Template-Type: ReDIF-Article 1.0
Author-Name: Sangit Chatterjee 
Author-X-Name-First: Sangit 
Author-X-Name-Last: Chatterjee 
Author-Name: Matthew Laudato 
Author-X-Name-First: Matthew 
Author-X-Name-Last: Laudato 
Title: Gender and performance of world-class athletes 
Abstract:
  The athletic performances of men and women are compared based on
 worldrecord times for various distance events in swimming, running and
 skating. The ratio of the times of women to those of men against years is
 modelled through a modified exponential distribution. The rate of
 improvement is found to be higher for women in the three sports. Law-like
 relationships are observed for world-record times against distance.
 Although men's absolute performance is generally superior, the disparity
 diminishes with increasing distance. 
Journal: Journal of Applied Statistics 
Pages: 3-10 
Issue: 1 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723846 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723846
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:1:p:3-10




Template-Type: ReDIF-Article 1.0
Author-Name: P. L. H. Yu 
Author-X-Name-First: P. L. H. 
Author-X-Name-Last: Yu 
Author-Name: K. Lam 
Author-X-Name-First: K. 
Author-X-Name-Last: Lam 
Title: How to predict election winners from a poll 
Abstract:
  Suppose that we have k candidates in an election and that the top m
 winners will be elected. Assume that the voters can select up to m ( 
Journal: Journal of Applied Statistics 
Pages: 11-24 
Issue: 1 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723855 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723855
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:1:p:11-24




Template-Type: ReDIF-Article 1.0
Author-Name: Philip Hans Franses 
Author-X-Name-First: Philip Hans 
Author-X-Name-Last: Franses 
Author-Name: Bart Hobijn 
Author-X-Name-First: Bart 
Author-X-Name-Last: Hobijn 
Title: Critical values for unit root tests in seasonal time series 
Abstract:
  In this paper, we present tables with critical values for a variety of
 tests for seasonal and non-seasonal unit roots in seasonal time series. We
 consider (extensions of) the Hylleberg et al. and Osborn et al. test
 procedures. These extensions concern time series with increasing seasonal
 variation and time series with structural breaks in the seasonal means.
 For each case, we give the appropriate auxiliary test regression, the test
 statistics, and the corresponding critical values for a selected set of
 sample sizes. We also illustrate the practical use of the auxiliary
 regressions for quarterly new car sales in the Netherlands. Supplementary
 to this paper, we provide Gauss programs with which one can generate
 critical values for particular seasonal frequencies and sample sizes. 
Journal: Journal of Applied Statistics 
Pages: 25-48 
Issue: 1 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723864 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723864
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:1:p:25-48




Template-Type: ReDIF-Article 1.0
Author-Name: C. Raju 
Author-X-Name-First: C. 
Author-X-Name-Last: Raju 
Author-Name: J. Jothikumar 
Author-X-Name-First: J. 
Author-X-Name-Last: Jothikumar 
Title: Procedures and tables for the construction and selection of chain sampling plans ChSP-4A(c1 ,c2 )r-Part 5 
Abstract:
  This paper presents a design procedure for ChSP-4A(c1,c2)r plans based on
 Kullback-Leibler information and the minimum sum of risks. A table that
 gives the values of the parameters n and k indexed by the acceptable
 quality level (AQL) and limiting quality level (LQL) is presented, from
 which one can select a plan which gives a desired AQL and LQL when the
 producer's risk alpha= 0.05 and the consumer's risk beta = 0.10. A
 concluding remark for this series of five papers is given at the end of
 this paper. 
Journal: Journal of Applied Statistics 
Pages: 49-76 
Issue: 1 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723873 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723873
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:1:p:49-76




Template-Type: ReDIF-Article 1.0
Author-Name: Ken Hung 
Author-X-Name-First: Ken 
Author-X-Name-Last: Hung 
Title: A comparison of two large sample confidence intervals for a proportion: A Monte Carlo simulation 
Abstract:
  Two pairs of confidence intervals for a proportion, similar to that of
 Larson, are compared. It can be shown through computer simulation
 experiments that, for certain values of p, the confidence interval
 obtained by the approximation is superior. 
Journal: Journal of Applied Statistics 
Pages: 77-84 
Issue: 1 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723882 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723882
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:1:p:77-84




Template-Type: ReDIF-Article 1.0
Author-Name: Dhaifalla Al-Mutairi 
Author-X-Name-First: Dhaifalla 
Author-X-Name-Last: Al-Mutairi 
Author-Name: Satish Agarwal 
Author-X-Name-First: Satish 
Author-X-Name-Last: Agarwal 
Title: Distributions of the lifetimes of system components operating under an unknown common environment 
Abstract:
  Families of joint distributions for describing the lifetimes of a system
 of components that operate under an unknown environment, when the
 environment follows a Weibull distribution, are derived. The reliability
 function for this system is calculated and several properties of the
 aforementioned joint distributions are investigated. 
Journal: Journal of Applied Statistics 
Pages: 85-96 
Issue: 1 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723891 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723891
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:1:p:85-96




Template-Type: ReDIF-Article 1.0
Author-Name: M. C. Agrawal 
Author-X-Name-First: M. C. 
Author-X-Name-Last: Agrawal 
Author-Name: A. B. Sthapit 
Author-X-Name-First: A. B. 
Author-X-Name-Last: Sthapit 
Title: Hierarchic predictive ratio-based and product-based estimators and their efficiency 
Abstract:
  Invoking the predictive approach with a fixed population set-up, and
 employing initially the customary ratio and product estimators as
 potential predictors for the non-surveyed part of the population, we have
 generated sequences of ratio-based and product-based estimators. The
 proposed ratio-based and product-based estimators of order k are-under
 some practical conditions-found to be more efficient than the customary
 ratio and product estimators and the usual simple mean when k is chosen
 optimally. Under the optimal value of k, the kth-order ratio-based and
 product-based estimators are found to be as efficient as the linear
 regression estimator. We have used real population data to illustrate the
 efficacy of the proposed ratio-based and product-based estimators relative
 to the usual simple mean and the customary ratio and product estimators. 
Journal: Journal of Applied Statistics 
Pages: 97-104 
Issue: 1 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723909 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723909
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:1:p:97-104




Template-Type: ReDIF-Article 1.0
Author-Name: Sergio Munoz 
Author-X-Name-First: Sergio 
Author-X-Name-Last: Munoz 
Author-Name: Shrikant Bangdiwala 
Author-X-Name-First: Shrikant 
Author-X-Name-Last: Bangdiwala 
Title: Interpretation of Kappa and B statistics measures of agreement 
Abstract:
  The Kappa statistic proposed by Cohen and the B statistic proposed by
 Bangdiwala are used to quantify the agreement between two observers,
 independently classifying the same n units into the same k categories.
 Both statistics correct for the agreement expected to result from chance
 alone, but the Kappa statistic is a measure that adjusts the observed
 proportion of agreement and ranges from- pc/(1- pc) to 1, where pc is the
 expected agreement that results from chance, and the B statistic is a
 measure that adjusts the observed area of agreement with that expected to
 result from chance, and ranges from 0 to 1. Statistical guidelines for the
 interpretation of either statistic are not available. For the Kappa
 statistic, the suggested arbitrary interpretation given by Landis and Koch
 is commonly quoted. This paper compares the behavior of the Kappa
 statistic and the B statistic in 3 3 and 4 4 contingency tables, under
 different agreement patterns. Based on simulation results, non-arbitrary
 guidelines for the interpretation of both statistics are provided. 
Journal: Journal of Applied Statistics 
Pages: 105-112 
Issue: 1 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723918 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723918
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:1:p:105-112




Template-Type: ReDIF-Article 1.0
Author-Name: Warren Gilchrist 
Author-X-Name-First: Warren 
Author-X-Name-Last: Gilchrist 
Title: Modelling with quantile distribution functions 
Abstract:
  The definition and construction of distributions are explored using
 parametric forms of the quantile distribution function. Short reviews are
 given of the identification and construction of such distribution
 functions, and of methods for estimation and testing. 
Journal: Journal of Applied Statistics 
Pages: 113-122 
Issue: 1 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723927 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723927
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:1:p:113-122




Template-Type: ReDIF-Article 1.0
Author-Name: L. I. Pettit 
Author-X-Name-First: L. I. 
Author-X-Name-Last: Pettit 
Author-Name: J. L. Palmer 
Author-X-Name-First: J. L. 
Author-X-Name-Last: Palmer 
Title: Seasonal patterns of fertility measures: A Bayesian approach 
Abstract:
  Becker (1981) presents some theory about related measures of fertility.
 He SUMMARY compares his theoretical predictions with observed
 relationships found in a set of data collected in Bangladesh. In general,
 he finds good agreement. In this paper, we reanalyse the data using
 Bayesian methods. In particular, we use Gibbs sampling to fit
 trigonometric regression models with autocorrelated errors. The results
 are generally in agreement with Becker's. However, evidence from one of
 the autocorrelation parameters and a residual analysis casts some doubt on
 whether the basic cosine model which is assumed fits the data well. 
Journal: Journal of Applied Statistics 
Pages: 139-146 
Issue: 2 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723756 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723756
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:2:p:139-146




Template-Type: ReDIF-Article 1.0
Author-Name: P. Prescott 
Author-X-Name-First: P. 
Author-X-Name-Last: Prescott 
Author-Name: N. R. Draper 
Author-X-Name-First: N. R. 
Author-X-Name-Last: Draper 
Author-Name: S. M. Lewis 
Author-X-Name-First: S. M. 
Author-X-Name-Last: Lewis 
Author-Name: A. M. Dean 
Author-X-Name-First: A. M. 
Author-X-Name-Last: Dean 
Title: Further properties of mixture designs for five components in orthogonal blocks 
Abstract:
  Orthogonally blocked experimental designs for mixtures of five
 ingredients, formed from Latin squares, were previously discussed by
 Prescott et al. Here, we extend this development by studying the
 properties of three classes of possible designs, with recommendations on
 their practical application. Restrictions on the design classes are
 explored and D-optimal (within the classes) versions are identified.
 Remarks on general D-optimality conclude the paper. 
Journal: Journal of Applied Statistics 
Pages: 147-156 
Issue: 2 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723765 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723765
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:2:p:147-156




Template-Type: ReDIF-Article 1.0
Author-Name: Tapio Nummi 
Author-X-Name-First: Tapio 
Author-X-Name-Last: Nummi 
Title: Estimation in a random effects growth curve model 
Abstract:
  This paper considers estimation under the growth curve model of Potthoff
 and Roy (1964) with random effects. Estimation under a multivariate model
 is also considered. Estimation under incomplete data and estimation of
 random effects are also discussed. A numerical example of data on bulls is
 presented to illustrate these techniques. 
Journal: Journal of Applied Statistics 
Pages: 157-168 
Issue: 2 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723774 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723774
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:2:p:157-168




Template-Type: ReDIF-Article 1.0
Author-Name: K. E. Basford 
Author-X-Name-First: K. E. 
Author-X-Name-Last: Basford 
Author-Name: G. J. Mclachlan 
Author-X-Name-First: G. J. 
Author-X-Name-Last: Mclachlan 
Author-Name: M. G. York 
Author-X-Name-First: M. G. 
Author-X-Name-Last: York 
Title: Modelling the distribution of stamp paper thickness via finite normal mixtures: The 1872 Hidalgo stamp issue of Mexico revisited 
Abstract:
  Izenman and Sommer (1988) used a non-parametric kernel density estimation
 technique to fit a seven-component model to the paper thickness of the
 1872 Hidalgo stamp issue of Mexico. They observed an apparent conflict
 when fitting a normal mixture model with three components with unequal
 variances. This conflict is examined further by investigating the most
 appropriate number of components when fitting a normal mixture of
 components with equal variances. 
Journal: Journal of Applied Statistics 
Pages: 169-180 
Issue: 2 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723783 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723783
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:2:p:169-180




Template-Type: ReDIF-Article 1.0
Author-Name: Y. Eric Shao 
Author-X-Name-First: Y. Eric 
Author-X-Name-Last: Shao 
Title: Multiple intervention analysis with application to sales promotion data 
Abstract:
  The sales promotion data resulting from multiple marketing strategies are
 usually autocorrelated. Consequently, the characteristics of those data
 sets can be analyzed using time-series and/or intervention analysis.
 Traditional time-series intervention analysis focuses on the effects of
 single or few interventions, and forecasts may be obtained as long as the
 future interventions can be assured. This study is different from
 traditional approaches, and considers the cases in which multiple
 interventions and the uncertainty of future interventions exist in the
 system. In addition, this study utilizes a set of real sales promotion
 data to demonstrate the effectiveness of the proposed approach. 
Journal: Journal of Applied Statistics 
Pages: 181-192 
Issue: 2 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723792 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723792
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:2:p:181-192




Template-Type: ReDIF-Article 1.0
Author-Name: Martin Schader 
Author-X-Name-First: Martin 
Author-X-Name-Last: Schader 
Author-Name: Friedrich Schmid 
Author-X-Name-First: Friedrich 
Author-X-Name-Last: Schmid 
Title: Power of tests for uniformity when limits are unknown 
Abstract:
  Power of modifications of the Kolmogorov, Cramer-von Mises, Watson and
 Anderson-Darling tests for testing uniformity when limits are unknown is
 compared. Power is computed by Monte Carlo simulation within one-parameter
 families of alternative distributions containing the uniform distribution
 as a special case. A table of mostly unpublished quantiles is given and
 continuous power curves are plotted. 
Journal: Journal of Applied Statistics 
Pages: 193-206 
Issue: 2 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723800 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723800
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:2:p:193-206




Template-Type: ReDIF-Article 1.0
Author-Name: V. Soundararajan 
Author-X-Name-First: V. 
Author-X-Name-Last: Soundararajan 
Author-Name: A. L. Christina 
Author-X-Name-First: A. L. 
Author-X-Name-Last: Christina 
Title: Selection of single sampling variables plans based on the minimum angle 
Abstract:
  This paper provides single sampling variables plans for given values of
 n, the acceptable quality level and limiting quality level. Tables are
 constructed using the minimum angle technique. 
Journal: Journal of Applied Statistics 
Pages: 207-218 
Issue: 2 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723819 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723819
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:2:p:207-218




Template-Type: ReDIF-Article 1.0
Author-Name: M. Mahibbur Rahman 
Author-X-Name-First: M. Mahibbur 
Author-X-Name-Last: Rahman 
Author-Name: Z. Govindarajulu 
Author-X-Name-First: Z. 
Author-X-Name-Last: Govindarajulu 
Title: A modification of the test of Shapiro and Wilk for normality 
Abstract:
  The W statistic of Shapiro and Wilk provides the best omnibus test of
 normality, but its application is limited up to n= 50. This study modifies
 W, such that it can be extended for all sample sizes. The critical values
 of W, i.e. the modification of W, is given for n up to 5000. The empirical
 moments show that the null distribution of W is skewed to the left and is
 consistant for all sample sizes. Empirical powers of W are also comparable
 with those of W. 
Journal: Journal of Applied Statistics 
Pages: 219-236 
Issue: 2 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723828 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723828
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:2:p:219-236




Template-Type: ReDIF-Article 1.0
Author-Name: L. H. Kao 
Author-X-Name-First: L. H. 
Author-X-Name-Last: Kao 
Author-Name: S. Chakraborti 
Author-X-Name-First: S. 
Author-X-Name-Last: Chakraborti 
Title: One-sided sign-type non-parametric procedures for comparing treatments with a control in a randomized complete block design 
Abstract:
  Non-parametric procedures are presented for comparing several treatments
 with a control when the data are collected in a randomized complete block
 design with no interaction. The procedures are generalizations of some
 well-known sign-type tests, and include both overall tests and multiple
 comparisons procedures. A numerical example is used to motivate the
 problem and illustrate the proposed methods. Some concluding remarks are
 offered. 
Journal: Journal of Applied Statistics 
Pages: 251-264 
Issue: 3 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723666 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723666
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:3:p:251-264




Template-Type: ReDIF-Article 1.0
Author-Name: H. H. Chen 
Author-X-Name-First: H. H. 
Author-X-Name-Last: Chen 
Author-Name: S. W. Duffy 
Author-X-Name-First: S. W. 
Author-X-Name-Last: Duffy 
Author-Name: L. Tabar 
Author-X-Name-First: L. 
Author-X-Name-Last: Tabar 
Title: A mover-stayer mixture of Markov chain models for the assessment of dedifferentiation and tumour progression in breast cancer 
Abstract:
  Malignancy grade is a histological measure of attributes related to a
 breast tumour's aggressive potential. It is not established whether the
 grade is an inate characteristic which remains unchanged throughout the
 tumour's development or whether it evolves as the tumour grows. It is
 likely that a proportion of tumours have the potential to evolve, and so a
 statistical method was required to assess this hypothesis and, if
 possible, to estimate the proportion with the potential for evolution.
 Therefore, a mover-stayer mixture of Markov chain models was developed,
 with the complication that 'movers' were unobservable because tumours were
 excised on diagnosis. A quasi-likelihood method was used for estimation.
 The methods are demonstrated using data from the Swedish twocounty trial
 of breast-cancer screening. 
Journal: Journal of Applied Statistics 
Pages: 265-278 
Issue: 3 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723675 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723675
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:3:p:265-278




Template-Type: ReDIF-Article 1.0
Author-Name: C. V. Rao 
Author-X-Name-First: C. V. 
Author-X-Name-Last: Rao 
Author-Name: S. Hari Krishna 
Author-X-Name-First: S. Hari 
Author-X-Name-Last: Krishna 
Title: A graphical method for testing the equality of several variances 
Abstract:
  The problem of testing the equality of several variances arises in many
 areas. For testing the equality of variances, several tests are available
 in the literature which demonstrate only the statistical significance of
 the variances. In this paper, a graphical method is presented for testing
 the equality of variances. This method simultaneously demonstrates the
 statistical and engineering significance. Two examples are given to
 illustrate the proposed graphical method, and the conclusions obtained are
 compared with the existing tests. 
Journal: Journal of Applied Statistics 
Pages: 279-288 
Issue: 3 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723684 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723684
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:3:p:279-288




Template-Type: ReDIF-Article 1.0
Author-Name: Anita Ghatak 
Author-X-Name-First: Anita 
Author-X-Name-Last: Ghatak 
Title: Unit roots and structural breaks: The case of India 1900-1988 
Abstract:
  This paper tests the hypothesis of difference stationarity of
 macro-economic time series against the alternative of trend stationarity,
 with and without allowing for possible structural breaks. The
 methodologies used are that of Dickey and Fuller familiarized by Nelson
 and Plosser, and that of dummy variables familiarized by Perron, including
 the Zivot and Andrews extension of Perron's tests. We have chosen 12
 macro-economic variables in the Indian economy during the period 1900-1988
 for this study. A study of this nature has not previously been undertaken
 for the Indian economy. The conventional Dickey-Fuller methodology without
 allowing for structural breaks cannot reject the unit root hypothesis
 (URH) for any series. Allowing for exogenous breaks in level and rate of
 growth in the years 1914, 1939 and 1951, Perron's tests reject the URH for
 three series after 1951, i.e. the year of introduction of economic
 planning in India. The Zivot and Andrews tests for endogenous breaks
 confirm the Perron tests and lead to the rejection of the URH for three
 more series. 
Journal: Journal of Applied Statistics 
Pages: 289-300 
Issue: 3 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723693 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723693
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:3:p:289-300




Template-Type: ReDIF-Article 1.0
Author-Name: H. J. Khamis 
Author-X-Name-First: H. J. 
Author-X-Name-Last: Khamis 
Title: The delta-corrected Kolmogorov-Smirnov test for the two-parameter Weibull distribution 
Abstract:
  Monte Carlo simulation techniques are used to create tables of critical
 values for the delta-corrected Kolmogorov-Smirnov statistic-a modification
 of the classical Kolmogorov-Smirnov statistic-for the Weibull distribution
 with known location parameter and unknown shape and scale parameters. The
 power of the proposed test is investigated relative to values of delta in
 the unit interval and relative to a wide variety of alternative
 distributions. The results indicate that using the delta-correction can
 lead to as many as 8.4 percentage points more power than can be achieved
 with the classical Kolmogorov-Smirnov test, with no change in the size of
 the test. Furthermore, carrying out the delta-corrected test involves no
 more steps or calculations than for the classical Kolmogorov-Smirnov test.
 In general, it is shown that a slight modification-or correction-in the
 definition of the empirical distribution function of the
 Kolmogorov-Smirnov test can lead to power enhancement without changing the
 type I error rate of the test. Two examples clearly show the effectiveness
 of the delta-corrected test. The delta-corrected Kolmogorov-Smirnov test
 is recommended for testing the goodness of fit to the twoparameter Weibull
 distribution. 
Journal: Journal of Applied Statistics 
Pages: 301-318 
Issue: 3 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723701 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723701
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:3:p:301-318




Template-Type: ReDIF-Article 1.0
Author-Name: Herbert Buning 
Author-X-Name-First: Herbert 
Author-X-Name-Last: Buning 
Title: Robust analysis of variance 
Abstract:
  For the c -sample location problem with equal and unequal variances, we
 compare the classical F -test and its robustified version-the Welch
 test-with some nonparametric counterparts defined for two-sided and
 one-sided ordered alternatives, such as trend and umbrella alternatives. A
 new rank test for long-tailed distributions is proposed. The comparison is
 referred to level alpha and power beta of the tests, and is carried out
 via Monte Carlo simulation, assuming short-, medium- and long-tailed as
 well as asymmetric distributions. It turns out that the Welch test is the
 best one in the case of unequal variances but in the case of equal
 variances special non-parametric tests are to prefer. 
Journal: Journal of Applied Statistics 
Pages: 319-332 
Issue: 3 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723710 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723710
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:3:p:319-332




Template-Type: ReDIF-Article 1.0
Author-Name: J. Munoz-Garcia 
Author-X-Name-First: J. 
Author-X-Name-Last: Munoz-Garcia 
Author-Name: R. Pino-Mejias 
Author-X-Name-First: R. 
Author-X-Name-Last: Pino-Mejias 
Author-Name: J. M. Munoz-Pichardo 
Author-X-Name-First: J. M. 
Author-X-Name-Last: Munoz-Pichardo 
Author-Name: M. D. Cubiles-De-La-Vega 
Author-X-Name-First: M. D. 
Author-X-Name-Last: Cubiles-De-La-Vega 
Title: Identification of outlier bootstrap samples 
Abstract:
  We define a variation of Efron's method II based on the outlier bootstrap
 sample concept. A criterion for the identification of such samples is
 given, with which a variation in the bootstrap sample generation algorithm
 is introduced. The results of several simulations are analyzed in which,
 in comparison with Efron's method II, a higher degree of closeness to the
 estimated quantities can be observed. 
Journal: Journal of Applied Statistics 
Pages: 333-342 
Issue: 3 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723729 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723729
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:3:p:333-342




Template-Type: ReDIF-Article 1.0
Author-Name: Jill Johnes 
Author-X-Name-First: Jill 
Author-X-Name-Last: Johnes 
Title: Inter-university variations in undergraduate non-completion rates: A statistical analysis by subject of study 
Abstract:
  Non-completion of higher education degree courses is a considerable
 problem, incurring costs on the taxpayer, higher education institutions
 and the students who fail to complete. Closer examination of the data
 reveals that non-completion rates in higher education vary substantially
 across institutions and by subject of degree. The purpose of this paper is
 to investigate, within each of 13 broad subject categories, the potential
 determinants of inter-university variations in non-completion rates.
 Published data are used to compute university non-completion rates over
 four time periods and to construct corresponding explanatory variables
 which could potentially be related to non-completion rates. The
 explanatory variables measure the characteristics (both academic and
 socioeconomic) of students recruited by universities and the
 characteristics of the institutions themselves. The significance of the
 relationship between the possible explanatory variables and non-completion
 rates within each given subject is assessed using both weighted
 leastsquares and weighted logit analysis. The conclusions drawn from the
 results of each technique are identical, and, therefore, for
 interpretation reasons, only the results of the weighted least-squares
 analysis are reported. As expected, the academic quality of student
 entrants is an important determinant of non-completion rates in the
 majority of subjects, although the magnitude of the effect varies
 according to subject. Variables reflecting the age and gender mix of
 university entrants are generally not significantly related to
 noncompletion rates. The characteristics of institutions which are
 significantly related to non-completion rates in specific subjects include
 the staff student ratio and the length of the degree course 
Journal: Journal of Applied Statistics 
Pages: 343-362 
Issue: 3 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723738 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723738
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:3:p:343-362




Template-Type: ReDIF-Article 1.0
Author-Name: Charles Katholi 
Author-X-Name-First: Charles 
Author-X-Name-Last: Katholi 
Author-Name: Anthony Merriweather 
Author-X-Name-First: Anthony 
Author-X-Name-Last: Merriweather 
Author-Name: Thomas Unnasch 
Author-X-Name-First: Thomas 
Author-X-Name-Last: Unnasch 
Title: An analysis of variance type test for comparing clusters of DNA sequences based on randomization test methodologies 
Abstract:
  A method for comparing groupings of DNA sequences is presented, which
 utilizes randomization test methods to assign significance levels to a
 test statistic defined in terms of the Hamming distance between two
 sequences. The method, which is intuitively motivated by the analysis of
 variance procedure, partitions the variation caused by differences between
 clusters from the variation attributable to differences at random base
 pair locations within clusers. Implementation issues are discussed, and an
 example of the application of the method is provided. 
Journal: Journal of Applied Statistics 
Pages: 371-382 
Issue: 4 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723585 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723585
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:4:p:371-382




Template-Type: ReDIF-Article 1.0
Author-Name: Shragga Irmay 
Author-X-Name-First: Shragga 
Author-X-Name-Last: Irmay 
Title: The relationship between Zipf's law and the distribution of first digits 
Abstract:
  Zipf 's experimental law states that, for a given large piece of text,
 the product of the relative frequency of a word and its order in
 descending frequency order is a constant, shown to be equal to 1 divided
 by the natural logarithm of the number of different words. It is shown to
 be approximately equal to Benford's logarithmic distribution of first
 significant digits in tables of numbers. Eleven samples allow comparison
 of observed and theoretical frequencies. 
Journal: Journal of Applied Statistics 
Pages: 383-394 
Issue: 4 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723594 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723594
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:4:p:383-394




Template-Type: ReDIF-Article 1.0
Author-Name: Ravindra Khattree 
Author-X-Name-First: Ravindra 
Author-X-Name-Last: Khattree 
Author-Name: Dayanand Naik 
Author-X-Name-First: Dayanand 
Author-X-Name-Last: Naik 
Author-Name: Robert Mason 
Author-X-Name-First: Robert 
Author-X-Name-Last: Mason 
Title: Estimation of variance components in staggered nested designs 
Abstract:
  Variance components are estimated by two different methods for a general
 p stage random-effects staggered nested design. In addition to estimation
 from an analysis of variance, a new approach is introduced. The main
 features of this new technique are its simplicity and its ability to yield
 non-negative estimates of the variance components. The performances of the
 two procedures are compared using simulation and the meansquared-error
 criterion. 
Journal: Journal of Applied Statistics 
Pages: 395-408 
Issue: 4 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723602 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723602
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:4:p:395-408




Template-Type: ReDIF-Article 1.0
Author-Name: Eric Schoen 
Author-X-Name-First: Eric 
Author-X-Name-Last: Schoen 
Author-Name: Kirsten Wolff 
Author-X-Name-First: Kirsten 
Author-X-Name-Last: Wolff 
Title: Design and analysis of a fractional 413125 split-plot experiment 
Abstract:
  This paper is a case study on two aspects of constructing mixed factorial
 experiments: (1) three equally sized fractions of a 2p+ 2 design are
 combined under a three level factor, yielding a 312p+ 2 experiment; (2)
 two carefully selected factors from a 2p+ 2 design are combined to obtain
 a 412p design. We consider both aspects for the design of a 1/8 fraction
 of a 413125 experiment (48 observations) to investigate a DNA
 amplification technique. The experiment is of the split-plot type, because
 the main effects of two factors had to be confounded with runs of a piece
 of equipment (whole-plots), while the other factors were varied between
 vials (subplots) contained within the equipment. We confounded an
 additional effect to avoid the usual difficulty in evaluating the
 whole-plot effects in unreplicated experiments. Both whole-plot and
 subplot effects can then be evaluated with half-normal plots. The analysis
 is illustrated with the results of the experiment. 
Journal: Journal of Applied Statistics 
Pages: 409-420 
Issue: 4 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723611 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723611
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:4:p:409-420




Template-Type: ReDIF-Article 1.0
Author-Name: Carmen Acuna 
Author-X-Name-First: Carmen 
Author-X-Name-Last: Acuna 
Author-Name: Joseph Horowitz 
Author-X-Name-First: Joseph 
Author-X-Name-Last: Horowitz 
Title: A statistical approach to the resolution of point sources 
Abstract:
  The Rayleigh criterion in optics states that two point sources of equal
 intensity are 'barely resolved' when the maximum of the diffraction
 pattern of one source overlaps the first minimum of the diffraction
 pattern of the second source. Although useful for rough comparisons of
 optical systems, such a criterion does not take into account the
 randomness in the detection process and does not tell whether sources can
 actually be distinguished. We present a statistical approach that
 addressed these issues. From quantum optics, the photon counts in the
 pixels are independent Poisson random variables with means that depend on
 the distance 2theta between the sources. Resolving the sources corresponds
 to testing H0: theta =0 vs Ha: theta >0, under conditions that make the
 information number zero at theta =0. We define resolution as the
 (asymptotic) power function of the likelihood ratio test rather than as a
 single number. The asymptotic distribution of the test statistic is
 derived under H0 and under contiguous alternatives. The results are
 illustrated by an application to a sky survey to detect binary stars using
 the Hubble space telescope. 
Journal: Journal of Applied Statistics 
Pages: 421-436 
Issue: 4 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723620 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723620
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:4:p:421-436




Template-Type: ReDIF-Article 1.0
Author-Name: M. L. Aggarwal 
Author-X-Name-First: M. L. 
Author-X-Name-Last: Aggarwal 
Author-Name: A. Goel 
Author-X-Name-First: A. 
Author-X-Name-Last: Goel 
Author-Name: S. R. Chowdhury 
Author-X-Name-First: S. R. 
Author-X-Name-Last: Chowdhury 
Title: Catalogue of group structures for two-level fractional factorial designs 
Abstract:
  Taguchi introduced the concept of split-unit design to sort factors into
 different groups with respect to difficulties involved in changing the
 levels of factors. Li et al. have developed all possible group structures
 for eight factors in an L16 orthogonal array for resolution IV with
 split-plot design. Chen et al. have searched for a best design, according
 to the various criteria for two-level fractional factorial design and have
 presented a catalogue. In this paper, we have developed an algorithm for
 generating group structure and possible allocations for various 2n- k
 fractional factorial designs that correspond to the designs given by Chen
 et al. 
Journal: Journal of Applied Statistics 
Pages: 437-452 
Issue: 4 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723639 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723639
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:4:p:437-452




Template-Type: ReDIF-Article 1.0
Author-Name: Raviprakash Salagame 
Author-X-Name-First: Raviprakash 
Author-X-Name-Last: Salagame 
Author-Name: Russell Barton 
Author-X-Name-First: Russell 
Author-X-Name-Last: Barton 
Title: Factorial hypercube designs for spatial correlation regression 
Abstract:
  The problem of generating a good experimental design for spatial
 correlation regression is studied in this paper. The quality of fit
 generated by random designs, Latin hypercube designs and factorial designs
 is studied for a particular response surface that arises in inkjet
 printhead design. These studies indicate that the quality of fit generated
 by spatial correlation models is highly dependent on the choice of design.
 A design strategy that we call 'factorial hypercubes' is introduced as a
 new method. This method can be thought of as an example of a more general
 class of hybrid designs. The quality of fit generated by these designs is
 compared with those of other methods. These comparisons indicate a better
 fit and less numerical problems with factorial hypercubes. 
Journal: Journal of Applied Statistics 
Pages: 453-474 
Issue: 4 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723648 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723648
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:4:p:453-474




Template-Type: ReDIF-Article 1.0
Author-Name: Nien Fan Zhang 
Author-X-Name-First: Nien Fan 
Author-X-Name-Last: Zhang 
Title: Detection capability of residual control chart for stationary process data 
Abstract:
  In recent years, methods for dealing with autocorrelated data in the
 statistical process control environment have been proposed. A primary
 method is based on modeling the process data and applying control charts
 to the residuals. However, the residual charts do not have the same
 properties as the traditional charts. In the literature, there has been no
 systematic study on the detection capability of the residual chart for the
 stationary processes. The article develops a measure of the detection
 capability of the residual chart for the general stationary processes.
 Conditions under which the residual chart reduces or increases the
 detection capability are given. The relationships between the detection
 capability and the average run length of the residual chart are also
 established. 
Journal: Journal of Applied Statistics 
Pages: 475-492 
Issue: 4 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723657 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723657
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:4:p:475-492




Template-Type: ReDIF-Article 1.0
Author-Name: Thaddeus Tarpey 
Author-X-Name-First: Thaddeus 
Author-X-Name-Last: Tarpey 
Title: Estimating principal points of univariate distributions 
Abstract:
  The term 'principal points' originated in a problem of determining
 'typical' heads for the design of protection masks, as described by Flury.
 Two principal points in the mask example correspond to a small and a large
 size. Principal points are cluster means for theoretical distributions,
 and sample cluster means from a k -means algorithm are non-parametric
 estimators of principal points. This paper demonstrates that maximum
 likelihood estimators and semi-parametric estimators based on symmetry
 constraints typically perform much better than the k -means estimators.
 Asymptotic results on the efficiency of these estimators of two principal
 points for four symmetric univariate distributions are given. Simulation
 results are provided to examine the performance of the estimators for
 finite sample sizes. Finally, the different estimators of two principal
 points are compared using the head dimension data for the design of
 protection masks. 
Journal: Journal of Applied Statistics 
Pages: 499-512 
Issue: 5 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723503 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723503
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:5:p:499-512




Template-Type: ReDIF-Article 1.0
Author-Name: I. L. Dryden 
Author-X-Name-First: I. L. 
Author-X-Name-Last: Dryden 
Author-Name: K. V. Mardia 
Author-X-Name-First: K. V. 
Author-X-Name-Last: Mardia 
Author-Name: A. N. Walder 
Author-X-Name-First: A. N. 
Author-X-Name-Last: Walder 
Title: Review of the use of context in statistical image analysis 
Abstract:
  This paper is a review of the use of contextual information in
 statistical image analysis. After defining what we mean by 'context', we
 describe the Bayesian approach to high-level image analysis using
 deformable templates. We describe important aspects of work on character
 recognition and syntactic pattern recognition; in particular, aspects of
 the work which are relevant to scene understanding. We conclude with a
 review of some work on knowledge-based systems which use context to aid
 object recognition. 
Journal: Journal of Applied Statistics 
Pages: 513-538 
Issue: 5 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723512 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723512
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:5:p:513-538




Template-Type: ReDIF-Article 1.0
Author-Name: Reay-Chen Wang 
Author-X-Name-First: Reay-Chen 
Author-X-Name-Last: Wang 
Author-Name: Chung-Ho Chen 
Author-X-Name-First: Chung-Ho 
Author-X-Name-Last: Chen 
Title: Minimum average fraction inspected for continuous sampling plan CSP-1 under inspection error 
Abstract:
  In this paper, we present a further modification of Endres's method to
 construct the problem of minimizing the average fraction inspected (AFI)
 for the continuous sampling plan CSP-1 under inspection error. The
 measures of average outgoing quality under perfect and imperfect
 replacement conditions are considered. The formulae for searching the
 smallest clearance number i for minimizing the AFI for a CSP-1 plan are
 also provided. The solution procedure of the proposed method is more
 reliable, clearer and easier than that of Endres. 
Journal: Journal of Applied Statistics 
Pages: 539-548 
Issue: 5 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723521 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723521
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:5:p:539-548




Template-Type: ReDIF-Article 1.0
Author-Name: Gilles Ducharme 
Author-X-Name-First: Gilles 
Author-X-Name-Last: Ducharme 
Title: Consistent selection of the actual model in regression analysis 
Abstract:
  In regression analysis, a best subset of regressors is usually selected
 by minimizing Mallows's C statistic or some other equivalent criterion,
 such as the Akaike lambda information criterion or cross-validation. It is
 known that the resulting procedure suffers from a lack of consistency that
 can lead to a model with too many variables. For this reason, corrections
 have been proposed that yield consistent procedures. The object of this
 paper is to show that these corrected criteria, although asymptotically
 consistent, are usually too conservative for finite sample sizes. The
 paper also proposes a new correction of Mallows's statistic that yields
 better results. A simulation study is conducted that shows that the
 proposed criterion performs well in a variety of situations. 
Journal: Journal of Applied Statistics 
Pages: 549-558 
Issue: 5 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723530 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723530
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:5:p:549-558




Template-Type: ReDIF-Article 1.0
Author-Name: E. Ayuga Tellez 
Author-X-Name-First: E. Ayuga 
Author-X-Name-Last: Tellez 
Author-Name: C. Ayuga Tellez 
Author-X-Name-First: C. Ayuga 
Author-X-Name-Last: Tellez 
Author-Name: C. Gonzalez Garcia 
Author-X-Name-First: C. Gonzalez 
Author-X-Name-Last: Garcia 
Author-Name: E. Martinez Falero 
Author-X-Name-First: E. Martinez 
Author-X-Name-Last: Falero 
Title: Estimation of non-parametric regression in the analysis of the anti-inflammatory activity of diverse extracts of Sideritis foetens 
Abstract:
  A procedure to choose the best non-parametric estimator from among all
 nonparametric methods to fit regression curves is described. The
 methodology that is proposed prevents a lack of fit at the edges of the
 regression curve. The method is summed up in a few steps to facilitate its
 application by researchers. The procedure is applied to the determination
 of various curves that explain the anti-inflammatory activity of diverse
 extracts of Sideritis foetens and phenylbutazone against the time elapsed
 from the application of the agent which provokes the inflammation.
 Discussion shows that it is possible to obtain valid conclusions about the
 effects of the different products and to establish comparisons between
 them. Such conclusions are not possible when starting from the classical
 statistics methods usually employed in pharmacology. 
Journal: Journal of Applied Statistics 
Pages: 559-572 
Issue: 5 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723549 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723549
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:5:p:559-572




Template-Type: ReDIF-Article 1.0
Author-Name: Wieslaw Madry 
Author-X-Name-First: Wieslaw 
Author-X-Name-Last: Madry 
Title: A statistical approach to multivariate evaluation of diversity with respect to quantitative characteristics in cereal germplasm collections 
Abstract:
  The aim of this paper is to undertake the problem of adapting some
 multivariate statistical methods (MANOVA, cluster analysis with
 simultaneous test procedures T 2 based on Roy's union-intersection rule
 and canonical variate analysis) max and describing their possible usage in
 the evaluation and interpretation of the phenotypic diversity with regard
 to quantitative traits in cereal collections. The presented procedures are
 used in a case where experimental data have been obtained from
 single-replicated trials conducted at the same location over a few years.
 In such cases, data can be nonorthogonal connected accessions x years
 cross-classification with none or one observation in a given subclass. The
 application of the suggested procedures is illustrated by a numerical
 example of a winter rye collection from the Plant Breeding and
 Acclimatization Institute in Radzikow near Warsaw (Poland). 
Journal: Journal of Applied Statistics 
Pages: 573-588 
Issue: 5 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723558 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723558
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:5:p:573-588




Template-Type: ReDIF-Article 1.0
Author-Name: Russell Boyles 
Author-X-Name-First: Russell 
Author-X-Name-Last: Boyles 
Title: Using the chi-square statistic to monitor compositional process data 
Abstract:
  We investigate the use of the chi-square control chart as a simple
 multivariate method for shopfloor monitoring of compositional process
 data. Although this chart is usually considered to be applicable only with
 multinomial process data, we show that it is also valid, in a certain
 asymptotic sense, for compositional data that arise from the Dirichlet
 distribution. For general compositional data, we show that the chi-square
 statistic can be used for process monitoring, provided that we make a
 simple adjustment to the degrees of freedom in the chi-square reference
 distribution. This method is illustrated and compared in four examples
 with the T 2 chart based on log-ratio transformation of the data. 
Journal: Journal of Applied Statistics 
Pages: 589-602 
Issue: 5 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723567 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723567
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:5:p:589-602




Template-Type: ReDIF-Article 1.0
Author-Name: Hisashi Tanizaki 
Author-X-Name-First: Hisashi 
Author-X-Name-Last: Tanizaki 
Title: Power comparison of non-parametric tests: Small-sample properties from Monte Carlo experiments 
Abstract:
  Non-parametric tests that deal with two samples include scores tests
 (such as the Wilcoxon rank sum test, normal scores test, logistic scores
 test, Cauchy scores test, etc.) and Fisher's randomization test. Because
 the non-parametric tests generally require a large amount of computational
 work, there are few studies on small-sample properties, although
 asymptotic properties with regard to various aspects were studied in the
 past. In this paper, the non-parametric tests are compared with the t
 -test through Monte Carlo experiments. Also, we consider testing
 structural changes as an application in economics. 
Journal: Journal of Applied Statistics 
Pages: 603-632 
Issue: 5 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723576 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723576
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:5:p:603-632




Template-Type: ReDIF-Article 1.0
Author-Name: Raymond Stefani 
Author-X-Name-First: Raymond 
Author-X-Name-Last: Stefani 
Title: Survey of the major world sports rating systems 
Abstract:
  Using a common framework, this paper presents a survey of the major world
 sports rating systems (WSRSs) in skiing (sponsored by the International
 Skiing Federation (FIS)), men's tennis (Association of Tennis
 Professionals (ATP)), women's tennis (Women's Tennis Association (WTA)),
 soccer (Federation of International Football Associations (FIFA)) and golf
 (Royal and Ancient Golf Club of St Andrews). These systems are not
 otherwise available in the literature. Each of the WSRSs has three phases:
 first, the observed results are weighted to provide points for each
 competition; second, these points are combined to provide a seasonal
 value; third, the seasonal values are combined to provide a rating. The
 final result or placement (and not the score or time) is the most
 important factor in determining points for a given competition. In skiing,
 men's tennis and women's tennis, the rating is calculated from results
 over one season, while three seasons are used in golf and six seasons are
 used in soccer. In cross-country skiing and men's tennis, the seasonal
 value is calculated from the sum of the best values from that season's
 competitions. In alpine skiing and women's tennis, the sum of all values
 from that season's competitions is used. In golf and soccer, an averaging
 process is used. Besides potentially encouraging more entries, a 'best'
 system and one using all values also generates simple integer ratings
 rather than decimal ratings as are obtained with an averaging system. The
 simplest system is that of FIS in skiing, where one table of points is
 used for all alpine and cross-country disciplines. In contrast,
 considering that soccer (as a sport) prides itself on the simplicity of
 the game, it is surprising that FIFA's system is so complex, It is also
 surprising in soccer that a 'friendly' (often a pick-up exhibition used
 for player development) counts two-thirds as much as does a World Cup
 final played before a worldwide TV audience. It is hoped that this survey
 will serve as a valuable resource for those studying sports rating
 systems. 
Journal: Journal of Applied Statistics 
Pages: 635-646 
Issue: 6 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723387 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723387
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:6:p:635-646




Template-Type: ReDIF-Article 1.0
Author-Name: E. M. Qannari 
Author-X-Name-First: E. M. 
Author-X-Name-Last: Qannari 
Author-Name: E. Vigneau 
Author-X-Name-First: E. 
Author-X-Name-Last: Vigneau 
Author-Name: M. Semenou 
Author-X-Name-First: M. 
Author-X-Name-Last: Semenou 
Title: New approach in biased regression 
Abstract:
  An optimization problem which provides a new characterization for ridge
 regression is discussed. A variant of this optimization problem leads to a
 new family of biased estimators that includes the Stein estimation method
 and principal components regression as particular cases. The whole
 approach is illustrated on the basis of real data sets. 
Journal: Journal of Applied Statistics 
Pages: 647-658 
Issue: 6 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723396 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723396
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:6:p:647-658




Template-Type: ReDIF-Article 1.0
Author-Name: Murari Singh 
Author-X-Name-First: Murari 
Author-X-Name-Last: Singh 
Author-Name: Michael Jones 
Author-X-Name-First: Michael 
Author-X-Name-Last: Jones 
Title: Estimating time to detect time trends in continuous cropping 
Abstract:
  In long-term field trials comparing different sequences of crops and
 husbandry practices, the identification and understanding of trends in
 productivity over time is an important issue of sustainable crop
 production. This paper presents a statistical technique for the estimation
 of time trends in yield variables of a seasonal annual crop under
 continuous cropping. The estimation procedure incorporates the correlation
 structure, which is assumed to follow first-order autocorrelation in the
 errors that arise over time on the same plot. Because large differences in
 annual rainfall have a major effect on crop performance, rainfall has been
 allowed for in the estimation of the time trends. Expressions for the
 number of years (time) required to detect statistically significant time
 trends have been obtained. Illustrations are based on a 7-year data set of
 grain and straw yields from a trial in northern Syria. Although agronomic
 interpretation is not intended in this paper, the barley yield data
 indicated that a significant time trend can apparently be detected even in
 a suboptimal data set of 7 years' duration. 
Journal: Journal of Applied Statistics 
Pages: 659-670 
Issue: 6 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723404 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723404
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:6:p:659-670




Template-Type: ReDIF-Article 1.0
Author-Name: M. A. Kaboudan 
Author-X-Name-First: M. A. 
Author-X-Name-Last: Kaboudan 
Title: Non-traditional analysis of stock returns 
Abstract:
  An investigation of the prices of eight individual stocks showed that
 pricechange returns are significantly less complex than are time-dependent
 returns. Timedependent returns computed every 15, 30 and 45 minutes were
 found to be more complex, using a complexity measure. Complexity is
 quantified by measuring the number of times that the estimated correlation
 dimension of an observed series is multiplied by when its original
 sequence is randomly shuffled. 
Journal: Journal of Applied Statistics 
Pages: 671-688 
Issue: 6 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723413 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723413
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:6:p:671-688




Template-Type: ReDIF-Article 1.0
Author-Name: S. Balamurali 
Author-X-Name-First: S. 
Author-X-Name-Last: Balamurali 
Author-Name: M. Kalyanasundaram 
Author-X-Name-First: M. 
Author-X-Name-Last: Kalyanasundaram 
Title: Determination of an attribute single sampling scheme 
Abstract:
  This paper presents procedures for the selection of a new sampling scheme
 called 'single sampling scheme' (SSS). It presents a compact table for the
 selection of an SSS indexed by various combinations of entry parameters.
 The advantages of SSSs are discussed. The basis for the construction of
 the table is also given. 
Journal: Journal of Applied Statistics 
Pages: 689-696 
Issue: 6 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723422 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723422
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:6:p:689-696




Template-Type: ReDIF-Article 1.0
Author-Name: Vic Barnett 
Author-X-Name-First: Vic 
Author-X-Name-Last: Barnett 
Author-Name: Karen Moore 
Author-X-Name-First: Karen 
Author-X-Name-Last: Moore 
Title: Best linear unbiased estimates in ranked-set sampling with particular reference to imperfect ordering 
Abstract:
  Ranked-set sampling is a widely used sampling procedure when sample
 observations are expensive or difficult to obtain. It departs from simple
 random sampling by seeking to spread the observations in the sample widely
 over the distribution or population. This is achieved by ranking methods
 which may need to employ concomitant information. The ranked-set sample
 mean is known to be more efficient than the corresponding simple random
 sample mean. Instead of the ranked-set sample mean, this paper considers
 the corresponding optimal estimator: the ranked-set best linear unbiased
 estimator. This is shown to be more efficient, even for normal data, but
 particularly for skew data, such as from an exponential distribution. The
 corresponding forms of the estimators are quite distinct from the
 ranked-set sample mean. Improvement holds where the ordering is perfect or
 imperfect, with this prospect of improper ordering being explored through
 the use of concomitants. In addition, the corresponding optimal linear
 estimator of a scale parameter is also discussed. The results are applied
 to a biological problem that involves the estimation of root weights for
 experimental plants, where the expense of measurement implies the need to
 minimize the number of observations taken. 
Journal: Journal of Applied Statistics 
Pages: 697-710 
Issue: 6 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723431 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723431
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:6:p:697-710




Template-Type: ReDIF-Article 1.0
Author-Name: A. I. Khuri 
Author-X-Name-First: A. I. 
Author-X-Name-Last: Khuri 
Title: Quantile dispersion graphs for analysis of variance estimates of variance components 
Abstract:
  The exact distribution of an analysis of variance estimator of a variance
 component is obtained by determining its quantiles on the basis of R. B.
 Davies' algorithm. A plot of these quantiles provides useful information
 concerning the efficiency of the estimator, including the extent to which
 it can be negative. Furthermore, the variability in the values of each
 quantile is assessed by varying the values of the variance components for
 the model under consideration. The maximum and minimum of such quantile
 values can then be determined. A plot of the maxima and minima for various
 selected quantiles produces the so-called 'quantile dispersion graphs'.
 These graphs can be used to provide a comprehensive picture of the quality
 of estimation obtained with a particular design. They also provide an
 effective graphical tool for comparing designs on the basis of their
 estimation capabilities. 
Journal: Journal of Applied Statistics 
Pages: 711-722 
Issue: 6 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723440 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723440
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:6:p:711-722




Template-Type: ReDIF-Article 1.0
Author-Name: Ram Mudambi 
Author-X-Name-First: Ram 
Author-X-Name-Last: Mudambi 
Title: Estimating turning points using polynomial regression 
Abstract:
  This paper describes a method for estimating regime switches in
 non-monotonic relationships, using polynomial regressions. Data from the
 UK financial services industry are used to illustrate the technique. The
 methodology provides a means of statistically ascertaining the existence
 of turning points, as well as a means of locating them, should they exist.
 While the methodology is most suited to applications that involve
 cross-sectional data, it may also be useful in short-horizon time series
 turning point prediction. 
Journal: Journal of Applied Statistics 
Pages: 723-732 
Issue: 6 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723459 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723459
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:6:p:723-732




Template-Type: ReDIF-Article 1.0
Author-Name: Eileen O'Donnell 
Author-X-Name-First: Eileen 
Author-X-Name-Last: O'Donnell 
Author-Name: G. Geoffrey Vining 
Author-X-Name-First: G. Geoffrey 
Author-X-Name-Last: Vining 
Title: Mean squared error of prediction approach to the analysis of a combined array 
Abstract:
  The combined array provides a powerful, more statistically rigorous
 alternative to Taguchi's crossed-array approach to robust parameter
 design. The combined array assumes a single linear model in the control
 and the noise factors. One may then find conditions for the control
 factors which will minimize an appropriate loss function that involves the
 noise factors. The most appropriate loss function is often simply the
 resulting process variance, recognizing that the noise factors are
 actually random effects in the process. Because the major focus of such an
 experiment is to optimize the estimated process variance, it is vital to
 understand the resulting prediction properties. This paper develops the
 mean squared error for the estimated process variance for the combined
 array approach, under the assumption that the model is correctly
 specified. Specific combined arrays are compared for robustness. A
 practical example outlines how this approach may be used to select
 appropriate combined arrays within a particular experimental situation. 
Journal: Journal of Applied Statistics 
Pages: 733-746 
Issue: 6 
Volume: 24 
Year: 1997 
X-DOI: 10.1080/02664769723468 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769723468
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:24:y:1997:i:6:p:733-746




Template-Type: ReDIF-Article 1.0
Author-Name: Eric Hillson 
Author-X-Name-First: Eric 
Author-X-Name-Last: Hillson 
Author-Name: Jaxk Reeves 
Author-X-Name-First: Jaxk 
Author-X-Name-Last: Reeves 
Author-Name: Charlotte Mcmillan 
Author-X-Name-First: Charlotte 
Author-X-Name-Last: Mcmillan 
Title: A statistical signalling model for use in surveillance of adverse drug reaction data 
Abstract:
  This paper presents a statistically superior lag-adjusted model for
 detecting increased frequency of reports of adverse drug event (ADE)
 rates. The effect of a significant lag time between ADE occurrence and
 report dates is studied. The approach in this paper to analyzing ADE data
 of this nature involves proposing a statistical model that utilizes a lag
 density function. The statistical method proposed was the development of
 an 'exact' procedure to monitor drugs that have a low incidence of ADEs.
 The approach determines statistically whether a change in the frequency of
 a specific ADE exists between two predetermined time intervals. There
 exist immense public health implications associated with the early
 detection of serious ADEs. The reduced risk of unfavorable outcomes
 associated with medication therapy is the goal of all involved. Simulated
 illustrations and discussion are provided, along with a detailed FORTRAN
 program used to implement the newly suggested lag-adjusted procedure. 
Journal: Journal of Applied Statistics 
Pages: 23-40 
Issue: 1 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769823287 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769823287
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:1:p:23-40




Template-Type: ReDIF-Article 1.0
Author-Name: Sang-Jun Park 
Author-X-Name-First: Sang-Jun 
Author-X-Name-Last: Park 
Author-Name: Bong-Jin Yum 
Author-X-Name-First: Bong-Jin 
Author-X-Name-Last: Yum 
Title: Optimal design of accelerated life tests under modified stress loading methods 
Abstract:
  Most of the previous work on optimal design of accelerated life test
 (ALT) plans has assumed instantaneous changes in stress levels, which may
 not be possible or desirable in practice, because of the limited
 capability of test equipment, possible stress shocks or the presence of
 undesirable failure modes. We consider the case in which stress levels are
 changed at a finite rate, and develop two types of ALT plan under the
 assumptions of exponential lifetimes of test units and type I censoring.
 One type of plan is the modified step-stress ALT plan, and the other type
 is the modified constant-stress ALT plan. These two plans are compared in
 terms of the asymptotic variance of the maximum likelihood estimator of
 the log mean lifetime for the use condition (i.e. avar\[ln (0)]).
 Computational results indicate that, for both types of plan, avar\[ln (0)]
 is not sensitive to the stress-increasing rate R, if R is greater than or
 equal to 10, say, in the standardized scale. This implies that the
 proposed stress loading method can be used effectively with little loss in
 statistical efficiency. In terms of avar\[ln (0)], the modified
 step-stress ALT generally performs better than the modified
 constant-stress ALT, unless R or the probability of failure until the
 censoring time under a certain stress-increasing rate is small. We also
 compare the progressive-stress ALT plan with the above two modified ALT
 plans in terms of avar\[ln (0)], using the optimal stress-increasing rate
 R* determined for the progressivestress ALT plan. We find that the
 proposed ALTs perform better than the progressivestress ALT for the
 parameter values considered. 
Journal: Journal of Applied Statistics 
Pages: 41-62 
Issue: 1 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769823296 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769823296
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:1:p:41-62




Template-Type: ReDIF-Article 1.0
Author-Name: John Stonehouse 
Author-X-Name-First: John 
Author-X-Name-Last: Stonehouse 
Author-Name: Guy Forrester 
Author-X-Name-First: Guy 
Author-X-Name-Last: Forrester 
Title: Robustness of the t and U tests under combined assumption violations 
Abstract:
  When the assumptions of parametric statistical tests for the difference
 between two means are violated, it is commonly advised that non-parametric
 tests are a more robust substitute. The history of the investigation of
 this issue is summarized. The robustness of the t -test was evaluated, by
 repeated computer testing for differences between samples from two
 populations of equal means but non-normal distributions and with different
 variances and sample sizes. Two common alternatives to t -Welch's
 approximate t and the Mann-Whitney U -test-were evaluated in the same way.
 The t -test is sufficiently robust for use in all likely cases, except
 when skew is severe or when population variances and sample sizes both
 differ. The Welch test satisfactorily addressed the latter problem, but
 was itself sensitive to departures from normality. Contrary to its popular
 reputation, the U -test showed a dramatic 'lack of robustness' in many
 cases-largely because it is sensitive to population differences other than
 between means, so it is not properly a 'non-parametric analogue' of the t
 -test, as it is too often described. 
Journal: Journal of Applied Statistics 
Pages: 63-74 
Issue: 1 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769823304 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769823304
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:1:p:63-74




Template-Type: ReDIF-Article 1.0
Author-Name: Siu-Keung Tse 
Author-X-Name-First: Siu-Keung 
Author-X-Name-Last: Tse 
Author-Name: Hak-Keung Yuen 
Author-X-Name-First: Hak-Keung 
Author-X-Name-Last: Yuen 
Title: Expected experiment times for the Weibull distribution under progressive censoring with random removals 
Abstract:
  This paper considers the expected experiment times for
 Weibull-distributed lifetimes under type II progressive censoring, with
 the numbers of removals being random. The formula to compute the expected
 experiment times is given. A detailed numerical study of this expected
 time is carried out for different combinations of model parameters.
 Furthermore, the ratio of the expected experiment time under this type of
 progressive censoring to the expected experiment time under complete
 sampling is studied. 
Journal: Journal of Applied Statistics 
Pages: 75-83 
Issue: 1 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769823313 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769823313
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:1:p:75-83




Template-Type: ReDIF-Article 1.0
Author-Name: Wai-Yuan Tan 
Author-X-Name-First: Wai-Yuan 
Author-X-Name-Last: Tan 
Author-Name: Si Chin Tang 
Author-X-Name-First: Si Chin 
Author-X-Name-Last: Tang 
Author-Name: Sho Rong Lee 
Author-X-Name-First: Sho Rong 
Author-X-Name-Last: Lee 
Title: Estimation of HIV seroconversion and effects of age in the San Francisco homosexual population 
Abstract:
  Using San Francisco city clinic cohort data, we estimate the HIV
 seroconversion distribution by both non-parametric and parametric methods,
 and illustrate the effects of age on this distribution. The non-parametric
 methods include the Turnbull method, the Bacchetti method, the
 expectation, maximization and smoothing (EMS) method and the penalized
 spline method. The seroconversion density curves estimated by these
 nonparametric methods are of bimodal nature with obvious effects of age.
 As a result of the bimodal nature of the seroconversion curves, the
 parametric models considered are mixtures of two distributions taken from
 the generalized log-logistic distribution with three parameters, the
 Weibull distribution and the log-normal distribution. In terms of the
 logarithm of the likelihood values, it appears that the non-parametric
 methods with smoothing as well as without smoothing (i.e. the Turnbull
 method) provided much better fits than did the parametric models. Among
 the non-parametric methods, the EMS and the spline estimates are more
 appealing, because the unsmoothed Turnbull estimates are very unstable and
 because the Bacchetti estimates have a longer tail. Among the parametric
 models, the mixture of a generalized log-logistic distribution with three
 parameters and a Weibull distribution or a log-normal distribution
 provided better fits than did other mixtures of parametric models. 
Journal: Journal of Applied Statistics 
Pages: 85-102 
Issue: 1 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769823322 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769823322
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:1:p:85-102




Template-Type: ReDIF-Article 1.0
Author-Name: K. Govindaraju 
Author-X-Name-First: K. 
Author-X-Name-Last: Govindaraju 
Author-Name: S. Balamurali 
Author-X-Name-First: S. 
Author-X-Name-Last: Balamurali 
Title: Chain sampling plan for variables inspection 
Abstract:
  This paper extends the concept of chain sampling to variables inspection
 when the standard deviation of the normally distributed characteristic is
 known. A discussion of the shape of the known sigma single-sampling
 variables plan is given. The chain sampling plan for variables inspection
 will be useful when testing is costly or destructive. 
Journal: Journal of Applied Statistics 
Pages: 103-109 
Issue: 1 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769823331 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769823331
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:1:p:103-109




Template-Type: ReDIF-Article 1.0
Author-Name: Hongzhu Qiao 
Author-X-Name-First: Hongzhu 
Author-X-Name-Last: Qiao 
Author-Name: Chris Tsokos 
Author-X-Name-First: Chris 
Author-X-Name-Last: Tsokos 
Title: Best efficient estimates of the intensity function of the power law process 
Abstract:
  We develop a general statistical procedure to obtain linearly the best
 efficient estimate of existing estimations of the parameter of a
 probability process. This procedure is used to obtain the best efficient
 estimates of the shape parameter, the intensity failure function and its
 reciprocal of the power law process. These estimates are important in the
 study of reliability growth modelling. The effectiveness of our findings
 is illustrated analytically and numerically, using real data and numerical
 simulations. 
Journal: Journal of Applied Statistics 
Pages: 111-120 
Issue: 1 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769823340 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769823340
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:1:p:111-120




Template-Type: ReDIF-Article 1.0
Author-Name: M. Yahyah 
Author-X-Name-First: M. 
Author-X-Name-Last: Yahyah 
Author-Name: A. Baines 
Author-X-Name-First: A. 
Author-X-Name-Last: Baines 
Author-Name: D. N. Joanes 
Author-X-Name-First: D. N. 
Author-X-Name-Last: Joanes 
Title: Graphical approach to model adequacy based on exact and near replicates 
Abstract:
  In this paper, we present an intuitive graphical approach to model
 validity, which, although to some extent subjective, can be extremely
 valuable for both presentation and interpretation purposes. In particular,
 the idea behind such a procedure arises naturally through the generation
 of a sequence of elements derived from the residuals about a fitted
 graduating function, based on datum points that are identical or that are
 relatively close together in a multi-dimensional factor space. 
Journal: Journal of Applied Statistics 
Pages: 121-129 
Issue: 1 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769823359 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769823359
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:1:p:121-129




Template-Type: ReDIF-Article 1.0
Author-Name: J. E. Toler 
Author-X-Name-First: J. E. 
Author-X-Name-Last: Toler 
Author-Name: P. M. Burrows 
Author-X-Name-First: P. M. 
Author-X-Name-Last: Burrows 
Title: Genotypic performance over environmental arrays: A non-linear grouping protocol 
Abstract:
  A non-linear model for examining genotypic responses across an array of
 environments is contrasted with the 'joint regression' formulation, and a
 rigorous approach to hypothesis testing using the conditional error
 principle is demonstrated. The model is extended to cater for situations
 where single straight-line response patterns fail to characterize
 genotypic behaviors over an environmental array: a combination of two
 straight lines, with slope in below-average and in above-average
 environments, is offered as the 1 2 simplest representation of convex and
 concave patterns. A protocol for classifying genotypes according to the
 results of hypothesis tests, i.e. H( = ) and H( = = = 1), is 1 2 1 2
 presented . A doubly desirable response pattern is convex ( &#x3c; 1&#x3c;
 ), while a doubly 1 2 undesirable pattern is concave ( &#x3e; 1&#x3e; ). 1
 2 
Journal: Journal of Applied Statistics 
Pages: 131-143 
Issue: 1 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769823368 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769823368
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:1:p:131-143




Template-Type: ReDIF-Article 1.0
Author-Name: Myung Geun Kim 
Author-X-Name-First: Myung Geun 
Author-X-Name-Last: Kim 
Title: Local influence on a test of linear hypothesis in multiple regression model 
Abstract:
  The local influence method is adapted to investigate the influence of
 observations on testing the linear hypothesis. The method provides
 information about individually or jointly influential observations in
 performing the test, which the usual diagnostic methods do not yield. An
 example is presented for illustration of the method. 
Journal: Journal of Applied Statistics 
Pages: 145-152 
Issue: 1 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769823377 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769823377
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:1:p:145-152




Template-Type: ReDIF-Article 1.0
Author-Name: C. A. Glasbey 
Author-X-Name-First: C. A. 
Author-X-Name-Last: Glasbey 
Author-Name: K. V. Mardia 
Author-X-Name-First: K. V. 
Author-X-Name-Last: Mardia 
Title: A review of image-warping methods 
Abstract:
  Image warping is a transformation which maps all positions in one image
 plane to positions in a second plane. It arises in many image analysis
 problems, whether in order to remove optical distortions introduced by a
 camera or a particular viewing perspective, to register an image with a
 map or template, or to align two or more images. The choice of warp is a
 compromise between a smooth distortion and one which achieves a good
 match. Smoothness can be ensured by assuming a parametric form for the
 warp or by constraining it using differential equations. Matching can be
 specified by points to be brought into alignment, by local measures of
 correlation between images, or by the coincidence of edges. Parametric and
 non-parametric approaches to warping, and matching criteria, are reviewed. 
Journal: Journal of Applied Statistics 
Pages: 155-171 
Issue: 2 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769823151 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769823151
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:2:p:155-171




Template-Type: ReDIF-Article 1.0
Author-Name: Edvin Bredrup 
Author-X-Name-First: Edvin 
Author-X-Name-Last: Bredrup 
Author-Name: Li-Chun Zhang 
Author-X-Name-First: Li-Chun 
Author-X-Name-Last: Zhang 
Title: Imperfectly shuffled decks in bridge 
Abstract:
  In this paper, we study the distribution tables of imperfectly shuffled
 hands in a bridge game under a conditional Markov chain model, based on
 which a simple approximate test on the randomness of the decks is derived.
 The idea is to examine whether a stochastic process is a compound
 hypergeometric process, through the number of its ties, and it is easily
 adapted to similar situations. 
Journal: Journal of Applied Statistics 
Pages: 173-179 
Issue: 2 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769823160 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769823160
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:2:p:173-179




Template-Type: ReDIF-Article 1.0
Author-Name: James Koziol 
Author-X-Name-First: James 
Author-X-Name-Last: Koziol 
Title: A non-parametric index of tracking 
Abstract:
  A two-sample version of the non-parametric index of tracking for
 longitudinal data introduced by Foulkes and Davis is described. The index
 is based on a multivariate U -statistic, and provides a measure of the
 stochastic ordering of the underlying growth curves of the samples. The
 utility of the U -statistic approach is explored with two applications
 related to growth curves and repeated measures analyses. 
Journal: Journal of Applied Statistics 
Pages: 181-191 
Issue: 2 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769823179 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769823179
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:2:p:181-191




Template-Type: ReDIF-Article 1.0
Author-Name: James Taylor 
Author-X-Name-First: James 
Author-X-Name-Last: Taylor 
Author-Name: Derek Bunn 
Author-X-Name-First: Derek 
Author-X-Name-Last: Bunn 
Title: Combining forecast quantiles using quantile regression: Investigating the derived weights, estimator bias and imposing constraints 
Abstract:
  A novel proposal for combining forecast distributions is to use quantile
 regression to combine quantile estimates. We consider the usefulness of
 the resultant linear combining weights. If the quantile estimates are
 unbiased, then there is strong intuitive appeal for omitting the constant
 and constraining the weights to sum to unity in the quantile regression.
 However, we show that suppressing the constant renders one of the main
 attractive features of quantile regression invalid. We establish necessary
 and sufficient conditions for unbiasedness of a quantile estimate, and
 show that a combination with zero constant and weights that sum to unity
 is not necessarily unbiased. 
Journal: Journal of Applied Statistics 
Pages: 193-206 
Issue: 2 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769823188 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769823188
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:2:p:193-206




Template-Type: ReDIF-Article 1.0
Author-Name: A. B. M. Zohrul Kabir 
Author-X-Name-First: A. B. M. Zohrul 
Author-X-Name-Last: Kabir 
Title: Estimation of Weibull distribution parameters for irregular interval group failure data with unknown failure times 
Abstract:
  This paper presents three methods for estimating Weibull distribution
 parameters for the case of irregular interval group failure data with
 unknown failure times. The methods are based on the concepts of the
 piecewise linear distribution function (PLDF), an average interval failure
 rate (AIFR) and sequential updating of the distribution function (SUDF),
 and use an analytical approach similar to that of Ackoff and Sasieni for
 regular interval group data. Results from a large number of simulated case
 problems generated with specified values of Weibull distribution
 parameters have been presented, which clearly indicate that the SUDF
 method produces near-perfect parameter estimates for all types of failure
 pattern. The performances of the PLDF and AIFR methods have been evaluated
 by goodness-of-fit testing and statistical confidence limits on the shape
 parameter. It has been found that, while the PLDF method produces
 acceptable parameter estimates, the AIFR method may fail for low and high
 shape parameter values that represent the cases of random and wear-out
 types of failure. A real-life application of the proposed methods is also
 presented, by analyzing failures of hydrogen make-up compressor valves in
 a petroleum refinery. 
Journal: Journal of Applied Statistics 
Pages: 207-219 
Issue: 2 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769823197 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769823197
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:2:p:207-219




Template-Type: ReDIF-Article 1.0
Author-Name: Mukhtar Ali 
Author-X-Name-First: Mukhtar 
Author-X-Name-Last: Ali 
Title: Probability models on horse-race outcomes 
Abstract:
  A number of models have been examined for modelling probability based on
 rankings. Most prominent among these are the gamma and normal probability
 models. The accuracy of these models in predicting the outcomes of horse
 races is investigated in this paper. The parameters of these models are
 estimated by the maximum likelihood method, using the information on win
 pool fractions. These models are used to estimate the probabilities that
 race entrants finish second or third in a race. These probabilities are
 then compared with the corresponding objective probabilities estimated
 from actual race outcomes. The data are obtained from over 15 000 races.
 it is found that all the models tend to overestimate the probability of a
 horse finishing second or third when the horse has a high probability of
 such a result, but underestimate the probability of a horse finishing
 second or third when this probability is low. 
Journal: Journal of Applied Statistics 
Pages: 221-229 
Issue: 2 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769823205 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769823205
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:2:p:221-229




Template-Type: ReDIF-Article 1.0
Author-Name: K. V. Mardia 
Author-X-Name-First: K. V. 
Author-X-Name-Last: Mardia 
Title: Fisher's repeated normal integral function and shape distributions 
Abstract:
  Fisher has presented various applications of the repeated integral of the
 normal distribution function. A new application has appeared in shape
 distributions. This work has led to the search for an explicit expression
 for the repeated normal integral function in place of an infinite series
 expansion for this function first given by Fisher. We provide an explicit
 expression for this function with a finite number of terms. 
Journal: Journal of Applied Statistics 
Pages: 231-235 
Issue: 2 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769823214 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769823214
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:2:p:231-235




Template-Type: ReDIF-Article 1.0
Author-Name: Dalton Andrade 
Author-X-Name-First: Dalton 
Author-X-Name-Last: Andrade 
Author-Name: Julio Singer 
Author-X-Name-First: Julio 
Author-X-Name-Last: Singer 
Title: Profile analysis for randomized complete block experiments 
Abstract:
  We consider the use of standard univariate and multivariate methods for
 profile analysis of randomized complete block experiments. Although the
 analysis for the case where the block time interaction is included in the
 model parallels that used for factorial experiments, situations where such
 interaction is not present may not be handled in the same way. We identify
 hypotheses for which the standard analysis may be applied, as well as
 those for which some adaptation is required. We also indicate how to
 implement such analyses via existing computer software. 
Journal: Journal of Applied Statistics 
Pages: 237-244 
Issue: 2 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769823223 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769823223
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:2:p:237-244




Template-Type: ReDIF-Article 1.0
Author-Name: Jin Zhang 
Author-X-Name-First: Jin 
Author-X-Name-Last: Zhang 
Title: Tests for multiple upper or lower outliers in an exponential sample 
Abstract:
  T = \[x + &#x2026; + x ]/ Sigma x (T*= \[x + &#x2026; + x ] Sigma x ) is
 the max k (n- k+ 1 ) (n) i k ( 1 ) (k) i imum likelihood ratio test
 statistic for k upper ( lower ) outliers in an exponential sample x ,
 &#x2026;, x . The null distributions of T for k= 1,2 were given by Fisher
 and by Kimber 1 n k and Stevens , while those of T*(k= 1,2) were given by
 Lewis and Fieller . In this paper , k the simple null distributions of T
 and T* are found for all possible values of k, and k k percentage points
 are tabulated for k= 1, 2, &#x2026;, 8. In addition , we find a way of
 determining k, which can reduce the masking or ' swamping ' effects . 
Journal: Journal of Applied Statistics 
Pages: 245-255 
Issue: 2 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769823232 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769823232
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:2:p:245-255




Template-Type: ReDIF-Article 1.0
Author-Name: Jin Zhang 
Author-X-Name-First: Jin 
Author-X-Name-Last: Zhang 
Author-Name: Xueren Wang 
Author-X-Name-First: Xueren 
Author-X-Name-Last: Wang 
Title: Unmasking test for multiple upper or lower outliers in normal samples 
Abstract:
  The discordancy test for multiple outliers is complicated by problems of
 masking and swamping. The key to the settlement of the question lies in
 the determination of k , i.e. the number of 'contaminants' in a sample.
 Great efforts have been made to solve this problem in recent years, but no
 effective method has been developed. In this paper, we present two ways of
 determining k , free from the effects of masking and swamping, when
 testing upper (lower) outliers in normal samples. Examples are given to
 illustrate the methods. 
Journal: Journal of Applied Statistics 
Pages: 257-261 
Issue: 2 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769823241 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769823241
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:2:p:257-261




Template-Type: ReDIF-Article 1.0
Author-Name: D. R. Anderson 
Author-X-Name-First: D. R. 
Author-X-Name-Last: Anderson 
Author-Name: K. P. Burnham 
Author-X-Name-First: K. P. 
Author-X-Name-Last: Burnham 
Author-Name: G. C. White 
Author-X-Name-First: G. C. 
Author-X-Name-Last: White 
Title: Comparison of Akaike information criterion and consistent Akaike information criterion for model selection and statistical inference from capture-recapture studies 
Abstract:
  We compare properties of parameter estimators under Akaike information
 criterion (AIC) and 'consistent' AIC (CAIC) model selection in a nested
 sequence of open population capture-recapture models. These models consist
 of product multinomials, where the cell probabilities are parameterized in
 terms of survival ( ) and capture ( p ) i i probabilities for each time
 interval i . The sequence of models is derived from 'treatment' effects
 that might be (1) absent, model H ; (2) only acute, model H ; or (3) acute
 and 0 2 p chronic, lasting several time intervals, model H . Using a 35
 factorial design, 1000 3 repetitions were simulated for each of 243 cases.
 The true number of parameters ranged from 7 to 42, and the sample size
 ranged from approximately 470 to 55 000 per case. We focus on the quality
 of the inference about the model parameters and model structure that
 results from the two selection criteria. We use achieved confidence
 interval coverage as an integrating metric to judge what constitutes a
 'properly parsimonious' model, and contrast the performance of these two
 model selection criteria for a wide range of models, sample sizes,
 parameter values and study interval lengths. AIC selection resulted in
 models in which the parameters were estimated with relatively little bias.
 However, these models exhibited asymptotic sampling variances that were
 somewhat too small, and achieved confidence interval coverage that was
 somewhat below the nominal level. In contrast, CAIC-selected models were
 too simple, the parameter estimators were often substantially biased, the
 asymptotic sampling variances were substantially too small and the
 achieved coverage was often substantially below the nominal level. An
 example case illustrates a pattern: with 20 capture occasions, 300
 previously unmarked animals are released at each occasion, and the
 survival and capture probabilities in the control group on each occasion
 were 0.9 and 0.8 respectively using model H . There was a strong acute
 treatment effect 3 on the first survival ( ) and first capture probability
 ( p ), and smaller, chronic effects 1 2 on the second and third survival
 probabilities ( and ) as well as on the second capture 2 3 probability ( p
 ); the sample size for each repetition was approximately 55 000. CAIC 3
 selection led to a model with exactly these effects in only nine of the
 1000 repetitions, compared with 467 times under AIC selection. Under CAIC
 selection, even the two acute effects were detected only 555 times,
 compared with 998 for AIC selection. AIC selection exhibited a balance
 between underfitted and overfitted models (270 versus 263), while CAIC
 tended strongly to select underfitted models. CAIC-selected models were
 overly parsimonious and poor as a basis for statistical inferences about
 important model parameters or structure. We recommend the use of the AIC
 and not the CAIC for analysis and inference from capture-recapture data
 sets. 
Journal: Journal of Applied Statistics 
Pages: 263-282 
Issue: 2 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769823250 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769823250
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:2:p:263-282




Template-Type: ReDIF-Article 1.0
Author-Name: Victor Guerrero 
Author-X-Name-First: Victor 
Author-X-Name-Last: Guerrero 
Author-Name: Edmundo Berumen 
Author-X-Name-First: Edmundo 
Author-X-Name-Last: Berumen 
Title: Forecasting electricity consumption with extra-model information provided by consumers 
Abstract:
  Univariate time series models make efficient use of available historical
 records of electricity consumption for short-term forecasting. However,
 the information (expectations) provided by electricity consumers in an
 energy-saving survey, even though qualitative, was considered to be
 particularly important, because the consumers' perception of the future
 may take into account the changing economic conditions. Our approach to
 forecasting electricity consumption combines historical data with
 expectations of the consumers in an optimal manner, using the technique of
 restricted forecasts. The same technique can be applied in some other
 forecasting situations in which additional information-besides the
 historical record of a variable-is available in the form of expectations. 
Journal: Journal of Applied Statistics 
Pages: 283-299 
Issue: 2 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769823269 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769823269
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:2:p:283-299




Template-Type: ReDIF-Article 1.0
Author-Name: Hans-Joachim Mittag 
Author-X-Name-First: Hans-Joachim 
Author-X-Name-Last: Mittag 
Author-Name: Dietmar Stemann 
Author-X-Name-First: Dietmar 
Author-X-Name-Last: Stemann 
Title: Gauge imprecision effect on the performance of the X-S control chart 
Abstract:
  This paper examines the effect of stochastic measurement error (gauge
 imprecision) on the performance of Shewhart-type X-S control charts. It is
 shown that gauge imprecision may seriously affect the ability of the chart
 to detect process disturbances quickly or, depending on the point in time
 when the error occurs, the probability of erroneously signalling an
 out-of-control process state. 
Journal: Journal of Applied Statistics 
Pages: 307-317 
Issue: 3 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769823043 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769823043
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:3:p:307-317




Template-Type: ReDIF-Article 1.0
Author-Name: Duolao Wang 
Author-X-Name-First: Duolao 
Author-X-Name-Last: Wang 
Author-Name: Mike Murphy 
Author-X-Name-First: Mike 
Author-X-Name-Last: Murphy 
Title: Use of a mixture model for the analysis of contraceptive-use duration among long-term users 
Abstract:
  This paper introduces a mixture model that combines proportional hazards
 regression with logistic regression for the analysis of survival data, and
 describes its parameter estimation via an expectation maximization
 algorithm. The mixture model is then applied to analyze the determinants
 of the timing of intrauterine device (IUD) discontinuation and long-term
 IUD use, utilizing 14 639 instances of IUD use by Chinese women. The
 results show that socio-economic and demographic characteristics of women
 have different influences on the acceleration or deceleration of the
 timing of stopping IUD use and on the likelihood of long-term IUD use. 
Journal: Journal of Applied Statistics 
Pages: 319-332 
Issue: 3 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769823052 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769823052
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:3:p:319-332




Template-Type: ReDIF-Article 1.0
Author-Name: Michael Sherman 
Author-X-Name-First: Michael 
Author-X-Name-Last: Sherman 
Author-Name: F. Michael Speed 
Author-X-Name-First: F. Michael 
Author-X-Name-Last: Speed 
Author-Name: F. Michael Speed 
Author-X-Name-First: F. Michael 
Author-X-Name-Last: Speed 
Title: Analysis of tidal data via the blockwise bootstrap 
Abstract:
  We analyze tidal data from Port Mansfield, TX, using Kunsch's blockwise
 bootstrap in the regression setting. In particular, we estimate the
 variability of parameter estimates in a harmonic analysis via block
 subsampling of residuals from a least-squares fit. We see that naive
 least-squares variance estimates can be either too large or too small,
 depending on the strength of correlation and the design matrix. We argue
 that the block bootstrap is a simple, omnibus method of accounting for
 correlation in a regression model with correlated errors. 
Journal: Journal of Applied Statistics 
Pages: 333-340 
Issue: 3 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769823061 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769823061
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:3:p:333-340




Template-Type: ReDIF-Article 1.0
Author-Name: R. Vijayaraghavan 
Author-X-Name-First: R. 
Author-X-Name-Last: Vijayaraghavan 
Author-Name: V. Soundararajan 
Author-X-Name-First: V. 
Author-X-Name-Last: Soundararajan 
Title: Design and evaluation of skip-lot sampling inspection plans with double-sampling plan as the reference plan 
Abstract:
  This paper presents a design for skip-lot sampling inspection plans with
 the double-sampling plan as the reference plan, so as to reduce the sample
 size and produce more efficient plans in return for the same sampling
 effort. The efficiency of the proposed plan compared with that of the
 conventional double-sampling plan is also discussed. The need for smaller
 acceptance numbers under the plan is highlighted. Methods of selecting the
 plan indexed by the acceptable quality level and limiting quality level,
 and by the acceptable quality level and average outgoing quality level are
 also presented. 
Journal: Journal of Applied Statistics 
Pages: 341-348 
Issue: 3 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769823070 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769823070
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:3:p:341-348




Template-Type: ReDIF-Article 1.0
Author-Name: D. K. Ghosh 
Author-X-Name-First: D. K. 
Author-X-Name-Last: Ghosh 
Author-Name: S. C. Bagui 
Author-X-Name-First: S. C. 
Author-X-Name-Last: Bagui 
Title: Identification of confounded design and its interactions 
Abstract:
  Kane has discussed a simple method for identifying the confounded
 interactions from 2n factorial experiments when a replication consists of
 (1) two blocks and (2) more than two blocks. It should be noted that
 Kane's method holds only for (1) regular design and (2) when one
 interaction is confounded. In the present investigation, we proposed a new
 way of identifying the confounded designs and the confounded interactions
 in 2n factorial experiments. Furthermore, the same method is extended to
 3n and Sn factorial experiments. 
Journal: Journal of Applied Statistics 
Pages: 349-356 
Issue: 3 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769823089 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769823089
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:3:p:349-356




Template-Type: ReDIF-Article 1.0
Author-Name: M. M. Shoukri 
Author-X-Name-First: M. M. 
Author-X-Name-Last: Shoukri 
Author-Name: M. Attanasio 
Author-X-Name-First: M. 
Author-X-Name-Last: Attanasio 
Author-Name: J. M. Sargeant 
Author-X-Name-First: J. M. 
Author-X-Name-Last: Sargeant 
Title: Parametric versus semi-parametric models for the analysis of correlated survival data: A case study in veterinary epidemiology 
Abstract:
  Correlated survival data arise frequently in biomedical and epidemiologic
 research, because each patient may experience multiple events or because
 there exists clustering of patients or subjects, such that failure times
 within the cluster are correlated. In this paper, we investigate the
 appropriateness of the semi-parametric Cox regression and of the
 generalized estimating equations as models for clustered failure time data
 that arise from an epidemiologic study in veterinary medicine. The
 semi-parametric approach is compared with a proposed fully parametric
 frailty model. The frailty component is assumed to follow a gamma
 distribution. Estimates of the fixed covariates effects were obtained by
 maximizing the likelihood function, while an estimate of the variance
 component ( frailty parameter) was obtained from a profile likelihood
 construction. 
Journal: Journal of Applied Statistics 
Pages: 357-374 
Issue: 3 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769823098 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769823098
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:3:p:357-374




Template-Type: ReDIF-Article 1.0
Author-Name: Christopher Todd Edwards 
Author-X-Name-First: Christopher Todd 
Author-X-Name-Last: Edwards 
Title: Non-parametric procedure for knockout tournaments 
Abstract:
  In a seeded knockout tournament, where teams have some preassigned
 strength, do we have any assurances that the best team in fact has won? Is
 there some insight to be gained by considering which teams beat which
 other teams solely examining the seeds? We pose an answer to these
 questions by using the difference in the seeds of the two players as the
 basis for a test statistic. We offer several models for the underlying
 probability structure to examine the null distribution and power functions
 and determine these for small tournaments (less than five teams). One
 structure each for 8 teams and 16 teams is examined, and we conjecture an
 asymptotic normal distribution for the test statistic. 
Journal: Journal of Applied Statistics 
Pages: 375-385 
Issue: 3 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769823106 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769823106
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:3:p:375-385




Template-Type: ReDIF-Article 1.0
Author-Name: Sadao Tomizawa 
Author-X-Name-First: Sadao 
Author-X-Name-Last: Tomizawa 
Author-Name: Takashi Seo 
Author-X-Name-First: Takashi 
Author-X-Name-Last: Seo 
Author-Name: Hideharu Yamamoto 
Author-X-Name-First: Hideharu 
Author-X-Name-Last: Yamamoto 
Title: Power-divergence-type measure of departure from symmetry for square contingency tables that have nominal categories 
Abstract:
  For square contingency tables that have nominal categories, Tomizawa
 considered two kinds of measure to represent the degree of departure from
 symmetry. This paper proposes a generalization of those measures. The
 proposed measure is expressed by using the average of the power divergence
 of Cressie and Read, or the average of the diversity index of Patil and
 Taillie. Special cases of the proposed measure include Tomizawa's
 measures. The proposed measure would be useful for comparing the degree of
 departure from symmetry in several tables. 
Journal: Journal of Applied Statistics 
Pages: 387-398 
Issue: 3 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769823115 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769823115
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:3:p:387-398




Template-Type: ReDIF-Article 1.0
Author-Name: K. Govindaraju 
Author-X-Name-First: K. 
Author-X-Name-Last: Govindaraju 
Author-Name: S. Ganesalingam 
Author-X-Name-First: S. 
Author-X-Name-Last: Ganesalingam 
Title: Zero acceptance number quick switching system for compliance sampling 
Abstract:
  The zero acceptance number plan is invariably used for compliance
 sampling and safety inspection of products. The disadvantage of such a
 plan is that its discriminating power between good and bad lots is poor.
 This paper presents a quick switching system that has zero acceptance
 numbers, with a provision for the resubmission of lots not accepted during
 normal inspection. The proposed system is found to require a smaller
 average sample size, and possesses greater discriminating power. 
Journal: Journal of Applied Statistics 
Pages: 399-407 
Issue: 3 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769823124 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769823124
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:3:p:399-407




Template-Type: ReDIF-Article 1.0
Author-Name: F. Javier Trivez 
Author-X-Name-First: F. Javier 
Author-X-Name-Last: Trivez 
Author-Name: Javier Nievas 
Author-X-Name-First: Javier 
Author-X-Name-Last: Nievas 
Title: Analyzing the effects of level shifts and temporary changes on the identification of ARIMA models 
Abstract:
  The presence of outliers in time series gives rise to important effects
 on the sample autocorrelation coefficients. In the case where these
 outliers are not adequately treated, their presence causes errors in the
 identification of the stochastic process generator of the time series
 under study. In this respect, Chan has demonstrated that, independent of
 the underlying process of the outlier-free series, a level shift (LS) at
 the limit (i.e. asymptotically and considering an LS of a sufficiently
 large size) will lead to the identification of non-stationary processes;
 with respect to a temporary change (TC), this will lead, again at the
 limit, to the identification of an AR(1) autoregressive process with a
 coefficient equal to the dampening factor that defines this TC. The
 objective of this paper is to analyze, by way of a simulation exercise,
 how large the LS and TC present in the time series must be for the
 limiting result to be relevant, in the sense of seriously affecting the
 instruments used at the identification stage of the ARIMA models, i.e. the
 sample autocorrelation function and the sample partial autocorrelation
 function. 
Journal: Journal of Applied Statistics 
Pages: 409-424 
Issue: 3 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769823133 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769823133
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:3:p:409-424




Template-Type: ReDIF-Article 1.0
Author-Name: Stan Li 
Author-X-Name-First: Stan 
Author-X-Name-Last: Li 
Title: Bayesian object matching 
Abstract:
  A Bayesian approach to object matching is presented. An object and a
 scene are each represented by features, such as critical points, line
 segments and surface patches, constrained by unary properties and
 contextual relations. The matching is presented as a labeling problem,
 where each feature in the scene is assigned (associated with) a feature of
 the known model objects. The prior distribution of a scene's labeling is
 modeled as a Markov random field, which encodes the between-object
 constraints. The conditional distribution of the observed features labeled
 is assumed to be Gaussian, which encodes the within-object constraints. An
 optimal solution is defined as a maximum a posteriori estimate.
 Relationships with previous work are discussed. Experimental results are
 shown. 
Journal: Journal of Applied Statistics 
Pages: 425-443 
Issue: 3 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769823142 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769823142
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:3:p:425-443




Template-Type: ReDIF-Article 1.0
Author-Name: K. Govindaraju 
Author-X-Name-First: K. 
Author-X-Name-Last: Govindaraju 
Author-Name: S. Balamurali 
Author-X-Name-First: S. 
Author-X-Name-Last: Balamurali 
Title: Tightened single-level continuous sampling plan 
Abstract:
  In this paper, a new tightening concept has been incorporated into the
 single-level continuous sampling plan CSP-1, such that quality degradation
 will warrant sampling inspection to cease beyond a certain number of
 sampled items, until new evidence of good quality is established. The
 expressions of the performance measures for this new plan, such as the
 operating characteristic, average outgoing quality and average fraction
 inspected, are derived using a Markov chain model. The advantage of the
 tightened CSP-1 plan is that it is possible to lower the average outgoing
 quality limit. 
Journal: Journal of Applied Statistics 
Pages: 451-461 
Issue: 4 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769822945 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769822945
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:4:p:451-461




Template-Type: ReDIF-Article 1.0
Author-Name: Fritz Efaw 
Author-X-Name-First: Fritz 
Author-X-Name-Last: Efaw 
Title: Test of alternative strike settlement models 
Abstract:
  This paper extends our understanding of what determines the length of
 strikes, by comparing two alternative models of the strike settlement
 process, while simultaneously allowing for variation in this process as a
 result of economic conditions and unobserved heterogeneity. Some
 inconclusive support is found for the view that settlement proceeds by one
 or both sides presenting terms for acceptance or rejection, rather than by
 both sides yielding ground toward an intermediate position. Strike
 duration is found to be shortest at peaks of business cycles, and
 settlement is not duration dependent. 
Journal: Journal of Applied Statistics 
Pages: 463-474 
Issue: 4 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769822954 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769822954
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:4:p:463-474




Template-Type: ReDIF-Article 1.0
Author-Name: Anita Ghatak 
Author-X-Name-First: Anita 
Author-X-Name-Last: Ghatak 
Title: Aggregate consumption functions for India: A cointegration analysis under structural changes, 1919-86 
Abstract:
  This paper extends cointegration methodology to include the effect of
 possible structural changes on aggregate consumption behaviour in India
 during 1919-86. The only cointegrated relation is found to be a dynamic
 linear regression of lag order two, with 1944 as the year in which
 structural change began. The estimated short-run marginal propensity to
 consume (MPC) is greater than the long-run MPC. The estimates of the MPC
 are different from previous estimates for the Indian economy based on
 conventional econometrics. The initial year of structural change has been
 selected by extending the method of Perron and that of Zivot and Andrews. 
Journal: Journal of Applied Statistics 
Pages: 475-488 
Issue: 4 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769822963 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769822963
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:4:p:475-488




Template-Type: ReDIF-Article 1.0
Author-Name: Gary Koop 
Author-X-Name-First: Gary 
Author-X-Name-Last: Koop 
Title: Carbon dioxide emissions and economic growth: A structural approach 
Abstract:
  This paper uses data for 44 countries from 1970-1990, to investigate the
 relationship between economic growth and carbon dioxide emissions.
 Empirical results are obtained from a structural model from the empirical
 growth literature modified to include environmental 'bads'. Results
 suggest that richer countries exhibit technical progress in a way that
 economizes on carbon dioxide emissions but that poorer countries do not.
 Furthermore, there is no indication that the growth process is leading
 poorer countries to move towards the adoption of the same
 pollution-ameliorating technology as characterizes richer countries. 
Journal: Journal of Applied Statistics 
Pages: 489-515 
Issue: 4 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769822972 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769822972
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:4:p:489-515




Template-Type: ReDIF-Article 1.0
Author-Name: Alexei Dmitrienko 
Author-X-Name-First: Alexei 
Author-X-Name-Last: Dmitrienko 
Author-Name: Z. Govindarajulu 
Author-X-Name-First: Z. 
Author-X-Name-Last: Govindarajulu 
Title: The 'demon' problem of Youden: Exponential case 
Abstract:
  We consider the problem of finding the probability of a sample mean
 falling above the (n - k)th-order statistic in a random sample of size n.
 Explicit expressions are obtained for the exponential distribution. Some
 applications that pertain to testing for outliers and goodness of fit are
 given. 
Journal: Journal of Applied Statistics 
Pages: 517-523 
Issue: 4 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769822981 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769822981
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:4:p:517-523




Template-Type: ReDIF-Article 1.0
Author-Name: Alan Hutson 
Author-X-Name-First: Alan 
Author-X-Name-Last: Hutson 
Title: Direct estimation of the percentile 'p-value' for the one-sample median test 
Abstract:
  In this paper we outline and illustrate an easy-to-use inference
 procedure for directly calculating the approximate bootstrap
 percentile-type p-value for the one-sample median test, i.e. we calculate
 the bootstrap p -value without resampling, by using a fractional order
 statistics based approach. The method parallels earlier work on
 fractionalorder-statistics-based non-parametric bootstrap percentile-type
 confidence intervals for quantiles. Monte Carlo simulation studies are
 performed, which illustrate that the fractional-order-statistics-based
 approach to the one-sample median test has accurate type I error control
 for small samples over a wide range of distributions; is easy to
 calculate; and is preferable to the sign test in terms of type I error
 control and power. Furthermore, the fractional-order-statistics-based
 median test is easily generalized to testing that any quantile has some
 hypothesized value; for example, tests for the upper or lower quartile may
 be performed using the same framework. 
Journal: Journal of Applied Statistics 
Pages: 525-533 
Issue: 4 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769822990 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769822990
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:4:p:525-533




Template-Type: ReDIF-Article 1.0
Author-Name: C. D. Lai 
Author-X-Name-First: C. D. 
Author-X-Name-Last: Lai 
Author-Name: K. Govindaraju 
Author-X-Name-First: K. 
Author-X-Name-Last: Govindaraju 
Author-Name: M. Xie 
Author-X-Name-First: M. 
Author-X-Name-Last: Xie 
Title: Effects of correlation on fraction non-conforming statistical process control procedures 
Abstract:
  High-yield production processes that involve a low fraction
 non-conforming are becoming more common, and the limitations of the
 standard control charting procedures for such processes are well known.
 This paper examines the control procedures based on the conforming unit
 run lengths applied to near-zero-defect processes in the presence of
 serial correlation. Using a correlation binomial model, a few control
 schemes are investigated and control limits are derived. The results
 reduce to the traditional case when the measurements are independent.
 However, it is shown that the false alarm rate cannot be reduced to below
 the amount of serial correlation present in the process. 
Journal: Journal of Applied Statistics 
Pages: 535-543 
Issue: 4 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769823007 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769823007
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:4:p:535-543




Template-Type: ReDIF-Article 1.0
Author-Name: Alan Veevers 
Author-X-Name-First: Alan 
Author-X-Name-Last: Veevers 
Title: Viability and capability indexes for multiresponse processes 
Abstract:
  The viability index Vr is introduced as an intuitively appealing measure
 of the capability potential of a process. It is related to the well-known
 index Cp but has some advantages over it. The statistical properties of Vr
 are readily obtainable and, unlike Cp, it extends naturally to
 multi-response processes. The multivariate viability index Vrn is defined,
 discussed and illustrated using an example from the minerals sector. 
Journal: Journal of Applied Statistics 
Pages: 545-558 
Issue: 4 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769823016 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769823016
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:4:p:545-558




Template-Type: ReDIF-Article 1.0
Author-Name: Nien Fan Zhang 
Author-X-Name-First: Nien Fan 
Author-X-Name-Last: Zhang 
Title: Estimating process capability indexes for autocorrelated data 
Abstract:
  Process capability indexes are widely used in the manufacturing
 industries and by supplier companies in process assessments and in the
 evaluation of purchasing decisions. One concern about using the process
 capability indexes is the assumption of the mutual independence of the
 process data, because, in process industries, process data are often
 autocorrelated. This paper discusses the use of the process capability
 indexes Cp and Cpk when the process data are autocorrelated. Interval
 estimation procedures for Cp and Cpk are proposed and their properties are
 studied. 
Journal: Journal of Applied Statistics 
Pages: 559-574 
Issue: 4 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769823025 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769823025
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:4:p:559-574




Template-Type: ReDIF-Article 1.0
Author-Name: Anita Ghatak 
Author-X-Name-First: Anita 
Author-X-Name-Last: Ghatak 
Title: Vector autoregression modelling and forecasting growth of South Korea 
Abstract:
	      In this paper, we have estimated vector
 autoregression (VAR), Bayesian vector autoregression (BVAR) and vector
 error-correction models (VECMs) using annual time-series data of South
 Korea for 1950-94. We find evidence supporting the view that growth of
 real per-capita income has been aided by income, investment and export
 growth, as well as government spending and exchange rate policies. The
 VECMs provide better forecasts of growth than do the VAR and BVAR models
 for both short-term and long-term predictions. 	 
Journal: Journal of Applied Statistics 
Pages: 579-592 
Issue: 5 
Volume: 25 
Year: 1998 
Month: 6 
X-DOI: 10.1080/02664769822837 
File-URL: http://hdl.handle.net/10.1080/02664769822837 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:5:p:579-592




Template-Type: ReDIF-Article 1.0
Author-Name: Larisa Matejic 
Author-X-Name-First: Larisa 
Author-X-Name-Last: Matejic 
Title: Testing for brain anomalies: A hippocampus study 
Abstract:
	      A mathematical classification method is
 presented to show how numerical tests for abnormal anatomical shape change
 can be used to study geometrical shape changes of the hippocampus in
 relation to the occurrence of schizophrenia. The method uses the
 well-known best Bayesian decision rule for two simple hypotheses.
 Furthermore, the technique is illustrated by applying the hypothesis
 testing method to some preliminary hippocampal data. The data pool
 available for the experiment consisted of 10 subjects, five of whom were
 diagnosed with schizophrenia and five of whom were not schizophrenics.
 Even though the information used in the experiment is limited and the
 number of subjects is relatively small, we are confident that the
 mathematical classification method presented is of significance and can be
 used successfully, given proper data, as a diagnostic tool.	      
Journal: Journal of Applied Statistics 
Pages: 593-600 
Issue: 5 
Volume: 25 
Year: 1998 
Month: 6 
X-DOI: 10.1080/02664769822846 
File-URL: http://hdl.handle.net/10.1080/02664769822846 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:5:p:593-600




Template-Type: ReDIF-Article 1.0
Author-Name: M. Hosseini 
Author-X-Name-First: M. 
Author-X-Name-Last: Hosseini 
Author-Name: R. G. Carpenter 
Author-X-Name-First: R. G. 
Author-X-Name-Last: Carpenter 
Author-Name: K. Mohammad 
Author-X-Name-First: K. 
Author-X-Name-Last: Mohammad 
Title: Identification of outlying height and weight data in the Iranian National Health Survey 1990-92 
Abstract:
	      Data on the weights and heights of
 children 2-18 yeas old in Iran were obtained in a National Health Survey
 of 10 660 families in 1990-92. Data were 'cleaned' in 1 year age groups.
 After excluding gross outliers by inspection of bivariate scatter plots,
 Box-Cox power transformations were used to normalize the distributions of
 height and weight. If a multivariate Box-Cox power transformation to
 normality exists, then it is equivalent to normalizing the data variable
 by variable. After excluding gross outliers, exclusions based on the
 Mahalanobis distance were almost identical to those identified by Hadi's
 iterative procedure, because the percentages of outliers were small. In
 all, 1% of the observations were gross outliers and a further 0.4% were
 identified by multivariate analysis. Review of records showed that the
 outliers identified by multivariate analysis resulted from data-processing
 errors. After transformation and 'cleaning', the data quality was
 excellent and suitable for the construction of growth charts.		
Journal: Journal of Applied Statistics 
Pages: 601-612 
Issue: 5 
Volume: 25 
Year: 1998 
Month: 6 
X-DOI: 10.1080/02664769822855 
File-URL: http://hdl.handle.net/10.1080/02664769822855 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:5:p:601-612




Template-Type: ReDIF-Article 1.0
Author-Name: Philip Prescott 
Author-X-Name-First: Philip 
Author-X-Name-Last: Prescott 
Author-Name: Norman R. Draper 
Author-X-Name-First: Norman R. 
Author-X-Name-Last: Draper 
Title: Mixture designs for constrained components in orthogonal blocks 
Abstract:
	      It is often the case in mixture
 experiments that some of the ingredients, such as additives or
 flavourings, are included with proportions constrained to lie in a
 restricted interval, while the majority of the mixture is made up of a
 particular ingredient used as a filler. The experimental region in such
 cases is restricted to a parallelepiped in or near one corner of the full
 simplex region. In this paper, orthogonally blocked designs with two
 experimental blends on each edge of the constrained region are considered
 for mixture experiments with three and four ingredients. The optimal
 symmetric orthogonally blocked designs within this class are determined
 and it is shown that even better designs are obtained for the asymmetric
 situation, in which some experimental blends are taken at the vertices of
 the experimental region. Some examples are given to show how these ideas
 may be extended to identify good designs in three and four blocks.
 Finally, an example is included to illustrate how to overcome the problems
 of collinearity that sometimes occur when fitting quadratic models to
 experimental data from mixture experiments in which some of the ingredient
 proportions are restricted to small values.	      
Journal: Journal of Applied Statistics 
Pages: 613-638 
Issue: 5 
Volume: 25 
Year: 1998 
Month: 6 
X-DOI: 10.1080/02664769822864 
File-URL: http://hdl.handle.net/10.1080/02664769822864 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:5:p:613-638




Template-Type: ReDIF-Article 1.0
Author-Name: James Carpenter 
Author-X-Name-First: James 
Author-X-Name-Last: Carpenter 
Title: Assessing parameter uncertainty via bootstrap likelihood ratio confidence regions 
Abstract:
	      In this paper, we show that, under
 certain regularity conditions, constructing likelihood ratio confidence
 regions using a boostrap estimate of the distribution of the likelihood
 ratio statistic-instead of the usual chi 2 approximation-leads to regions
 which have a coverage error of O(n- 2), which is the same as that achieved
 using a Bartlett-corrected likelihood ratio statistic. We use the boostrap
 method to assess the uncertainty associated with dose-response parameters
 that arise in models for the Japanese atomic bomb survivors data.	    
Journal: Journal of Applied Statistics 
Pages: 639-649 
Issue: 5 
Volume: 25 
Year: 1998 
Month: 6 
X-DOI: 10.1080/02664769822873 
File-URL: http://hdl.handle.net/10.1080/02664769822873 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:5:p:639-649




Template-Type: ReDIF-Article 1.0
Author-Name: James F. Reed 
Author-X-Name-First: James F. 
Author-X-Name-Last: Reed 
Title: Contributions to adaptive estimation 
Abstract:
	      There are many statistics which can be
 used to characterize data sets and provide valuable information regarding
 the data distribution, even for large samples. Traditional measures, such
 as skewness and kurtosis, mentioned in introductory statistics courses,
 are rarely applied. A variety of other measures of tail length, skewness
 and tail weight have been proposed, which can be used to describe the
 underlying population distribution. Adaptive statistical procedures change
 the estimator of location, depending on sample characteristics. The
 success of these estimators depends on correctly classifying the
 underlying distribution model. Advocates of adaptive distribution testing
 propose to proceed by assuming (1) that an appropriate model, say Omega ,
 is such that Omega { Omega , Omega , i i 1 2 ... , Omega }, and (2) that
 the character of the model selection process is statistically k
 independent of the hypothesis testing. We review the development of
 adaptive linear estimators and adaptive maximum-likelihood estimators.     
Journal: Journal of Applied Statistics 
Pages: 651-669 
Issue: 5 
Volume: 25 
Year: 1998 
Month: 6 
X-DOI: 10.1080/02664769822882 
File-URL: http://hdl.handle.net/10.1080/02664769822882 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:5:p:651-669




Template-Type: ReDIF-Article 1.0
Author-Name: M. J. Baxter 
Author-X-Name-First: M. J. 
Author-X-Name-Last: Baxter 
Author-Name: N. H. Gale 
Author-X-Name-First: N. H. 
Author-X-Name-Last: Gale 
Title: Testing for multivariate normality via univariate tests: A case study using lead isotope ratio data 
Abstract:
	      Samples from ore bodies, mined for
 copper in antiquity, can be characterized by measurements on three lead
 isotope ratios. Given sufficient samples, it is possible to estimate the
 lead isotope field-a three-dimensional construct-that characterizes the
 ore body. For the purposes of estimating the extent of a field, or
 assessing whether bronze artefacts could have been made using copper from
 a particular field, it is often assumed that fields have a trivariate
 normal distribution. Using recently published data, for which the sample
 sizes are larger than usual, this paper casts doubt on this assumption. A
 variety of tests of univariate normality are applied, both to the original
 lead isotope ratios and to transformations of them based on principal
 component analysis; the paper can be read as a case study in the use of
 tests of univariate normality for assessing multivariate normality. This
 is not an optimal approach, but is sufficient in the cases considered to
 suggest that fields are, in fact, 'non-normal'. A direct test of
 multivariate normality confirms this. Some implications for the use of
 lead isotope ratio data in archaeology are discussed.		
Journal: Journal of Applied Statistics 
Pages: 671-683 
Issue: 5 
Volume: 25 
Year: 1998 
Month: 6  
X-DOI: 10.1080/02664769822891 
File-URL: http://hdl.handle.net/10.1080/02664769822891 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:5:p:671-683




Template-Type: ReDIF-Article 1.0
Author-Name: Christine A. Ribic 
Author-X-Name-First: Christine A. 
Author-X-Name-Last: Ribic 
Author-Name: Thomas W. Miller 
Author-X-Name-First: Thomas W. 
Author-X-Name-Last: Miller 
Title: Evaluation of alternative model selection criteria in the analysis of unimodal response curves using CART 
Abstract:
	      We investigated CART performance with a
 unimodal response curve for one continuous response and four continuous
 explanatory variables, where two variables were important (i.e. directly
 related to the response) and the other two were not. We explored
 performance under three relationship strengths and two explanatory
 variable conditions: equal importance and one variable four times as
 important as the other. We compared CART variable selection performance
 using three tree-selection rules ('minimum risk', 'minimum risk
 complexity', 'one standard error') to stepwise polynomial ordinary least
 squares (OLS) under four sample size conditions. The one-standard-error
 and minimum risk-complexity methods performed about as well as stepwise
 OLS with large sample sizes when the relationship was strong. With weaker
 relationships, equally important explanatory variables and larger sample
 sizes, the one-standard-error and minimum-risk-complexity rules performed
 better than stepwise OLS. With weaker relationships and explanatory
 variables of unequal importance, tree-structured methods did not perform
 as well as stepwise OLS. Comparing performance within tree-structured
 methods, with a strong relationship and equally important explanatory
 variables, the one-standard-error rule was more likely to choose the
 correct model than were the other tree-selection rules. The
 minimum-risk-complexity rule was more likely to choose the correct model
 than were the other tree-selection rules (1) with weaker relationships and
 equally important explanatory variables; and (2) under all relationship
 strengths when explanatory variables were of unequal importance and sample
 sizes were lower.	    
Journal: Journal of Applied Statistics 
Pages: 685-698 
Issue: 5 
Volume: 25 
Year: 1998 
Month: 6 
X-DOI: 10.1080/02664769822909 
File-URL: http://hdl.handle.net/10.1080/02664769822909 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:5:p:685-698




Template-Type: ReDIF-Article 1.0
Author-Name: Christine Gunter 
Author-X-Name-First: Christine 
Author-X-Name-Last: Gunter 
Author-Name: Colin Rallings 
Author-X-Name-First: Colin 
Author-X-Name-Last: Rallings 
Author-Name: Michael Thrasher 
Author-X-Name-First: Michael 
Author-X-Name-Last: Thrasher 
Title: Calculating the total vote where the district magnitude is greater than one: A test of some algorithms using British local election data 
Abstract:
	      Electoral analysis using aggregate data
 relies on the availability of accurate voting statistics. One vital piece
 of information, often missing from official electoral returns,
 particularly British local government elections, is the total number of
 valid ballot papers. This figure is essential for the calculation of
 electoral turnout. When voters have a single vote and official information
 about the number of ballot papers issued is missing, a figure for the
 total vote can still be derived. However, local elections in Britain
 frequently use a system of multiple-member wards, where voters have as
 many votes as there are seats to be filled. In such cases, calculating the
 total vote and, hence, the turnout does present a real problem. It cannot
 be assumed that all voters will use their full quota of votes or that
 voters will cast a ballot in favour of a single party. This paper develops
 and tests diff erent algorithms for calculating the total vote in such
 circumstances. We conclude that the accuracy of an algorithm is closely
 related to the structure of party competition. The findings of this paper
 have a number of important implications. First, the difficulties in
 calculating the turnout in multiple-member wards are identified. This will
 inform the debate about public participation in the local electoral
 process. Second, the method for deriving a figure for the total vote has
 an important bearing on a number of other statistics widely employed in
 electoral analysis.	      
Journal: Journal of Applied Statistics 
Pages: 699-706 
Issue: 5 
Volume: 25 
Year: 1998 
Month: 6 
X-DOI: 10.1080/02664769822918 
File-URL: http://hdl.handle.net/10.1080/02664769822918 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:5:p:699-706




Template-Type: ReDIF-Article 1.0
Author-Name: Uditha Balasooriya 
Author-X-Name-First: Uditha 
Author-X-Name-Last: Balasooriya 
Author-Name: Sutaip L. C. Saw 
Author-X-Name-First: Sutaip L. C. 
Author-X-Name-Last: Saw 
Title: Reliability sampling plans for the two-parameter exponential distribution under progressive censoring 
Abstract:
	      This paper presents reliability sampling
 plans for the two-parameter exponential distribution under progressive
 censoring. These sampling plans are quite useful to practitioners, because
 they provide savings in resources and in total test time. Furthermore,
 they off er the flexibility to remove functioning test specimens from
 further testing at various stages of the experimentation. In the
 construction of these sampling plans, the operating characteristic curve
 is derived using the exact distributional properties of maximum likelihood
 estimators. An example is given to illustrate the application of the
 proposed sampling plans.	   
Journal: Journal of Applied Statistics 
Pages: 707-714 
Issue: 5 
Volume: 25 
Year: 1998 
Month: 6 
X-DOI: 10.1080/02664769822927 
File-URL: http://hdl.handle.net/10.1080/02664769822927 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:5:p:707-714




Template-Type: ReDIF-Article 1.0
Author-Name: Vladimir Brajkovic 
Author-X-Name-First: Vladimir 
Author-X-Name-Last: Brajkovic 
Title: Mechanics of microelectrics examined by design of experiments techniques 
Abstract:
  We live in a world full of variations. We need to understand its sources
 and we need a scientific method for predicting it, for reducing it and for
 controlling it. Statistical thinking is the only way to deal with
 variations. Continuous improvement means continuously solving the
 variation problem. But this relies on a successful marriage of theory and
 practice; experience is insufficient without theory. The theory needs to
 be taught. There is no substitute for knowledge (Logothetis, 1991). 
Journal: Journal of Applied Statistics 
Pages: 723-731 
Issue: 6 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769822710 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769822710
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:6:p:723-731




Template-Type: ReDIF-Article 1.0
Author-Name: Reay-Chen Wang 
Author-X-Name-First: Reay-Chen 
Author-X-Name-Last: Wang 
Title: Minimum average fraction inspected for short-run CSP-1 plan 
Abstract:
  This paper presents details of the calculation of the average outgoing
 quality limit (AOQL) for a short-run CSP-1 plan based on Y ang's renewal
 process approach. A solution procedure is developed to find the unique
 combination (i,f) that will meet the AOQL requirement, while also
 minimizing the average fraction inspected for the shortrun CSP-1 plan when
 the process average p (&#x3e; AOQL) and production run length R are known. 
Journal: Journal of Applied Statistics 
Pages: 733-738 
Issue: 6 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769822729 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769822729
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:6:p:733-738




Template-Type: ReDIF-Article 1.0
Author-Name: Ling Chen 
Author-X-Name-First: Ling 
Author-X-Name-Last: Chen 
Title: Improved penalized mean for estimating the mean concentration of contaminants 
Abstract:
  Chen and Jernigan proposed a non-parametric, conservative method that
 involved using a penalized mean to estimate the average concentration of
 contaminants in soils. The method assumes a random sample obtained from a
 whole site involved in the US Superfund program. However, in some cases,
 about 10% of known data are collected from the 'hot spots'. In this paper,
 two procedures are proposed to use the information from hot spots data or
 an extreme value to estimate the mean concentration of contaminants. These
 procedures are evaluated using a data set of chromium concentrations from
 one of the Environmental Protection Agency's toxic waste sites. The
 simulation results show that these new procedures are cost-eff ective. 
Journal: Journal of Applied Statistics 
Pages: 739-750 
Issue: 6 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769822738 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769822738
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:6:p:739-750




Template-Type: ReDIF-Article 1.0
Author-Name: Brenton Clarke 
Author-X-Name-First: Brenton 
Author-X-Name-Last: Clarke 
Author-Name: Toby Lewis 
Author-X-Name-First: Toby 
Author-X-Name-Last: Lewis 
Title: An outlier problem in the determination of ore grade 
Abstract:
  Data from recordings of ore assays from the Western Australian goldfields
 provide motivation to devise new tests for outliers when observations are
 distributed with the same mean but diff ering variances. In the case of
 equal variances, tests for a single outlier reduce to well-known tests of
 discordancy. A block discordancy test for k outliers is also described.
 The question of whether or not one should omit any observation(s) in the
 calculation of the mean recoverable gold content is addressed in the
 context of whether or not the data contain outliers, as judged by a normal
 model for the 'logged' ore assay values. The given data suggest that
 models with 'logged' values that follow long-tailed approximately normal
 distributions may be appropriate. 
Journal: Journal of Applied Statistics 
Pages: 751-762 
Issue: 6 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769822747 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769822747
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:6:p:751-762




Template-Type: ReDIF-Article 1.0
Author-Name: Xavier De Luna 
Author-X-Name-First: Xavier 
Author-X-Name-Last: De Luna 
Title: Projected polynomial autoregression for prediction of stationary time series 
Abstract:
  Polynomial autoregressions are usually considered to be unrealistic
 models for time series. However, this paper shows that they can
 successfully be used when the purpose of the time series study is to
 provide forecasts. A projection scheme inspired from projection pursuit
 regression and feedforward artificial neural networks is used in order to
 avoid an explosion of the number of parameters when considering a large
 number of lags. The estimation of the parameters of the projected
 polynomial autoregressions is a non-linear least-squares problem. A
 consistency result is proved. A simulation study shows that the naive use
 of the common final prediction error criterion is inappropriate to
 identify the best projected polynomial autoregression. An explanation of
 this phenomenon is given and a correction to the criterion is proposed. An
 important feature of the polynomial predictors introduced in this paper is
 their simple implementation, which allows for automatic use. This is
 illustrated with real data for the three-month US Treasury Bill. 
Journal: Journal of Applied Statistics 
Pages: 763-775 
Issue: 6 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769822756 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769822756
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:6:p:763-775




Template-Type: ReDIF-Article 1.0
Author-Name: Reza Modarres 
Author-X-Name-First: Reza 
Author-X-Name-Last: Modarres 
Author-Name: Joseph Gastwirth 
Author-X-Name-First: Joseph 
Author-X-Name-Last: Gastwirth 
Title: Hybrid test for the hypothesis of symmetry 
Abstract:
  In recent years, McWilliams and Tajuddin have proposed new and more
 powerful non-parametric tests of symmetry for continuous distributions
 about a known center. In this paper, we propose a simple non-parametric
 two-stage procedure based on the sign test and a percentile-modified
 two-sample Wilcoxon test. The small-sample properties of this test,
 Tajuddin's test, McWilliams' test and a modified runs test of Modarres and
 Gastwirth are investigated in a Monte Carlo simulation study. The
 simulations indicate that, for a wide variety of asymmetric alternatives
 in the lambda family, the hybrid test is more powerful than are existing
 tests in the literature. 
Journal: Journal of Applied Statistics 
Pages: 777-783 
Issue: 6 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769822765 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769822765
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:6:p:777-783




Template-Type: ReDIF-Article 1.0
Author-Name: Yoshikazu Ojima 
Author-X-Name-First: Yoshikazu 
Author-X-Name-Last: Ojima 
Title: General formulae for expectations, variances and covariances of the mean squares for staggered nested designs 
Abstract:
  Staggered nested experimental designs are the most popular class of
 unbalanced nested designs. Using a special notation which covers the
 particular structure of the staggered nested design, this paper
 systematically derives the canonical form for the arbitrary m-factors.
 Under the normality assumption for every random variable, a vector
 comprising m canonical variables from each experimental unit is normally
 independently and identically distributed. Every sum of squares used in
 the analysis of variance (ANOVA) can be expressed as the sum of squares of
 the corresponding canonical variables. Hence, general formulae for the
 expectations, variances and covariances of the mean squares are directly
 obtained from the canonical form. Applying the formulae, the explicit
 forms of the ANOVA estimators of the variance components and unbiased
 estimators of the ratios of the variance components are introduced in this
 paper. The formulae are easily applied to obtain the variances and
 covariances of any linear combinations of the mean squares, especially the
 ANOVA estimators of the variance components. These results are eff
 ectively applied for the standardization of measurement methods. 
Journal: Journal of Applied Statistics 
Pages: 785-799 
Issue: 6 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769822774 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769822774
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:6:p:785-799




Template-Type: ReDIF-Article 1.0
Author-Name: W. L. Pearn 
Author-X-Name-First: W. L. 
Author-X-Name-Last: Pearn 
Title: New generalization of process capability index Cpk 
Abstract:
  The process capability index Cpk has been widely used in manufacturing
 industry to provide numerical measures of process potential and
 performance. As noted by many quality control researchers and
 practitioners, Cpk is yield-based and is independent of the target T. This
 fails to account for process centering with symmetric tolerances, and
 presents an even greater problem with asymmetric tolerances. To overcome
 the problem, several generalizations of Cpk have been proposed to handle
 processes with asymmetric tolerances. Unfortunately, these generalizations
 understate or overstate the process capability in many cases, so reflect
 the process potential and performance inaccurately. In this paper, we
 first introduce a new index Cp&#x22;k, which is shown to be superior to
 the existing generalizations of Cpk. We then investigate the statistical
 properties of the natural estimator of Cp&#x22;k, assuming that the
 process is normally distributed. 
Journal: Journal of Applied Statistics 
Pages: 801-810 
Issue: 6 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769822783 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769822783
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:6:p:801-810




Template-Type: ReDIF-Article 1.0
Author-Name: Graham Upton 
Author-X-Name-First: Graham 
Author-X-Name-Last: Upton 
Title: Rounding halves 
Abstract:
  This paper examines the consequences of requiring that data measured as
 multiples of a half should be reported as integers. General formulae are
 given for the mean and variance of rounded values. The formulae are
 applied in the context of fibre counting, where fibres that overlap a
 boundary are given a value of 1/2. 
Journal: Journal of Applied Statistics 
Pages: 811-816 
Issue: 6 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769822792 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769822792
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:6:p:811-816




Template-Type: ReDIF-Article 1.0
Author-Name: Grace Montepiedra 
Author-X-Name-First: Grace 
Author-X-Name-Last: Montepiedra 
Title: Application of genetic algorithms to the construction of exact D-optimal designs 
Abstract:
  This paper studies the application of genetic algorithms to the
 construction of exact D-optimal experimental designs. The concept of
 genetic algorithms is introduced in the general context of the problem of
 finding optimal designs. The algorithm is then applied specifically to
 finding exact D-optimal designs for three different types of model. The
 performance of genetic algorithms is compared with that of the modified
 Fedorov algorithm in terms of computing time and relative efficiency.
 Finally, potential applications of genetic algorithms to other optimality
 criteria and to other types of model are discussed, along with some open
 problems for possible future research. 
Journal: Journal of Applied Statistics 
Pages: 817-826 
Issue: 6 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769822800 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769822800
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:6:p:817-826




Template-Type: ReDIF-Article 1.0
Author-Name: D. K. Ghosh 
Author-X-Name-First: D. K. 
Author-X-Name-Last: Ghosh 
Title: Robustness of complete diallel crosses plans to the unavailability of one block 
Abstract:
  The present investigation involved the estimation of the general
 combining ability of CDC plans subject to the unavailability of one block
 for Griffing's system IV. Further, it has been shown that CDC plans are
 fairly robust to the unavailability of one block. 
Journal: Journal of Applied Statistics 
Pages: 827-837 
Issue: 6 
Volume: 25 
Year: 1998 
X-DOI: 10.1080/02664769822819 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769822819
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:25:y:1998:i:6:p:827-837




Template-Type: ReDIF-Article 1.0
Author-Name: Saling Huang 
Author-X-Name-First: Saling 
Author-X-Name-Last: Huang 
Author-Name: Morton Brown 
Author-X-Name-First: Morton 
Author-X-Name-Last: Brown 
Title: A Markov chain model for longitudinal categorical data when there may be non-ignorable non-response 
Abstract:
  Longitudinal data with non-response occur in studies where the same
 subject is followed over time but data for each subject may not be
 available at every time point. When the response is categorical and the
 response at time t depends on the response at the previous time points, it
 may be appropriate to model the response using a Markov model. We
 generalize a second-order Markov model to include a non-ignorable
 non-response mechanism. Simulation is used to study the properties of the
 estimators. Large sample sizes are necessary to ensure that the algorithm
 converges and that the asymptotic properties of the estimators can be
 used. 
Journal: Journal of Applied Statistics 
Pages: 5-18 
Issue: 1 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922610 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922610
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:1:p:5-18




Template-Type: ReDIF-Article 1.0
Author-Name: L. Y. Chan 
Author-X-Name-First: L. Y. 
Author-X-Name-Last: Chan 
Title: Optimal orthogonal block designs for a quadratic mixture model for three components 
Abstract:
  In experiments with mixtures that involve process variables, if the
 response function is expressed as the sum of a function of mixture
 components and a function of process variables, then the parameters in the
 mixture part and in the process part can be estimated independently using
 orthogonal block designs. This paper is concerned with such a block design
 for parameter estimation in the mixture part of a quadratic mixture model
 for three mixture components. The behaviour of the eigenvalues of the
 moment matrix of the design is investigated in detail, the design is
 optimized according to E- and Aoptimality criteria, and the results are
 compared together with a known result on Doptimality. It is found that
 this block design is robust with respect to these diff erent optimality
 criteria against the shifting of experimental points. As a result, we
 recommend experimental points of the form (a, b, c) in the simplex S2,
 where c=0, b=1-a, and a can be any value in the range 0.17+/-0.02. 
Journal: Journal of Applied Statistics 
Pages: 19-34 
Issue: 1 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922629 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922629
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:1:p:19-34




Template-Type: ReDIF-Article 1.0
Author-Name: Wai-Sum Chan 
Author-X-Name-First: Wai-Sum 
Author-X-Name-Last: Chan 
Title: Exact joint forecast regions for vector autoregressive models 
Abstract:
  Assume that a k-element vector time series follows a vector
 autoregressive (VAR) model. Obtaining simultaneous forecasts of the k
 elements of the vector time series is an important problem. Based on the
 Bonferroni inequality, Lutkepohl (1991) derived the procedures which
 construct the conservative joint forecast regions for the VAR model. In
 this paper, we propose to use an exact method which provides shorter
 prediction intervals than does the Bonferroni method. Three illustrative
 examples are given for comparison of the various VAR forecasting
 procedures. 
Journal: Journal of Applied Statistics 
Pages: 35-44 
Issue: 1 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922638 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922638
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:1:p:35-44




Template-Type: ReDIF-Article 1.0
Author-Name: Guadalupe Gomez 
Author-X-Name-First: Guadalupe 
Author-X-Name-Last: Gomez 
Author-Name: M. Luz Calle 
Author-X-Name-First: M. Luz 
Author-X-Name-Last: Calle 
Title: Non-parametric estimation with doubly censored data 
Abstract:
  Data from longitudinal studies in which an initiating event and a
 subsequent event occur in sequence are called 'doubly censored' data if
 the time of both events is interval-censored. This paper is concerned with
 using doubly censored data to estimate the distribution function of the
 so-called 'duration time', i.e. the elapsed time between the originating
 event and the subsequent event. The paper proposes a generalization of the
 Gomez and Lagakos two-step method for the case where both the time to the
 initiating event and the duration time are continuous. This approach is
 applied to estimate the AIDS-latency time from a haemophiliacs cohort. 
Journal: Journal of Applied Statistics 
Pages: 45-58 
Issue: 1 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922647 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922647
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:1:p:45-58




Template-Type: ReDIF-Article 1.0
Author-Name: W. J. Krzanowski 
Author-X-Name-First: W. J. 
Author-X-Name-Last: Krzanowski 
Title: Antedependence models in the analysis of multi-group high-dimensional data 
Abstract:
  Antedependence modelling has previously been shown to be useful for
 twogroup discriminant analysis of high-dimensional data. In this paper,
 the theory of such models is extended to multi-group discriminant analysis
 and to canonical variate analysis for data display. The application of
 antedependence models of orders 1, 2 and 3 to spectroscopic analyses of
 rice samples is described, and the results are compared with those from
 standard methods based on principal component scores calculated from the
 data. 
Journal: Journal of Applied Statistics 
Pages: 59-67 
Issue: 1 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922656 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922656
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:1:p:59-67




Template-Type: ReDIF-Article 1.0
Author-Name: Donald Martin 
Author-X-Name-First: Donald 
Author-X-Name-Last: Martin 
Title: Paired comparison models applied to the design of the Major League baseball play-offs 
Abstract:
  This paper presents an analysis of the eff ect of various baseball
 play-off configurations on the probability of advancing to the World
 Series. Play-off games are assumed to be independent. Several paired
 comparisons models are considered for modeling the probability of a home
 team winning a single game as a function of the winning percentages of the
 contestants over the course of the season. The uniform and logistic
 regression models are both adequate, whereas the Bradley-Terry model
 (modified for within-pair order eff ects, i.e. the home field advantage)
 is not. The single-game probabilities are then used to compute the
 probability of winning the play-off s under various structures. The extra
 round of play-off s, instituted in 1994, significantly lowers the
 probability of the team with the best record advancing to the World
 Series, whereas home field advantage and the diff erent possible
 play-offdraws have a minimal eff ect. 
Journal: Journal of Applied Statistics 
Pages: 69-80 
Issue: 1 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922665 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922665
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:1:p:69-80




Template-Type: ReDIF-Article 1.0
Author-Name: Glen Meeden 
Author-X-Name-First: Glen 
Author-X-Name-Last: Meeden 
Title: Interval estimators for the population mean for skewed distributions with a small sample size 
Abstract:
  In finite population sampling, it has long been known that, for small
 sample sizes, when sampling from a skewed population, the usual
 frequentist intervals for the population mean cover the true value less
 often than their stated frequency of coverage. Recently, a non-informative
 Bayesian approach to some problems in finite population sampling has been
 developed, which is based on the 'Polya posterior'. For large sample
 sizes, these methods often closely mimic standard frequentist methods. In
 this paper, a modification of the 'Polya posterior', which employs the
 weighted Polya distribution, is shown to give interval estimators with
 improved coverage properties for problems with skewed populations and
 small sample sizes. This approach also yields improved tests for
 hypotheses about the mean of a skewed distribution. 
Journal: Journal of Applied Statistics 
Pages: 81-96 
Issue: 1 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922674 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922674
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:1:p:81-96




Template-Type: ReDIF-Article 1.0
Author-Name: Seymour Geisser 
Author-X-Name-First: Seymour 
Author-X-Name-Last: Geisser 
Title: Remarks on the 'Bayesian' method of moments 
Abstract:
  Zellner has proposed a novel methodology for estimating structural
 parameters and predicting future observables based on two moments of a
 subjective distribution and the application of the maximum entropy
 principle-all in the absence of an explicit statistical model or
 likelihood function for the data. He calls his procedure the 'Bayesian
 method of moments' (BMOM). In a recent paper in this journal, Green and
 Strawderman applied the BMOM to a model for slash pine plantations. It is
 our view that there are inconsistencies between BMOM and Bayesian
 (conditional) probability, as we explain in this paper. 
Journal: Journal of Applied Statistics 
Pages: 97-101 
Issue: 1 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922683 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922683
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:1:p:97-101




Template-Type: ReDIF-Article 1.0
Author-Name: Peter Young 
Author-X-Name-First: Peter 
Author-X-Name-Last: Young 
Title: Recursive and en-bloc approaches to signal extraction 
Abstract:
  In the literature on unobservable component models , three main
 statistical instruments have been used for signal extraction: fixed
 interval smoothing (FIS), which derives from Kalman's seminal work on
 optimal state-space filter theory in the time domain;
 Wiener-Kolmogorov-Whittle optimal signal extraction (OSE) theory, which is
 normally set in the frequency domain and dominates the field of classical
 statistics; and regularization , which was developed mainly by numerical
 analysts but is referred to as 'smoothing' in the statistical literature
 (such as smoothing splines, kernel smoothers and local regression).
 Although some minor recognition of the interrelationship between these
 methods can be discerned from the literature, no clear discussion of their
 equivalence has appeared. This paper exposes clearly the
 interrelationships between the three methods; highlights important
 properties of the smoothing filters used in signal extraction; and
 stresses the advantages of the FIS algorithms as a practical solution to
 signal extraction and smoothing problems. It also emphasizes the
 importance of the classical OSE theory as an analytical tool for obtaining
 a better understanding of the problem of signal extraction. 
Journal: Journal of Applied Statistics 
Pages: 103-128 
Issue: 1 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922692 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922692
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:1:p:103-128




Template-Type: ReDIF-Article 1.0
Author-Name: M. F. Ramalhoto 
Author-X-Name-First: M. F. 
Author-X-Name-Last: Ramalhoto 
Author-Name: M. Morais 
Author-X-Name-First: M. 
Author-X-Name-Last: Morais 
Title: Shewhart control charts for the scale parameter of a Weibull control variable with fixed and variable sampling intervals 
Abstract:
  In this paper, we are concerned with pure statistical Shewhart control
 charts for the scale parameter of the three-parameter Weibull control
 variable, where, and are the location, the scale and the shape parameters,
 respectively, with fixed (FSI) and variable (VSI) sampling intervals. The
 parameters and are assumed to be known. We consider two-sided, and lower
 and upper one-sided Shewhart control charts and their FSI and VSI versions
 . They jointly control the mean and the variance of the Weibull control
 variable X. The pivotal statistic of those control charts is the
 maximum-likelihood estimator of for the Nth random sample
 XN=(X1N,X2N,&#x2026;,XnN) of the Weibull control variable X. The design
 and performance of these control charts are studied. Two criteria, i.e.
 'comparability criterion' (or 'matched criterion') under control and
 'primordial criterion', are imposed on their design. The performance of
 these control charts is measured using the function average time to
 signal. For the VSI versions, the constant which defines the partition of
 the 'continuation region' is obtained through the 'comparability
 criterion' under control. The monotonic behaviour of the function average
 time to signal in terms of the parameters (magnitude of the shift suff
 ered by the target value 0), and is studied. We show that the function
 average time to signal of all the control charts studied in this paper
 does not depend on the value of the parameter or on 0, and, under control,
 does not depend on the parameter, when Delta (the probability of a false
 alarm) and n (sample size) are fixed. All control charts satisfy the
 'primordial criterion' and, for fixed, on average, they all (except the
 two-sided VSI, for which we were not able to ascertain proof) are quicker
 in detecting the shift as increases. We conjecture - and we are not
 contradicted by the numerical example considered - that the same is true
 for the two-sided VSI control chart. We prove that, under the average time
 to signal criterion, the VSI versions are always preferable to their FSI
 versions. In the case of one-sided control charts, under the
 'comparability criterion', the VSI version is always preferable to the FSI
 version, and this advantage increases with and the extent of the shift.
 Our one-sided control charts perform better and have more powerful
 statistical properties than does our two-sided control chart. The
 numerical example where n=5,0=1,=0.5, 1.0, 2.0, and Delta=1/370.4 is
 presented for the two-sided, and the lower and upper one-sided control
 charts. These numerical results are presented in tables and in figures.
 The joint influence of the parameters and in the function average time to
 signal is illustrated. 
Journal: Journal of Applied Statistics 
Pages: 129-160 
Issue: 1 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922700 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922700
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:1:p:129-160




Template-Type: ReDIF-Article 1.0
Author-Name: Joseph Brian Adams 
Author-X-Name-First: Joseph Brian 
Author-X-Name-Last: Adams 
Title: Predicting pickle harvests using a parametric feedforward neural network 
Abstract:
  Feedforward networks have demonstrated their ability to model non-linear
 data. Despite this success, their use as a statistical analysis tool has
 been limited by the persistent assumption that these networks can only be
 implemented as non-parametric models. In fact, a feedforward network can
 be used for parametric modeling, with the result that many of the common
 parametric testing procedures can be applied to the nonlinear network. In
 this paper, a feedforward network for predicting the biological growth
 rate of pickles is developed. Using this network, the parametric nature of
 the network is demonstrated. Once trained, the network model is tested
 using standard parametric methods. In order to facilitate this testing, it
 is first necessary to develop a method for calculating the degrees of
 freedom for the neural network, and the residual covariance matrix. It is
 shown that the degrees of freedom is determined by the number of
 parameters that actually contribute to an output. With this information,
 the covariance matrix can be created by adapting the error matrix. Using
 these results, the trained network is tested using a simple F-statistic. 
Journal: Journal of Applied Statistics 
Pages: 165-176 
Issue: 2 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922502 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922502
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:2:p:165-176




Template-Type: ReDIF-Article 1.0
Author-Name: Nedret Billor 
Author-X-Name-First: Nedret 
Author-X-Name-Last: Billor 
Title: An application of the local influence approach to ridge regression 
Abstract:
  In this study, the method of local influence, which was introduced by
 Cook as a general tool for assessing the influence of local departures
 from the underlying assumptions, is applied to ridge regression, by
 defining the maximum pseudo-likelihood ridge estimator obtained using the
 augmentation approach, because this method is suitable for
 likelihood-based models. In addition, an alternative local influence
 approach suggested by Billor and Loynes is applied to ridge regression. A
 comparison of these approaches and an example are given. 
Journal: Journal of Applied Statistics 
Pages: 177-183 
Issue: 2 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922511 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922511
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:2:p:177-183




Template-Type: ReDIF-Article 1.0
Author-Name: K. Kalirajan 
Author-X-Name-First: K. 
Author-X-Name-Last: Kalirajan 
Title: Stochastic varying coefficients gravity model: An application in trade analysis 
Abstract:
  In international trade analysis between countries, the central theme is
 the examination of whether or not there are any significant diff erences
 between the actual trade and potential trade, given the determinants of
 trade flows. Thus, estimating the potential trade is an important
 component in trade analysis. The objective of this paper is to suggest a
 methodology to estimate the potential trade flows between countries, using
 the gravity model, which has been established in the literature as the
 most successful empirical trade flow equation, usually producing a good
 fit. The application of the method has been demonstrated using trade flows
 between Australia and its trading partners in the Indian Ocean Rim. 
Journal: Journal of Applied Statistics 
Pages: 185-193 
Issue: 2 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922520 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922520
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:2:p:185-193




Template-Type: ReDIF-Article 1.0
Author-Name: L. S. Kaushik 
Author-X-Name-First: L. S. 
Author-X-Name-Last: Kaushik 
Title: Partial diallel crosses based on three associate class association schemes 
Abstract:
  Designs of partial diallel crosses obtained by including parents based on
 rectangular and cubic association schemes have been presented. In
 addition, a simplified method of their analysis by making use of latent
 roots and idempotent matrices has also been presented. The method has been
 illustrated with the help of numerical data. 
Journal: Journal of Applied Statistics 
Pages: 195-201 
Issue: 2 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922539 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922539
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:2:p:195-201




Template-Type: ReDIF-Article 1.0
Author-Name: Scott Richter 
Author-X-Name-First: Scott 
Author-X-Name-Last: Richter 
Title: Nearly exact tests in factorial experiments using the aligned rank transform 
Abstract:
  A procedure is studied that uses rank-transformed data to perform exact
 and estimated exact tests, which is an alternative to the commonly used
 F-ratio test procedure. First, a common parametric test statistic is
 computed using rank-transformed data, where two methods of ranking-ranks
 taken for the original observations and ranks taken after aligning the
 observations-are studied. Significance is then determined using either the
 exact permutation distribution of the statistic or an estimate of this
 distribution based on a random sample of all possible permutations.
 Simulation studies compare the performance of this method with the normal
 theory parametric F-test and the traditional rank transform procedure.
 Power and nominal type I error rates are compared under conditions when
 normal theory assumptions are satisfied, as well as when these assumptions
 are violated. The method is studied for a two-factor factorial arrangement
 of treatments in a completely randomized design and for a split-unit
 experiment. The power of the tests rivals the parametric F-test when
 normal theory assumptions are satisfied, and is usually superior when
 normal theory assumptions are not satisfied. Based on the evidence of this
 study, the exact aligned rank procedure appears to be the overall best
 choice for performing tests in a general factorial experiment. 
Journal: Journal of Applied Statistics 
Pages: 203-217 
Issue: 2 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922548 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922548
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:2:p:203-217




Template-Type: ReDIF-Article 1.0
Author-Name: James Hughes 
Author-X-Name-First: James 
Author-X-Name-Last: Hughes 
Author-Name: Elizabeth Savoca 
Author-X-Name-First: Elizabeth 
Author-X-Name-Last: Savoca 
Title: Accounting for censoring in duration data: An application to estimating the effect of legal reforms on the duration of medical malpractice disputes 
Abstract:
  Using a sample of medical malpractice insurance claims closed between 1
 October 1985 and 1 October 1989 in the USA, we estimate the impact of
 legal reforms on the longevity of disputes, via a competing risks model
 that accounts for length-biased sampling and a finite sampling horizon. We
 find that only the 'English rule'-a rule which requires the loser at trial
 to pay all legal expenses-shortens the duration of disputes. Our results
 for this law also show that failure to correct for length-biased sampling
 can incorrectly imply that the English rule lengthens the time needed for
 settlement and litigation. Our estimates also suggest that tort reforms
 that place additional procedural hurdles in the plaintiff s' paths tend to
 lengthen the time to disposition. Here, correction for a finite sampling
 horizon substantially changes the inferences with regard to the eff ect of
 this reform on duration. 
Journal: Journal of Applied Statistics 
Pages: 219-228 
Issue: 2 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922557 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922557
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:2:p:219-228




Template-Type: ReDIF-Article 1.0
Author-Name: R. Vijayaraghavan 
Author-X-Name-First: R. 
Author-X-Name-Last: Vijayaraghavan 
Title: Procedure for the selection of CSP-M one level skip-lot sampling inspection plans that have a single-sampling plan with acceptance number zero as the reference plan 
Abstract:
  This paper presents a procedure for the selection of CSP-M one-level
 skip-lot sampling plans, designated as CSP-MSkSP, that have a
 single-sampling plan with acceptance number zero as the reference plan.
 The parameters of the plan are determined when two points on the operating
 characteristic curve are specified, the two points being (p1,) and (p2,),
 where p1 is the acceptable quality level, is the producer's risk, p2 is
 the limiting quality level and is the consumer's risk. 
Journal: Journal of Applied Statistics 
Pages: 229-233 
Issue: 2 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922566 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922566
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:2:p:229-233




Template-Type: ReDIF-Article 1.0
Author-Name: Sture Holm 
Author-X-Name-First: Sture 
Author-X-Name-Last: Holm 
Author-Name: Kerstin Wiklander 
Author-X-Name-First: Kerstin 
Author-X-Name-Last: Wiklander 
Title: Simultaneous estimation of location and dispersion in two-level fractional factorial designs 
Abstract:
  The reduction of variation is one of the obvious goals in quality
 improvement. The identification of factors aff ecting the dispersion is a
 step towards this goal. In this paper, the problem of estimating location
 effects and dispersion eff ects simultaneously in unreplicated factorial
 experiments is considered. By making a one-to-one transformation of the
 response variables, the study of the quadratic functions becomes clearer.
 The transformation also gives a natural motivation to the model of the
 variances of the original variables. The covariances of the transformed
 responses appear as parameters in the variances of the original variables.
 Results of Hadamard products are used for deriving these covariances. The
 method of estimating dispersion effects is shown in two illustrations. In
 a 24 factorial design, the essential covariance matrix of the transformed
 variables is also presented. The method is also illustrated in a 25-1
 fractional design with a model which is saturated in this context. 
Journal: Journal of Applied Statistics 
Pages: 235-242 
Issue: 2 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922575 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922575
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:2:p:235-242




Template-Type: ReDIF-Article 1.0
Author-Name: Peter Wludyka 
Author-X-Name-First: Peter 
Author-X-Name-Last: Wludyka 
Title: Two non-parametric, analysis-of-means-type tests for homogeneity of variances 
Abstract:
  After a brief review of the literature, two non-parametric tests for
 homogeneity of variances are presented. The first test is based on the
 analysis of means for ranks, which is a non-parametric version of the
 analysis of means (ANOM) that uses ranks as input for an ANOM test. The
 second test uses inverse normal scores of the ranks of scale
 transformations of the observations as input to the ANOM. Both homogeneity
 of variances tests can be presented in a graphical form, which makes it
 easy for practitioners to assess the practical and the statistical
 significance. A Monte Carlo study is used to show that these tests have
 power comparable with that of well-known robust tests for homogeneity of
 variances. 
Journal: Journal of Applied Statistics 
Pages: 243-256 
Issue: 2 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922584 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922584
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:2:p:243-256




Template-Type: ReDIF-Article 1.0
Author-Name: K. K. W. Yau 
Author-X-Name-First: K. K. W. 
Author-X-Name-Last: Yau 
Title: Estimation of surgeon effects in the analysis of post-operative colorectal cancer patients data 
Abstract:
  There has been increasing interest in the assessment of surgeon effects
 for survival data of post-operative cancer patients. In particular, the
 measurement of surgeon's surgical performance after eliminating
 significant risk variables is considered. The generalized linear mixed
 model approach, which assumes a log-normal-distributed surgeon effects in
 the hazard function, is adopted to assess the random surgeon effects of
 post-operative colorectal cancer patients data. The method extends the
 traditional Cox's proportional hazards regression model, by including a
 random component in the linear predictor. Estimation is accomplished by
 constructing an appropriate log-likelihood function in the spirit of the
 best linear unbiased predictor method and extends to obtain residual
 maximum likelihood estimates. As a result of the non-proportionality of
 the hazard of colon and rectal cancer, the data are analyzed separately
 according to these two kinds of cancer. Significant risk variables are
 identified. The 'predictions' of random surgeon effects are obtained and
 their association with the rank of surgeon is examined. 
Journal: Journal of Applied Statistics 
Pages: 257-272 
Issue: 2 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922593 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922593
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:2:p:257-272




Template-Type: ReDIF-Article 1.0
Author-Name: P. J. Harrison 
Author-X-Name-First: P. J. 
Author-X-Name-Last: Harrison 
Title: Statistical process control and model monitoring 
Abstract:
  This paper is concerned with model monitoring and quality control
 schemes, which are founded on a decision theoretic formulation. After
 identifying unacceptable weaknesses associated with Wald, sequential
 probability ratio test (SPRT) and Cuscore monitors, the Bayes decision
 monitor is developed. In particular, the paper focuses on what is termed a
 'popular decision scheme' (PDS) for which the monitoring run loss
 functions are specified simply in terms of two indiff erence qualities.
 For most applications, the PDS results in forward cumulative sum tests of
 functions of the observations. For many exponential family applications,
 the PDS is equivalent to well-used SPRTs and Cusums. In particular, a neat
 interpretation of V-mask cusum chart settings is derived when
 simultaneously running two symmetric PDSs. However, apart from providing a
 decision theoretic basis for monitoring, sensible procedures occur in
 applications for which SPRTs and Cuscores are particularly unsatisfactory.
 Average run lengths (ARLs) are given for two special cases, and the
 inadequacy of the Wald and similar ARL approximations is revealed.
 Generalizations and applications to normal and dynamic linear models are
 discussed. The paper concludes by deriving conditions under which
 sequences of forward and backward sequential or Cusum chart tests are
 equivalent. 
Journal: Journal of Applied Statistics 
Pages: 273-292 
Issue: 2 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922601 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922601
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:2:p:273-292




Template-Type: ReDIF-Article 1.0
Author-Name: Uttam Bandyopadhyay 
Author-X-Name-First: Uttam 
Author-X-Name-Last: Bandyopadhyay 
Author-Name: Atanu Biswas 
Author-X-Name-First: Atanu 
Author-X-Name-Last: Biswas 
Title: Sequential-type nonparametric test using Mann-Whitney statistics 
Abstract:
  The paper provides a nonparametric test for the identity of two
 continuous univariate distribution functions when observations are drawn
 in pairs from the populations, by adopting a sampling scheme which, using
 Mann-Whitney scores, generalizes the existing inverse binomial sampling
 technique. Some exact performance characteristics of the proposed test are
 formulated and compared numerically with existing competitors of the
 proposed test. The applicability of the proposed test is illustrated using
 real-life data. 
Journal: Journal of Applied Statistics 
Pages: 301-308 
Issue: 3 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922412 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922412
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:3:p:301-308




Template-Type: ReDIF-Article 1.0
Author-Name: Reay-Chen Wang 
Author-X-Name-First: Reay-Chen 
Author-X-Name-Last: Wang 
Title: Designing a variable sampling plan based on Taguchi's loss function 
Abstract:
  This paper discusses the problem of designing a new variable sampling
 plan. Suppose that the lot quality characteristic obeys an exponential
 distribution. Adopting Taguchi's loss function, the objective is to design
 a plan under which the producer's risk of rejecting a lot that has a
 specified average loss per item is no greater than alpha, and the
 consumer's risk of accepting a lot that has a specified average loss per
 item is no greater than beta. The method of designing this plan is an
 extension of the method used by Derman and Ross. 
Journal: Journal of Applied Statistics 
Pages: 309-313 
Issue: 3 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922421 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922421
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:3:p:309-313




Template-Type: ReDIF-Article 1.0
Author-Name: G. Galliani 
Author-X-Name-First: G. 
Author-X-Name-Last: Galliani 
Author-Name: F. Filippini 
Author-X-Name-First: F. 
Author-X-Name-Last: Filippini 
Author-Name: F. Screpanti 
Author-X-Name-First: F. 
Author-X-Name-Last: Screpanti 
Title: A queuing-theory-based approach to evaluate the efficiency of a network of automated stations and of a communication system 
Abstract:
  The Regional Meteorological Service for the Emilia-Romagna Region manages
 a network of automatic weather stations equipped with electronic sensors
 suitable for measuring meteorological parameters. The automatic stations
 consist of electronic instruments, which are subject to failures at more
 or less frequent intervals. A summary of their performance is necessary.
 In this paper, we compare the results of the summary, such as the
 contiguous absence or simultaneous inactivity of different stations, with
 theoretical simulations in order to evaluate the nature and recurrence of
 the failures. A single- and multi-server queue simulation model was also
 used to evaluate the performance of the data transmission system, so as to
 optimize the communications system. 
Journal: Journal of Applied Statistics 
Pages: 315-326 
Issue: 3 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922430 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922430
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:3:p:315-326




Template-Type: ReDIF-Article 1.0
Author-Name: U. S. Pasaribu 
Author-X-Name-First: U. S. 
Author-X-Name-Last: Pasaribu 
Title: Statistical assumptions underlying the fitting of the Michaelis-Menten equation 
Abstract:
  An experiment was carried out to test the various assumptions usually
 made when evaluating statistical procedures for estimating the parameters
 of the Michaelis Menten equation, which describes enzyme-catalyzed
 reactions. The usual assumption of normality is not strongly supported,
 but is probably not too unreasonable. We study the variation in
 experimental results and, in consequence, a more complex model is
 proposed, which incorporates extra components of variation associated with
 substrate levels and diff erent days. The model is fitted using the EM
 algorithm. 
Journal: Journal of Applied Statistics 
Pages: 327-341 
Issue: 3 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922449 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922449
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:3:p:327-341




Template-Type: ReDIF-Article 1.0
Author-Name: Alan Hutson 
Author-X-Name-First: Alan 
Author-X-Name-Last: Hutson 
Title: Calculating nonparametric confidence intervals for quantiles using fractional order statistics 
Abstract:
  In this paper, we provide an easy-to-program algorithm for constructing
 the preselected 100(1 - alpha)% nonparametric confidence interval for an
 arbitrary quantile, such as the median or quartile, by approximating the
 distribution of the linear interpolation estimator of the quantile
 function Q L ( u ) = (1 - epsilon) X \[ n u ] + epsilon X \[ n u ] + 1
 with the distribution of the fractional order statistic Q I ( u ) = Xn u ,
 as defined by Stigler, where n = n + 1 and \[ . ] denotes the floor
 function. A simulation study verifies the accuracy of the coverage
 probabilities. An application to the extreme-value problem in flood data
 analysis in hydrology is illustrated. 
Journal: Journal of Applied Statistics 
Pages: 343-353 
Issue: 3 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922458 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922458
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:3:p:343-353




Template-Type: ReDIF-Article 1.0
Author-Name: Takafumi Isogai 
Author-X-Name-First: Takafumi 
Author-X-Name-Last: Isogai 
Title: Power transformation of the F distribution and a power normal family 
Abstract:
  To transform the F distribution to a normal distribution, two types of
 formula for power transformation of the F variable are introduced. One
 formula is an extension of the Wilson-Hilferty transformation for the chi
 2 variable, and the other type is based on the median of the F
 distribution. Combining those two formulas, a simple formula for the
 median of the F distribution is derived, and its numerical accuracy is
 evaluated. Simplification of the formula of the Wilson-Hilferty
 transformation, through the median formula, leads us to construct a power
 normal family from the generalized F distribution. Unlike the Box-Cox
 power normal family, our family has a property that the covariance
 structure of the maximum-likelihood estimates of the parameters is
 invariant under a scale transformation of the response variable. Numerical
 examples are given to show the diff erence between two power normal
 families. 
Journal: Journal of Applied Statistics 
Pages: 355-371 
Issue: 3 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922467 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922467
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:3:p:355-371




Template-Type: ReDIF-Article 1.0
Author-Name: K. V. Mardia 
Author-X-Name-First: K. V. 
Author-X-Name-Last: Mardia 
Title: Estimation of torsion 
Abstract:
  Spinal deformity is a more common problem in children than is usually
 realized, and early diagnosis is highly desirable. One current measure of
 detection is quite crude, with an angle being taken by hand from X-rays.
 In this paper, we present some thoughts and exploratory results for
 assisting orthopaedic surgeons, by estimating torsion. 
Journal: Journal of Applied Statistics 
Pages: 373-381 
Issue: 3 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922476 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922476
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:3:p:373-381




Template-Type: ReDIF-Article 1.0
Author-Name: Wai-Yin Poon 
Author-X-Name-First: Wai-Yin 
Author-X-Name-Last: Poon 
Title: Sources of heterogeneity in distributions with ordered categorical variables 
Abstract:
  The chi-squared statistic is used to test the homogeneity for several
 groups in a contingency table. However, it may be inappropriate to apply
 the test when ordinal categories are involved. If it can be assumed that
 the ordinal categorical variables are realizations of underlying
 continuous random variables, then it is possible to study the properties
 of different groups in a relative sense. Assuming that the distributions
 of the continuous variables are in the same family and that the thresholds
 that define the categories are invariant across groups, we propose a
 procedure to test homogeneity and to address the sources of heterogeneity
 in different groups. An example based on a real data set is used to
 demonstrate the practical applicability of the suggested method. 
Journal: Journal of Applied Statistics 
Pages: 383-392 
Issue: 3 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922485 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922485
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:3:p:383-392




Template-Type: ReDIF-Article 1.0
Author-Name: Luis Rodriguez-Carvajal 
Author-X-Name-First: Luis 
Author-X-Name-Last: Rodriguez-Carvajal 
Title: Multivariate AB-BA crossover trial 
Abstract:
  One way to analyze the AB-BA crossover trial with multivariate response
 is proposed. The multivariate model is given and the assumptions
 discussed. Two possibilities for the treatment eff ects hypothesis are
 considered. The statistical tests include the use of Hotelling's T 2
 statistic, and a transformation equivalent to that of Jones and Kenward
 for the univariate case. Data from a nutrition experiment in Mexico
 illustrate the method. The multiple comparisons are carried out using
 Bonferroni intervals and the validity of the assumptions is explored. The
 main conclusions include the finding that some of the assumptions are not
 a requirement for the multivariate analysis; however, the sample sizes are
 important. 
Journal: Journal of Applied Statistics 
Pages: 393-403 
Issue: 3 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922494 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922494
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:3:p:393-403




Template-Type: ReDIF-Article 1.0
Author-Name: Pai-Lien Chen 
Author-X-Name-First: Pai-Lien 
Author-X-Name-Last: Chen 
Author-Name: Estrada Bernard 
Author-X-Name-First: Estrada 
Author-X-Name-Last: Bernard 
Author-Name: Pranab Sen 
Author-X-Name-First: Pranab 
Author-X-Name-Last: Sen 
Title: A Markov chain model used in analyzing disease history applied to a stroke study 
Abstract:
  In clinical research, study subjects may experience multiple events that
 are observed and recorded periodically. To analyze transition patterns of
 disease processes, it is desirable to use those multiple events over time
 in the analysis. This study proposes a multi-state Markov model with
 piecewise transition probability, which is able to accommodate
 periodically observed clinical data without a time homogeneity assumption.
 Models with ordinal outcomes that incorporate covariates are also
 discussed. The proposed models are illustrated by an analysis of the
 severity of morbidity in a monthly follow-up study for patients with
 spontaneous intracerebral hemorrhage. 
Journal: Journal of Applied Statistics 
Pages: 413-422 
Issue: 4 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922304 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922304
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:4:p:413-422




Template-Type: ReDIF-Article 1.0
Author-Name: Kaushik Ghosh 
Author-X-Name-First: Kaushik 
Author-X-Name-Last: Ghosh 
Author-Name: S. Rao Jammalamadaka 
Author-X-Name-First: S. Rao 
Author-X-Name-Last: Jammalamadaka 
Author-Name: Mangalam Vasudaven 
Author-X-Name-First: Mangalam 
Author-X-Name-Last: Vasudaven 
Title: Change-point problems for the von Mises distribution 
Abstract:
  A generalized likelihood ratio procedure and a Bayes procedure are
 considered for change-point problems for the mean direction of the von
 Mises distribution, both when the concentration parameter is known and
 when it is unknown. These tests are based on sample resultant lengths.
 Tables that list critical values of these test statistics are provided.
 These tests are shown to be valid even when the data come from other
 similar unimodal circular distributions. Some empirical studies of powers
 of these test procedures are also incorporated. 
Journal: Journal of Applied Statistics 
Pages: 423-434 
Issue: 4 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922313 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922313
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:4:p:423-434




Template-Type: ReDIF-Article 1.0
Author-Name: Claes Cassel 
Author-X-Name-First: Claes 
Author-X-Name-Last: Cassel 
Author-Name: Peter Hackl 
Author-X-Name-First: Peter 
Author-X-Name-Last: Hackl 
Author-Name: Anders Westlund 
Author-X-Name-First: Anders 
Author-X-Name-Last: Westlund 
Title: Robustness of partial least-squares method for estimating latent variable quality structures 
Abstract:
  Latent variable structural models and the partial least-squares (PLS)
 estimation procedure have found increased interest since being used in the
 context of customer satisfaction measurement. The well-known property that
 the estimates of the inner structure model are inconsistent implies biased
 estimates for finite sample sizes. A simplified version of the structural
 model that is used for the Swedish Customer Satisfaction Index (SCSI)
 system has been used to generate simulated data and to study the PLS
 algorithm in the presence of three inadequacies: (i) skew instead of
 symmetric distributions for manifest variables; (ii) multi-collinearity
 within blocks of manifest and between latent variables; and (iii)
 misspecification of the structural model (omission of regressors). The
 simulation results show that the PLS method is quite robust against these
 inadequacies. The bias that is caused by the inconsistency of PLS
 estimates is substantially increased only for extremely skewed
 distributions and for the erroneous omission of a highly relevant latent
 regressor variable. The estimated scores of the latent variables are
 always in very good agreement with the true values and seem to be
 unaffected by the inadequacies under investigation. 
Journal: Journal of Applied Statistics 
Pages: 435-446 
Issue: 4 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922322 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922322
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:4:p:435-446




Template-Type: ReDIF-Article 1.0
Author-Name: Magnar Lillegard 
Author-X-Name-First: Magnar 
Author-X-Name-Last: Lillegard 
Author-Name: Steinar Engen 
Author-X-Name-First: Steinar 
Author-X-Name-Last: Engen 
Title: Exact confidence intervals generated by conditional parametric bootstrapping 
Abstract:
  Conditional parametric bootstrapping is defined as the samples obtained
 by performing the simulations in such a way that the estimator is kept
 constant and equal to the estimate obtained from the data. Order
 statistics of the bootstrap replicates of the parameter chosen in each
 simulation provide exact confidence intervals, in a probabilistic sense,
 in models with one parameter under quite general conditions. The method is
 still exact in the case of nuisance parameters when these are location and
 scale parameters, and the bootstrapping is based on keeping the
 maximum-likelihood estimates constant. The method is also exact if there
 exists a sufficient statistic for the nuisance parameters and if the
 simulations are performed conditioning on this statistic. The technique
 may also be used to construct prediction intervals. These are generally
 not exact, but are likely to be good approximations. 
Journal: Journal of Applied Statistics 
Pages: 447-459 
Issue: 4 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922331 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922331
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:4:p:447-459




Template-Type: ReDIF-Article 1.0
Author-Name: Jan Magnus 
Author-X-Name-First: Jan 
Author-X-Name-Last: Magnus 
Author-Name: Franc Klaassen 
Author-X-Name-First: Franc 
Author-X-Name-Last: Klaassen 
Title: The final set in a tennis match: Four years at Wimbledon 
Abstract:
  We consider the 'final' (deciding) set in a tennis match. We examine
 whether it is true that the chances for both players to win the match are
 equal at the beginning of the final set, even though they were not equal
 at the beginning of the match. We also test whether it is easier for an
 unseeded woman to beat a seeded player than it is for an unseeded man, and
 whether male players are more closely equal in quality than are females.
 We examine whether the service dominance decreases in long matches, and
 whether winning the 'pre-final' set provides an advantage in the final
 set. We use almost 90 000 points at Wimbledon to test all five hypotheses. 
Journal: Journal of Applied Statistics 
Pages: 461-468 
Issue: 4 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922340 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922340
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:4:p:461-468




Template-Type: ReDIF-Article 1.0
Author-Name: Sueli Mingoti 
Author-X-Name-First: Sueli 
Author-X-Name-Last: Mingoti 
Title: Bayesian estimator for the total number of distinct species when quadrat sampling is used 
Abstract:
  A Bayesian estimator for the total number of distinct species present in
 the region of investigation is constructed when the quadrat sampling
 procedure is used to collect a sample of species. The estimator is based
 on a model similar to that used by Mingoti and Meeden, and uses as a
 special case the zero truncated negative binomial distribution as a prior
 distribution for the true number S of distinct species in the region.
 Confidence intervals are also obtained. Simple comparisons with the
 first-order jackknife estimator and the empirical Bayesian estimator are
 performed. 
Journal: Journal of Applied Statistics 
Pages: 469-483 
Issue: 4 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922359 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922359
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:4:p:469-483




Template-Type: ReDIF-Article 1.0
Author-Name: Francisco Fernando 
Author-X-Name-First: Francisco 
Author-X-Name-Last: Fernando 
Author-Name: Ribeiro Ramos 
Author-X-Name-First: Ribeiro 
Author-X-Name-Last: Ramos 
Title: Underreporting of purchases of port wine 
Abstract:
  In this paper, we develop a new approach for modelling underreported
 Poisson counts. The parameters of the model are estimated by Markov chain
 Monte Carlo simulation. An application to a real data set from a
 Portuguese marketing survey illustrates the fruitfulness of the approach.
 We find that purchases of bottles of port wine increase significantly with
 income class and the size of the household. 
Journal: Journal of Applied Statistics 
Pages: 485-494 
Issue: 4 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922368 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922368
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:4:p:485-494




Template-Type: ReDIF-Article 1.0
Author-Name: Eric Schoen 
Author-X-Name-First: Eric 
Author-X-Name-Last: Schoen 
Title: Designing fractional two-level experiments with nested error structures 
Abstract:
  A common feature of experiments with a random blocking factor and
 splitplot experiments is their nested error structure. This paper proposes
 a general strategy to handle fractional two-level experiments with such
 error structures. The strategy aims to create error strata with sufficient
 numbers of contrasts to separate active effects from inactive effects. The
 strategy also details the construction of treatment generators, given the
 constraints of a predetermined error structure. The key elements of the
 strategy are illustrated with a chemical experiment that has 16 factors
 and 32 runs blocked according to working days, and a cheese-making
 experiment that has 11 factors and 128 runs, divided over milk supplies as
 whole plots, curds productions as subplots and sets of identically treated
 cheeses as sub-subplots. 
Journal: Journal of Applied Statistics 
Pages: 495-508 
Issue: 4 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922377 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922377
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:4:p:495-508




Template-Type: ReDIF-Article 1.0
Author-Name: Stephen Walker 
Author-X-Name-First: Stephen 
Author-X-Name-Last: Walker 
Title: The uniform power distribution 
Abstract:
  This paper introduces a generalization of the normal distribution: the
 uniform power distribution. It is a symmetric and unimodal family of
 distributions, defined on the real line, and is closely related to the
 exponential power family. The exponential power family was introduced to
 allow the modelling of kurtosis. The uniform power family matches the
 exponential power family with respect to the range of kurtosis. However,
 whereas the exponential is somewhat difficult to work with, the contrary
 is true for the uniform power family. 
Journal: Journal of Applied Statistics 
Pages: 509-517 
Issue: 4 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922386 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922386
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:4:p:509-517




Template-Type: ReDIF-Article 1.0
Author-Name: Paul Zhang 
Author-X-Name-First: Paul 
Author-X-Name-Last: Zhang 
Title: Omnibus test of normality using the Q statistic 
Abstract:
  A new statistical procedure for testing normality is proposed. The Q
 statistic is derived as the ratio of two linear combinations of the
 ordered random observations. The coefficients of the linear combinations
 are utilizing the expected values of the order statistics from the
 standard normal distribution. This test is omnibus to detect the
 deviations from normality that result from either skewness or kurtosis.
 The statistic is independent of the origin and the scale under the null
 hypothesis of normality, and the null distribution of Q can be very well
 approximated by the Cornish-Fisher expansion. The powers for various
 alternative distributions were compared with several other test statistics
 by simulations. 
Journal: Journal of Applied Statistics 
Pages: 519-528 
Issue: 4 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922395 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922395
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:4:p:519-528




Template-Type: ReDIF-Article 1.0
Author-Name: Herbert BUNing 
Author-X-Name-First: Herbert 
Author-X-Name-Last: BUNing 
Title: Adaptive Jonckheere-type tests for ordered alternatives 
Abstract:
  Testing against ordered alternatives in the c -sample location problem
 plays an important role in statistical practice. The parametric test
 proposed by Barlow et al .-in the following, called the 'B-test'-is an
 appropriate test under the model of normality. For non-normal data,
 however, there are rank tests which have higher power than the B-test,
 such as the Jonckheere test or so-called Jonckheere-type tests introduced
 and studied by Buning and Kossler. However, we usually have no information
 about the underlying distribution. Thus, an adaptive test should be
 applied which takes into account the given data set. Two versions of such
 an adaptive test are proposed, which are based on the concept introduced
 by Hogg in 1974. These adaptive tests are compared with each of the single
 Jonckheere-type tests in the adaptive scheme and also with the B-test. It
 is shown via Monte Carlo simulation that the adaptive tests behave well
 over a broad class of symmetric distributions with short, medium and long
 tails, as well as for asymmetric distributions. 
Journal: Journal of Applied Statistics 
Pages: 541-551 
Issue: 5 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922214 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922214
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:5:p:541-551




Template-Type: ReDIF-Article 1.0
Author-Name: Jerry Dechert 
Author-X-Name-First: Jerry 
Author-X-Name-Last: Dechert 
Author-Name: Kenneth Case 
Author-X-Name-First: Kenneth 
Author-X-Name-Last: Case 
Title: An economic model for clinical quality control 
Abstract:
  With increased focus on reducing costs in the healthcare industry, the
 economic aspects of quality control for clinical laboratories must be
 addressed. In order to evaluate the economic performance of statistical
 quality control approaches used in the clinical setting, an economic model
 is developed. Although the economic model is applied specifically to the
 clinical laboratory in this research, it is easily generalized for use in
 a wide variety of industry applications. Use of the economic model is
 illustrated through the comparison of traditional approaches to clinical
 quality control. Recommendations concerning the performance of the
 traditional approaches to clinical quality control are provided. 
Journal: Journal of Applied Statistics 
Pages: 553-562 
Issue: 5 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922223 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922223
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:5:p:553-562




Template-Type: ReDIF-Article 1.0
Author-Name: D. K. Ghosh 
Author-X-Name-First: D. K. 
Author-X-Name-Last: Ghosh 
Author-Name: Naimesh Desai 
Author-X-Name-First: Naimesh 
Author-X-Name-Last: Desai 
Title: Robustness of a complete diallel crosses plan with an unequal number of crosses to the unavailability of one block 
Abstract:
  The present investigation involved the estimation of the general
 combining ability of complete diallel crosses (CDC) plans with unequal
 numbers of crosses, subject to the unavailability of one block for
 Griffing's system IV . Further, it has been shown that CDC plans with
 unequal numbers of crosses are fairly robust to the unavailability of one
 block. 
Journal: Journal of Applied Statistics 
Pages: 563-577 
Issue: 5 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922232 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922232
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:5:p:563-577




Template-Type: ReDIF-Article 1.0
Author-Name: Paul Taylor 
Author-X-Name-First: Paul 
Author-X-Name-Last: Taylor 
Author-Name: David Hand 
Author-X-Name-First: David 
Author-X-Name-Last: Hand 
Title: Finding 'superclassifications' with an acceptable misclassification rate 
Abstract:
  Cluster analysis methods are based on measures of 'distance' between
 objects. Sometimes the objects have an internal structure, and use of this
 can be made when defining such distances. This leads to non-standard
 cluster analysis methods. We illustrate with an application in which the
 objects are themselves classes and the aim is to produce clusters of
 classes which minimize the error rate of a supervised classification rule.
 For supervised classification problems with more than a handful of
 classes, there may exist groups of classes which are well separated from
 other groups, even though individual classes are not all well separated.
 In such cases, the overall misclassification rate is a crude measure of
 performance and more subtle measures, taking note of subgroup separation,
 are desirable. The fact that points can be assigned accurately to groups,
 if not to individual classes, can sometimes be practically useful. 
Journal: Journal of Applied Statistics 
Pages: 579-590 
Issue: 5 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922241 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922241
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:5:p:579-590




Template-Type: ReDIF-Article 1.0
Author-Name: Habshah Midi 
Author-X-Name-First: Habshah 
Author-X-Name-Last: Midi 
Title: Preliminary estimators for robust non-linear regression estimation 
Abstract:
  In this paper, the robustness of weighted non-linear least-squares
 estimation based on some preliminary estimators is examined. The
 preliminary estimators are the Lnorm estimates proposed by Schlossmacher,
 by El-Attar et al., by Koenker and Park, and by Lawrence and Arthur. A
 numerical example is presented to compare the robustness of the weighted
 non-linear least-squares approach when based on the preliminary estimators
 of Schlossmacher (HS), El-Attar et al. (HEA), Koenker and Park (HKP), and
 Lawrence and Arthur (HLA). The study shows that the HEA estimator is as
 robust as the HKP estimator. However, the HEA estimator posed certain
 computational problems and required more storage and computing time. 
Journal: Journal of Applied Statistics 
Pages: 591-600 
Issue: 5 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922250 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922250
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:5:p:591-600




Template-Type: ReDIF-Article 1.0
Author-Name: M. Carme Ruiz De Villa 
Author-X-Name-First: M. Carme Ruiz 
Author-X-Name-Last: De Villa 
Author-Name: M. Salome 
Author-X-Name-First: M. 
Author-X-Name-Last: Salome 
Author-Name: E. Cabral 
Author-X-Name-First: E. 
Author-X-Name-Last: Cabral 
Author-Name: Eduardo Escrich Escriche 
Author-X-Name-First: Eduardo Escrich 
Author-X-Name-Last: Escriche 
Author-Name: Montse Solanas 
Author-X-Name-First: Montse 
Author-X-Name-Last: Solanas 
Title: A non-parametric regression approach to repeated measures analysis in cancer experiments 
Abstract:
  The validity conditions for univariate or multivariate analyses of
 repeated measures are highly sensitive to the usual assumptions. In cancer
 experiments, the data are frequently heteroscedastic and strongly
 correlated with time, and standard analyses do not perform well.
 Alternative non-parametric approaches can contribute to an analysis of
 these longitudinal data. This paper describes a method for such
 situations, using the results from a comparative experiment in which
 tumour volume is evaluated over time. First, we apply the non-parametric
 approach proposed by Raz in constructing a randomization Ftest for
 comparing treatments. A local polynomial fit is conducted to estimate the
 growth curves and confidence intervals for each treatment. Finally, this
 technique is used to estimate the velocity of tumour growth. 
Journal: Journal of Applied Statistics 
Pages: 601-611 
Issue: 5 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922269 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922269
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:5:p:601-611




Template-Type: ReDIF-Article 1.0
Author-Name: A. J. Scallan 
Author-X-Name-First: A. J. 
Author-X-Name-Last: Scallan 
Title: Regression modelling of interval-censored failure time data using the Weibull distribution 
Abstract:
  A method is described for fitting the Weibull distribution to
 failure-time data which may be left, right or interval censored. The
 method generalizes the auxiliary Poisson approach and, as such, means that
 it can be easily programmed in statistical packages with macro programming
 capabilities. Examples are given of fitting such models and an
 implementation in the GLIM package is used for illustration. 
Journal: Journal of Applied Statistics 
Pages: 613-618 
Issue: 5 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922278 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922278
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:5:p:613-618




Template-Type: ReDIF-Article 1.0
Author-Name: Alexandra Mello Schmidt 
Author-X-Name-First: Alexandra Mello 
Author-X-Name-Last: Schmidt 
Author-Name: Dani Gamerman 
Author-X-Name-First: Dani 
Author-X-Name-Last: Gamerman 
Author-Name: Ajax Moreira 
Author-X-Name-First: Ajax 
Author-X-Name-Last: Moreira 
Title: An adaptive resampling scheme for cycle estimation 
Abstract:
  Bayesian dynamic linear models (DLMs) are useful in time series
 modelling, because of the flexibility that they off er for obtaining a
 good forecast. They are based on a decomposition of the relevant factors
 which explain the behaviour of the series through a series of state
 parameters. Nevertheless, the DLM as developed by West and Harrison depend
 on additional quantities, such as the variance of the system disturbances,
 which, in practice, are unknown. These are referred to here as
 'hyper-parameters' of the model. In this paper, DLMs with autoregressive
 components are used to describe time series that show cyclic behaviour.
 The marginal posterior distribution for state parameters can be obtained
 by weighting the conditional distribution of state parameters by the
 marginal distribution of hyper-parameters. In most cases, the joint
 distribution of the hyperparameters can be obtained analytically but the
 marginal distributions of the components cannot, so requiring numerical
 integration. We propose to obtain samples of the hyperparameters by a
 variant of the sampling importance resampling method. A few applications
 are shown with simulated and real data sets. 
Journal: Journal of Applied Statistics 
Pages: 619-641 
Issue: 5 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922287 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922287
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:5:p:619-641




Template-Type: ReDIF-Article 1.0
Author-Name: Sharifah Sakinah Aidid 
Author-X-Name-First: Sharifah Sakinah 
Author-X-Name-Last: Aidid 
Author-Name: Mick Silver 
Author-X-Name-First: Mick 
Author-X-Name-Last: Silver 
Title: Modelling market shares by segments using volatility 
Abstract:
  This paper presents the results of market share modelling for individual
 segments of the UK tea market using scanner panel data. The study is novel
 in its introduction of the use of volatility as one of the bases for
 segmentation, others being usage, loyalty or switching between product
 types and product forms. The segmentation is undertaken on an a priori,
 quasi-experimental basis, allowing nested tests of constancy of
 elasticities across segments. The estimated equations (using seemingly
 unrelated regressions) benefit from extensive specification, including
 four diff erent forms for the price variable, four variables for
 promotion, and six for product characteristic, distribution and
 macroeconomic variables. Tests for the constancy of the parameters across
 segments show the segmentation to be successful. 
Journal: Journal of Applied Statistics 
Pages: 643-660 
Issue: 5 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922296 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922296
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:5:p:643-660




Template-Type: ReDIF-Article 1.0
Author-Name: Jorge Alberto Achcar 
Author-X-Name-First: Jorge Alberto 
Author-X-Name-Last: Achcar 
Author-Name: Gilberto De AraUJo Pereira 
Author-X-Name-First: Gilberto De AraUJo 
Author-X-Name-Last: Pereira 
Title: Use of exponential power distributions for mixture models in the presence of covariates 
Abstract:
  In this paper, we present a Bayesian analysis of exponential power
 mixture models in the presence of a covariate. Considering Gibbs sampling
 with MetropolisHastings algorithms, we obtain Monte Carlo estimates for
 the posterior quantities of interest. 
Journal: Journal of Applied Statistics 
Pages: 669-679 
Issue: 6 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922115 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922115
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:6:p:669-679




Template-Type: ReDIF-Article 1.0
Author-Name: Yssa Dewoody 
Author-X-Name-First: Yssa 
Author-X-Name-Last: Dewoody 
Author-Name: V. T. Gururaj 
Author-X-Name-First: V. T. 
Author-X-Name-Last: Gururaj 
Author-Name: Clyde Martin 
Author-X-Name-First: Clyde 
Author-X-Name-Last: Martin 
Title: Assessing risk for rare events 
Abstract:
  This paper develops a method for assessing the risk for rare events based
 on the following scenario. There exists a large population with an unknown
 percentage p of defects. A sample of size N is drawn from the population
 and, in the sample, 0 defects are drawn. Given these data, we want to
 determine the probability that no more than n defects will be found in
 another random sample of N drawn from the population. Estimates on the
 range of p and n are calculated from a derived joint distribution which
 depends on p, n and N. Asymptotic risk results based on an infinite sample
 are then developed. It is shown that these results are applicable even
 with relatively small sample spaces. 
Journal: Journal of Applied Statistics 
Pages: 681-687 
Issue: 6 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922124 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922124
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:6:p:681-687




Template-Type: ReDIF-Article 1.0
Author-Name: L. Duchateau 
Author-X-Name-First: L. 
Author-X-Name-Last: Duchateau 
Author-Name: D. L. Berkvens 
Author-X-Name-First: D. L. 
Author-X-Name-Last: Berkvens 
Author-Name: G. J. Rowlands 
Author-X-Name-First: G. J. 
Author-X-Name-Last: Rowlands 
Title: Decision rules for small vaccine experiments with binary outcomes based on conditional and expected power and size of the Fisherexact test 
Abstract:
  Vaccine experiments with a binary outcome typically use a small number of
 animals for financial and ethical reasons. The choice of a design,
 characterized by the total number of animals and the allocation of animals
 to treated and control groups, needs to be based on an assessment of
 change in expected size and power, with corresponding changes in the
 nominal significance level. This paper shows how an analysis of the
 conditional and the expected size and power of the Fisher-exact test,
 given predicted values for the proportions of success in control and
 treated groups, can lead to appropriate decision rules. 
Journal: Journal of Applied Statistics 
Pages: 689-699 
Issue: 6 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922133 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922133
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:6:p:689-699




Template-Type: ReDIF-Article 1.0
Author-Name: Yue Fang 
Author-X-Name-First: Yue 
Author-X-Name-Last: Fang 
Author-Name: John Zhang 
Author-X-Name-First: John 
Author-X-Name-Last: Zhang 
Title: Performance of control charts for autoregressive conditional heteroscedastic processes 
Abstract:
  This paper examines the robustness of control schemes to data conditional
 heteroscedasticity. Overall, the results show that the control schemes
 which do not account for heteroscedasticity fail in providing reliable
 information on the status of the process. Consequently, incorrect
 conclusions will be drawn by applying these procedures in the presence of
 data conditional heteroscedasticity. Control charts with time-varying
 control limits are shown to be useful in that context. 
Journal: Journal of Applied Statistics 
Pages: 701-714 
Issue: 6 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922142 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922142
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:6:p:701-714




Template-Type: ReDIF-Article 1.0
Author-Name: Peter Harris 
Author-X-Name-First: Peter 
Author-X-Name-Last: Harris 
Author-Name: Mark Hann 
Author-X-Name-First: Mark 
Author-X-Name-Last: Hann 
Author-Name: Simon Kirby 
Author-X-Name-First: Simon 
Author-X-Name-Last: Kirby 
Author-Name: John Dearden 
Author-X-Name-First: John 
Author-X-Name-Last: Dearden 
Title: Interval estimation of the median effective dose for a logistic dose-response curve 
Abstract:
  In 1986, Williams showed how, assuming a logistic dose-response curve,
 one can construct a confidence interval for the median effective dose from
 the asymptotic likelihood ratio test. He gave reasons for preferring this
 likelihood ratio interval to the established interval calculated by
 applying Fieller's theorem to the maximum-likelihood estimates. Here, we
 assess the impact of applying a Bartlett adjustment to the likelihood
 ratio statistic and introduce the score test as an alternative approach
 for constructing a confidence interval for the median effective dose. 
Journal: Journal of Applied Statistics 
Pages: 715-722 
Issue: 6 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922151 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922151
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:6:p:715-722




Template-Type: ReDIF-Article 1.0
Author-Name: Ulric Lund 
Author-X-Name-First: Ulric 
Author-X-Name-Last: Lund 
Title: Least circular distance regression for directional data 
Abstract:
  Least-squares regression is not appropriate when the response variable is
 circular, and can lead to erroneous results. The reason for this is that
 the squared difference is not an appropriate measure of distance on the
 circle. In this paper, a circular analog to least-squares regression is
 presented for predicting a circular response variable by another circular
 variable and a set of linear covariates. An alternative maximum-likelihood
 formulation yields the same regression parameter estimates. Under the
 maximum-likelihood model, asymptotic standard errors of the parameter
 estimates are obtained. As an example, the regression model is used to
 model data from a marine biology study. 
Journal: Journal of Applied Statistics 
Pages: 723-733 
Issue: 6 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922160 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922160
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:6:p:723-733




Template-Type: ReDIF-Article 1.0
Author-Name: K. V. Mardia 
Author-X-Name-First: K. V. 
Author-X-Name-Last: Mardia 
Author-Name: A. N. Walder 
Author-X-Name-First: A. N. 
Author-X-Name-Last: Walder 
Author-Name: E. Berry 
Author-X-Name-First: E. 
Author-X-Name-Last: Berry 
Author-Name: D. Sharples 
Author-X-Name-First: D. 
Author-X-Name-Last: Sharples 
Author-Name: P. A. Millner 
Author-X-Name-First: P. A. 
Author-X-Name-Last: Millner 
Author-Name: R. A. Dickson 
Author-X-Name-First: R. A. 
Author-X-Name-Last: Dickson 
Title: Assessing spinal shape 
Abstract:
  Idiopathic scoliosis is the most common spinal deformity, affecting
 perhaps as many as 5% of children. Early recognition of the condition is
 essential for optimal treatment. A widely used technique for
 identification is based on a somewhat crude angle measurement from a
 frontal spinal X-ray. Here, we provide a technique and new summary
 statistical measures for classifying spinal shape, and present results
 obtained from clinical X-rays. 
Journal: Journal of Applied Statistics 
Pages: 735-745 
Issue: 6 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922179 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922179
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:6:p:735-745




Template-Type: ReDIF-Article 1.0
Author-Name: A. J. Scallan 
Author-X-Name-First: A. J. 
Author-X-Name-Last: Scallan 
Title: Fitting a mixture distribution to complex censored survival data using generalized linear models 
Abstract:
  Mixture models may arise for a variety of reasons in survival data
 analysis. This paper shows how such models that involve potentially
 complex cross-classification by covariates may be easily fitted using a
 package such as GLIM. The method employs an auxiliary Poisson-binomial
 model in order to find the maximum-likelihood estimates of the model
 parameters, and has been implemented using GLIM macros. 
Journal: Journal of Applied Statistics 
Pages: 747-753 
Issue: 6 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922188 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922188
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:6:p:747-753




Template-Type: ReDIF-Article 1.0
Author-Name: Yuehjen Shao 
Author-X-Name-First: Yuehjen 
Author-X-Name-Last: Shao 
Author-Name: Yue-Fa Lin 
Author-X-Name-First: Yue-Fa 
Author-X-Name-Last: Lin 
Author-Name: Soe-Tsyr Yuan 
Author-X-Name-First: Soe-Tsyr 
Author-X-Name-Last: Yuan 
Title: Integrated application of time series multiple-interventions analysis and knowledge-based reasoning 
Abstract:
  This study examines the data that result from multiple promotional
 strategies when the data are autocorrelated. Time series intervention
 analysis is the traditional way to analyze such data, focusing on the
 effects of a single or a few interventions. Time series intervention
 analysis delivers good results, provided that there is a known and
 predetermined schedule of future interventions. This study opts for a
 different type of analysis. Instead of adopting the traditional time
 series intervention analysis with only one or a few interventions, this
 study explores the possibility of integrating time series intervention
 analysis and a knowledge-based system to analyze multiple-interventions
 data. This integrated approach does not require attempts to ascertain the
 effects of future interventions. Through the analysis of actual promotion
 data, this study shows the benefits of using the proposed method. 
Journal: Journal of Applied Statistics 
Pages: 755-766 
Issue: 6 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922197 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922197
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:6:p:755-766




Template-Type: ReDIF-Article 1.0
Author-Name: I. H. Tajuddin 
Author-X-Name-First: I. H. 
Author-X-Name-Last: Tajuddin 
Title: A comparison between two simple measures of skewness 
Abstract:
  In 1995, Arnold and Groeneveld introduced the measure of skewness gammaM
 in terms of F(mode)-the cumulative probability of a random variable less
 than or equal to the mode of the distribution. They assumed that the mode
 of a distribution exists and is unique. Independently, in 1996, the
 present author arrived at the measure of skewness T, which is given in
 terms of F(mean). This measure possesses desirable properties and is
 equally simple. The measure gammaM satisfies - 1 gammaM 1 , with 1 (- 1)
 indicating extreme right (left) skewness. However, the measure T can take
 on any value on the real line; hence, an equivalent measure gammaT is
 considered and is compared with gammaM. We consider a variety of families
 of distributions and include in our study other measures of skewness of
 interest. Skewness values are easily obtained using MINITAB programs. 
Journal: Journal of Applied Statistics 
Pages: 767-774 
Issue: 6 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922205 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922205
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:6:p:767-774




Template-Type: ReDIF-Article 1.0
Author-Name: JosE Nilo Binongo 
Author-X-Name-First: JosE Nilo 
Author-X-Name-Last: Binongo 
Author-Name: M. W. A. Smith 
Author-X-Name-First: M. W. A. 
Author-X-Name-Last: Smith 
Title: A bridge between statistics and literature: The graphs of Oscar Wilde's literary genres 
Abstract:
  The availability of computing devices and the proliferation of electronic
 texts (the so-called 'e-texts') in centres for literary and linguistic
 computing in major universities have encouraged non-traditional
 applications of statistics. With the drudgery of computation and text
 encoding diminished, research in the field of computational stylistics is
 accelerating. In this paper, it is shown how projections onto the
 Cartesian plane of 25-dimensional vectors related to the frequency of
 occurrence of 25 prepositions can distinguish between Oscar Wilde's plays
 and essays. Such an application illustrates that it is possible to find
 unusual and intriguing examples of how statistics can impinge on
 unexpected territory. 
Journal: Journal of Applied Statistics 
Pages: 781-787 
Issue: 7 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922025 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922025
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:7:p:781-787




Template-Type: ReDIF-Article 1.0
Author-Name: Zhenmin Chen 
Author-X-Name-First: Zhenmin 
Author-X-Name-Last: Chen 
Title: A simple exact method for testing hypotheses about the shape parameter of a log-normal distribution 
Abstract:
  The log-normal distribution is a useful lifetime distribution in many
 areas. The survival function of a log-normal distribution cannot be
 expressed in close forms. This makes it difficult to develop exact
 statistical methods for parameter estimation when censoring occurs. This
 article proposes a simple and exact method for conducting statistical
 tests about the shape parameter of a log-normal distribution. Necessary
 tables are provided based on Monte Carlo simulation. The method can be
 used for type II censored data. Comparing with existing exact methods,
 this method uses fewer tables and is easier for calculations. 
Journal: Journal of Applied Statistics 
Pages: 789-805 
Issue: 7 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922034 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922034
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:7:p:789-805




Template-Type: ReDIF-Article 1.0
Author-Name: Paul De Bruin 
Author-X-Name-First: Paul 
Author-X-Name-Last: De Bruin 
Author-Name: Philip Hans Franses 
Author-X-Name-First: Philip Hans 
Author-X-Name-Last: Franses 
Title: Forecasting power-transformed time series data 
Abstract:
  When there is an interest in forecasting the growth rates as well as the
 levels of a single macro-economic time series, a practitioner faces the
 question of whether a forecasting model should be constructed for growth
 rates, for levels, or for both. In this paper, we investigate this issue
 for 10 US (un-)employment series, where we evaluate the forecasts from a
 non-linear time series model for power-transformed data. Our main finding
 is that models for growth rates (levels) do not automatically result in
 the most accurate forecasts of growth rates (levels). 
Journal: Journal of Applied Statistics 
Pages: 807-815 
Issue: 7 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922043 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922043
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:7:p:807-815




Template-Type: ReDIF-Article 1.0
Author-Name: Yadolah Dodge 
Author-X-Name-First: Yadolah 
Author-X-Name-Last: Dodge 
Author-Name: Ali Hadi 
Author-X-Name-First: Ali 
Author-X-Name-Last: Hadi 
Title: Simple graphs and bounds for the elements of the hat matrix 
Abstract:
  In regression analysis, the matrix H = X (XTX)-1XT is known as the 'hat'
 or 'projection' matrix, among other names. It has been studied by many
 authors from different perspectives. The main area of study has been the
 type of measure best adapted to detect leverage points in linear
 regression. For computational reasons, these measures were originally
 based on the diagonal elements of the hat matrix. In the present paper, we
 propose a very simple procedure for identifying leverage groups. The
 procedure is based on upper and lower bounds for the diagonal and the
 off-diagonal elements of H. These upper and lower bounds can easily be
 shown on an index plot of the elements of H. 
Journal: Journal of Applied Statistics 
Pages: 817-823 
Issue: 7 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922052 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922052
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:7:p:817-823




Template-Type: ReDIF-Article 1.0
Author-Name: T. Lehtonen 
Author-X-Name-First: T. 
Author-X-Name-Last: Lehtonen 
Author-Name: J. -O. Malmberg 
Author-X-Name-First: J. -O. 
Author-X-Name-Last: Malmberg 
Title: Do two competing frequencies differ significantly? 
Abstract:
  When testing the equality of two population frequencies, one well-known
 and common situation is that the test is based on two independent samples.
 In this paper, we consider the other interesting case, in which the
 comparison is actually within a single population and the test is based on
 a single sample. 
Journal: Journal of Applied Statistics 
Pages: 825-830 
Issue: 7 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922061 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922061
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:7:p:825-830




Template-Type: ReDIF-Article 1.0
Author-Name: Ralf Ostermark 
Author-X-Name-First: Ralf 
Author-X-Name-Last: Ostermark 
Author-Name: Rune Hoglund 
Author-X-Name-First: Rune 
Author-X-Name-Last: Hoglund 
Title: Simulating competing cointegration tests in a bivariate system 
Abstract:
  In this paper, we consider the size and power of a set of cointegration
 tests in a number of Monte Carlo simulations. The behaviour of the
 competing methods is investigated in diff erent situations, including diff
 erent levels of variance and correlation in the error processes. The
 impact of violations of the common factor restriction (CFR) implied by the
 Engle-Granger framework is studied in these situations. The reactions to
 changes in the CFR condition depend on the error correlation. When the
 correlation is non-positive, the power increases with increasing CFR
 violations for the error correction model (ECM) test, while the other
 tests react in the opposite direction. We also note the reaction to diff
 erences in the error variances in the data-generating process. For
 positive correlation and equal variances, the reaction to changes in the
 CFR violations diff ers somewhat between the tests. We conclude that the
 ECM and the Z-tests show the best performance over diff erent parameter
 combinations. In most situations the ECM is best. Therefore, if we had to
 recommend a unit root test, it would be the ECM, especially for small
 samples. However, we do not think that one should use just one test, but
 two or more. Of course, the portfolio of tests we have considered here
 only represents a subset of the possible tests. 
Journal: Journal of Applied Statistics 
Pages: 831-846 
Issue: 7 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922070 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922070
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:7:p:831-846




Template-Type: ReDIF-Article 1.0
Author-Name: Joseph Pigeon 
Author-X-Name-First: Joseph 
Author-X-Name-Last: Pigeon 
Author-Name: Joseph Heyse 
Author-X-Name-First: Joseph 
Author-X-Name-Last: Heyse 
Title: A cautionary note about assessing the fit of logistic regression models 
Abstract:
  Logistic regression is a popular method of relating a binary response to
 one or more potential covariables or risk factors. In 1980, Hosmer and
 Lemeshow proposed a method for assessing the goodness of fit of logistic
 regression models. This test is based on a chi-squared statistic that
 compares the observed and expected cell frequencies in the 2 g table, as
 found by sorting the observations by predicted probabilities and forming g
 groups. We have noted that the test may be sensitive to situations where
 there are low expected cell frequencies. Further, several commonly used
 statistical packages apply the Hosmer-Lemeshow test, but do so in diff
 erent ways, and none of the packages we considered alerted the user to the
 potential difficulty with low expected cell frequencies. An alternative
 goodness-of-fit test is illustrated which seems to off er an advantage
 over the popular Hosmer-Lemeshow test, by reducing the likelihood of small
 expected counts and, potentially, sharpening the interpretation. An
 example is provided which demonstrates these ideas. 
Journal: Journal of Applied Statistics 
Pages: 847-853 
Issue: 7 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922089 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922089
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:7:p:847-853




Template-Type: ReDIF-Article 1.0
Author-Name: Peiming Wang 
Author-X-Name-First: Peiming 
Author-X-Name-Last: Wang 
Author-Name: Martin Puterman 
Author-X-Name-First: Martin 
Author-X-Name-Last: Puterman 
Title: Markov Poisson regression models for discrete time series. Part 1: Methodology 
Abstract:
  This paper proposes and investigates a class of Markov Poisson regression
 models in which Poisson rate functions of covariates are conditional on
 unobserved states which follow a finite-state Markov chain. Features of
 the proposed model, estimation, inference, bootstrap confidence intervals,
 model selection and other implementation issues are discussed. Monte Carlo
 studies suggest that the proposed estimation method is accurate and
 reliable for single- and multiple-subject time series data; the choice of
 starting probabilities for the Markov process has little eff ect on the
 parameter estimates; and penalized likelihood criteria are reliable for
 determining the number of states. Part 2 provides applications of the
 proposed model. 
Journal: Journal of Applied Statistics 
Pages: 855-869 
Issue: 7 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922098 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922098
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:7:p:855-869




Template-Type: ReDIF-Article 1.0
Author-Name: Peiming Wang 
Author-X-Name-First: Peiming 
Author-X-Name-Last: Wang 
Author-Name: Martin Puterman 
Author-X-Name-First: Martin 
Author-X-Name-Last: Puterman 
Title: Markov Poisson regression models for discrete time series. Part 2: Applications 
Abstract:
  This paper applies the Markov Poisson regression methodology of Wang and
 Puterman to the analysis of seizure frequencies in an epilepsy clinical
 trial and counts of poliomyelitis cases. The analysis of the poliomyelitis
 data is compared with that of Zeger. 
Journal: Journal of Applied Statistics 
Pages: 871-882 
Issue: 7 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769922106 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769922106
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:7:p:871-882




Template-Type: ReDIF-Article 1.0
Author-Name: S. Balamurali 
Author-X-Name-First: S. 
Author-X-Name-Last: Balamurali 
Author-Name: M. Kalyanasundaram 
Author-X-Name-First: M. 
Author-X-Name-Last: Kalyanasundaram 
Title: Determination of conditional double sampling scheme 
Abstract:
  In this paper, a new sampling scheme called the 'conditional double
 sampling scheme' (CDSS) has been proposed. A compact table is presented
 for the selection of a CDSS indexed by various combinations of entry
 parameters. Advantages of the CDSS over the single sampling scheme have
 been discussed. The basis for the construction of the table is given. 
Journal: Journal of Applied Statistics 
Pages: 893-902 
Issue: 8 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769921909 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769921909
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:8:p:893-902




Template-Type: ReDIF-Article 1.0
Author-Name: Chung-Ho Chen 
Author-X-Name-First: Chung-Ho 
Author-X-Name-Last: Chen 
Title: Specification limit under a quality loss function 
Abstract:
  The purpose of this paper is to present the problem of selecting a lower
 specification limit under Taguchi's quality loss function. Considering
 that the product quality characteristic obeys an exponential distribution,
 we propose a modification of the method of Kapur and Wang for the economic
 design of the specification limit. 
Journal: Journal of Applied Statistics 
Pages: 903-908 
Issue: 8 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769921918 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769921918
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:8:p:903-908




Template-Type: ReDIF-Article 1.0
Author-Name: John Cooper 
Author-X-Name-First: John 
Author-X-Name-Last: Cooper 
Title: Artificial neural networks versus multivariate statistics: An application from economics 
Abstract:
  An artificial neural network is a computer model that mimics the brain's
 ability to classify patterns or to make forecasts based on past
 experience. This paper explains the underlying theory of the widely used
 back-propagation algorithm and applies this procedure to a problem from
 the field of international economics, namely the identification of
 countries that are likely to seek a rescheduling of their international
 debt-service obligations. A comparison of the results with those obtained
 from three multivariate statistical procedures applied to the same data
 set suggests that neural networks are worthy of consideration by the
 applied economist. 
Journal: Journal of Applied Statistics 
Pages: 909-921 
Issue: 8 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769921927 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769921927
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:8:p:909-921




Template-Type: ReDIF-Article 1.0
Author-Name: Graham Horgan 
Author-X-Name-First: Graham 
Author-X-Name-Last: Horgan 
Title: Using wavelets for data smoothing: A simulation study 
Abstract:
  Wavelet shrinkage has been proposed as a highly adaptable approach to
 signal smoothing, which can produce optimum results in some senses. This
 paper examines the performance of the method as a function of its
 parameters, by simulation for time series showing gradual, rapid and
 discontinuous variations, for a range of signal-to-noise ratios. Some
 general conclusions are drawn. The effects of the choice of wavelet,
 choice of threshold and choice of resolution cut-off are considered. The
 use of the residual autocorrelation as a diagnostic tool is suggested. 
Journal: Journal of Applied Statistics 
Pages: 923-932 
Issue: 8 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769921936 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769921936
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:8:p:923-932




Template-Type: ReDIF-Article 1.0
Author-Name: Juneyoung Lee 
Author-X-Name-First: Juneyoung 
Author-X-Name-Last: Lee 
Author-Name: Andre Khuri 
Author-X-Name-First: Andre 
Author-X-Name-Last: Khuri 
Title: Graphical technique for comparing designs for random models 
Abstract:
  Methods for comparing designs for a random (or mixed) linear model have
 focused primarily on criteria based on single-valued functions. In
 general, these functions are difficult to use, because of their complex
 forms, in addition to their dependence on the model's unknown variance
 components. In this paper, a graphical approach is presented for comparing
 designs for random models. The one-way model is used for illustration. The
 proposed approach is based on using quantiles of an estimator of a
 function of the variance components. The dependence of these quantiles on
 the true values of the variance components is depicted by plotting the
 so-called quantile dispersion graphs (QDGs), which provide a comprehensive
 picture of the quality of estimation obtained with a given design. The
 QDGs can therefore be used to compare several candidate designs. Two
 methods of estimation of variance components are considered, namely
 analysis of variance and maximum-likelihood estimation. 
Journal: Journal of Applied Statistics 
Pages: 933-947 
Issue: 8 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769921945 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769921945
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:8:p:933-947




Template-Type: ReDIF-Article 1.0
Author-Name: K. V. Mardia 
Author-X-Name-First: K. V. 
Author-X-Name-Last: Mardia 
Title: Directional statistics and shape analysis 
Abstract:
  This paper highlights distributional connections between directional
 statistics and shape analysis. In particular, we provide a test of
 uniformity for highly dispersed shapes, using the standard techniques of
 directional statistics. We exploit the isometric transformation from
 triangular shapes to a sphere in three dimensions, to provide a rich class
 of shape distributions. A link between the Fisher distribution and the
 complex Bingham distribution is re-examined. Some extensions to
 higher-dimensional shapes are outlined. 
Journal: Journal of Applied Statistics 
Pages: 949-957 
Issue: 8 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769921954 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769921954
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:8:p:949-957




Template-Type: ReDIF-Article 1.0
Author-Name: Antonietta Mira 
Author-X-Name-First: Antonietta 
Author-X-Name-Last: Mira 
Title: Distribution-free test for symmetry based on Bonferroni's measure 
Abstract:
  We propose a test based on Bonferroni's measure of skewness. The test
 detects the asymmetry of a distribution function about an unknown median.
 We study the asymptotic distribution of the given test statistic and
 provide a consistent estimate of its variance. The asymptotic relative
 efficiency of the proposed test is computed along with Monte Carlo
 estimates of its power. This allows us to perform a comparison of the test
 based on Bonferroni's measure with other tests for symmetry. 
Journal: Journal of Applied Statistics 
Pages: 959-972 
Issue: 8 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769921963 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769921963
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:8:p:959-972




Template-Type: ReDIF-Article 1.0
Author-Name: Ray Okafor 
Author-X-Name-First: Ray 
Author-X-Name-Last: Okafor 
Title: Using an empirical Bayes model to estimate currency exchange rate 
Abstract:
  An empirical Bayes (EB) model to estimate the exchange rate of a national
 currency is described. The national currency under consideration is
 typically non-convertible, and is generally associated with a weak economy
 of a Third World country. We take the Nigerian currency as an example.
 Using theta as a generic notation for the exchange rate parameter, a
 sequence of sample mean estimates theta i MN (i = 1, 2, &#x2026;, m) is
 generated over m time periods. An EB model is formulated for the theta MN
 , from which the empirical Bayes estimates theta i EB are calculated. The
 performances of theta EB and the Central Bank of Nigeria (CBN) estimate
 theta CBN are compared. On several performance measures, theta EB is shown
 to be superior to theta CBN . 
Journal: Journal of Applied Statistics 
Pages: 973-983 
Issue: 8 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769921972 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769921972
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:8:p:973-983




Template-Type: ReDIF-Article 1.0
Author-Name: Paulo Rodrigues 
Author-X-Name-First: Paulo 
Author-X-Name-Last: Rodrigues 
Author-Name: Denise Osborn 
Author-X-Name-First: Denise 
Author-X-Name-Last: Osborn 
Title: Performance of seasonal unit root tests for monthly data 
Abstract:
  This paper uses Monte Carlo simulations to analyze the performance of
 several seasonal unit root tests for monthly time series. The tests are
 those of Dickey, Hasza and Fuller (DHF), Hylleberg, Engle, Granger and Yoo
 (HEGY), and Osborn, Chui, Smith and Birchenhall (OCSB). The unit root test
 of Dickey and Fuller (DF) is also considered. The results indicate that
 users have to be particularly cautious when applying the monthly version
 of the HEGY test. In general, the DHF and OCSB tests are preferable in
 terms of size and power, but these procedures may impose invalid
 restrictions. An empirical illustration is undertaken for UK two-digit
 industrial production indicators. 
Journal: Journal of Applied Statistics 
Pages: 985-1004 
Issue: 8 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769921981 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769921981
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:8:p:985-1004




Template-Type: ReDIF-Article 1.0
Author-Name: Rosa Bernardini Papalia 
Author-X-Name-First: Rosa Bernardini 
Author-X-Name-Last: Papalia 
Title: Local generalized method of moments estimation based on kernel weights: An application to panel data 
Abstract:
  This paper presents and applies a local generalized method of moments
 (LGMM) estimator for regression functions. The method is an extension of
 previous results obtained by Gozalo and Linton. The LGMM estimation
 procedure can be applied to estimate a mean regression function and its
 derivatives at an interior point x , without making explicit assumptions
 about its functional form. The method has been applied to estimate dynamic
 models based on panel data. 
Journal: Journal of Applied Statistics 
Pages: 1005-1015 
Issue: 8 
Volume: 26 
Year: 1999 
X-DOI: 10.1080/02664769921990 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664769921990
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:26:y:1999:i:8:p:1005-1015




Template-Type: ReDIF-Article 1.0
Author-Name: R. D. Baker 
Author-X-Name-First: R. D. 
Author-X-Name-Last: Baker 
Title: Application of a new discrete distribution 
Abstract:
  In epidemiology, an infection lasting n weeks may be monitored by taking
 weekly serum samples. If tests on samples are independent Bernoulli trials
 with probability q of correctly testing positive, the apparent duration of
 infection ( from the first positive test to the last positive test
 inclusive) may be less than n weeks. This distribution of apparent length
 also arises when plants in a row of n each have a probability q of
 germinating, for example. This distribution is shown to be related to that
 of the number of tails obtained when tossing a coin until two heads are
 obtained, in a maximum of n tosses. The properties of the 'apparent
 length' distribution are described, and some compounded (mixed)
 distributions that can be derived from it are also discussed. The
 distribution was used to estimate the underlying distribution of the
 duration of infection, in a longitudinal study of infections of children.
 The methodology was also used to estimate the proportion of infectious
 episodes that were not detected. It can be similarly used to correct
 episode durations and rates in longitudinal studies in which episodes of
 any kind are detected by regular sampling. 
Journal: Journal of Applied Statistics 
Pages: 5-21 
Issue: 1 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760021790 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760021790
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:1:p:5-21




Template-Type: ReDIF-Article 1.0
Author-Name: S. Balamurali 
Author-X-Name-First: S. 
Author-X-Name-Last: Balamurali 
Author-Name: M. Kalyanasundaram 
Author-X-Name-First: M. 
Author-X-Name-Last: Kalyanasundaram 
Title: Generalized tightened two-level continuous sampling plans 
Abstract:
  In 1955, Lieberman and Solomon introduced multi-level (MLP) continuous
 sampling plans. Derman et al . then extended the multi-level plans as
 tightened multi-level plans (MLP-T). In this paper, a generalization of
 MLP-T with two sampling levels is presented. Using a Markov chain model,
 expressions for the performance measures of the general MLP-T plans are
 derived. Tables are also presented for the selection of general MLP-T
 plans with two sampling levels when the acceptable quality level, limiting
 quality level, indiff erence quality level and average outgoing quality
 level are specified. 
Journal: Journal of Applied Statistics 
Pages: 23-38 
Issue: 1 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760021808 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760021808
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:1:p:23-38




Template-Type: ReDIF-Article 1.0
Author-Name: Olcay Arslan 
Author-X-Name-First: Olcay 
Author-X-Name-Last: Arslan 
Author-Name: Nedret Billor 
Author-X-Name-First: Nedret 
Author-X-Name-Last: Billor 
Title: Robust Liu estimator for regression based on an M-estimator 
Abstract:
  Consider the regression model y = beta 0 1 + Xbeta + epsilon. Recently,
 the Liu estimator, which is an alternative biased estimator beta L (d) =
 (X'X + I) -1 (X'X + dI)beta OLS , where 0&#x3c;d&#x3c;1 is a parameter,
 has been proposed to overcome multicollinearity . The advantage of beta L
 (d) over the ridge estimator beta R (k) is that beta L (d) is a linear
 function of d. Therefore, it is easier to choose d than to choose k in the
 ridge estimator. However, beta L (d) is obtained by shrinking the ordinary
 least squares (OLS) estimator using the matrix (X'X + I) -1 (X'X + dI) so
 that the presence of outliers in the y direction may affect the beta L (d)
 estimator. To cope with this combined problem of multicollinearity and
 outliers, we propose an alternative class of Liu-type M-estimators
 (LM-estimators) obtained by shrinking an M-estimator beta M , instead of
 the OLS estimator using the matrix (X'X + I) -1 (X'X + dI). 
Journal: Journal of Applied Statistics 
Pages: 39-47 
Issue: 1 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760021817 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760021817
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:1:p:39-47




Template-Type: ReDIF-Article 1.0
Author-Name: Jyoti Divecha 
Author-X-Name-First: Jyoti 
Author-X-Name-Last: Divecha 
Title: Search for suitable incomplete block designs for complete diallel cross systems 
Abstract:
  The suitability of incomplete block designs for each complete diallel
 cross system I, II, III and IV, under the general genetic model is
 examined, and a set of necessary conditions obtained. In this connection,
 modifications in available designs are suggested and illustrated. A table
 of suitable designs with higher efficiency for complete diallel cross
 systems is presented. 
Journal: Journal of Applied Statistics 
Pages: 49-62 
Issue: 1 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760021826 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760021826
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:1:p:49-62




Template-Type: ReDIF-Article 1.0
Author-Name: Yangxin Huang Simon 
Author-X-Name-First: Yangxin Huang 
Author-X-Name-Last: Simon 
Author-Name: P. J. Kirby Peter 
Author-X-Name-First: P. J. Kirby 
Author-X-Name-Last: Peter 
Author-Name: Harris John 
Author-X-Name-First: Harris 
Author-X-Name-Last: John 
Author-Name: C. Dearden 
Author-X-Name-First: C. 
Author-X-Name-Last: Dearden 
Title: Interval estimation of the 90% effective dose: A comparison of bootstrap resampling methods with some large-sample approaches 
Abstract:
  A number of recent studies have looked at the coverage probabilities of
 various common parametric methods of interval estimation of the median
 effective dose (ED 50 ) for a logistic dose-response curve. There has been
 comparatively little work done on more extreme effective doses. In this
 paper, the interval estimation of the 90% effective dose (ED 90 ) will be
 of principal interest. We provide a comparison of four parametric methods
 of interval construction with four methods based on bootstrap resampling. 
Journal: Journal of Applied Statistics 
Pages: 63-73 
Issue: 1 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760021835 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760021835
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:1:p:63-73




Template-Type: ReDIF-Article 1.0
Author-Name: Takafumi Isogai 
Author-X-Name-First: Takafumi 
Author-X-Name-Last: Isogai 
Title: Analysis of factorial experiments for survival data with long-tailed distributions 
Abstract:
  Two left-truncated survival data sets are collected in one-way factorial
 designs to examine the quality of products. We cannot specify our survival
 function completely, and only know that the tail has a power functional
 form of its argument. Thus, our problem is a left-truncated one with
 incomplete survivor functions. One of our data sets is the case where the
 usual analysis of variance (ANOVA) may be adapted. The other is a repeated
 measurement case. We note that the likelihood function is expressed as a
 product of conditional and marginal likelihood functions. Estimates of
 power parameters are always obtained by the conditional likelihood.
 Location parameters describing treatment eff ects are included in the
 marginal likelihood only, and their estimates are undetermined, because of
 missing values resulting from left truncation. However, in the ANOVA case,
 we show that a common structure of power parameters and some simple
 assumptions about the missing values enable us to construct an approximate
 F test for treatment effects through the marginal likelihood. This result
 is extended to a regression case. With the data in repeated measurements,
 a systematic variation of the power parameters and an apparent deviation
 from our presupposed model make an application of the ANOVA mentioned
 impossible, and compel us to generalize our model. By using the ratio of
 those generalized models, we show that a descriptive model for evaluating
 treatment effects can be constructed. 
Journal: Journal of Applied Statistics 
Pages: 75-101 
Issue: 1 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760021844 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760021844
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:1:p:75-101




Template-Type: ReDIF-Article 1.0
Author-Name: Dejian Lai 
Author-X-Name-First: Dejian 
Author-X-Name-Last: Lai 
Author-Name: Barry Davis 
Author-X-Name-First: Barry 
Author-X-Name-Last: Davis 
Author-Name: Robert Hardy 
Author-X-Name-First: Robert 
Author-X-Name-Last: Hardy 
Title: Fractional Brownian motion and clinical trials 
Abstract:
  The purpose of this paper is to extend the widely used classical Brownian
 motion technique for monitoring clinical trial data to a larger class of
 stochastic processes, i.e. fractional Brownian motion, and compare these
 results. The beta-blocker heart attack trial is presented as an example to
 illustrate both methods. 
Journal: Journal of Applied Statistics 
Pages: 103-108 
Issue: 1 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760021853 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760021853
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:1:p:103-108




Template-Type: ReDIF-Article 1.0
Author-Name: J. A. Nelder 
Author-X-Name-First: J. A. 
Author-X-Name-Last: Nelder 
Title: Functional marginality and response-surface fitting 
Abstract:
  Well-formed polynomials contain the marginal terms of all terms; for
 example, they contain both x 1 and x 2 if x 1 x 2 is present. Such models
 have a goodness of fit that is invariant to linear transformations of the
 x variables. Recently, selection procedures have been proposed which may
 not give well-formed polynomials. Analysis of two data sets for which
 non-well-formed polynomials have been selected shows that conversion to
 well-formed polynomials is beneficial in terms of goodness of fit, as well
 as giving fits invariant to linear transformation of the x variables. It
 is concluded that selection procedures should search among well-formed
 polynomials only. 
Journal: Journal of Applied Statistics 
Pages: 109-112 
Issue: 1 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760021862 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760021862
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:1:p:109-112




Template-Type: ReDIF-Article 1.0
Author-Name: Erhard Reschenhofer 
Author-X-Name-First: Erhard 
Author-X-Name-Last: Reschenhofer 
Title: Modification of autoregressive fractionally integrated moving average models for the estimation of persistence 
Abstract:
  In this paper, it is proposed to modify autoregressive fractionally
 integrated moving average (ARFIMA) processes by introducing an additional
 parameter to comply with the criticism of Hauser et al . (1999) that
 ARFIMA processes are not appropriate for the estimation of persistence,
 because of the degenerate behavior of their spectral densities at
 frequency zero. When fitting these modified ARFIMA processes to the US
 GNP, it turns out that the estimated spectra are very similar to those
 obtained with conventional ARFIMA models, indicating that, in this special
 case, the disadvantage of ARFIMA models cited by Hauser et al. (1999) does
 not seriously aff ect the estimation of persistence. However, according to
 the results of a goodness-of-fit test applied to the estimated spectra,
 both the ARFIMA models and the modified ARFIMA models seem to overfit the
 data in the neighborhood of frequency zero. 
Journal: Journal of Applied Statistics 
Pages: 113-118 
Issue: 1 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760021871 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760021871
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:1:p:113-118




Template-Type: ReDIF-Article 1.0
Author-Name: Jon Vilasuso 
Author-X-Name-First: Jon 
Author-X-Name-Last: Vilasuso 
Author-Name: David Katz 
Author-X-Name-First: David 
Author-X-Name-Last: Katz 
Title: Estimates of the likelihood of extreme returns in international stock markets 
Abstract:
  This study applies extreme-value theory to daily international
 stock-market returns to determine (1) whether or not returns follow a
 heavy-tailed stable distribution, (2) the likelihood of an extreme return,
 such as a 20% drop in a single day, and (3) whether or not the likelihood
 of an extreme event has changed since October 1987. Empirical results
 reject a heavy-tailed stable distribution for returns. Instead, a
 Student-t distribution or an autoregressive conditional heteroscedastic
 process is better able to capture the salient features of returns. We find
 that the likelihood of a large single-day return diff ers widely across
 markets and, for the G-7 countries, the 1987 stock-market drop appears to
 be largely an isolated event. A drop of this magnitude, however, is not
 rare in the case of Hong Kong. Finally, there is only limited evidence
 that the chance of a large single-day decline is more likely since the
 October 1987 market drop; however, exceptions include stock markets in
 Germany, The Netherlands and the UK. 
Journal: Journal of Applied Statistics 
Pages: 119-130 
Issue: 1 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760021880 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760021880
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:1:p:119-130




Template-Type: ReDIF-Article 1.0
Author-Name: David Aadland 
Author-X-Name-First: David 
Author-X-Name-Last: Aadland 
Title: Distribution and interpolation using transformed data 
Abstract:
  This paper addresses the distribution and interpolation of time series
 that have been subject to various data transformations. Monte Carlo
 experiments are performed, which suggest that failure to account for these
 data transformations may lead to serious errors in estimation. 
Journal: Journal of Applied Statistics 
Pages: 141-156 
Issue: 2 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760021682 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760021682
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:2:p:141-156




Template-Type: ReDIF-Article 1.0
Author-Name: H. Oztas Ayhan 
Author-X-Name-First: H. Oztas 
Author-X-Name-Last: Ayhan 
Title: Estimators of vital events in dual-record systems 
Abstract:
  Dual-record system estimation has been widely used to obtain vital events
 in the past. Because of the weakness of the statistical assumptions of the
 model, as well as the biases involved in the estimators, its use became
 limited. The proposed estimators for dual-record systems are based on
 further division of the cells of the original table. The results have
 shown that they improved the underestimation of the total counts when
 compared with the classical Chandra Sekar-Deming estimator. 
Journal: Journal of Applied Statistics 
Pages: 157-169 
Issue: 2 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760021691 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760021691
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:2:p:157-169




Template-Type: ReDIF-Article 1.0
Author-Name: M. Kalyanasundaram 
Author-X-Name-First: M. 
Author-X-Name-Last: Kalyanasundaram 
Author-Name: S. Balamurali 
Author-X-Name-First: S. 
Author-X-Name-Last: Balamurali 
Title: Determination of variable-lot-size attribute sampling plan indexed by the acceptable quality level and average outgoing quality level for continuous production 
Abstract:
  In this paper, procedures and tables for the selection of a
 variable-lot-size attribute sampling plan for continuous production are
 given, and the advantages of this plan relative to a fixed-lot-size plan
 are also discussed. 
Journal: Journal of Applied Statistics 
Pages: 171-175 
Issue: 2 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760021709 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760021709
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:2:p:171-175




Template-Type: ReDIF-Article 1.0
Author-Name: Alberto Luceno George 
Author-X-Name-First: Alberto Luceno 
Author-X-Name-Last: George 
Author-Name: E. P. Box 
Author-X-Name-First: E. P. 
Author-X-Name-Last: Box 
Title: Influence of the sampling interval, decision limit and autocorrelation on the average run length in Cusum charts 
Abstract:
  This paper shows how the average run length for a one-sided Cusum chart
 varies as a function of the length of the sampling interval between
 consecutive observations, the decision limit for the Cusum statistic, and
 the amount of autocorrelation between successive observations. It is shown
 that the rate of false alarms can be decreased considerably, without
 modifying the rate of valid alarms, by decreasing the sampling interval
 and appropriately increasing the decision interval. It is also shown that
 this can be done even when the shorter sampling interval induces moderate
 autocorrelation between successive observations. 
Journal: Journal of Applied Statistics 
Pages: 177-183 
Issue: 2 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760021718 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760021718
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:2:p:177-183




Template-Type: ReDIF-Article 1.0
Author-Name: Victor Guerrero 
Author-X-Name-First: Victor 
Author-X-Name-Last: Guerrero 
Title: Selecting a linearizing power transformation for time series 
Abstract:
  A method is proposed for choosing a power transformation that allows a
 univariate time series to be adequately represented by a straight line, in
 an exploratory analysis of the data. The method is quite simple and
 enables the analyst to measure local and global curvature in the data. A
 description of the pattern followed by the data is obtained as a
 by-product of the method. A specific form of the coefficient of
 determination is suggested to discriminate among several combinations of
 estimates of the index of the transformation and the slope of the straight
 line. Some results related to the degree of diff erencing required to make
 the time series stationary are also exploited. The usefulness of the
 proposal is illustrated with four empirical applications-two using
 demographic data and the other two concerning market studies. These
 examples are provided in line with the spirit of an exploratory analysis,
 rather than as a complete or confirmatory analysis of the data. 
Journal: Journal of Applied Statistics 
Pages: 185-195 
Issue: 2 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760021727 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760021727
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:2:p:185-195




Template-Type: ReDIF-Article 1.0
Author-Name: Jiin-Huarng Guo 
Author-X-Name-First: Jiin-Huarng 
Author-X-Name-Last: Guo 
Author-Name: Wei-Ming Luh 
Author-X-Name-First: Wei-Ming 
Author-X-Name-Last: Luh 
Title: Normalized Johnson's transformation one-sample trimmed t for non-normality 
Abstract:
  The present study suggests the use of the normalized Johnson
 transformation trimmed t statistic in the one-sample case when the
 assumption of normality is violated. The performance of the proposed
 method was evaluated by Monte Carlo simulation, and was compared with the
 conventional Student t statistic, the trimmed t statistic and the
 normalized Johnson's transformation untrimmed t statistic respectively.
 The simulated results indicate that the proposed method can control type I
 error very well and that its power is greater than the other competitors
 for various conditions of non-normality. The method can be easily computer
 programmed and provides an alternative for the conventional t test. 
Journal: Journal of Applied Statistics 
Pages: 197-203 
Issue: 2 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760021736 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760021736
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:2:p:197-203




Template-Type: ReDIF-Article 1.0
Author-Name: R. Southworth 
Author-X-Name-First: R. 
Author-X-Name-Last: Southworth 
Author-Name: K. V. Mardia 
Author-X-Name-First: K. V. 
Author-X-Name-Last: Mardia 
Author-Name: C. C. Taylor 
Author-X-Name-First: C. C. 
Author-X-Name-Last: Taylor 
Title: Transformation- and label-invariant neural network for the classification of landmark data 
Abstract:
  One method of expressing coarse information about the shape of an object
 is to describe the shape by its landmarks, which can be taken as
 meaningful points on the outline of an object. We consider a situation in
 which we want to classify shapes into known populations based on their
 landmarks, invariant to the location, scale and rotation of the shapes. A
 neural network method for transformation-invariant classification of
 landmark data is presented. The method is compared with the
 (non-transformation-invariant) complex Bingham rule; the two techniques
 are tested on two sets of simulated data, and on data that arise from mice
 vertebrae. Despite the obvious advantage of the complex Bingham rule
 because of information about rotation, the neural network method compares
 favourably. 
Journal: Journal of Applied Statistics 
Pages: 205-215 
Issue: 2 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760021745 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760021745
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:2:p:205-215




Template-Type: ReDIF-Article 1.0
Author-Name: Leann Myers 
Author-X-Name-First: Leann 
Author-X-Name-Last: Myers 
Author-Name: Stephanie Broyles 
Author-X-Name-First: Stephanie 
Author-X-Name-Last: Broyles 
Title: Regression coefficient analysis for correlated binomial outcomes 
Abstract:
  At present, the generalized estimating equation (GEE) and weighted
 least-squares (WLS) regression methods are the most widely used methods
 for analyzing correlated binomial data; both are easily implemented using
 existing software packages. We propose an alternative technique, i.e.
 regression coefficient analysis (RCA), for this type of data. In RCA, a
 regression equation is computed for each of n individuals; regression
 coefficients are averaged across the n equations to produce a regression
 equation, which predicts marginal probabilities and which can be tested to
 address hypotheses of different slopes between groups, slopes different
 from zero, different intercepts, etc. The method is computationally simple
 and can be performed using standard software. Simulations and examples are
 used to compare the power and robustness of RCA with those of the standard
 GEE and WLS methods. We find that RCA is comparable with the GEE method
 under the conditions tested, and suggest that RCA, within specified
 limitations, is a viable alternative to the GEE and WLS methods in the
 analysis of correlated binomial data. 
Journal: Journal of Applied Statistics 
Pages: 217-234 
Issue: 2 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760021754 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760021754
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:2:p:217-234




Template-Type: ReDIF-Article 1.0
Author-Name: Tapio Nummi 
Author-X-Name-First: Tapio 
Author-X-Name-Last: Nummi 
Title: Analysis of growth curves under measurement errors 
Abstract:
  In this paper, we propose a method for the analysis of growth curve
 models when also the regressor variable may be measured with errors. Two
 classes of structure for errors in regressors are discussed. For complete
 and balanced data, estimators for the model parameters are derived under
 the maximum-likelihood framework. Numerical examples are provided to
 illustrate the proposed technique. 
Journal: Journal of Applied Statistics 
Pages: 235-243 
Issue: 2 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760021763 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760021763
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:2:p:235-243




Template-Type: ReDIF-Article 1.0
Author-Name: J. Tyrcha 
Author-X-Name-First: J. 
Author-X-Name-Last: Tyrcha 
Author-Name: R. Sundberg 
Author-X-Name-First: R. 
Author-X-Name-Last: Sundberg 
Author-Name: P. Lindskog 
Author-X-Name-First: P. 
Author-X-Name-Last: Lindskog 
Author-Name: B. Sundstrom 
Author-X-Name-First: B. 
Author-X-Name-Last: Sundstrom 
Title: Statistical modelling and saddle-point approximation of tail probabilities for accumulated splice loss in fibre-optic networks 
Abstract:
  Tail probabilities are calculated by saddle-point approximation in a
 probabilistic-statistical model for the accumulated splice loss that
 results from a number of fusion splices in the installation of fibre-optic
 networks. When these probabilities, representing the risk of exceeding a
 specified total loss, can be controlled and kept low, the requirements on
 the individual losses can be substantially relaxed from their customary
 settings. As a consequence, it should be possible to save considerable
 installation time and cost. The probabilistic model, which can be
 theoretically motivated, states that the individual loss is basically
 exponentially distributed, but with a Gaussian contribution added and
 truncated at a set value, and that the loss is additive over splices. An
 extensive set of installation data fitted well with this model, except for
 occasional high losses. Therefore, the model described was extended to
 allow for a frequency of unspecified high losses of this sort. It is also
 indicated how the model parameters can be estimated from data. 
Journal: Journal of Applied Statistics 
Pages: 245-256 
Issue: 2 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760021772 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760021772
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:2:p:245-256




Template-Type: ReDIF-Article 1.0
Author-Name: J. A. Amaral 
Author-X-Name-First: J. A. 
Author-X-Name-Last: Amaral 
Author-Name: M. B. RosARio 
Author-X-Name-First: M. B. 
Author-X-Name-Last: RosARio 
Author-Name: M. T. Paixao 
Author-X-Name-First: M. T. 
Author-X-Name-Last: Paixao 
Title: Data and projections of HIV and AIDS in Portugal 
Abstract:
  Projections of AIDS incidence are critical for assessing future
 healthcare needs. This paper focuses on the method of back-calculation for
 obtaining forecasts. The first problem faced was the need to account for
 delays and underreporting in reporting of cases and to adjust the
 incidence data. The method used to estimate the reporting delay
 distribution is based on Poisson regression and involves cross-classifying
 each reported case by calendar time of diagnosis and reporting delay. The
 adjusted AIDS incidence data are then used to obtain short-term
 projections and lower bounds on the size of the AIDS epidemic. The
 estimation procedure 'back-calculates' from AIDS incidence data using the
 incubation period distribution to obtain estimates of the numbers
 previously infected. These numbers are then projected forward. The problem
 can be shown to reduce to estimating the size of a multinomial population.
 The expectation-maximization (EM) algorithm is used to obtain
 maximum-likelihood estimates when the density of infection times is
 parametrized as a step function. The methodology is applied to AIDS
 incidence data in Portugal for four different transmission categories:
 injecting drug users, sexual transmission (homosexual/bisexual and
 heterosexual contact) and other, mainly haemophilia and blood transfusion
 related, to obtain short-term projections and an estimate of the minimum
 size of the epidemic. 
Journal: Journal of Applied Statistics 
Pages: 269-279 
Issue: 3 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760021592 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760021592
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:3:p:269-279




Template-Type: ReDIF-Article 1.0
Author-Name: Ying Wang Wong 
Author-X-Name-First: Ying Wang 
Author-X-Name-Last: Wong 
Author-Name: Siu Hung Cheung 
Author-X-Name-First: Siu Hung 
Author-X-Name-Last: Cheung 
Title: Simultaneous pairwise multiple comparisons in a two-way analysis of covariance model 
Abstract:
  Pairwise comparison procedures are important and popular statistical
 techniques in many disciplines, such as physiology and agrobiology. In
 this paper, we seek to derive the statistical methods which enable one to
 perform pairwise comparisons in a two-way analysis of covariance model.
 The overall family-wise type I error rate is controlled at a designated
 level. The procedures are outlined for simultaneous inferences among
 treatment means. Numerical examples are given to illustrate our testing
 procedure. 
Journal: Journal of Applied Statistics 
Pages: 281-291 
Issue: 3 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760021600 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760021600
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:3:p:281-291




Template-Type: ReDIF-Article 1.0
Author-Name: Kang-Mo Jung 
Author-X-Name-First: Kang-Mo 
Author-X-Name-Last: Jung 
Title: Local influence assessment in canonical correlation analysis 
Abstract:
  The local influence method is adapted to canonical correlation analysis
 for the purpose of investigating the influence of observations. We
 consider a perturbation based on the empirical distribution function. An
 illustrative example is given to show the effectiveness of the local
 influence method for the identification of influential observations. 
Journal: Journal of Applied Statistics 
Pages: 293-301 
Issue: 3 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760021619 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760021619
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:3:p:293-301




Template-Type: ReDIF-Article 1.0
Author-Name: Trine Kvist 
Author-X-Name-First: Trine 
Author-X-Name-Last: Kvist 
Author-Name: Henrik Gislason 
Author-X-Name-First: Henrik 
Author-X-Name-Last: Gislason 
Author-Name: Poul Thyregod 
Author-X-Name-First: Poul 
Author-X-Name-Last: Thyregod 
Title: Using continuation-ratio logits to analyze the variation of the age composition of fish catches 
Abstract:
  Major sources of information for the estimation of the size of the fish
 stocks and the rate of their exploitation are samples from which the age
 composition of catches may be determined. However, the age composition in
 the catches often varies as a result of several factors. Stratification of
 the sampling is desirable, because it leads to better estimates of the age
 composition, and the corresponding variances and covariances. The analysis
 is impeded by the fact that the response is ordered categorical. This
 paper introduces an easily applicable method to analyze such data. The
 method combines continuation-ratio logits and the theory for generalized
 linear mixed models. Continuation-ratio logits are designed for ordered
 multinomial response and have the feature that the associated
 log-likelihood splits into separate terms for each category levels. Thus,
 generalized linear mixed models can be applied separately to each level of
 the logits. The method is illustrated by the analysis of age-composition
 data collected from the Danish sandeel fishery in the North Sea in 1993.
 The significance of possible sources of variation is evaluated, and
 formulae for estimating the proportions of each age group and their
 variance-covariance matrix are derived. 
Journal: Journal of Applied Statistics 
Pages: 303-319 
Issue: 3 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760021628 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760021628
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:3:p:303-319




Template-Type: ReDIF-Article 1.0
Author-Name: Jack Lee 
Author-X-Name-First: Jack 
Author-X-Name-Last: Lee 
Author-Name: Kuo-Ching Liu 
Author-X-Name-First: Kuo-Ching 
Author-X-Name-Last: Liu 
Title: Bayesian analysis of a general growth curve model with predictions using power transformations and AR(1) autoregressive dependence 
Abstract:
  In this paper, we consider a Bayesian analysis of the unbalanced
 (general) growth curve model with AR(1) autoregressive dependence, while
 applying the Box-Cox power transformations. We propose exact, simple and
 Markov chain Monte Carlo approximate parameter estimation and prediction
 of future values. Numerical results are illustrated with real and
 simulated data. 
Journal: Journal of Applied Statistics 
Pages: 321-336 
Issue: 3 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760021637 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760021637
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:3:p:321-336




Template-Type: ReDIF-Article 1.0
Author-Name: Yeong-Tzay Su 
Author-X-Name-First: Yeong-Tzay 
Author-X-Name-Last: Su 
Author-Name: Chyi-Lyi Kathleen Liang 
Author-X-Name-First: Chyi-Lyi Kathleen 
Author-X-Name-Last: Liang 
Title: Using multivariate rank sum tests to evaluate effectiveness of computer applications in teaching business statistics 
Abstract:
  Arguments about using computer facilities in classroom teaching have
 received a lot of attention over time. Using the computer facilities will
 be helpful to demonstrate real-world applications, while poor data or
 inappropriate case studies might hinder the applications of the computer
 programs in classroom teaching. In this paper, we examine the impacts that
 using computer programs to teach business statistics have on students in
 the Krannert School of Management at Purdue University. The results show
 that students are attracted to the interactive computer programs designed
 for the business statistics course, and students are more motivated to
 attend classes when computer programs are applied in teaching.
 Furthermore, computer programs help students to understand confusing
 topics, and students feel that teaching them to use computer facilities
 really improves their own abilities to apply similar programs in analyzing
 real-world problems. 
Journal: Journal of Applied Statistics 
Pages: 337-345 
Issue: 3 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760021646 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760021646
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:3:p:337-345




Template-Type: ReDIF-Article 1.0
Author-Name: Sifa Mvoi 
Author-X-Name-First: Sifa 
Author-X-Name-Last: Mvoi 
Author-Name: Yan-Xia Lin 
Author-X-Name-First: Yan-Xia 
Author-X-Name-Last: Lin 
Title: Criteria for estimating the variance function used in the asymptotic quasi-likelihood approach 
Abstract:
  The estimation of the variance function of a linear regression model used
 in the asymptotic quasi-likelihood approach is considered. It is shown
 that the variance function used in the determination of the asymptotic
 quasi-likelihood estimates encompasses the variance functions commonly
 found in the literature. Selection criteria of the most appropriate
 estimate of the variance function for given data are established. These
 criteria are based on a graphical technique and a chi-squared test. 
Journal: Journal of Applied Statistics 
Pages: 347-362 
Issue: 3 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760021655 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760021655
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:3:p:347-362




Template-Type: ReDIF-Article 1.0
Author-Name: Milad Sawiris 
Author-X-Name-First: Milad 
Author-X-Name-Last: Sawiris 
Title: Optimum grouping and the boundary problem 
Abstract:
  Given a set of n elements or observations that form a continuous
 variable, it is required to divide their distribution into k homogenous
 groups where k &#x3e; 2, and the purpose is to minimize the within-groups
 variance. This paper investigates procedures for such a division and shows
 how to find the boundaries separating the groups. 
Journal: Journal of Applied Statistics 
Pages: 363-371 
Issue: 3 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760021664 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760021664
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:3:p:363-371




Template-Type: ReDIF-Article 1.0
Author-Name: A. M. C. Vieira 
Author-X-Name-First: A. M. C. 
Author-X-Name-Last: Vieira 
Author-Name: J. P. Hinde 
Author-X-Name-First: J. P. 
Author-X-Name-Last: Hinde 
Author-Name: C. G. B. Demetrio 
Author-X-Name-First: C. G. B. 
Author-X-Name-Last: Demetrio 
Title: Zero-inflated proportion data models applied to a biological control assay 
Abstract:
  Biological control of pests is an important branch of entomology,
 providing environmentally friendly forms of crop protection. Bioassays are
 used to find the optimal conditions for the production of parasites and
 strategies for application in the field. In some of these assays,
 proportions are measured and, often, these data have an inflated number of
 zeros. In this work, six models will be applied to data sets obtained from
 biological control assays for Diatraea saccharalis , a common pest in
 sugar cane production. A natural choice for modelling proportion data is
 the binomial model. The second model will be an overdispersed version of
 the binomial model, estimated by a quasi-likelihood method. This model was
 initially built to model overdispersion generated by individual
 variability in the probability of success. When interest is only in the
 positive proportion data, a model can be based on the truncated binomial
 distribution and in its overdispersed version. The last two models include
 the zero proportions and are based on a finite mixture model with the
 binomial distribution or its overdispersed version for the positive data.
 Here, we will present the models, discuss their estimation and compare the
 results. 
Journal: Journal of Applied Statistics 
Pages: 373-389 
Issue: 3 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760021673 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760021673
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:3:p:373-389




Template-Type: ReDIF-Article 1.0
Author-Name: S. Balamurali 
Author-X-Name-First: S. 
Author-X-Name-Last: Balamurali 
Author-Name: K. Govindaraju 
Author-X-Name-First: K. 
Author-X-Name-Last: Govindaraju 
Title: Modified tightened two-level continuous sampling plans 
Abstract:
  In this paper, a modification is proposed on the tightened two-level
 continuous sampling plan. The tightened two-level plan is one of the three
 tightened multi-level continuous sampling plans of Derman et al. (1957)
 with two sampling levels. A modified tightened two-level continuous
 sampling plan is considered, for which the rules concerning partial
 inspection depend, in part, on the length of time it takes to decide that
 the process quality is good enough that 100% inspection may be suspended
 (e.g. the time required to find i consecutive items free of defects).
 Using a Markov chain model, expressions for the performance measures of
 the modified MLP-T-2 plan are derived. The modified MLP-T-2 plan is shown
 to be identical to the MLP-T-2 plan. Tables are also presented for the
 selection of the modified MLP-T-2 plan when the AQL or LQL and AOQL are
 specified. 
Journal: Journal of Applied Statistics 
Pages: 397-409 
Issue: 4 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050003597 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050003597
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:4:p:397-409




Template-Type: ReDIF-Article 1.0
Author-Name: Bei-Hung Chang 
Author-X-Name-First: Bei-Hung 
Author-X-Name-Last: Chang 
Author-Name: Stuart Lipsitz 
Author-X-Name-First: Stuart 
Author-X-Name-Last: Lipsitz 
Author-Name: Christine Waternaux 
Author-X-Name-First: Christine 
Author-X-Name-Last: Waternaux 
Title: Logistic regression in meta-analysis using aggregate data 
Abstract:
  We derived two methods to estimate the logistic regression coefficients
 in a meta-analysis when only the 'aggregate' data (mean values) from each
 study are available. The estimators we proposed are the discriminant
 function estimator and the reverse Taylor series approximation. These two
 methods of estimation gave similar estimators using an example of
 individual data. However, when aggregate data were used, the discriminant
 function estimators were quite different from the other two estimators. A
 simulation study was then performed to evaluate the performance of these
 two estimators as well as the estimator obtained from the model that
 simply uses the aggregate data in a logistic regression model. The
 simulation study showed that all three estimators are biased. The bias
 increases as the variance of the covariate increases. The distribution
 type of the covariates also affects the bias. In general, the estimator
 from the logistic regression using the aggregate data has less bias and
 better coverage probabilities than the other two estimators. We concluded
 that analysts should be cautious in using aggregate data to estimate the
 parameters of the logistic regression model for the underlying individual
 data. 
Journal: Journal of Applied Statistics 
Pages: 411-424 
Issue: 4 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050003605 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050003605
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:4:p:411-424




Template-Type: ReDIF-Article 1.0
Author-Name: Jan-Olof Johansson 
Author-X-Name-First: Jan-Olof 
Author-X-Name-Last: Johansson 
Title: Modelling the surface structure of newsprint 
Abstract:
  The Gibbs distribution is often used to model micro-textures. This
 includes a definition of a neighbourhood system. If a micro-texture
 contains a large-scale variation, the neighbourhood system will be large,
 which implies many parameters in the corresponding Gibbs distribution. The
 estimation of the parameters for such models will be difficult and time
 consuming. I suggest, in this paper, a separation of the micro-texture
 into a large-scale variation and a small-scale variation and model each
 source of variation with a Gibbs distribution. This method is applied on
 full-tone print of newsprint to model the variation caused by print
 mottle. In this application, the large-scale variation is mainly caused by
 fibre flocculation and clustering and the small-scale variation contains
 the variation of the fibres and fines on and between the clusters. The
 separate description of these two variations makes it possible to relate
 different kinds of paper qualities to the appropriate source of variation. 
Journal: Journal of Applied Statistics 
Pages: 425-438 
Issue: 4 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050003614 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050003614
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:4:p:425-438




Template-Type: ReDIF-Article 1.0
Author-Name: H. J. Khamis 
Author-X-Name-First: H. J. 
Author-X-Name-Last: Khamis 
Title: The two-stage i -corrected Kolmogorov-Smirnov test 
Abstract:
  The delta-corrected Kolmogorov-Smirnov test has been shown to be
 uniformly more powerful than the classical Kolmogorov-Smirnov test for
 small to moderate sample sizes. However, the delta-corrected test consists
 of two tests, leading to a slight inflation of the experimentwise type I
 error rate. The critical values of the delta-corrected test are adjusted
 to take into account the two-stage nature of the test, ensuring an
 experimentwise error rate at the nominal level. A power study confirms
 that the resulting so-called two-stage delta-corrected test is uniformly
 more powerful than the classical Kolmogorov-Smirnov test, with power
 improvements of up to 46 percentage points. 
Journal: Journal of Applied Statistics 
Pages: 439-450 
Issue: 4 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050003623 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050003623
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:4:p:439-450




Template-Type: ReDIF-Article 1.0
Author-Name: Myung Geun Kim 
Author-X-Name-First: Myung Geun 
Author-X-Name-Last: Kim 
Title: Outliers and influential observations in the structural errors-in-variables model 
Abstract:
  The influence of observations on the parameter estimates for the simple
 structural errors-in-variables model with no equation error is
 investigated using the local influence method. Residuals themselves are
 not sufficient for detecting outliers. The likelihood displacement
 approach is useful for outlier detection especially when a masking
 phenomenon is present. An illustrative example is provided. 
Journal: Journal of Applied Statistics 
Pages: 451-460 
Issue: 4 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050003632 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050003632
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:4:p:451-460




Template-Type: ReDIF-Article 1.0
Author-Name: C. D. Lai 
Author-X-Name-First: C. D. 
Author-X-Name-Last: Lai 
Author-Name: M. Xie 
Author-X-Name-First: M. 
Author-X-Name-Last: Xie 
Author-Name: K. Govindaraju 
Author-X-Name-First: K. 
Author-X-Name-Last: Govindaraju 
Title: Study of a Markov model for a high-quality dependent process 
Abstract:
  For high-quality processes, non-conforming items are seldom observed and
 the traditional p (or np) charts are not suitable for monitoring the state
 of the process. A type of chart based on the count of cumulative
 conforming items has recently been introduced and it is especially useful
 for automatically collected one-at-a-time data. However, in such a case,
 it is common that the process characteristics become dependent as items
 produced one after another are inspected. In this paper, we study the
 problem of process monitoring when the process is of high quality and
 measurement values possess a certain serial dependence. The problem of
 assuming independence is examined and a Markov model for this type of
 process is studied, upon which suitable control procedures can be
 developed. 
Journal: Journal of Applied Statistics 
Pages: 461-473 
Issue: 4 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050003641 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050003641
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:4:p:461-473




Template-Type: ReDIF-Article 1.0
Author-Name: M. M. Shoukri 
Author-X-Name-First: M. M. 
Author-X-Name-Last: Shoukri 
Author-Name: O. Demirkaya 
Author-X-Name-First: O. 
Author-X-Name-Last: Demirkaya 
Title: Sample size requirements to test the equality of raters' precision 
Abstract:
  Exact and approximate methods are developed to calculate the required
 number of subjects n in a repeatability study, where repeatability is
 measured by the precision of measurements made by a rater. The exact
 method is based on power calculations under the non-null distribution of
 the multiple coefficient of determination, which requires intensive
 numerical computation. The approximate method is based on predictions from
 families of non-linear curves fitted by the method of least squares. 
Journal: Journal of Applied Statistics 
Pages: 483-494 
Issue: 4 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050003669 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050003669
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:4:p:483-494




Template-Type: ReDIF-Article 1.0
Author-Name: Zheng Wang 
Author-X-Name-First: Zheng 
Author-X-Name-Last: Wang 
Title: An algorithm for generalized monotonic smoothing 
Abstract:
  In this paper, an algorithm for Generalized Monotonic Smoothing (GMS) is
 developed as an extension to exponential family models of the monotonic
 smoothing techniques proposed by Ramsay (1988, 1998a,b). A two-step
 algorithm is used to estimate the coefficients of bases and the linear
 term. We show that the algorithm can be embedded into the iterative
 re-weighted least square algorithm that is typically used to estimate the
 coefficients in Generalized Linear Models. Thus, the GMS estimator can be
 computed using existing routines in S-plus and other statistical software.
 We apply the GMS model to the Down's syndrome data set and compare the
 results with those from Generalized Additive Model estimation. The choice
 of smoothing parameter and testing of monotonicity are also discussed. 
Journal: Journal of Applied Statistics 
Pages: 495-507 
Issue: 4 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050003678 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050003678
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:4:p:495-507




Template-Type: ReDIF-Article 1.0
Author-Name: Jon Woodroof 
Author-X-Name-First: Jon 
Author-X-Name-Last: Woodroof 
Title: Bootstrapping: As easy as 1-2-3 
Abstract:
  The bootstrap is a powerful non-parametric statistical technique for
 making probability-based inferences about a population parameter. Through
 a Monte-Carlo resampling simulation, bootstrapping empirically generates a
 statistic's entire distribution. From this simulated distribution,
 inferences can be made about a population parameter. Assumptions about
 normality are not required. In general, despite its power, bootstrapping
 has been used relatively infrequently in social science research, and this
 is particularly true for business research. This under-utilization is
 likely due to a combination of a general lack of understanding of the
 bootstrap technique and the difficulty with which it has traditionally
 been implemented. Researchers in the various fields of business should be
 familiar with this powerful statistical technique. The purpose of this
 paper is to explain how this technique works using Lotus 1-2-3, a software
 package with which business people are very familiar. 
Journal: Journal of Applied Statistics 
Pages: 509-517 
Issue: 4 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050003687 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050003687
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:4:p:509-517




Template-Type: ReDIF-Article 1.0
Author-Name: David Hand 
Author-X-Name-First: David 
Author-X-Name-Last: Hand 
Author-Name: Niall Adams 
Author-X-Name-First: Niall 
Author-X-Name-Last: Adams 
Title: Defining attributes for scorecard construction in credit scoring 
Abstract:
  In many domains, simple forms of classification rules are needed because
 of requirements such as ease of use. A particularly simple form splits
 each variable into just a few categories, assigns weights to the
 categories, sums the weights for a new object to be classified, and
 produces a classification by comparing the score with a threshold. Such
 instruments are often called scorecards. We describe a way to find the
 best partition of each variable using a simulated annealing strategy. We
 present theoretical and empirical comparisons of two such additive models,
 one based on weights of evidence and another based on logistic regression. 
Journal: Journal of Applied Statistics 
Pages: 527-540 
Issue: 5 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050076371 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050076371
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:5:p:527-540




Template-Type: ReDIF-Article 1.0
Author-Name: Yoshikazu Ojima 
Author-X-Name-First: Yoshikazu 
Author-X-Name-Last: Ojima 
Title: Generalized staggered nested designs for variance components estimation 
Abstract:
  Staggered nested experimental designs are the most popular class of
 unbalanced nested designs in practical fields. The most important features
 of the staggered nested design are that it has a very simple open-ended
 structure and each sum of squares in the analysis of variance has almost
 the same degrees of freedom. Based on the features, a class of unbalanced
 nested designs that is a generalization of the staggered nested design is
 proposed in this paper. Formulae for the estimation of variance components
 and their sums are provided. Comparing the variances of the estimators to
 the staggered nested designs, it is found that some of the generalized
 staggered nested designs are more efficient than the traditional staggered
 nested design in estimating some of the variance components and their
 sums. An example is provided for illustration. 
Journal: Journal of Applied Statistics 
Pages: 541-553 
Issue: 5 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050076380 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050076380
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:5:p:541-553




Template-Type: ReDIF-Article 1.0
Author-Name: Mustafa Yilmaz 
Author-X-Name-First: Mustafa 
Author-X-Name-Last: Yilmaz 
Author-Name: Sangit Chatterjee 
Author-X-Name-First: Sangit 
Author-X-Name-Last: Chatterjee 
Title: Patterns of NBA team performance from 1950 to 1998 
Abstract:
  This paper examines team performance in the NBA over the last five
 decades. It was motivated by two previous observational studies, one of
 which studied the winning percentages of professional baseball teams over
 time, while the other examined individual player performance in the NBA.
 These studies considered professional sports as evolving systems, a view
 proposed by evolutionary biologist Stephen Jay Gould, who wrote
 extensively on the disappearance of .400 hitters in baseball. Gould argued
 that the disappearance is actually a sign of improvement in the quality of
 play, reflected in the reduction of variability in hitting performance.
 The previous studies reached similar conclusions in terms of winning
 percentages of baseball teams, and performance of individual players in
 basketball. This paper uses multivariate measures of team performance in
 the NBA to see if similar characteristics of evolution can be observed.
 The conclusion does not appear to be clearly affirmative, as in previous
 studies, and possible reasons for this are discussed. 
Journal: Journal of Applied Statistics 
Pages: 555-566 
Issue: 5 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050076399 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050076399
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:5:p:555-566




Template-Type: ReDIF-Article 1.0
Author-Name: Alan Hutson 
Author-X-Name-First: Alan 
Author-X-Name-Last: Hutson 
Title: A composite quantile function estimator with applications in bootstrapping 
Abstract:
  In this note we define a composite quantile function estimator in order
 to improve the accuracy of the classical bootstrap procedure in small
 sample setting. The composite quantile function estimator employs a
 parametric model for modelling the tails of the distribution and uses the
 simple linear interpolation quantile function estimator to estimate
 quantiles lying between 1/(n+1) and n/(n+1). The method is easily
 programmed using standard software packages and has general applicability.
 It is shown that the composite quantile function estimator improves the
 bootstrap percentile interval coverage for a variety of statistics and is
 robust to misspecification of the parametric component. Moreover, it is
 also shown that the composite quantile function based approach
 surprisingly outperforms the parametric bootstrap for a variety of small
 sample situations. 
Journal: Journal of Applied Statistics 
Pages: 567-577 
Issue: 5 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050076407 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050076407
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:5:p:567-577




Template-Type: ReDIF-Article 1.0
Author-Name: Hui Li 
Author-X-Name-First: Hui 
Author-X-Name-Last: Li 
Author-Name: Robert Malkin 
Author-X-Name-First: Robert 
Author-X-Name-Last: Malkin 
Title: An approximate Bayesian up-down method for estimating a percentage point on a dose-response curve 
Abstract:
  While the up-down method for estimating a percentage point on a
 dose-response curve has received considerable attention, a general
 Bayesian solution to the up-down design and estimation has never been
 presented, probably due to its computational complexity both in design and
 use. This paper presents a theoretical approach for up-down experimental
 designs with unknown location and slope parameters, and a practical
 approach for their use. The simplex method is used to find the optimal
 starting dose level and step sizes that minimize the expected root mean
 square error for a fixed number of observations and a reduced number of
 step sizes. The Bayesian estimate is then approximated by a polynomial
 formula. The coefficients of the formula are also chosen using simplex
 minimization. Two example solutions are given with uniform-uniform and
 normal-gamma joint prior distributions, showing that the simplifying
 assumptions make the method far easier to use with only a marginal
 increase in expected root mean square error. We show how to adapt these
 prior distributions to a wide range of frequently encountered
 applications. 
Journal: Journal of Applied Statistics 
Pages: 579-587 
Issue: 5 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050076416 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050076416
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:5:p:579-587




Template-Type: ReDIF-Article 1.0
Author-Name: Murari Singh 
Author-X-Name-First: Murari 
Author-X-Name-Last: Singh 
Author-Name: Michael Jones 
Author-X-Name-First: Michael 
Author-X-Name-Last: Jones 
Title: Statistical estimation of time-trends in two-course crop rotations 
Abstract:
  An assessment of time-trends in yield parameters is essential to the
 utilization of data from long-term field trials for the comparison of
 different crop rotations and input regimes, and the identification of
 sustainable production systems. The barley-vetch rotation established at
 Breda in northern Syria has provided the basis for estimation of the
 time-trends in yield data from selected treatments in a two-course crop
 rotation trial. The model used for the estimation accounts for the effect
 of rainfall, a major determinant of each annual yield value, and the
 first-order autocorrelation structure in the errors arising from the same
 plot over time. An expression for the minimum number of cycles required to
 detect a significant time-trend has been obtained. Results from the
 barley-vetch rotation under two fertilizer regimes have been discussed. 
Journal: Journal of Applied Statistics 
Pages: 589-597 
Issue: 5 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050076425 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050076425
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:5:p:589-597




Template-Type: ReDIF-Article 1.0
Author-Name: Govind Mudholkar 
Author-X-Name-First: Govind 
Author-X-Name-Last: Mudholkar 
Author-Name: Deo Kumar Srivastava 
Author-X-Name-First: Deo Kumar 
Author-X-Name-Last: Srivastava 
Title: A class of robust stepwise alternatives to Hotelling's T 2 tests 
Abstract:
  Hotelling's T 2 test is known to be optimal under multivariate normality
 and is reasonably validity-robust when the assumption fails. However, some
 recently introduced robust test procedures have superior power properties
 and reasonable type I error control with non-normal populations. These,
 including the tests due to Tiku & Singh (1982), Tiku & Balakrishnan (1988)
 and Mudholkar & Srivastava (1999b, c), are asymptotically valid but are
 useful with moderate size samples only if the population dimension is
 small. A class of B-optimal modifications of the stepwise alternatives to
 Hotellings T 2 introduced by Mudholkar & Subbaiah (1980) are simple to
 implement and essentially equivalent to the T 2 test even with small
 samples. In this paper we construct and study the robust versions of these
 modified stepwise tests using trimmed means instead of sample means. We
 use the robust one- and two-sample trimmed- t procedures as in Mudholkar
 et al. (1991) and propose statistics based on combining them. The results
 of an extensive Monte Carlo experiment show that the robust alternatives
 provide excellent type I error control and a substantial gain in power. 
Journal: Journal of Applied Statistics 
Pages: 599-619 
Issue: 5 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050076434 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050076434
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:5:p:599-619




Template-Type: ReDIF-Article 1.0
Author-Name: Kelly Zou 
Author-X-Name-First: Kelly 
Author-X-Name-Last: Zou 
Author-Name: W. J. Hall 
Author-X-Name-First: W. J. 
Author-X-Name-Last: Hall 
Title: Two transformation models for estimating an ROC curve derived from continuous data 
Abstract:
  A receiver operating characteristic (ROC) curve is a plot of two survival
 functions, derived separately from the diseased and healthy samples. A
 special feature is that the ROC curve is invariant to any monotone
 transformation of the measurement scale. We propose and analyse
 semiparametric and parametric transformation models for this two-sample
 problem. Following an unspecified or specified monotone transformation, we
 assume that the healthy and diseased measurements have two normal
 distributions with different means and variances. Maximum likelihood
 algorithms for estimating ROC curve parameters are developed. The proposed
 methods are illustrated on the marker CA125 in the diagnosis of gastric
 cancer. 
Journal: Journal of Applied Statistics 
Pages: 621-631 
Issue: 5 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050076443 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050076443
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:5:p:621-631




Template-Type: ReDIF-Article 1.0
Author-Name: Fred Huffer 
Author-X-Name-First: Fred 
Author-X-Name-Last: Huffer 
Author-Name: Cheolyong Park 
Author-X-Name-First: Cheolyong 
Author-X-Name-Last: Park 
Title: A test for multivariate structure 
Abstract:
  We present a test for detecting 'multivariate structure' in data sets.
 This procedure consists of transforming the data to remove the
 correlations, then discretizing the data and, finally, studying the cell
 counts in the resulting contingency table. A formal test can be performed
 using the usual chi-squared test statistic. We give the limiting
 distribution of the chi-squared statistic and also present simulation
 results to examine the accuracy of this limiting distribution in finite
 samples. Several examples show that our procedure can detect a variety of
 different types of structure. Our examples include data with clustering,
 digitized speech data, and residuals from a fitted time series model. The
 chi-squared statistic can also be used as a test for multivariate
 normality. 
Journal: Journal of Applied Statistics 
Pages: 633-650 
Issue: 5 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050076452 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050076452
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:5:p:633-650




Template-Type: ReDIF-Article 1.0
Author-Name: Sueli Mingoti 
Author-X-Name-First: Sueli 
Author-X-Name-Last: Mingoti 
Title: A stepwise Bayesian estimator for the total number of distinct species in finite populations: Sampling by elements 
Abstract:
  A stepwise Bayesian estimator for the total number of distinct species in
 the region of investigation is constructed when sampling by elements is
 used to collect the sample of species. The species in the region are
 supposed to be divided into two groups: the first containing those species
 the researcher believes are present in the region and the second group
 containing the species in the region which are completely unknown to the
 researcher. The abundance values of the second group are supposed to
 follow a Dirichlet distribution. Under this model, the obtained stepwise
 Bayesian estimator is an extension of that proposed by Lewins & Joanes
 (1984). When the negative binomial distribution is chosen as a prior
 distribution for the true value T of species in the region, the stepwise
 estimator takes a simple form. It is then shown that the estimator
 proposed by Hill (1979) is a particular case and that the stepwise
 Bayesian estimator can also be similar to the estimator proposed by
 Mingoti (1999) for quadrat sampling. Some results of a simulation study
 are presented as well as one application using abundance data and another
 in the estimation of population size when capture and recapture methods
 are used. 
Journal: Journal of Applied Statistics 
Pages: 651-670 
Issue: 5 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050076461 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050076461
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:5:p:651-670




Template-Type: ReDIF-Article 1.0
Author-Name: Jason Abrevaya 
Author-X-Name-First: Jason 
Author-X-Name-Last: Abrevaya 
Title: Testing for a treatment effect in a heterogeneous population: A modified sign-test statistic and a leapfrog statistic 
Abstract:
  This paper proposes two non-parametric statistics that test for a
 treatment effect in a heterogeneous population. In the model considered,
 data on two examinations for both a control and a treatment group are
 needed to perform the test. The model allows for individual (fixed)
 effects that may be correlated with the choice of treatment. In addition,
 the model allows for an unspecified, monotonic transformation of the
 response variable. The techniques are illustrated by testing whether high
 levels of unemploymentbenefit eligibility affect the consumption patterns
 of unemployed American workers. 
Journal: Journal of Applied Statistics 
Pages: 679-687 
Issue: 6 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050081852 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050081852
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:6:p:679-687




Template-Type: ReDIF-Article 1.0
Author-Name: J. M. Tapia Garcia 
Author-X-Name-First: J. M. Tapia 
Author-X-Name-Last: Garcia 
Author-Name: A. Martin Andres 
Author-X-Name-First: A. Martin 
Author-X-Name-Last: Andres 
Title: Optimal unconditional critical regions for 2 2 2 multinomial trials 
Abstract:
  Analysing a 2 2 2 table is one of the most frequent problems in applied
 research (particularly in epidemiology). When the table arises from a 2 2
 2 multinomial trial (or the case of double dichotomy), the appropriate
 test for independence is an unconditional one, like those of Barnard
 (1947), which, although they date from a long time ago, have not been
 developed (because of computational problems) until the last ten years.
 Among the different possible versions, the optimal (Martin Andres & Tapia
 Garcia, 1999) is Barnard's original one, but the calculation time (even
 today) is excessive. This paper offers critical region tables for that
 version, which behave well compared to those of Shuster (1992). The tables
 are of particular use for researchers wishing to obtain significant
 results for very small sample sizes (N h 50). 
Journal: Journal of Applied Statistics 
Pages: 689-695 
Issue: 6 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050081861 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050081861
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:6:p:689-695




Template-Type: ReDIF-Article 1.0
Author-Name: Chung-Ho Chen 
Author-X-Name-First: Chung-Ho 
Author-X-Name-Last: Chen 
Author-Name: Chao-Yu Chou 
Author-X-Name-First: Chao-Yu 
Author-X-Name-Last: Chou 
Title: Design of a CSP-1 plan based on regret-balanced criterion 
Abstract:
  This article explores the problem of designing a CSP-1 plan with the
 specified average outgoing quality limit (AOQL), the acceptable quality
 level (AQL), and the limiting quality level (LQL) value. By adopting the
 regret-balanced criterion under the producer's and consumer's interests of
 quality, we can design the optimal CSP-1 plan. 
Journal: Journal of Applied Statistics 
Pages: 697-701 
Issue: 6 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050081870 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050081870
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:6:p:697-701




Template-Type: ReDIF-Article 1.0
Author-Name: Wilfried De Corte 
Author-X-Name-First: Wilfried 
Author-X-Name-Last: De Corte 
Title: Using order statistics to assess the sampling variability of personnel selection utility estimates 
Abstract:
  Virtually all models for the utility of personnel selection are based on
 the average criterion score of the predictor selected applicants. This
 paper indicates how standard results from the theory on order statistics
 can be used to determine the expected value, the standard error and the
 sampling distribution of the average criterion score statistic when a
 finite number of employees is selected. Exact as well as approximate
 results are derived and it is shown how these results can be used to
 construct intervals that will contain, with a given probability 1 - f ,
 the average criterion score associated with a particular implementation of
 the personnel selection. These interval estimates are particularly helpful
 to the selection practitioner because they can be used to state the
 confidence level with which the selection payoff will be above a specific
 value. In addition, for most realistic selection scenarios, it is found
 that the corresponding utility interval estimate is quite large. For
 situations in which multiple selections are performed over time, the
 utility intervals are, however, smaller. 
Journal: Journal of Applied Statistics 
Pages: 703-713 
Issue: 6 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050081889 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050081889
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:6:p:703-713




Template-Type: ReDIF-Article 1.0
Author-Name: D. K. Ghosh 
Author-X-Name-First: D. K. 
Author-X-Name-Last: Ghosh 
Author-Name: P. C. Biswas 
Author-X-Name-First: P. C. 
Author-X-Name-Last: Biswas 
Title: Robust designs for diallel crosses against the missing of one block 
Abstract:
  Dey & Midha (1996) showed that some of the complete diallel crosses
 plans, obtained by using triangular partially balanced designs with two
 associate classes, are optimal. In this investigation, it is derived that
 these optimal designs for diallel crosses are robust also against the
 unavailability of one block. 
Journal: Journal of Applied Statistics 
Pages: 715-723 
Issue: 6 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050081898 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050081898
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:6:p:715-723




Template-Type: ReDIF-Article 1.0
Author-Name: K. Govindaraju 
Author-X-Name-First: K. 
Author-X-Name-Last: Govindaraju 
Author-Name: M. Bebbington 
Author-X-Name-First: M. 
Author-X-Name-Last: Bebbington 
Title: Combined continuous lot by lot acceptance sampling plan 
Abstract:
  For production processes involving low fraction non-conforming, the
 sample sizes of the usual attribute inspection plans are very large. A
 continuous sampling plan for such processes would also require either a
 large clearance interval or a large sampling fraction. This paper
 simplifies the approach of combining the lot by lot and continuous
 sampling plans recommended by Pesotchinsky (1987) and provides various
 performance measures for the combined plan. A discussion of the choice of
 the parameters is also given. 
Journal: Journal of Applied Statistics 
Pages: 725-730 
Issue: 6 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050081906 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050081906
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:6:p:725-730




Template-Type: ReDIF-Article 1.0
Author-Name: Jiin-Huarng Guo 
Author-X-Name-First: Jiin-Huarng 
Author-X-Name-Last: Guo 
Author-Name: Wei-Ming Luh 
Author-X-Name-First: Wei-Ming 
Author-X-Name-Last: Luh 
Title: Testing methods for the one-way fixed effects ANOVA models of log-normal samples 
Abstract:
  For one-way fixed effects of log-normal data with unequal variance, the
 present study proposes a method to deal with heterogeneity. An appropriate
 hypothesis testing is demonstrated; and one of the approximate tests, such
 as the Alexander-Govern test, Welch test or James second-order test, is
 applied to control Type I error rate. Monte Carlo simulation is used to
 investigate the performance of the F test for log-scale, the F test for
 original scale, the James second-order test, the Welch test, and the
 Alexander-Govern test. The simulated results and real data analysis show
 that the proposed method is valid and powerful. 
Journal: Journal of Applied Statistics 
Pages: 731-738 
Issue: 6 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050081915 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050081915
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:6:p:731-738




Template-Type: ReDIF-Article 1.0
Author-Name: Dov Ingman 
Author-X-Name-First: Dov 
Author-X-Name-Last: Ingman 
Author-Name: Boris Lipnik 
Author-X-Name-First: Boris 
Author-X-Name-Last: Lipnik 
Title: Loss-based optimal control statistics for control charts 
Abstract:
  This work proposes a means for interconnecting optimal sample statistics
 with parameters of the process output distribution irrespective of the
 specific way in which these parameters change during transition to the
 out-of-control state (jumps, trends, cycles, etc). The approach, based on
 minimization of the loss incurred by the two types of decision errors,
 leads to a unique sample statistic and, therefore, to a single control
 chart. The optimal sample statistics are obtained as a solution of the
 developed optional boundary equation. The paper demonstrates that, for
 particular conditions, this equation leads to the same statistics as are
 obtained through the Neyman-Pearson fundamental lemma. Application
 examples of the approach when the process output distribution is Gamma and
 Weibull are given. A special loss function representing out-of-control
 state detection as a pattern recognition problem is presented. 
Journal: Journal of Applied Statistics 
Pages: 739-756 
Issue: 6 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050081924 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050081924
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:6:p:739-756




Template-Type: ReDIF-Article 1.0
Author-Name: G. K. Kanji 
Author-X-Name-First: G. K. 
Author-X-Name-Last: Kanji 
Author-Name: Osama Hasan Arif 
Author-X-Name-First: Osama Hasan 
Author-X-Name-Last: Arif 
Title: Median rankit control chart by the quantile approach 
Abstract:
  It is desirable that the data for a statistical control chart be normally
 distributed. However, if the data are not normal, then a transformation
 can be used, e.g. Box-Cox transformations, to produce a suitable control
 chart. In this paper we will discuss a quantile approach to produce a
 control chart and to estimate median rankit for various non-normal
 distributions. We will also provide examples of logistic data to indicate
 how a quantile approach could be used to construct a control chart for a
 non-normal distribution using a median rankit. 
Journal: Journal of Applied Statistics 
Pages: 757-770 
Issue: 6 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050081933 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050081933
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:6:p:757-770




Template-Type: ReDIF-Article 1.0
Author-Name: V. Soundararajan 
Author-X-Name-First: V. 
Author-X-Name-Last: Soundararajan 
Author-Name: M. Palanivel 
Author-X-Name-First: M. 
Author-X-Name-Last: Palanivel 
Title: Quick switching variables single sampling (QSVSS) system indexed by AQL and AOQL 
Abstract:
  Procedures and tables are given for the selection of a 'Quick Switching
 Single Sampling Variable System' for given AQL and AOQL, whenever rejected
 lots are 100% inspected and for replacement of non-conforming units. 
Journal: Journal of Applied Statistics 
Pages: 771-778 
Issue: 6 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050081942 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050081942
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:6:p:771-778




Template-Type: ReDIF-Article 1.0
Author-Name: Leopold Simar 
Author-X-Name-First: Leopold 
Author-X-Name-Last: Simar 
Author-Name: Paul Wilson 
Author-X-Name-First: Paul 
Author-X-Name-Last: Wilson 
Title: A general methodology for bootstrapping in non-parametric frontier models 
Abstract:
  The Data Envelopment Analysis method has been extensively used in the
 literature to provide measures of firms' technical efficiency. These
 measures allow rankings of firms by their apparent performance. The
 underlying frontier model is non-parametric since no particular functional
 form is assumed for the frontier model. Since the observations result from
 some data-generating process, the statistical properties of the estimated
 efficiency measures are essential for their interpretations. In the
 general multi-output multi-input framework, the bootstrap seems to offer
 the only means of inferring these properties (i.e. to estimate the bias
 and variance, and to construct confidence intervals). This paper proposes
 a general methodology for bootstrapping in frontier models, extending the
 more restrictive method proposed in Simar & Wilson (1998) by allowing for
 heterogeneity in the structure of efficiency. A numerical illustration
 with real data is provided to illustrate the methodology. 
Journal: Journal of Applied Statistics 
Pages: 779-802 
Issue: 6 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050081951 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050081951
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:6:p:779-802




Template-Type: ReDIF-Article 1.0
Author-Name: A. Martin Andres 
Author-X-Name-First: A. Martin 
Author-X-Name-Last: Andres 
Author-Name: I. Herranz Tejedor 
Author-X-Name-First: I. Herranz 
Author-X-Name-Last: Tejedor 
Title: On the minimum expected quantity for the validity of the chi-squared test in 2 2 2 tables 
Abstract:
  A 2 2 2 contingency table can often be analysed in an exact fashion by
 using Fisher's exact test and in an approximate fashion by using the
 chi-squared test with Yates' continuity correction, and it is
 traditionally held that the approximation is valid when the minimum
 expected quantity E is E S 5. Unfortunately, little research has been
 carried out into this belief, other than that it is necessary to establish
 a bound E&#x3e;E*, that the condition E S 5 may not be the most
 appropriate (Martin Andres et al., 1992) and that E* is not a constant,
 but usually increasing with the growth of the sample size (Martin Andres &
 Herranz Tejedor, 1997). In this paper, the authors conduct a theoretical
 experimental study from which they ascertain that E* value (which is very
 variable and frequently quite a lot greater than 5) is strongly related to
 the magnitude of the skewness of the underlying hypergeometric
 distribution, and that bounding the skewness is equivalent to bounding E
 (which is the best control procedure). The study enables estimating the
 expression for the above-mentioned E* (which in turn depends on the number
 of tails in the test, the alpha error used, the total sample size, and the
 minimum marginal imbalance) to be estimated. Also the authors show that E*
 increases generally with the sample size and with the marginal imbalance,
 although it does reach a maximum. Some general and very conservative
 validity conditions are E S 35.53 (one-tailed test) and E S 7.45
 (two-tailed test) for alpha nominal errors in 1% h f h 10%. The
 traditional condition E S 5 is only valid when the samples are small and
 one of the marginals is very balanced; alternatively, the condition E S
 5.5 is valid for small samples or a very balanced marginal. Finally, it is
 proved that the chi-squared test is always valid in tables where both
 marginals are balanced, and that the maximum skewness permitted is related
 to the maximum value of the bound E*, to its value for tables with at
 least one balanced marginal and to the minimum value that those marginals
 must have (in non-balanced tables) for the chi-squared test to be valid. 
Journal: Journal of Applied Statistics 
Pages: 807-820 
Issue: 7 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050120506 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050120506
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:7:p:807-820




Template-Type: ReDIF-Article 1.0
Author-Name: Jesus Gonzalo 
Author-X-Name-First: Jesus 
Author-X-Name-Last: Gonzalo 
Author-Name: Tae-Hwy Lee 
Author-X-Name-First: Tae-Hwy 
Author-X-Name-Last: Lee 
Title: On the robustness of cointegration tests when series are fractionally intergrated 
Abstract:
  This paper shows that when series are fractionally integrated, but unit
 root tests wrongly indicate that they are I(1), Johansen likelihood ratio
 (LR) tests tend to find too much spurious cointegration, while the
 Engle-Granger test presents a more robust performance. This result holds
 asymptotically as well as infinite samples. The different performance of
 these two methods is due to the fact that they are based on different
 principles. The Johansen procedure is based on maximizing correlations
 (canonical correlation) while Engle-Granger minimizes variances (in the
 spirit of principal components). 
Journal: Journal of Applied Statistics 
Pages: 821-827 
Issue: 7 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050120515 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050120515
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:7:p:821-827




Template-Type: ReDIF-Article 1.0
Author-Name: K. Govindaraju 
Author-X-Name-First: K. 
Author-X-Name-Last: Govindaraju 
Author-Name: C. Kandasamy 
Author-X-Name-First: C. 
Author-X-Name-Last: Kandasamy 
Title: Design of generalized CSP-C continuous sampling plan 
Abstract:
  In this paper, the concept acceptance number has been incorporated to the
 single level continuous sampling plan CSP-1. The advantage of the proposed
 plan, designated as the CSP-C plan, is to achieve a reduction in the
 average fraction inspected at good quality levels. Nomographs for the
 design of the proposed plan are presented. The expressions of the
 performance measures for this new plan such as OC, AOQ and AFI are also
 provided. 
Journal: Journal of Applied Statistics 
Pages: 829-841 
Issue: 7 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050120524 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050120524
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:7:p:829-841




Template-Type: ReDIF-Article 1.0
Author-Name: Wei-Ming Luh 
Author-X-Name-First: Wei-Ming 
Author-X-Name-Last: Luh 
Author-Name: Jiin-Huarng Guo 
Author-X-Name-First: Jiin-Huarng 
Author-X-Name-Last: Guo 
Title: Approximate transformation trimmed mean methods to the test of simple linear regression slope equality 
Abstract:
  To deal with the problem of non-normality and heteroscedasticity, the
 current study proposes applying approximate transformation trimmed mean
 methods to the test of simple linear regression slope equality. The
 distribution-free slope estimates are first trimmed on both sides and then
 the test statistic t is transformed by Johnson's method for each group to
 correct non-normality. Lastly, an approximate test such as the James
 second-order test, the Welch test, or the DeShon-Alexander test, which are
 robust for heterogeneous variances, is applied to test the equality of
 regression slopes. Bootstrap methods and Monte Carlo simulation results
 show that the proposed methods provide protection against both unusual y
 values, as well as unusual x values. The new methods are valid
 alternatives for testing the simple linear regression slopes when
 heteroscedastic variances and nonnormality are present. 
Journal: Journal of Applied Statistics 
Pages: 843-857 
Issue: 7 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050120533 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050120533
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:7:p:843-857




Template-Type: ReDIF-Article 1.0
Author-Name: Arthur Pewsey 
Author-X-Name-First: Arthur 
Author-X-Name-Last: Pewsey 
Title: Problems of inference for Azzalini's skewnormal distribution 
Abstract:
  This paper considers various unresolved inference problems for the
 skewnormal distribution. We give reasons as to why the direct
 parameterization should not be used as a general basis for estimation, and
 consider method of moments and maximum likelihood estimation for the
 distribution's centred parameterization. Large sample theory results are
 given for the method of moments estimators, and numerical approaches for
 obtaining maximum likelihood estimates are discussed. Simulation is used
 to assess the performance of the two types of estimation. We also present
 procedures for testing for departures from the limiting folded normal
 distribution. Data on the percentage body fat of elite athletes are used
 to illustrate some of the issues raised. 
Journal: Journal of Applied Statistics 
Pages: 859-870 
Issue: 7 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050120542 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050120542
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:7:p:859-870




Template-Type: ReDIF-Article 1.0
Author-Name: Man-Lai Tang 
Author-X-Name-First: Man-Lai 
Author-X-Name-Last: Tang 
Title: On tests of linearity for dose response data: Asymptotic, exact conditional and exact unconditional tests 
Abstract:
  The approximate chi-square statistic, X 2 Q , which is calculated as the
 difference between the usual chi-square statistic for heterogeneity and
 the Cochran-Armitage trend test statistic, has been widely applied to test
 the linearity assumption for dose-response data. This statistic can be
 shown to be asymptotically distributed as chi-square with K - 2 degrees of
 freedom. However, this asymptotic property could be quite questionable if
 the sample size is small, or if there is a high degree of sparseness or
 imbalance in the data. In this article, we consider how exact tests based
 on this X 2 Q statistic can be performed. Both the exact conditional and
 unconditional versions will be studied. Interesting findings include: (i)
 the exact conditional test is extremely sensitive to a small change in
 dosages, which may eventually produce a degenerate exact conditional
 distribution; and (ii) the exact unconditional test avoids the problem of
 degenerate distribution and is shown to be less sensitive to the change in
 dosages. A real example involving an animal carcinogenesis experiment as
 well as a fictitious data set will be used for illustration purposes. 
Journal: Journal of Applied Statistics 
Pages: 871-880 
Issue: 7 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050120551 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050120551
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:7:p:871-880




Template-Type: ReDIF-Article 1.0
Author-Name: Stephen Salter 
Author-X-Name-First: Stephen 
Author-X-Name-Last: Salter 
Author-Name: Neville Topham 
Author-X-Name-First: Neville 
Author-X-Name-Last: Topham 
Title: Side betting and playing the National Lottery: An exercise in policy design 
Abstract:
  This paper demonstrates a methodology for estimating the frequencies by
 which numbers are selected by National Lottery players by utilizing a
 twofold approach of a Multi-Response Non-Linear Regression model in
 conjunction with a suggested approximation function for number selections,
 which leads to an explanation of number choice in terms of the spatial
 effects of form design. It shows that in a marketplace, side betting is
 complementary with the main online draw product, and market forces produce
 close substitutes if side betting on the National Lottery is prohibited. 
Journal: Journal of Applied Statistics 
Pages: 881-899 
Issue: 7 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050120560 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050120560
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:7:p:881-899




Template-Type: ReDIF-Article 1.0
Author-Name: R. Vijayaraghavan 
Author-X-Name-First: R. 
Author-X-Name-Last: Vijayaraghavan 
Title: Design and evaluation of skip-lot sampling plans of type SkSP-3 
Abstract:
  This paper presents a system of skip-lot sampling inspection plans
 designated as SkSP-3 based on the principle of a continuous sampling plan
 of type CSP-2. Expressions for performance measures such as Operating
 Characteristic function and ASN function are derived by the Markov chain
 approach. Selection of SkSP-3 with a single sampling plan having
 acceptance number zero as the reference plan is discussed. 
Journal: Journal of Applied Statistics 
Pages: 901-908 
Issue: 7 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050120579 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050120579
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:7:p:901-908




Template-Type: ReDIF-Article 1.0
Author-Name: George Wesolowsky 
Author-X-Name-First: George 
Author-X-Name-Last: Wesolowsky 
Title: Detecting excessive similarity in answers on multiple choice exams 
Abstract:
  This paper provides a simple and robust method for detecting cheating.
 Unlike some methods, non-cheating behaviour and not cheating behaviour is
 modelled because this requires the fewest assumptions. The main concern is
 the prevention of false accusations. The model is suitable for screening
 large classes and the results are simple to interpret. Simulation and the
 Bonferroni inequality are used to prevent false accusation due to 'data
 dredging'. The model has received considerable application in practice and
 has been verified through the adjacent seating method. 
Journal: Journal of Applied Statistics 
Pages: 909-921 
Issue: 7 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050120588 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050120588
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:7:p:909-921




Template-Type: ReDIF-Article 1.0
Author-Name: Beatrice Giglio 
Author-X-Name-First: Beatrice 
Author-X-Name-Last: Giglio 
Author-Name: Eva Riccomagno 
Author-X-Name-First: Eva 
Author-X-Name-Last: Riccomagno 
Author-Name: Henry Wynn 
Author-X-Name-First: Henry 
Author-X-Name-Last: Wynn 
Title: Gro&#xa8;bner basis strategies in regression 
Abstract:
  The Grobner basis method in experimental design (Pistone & Wynn, 1996) is
 developed in a practical setting. The computational algebraic techniques
 (Grobner bases in particular) are coupled with statistical strategies and
 the links to more standard approaches made. A new method of analysing a
 non-orthogonal experiment based on the Grobner basis method is introduced.
 Examples are given utilizing the approaches. 
Journal: Journal of Applied Statistics 
Pages: 923-938 
Issue: 7 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050120597 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050120597
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:7:p:923-938




Template-Type: ReDIF-Article 1.0
Author-Name: Chao-Yu Chou 
Author-X-Name-First: Chao-Yu 
Author-X-Name-Last: Chou 
Author-Name: Chung-Ho Chen 
Author-X-Name-First: Chung-Ho 
Author-X-Name-Last: Chen 
Author-Name: Hui-Rong Liu 
Author-X-Name-First: Hui-Rong 
Author-X-Name-Last: Liu 
Title: Economic-statistical design of X &#xa5; charts for non-normal data by considering quality loss 
Abstract:
  When the X &#xa5; control chart is used to monitor a process, three
 parameters should be determined: the sample size, the sampling interval
 between successive samples, and the control limits of the chart. Duncan
 presented a cost model to determine the three parameters for an X &#xa5;
 chart. Alexander et al. combined Duncan's cost model with the Taguchi loss
 function to present a loss model for determining the three parameters. In
 this paper, the Burr distribution is employed to conduct the
 economic-statistical design of X &#xa5; charts for non-normal data.
 Alexander's loss model is used as the objective function, and the
 cumulative function of the Burr distribution is applied to derive the
 statistical constraints of the design. An example is presented to
 illustrate the solution procedure. From the results of the sensitivity
 analyses, we find that small values of the skewness coefficient have no
 significant effect on the optimal design; however, a larger value of
 skewness coefficient leads to a slightly larger sample size and sampling
 interval, as well as wider control limits. Meanwhile, an increase on the
 kurtosis coefficient results in an increase on the sample size and wider
 control limits. 
Journal: Journal of Applied Statistics 
Pages: 939-951 
Issue: 8 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050173274 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050173274
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:8:p:939-951




Template-Type: ReDIF-Article 1.0
Author-Name: Peter Fader 
Author-X-Name-First: Peter 
Author-X-Name-Last: Fader 
Author-Name: Bruce Hardie 
Author-X-Name-First: Bruce 
Author-X-Name-Last: Hardie 
Title: A note on modelling underreported Poisson counts 
Abstract:
  In this paper we present a parsimonious model for the analysis of
 underreported Poisson count data. In contrast to previously developed
 methods, we are able to derive analytic expressions for the key marginal
 posterior distributions that are of interest. The usefulness of this model
 is explored via a re-examination of previously analysed data covering the
 purchasing of port wine (Ramos, 1999). 
Journal: Journal of Applied Statistics 
Pages: 953-964 
Issue: 8 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050173283 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050173283
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:8:p:953-964




Template-Type: ReDIF-Article 1.0
Author-Name: Wei-Ming Luh 
Author-X-Name-First: Wei-Ming 
Author-X-Name-Last: Luh 
Author-Name: Jiin-Huarng Guo 
Author-X-Name-First: Jiin-Huarng 
Author-X-Name-Last: Guo 
Title: Johnson's transformation two-sample trimmed t and its bootstrap method for heterogeneity and non-normality 
Abstract:
  The present study investigates the performance of Johnson's
 transformation trimmed t statistic, Welch's t test, Yuen's trimmed t ,
 Johnson's transformation untrimmed t test, and the corresponding bootstrap
 methods for the two-sample case with small/unequal sample sizes when the
 distribution is non-normal and variances are heterogeneous. The Monte
 Carlo simulation is conducted in two-sided as well as one-sided tests.
 When the variance is proportional to the sample size, Yuen's trimmed t is
 as good as Johnson's transformation trimmed t . However, when the variance
 is disproportional to the sample size, the bootstrap Yuen's trimmed t and
 the bootstrap Johnson's transformation trimmed t are recommended in
 one-sided tests. For two-sided tests, Johnson's transformation trimmed t
 is not only valid but also powerful in comparison to the bootstrap
 methods. 
Journal: Journal of Applied Statistics 
Pages: 965-973 
Issue: 8 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050173292 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050173292
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:8:p:965-973




Template-Type: ReDIF-Article 1.0
Author-Name: Steen Magnussen 
Author-X-Name-First: Steen 
Author-X-Name-Last: Magnussen 
Title: Unequal probability sampling in fixed area plots of stem volume with and without prior inclusion probabilities 
Abstract:
  The impact of guessing auxiliary population attributes, as opposed to
 relying on actual values from a prior survey, was quantified for three
 unequal probability sampling methods of tree stem volume (biomass).
 Reasonable prior guesses (no-list sampling) yielded, in five populations
 and 35 combinations of population size and sample size, results at par
 with sampling with known auxiliary predictors (list sampling). Realized
 sample sizes were slightly inflated in no-list sampling with probability
 proportional to predictions ( PPP ). Mean absolute differences from true
 totals and root mean square errors in no-list-sampling schemes were only
 slightly above those achieved with list sampling. Stratified sampling
 generally outperformed PPP and systematic sampling, yet the latter is
 recommended due to consistency between observed and expected mean square
 errors and overall robustness against a systematic bias in no-list
 settings. 
Journal: Journal of Applied Statistics 
Pages: 975-990 
Issue: 8 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050173300 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050173300
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:8:p:975-990




Template-Type: ReDIF-Article 1.0
Author-Name: Carlos Mate 
Author-X-Name-First: Carlos 
Author-X-Name-Last: Mate 
Author-Name: Rafael Calderon 
Author-X-Name-First: Rafael 
Author-X-Name-Last: Calderon 
Title: Exploring the characteristics of rotating electric machines with factor analysis 
Abstract:
  Applications of multivariate statistics in engineering are hard to find,
 apart from those in quality control. However, we think that further
 insight into some technological cases may be gained by using adequate
 multivariate analysis tools. In this paper, we propose a review of the key
 parameters of rotating electric machines with factor analysis. This
 statistical technique allows not only the reduction of the dimension of
 the case we are analysing, but also reveals subtle relationships between
 the variables under study. We show an application of this methodology by
 studying the interrelations between the key variables in an electric
 machine, in this case the squirrel-cage induction motor. Through a
 step-by-step presentation of the case study, we deal with some of the
 topics an applied researcher may face, such as the rotation of the
 original factors, the extraction of higher-order factors and the
 development of the exploratory model. As a result, we present a worthwhile
 framework to both confirm our previous knowledge and capture unexplored
 facts. Moreover, it may provide a new approach to describing and
 understanding the design, performance and operating characteristics of
 these machines. 
Journal: Journal of Applied Statistics 
Pages: 991-1006 
Issue: 8 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050173319 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050173319
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:8:p:991-1006




Template-Type: ReDIF-Article 1.0
Author-Name: J. A. Nelder 
Author-X-Name-First: J. A. 
Author-X-Name-Last: Nelder 
Title: Quasi-likelihood and pseudo-likelihood are not the same thing 
Abstract:
  Models described as using quasi-likelihood (QL) are often using a
 different approach based on the normal likelihood, which I call
 pseudo-likelihood. The two approaches are described and contrasted, and an
 example is used to illustrate the advantages of the QL approach proper. 
Journal: Journal of Applied Statistics 
Pages: 1007-1011 
Issue: 8 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050173328 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050173328
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:8:p:1007-1011




Template-Type: ReDIF-Article 1.0
Author-Name: M. K. Sharma 
Author-X-Name-First: M. K. 
Author-X-Name-Last: Sharma 
Title: Application of PBIB designs in CDC Method IV 
Abstract:
  In this paper we propose the use of some partially balanced incomplete
 block designs for blocking in complete diallel cross Method IV (Griffing,
 1956) to deal with the situation when it is not desirable for all crosses
 to be accommodated in the block of a traditional randomized block design.
 A method is also proposed to analyse the MatingEnvironment designs for
 estimating the general combining ability effect of lines. 
Journal: Journal of Applied Statistics 
Pages: 1013-1019 
Issue: 8 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050173337 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050173337
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:8:p:1013-1019




Template-Type: ReDIF-Article 1.0
Author-Name: Ghazi Shukur 
Author-X-Name-First: Ghazi 
Author-X-Name-Last: Shukur 
Author-Name: Panagiotis Mantalos 
Author-X-Name-First: Panagiotis 
Author-X-Name-Last: Mantalos 
Title: A simple investigation of the Granger-causality test in integrated-cointegrated VAR systems 
Abstract:
  The size and power of various generalization tests for the
 Granger-causality in integrated-cointegrated VAR systems are considered.
 By using Monte Carlo methods, properties of eight versions of the test are
 studied in two different forms, the standard form and the modified form by
 Dolado & Lutkepohl (1996) in a study confined to properties of the Wald
 test only. In their study as well as in ours, both the standard and the
 modified Wald tests are shown to perform badly especially in small
 samples. We find, however, that the corrected LR tests exhibit correct
 size even in small samples. The power of the test is higher when the true
 VAR(2) model is estimated, and the modified test loses information by
 estimating the extra coefficients. The same is true when considering the
 power results in the VAR(3) model, and the power of the tests is somewhat
 lower than those in the VAR(2). 
Journal: Journal of Applied Statistics 
Pages: 1021-1031 
Issue: 8 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050173346 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050173346
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:8:p:1021-1031




Template-Type: ReDIF-Article 1.0
Author-Name: Siu Keung Tse 
Author-X-Name-First: Siu Keung 
Author-X-Name-Last: Tse 
Author-Name: Chunyan Yang 
Author-X-Name-First: Chunyan 
Author-X-Name-Last: Yang 
Author-Name: Hak-Keung Yuen 
Author-X-Name-First: Hak-Keung 
Author-X-Name-Last: Yuen 
Title: Statistical analysis of Weibull distributed lifetime data under Type II progressive censoring with binomial removals 
Abstract:
  This paper considers the analysis of Weibull distributed lifetime data
 observed under Type II progressive censoring with random removals, where
 the number of units removed at each failure time follows a binomial
 distribution. Maximum likelihood estimators of the parameters and their
 asymptotic variances are derived. The expected time required to complete
 the life test under this censoring scheme is investigated. 
Journal: Journal of Applied Statistics 
Pages: 1033-1043 
Issue: 8 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050173355 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050173355
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:8:p:1033-1043




Template-Type: ReDIF-Article 1.0
Author-Name: Peter Wright 
Author-X-Name-First: Peter 
Author-X-Name-Last: Wright 
Title: Choosing a lower specification limit for an exponential process with 'the larger the better' tolerance: A simple, exact solution 
Abstract:
  Chen (1999) proposed an economic design, using Taguchi's quality loss
 function, for choosing a producer's lower specification limit eta for a
 product with a quality characteristic that has an exponential distribution
 with mean &#x3b8; and 'the larger the better' tolerance. Chen (1999)
 developed an approximate solution that is applicable when 0.5 r m /&#x3b8;
 r 0.7 and that requires numerical minimization. We derive a simple, exact
 solution that is applicable for all values of m /&#x3b8; and does not
 require numerical minimization. 
Journal: Journal of Applied Statistics 
Pages: 1045-1049 
Issue: 8 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050173364 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050173364
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:8:p:1045-1049




Template-Type: ReDIF-Article 1.0
Author-Name: Zhenlin Yang 
Author-X-Name-First: Zhenlin 
Author-X-Name-Last: Yang 
Author-Name: Min Xie 
Author-X-Name-First: Min 
Author-X-Name-Last: Xie 
Title: Process monitoring of exponentially distributed characteristics through an optimal normalizing transformation 
Abstract:
  Many process characteristics follow an exponential distribution, and
 control charts based on such a distribution have attracted a lot of
 attention. However, traditional control limits may be not appropriate
 because of the lack of symmetry. In this paper, process monitoring through
 a normalizing power transformation is studied. The traditional individual
 measurement control charts can be used based on the transformed data. The
 properties of this control chart are investigated. A comparison with the
 chart when using probability limits is also carried out for cases of known
 and estimated parameters. Without losing much accuracy, even compared with
 the exact probability limits, the power transformation approach can easily
 be used to produce charts that can be interpreted when the normality
 assumption is valid. 
Journal: Journal of Applied Statistics 
Pages: 1051-1063 
Issue: 8 
Volume: 27 
Year: 2000 
X-DOI: 10.1080/02664760050173373 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760050173373
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:27:y:2000:i:8:p:1051-1063




Template-Type: ReDIF-Article 1.0
Author-Name: S. A. Al-Awadhi 
Author-X-Name-First: S. A. 
Author-X-Name-Last: Al-Awadhi 
Author-Name: P. H. Garthwaite 
Author-X-Name-First: P. H. 
Author-X-Name-Last: Garthwaite 
Title: Prior distribution assessment for a multivariate normal distribution: An experimental study 
Abstract:
  A variety of methods of eliciting a prior distribution for a multivariate
 normal (MVN) distribution have recently been proposed. This paper reports
 an experiment in which 16 meteorologists used the methods to quantify
 their opinions about climatology variables. Our results compare prior
 models and show, in particular, that it can be better to assume the mean
 and variance of an MVN distribution are independent a priori, rather than
 to model opinion by the conjugate prior distribution. Using a proper
 scoring rule, different forms of assessment task are examined and
 alternative ways of estimating parameters are compared. To quantify
 opinion about means, it proved preferable to ask directly about the means
 rather than individual observations while, to quantify opinion about the
 variance matrix, it was best to ask about deviations from the mean.
 Further results include recommendations for the way parameters of the
 prior distribution are estimated. 
Journal: Journal of Applied Statistics 
Pages: 5-23 
Issue: 1 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120011563 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120011563
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:1:p:5-23




Template-Type: ReDIF-Article 1.0
Author-Name: P. K. Tsay 
Author-X-Name-First: P. K. 
Author-X-Name-Last: Tsay 
Author-Name: A. Chao 
Author-X-Name-First: A. 
Author-X-Name-Last: Chao 
Title: Population size estimation for capture-recapture models with applications to epidemiological data 
Abstract:
  The capture-recapture method is applied to estimate the population size
 of a target population based on ascertainment data in epidemiological
 applications. We generalize the three-list case of Chao & Tsay (1998) to
 situations where more than three lists are available. An estimation
 procedure is presented using the concept of sample coverage, which can be
 interpreted as a measure of overlap information among multiple list
 records. When there is enough overlap, an estimator of the total
 population size is proposed. The bootstrap method is used to construct a
 variance estimator and confidence interval. If the overlap rate is
 relatively low, then the population size cannot be precisely estimated and
 thus only a lower (upper) bound is proposed for positively (negatively)
 dependent lists. The proposed method is applied to two data sets, one with
 a high and one with a low overlap rate. 
Journal: Journal of Applied Statistics 
Pages: 25-36 
Issue: 1 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120011572 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120011572
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:1:p:25-36




Template-Type: ReDIF-Article 1.0
Author-Name: Camil Fuchs 
Author-X-Name-First: Camil 
Author-X-Name-Last: Fuchs 
Author-Name: Morton Brown 
Author-X-Name-First: Morton 
Author-X-Name-Last: Brown 
Title: Summary measurements and screening in clinical trials with replicate observations 
Abstract:
  Repeating measurements of efficacy variables in clinical trials may be
 desirable when the measurement may be affected by ambient conditions. When
 such measurements are repeated at baseline and at the end of therapy,
 statistical questions relate to: (1) the best summary measurement to use
 for a subject when there is a possibility that some observations are
 contaminated and have increased variances; and (2) the effect of screening
 procedures which exclude outliers based on within- and between-subject
 contamination tests. We study these issues in two stages, each using a
 different set of models. The first stage deals only with the choice of the
 summary measure. The simulation results show that in some cases of
 contamination, the power achieved by the tests based on the median exceeds
 that achieved by the tests based on the mean of the replicates. However,
 even when we use the median, there are cases when contamination leads to a
 considerable loss in power. The combined issue of the best summary
 measurement and the effect of screening is studied in the second stage.
 The tests use either the observed data or the data after screening for
 outliers. The simulation results demonstrate that the power depends on the
 screening procedure as well as on the test statistic used in the study. We
 found that for the extent and magnitude of contamination considered,
 within-subject screening has a minimal effect on the power of the tests
 when there are at least three replicates; as a result, we found no
 advantage in the use of screening procedures for within-subject
 contamination. On the other hand, the use of a between-subject screening
 for outliers increases the power of the test procedures. However, even
 with the use of screening procedures, heterogeneity of variances can
 greatly reduce the power of the study. 
Journal: Journal of Applied Statistics 
Pages: 37-51 
Issue: 1 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120011581 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120011581
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:1:p:37-51




Template-Type: ReDIF-Article 1.0
Author-Name: D. Gregori 
Author-X-Name-First: D. 
Author-X-Name-Last: Gregori 
Author-Name: C. Rocco 
Author-X-Name-First: C. 
Author-X-Name-Last: Rocco 
Author-Name: S. Miocic 
Author-X-Name-First: S. 
Author-X-Name-Last: Miocic 
Author-Name: L. Mestroni 
Author-X-Name-First: L. 
Author-X-Name-Last: Mestroni 
Title: Estimating the frequency of familial dilated cardiomyopathy in the presence of misclassification errors 
Abstract:
  Dilated cardiomyopathy is a disease of unknown cause characterized by
 dilation and impaired function of one or both ventricles. Most cases are
 believed to be sporadic, although familial forms have been detected. The
 familial form has been estimated to have a relative frequency of about
 25%. Since, except for familial history, familial form has no other
 characteristics that could help in classifying the two diseases, the
 estimate of the frequency of the familial form should take into account a
 possible misclassification error. In our study, 100 cases were randomly
 selected in a prospective series of 350 patients. Out of them, 28 index
 cases were included in the analysis: 12 were known to be familial, and 88
 were believed to be sporadic. After extensive clinical examination of the
 relatives, 3 patients supposed to have a sporadic form were found to have
 a familial form. 13 cases had a confirmed sporadic disease. Models in the
 Log-Linear Product class (LLP) have been used to separate classification
 errors from underlying patterns of disease incidence. The most
 conservative crude estimate of the misclassification error is 16.1% (CI
 0.22- 23.27%), which leads to a crude estimate of the frequency of the
 familiar form of about 60%. An estimate of the disease frequency, adjusted
 for taking into consideration the sampling plan, is 40.93% (CI
 32.29-44.17%). The results are consistent with the hypothesis that genetic
 factors are still underestimated, although they represent a major cause of
 the disease. 
Journal: Journal of Applied Statistics 
Pages: 53-62 
Issue: 1 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120011590 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120011590
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:1:p:53-62




Template-Type: ReDIF-Article 1.0
Author-Name: Krishan Lal 
Author-X-Name-First: Krishan 
Author-X-Name-Last: Lal 
Author-Name: V. K. Gupta 
Author-X-Name-First: V. K. 
Author-X-Name-Last: Gupta 
Author-Name: Lalmohan Bhar 
Author-X-Name-First: Lalmohan 
Author-X-Name-Last: Bhar 
Title: Robustness of designed experiments against missing data 
Abstract:
  This paper investigates the robustness of designed experiments for
 estimating linear functions of a subset of parameters in a general linear
 model against the loss of any t( U 1) observations. Necessary and
 sufficient conditions for robustness of a design under a homoscedastic
 model are derived. It is shown that a design robust under a homoscedastic
 model is also robust under a general heteroscedastic model with correlated
 observations. As a particular case, necessary and sufficient conditions
 are obtained for the robustness of block designs against the loss of data.
 Simple sufficient conditions are also provided for the binary block
 designs to be robust against the loss of data. Some classes of designs,
 robust up to three missing observations, are identified. A-efficiency of
 the residual design is evaluated for certain block designs for several
 patterns of two missing observations. The efficiency of the residual
 design has also been worked out when all the observations in any two
 blocks, not necessarily disjoint, are lost. The lower bound to
 A-efficiency has also been obtained for the loss of t observations.
 Finally, a general expression is obtained for the efficiency of the
 residual design when all the observations of m ( U 1) disjoint blocks are
 lost. 
Journal: Journal of Applied Statistics 
Pages: 63-79 
Issue: 1 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120011608 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120011608
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:1:p:63-79




Template-Type: ReDIF-Article 1.0
Author-Name: P. J. Lindsey 
Author-X-Name-First: P. J. 
Author-X-Name-Last: Lindsey 
Title: Adapting sample size calculations to repeated measurements in clinical trials 
Abstract:
  Many of the repeated-measures sample size calculation methods presented
 in the literature are not suitable when: &#x201d; the different treatments
 are assumed to be equal on average at baseline time due to randomization,
 &#x201d; and the experimenters are interested in a pre-specified
 difference to be detected after a specific time period. The method
 presented here has been developed for those cases where a multivariate
 normal distribution can reasonably be assumed. It is likelihood-based and
 has been designed to be flexible enough to handle repeated-measures
 models, including a non-linear change in time, and an arbitrary
 correlation structure. 
Journal: Journal of Applied Statistics 
Pages: 81-89 
Issue: 1 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120011617 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120011617
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:1:p:81-89




Template-Type: ReDIF-Article 1.0
Author-Name: Dayanand Naik 
Author-X-Name-First: Dayanand 
Author-X-Name-Last: Naik 
Author-Name: Shantha Rao 
Author-X-Name-First: Shantha 
Author-X-Name-Last: Rao 
Title: Analysis of multivariate repeated measures data with a Kronecker product structured covariance matrix 
Abstract:
  In this article we consider a set of t repeated measurements on p
 variables (or characteristics) on each of the n individuals. Thus, data on
 each individual is a p 2 t matrix. The n individuals themselves may be
 divided and randomly assigned to g groups. Analysis of these data using a
 MANOVA model, assuming that the data on an individual has a covariance
 matrix which is a Kronecker product of two positive definite matrices, is
 considered. The well-known Satterthwaite type approximation to the
 distribution of a quadratic form in normal variables is extended to the
 distribution of a multivariate quadratic form in multivariate normal
 variables. The multivariate tests using this approximation are developed
 for testing the usual hypotheses. Results are illustrated on a data set. A
 method for analysing unbalanced data is also discussed. 
Journal: Journal of Applied Statistics 
Pages: 91-105 
Issue: 1 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120011626 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120011626
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:1:p:91-105




Template-Type: ReDIF-Article 1.0
Author-Name: Raffaella Piccarreta 
Author-X-Name-First: Raffaella 
Author-X-Name-Last: Piccarreta 
Title: A new measure of nominal-ordinal association 
Abstract:
  A new measure for evaluating the strength of the association between a
 nominal variable and an ordered categorical response variable is
 introduced. The introduction of a new measure is justified by analysing
 the characteristics of a measure of the nominal-ordinal association
 proposed by Agresti (1981), especially with respect to the problem of the
 'choice' of a predictive variable. The sample-based version of the index
 is studied, and its asymptotic standard error and asymptotic distribution
 are derived. Simulations are considered to evaluate the adequacy of the
 asymptotic approximation determined, following Goodman & Kruskal (1963). 
Journal: Journal of Applied Statistics 
Pages: 107-120 
Issue: 1 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120011635 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120011635
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:1:p:107-120




Template-Type: ReDIF-Article 1.0
Author-Name: R. R. L. Kantam 
Author-X-Name-First: R. R. L. 
Author-X-Name-Last: Kantam 
Author-Name: K. Rosaiah 
Author-X-Name-First: K. 
Author-X-Name-Last: Rosaiah 
Author-Name: G. Srinivasa Rao 
Author-X-Name-First: G. Srinivasa 
Author-X-Name-Last: Rao 
Title: Acceptance sampling based on life tests: Log-logistic model 
Abstract:
  The problem of acceptance sampling when the life test is truncated at a
 preassigned time is considered. For various acceptance numbers, confidence
 levels and values of the ratio of the fixed experimental time to the
 specified average life, the minimum sample size necessary to ensure the
 specified average life, are obtained under the assumption that the
 lifetime variate of the test items follows a distribution belonging to
 Burr's family XII of distributions - called the log-logistic model. The
 operating characteristic values of the sampling plans and producer's risk
 are presented. The results are illustrated by an example. 
Journal: Journal of Applied Statistics 
Pages: 121-128 
Issue: 1 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120011644 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120011644
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:1:p:121-128




Template-Type: ReDIF-Article 1.0
Author-Name: Ashis Sengupta 
Author-X-Name-First: Ashis 
Author-X-Name-Last: Sengupta 
Author-Name: Chandranath Pal 
Author-X-Name-First: Chandranath 
Author-X-Name-Last: Pal 
Title: On optimal tests for isotropy against the symmetric wrapped stable-circular uniform mixture family 
Abstract:
  The family of Symmetric Wrapped Stable (SWS) distributions can be widely
 used for modelling circular data. Mixtures of Circular Uniform (CU) with
 the former also have applications as a larger family of circular
 distributions to incorporate possible outliers. Restricting ourselves to
 such a mixture, we derive the locally most powerful invariant (LMPI) test
 for the hypothesis of isotropy or randomness of directions-expressed in
 terms of the null value of the mixing proportion, p, in the model. Global
 monotonicity of the power function of the test is established. The test is
 also consistent. Power values of the test for some selected parameter
 combinations, obtained through simulation reveal quite encouraging
 performances even for moderate sample sizes. The P 3 approach (SenGupta,
 1991; Pal & SenGupta, 2000) for unknown p and rho and the non-regular case
 of unknown a, the index parameter, are also discussed. A real-life example
 is presented to illustrate the inadequacy of the circular normal
 distribution as a circular model. This example is also used to demonstrate
 the applications of the LMPI test, optimal P 3 test and a Daviesmotivated
 test (Davies, 1977, 1987). Finally, a goodness-of-fit test performed on
 the data establishes the plausibility of the above SWS-CU mixture model
 for real-life problems encountered in practical situations. 
Journal: Journal of Applied Statistics 
Pages: 129-143 
Issue: 1 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120011653 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120011653
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:1:p:129-143




Template-Type: ReDIF-Article 1.0
Author-Name: Mehmetcik Bayazit 
Author-X-Name-First: Mehmetcik 
Author-X-Name-Last: Bayazit 
Author-Name: Hafzullah Aksoy 
Author-X-Name-First: Hafzullah 
Author-X-Name-Last: Aksoy 
Title: Using wavelets for data generation 
Abstract:
  Wavelets are proposed as a non-parametric data generation tool. The idea
 behind the suggested method is decomposition of data into its details and
 later reconstruction by summation of the details randomly to generate new
 data. A Haar wavelet is used because of its simplicity. The method is
 applied to annual and monthly streamflow series taken from Turkey and USA.
 It is found to give good results for non-skewed data, as well as in the
 presence of auto-correlation. 
Journal: Journal of Applied Statistics 
Pages: 157-166 
Issue: 2 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760020016073 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760020016073
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:2:p:157-166




Template-Type: ReDIF-Article 1.0
Author-Name: Teresa Aparicio 
Author-X-Name-First: Teresa 
Author-X-Name-Last: Aparicio 
Author-Name: Inmaculada Villanua 
Author-X-Name-First: Inmaculada 
Author-X-Name-Last: Villanua 
Title: The asymptotically efficient version of the information matrix test in binary choice models. A study of size and power 
Abstract:
  As Newey (1985) and Orme (1988) argue in the context of discrete binary
 choice models, the test of the information matrix (IM) is sensitive to
 heteroscedasticity and the incorrect distribution of the error term, with
 both these problems leading to inconsistency of the estimators obtained.
 This paper uses simulation experiments to analyse the size and power of
 the asymptotically efficient version of this test, with the aim of
 obtaining evidence on its capacity to detect such specification errors,
 considering different alternatives. 
Journal: Journal of Applied Statistics 
Pages: 167-182 
Issue: 2 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760020016082 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760020016082
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:2:p:167-182




Template-Type: ReDIF-Article 1.0
Author-Name: Peter Congdon 
Author-X-Name-First: Peter 
Author-X-Name-Last: Congdon 
Title: Predicting adverse infant health outcomes using routine screening variables: Modelling the impact of interdependent risk factors 
Abstract:
  This paper sets out a methodology for risk assessment of pregnancies in
 terms of adverse outcomes such as low birth-weight and neonatal mortality
 in a situation of multiple but possibly interdependent major dimensions of
 risk. In the present analysis, the outcome is very low birth-weight and
 the observed risk indicators are assumed to be linked to three main
 dimensions: socio-demographic, bio-medical status, and fertility history.
 Summary scores for each mother under each risk dimension are derived from
 observed indicators and used as the basis for a multidimensional
 classification to high or low risk. A fully Bayesian method of
 implementation is applied to estimation and prediction. A case study is
 presented of very low birth-weight singleton livebirths over 1991-93 in a
 health region covering North West London and parts of the adjacent South
 East of England, with validating predictions to maternities in 1994. 
Journal: Journal of Applied Statistics 
Pages: 183-197 
Issue: 2 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760020016091 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760020016091
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:2:p:183-197




Template-Type: ReDIF-Article 1.0
Author-Name: Abhijit Gupta 
Author-X-Name-First: Abhijit 
Author-X-Name-Last: Gupta 
Title: Optimization of product performance of a paint formulation using a mixture experiment 
Abstract:
  A paint manufacturing company was facing the problem of Vehicle
 Separation and Settling in one of its prime products. These two
 abnormalities are, in general, opposing in nature. The manufacturer tried
 several modifications in the existing recipe for the product but failed to
 control them. Experimentation was carried out using mixture design, a
 special type of designed experiment, and quadratic response surface models
 were fitted for both the responses. Finally, optimum formulation was
 obtained by simultaneously optimizing the two response surface models.
 During the determination of optimal formulation, different methods were
 compared. The optimum formulation is currently being used for regular
 manufacturing. 
Journal: Journal of Applied Statistics 
Pages: 199-213 
Issue: 2 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760020016109 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760020016109
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:2:p:199-213




Template-Type: ReDIF-Article 1.0
Author-Name: Dog-super-&#x2dc;an Argac 
Author-X-Name-First: Dog-super-&#x2dc;an 
Author-X-Name-Last: Argac 
Author-Name: Kepher Makambi 
Author-X-Name-First: Kepher 
Author-X-Name-Last: Makambi 
Author-Name: Joachim Hartung 
Author-X-Name-First: Joachim 
Author-X-Name-Last: Hartung 
Title: A note on testing the nullity of the between group variance in the one-way random effects model under variance heterogeneity 
Abstract:
  In an unbalanced and heteroscedastic one-way random effects model, we
 compare, by way of simulation, several test statistics for testing the
 null hypothesis that the variance of the random effects, also named the
 between group variance, is zero. These tests are the classical F-test, the
 test proposed by Jeyaratnam & Othman, the Welch test, and a modified
 version of Welch's test. 
Journal: Journal of Applied Statistics 
Pages: 215-222 
Issue: 2 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760020016118 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760020016118
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:2:p:215-222




Template-Type: ReDIF-Article 1.0
Author-Name: Jack Lee 
Author-X-Name-First: Jack 
Author-X-Name-Last: Lee 
Author-Name: W. H. Lien 
Author-X-Name-First: W. H. 
Author-X-Name-Last: Lien 
Title: Bayesian analysis of a growth curve model with power transformation, random effects and AR(1) dependence 
Abstract:
  In this paper we devote ourselves to a general growth curve model with
 power transformation, random effects and AR(1) dependence via a Bayesian
 approach. Two priors are proposed and both parameter estimation and
 prediction of future values are considered. Some numerical results with a
 set of real data are also given. 
Journal: Journal of Applied Statistics 
Pages: 223-238 
Issue: 2 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760020016127 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760020016127
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:2:p:223-238




Template-Type: ReDIF-Article 1.0
Author-Name: Youngjo Lee 
Author-X-Name-First: Youngjo 
Author-X-Name-Last: Lee 
Title: Can we recover information from concordant pairs in binary matched pairs? 
Abstract:
  When possible values of a response variable are limited, distributional
 assumptions about random effects may not be checkable. This may cause a
 distribution-robust estimator, such as the conditional maximum likelihood
 estimator to be recommended; however, it does not utilize all the
 information in the data. We show how, with binary matched pairs, the
 hierarchical likelihood can be used to recover information from concordant
 pairs, giving an improvement over the conditional maximum likelihood
 estimator without losing distribution-robustness. 
Journal: Journal of Applied Statistics 
Pages: 239-246 
Issue: 2 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760020016136 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760020016136
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:2:p:239-246




Template-Type: ReDIF-Article 1.0
Author-Name: Donald Martin 
Author-X-Name-First: Donald 
Author-X-Name-Last: Martin 
Title: Influence functions applied to the estimation of mean rain rate 
Abstract:
  In this paper we illustrate the usefulness of influence functions for
 studying properties of various statistical estimators of mean rain rate
 using space-borne radar data. In Martin (1999), estimators using
 censoring, minimum chi-square, and least squares are compared in terms of
 asymptotic variance. Here, we use influence functions to consider
 robustness properties of the same estimators. We also obtain formulas for
 the asymptotic variance of the estimators using influence functions, and
 thus show that they may also be used for studying relative efficiency. The
 least squares estimator, although less efficient, is shown to be more
 robust in the sense that it has the smallest gross-error sensitivity. In
 some cases, influence functions associated with the estimators reveal
 counterintuitive behaviour. For example, observations that are less than
 the mean rain rate may increase the estimated mean. The additional
 information gleaned from influence functions may be used to understand
 better and improve the estimation procedures themselves. 
Journal: Journal of Applied Statistics 
Pages: 247-258 
Issue: 2 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760020016145 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760020016145
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:2:p:247-258




Template-Type: ReDIF-Article 1.0
Author-Name: Man-Suk Oh 
Author-X-Name-First: Man-Suk 
Author-X-Name-Last: Oh 
Author-Name: Yong Bin Lim 
Author-X-Name-First: Yong Bin 
Author-X-Name-Last: Lim 
Title: Bayesian analysis of time series Poisson data 
Abstract:
  This paper provides a practical simulation-based Bayesian analysis of
 parameter-driven models for time series Poisson data with the AR(1) latent
 process. The posterior distribution is simulated by a Gibbs sampling
 algorithm. Full conditional posterior distributions of unknown variables
 in the model are given in convenient forms for the Gibbs sampling
 algorithm. The case with missing observations is also discussed. The
 methods are applied to real polio data from 1970 to 1983. 
Journal: Journal of Applied Statistics 
Pages: 259-271 
Issue: 2 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760020016154 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760020016154
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:2:p:259-271




Template-Type: ReDIF-Article 1.0
Author-Name: Andrew Roddam 
Author-X-Name-First: Andrew 
Author-X-Name-Last: Roddam 
Title: An approximate maximum likelihood procedure for parameter estimation in multivariate discrete data regression models 
Abstract:
  This paper considers an alternative to iterative procedures used to
 calculate maximum likelihood estimates of regression coefficients in a
 general class of discrete data regression models. These models can include
 both marginal and conditional models and also local regression models. The
 classical estimation procedure is generally via a Fisher-scoring algorithm
 and can be computationally intensive for high-dimensional problems. The
 alternative method proposed here is non-iterative and is likely to be more
 efficient in high-dimensional problems. The method is demonstrated on two
 different classes of regression models. 
Journal: Journal of Applied Statistics 
Pages: 273-279 
Issue: 2 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760020016163 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760020016163
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:2:p:273-279




Template-Type: ReDIF-Article 1.0
Author-Name: M. Arvidsson 
Author-X-Name-First: M. 
Author-X-Name-Last: Arvidsson 
Author-Name: P. Kammerlind 
Author-X-Name-First: P. 
Author-X-Name-Last: Kammerlind 
Author-Name: A. Hynen 
Author-X-Name-First: A. 
Author-X-Name-Last: Hynen 
Author-Name: B. Bergman 
Author-X-Name-First: B. 
Author-X-Name-Last: Bergman 
Title: Identification of factors influencing dispersion in split-plot experiments 
Abstract:
  As split-plot designs are commonly used in robust design it is important
 to identify factors in these designs that influence the dispersion of the
 response variable. In this article, the Bergman-Hynen method, developed
 for identification of dispersion effects in unreplicated experiments, is
 modified to be used in the context of split-plot experiments. The
 modification of the Bergman-Hynen method enables identification of factors
 that influence specific variance components in unreplicated two-level
 fractional factorial splitplot experiments. An industrial example is used
 to illustrate the proposed method. 
Journal: Journal of Applied Statistics 
Pages: 269-283 
Issue: 3-4 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120034027 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120034027
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:3-4:p:269-283




Template-Type: ReDIF-Article 1.0
Author-Name: George Box 
Author-X-Name-First: George 
Author-X-Name-Last: Box 
Title: Statistics for discovery 
Abstract:
  The question is discussed of why investigators in engineering and the
 physical sciences rarely use statistical methods. It is argued that
 statistics has in the past been overly influenced by the needs of
 mathematics rather than those of scientific learning and discovery.
 Remedies are suggested. 
Journal: Journal of Applied Statistics 
Pages: 285-299 
Issue: 3-4 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120034036 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120034036
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:3-4:p:285-299




Template-Type: ReDIF-Article 1.0
Author-Name: Roland Caulcutt 
Author-X-Name-First: Roland 
Author-X-Name-Last: Caulcutt 
Title: Why is Six Sigma so successful? 
Abstract:
  There can be little doubt that Motorola, General Electric, Black and
 Decker, Allied Signal (now Honeywell), ABB and Bombardier, have achieved
 impressive business performance in recent years. Their annual reports
 document this success. Furthermore, in several cases, the Annual Report
 clearly attributes this success to having followed a Six Sigma strategy.
 Not surprisingly, many other companies wish to learn what Six Sigma can do
 for them, and their first question is 'What exactly is Six Sigma?'.
 Unfortunately it is rather difficult, if not impossible, to define Six
 Sigma in one or two sentences. This paper identifies the essential
 elements of Six Sigma. Some are obvious, such as the extensive use of
 statistical techniques by employees known as Blackbelts. However, other
 more subtle, but very important, features of Six Sigma are concealed
 within the business culture of these successful companies. It is clear to
 those who have participated in this success, that any company embarking on
 Six Sigma will not succeed if it focuses on statistics whilst failing to
 develop a supporting culture. 
Journal: Journal of Applied Statistics 
Pages: 301-306 
Issue: 3-4 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120034045 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120034045
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:3-4:p:301-306




Template-Type: ReDIF-Article 1.0
Author-Name: P. R. G. Chambers 
Author-X-Name-First: P. R. G. 
Author-X-Name-Last: Chambers 
Author-Name: J. L. Piggott 
Author-X-Name-First: J. L. 
Author-X-Name-Last: Piggott 
Author-Name: S. Y. Coleman 
Author-X-Name-First: S. Y. 
Author-X-Name-Last: Coleman 
Title: SPC&#x2014;a team effort for process improvement across four Area Control Centres 
Abstract:
  This paper describes an innovative application of statistical process
 control to the online remote control of the UK's gas transportation
 networks. The gas industry went through a number of changes in ownership,
 regulation, access to networks, organization and management culture in the
 1990s. The application of SPC was motivated by these changes along with
 the desire to apply the best industrial statistics theory to practical
 problems. The work was initiated by a studentship, with the technology
 gradually being transferred to the industry. The combined efforts of
 control engineers and statisticians helped develop a novel SPC system.
 Having set up the control limits, a system was devised to automatically
 update and publish the control charts on a daily basis. The charts and an
 associated discussion forum are available to both managers and control
 engineers throughout the country at their desktop PCs. The paper describes
 methods of involving people to design first-class systems to achieve
 continual process improvement. It describes how the traditional benefits
 of SPC can be realized in a 'distal team working', and 'soft systems',
 context of four Area Control Centres, controlling a system delivering two
 thirds of the UK's energy needs. 
Journal: Journal of Applied Statistics 
Pages: 307-324 
Issue: 3-4 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120034054 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120034054
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:3-4:p:307-324




Template-Type: ReDIF-Article 1.0
Author-Name: S. Y. Coleman 
Author-X-Name-First: S. Y. 
Author-X-Name-Last: Coleman 
Author-Name: G. Arunakumar 
Author-X-Name-First: G. 
Author-X-Name-Last: Arunakumar 
Author-Name: F. Foldvary 
Author-X-Name-First: F. 
Author-X-Name-Last: Foldvary 
Author-Name: R. Feltham 
Author-X-Name-First: R. 
Author-X-Name-Last: Feltham 
Title: SPC as a tool for creating a successful business measurement framework 
Abstract:
  Many companies are trying to get to the bottom of what their main
 objectives are and what their business should be doing. The new Six Sigma
 approach concentrates on clarifying business strategy and making sure that
 everything relates to company objectives. It is vital to clarify each part
 of the business in such a way that everyone can understand the causes of
 variation that can lead to improvements in processes and performance. This
 paper describes a situation where the full implementation of SPC
 methodology has made possible a visual and widely appreciated summary of
 the performance of one important aspect of the business. The major part of
 the work was identifying the core objectives and deciding how to
 encapsulate each of them in one or more suitable measurements. The next
 step was to review the practicalities of obtaining the measurements and
 their reliability and representativeness. Finally, the measurements were
 presented in chart form and the more traditional steps of SPC analysis
 were commenced. Data from fast changing business environments are prone to
 many different problems, such as the short previous span of typical data,
 strange distributions and other uncertainties. Issues surrounding these
 and the eventual extraction of a meaningful set of information will be
 discussed in the paper. The measurement framework has proved very useful
 and, from an initial circulation of a handful of people, it now forms an
 important part of an information process that provides responsible
 managers with valuable control information. The measurement framework is
 kept fresh and vital by constant review and modifications. Improved
 electronic data collection and dissemination of the report has proved very
 important. 
Journal: Journal of Applied Statistics 
Pages: 325-334 
Issue: 3-4 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120034063 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120034063
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:3-4:p:325-334




Template-Type: ReDIF-Article 1.0
Author-Name: David Bruce 
Author-X-Name-First: David 
Author-X-Name-Last: Bruce 
Author-Name: Shirley Coleman 
Author-X-Name-First: Shirley 
Author-X-Name-Last: Coleman 
Title: Improving communication via quantitative management 
Abstract:
  Villa Soft Drinks Ltd, established in 1884, manufactures and bottles
 spring waters and carbonates for both the growing adult soft drinks market
 and the more traditional soft drinks market. The company employs just over
 100 people split between the manufacturing site in Sunderland and the head
 office and distribution centre in Washington. One of the fundamental
 problems affecting the day-to-day running of Villa, and most companies, is
 communication. There is a lack of awareness of the impact that changes in
 one department have on other departments (e.g. if production efficiency is
 increased by 10%, what impact will this have on warehousing?). Villa had
 recently identified key performance indicators (KPIs) to monitor all
 aspects of manufacturing performance on a regular basis. This enabled the
 current production situation to be evaluated and helped familiarize staff
 with charts and measurements. The use of Pareto analysis and problem
 solving techniques helped to boost efficiency and utilization. Key
 performance indicators were then developed in most other departments and
 are monitored and displayed regularly. The KPIs can be used further to
 improve transparency across the company by incorporating them in an
 interactive, interpretative tool to aid communication and understanding at
 all levels of the company. Individual departmental flow diagrams will be
 linked together to represent how the company operates. The diagrams will
 include both material flow and information flow. These data will then be
 organized in a software package and the end result will be a fully
 integrated simulation of the company in which any variable can be altered
 to demonstrate the effect this has on other departments and therefore the
 company as a whole. This will be an extremely valuable tool for the
 company as it will have many different applications, such as calculating
 manning requirements, identifying potential cycle time reductions and
 optimizing warehouse space. 
Journal: Journal of Applied Statistics 
Pages: 335-341 
Issue: 3-4 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120034072 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120034072
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:3-4:p:335-341




Template-Type: ReDIF-Article 1.0
Author-Name: S. Y. Coleman 
Author-X-Name-First: S. Y. 
Author-X-Name-Last: Coleman 
Author-Name: A. Gordon 
Author-X-Name-First: A. 
Author-X-Name-Last: Gordon 
Author-Name: P. R. Chambers 
Author-X-Name-First: P. R. 
Author-X-Name-Last: Chambers 
Title: SPC&#x2014;making it work for the gas transportation business 
Abstract:
  Transco is the main provider of gas transportation to domestic and
 commercial customers in mainland Britain. Gas arrives in Britain at a
 steady rate but is consumed with a distinct diurnal pattern. The safe and
 timely movement of gas from arrival at the beach in various places in
 Britain to delivery at burners is the main driver for System Operations.
 The movement of gas is meticulously controlled and monitored resulting in
 a mass of information on pressure, flow and temperature. Gas is stored
 temporarily in various storage vessels and is moved around the pipes and
 in and out of storage as demand dictates. Demand is mostly dictated by the
 weather and is therefore subject to much variation. Transco and its
 predecessors have been transporting gas for over 50 years and are very
 successful as judged by their excellent safety record and the continual
 delivery of gas. Nevertheless, the company wished to improve itself and
 make further use of the many measurements collected. SPC is ideal for
 improving communication and understanding through increased visibility of
 data. All companies have special issues to face when they implement SPC,
 and this paper describes the way these were dealt with in System
 Operations and the lessons learnt along the way. The first part describes
 how performance measures were chosen for investigation. It includes a
 novel use of correlation between output and day-to-day conditions, which
 was successfully turned into a measure to check the uncheckable. The
 second part is about the issues involved with early application of SPC
 when features of the system are still unexplained. SPC has helped enhance
 understanding of the complex transportation process, encouraged team work,
 improved performance and provided an objective means of decision making. 
Journal: Journal of Applied Statistics 
Pages: 343-351 
Issue: 3-4 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120034081 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120034081
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:3-4:p:343-351




Template-Type: ReDIF-Article 1.0
Author-Name: M. A. A. Cox 
Author-X-Name-First: M. A. A. 
Author-X-Name-Last: Cox 
Title: Towards the implementation of a universal control chart and estimation of its average run length using a spreadsheet: An artificial neural network is employed to model the parameters in a special case 
Abstract:
  A control chart procedure has previously been proposed (Champ et al.,
 1991) for which the Shewhart X &#xa5; -chart, the cumulative sum chart,
 and the exponentially weighted moving average chart are special cases. The
 rapid and easy production of these charts, plus many others, is proposed
 using spreadsheets. In addition, for all these novel charts, the average
 run lengths are generated as a guide to their likely behaviour. The
 cumulative sum chart is widely employed in quality control and is
 considered in greater detail. Charts are designed to exhibit acceptable
 average run lengths both when the process is in and out of control. A
 functional technique for parameter selection for such a chart is
 introduced that results in target average run lengths. It employs the
 method of artificial neural networks to derive appropriate coefficients.
 This approach may be extended to any of the charts previously introduced. 
Journal: Journal of Applied Statistics 
Pages: 353-364 
Issue: 3-4 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120034090 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120034090
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:3-4:p:353-364




Template-Type: ReDIF-Article 1.0
Author-Name: Trevor Cox 
Author-X-Name-First: Trevor 
Author-X-Name-Last: Cox 
Title: Multidimensional scaling used in multivariate statistical process control 
Abstract:
  This paper considers the use of multidimensional scaling techniques in
 multivariate statistical process control. Principal components analysis,
 multiple principal components analysis, partial least squares and PARAFAC
 models have already been established as useful methods for such, but it
 should be possible to widen the portfolio of techniques to include others
 that come under the multidimensional scaling class. Some of these are
 briefly described-namely classical scaling, non-metric scaling, biplots,
 Procrustes analysis-and are then used on some gas transportation data
 provided by Transco. 
Journal: Journal of Applied Statistics 
Pages: 365-378 
Issue: 3-4 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120034108 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120034108
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:3-4:p:365-378




Template-Type: ReDIF-Article 1.0
Author-Name: E. J. Godolphin 
Author-X-Name-First: E. J. 
Author-X-Name-Last: Godolphin 
Title: Observable trend-projecting state-space models 
Abstract:
  Much attention has focused in recent years on the use of state-space
 models for describing and forecasting industrial time series. However,
 several state-space models that are proposed for such data series are not
 observable and do not have a unique representation, particularly in
 situations where the data history suggests marked seasonal trends. This
 raises major practical difficulties since it becomes necessary to impose
 one or more constraints and this implies a complicated error structure on
 the model. The purpose of this paper is to demonstrate that state-space
 models are useful for describing time series data for forecasting purposes
 and that there are trend-projecting state-space components that can be
 combined to provide observable state-space representations for specified
 data series. This result is particularly useful for seasonal or
 pseudo-seasonal time series. A well-known data series is examined in some
 detail and several observable state-space models are suggested and
 compared favourably with the constrained observable model. 
Journal: Journal of Applied Statistics 
Pages: 379-389 
Issue: 3-4 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120034117 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120034117
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:3-4:p:379-389




Template-Type: ReDIF-Article 1.0
Author-Name: T. N. Goh 
Author-X-Name-First: T. N. 
Author-X-Name-Last: Goh 
Title: A pragmatic approach to experimental design in industry 
Abstract:
  The importance of statistically designed experiments in industry has been
 well recognized. However, the use of 'design of experiments' is still not
 pervasive, owing in part to the inefficient learning process experienced
 by many non-statisticians. In this paper, the nature of design of
 experiments, in contrast to the usual statistical process control
 techniques, is discussed. It is then pointed out that for design of
 experiments to be appreciated and applied, appropriate approaches should
 be taken in training, learning and application. Perspectives based on the
 concepts of objective setting and design under constraints can be used to
 facilitate the experimenters' formulation of plans for collection,
 analysis and interpretation of empirical information. A review is made of
 the expanding role of design of experiments in the past several decades,
 with comparisons made of the various formats and contexts of experimental
 design applications, such as Taguchi methods and Six Sigma. The trend of
 development shows that, from the realm of scientific research to business
 improvement, the competitive advantage offered by design of experiments is
 being increasingly felt. 
Journal: Journal of Applied Statistics 
Pages: 391-398 
Issue: 3-4 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120034126 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120034126
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:3-4:p:391-398




Template-Type: ReDIF-Article 1.0
Author-Name: G. Robin Henderson 
Author-X-Name-First: G. Robin 
Author-X-Name-Last: Henderson 
Title: EWMA and industrial applications to feedback adjustment and control 
Abstract:
  In his book 'Out of the Crisis' the late Dr Edwards Deming asserted that
 'if anyone adjusts a stable process to try to compensate for a result that
 is undesirable, or for a result that is extra good, the output will be
 worse than if he had left the process alone'. His famous funnel
 experiments supported this assertion. The development of the control chart
 by Dr Walter Shewhart stemmed from an approach made to him by the
 management of a Western Electric Company plant because of their awareness
 that adjustments made to processes often made matters worse. However, many
 industrial processes are such that the mean values of product quality
 characteristics shift and drift over time so that, instead of sequences of
 independent observations to which Deming's assertion applies, process
 owners are faced with autocorrelated data. The truth of Dr Deming's
 assertion is demonstrated, both theoretically and via computer simulation.
 The use of the Exponentially Weighted Moving Average (EWMA) for process
 monitoring is demonstrated and, for situations where process data exhibit
 autocorrelation, its use for feedback adjustment is discussed and
 demonstrated. Finally, successful applications of process improvements
 using EWMA-based control algorithms is discussed. 
Journal: Journal of Applied Statistics 
Pages: 399-407 
Issue: 3-4 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120034135 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120034135
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:3-4:p:399-407




Template-Type: ReDIF-Article 1.0
Author-Name: M. Weighell 
Author-X-Name-First: M. 
Author-X-Name-Last: Weighell 
Author-Name: E. B. Martin 
Author-X-Name-First: E. B. 
Author-X-Name-Last: Martin 
Author-Name: A. J. Morris 
Author-X-Name-First: A. J. 
Author-X-Name-Last: Morris 
Title: The statistical monitoring of a complex manufacturing process 
Abstract:
  This paper describes the development of a multivariate statistical
 process performance monitoring scheme for a high-speed polyester film
 production facility. The objective for applying multivariate statistical
 process control (MSPC) was to improve product consistency, detect process
 changes and disturbances and increase operator awareness of the impact of
 both routine maintenance and unusual events. The background to MSPC is
 briefly described and the various stages in the development of an at-line
 MSPC representation for the production line are described. A number of
 case studies are used to illustrate the power of the methodology,
 highlighting its potential to assist in process maintenance, the detection
 of changes in process operation and the potential for the identification
 of badly tuned controller loops. 
Journal: Journal of Applied Statistics 
Pages: 409-425 
Issue: 3-4 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120034144 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120034144
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:3-4:p:409-425




Template-Type: ReDIF-Article 1.0
Author-Name: Douglas Montgomery 
Author-X-Name-First: Douglas 
Author-X-Name-Last: Montgomery 
Title: Opportunities and challenges for industrial statisticians 
Abstract:
  The last 20 years have seen significant advances in the use of
 statistical methodology in industry, with applications in new product
 design and development, optimization and control of manufacturing
 processes, and in the service industries. The field of industrial
 statistics has emerged as an important branch of statistical science that
 focuses on this application environment. Yet as applications of statistics
 in industry have expanded, creating many new opportunities for the modern
 industrial statistician, many new challenges have arisen. Some of these
 challenges are technical, while others have managerial and organizational
 aspects. There are also important concerns pertaining to training and
 education. This presentation focuses on some of these issues, and
 identifies some potential solutions. 
Journal: Journal of Applied Statistics 
Pages: 427-439 
Issue: 3-4 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120034153 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120034153
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:3-4:p:427-439




Template-Type: ReDIF-Article 1.0
Author-Name: P. Pongcharoen 
Author-X-Name-First: P. 
Author-X-Name-Last: Pongcharoen 
Author-Name: D. J. Stewardson 
Author-X-Name-First: D. J. 
Author-X-Name-Last: Stewardson 
Author-Name: C. Hicks 
Author-X-Name-First: C. 
Author-X-Name-Last: Hicks 
Author-Name: P. M. Braiden 
Author-X-Name-First: P. M. 
Author-X-Name-Last: Braiden 
Title: Applying designed experiments to optimize the performance of genetic algorithms used for scheduling complex products in the capital goods industry 
Abstract:
  Conventional optimization approaches, such as Linear Programming, Dynamic
 Programming and Branch-and-Bound methods are well established for solving
 relatively simple scheduling problems. Algorithms such as Simulated
 Annealing, Taboo Search and Genetic Algorithms (GA) have recently been
 applied to large combinatorial problems. Owing to the complex nature of
 these problems it is often impossible to search the whole problem space
 and an optimal solution cannot, therefore, be guaranteed. A BiCriteria
 Genetic Algorithm (BCGA) has been developed for the scheduling of complex
 products with multiple resource constraints and deep product structure.
 This GA identifies and corrects infeasible schedules and takes account of
 the early supply of components and assemblies, late delivery of final
 products and capacity utilization. The research has used manufacturing
 data obtained from a capital goods company. Genetic Algorithms include a
 number of parameters, including the probabilities of crossover and
 mutation, the population size and the number of generations. The BCGA
 scheduling tool provides 16 alternative crossover operations and eight
 different mutation mechanisms. The overall objective of this study was to
 develop an efficient design-of-experiments approach to identify genetic
 algorithm operators and parameters that produce solutions with minimum
 total cost. The case studies were based upon a complex, computationally
 intensive scheduling problem that was insoluble using conventional
 approaches. This paper describes an efficient sequential experimental
 strategy that enabled this work to be performed within a reasonable time.
 The first stage was a screening experiment, which had a fractional
 factorial embedded within a half Latin-square design. The second stage was
 a half-fraction design with a reduced number of GA operators. The results
 are compared with previous studies. It is demonstrated that, in this case,
 improved GA performance was achieved using the experimental strategy
 proposed. The appropriate genetic operators and parameters may be case
 specific, leading to the view that experimental design may be the best way
 to proceed when finding the 'best' combination of GA operators and
 parameters. 
Journal: Journal of Applied Statistics 
Pages: 441-455 
Issue: 3-4 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120034162 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120034162
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:3-4:p:441-455




Template-Type: ReDIF-Article 1.0
Author-Name: Elsayed Elamir 
Author-X-Name-First: Elsayed 
Author-X-Name-Last: Elamir 
Author-Name: Allan Seheult 
Author-X-Name-First: Allan 
Author-X-Name-Last: Seheult 
Title: Control charts based on linear combinations of order statistics 
Abstract:
  The last 20 years have seen an increasing emphasis on statistical process
 control as a practical approach to reducing variability in industrial
 applications. Control charts are used to detect problems such as outliers
 or excess variability in subgroup means that may have a special cause. We
 describe an approach to the computation of control limits for
 exponentially weighted moving average control charts where the usual
 statistics in classical charts are replaced by linear combinations of
 order statistics; in particular, the trimmed mean and Gini's mean
 difference instead of the mean and range, respectively. Control limits are
 derived, and simulated average run length experiments show the trimmed
 control charts to be less influenced by extreme observations than their
 classical counterparts, and lead to tighter control limits. An example is
 given that illustrates the benefits of the proposed charts. parameters;
 see, for example, Hunter (1986) and Montgomery (1996). On the other hand,
 EWMA charts have been shown to be more efficient than Shewharttype charts
 in detecting small shifts in the process mean; see, for example, Ng & Case
 (1989), Crowder (1989), Lucas & Saccucci (1990), Amin & Searcy (1991) and
 Wetherill & Brown (1991). In fact, the EWMA control chart has become
 popular for monitoring a process mean; see Hunter (1986) for a good
 discussion. More recently, EWMA charts have been developed for monitoring
 process variability; 
Journal: Journal of Applied Statistics 
Pages: 457-468 
Issue: 3-4 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120034171 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120034171
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:3-4:p:457-468




Template-Type: ReDIF-Article 1.0
Author-Name: Dave Stewardson 
Author-X-Name-First: Dave 
Author-X-Name-Last: Stewardson 
Author-Name: Shirley Coleman 
Author-X-Name-First: Shirley 
Author-X-Name-Last: Coleman 
Title: Using the Summed Rank Cusum for monitoring environmental data from industrial processes 
Abstract:
  Environmental issues have become a hot topic recently, especially those
 surrounding industrial outputs. Effluents, emissions, outflows,
 by-products, waste materials, product de-commissioning, land reclamation
 and energy consumption are all the subject of monitoring, either under new
 legislation or through economic necessity. Many types of environmental
 data are often difficult to understand or measure because of their unusual
 distribution of values however. Standard methods of monitoring these data
 types often fail or are unwieldy. The scarcity of events, small volume
 measurements and the unusual time scales sometimes involved add to the
 complexity of the task. One recently developed monitoring technique is the
 Summed Rank Cusum (SRC) that applies non-parametric methods to a standard
 chart. The SRC can be used diagnostically and this paper describes the
 application of this new tool to three data sets, each derived from a
 different problem area. These are measuring industrial effluent, assessing
 the levels of potentially harmful proteins produced by an industrial
 process and industrial land reclamation in the face of harmful waste
 materials. The use of the SRC to spot change points in time
 retrospectively is described. The paper also shows the use of SRC in the
 significant-difference testing mode, which is applied via the use of
 spreadsheets. Links to other similar methods described in the literature
 are given and formulae describing the statistical nature of the
 transformation are shown. These practical demonstrations illustrate that
 the graphical interpretation of the method appears to help considerably in
 practice when trying to find time-series change points. The charts are an
 effective graphical retrospective monitoring technique when dealing with
 non-normal data. The method is easy to apply and may help considerably in
 dealing with environmental data in the industrial setting when standard
 methods are not appropriate. Further work is continuing on the more
 theoretical aspects of the method. 
Journal: Journal of Applied Statistics 
Pages: 469-484 
Issue: 3-4 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120034180 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120034180
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:3-4:p:469-484




Template-Type: ReDIF-Article 1.0
Author-Name: Dave Stewardson 
Author-X-Name-First: Dave 
Author-X-Name-Last: Stewardson 
Author-Name: David Porter 
Author-X-Name-First: David 
Author-X-Name-Last: Porter 
Author-Name: Tony Kelly 
Author-X-Name-First: Tony 
Author-X-Name-Last: Kelly 
Title: The dangers posed by saddle points, and other problems, when using central composite designs 
Abstract:
  This paper discusses two problems, which can occur when using central
 composite designs (CCDs), that are not generally covered in the literature
 but can lead to wrong decisions-and therefore incorrect models-if they are
 ignored. Most industrialbased experimental designs are sequential. This
 usually involves running as few initial tests as possible, while getting
 enough information as is needed to provide a reasonable approximation to
 reality (the screening stage). The CCD design strategy generally requires
 the running of a full or fractional factorial design (the cube or
 hypercube) with one or more additional centre points. The cube is
 augmented, if deemed necessary, by additional experiments known as
 star-points. The major problems highlighted here concern the decision to
 run the star points or not. If the difference between the average response
 at the centre of the design and the average of the cube results is
 significant, there is probably a need for one or more quadratic terms in
 the predictive model. If not, then a simpler model that includes only main
 effects and interactions is usually considered sufficient. This test for
 'curvature' in a main effect will often fail if the design space contains
 or surrounds a saddle-point. Such a point may disguise the need for a
 quadratic term. This paper describes the occurrence of a real saddle-point
 from an industrial project and how this was overcome. The second problem
 occurs because the cube and star point portions of a CCD are sometimes run
 as orthogonal blocks. Indeed, theory would suggest that this is the
 correct procedure. However in the industrial context, where minimizing the
 total number of tests is at a premium, this can lead to designs with star
 points a long way from the cube. In such a situation, were the curvature
 test to be found non-significant, we could end with a model that predicted
 well within the cube portion of the design space but that would be
 unreliable in the balance of the total area of investigation. The paper
 discusses just such a design, one that disguised the real need for a
 quadratic term. 
Journal: Journal of Applied Statistics 
Pages: 485-495 
Issue: 3-4 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120034199 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120034199
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:3-4:p:485-495




Template-Type: ReDIF-Article 1.0
Author-Name: Carl Scarrott 
Author-X-Name-First: Carl 
Author-X-Name-Last: Scarrott 
Author-Name: Granville Tunnicliffe Wilson 
Author-X-Name-First: Granville Tunnicliffe 
Author-X-Name-Last: Wilson 
Title: Building a statistical model to predict reactor temperatures 
Abstract:
  This paper describes the various stages in building a statistical model
 to predict temperatures in the core of a reactor, and compares the
 benefits of this model with those of a physical model. We give a brief
 background to this study and the applications of the model to rapid online
 monitoring and safe operation of the reactor. We describe the methods, of
 correlation and two dimensional spectral analysis, which we use to
 identify the effects that are incorporated in a spatial regression model
 for the measured temperatures. These effects are related to the age of the
 reactor fuel and the spatial geometry of the reactor. A remaining
 component of the temperature variation is a slowly varying temperature
 surface modelled by smooth functions with constrained coefficients. We
 assess the accuracy of the model for interpolating temperatures throughout
 the reactor, when measurements are available only at a reduced set of
 spatial locations, as is the case in most reactors. Further possible
 improvements to the model are discussed. 
Journal: Journal of Applied Statistics 
Pages: 497-511 
Issue: 3-4 
Volume: 28 
Year: 2001 
Keywords: Spatial Prediction Two-DIMENSIONAL Spectra Linear Mixed Model, 
X-DOI: 10.1080/02664760120034207 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120034207
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:3-4:p:497-511




Template-Type: ReDIF-Article 1.0
Author-Name: Seung-Ho Kang 
Author-X-Name-First: Seung-Ho 
Author-X-Name-Last: Kang 
Author-Name: Chul Ahn 
Author-X-Name-First: Chul 
Author-X-Name-Last: Ahn 
Title: Regression coefficient analysis for correlated binomial outcomes 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 513-514 
Issue: 3-4 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120034216 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120034216
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:3-4:p:513-514




Template-Type: ReDIF-Article 1.0
Author-Name: Leann Myers 
Author-X-Name-First: Leann 
Author-X-Name-Last: Myers 
Author-Name: Stephanie Broyles 
Author-X-Name-First: Stephanie 
Author-X-Name-Last: Broyles 
Title: Response 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 515-515 
Issue: 3-4 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/026647601300073221 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/026647601300073221
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:3-4:p:515-515




Template-Type: ReDIF-Article 1.0
Author-Name: I. Bairamov 
Author-X-Name-First: I. 
Author-X-Name-Last: Bairamov 
Author-Name: S. Kotz 
Author-X-Name-First: S. 
Author-X-Name-Last: Kotz 
Author-Name: M. Bekci 
Author-X-Name-First: M. 
Author-X-Name-Last: Bekci 
Title: New generalized Farlie-Gumbel-Morgenstern distributions and concomitants of order statistics 
Abstract:
  We consider a generalization of the bivariate Farlie-Gumbel-Morgenstern
 (FGM) distribution by introducing additional parameters. For the
 generalized FGM distribution, the admissible range of the association
 parameter allowing positive quadrant dependence property is shown.
 Distributional properties of concomitants for this generalized FGM
 distribution are studied. Recurrence relations between moments of
 concomitants are presented. 
Journal: Journal of Applied Statistics 
Pages: 521-536 
Issue: 5 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120047861 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120047861
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:5:p:521-536




Template-Type: ReDIF-Article 1.0
Author-Name: Ling-Yau Chan 
Author-X-Name-First: Ling-Yau 
Author-X-Name-Last: Chan 
Author-Name: Ying-Nan Guan 
Author-X-Name-First: Ying-Nan 
Author-X-Name-Last: Guan 
Title: A- and D-optimal designs for a log contrast model for experiments with mixtures 
Abstract:
  A- and D-optimal designs are investigated for a log contrast model
 suggested by Aitchison & Bacon-Shone for experiments with mixtures. It is
 proved that when the number of mixture components q is an even integer, A-
 and D-optimal designs are identical; and when q is an odd integer, A- and
 D-optimal designs are different, but they share some common support points
 and are very close to each other in efficiency. Optimal designs with a
 minimum number of support points are also constructed for 3, 4, 5 and 6
 mixture components. 
Journal: Journal of Applied Statistics 
Pages: 537-546 
Issue: 5 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120047870 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120047870
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:5:p:537-546




Template-Type: ReDIF-Article 1.0
Author-Name: Francesco Pauli 
Author-X-Name-First: Francesco 
Author-X-Name-Last: Pauli 
Author-Name: Stuart Coles 
Author-X-Name-First: Stuart 
Author-X-Name-Last: Coles 
Title: Penalized likelihood inference in extreme value analyses 
Abstract:
  Models for extreme values are usually based on detailed asymptotic
 argument, for which strong ergodic assumptions such as stationarity, or
 prescribed perturbations from stationarity, are required. In most
 applications of extreme value modelling such assumptions are not
 satisfied, but the type of departure from stationarity is either unknown
 or complex, making asymptotic calculations unfeasible. This has led to
 various approaches in which standard extreme value models are used as
 building blocks for conditional or local behaviour of processes, with more
 general statistical techniques being used at the modelling stage to handle
 the non-stationarity. This paper presents another approach in this
 direction based on penalized likelihood. There are some advantages to this
 particular approach: the method has a simple interpretation; computations
 for estimation are relatively straightforward using standard algorithms;
 and a simple reinterpretation of the model enables broader inferences,
 such as confidence intervals, to be obtained using MCMC methodology.
 Methodological details together with applications to both athletics and
 environmental data are given. 
Journal: Journal of Applied Statistics 
Pages: 547-560 
Issue: 5 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120047889 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120047889
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:5:p:547-560




Template-Type: ReDIF-Article 1.0
Author-Name: Steven Garren 
Author-X-Name-First: Steven 
Author-X-Name-Last: Garren 
Author-Name: Richard Smith 
Author-X-Name-First: Richard 
Author-X-Name-Last: Smith 
Author-Name: Walter Piegorsch 
Author-X-Name-First: Walter 
Author-X-Name-Last: Piegorsch 
Title: Bootstrap goodness-of-fit test for the beta-binomial model 
Abstract:
  A common question in the analysis of binary data is how to deal with
 overdispersion. One widely advocated sampling distribution for
 overdispersed binary data is the beta-binomial model. For example, this
 distribution is often used to model litter effects in toxicological
 experiments. Testing the null hypothesis of a beta-binomial distribution
 against all other distributions is difficult, however, when the litter
 sizes vary greatly. Herein, we propose a test statistic based on combining
 Pearson statistics from individual litter sizes, and estimate the p-value
 using bootstrap techniques. A Monte Carlo study confirms the accuracy and
 power of the test against a beta-binomial distribution contaminated with a
 few outliers. The method is applied to data from environmental toxicity
 studies. 
Journal: Journal of Applied Statistics 
Pages: 561-571 
Issue: 5 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120047898 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120047898
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:5:p:561-571




Template-Type: ReDIF-Article 1.0
Author-Name: Subrata Ghatak 
Author-X-Name-First: Subrata 
Author-X-Name-Last: Ghatak 
Author-Name: Jalal Siddiki 
Author-X-Name-First: Jalal 
Author-X-Name-Last: Siddiki 
Title: The use of the ARDL approach in estimating virtual exchange rates in India 
Abstract:
  This paper applies the autoregressive distributed lag approach to
 cointegration analysis in estimating the 'virtual exchange rate' (VER) in
 India. The VER would have prevailed if the unconstrained import demand
 were equal to the constraint imposed due to foreign exchange rationing and
 the VER is used to approximate the 'price' of rationed foreign exchange
 reserves. We highlight the shortcomings of the existing literature in
 approximating equilibrium exchange rates in a less developed country such
 as India and propose the VER approach for equilibrium rates, which uses
 information from an estimated structural model. In this relationship,
 black market real exchange rate (E U ) is a dependent variable and real
 official exchange rates (E O ), the ratio of the foreign (r*) to the
 domestic (r) interest rate (I), and official forex reserves (Q) are
 explanatory variables. In our estimation, the VERs are higher than E O by
 about 10% in the short-run and 16% in the long-run. 
Journal: Journal of Applied Statistics 
Pages: 573-583 
Issue: 5 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120047906 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120047906
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:5:p:573-583




Template-Type: ReDIF-Article 1.0
Author-Name: W. J. Krzanowski 
Author-X-Name-First: W. J. 
Author-X-Name-Last: Krzanowski 
Title: Data-based interval estimation of classification error rates 
Abstract:
  Leave-one-out and 632 bootstrap are popular data-based methods of
 estimating the true error rate of a classification rule, but practical
 applications almost exclusively quote only point estimates. Interval
 estimation would provide better assessment of the future performance of
 the rule, but little has been published on this topic. We first review
 general-purpose jackknife and bootstrap methodology that can be used in
 conjunction with leave-one-out estimates to provide prediction intervals
 for true error rates of classification rules. Monte Carlo simulation is
 then used to investigate coverage rates of the resulting intervals for
 normal data, but the results are disappointing; standard intervals show
 considerable overinclusion, intervals based on Edgeworth approximations or
 random weighting do not perform well, and while a bootstrap approach
 provides intervals with coverage rates closer to the nominal ones there is
 still marked underinclusion. We then turn to intervals constructed from
 632 bootstrap estimates, and show that much better results are obtained.
 Although there is now some overinclusion, particularly for large training
 samples, the actual coverage rates are sufficiently close to the nominal
 rates for the method to be recommended. An application to real data
 illustrates the considerable variability that can arise in practical
 estimation of error rates. 
Journal: Journal of Applied Statistics 
Pages: 585-595 
Issue: 5 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120047915 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120047915
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:5:p:585-595




Template-Type: ReDIF-Article 1.0
Author-Name: Yong Lim 
Author-X-Name-First: Yong 
Author-X-Name-Last: Lim 
Author-Name: B. S. So 
Author-X-Name-First: B. S. 
Author-X-Name-Last: So 
Title: A note on the optimal number of centre runs in a second phase design of response surface methods 
Abstract:
  In searching for optimum conditions, the response surface methods
 comprise two phases. In the first phase, the method of the steepest ascent
 with a 2 k-p design is used in searching for a region of improved
 response. The curvature of the response surface is checked in the second
 phase. For testing the evidence of curvature, a reasonable design is a 2
 k-p fractional factorial design augmented by centre runs. Using
 c-optimality criterion, the optimal number of centre runs is investigated.
 Incorporating c-efficiencies for the curvature test with D-efficiencies
 and G-efficiencies of CCDs for the quadratic response surfaces and then,
 adopting the Mini-Max principle, i.e. maximizing the worst efficiency, we
 propose robust centre runs with respect to the three optimality criteria
 to be chosen. 
Journal: Journal of Applied Statistics 
Pages: 597-602 
Issue: 5 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120047924 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120047924
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:5:p:597-602




Template-Type: ReDIF-Article 1.0
Author-Name: Alvaro Montenegro 
Author-X-Name-First: Alvaro 
Author-X-Name-Last: Montenegro 
Title: On sample size and precision in ordinary least squares 
Abstract:
  An expression relating estimation precision in the classical linear model
 to the number of parameters k and the sample size n is illustrated. A rule
 of thumb for the sample size is suggested. 
Journal: Journal of Applied Statistics 
Pages: 603-605 
Issue: 5 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120047933 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120047933
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:5:p:603-605




Template-Type: ReDIF-Article 1.0
Author-Name: G. Nuel 
Author-X-Name-First: G. 
Author-X-Name-Last: Nuel 
Author-Name: S. Robin 
Author-X-Name-First: S. 
Author-X-Name-Last: Robin 
Author-Name: C. P. Baril 
Author-X-Name-First: C. P. 
Author-X-Name-Last: Baril 
Title: Predicting distances using a linear model: The case of varietal distinctness 
Abstract:
  Differences between plant varieties are based on phenotypic observations,
 which are both space and time consuming. Moreover, the phenotypic data
 result from the combined effects of genotype and environment. On the
 contrary, molecular data are easier to obtain and give a direct access to
 the genotype. In order to save experimental trials and to concentrate
 efforts on the relevant comparisons between varieties, the relationship
 between phenotypic and genetic distances is studied. It appears that the
 classical genetic distances based on molecular data are not appropriate
 for predicting phenotypic distances. In the linear model framework, we
 define a new pseudo genetic distance, which is a prediction of the
 phenotypic one. The distribution of this distance given the pseudo genetic
 distance is established. Statistical properties of the predicted distance
 are derived when the parameters of the model are either given or
 estimated. We finally apply these results to distinguishing between 144
 maize lines. This case study is very satisfactory because the use of
 anonymous molecular markers (RFLP) leads to saving 29% of the trials with
 an acceptable error risk. These results need to be confirmed on other
 varieties and species and would certainly be improved by using genes
 coding for phenotypic traits. 
Journal: Journal of Applied Statistics 
Pages: 607-621 
Issue: 5 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120047942 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120047942
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:5:p:607-621




Template-Type: ReDIF-Article 1.0
Author-Name: Peiming Wang 
Author-X-Name-First: Peiming 
Author-X-Name-Last: Wang 
Title: Markov zero-inflated Poisson regression models for a time series of counts with excess zeros 
Abstract:
  This paper discusses a class of Markov zero-inflated Poisson regression
 models for a time series of counts with the presence of excess zero
 relative to a Poisson distribution, in which the frequency distribution
 changes according to an underlying two-state Markov chain. Features of the
 proposed model, estimation method based on the EM and quasi-Newton
 algorithms, and other implementation issues are discussed. A Monte Carlo
 study shows that the estimation method is accurate and reliable as long as
 the sample size is reasonably large, and the choice of starting
 probabilities for the Markov process has little impact on the parameter
 estimates. The methodology is illustrated using daily numbers of phone
 calls reporting faults for a mainframe computer system. 
Journal: Journal of Applied Statistics 
Pages: 623-632 
Issue: 5 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120047951 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120047951
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:5:p:623-632




Template-Type: ReDIF-Article 1.0
Author-Name: Seung-Ho Kang 
Author-X-Name-First: Seung-Ho 
Author-X-Name-Last: Kang 
Author-Name: Chul Ahn 
Author-X-Name-First: Chul 
Author-X-Name-Last: Ahn 
Title: Regression coefficient analysis for correlated binomial outcomes 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 633-634 
Issue: 5 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120047960 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120047960
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:5:p:633-634




Template-Type: ReDIF-Article 1.0
Author-Name: Leann Myers 
Author-X-Name-First: Leann 
Author-X-Name-Last: Myers 
Author-Name: Stephanie Broyles 
Author-X-Name-First: Stephanie 
Author-X-Name-Last: Broyles 
Title: Authors' reply 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 635-635 
Issue: 5 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/026647601750235952 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/026647601750235952
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:5:p:635-635




Template-Type: ReDIF-Article 1.0
Author-Name: Rainer Winkelmann 
Author-X-Name-First: Rainer 
Author-X-Name-Last: Winkelmann 
Title: 'Under-reporting of purchases of port wine': A correction 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 637-637 
Issue: 5 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/026647601750235961 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/026647601750235961
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:5:p:637-637




Template-Type: ReDIF-Article 1.0
Author-Name: S. C. Bagui 
Author-X-Name-First: S. C. 
Author-X-Name-Last: Bagui 
Author-Name: D. K. Ghosh 
Author-X-Name-First: D. K. 
Author-X-Name-Last: Ghosh 
Title: Efficiency balanced designs through reinforcement 
Abstract:
  In this investigation, general efficiency balanced (GEB) and efficiency
 balanced (EB) designs with (v + t) treatments, using (i) balanced
 incomplete block (BIB), (ii) symmetrical BIB, (iii) f -resolvable BIB,
 (iv) group divisible (GD) and (v) resolvable GD designs have been
 constructed with smaller number of replications and block sizes. 
Journal: Journal of Applied Statistics 
Pages: 649-658 
Issue: 6 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120059192 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120059192
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:6:p:649-658




Template-Type: ReDIF-Article 1.0
Author-Name: Vicente Cancho 
Author-X-Name-First: Vicente 
Author-X-Name-Last: Cancho 
Author-Name: Heleno Bolfarine 
Author-X-Name-First: Heleno 
Author-X-Name-Last: Bolfarine 
Title: Modeling the presence of immunes by using the exponentiated-Weibull model 
Abstract:
  In this paper the exponentiated-Weibull model is modified to model the
 possibility that long-term survivors are present in the data. The
 modification leads to an exponentiated-Weibull mixture model which
 encompasses as special cases the exponential and Weibull mixture models
 typically used to model such data. Inference for the model parameters is
 considered via maximum likelihood and also via Bayesian inference by using
 Markov chain Monte Carlo simulation. Model comparison is considered by
 using likelihood ratio statistics and also the pseudo Bayes factor, which
 can be computed by using the generated samples. An example of a data set
 is considered for which the exponentiated-Weibull mixture model presents a
 better fit than the Weibull mixture model. Results of simulation studies
 are also reported, which show that the likelihood ratio statistics seems
 to be somewhat deficient for small and moderate sample sizes. 
Journal: Journal of Applied Statistics 
Pages: 659-671 
Issue: 6 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120059200 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120059200
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:6:p:659-671




Template-Type: ReDIF-Article 1.0
Author-Name: Mark Glickman 
Author-X-Name-First: Mark 
Author-X-Name-Last: Glickman 
Title: Dynamic paired comparison models with stochastic variances 
Abstract:
  In paired comparison experiments, the worth or merit of a unit is
 measured through comparisons against other units. When paired comparison
 outcomes are collected over time and the merits of the units may be
 changing, it is often convenient to assume the data follow a non-linear
 state-space model. Typical paired comparison state-space models that
 assume a fixed (unknown) autoregressive variance do not account for the
 possibility of sudden changes in the merits. This is a particular concern,
 for example, in modeling cognitive ability in human development; cognitive
 ability not only changes over time, but also can change abruptly. We
 explore a particular extension of conventional state-space models for
 paired comparison data that allows the state variance to vary
 stochastically. Models of this type have recently been developed and
 applied to modeling financial data, but can be seen to have applicability
 in modeling paired comparison data. A filtering algorithm is also derived
 that can be used in place of likelihood-based computations when the number
 of objects being compared is large. Applications to National Football
 League game outcomes and chess game outcomes are presented. 
Journal: Journal of Applied Statistics 
Pages: 673-689 
Issue: 6 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120059219 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120059219
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:6:p:673-689




Template-Type: ReDIF-Article 1.0
Author-Name: Andy Lee 
Author-X-Name-First: Andy 
Author-X-Name-Last: Lee 
Author-Name: John Yick 
Author-X-Name-First: John 
Author-X-Name-Last: Yick 
Author-Name: Yer Van Hui 
Author-X-Name-First: Yer 
Author-X-Name-Last: Van Hui 
Title: Sensitivity of the portmanteau statistic in time series modeling 
Abstract:
  The portmanteau statistic is commonly used for testing goodness-of-fit of
 time series models. However, this lack of fit test may depend on one or
 several atypical observations in the series. We investigate the
 sensitivity of the portmanteau statistic in the presence of additive
 outliers. Diagnostics are developed to assess both local and global
 influence. Three practical examples demonstrate the usefulness of the
 proposed diagnostics. 
Journal: Journal of Applied Statistics 
Pages: 691-702 
Issue: 6 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120059228 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120059228
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:6:p:691-702




Template-Type: ReDIF-Article 1.0
Author-Name: Francisco Louzada-Neto 
Author-X-Name-First: Francisco 
Author-X-Name-Last: Louzada-Neto 
Author-Name: Juan Carlos Pardo-Fernandez 
Author-X-Name-First: Juan Carlos 
Author-X-Name-Last: Pardo-Fernandez 
Title: The effect of reparametrization on accelerated lifetime tests 
Abstract:
  Efficient reliability industrial experiments consist of submitting items
 to accelerated life tests. It is of interest to obtain measures of the
 realiability of the devices under the usual working conditions,
 represented here by the mean lifetime. A practical problem refers to the
 accuracy of interval estimation of the parameter of interest when the
 sample size is small or moderate. In this paper, we describe the effect of
 several reparametrizations on the accuracy of the interval estimation. We
 propose a reparametrization that leads to accuracy while allowing
 orthogonality between the parameters. The idea is to consider a
 logarithmic reparametrization on orthogonal parameters in order to have
 independent maximum likelihood estimates with good asymptotic normal
 approximation. The study is illustrated by a data set on an accelerated
 life test at pressurized containers of Kevlan/Epoxy 49. 
Journal: Journal of Applied Statistics 
Pages: 703-711 
Issue: 6 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120059237 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120059237
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:6:p:703-711




Template-Type: ReDIF-Article 1.0
Author-Name: Terence Mills 
Author-X-Name-First: Terence 
Author-X-Name-Last: Mills 
Title: Business cycle asymmetry and duration dependence: An international perspective 
Abstract:
  The business cycle behaviour of macroeconomic variables has long been of
 interest to economists, and attention has recently focused on two aspects
 of this behaviour - the 'stylized facts' of cyclical asymmetry and
 duration dependence. Cyclical asymmetry is where the economy behaves
 differently over the expansion and recession phases of the business cycle.
 Duration dependence, on the other hand, concerns the question of whether,
 for example, the probability of a cyclical expansion is dependent on how
 long the expansion has been running, or whether business cycle lengths
 tend to cluster around a particular duration. Using an international data
 set containing annual output per capita for 22 countries, we focus
 attention on non-parametric techniques for extracting cyclical components
 and for modelling and testing asymmetry and duration dependence. Once
 outliers, primarily associated with wars, are omitted, there is little
 international evidence of asymmetry. There is considerably more evidence
 of duration dependence, which is detected in the majority of countries
 using a variety of non-parametric tests. There is thus widespread evidence
 against the constant hazard hypothesis that cyclical patterns occur simply
 by chance. Business cycle durations do appear to cluster around certain
 values, with the average duration being about 3.6 years. 
Journal: Journal of Applied Statistics 
Pages: 713-724 
Issue: 6 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120059246 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120059246
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:6:p:713-724




Template-Type: ReDIF-Article 1.0
Author-Name: Reik Oberrath 
Author-X-Name-First: Reik 
Author-X-Name-Last: Oberrath 
Author-Name: Katrin Bohning-Gaese 
Author-X-Name-First: Katrin 
Author-X-Name-Last: Bohning-Gaese 
Title: The Signed Mantel test to cope with autocorrelation in comparative analyses 
Abstract:
  In biology, medicine and anthropology, scientists try to reveal general
 patterns when comparing different sampling units such as biological taxa,
 diseases or cultures. A problem of such comparative data is that standard
 statistical procedures are often inappropriate due to possible
 autocorrelation within the data. Widespread causes of autocorrelation are
 a shared geography or phylogeny of the sampling units. To cope with
 possible autocorrelations within comparative data, we suggest a new kind
 of the Mantel test. The Signed Mantel test evaluates the relationship
 between two or more distance matrices and allows trait variables
 facultatively to be represented as signed distances (calculated as signed
 differences or quotients). Considering the sign of distances takes into
 account the direction of an effect found in the data. Since different
 metrics exist to calculate the distance between two sampling units from
 the raw data and because the test results often depend on the kind of
 metric used, we suggest validating analysis by comparing the structures of
 the raw and the distance data. We offer a computer program that is able to
 construct both signed and absolute distance matrices, to perform both
 customary and Signed Mantel tests, and to explore raw and distance data
 visually. 
Journal: Journal of Applied Statistics 
Pages: 725-736 
Issue: 6 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120059255 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120059255
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:6:p:725-736




Template-Type: ReDIF-Article 1.0
Author-Name: Wai-Yin Poon 
Author-X-Name-First: Wai-Yin 
Author-X-Name-Last: Poon 
Author-Name: Man-Lai Tang 
Author-X-Name-First: Man-Lai 
Author-X-Name-Last: Tang 
Title: Influence measure in maximum likelihood estimate for models of lifetime data 
Abstract:
  We use the local influence approach to develop influence measures for
 identifying observations that strike a disproportionate effect on the
 maximum likelihood estimate of parameters in models for lifetime data. The
 proposed method for developing influence measures can be applied to a wide
 variety of models and we use the exponential model to illustrate the
 details. In particular, we show that the proposed measure is equivalent to
 the martingale residual under the exponential model. 
Journal: Journal of Applied Statistics 
Pages: 737-742 
Issue: 6 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120059264 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120059264
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:6:p:737-742




Template-Type: ReDIF-Article 1.0
Author-Name: Ralph Mansson 
Author-X-Name-First: Ralph 
Author-X-Name-Last: Mansson 
Author-Name: Philip Prescott 
Author-X-Name-First: Philip 
Author-X-Name-Last: Prescott 
Title: Missing values in replicated Latin squares 
Abstract:
  Designs based on any number of replicated Latin squares are examined for
 their robustness against the loss of up to three observations randomly
 scattered throughout the design. The information matrix for the treatment
 effects is used to evaluate the average variances of the treatment
 differences for each design in terms of the number of missing values and
 the size of the design. The resulting average variances are used to assess
 the overall robustness of the designs. In general, there are 16 different
 situations for the case of three missing values when there are at least
 three Latin square replicates in the design. Algebraic expressions may be
 determined for all possible configurations, but here the best and worst
 cases are given in detail. Numerical illustrations are provided for the
 average variances, relative efficiencies, minimum and maximum variances
 and the frequency counts, showing the effects of the missing values for a
 range of design sizes and levels of replication. 
Journal: Journal of Applied Statistics 
Pages: 743-757 
Issue: 6 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120059273 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120059273
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:6:p:743-757




Template-Type: ReDIF-Article 1.0
Author-Name: Thomas Wenzel 
Author-X-Name-First: Thomas 
Author-X-Name-Last: Wenzel 
Title: Hits-and-misses for the evaluation and combination of forecasts 
Abstract:
  Error measures for the evaluation of forecasts are usually based on the
 size of the forecast errors. Common measures are, e.g. the mean squared
 error (MSE), the mean absolute deviation (MAD) or the mean absolute
 percentage error (MAPE). Alternative measures for the comparison of
 forecasts are turning points or hits-and-misses, where an indicator loss
 function is used to decide if a forecast is of high quality or not. Here,
 we discuss the latter to obtain reliable combined forecasts. We apply
 several combination techniques to a set of German macroeconomic data.
 Furthermore, we perform a small simulation study for the combination of
 two biased forecasts. 
Journal: Journal of Applied Statistics 
Pages: 759-773 
Issue: 6 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120059282 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120059282
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:6:p:759-773




Template-Type: ReDIF-Article 1.0
Author-Name: Arnold Zellner 
Author-X-Name-First: Arnold 
Author-X-Name-Last: Zellner 
Title: Remarks on a 'critique' of the Bayesian Method of Moments 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 775-778 
Issue: 6 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120059291 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120059291
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:6:p:775-778




Template-Type: ReDIF-Article 1.0
Author-Name: Chung-Ho Chen 
Author-X-Name-First: Chung-Ho 
Author-X-Name-Last: Chen 
Author-Name: Te-Shiang Cheng 
Author-X-Name-First: Te-Shiang 
Author-X-Name-Last: Cheng 
Author-Name: Chao-Yu Chou 
Author-X-Name-First: Chao-Yu 
Author-X-Name-Last: Chou 
Title: Minimum average fraction inspected for TCSP-1 plan 
Abstract:
  This paper presents the calculation of the average outgoing quality limit
 (AOQL) for the tightened single-level continuous sampling plan (TCSP-1
 plan) based on a numerical method. A solution procedure is developed to
 find the parameters (i, f, k) that will meet the AOQL requirement, while
 also minimizing the average fraction inspected (AFI) for the TCSP-1 plan
 when the process average p-super-&#xaf; (&#x3e; AOQL) is known. 
Journal: Journal of Applied Statistics 
Pages: 793-799 
Issue: 7 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120074906 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120074906
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:7:p:793-799




Template-Type: ReDIF-Article 1.0
Author-Name: Jeongwen Chiang 
Author-X-Name-First: Jeongwen 
Author-X-Name-Last: Chiang 
Author-Name: Ching-Fan Chung 
Author-X-Name-First: Ching-Fan 
Author-X-Name-Last: Chung 
Author-Name: Emily Cremers 
Author-X-Name-First: Emily 
Author-X-Name-Last: Cremers 
Title: Promotions and the pattern of grocery shopping time 
Abstract:
  The histograms of interpurchase times for frequently purchased packaged
 goods have consistently shown pronounced seven-day cycles. Evidence
 supports that the weekly spike phenomenon is the result of consumers'
 regular shopping trip schedules. We explore the implications of this
 peculiar regularity on the issue of consumer purchase timing acceleration.
 Data for five product categories are examined. Promotions are found to
 have little effect in accelerating purchase timing. In contrast,
 conventional interpurchase time models are shown to overstate the effect
 of promotions. 
Journal: Journal of Applied Statistics 
Pages: 801-819 
Issue: 7 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120074997 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120074997
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:7:p:801-819




Template-Type: ReDIF-Article 1.0
Author-Name: D. K. Ghosh 
Author-X-Name-First: D. K. 
Author-X-Name-Last: Ghosh 
Author-Name: S. B. Shrivastava 
Author-X-Name-First: S. B. 
Author-X-Name-Last: Shrivastava 
Title: A class of BIB designs with repeated blocks 
Abstract:
  Balanced incomplete block design (BIBD) with repeated blocks is studied
 in detail. Methods of construction of BIB designs with repeated blocks are
 developed so as to distinguish the usual BIBD and BIBD with repeated
 blocks. One additional parameter, say d, is considered here, where d
 denotes the number of distinct blocks present in the BIB design with
 repeated blocks. Further, a class of BIB design with parameters: v = 7, b
 = 28, r = 12, k = 3, u = 4, has been constructed where, out of 15, 14 BIB
 designs have repeated blocks. These 15 BIB designs, which have the same
 parameters, are compared on the basis of number of distinct blocks (d) and
 the multiplicities of variance of elementary contrasts of the block
 effect. 
Journal: Journal of Applied Statistics 
Pages: 821-833 
Issue: 7 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120074915 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120074915
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:7:p:821-833




Template-Type: ReDIF-Article 1.0
Author-Name: Tsung-Wu Ho 
Author-X-Name-First: Tsung-Wu 
Author-X-Name-Last: Ho 
Title: Finite-sample properties of the bootstrap estimator in a Markov-switching model 
Abstract:
  The size distortion problem is clearly indicative of the small-sample
 approximation in the Markov-switching regression model. This paper shows
 that the bootstrap procedure can relieve the effects that this problem
 has. Our Monte Carlo simulation results reveal that the bootstrap maximum
 likelihood asymptotic approximations to the distribution can often be good
 when the sample size is small. 
Journal: Journal of Applied Statistics 
Pages: 835-842 
Issue: 7 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120074924 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120074924
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:7:p:835-842




Template-Type: ReDIF-Article 1.0
Author-Name: F. Huettmann 
Author-X-Name-First: F. 
Author-X-Name-Last: Huettmann 
Author-Name: A. W. Diamond 
Author-X-Name-First: A. W. 
Author-X-Name-Last: Diamond 
Title: Using PCA scores to classify species communities: An example for pelagic seabird distribution 
Abstract:
  Using Principal Component Analysis (PCA) in order to classify animal
 communities from transect counts is a widely used method. One problem with
 this approach is determining an appropriate cut-off point on the Principal
 Component (PC) axis to separate communities. We have developed a method
 using the distribution of PC scores of individual species along transects
 from the PIROP (Programme Integrede Recherches sur les Oiseaux Pelagiques)
 database for seabirds at sea in the Northwest Atlantic in winter 1965-
 1992. This method can be applied generally to wildlife species, and also
 facilitates the evaluation, justification and stratification of PCs and
 community classifications in a transparent way. A typical application of
 this method is shown for three Principal Components; spatial implications
 of the cut-off decision for PCs are also discussed, e.g. for habitat
 studies. 
Journal: Journal of Applied Statistics 
Pages: 843-853 
Issue: 7 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120074933 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120074933
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:7:p:843-853




Template-Type: ReDIF-Article 1.0
Author-Name: Miguel Garcia-Perez 
Author-X-Name-First: Miguel 
Author-X-Name-Last: Garcia-Perez 
Author-Name: Vicente Nunez-Anton 
Author-X-Name-First: Vicente 
Author-X-Name-Last: Nunez-Anton 
Title: Small-sample comparisons for powerdivergence goodness-of-fit statistics for symmetric and skewed simple null hypotheses 
Abstract:
  Power-divergence goodness-of-fit statistics have asymptotically a
 chi-squared distribution. Asymptotic results may not apply in small-sample
 situations, and the exact significance of a goodness-of-fit statistic may
 potentially be over- or under-stated by the asymptotic distribution.
 Several correction terms have been proposed to improve the accuracy of the
 asymptotic distribution, but their performance has only been studied for
 the equiprobable case. We extend that research to skewed hypotheses.
 Results are presented for one-way multinomials involving k = 2 to 6 cells
 with sample sizes N = 20, 40, 60, 80 and 100 and nominal test sizes f =
 0.1, 0.05, 0.01 and 0.001. Six power-divergence goodness-of-fit statistics
 were investigated, and five correction terms were included in the study.
 Our results show that skewness itself does not affect the accuracy of the
 asymptotic approximation, which depends only on the magnitude of the
 smallest expected frequency (whether this comes from a small sample with
 the equiprobable hypothesis or a large sample with a skewed hypothesis).
 Throughout the conditions of the study, the accuracy of the asymptotic
 distribution seems to be optimal for Pearson's X2 statistic (the
 power-divergence statistic of index u = 1) when k &#x3e; 3 and the
 smallest expected frequency is as low as between 0.1 and 1.5 (depending on
 the particular k, N and nominal test size), but a computationally
 inexpensive improvement can be obtained in these cases by using a
 moment-corrected h2 distribution. If the smallest expected frequency is
 even smaller, a normal correction yields accurate tests through the
 log-likelihood-ratio statistic G2 (the power-divergence statistic of index
 u = 0). 
Journal: Journal of Applied Statistics 
Pages: 855-874 
Issue: 7 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120074942 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120074942
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:7:p:855-874




Template-Type: ReDIF-Article 1.0
Author-Name: Antonio Costa 
Author-X-Name-First: Antonio 
Author-X-Name-Last: Costa 
Author-Name: M. A. Rahim 
Author-X-Name-First: M. A. 
Author-X-Name-Last: Rahim 
Title: Economic design of X charts with variable parameters: The Markov chain approach 
Abstract:
  This paper presents an economic design of X control charts with variable
 sample sizes, variable sampling intervals, and variable control limits.
 The sample size n, the sampling interval h, and the control limit
 coefficient k vary between minimum and maximum values, tightening or
 relaxing the control. The control is relaxed when an X value falls close
 to the target and is tightened when an X value falls far from the target.
 A cost model is constructed that involves the cost of false alarms, the
 cost of finding and eliminating the assignable cause, the cost associated
 with production in an out-of-control state, and the cost of sampling and
 testing. The assumption of an exponential distribution to describe the
 length of time the process remains in control allows the application of
 the Markov chain approach for developing the cost function. A
 comprehensive study is performed to examine the economic advantages of
 varying the X chart parameters. 
Journal: Journal of Applied Statistics 
Pages: 875-885 
Issue: 7 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120074951 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120074951
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:7:p:875-885




Template-Type: ReDIF-Article 1.0
Author-Name: John Roberts 
Author-X-Name-First: John 
Author-X-Name-Last: Roberts 
Author-Name: Devon Brewer 
Author-X-Name-First: Devon 
Author-X-Name-Last: Brewer 
Title: Measures and tests of heaping in discrete quantitative distributions 
Abstract:
  Heaping is often found in discrete quantitative data based on subject
 responses to open-ended interview questions or observer assessments.
 Heaping occurs when subjects or observers prefer some set of numbers as
 responses (e.g. multiples of 5) simply because of the features of this
 set. Although heaping represents a common type of measurement error,
 apparently no prior general measure of heaping exists. We present simple
 measures and tests of heaping in discrete quantitative data, illustrate
 them with data from an epidemiologic study, and evaluate the bias of these
 statistics. These techniques permit formal measurement of heaping and
 facilitate comparisons of the degree of heaping in data from different
 samples, substantive domains, and data collection methods. 
Journal: Journal of Applied Statistics 
Pages: 887-896 
Issue: 7 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120074960 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120074960
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:7:p:887-896




Template-Type: ReDIF-Article 1.0
Author-Name: Peter Rothery 
Author-X-Name-First: Peter 
Author-X-Name-Last: Rothery 
Author-Name: David Roy 
Author-X-Name-First: David 
Author-X-Name-Last: Roy 
Title: Application of generalized additive models to butterfly transect count data 
Abstract:
  We investigate the use of generalized additive models for describing
 patterns in butterfly transect counts during the flight period. Models
 were applied to sets of simulated data and to transect counts from the
 British Butterfly Monitoring Scheme (BMS) recorded at a large number of
 sites in the UK. The models successfully described patterns in counts in a
 range of species with different life cycles and the approach can be used
 to estimate an index of butterfly abundance allowing for missing counts.
 The method could be extended to include other factors such as temperature,
 sunshine, windspeed and time of day, and to examine potential biases
 arising from variation in these factors. 
Journal: Journal of Applied Statistics 
Pages: 897-909 
Issue: 7 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120074979 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120074979
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:7:p:897-909




Template-Type: ReDIF-Article 1.0
Author-Name: Stephen Walker 
Author-X-Name-First: Stephen 
Author-X-Name-Last: Walker 
Author-Name: Christopher Page 
Author-X-Name-First: Christopher 
Author-X-Name-Last: Page 
Title: Generalized ridge regression and a generalization of the CP statistic 
Abstract:
  We consider a generalization of ridge regression and demonstrate
 advantages over ridge regression. We provide an empirical Bayes method for
 determining the ridge constants, using the Bayesian interpretation of
 ridge estimators, and show that this coincides with a method based on a
 generalization of the CP statistic and the non-negative garrote. These
 provide an automatic variable selection procedure for the canonical
 variables. 
Journal: Journal of Applied Statistics 
Pages: 911-922 
Issue: 7 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120074988 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120074988
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:7:p:911-922




Template-Type: ReDIF-Article 1.0
Author-Name: Vic Barnett 
Author-X-Name-First: Vic 
Author-X-Name-Last: Barnett 
Author-Name: Maria Cecilia Mendes Barreto 
Author-X-Name-First: Maria Cecilia Mendes 
Author-X-Name-Last: Barreto 
Title: Estimators for a Poisson parameter using ranked set sampling 
Abstract:
  Using ranked set sampling, a viable BLUE estimator is obtained for
 estimating the mean of a Poisson distribution. Its properties, such as
 efficiency relative to the ranked set sample mean and to the maximum
 likelihood estimator, have been calculated for different sample sizes and
 values of the Poisson parameter. The estimator (termed the normal modified
 r.s.s. estimator is more efficient than both the ranked set sample mean
 and the MLE. It is recommended as a reasonable estimator of the Poisson
 mean ( u ) to be used in a ranked set sampling environment. 
Journal: Journal of Applied Statistics 
Pages: 929-941 
Issue: 8 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120076616 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120076616
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:8:p:929-941




Template-Type: ReDIF-Article 1.0
Author-Name: Soren Bisgaard 
Author-X-Name-First: Soren 
Author-X-Name-Last: Bisgaard 
Author-Name: Murat Kulahci 
Author-X-Name-First: Murat 
Author-X-Name-Last: Kulahci 
Title: Switching-one-column follow-up experiments for Plackett-Burman designs 
Abstract:
  Industrial experiments are frequently performed sequentially using
 two-level fractional factorial designs. In this context, a common strategy
 for the design of follow-up experiments is to switch the signs in one
 column. It is well known that this strategy, when applied to two-level
 fractional factorial resolution III designs, will clear the main effect,
 for which the switch was performed, from any confounding with any other
 two-factor interactions and will also clear all the two-factor
 interactions between that factor and the other main effects from any
 confounding with other two-factor interactions. In this article, we extend
 this result and show that this strategy applies to any orthogonal
 two-level resolution III design and therefore specifically to any
 two-level Plackett- Burman design . 
Journal: Journal of Applied Statistics 
Pages: 943-949 
Issue: 8 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120076625 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120076625
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:8:p:943-949




Template-Type: ReDIF-Article 1.0
Author-Name: Patrick Bourke 
Author-X-Name-First: Patrick 
Author-X-Name-Last: Bourke 
Title: The geometric CUSUM chart with sampling inspection for monitoring fraction defective 
Abstract:
  The detection of an upward shift in the fraction defective of a
 repetitive process is considered using the geometric CUSUM. This CUSUM
 makes use of the information provided by the run-lengths of non-defective
 items between successive defective items, and was initially developed for
 the case of 100% inspection. This paper considers the geometric CUSUM
 under sampling inspection, and emphasizes that the pattern of sampling
 inspection can be quite haphazard without causing any difficulty for the
 operation of the CUSUM. Two separate mechanisms for the occurrence of a
 shift are considered. Methods for evaluating zero-state and steady-state
 ARL are presented for both 100% inspection and sampling inspection.
 Parameter choice is also considered, and recommendations made. Comparisons
 with some np -charts are provided. 
Journal: Journal of Applied Statistics 
Pages: 951-972 
Issue: 8 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120076643 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120076643
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:8:p:951-972




Template-Type: ReDIF-Article 1.0
Author-Name: George Box 
Author-X-Name-First: George 
Author-X-Name-Last: Box 
Author-Name: Ian Hau 
Author-X-Name-First: Ian 
Author-X-Name-Last: Hau 
Title: Experimental designs when there are one or more factor constraints 
Abstract:
  In response surface methodology, designs of orders one or two are often
 needed such that some or all the factor levels satisfy one or more linear
 constraints. A method is discussed for obtaining such designs by
 projection of a standard design onto the constraint hyperplane. It is
 shown that a projected design obtained from a rotatable design is also
 rotatable, and for a rotatable design that is also orthogonal (in
 particular any orthogonal first-order design) a least squares analysis
 carried out on the generating design supplies a least squares solution for
 the constrained design subject to the constraints. Some useful properties
 of the generating design, such as orthogonal blocking and fractionation
 are retained in the projected design. Some second-order mixture designs
 generated by two-level factorials are discussed. 
Journal: Journal of Applied Statistics 
Pages: 973-989 
Issue: 8 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120076652 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120076652
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:8:p:973-989




Template-Type: ReDIF-Article 1.0
Author-Name: V. R. Prayag 
Author-X-Name-First: V. R. 
Author-X-Name-Last: Prayag 
Author-Name: S. A. Chiplonkar 
Author-X-Name-First: S. A. 
Author-X-Name-Last: Chiplonkar 
Title: A multiple test for comparing two treatments with control: Interval hypotheses approach 
Abstract:
  In biological experiments, multiple comparison test procedures may lead
 to a statistically significant difference in means. However, sometimes the
 difference is not worthy of attention considering the inherent variation
 in the characteristic. This may be due to the fact that the magnitude of
 the change in the characteristic under study after receiving the treatment
 is small, less than the natural biological variation. It then becomes the
 job of the statistician to design a test that will remove this paradox,
 such that the statistical significance will coincide with the biological
 one. The present paper develops a multiple comparison test for comparing
 two treatments with control by incorporating within-person variation in
 forming interval hypotheses. Assuming common variance (unknown) for the
 three groups (control and two treatments) and the width of the interval as
 intra-individual variation (known), the distribution of the test statistic
 is obtained as bivariate non-central t . A level f test procedure is
 designed. A table of critical values for carrying out the test is
 constructed for f = 0.05. The exact powers are computed for various values
 of small sample sizes and parameters. The test is powerful for all values
 of the parameters. The test was used to detect differences in zinc
 absorption for two cereal diets compared with a control diet. After
 application of our test, we arrived at the conclusion of homogeneity of
 diets with the control diet. Dunnett's procedure, when applied to the same
 data, concluded otherwise. The new test can also be applied to other data
 situations in biology, medicine and agriculture. 
Journal: Journal of Applied Statistics 
Pages: 991-1001 
Issue: 8 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120076661 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120076661
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:8:p:991-1001




Template-Type: ReDIF-Article 1.0
Author-Name: Hassen Muttlak 
Author-X-Name-First: Hassen 
Author-X-Name-Last: Muttlak 
Title: Regression estimators in extreme and median ranked set samples 
Abstract:
  The ranked set sampling (RSS) method as suggested by McIntyre (1952) may
 be modified to come up with new sampling methods that can be made more
 efficient than the usual RSS method. Two such modifications, namely
 extreme and median ranked set sampling methods, are considered in this
 study. These two methods are generally easier to use in the field and less
 prone to problems resulting from errors in ranking. Two regression-type
 estimators based on extreme ranked set sampling (ERSS) and median ranked
 set sampling (MRSS) for estimating the population mean of the variable of
 interest are considered in this study and compared with the
 regression-type estimators based on RSS suggested by Yu & Lam (1997). It
 turned out that when the variable of interest and the concomitant variable
 jointly followed a bivariate normal distribution, the regression-type
 estimator of the population mean based on ERSS dominates all other
 estimators considered. 
Journal: Journal of Applied Statistics 
Pages: 1003-1017 
Issue: 8 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120076670 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120076670
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:8:p:1003-1017




Template-Type: ReDIF-Article 1.0
Author-Name: Key-Il Shin 
Author-X-Name-First: Key-Il 
Author-X-Name-Last: Shin 
Author-Name: Hee-Jeong Kang 
Author-X-Name-First: Hee-Jeong 
Author-X-Name-Last: Kang 
Title: A study on the effect of power transformation in the ARMA(p,q) model 
Abstract:
  In time series analysis, the Box-Cox power transformation is generally
 used for variance stabilization. In this paper we show that the order and
 the first step ahead forecast of the transformed model are approximately
 invariant to those of the original model under certain assumptions on the
 mean and variance. A small Monte Carlo simulation is performed to support
 the results. 
Journal: Journal of Applied Statistics 
Pages: 1019-1028 
Issue: 8 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120076689 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120076689
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:8:p:1019-1028




Template-Type: ReDIF-Article 1.0
Author-Name: X. M. Tu 
Author-X-Name-First: X. M. 
Author-X-Name-Last: Tu 
Author-Name: J. Kowalski 
Author-X-Name-First: J. 
Author-X-Name-Last: Kowalski 
Author-Name: A. Begley 
Author-X-Name-First: A. 
Author-X-Name-Last: Begley 
Author-Name: P. Houck 
Author-X-Name-First: P. 
Author-X-Name-Last: Houck 
Author-Name: S. Mazumdar 
Author-X-Name-First: S. 
Author-X-Name-Last: Mazumdar 
Author-Name: J. Miewald 
Author-X-Name-First: J. 
Author-X-Name-Last: Miewald 
Author-Name: D. J. Buysse 
Author-X-Name-First: D. J. 
Author-X-Name-Last: Buysse 
Author-Name: D. J. Kupfer 
Author-X-Name-First: D. J. 
Author-X-Name-Last: Kupfer 
Title: Data recycling: A response to the changing technology from the statistical perspective with application to psychiatric sleep research 
Abstract:
  Rapid technological advances have resulted in continual changes in data
 acquisition and reporting processes. While such advances have benefited
 research in these areas, the changing technologies have, at the same time,
 created difficulty for statistical analysis by generating outdated data
 which are incompatible with data based on newer technology. Relationships
 between these incompatible variables are complicated; not only they are
 stochastic, but also often depend on other variables, rendering even a
 simple statistical analysis, such as estimation of a population mean,
 difficult in the presence of mixed data formats. Thus, technological
 advancement has brought forth, from the statistical perspective, a
 methodological problem of the analysis of newer data with outdated data.
 In this paper, we discuss general principles for addressing the
 statistical issues related to the analysis of incompatible data. The
 approach taken to the task at hand has three desirable properties, it is
 readily understood, since it builds upon a linear regression setting, it
 is flexible to allow for data incompatibility in either the response or
 covariate, and it is not computationally intensive. In addition,
 inferences may be made for a latent variable of interest. Our
 considerations to this problem are motivated by the analysis of delta wave
 counts, as a surrogate for sleep disorder, in the sleep laboratory of the
 Department of Psychiatry, University of Pittsburgh Medical Center, where
 two major changes had occurred in the acquisition of this data, resulting
 in three mixed formats. By developing appropriate methods for addressing
 this issue, we provide statistical advancement that is compatible with
 technological advancement. 
Journal: Journal of Applied Statistics 
Pages: 1029-1049 
Issue: 8 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120076698 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120076698
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:8:p:1029-1049




Template-Type: ReDIF-Article 1.0
Author-Name: Samia Adham 
Author-X-Name-First: Samia 
Author-X-Name-Last: Adham 
Author-Name: Stephen Walker 
Author-X-Name-First: Stephen 
Author-X-Name-Last: Walker 
Title: A multivariate Gompertz-type distribution 
Abstract:
  The Gompertz distribution has many applications, particularly in medical
 and actuarial studies. However, there has been little recent work on the
 Gompertz in comparison with its early investigation. The problem of
 finding and analysing a bivariate (or multivariate) Gompertz distribution
 is of interest and the focus of this paper. A search of the literature
 suggests there is currently no multivariate or even useful bivariate
 Gompertz distribution. 
Journal: Journal of Applied Statistics 
Pages: 1051-1065 
Issue: 8 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120076706 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120076706
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:8:p:1051-1065




Template-Type: ReDIF-Article 1.0
Author-Name: Man-Yu Wong 
Author-X-Name-First: Man-Yu 
Author-X-Name-Last: Wong 
Author-Name: Shuanglin Zhang 
Author-X-Name-First: Shuanglin 
Author-X-Name-Last: Zhang 
Title: Degrees of freedom and the likelihood ratio test for the generalized Behrens-Fisher problem 
Abstract:
  Several methods for testing the difference between two group means of k
 independent populations are compared. Simulation shows that the likelihood
 ratio test with the Bartlett correction factor and the t test with
 appropriate degrees of freedom perform better, particularly when the
 sample size is small. However, the latter is very good for all
 configurations. 
Journal: Journal of Applied Statistics 
Pages: 1067-1074 
Issue: 8 
Volume: 28 
Year: 2001 
X-DOI: 10.1080/02664760120076715 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120076715
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:28:y:2001:i:8:p:1067-1074




Template-Type: ReDIF-Article 1.0
Author-Name: Emmanuelle Cam 
Author-X-Name-First: Emmanuelle 
Author-X-Name-Last: Cam 
Author-Name: Bernard Cadiou 
Author-X-Name-First: Bernard 
Author-X-Name-Last: Cadiou 
Author-Name: James Hines 
Author-X-Name-First: James 
Author-X-Name-Last: Hines 
Author-Name: Jean Yves Monnat 
Author-X-Name-First: Jean Yves 
Author-X-Name-Last: Monnat 
Title: Influence of behavioural tactics on recruitment and reproductive trajectory in the kittiwake 
Abstract:
  Many studies have provided evidence that, in birds, inexperienced
 breeders have a lower probability of breeding successfully. This is often
 explained by lack of skills and knowledge, and sometimes late laying dates
 in the first breeding attempt. There is growing evidence that in many
 species with deferred reproduction, some prebreeders attend breeding
 places, acquire territories and form pairs. Several behavioural tactics
 assumed to be associated with territory acquisition have been described in
 different species. These tactics may influence the probability of
 recruiting in the breeding segment of the population, age of first
 breeding, and reproductive success in the first breeding attempt. Here we
 addressed the influence of behaviour ('squatting') during the prebreeding
 period on demographic parameters (survival and recruitment probability) in
 a long-lived colonial seabird species: the kittiwake. We also investigated
 the influence of behaviour on reproductive trajectory. Squatters have a
 higher survival and recruitment probability, and a higher probability of
 breeding successfully in the first breeding attempt in all age-classes
 where this category is represented. The influence of behaviour is mainly
 expressed in the first reproduction. However, there is a relationship
 between breeding success in the first occasion and subsequent occasions.
 The influence of breeding success in the first breeding attempt on the
 rest of the trajectory may indirectly reflect the influence of behaviour
 on breeding success in the first occasion. The shape of the reproductive
 trajectory is influenced by behaviour and age of first breeding. There is
 substantial individual variation from the mean reproductive trajectory,
 which is accounted for by heterogeneity in performance among individuals
 in the first attempt, but there is no evidence of individual heterogeneity
 in the rate of change over time in performance in subsequent breeding
 occasions 
Journal: Journal of Applied Statistics 
Pages: 163-185 
Issue: 1-4 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120108502 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120108502
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:163-185




Template-Type: ReDIF-Article 1.0
Author-Name: William Link 
Author-X-Name-First: William 
Author-X-Name-Last: Link 
Author-Name: Evan Cooch 
Author-X-Name-First: Evan 
Author-X-Name-Last: Cooch 
Author-Name: Emmanuelle Cam 
Author-X-Name-First: Emmanuelle 
Author-X-Name-Last: Cam 
Title: Model-based estimation of individual fitness 
Abstract:
  Fitness is the currency of natural selection, a measure of the
 propagation rate of genotypes into future generations. Its various
 definitions have the common feature that they are functions of survival
 and fertility rates. At the individual level, the operative level for
 natural selection, these rates must be understood as latent features,
 genetically determined propensities existing at birth. This conception of
 rates requires that individual fitness be defined and estimated by
 consideration of the individual in a modelled relation to a group of
 similar individuals; the only alternative is to consider a sample of size
 one, unless a clone of identical individuals is available. We present
 hierarchical models describing individual heterogeneity in survival and
 fertility rates and allowing for associations between these rates at the
 individual level. We apply these models to an analysis of life histories
 of Kittiwakes ( Rissa tridactyla ) observed at several colonies on the
 Brittany coast of France. We compare Bayesian estimation of the population
 distribution of individual fitness with estimation based on treating
 individual life histories in isolation, as samples of size one (e.g.
 McGraw & Caswell, 1996). 
Journal: Journal of Applied Statistics 
Pages: 207-224 
Issue: 1-4 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120108700a 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120108700a
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:207-224




Template-Type: ReDIF-Article 1.0
Author-Name: Kenneth Burnham 
Author-X-Name-First: Kenneth 
Author-X-Name-Last: Burnham 
Author-Name: Gary White 
Author-X-Name-First: Gary 
Author-X-Name-Last: White 
Title: Evaluation of some random effects methodology applicable to bird ringing data 
Abstract:
  Existing models for ring recovery and recapture data analysis treat
 temporal variations in annual survival probability (S) as fixed effects.
 Often there is no explainable structure to the temporal variation in S 1 ,
 &#x2026; , S k ; random effects can then be a useful model: Si = E(S) + k
 i . Here, the temporal variation in survival probability is treated as
 random with average value E( k 2 ) = &#x2020; 2 . This random effects
 model can now be fit in program MARK. Resultant inferences include point
 and interval estimation for process variation, &#x2020; 2 , estimation of
 E(S) and var(E(S)) where the latter includes a component for &#x2020; 2 as
 well as the traditional component for v ar(S&7CS). Furthermore, the random
 effects model leads to shrinkage estimates, S i , as improved (in mean
 square error) estimators of Si compared to the MLE, S i , from the
 unrestricted time-effects model. Appropriate confidence intervals based on
 the S i are also provided. In addition, AIC has been generalized to random
 effects models. This paper presents results of a Monte Carlo evaluation of
 inference performance under the simple random effects model. Examined by
 simulation, under the simple one group Cormack-Jolly-Seber (CJS) model,
 are issues such as bias of &#x2020; 2 , confidence interval coverage on
 &#x2020; 2 , coverage and mean square error comparisons for inference
 about Si based on shrinkage versus maximum likelihood estimators, and
 performance of AIC model selection over three models: S i = S (no
 effects), Si = E(S) + k i (random effects), and S 1 , &#x2026; , S k
 (fixed effects). For the cases simulated, the random effects methods
 performed well and were uniformly better than fixed effects MLE for the S
 i . 
Journal: Journal of Applied Statistics 
Pages: 245-264 
Issue: 1-4 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120108755 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120108755
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:245-264




Template-Type: ReDIF-Article 1.0
Author-Name: Ian Nisbet 
Author-X-Name-First: Ian 
Author-X-Name-Last: Nisbet 
Author-Name: Emmanuelle Cam 
Author-X-Name-First: Emmanuelle 
Author-X-Name-Last: Cam 
Title: Test for age-specificity in survival of the common tern 
Abstract:
  Much effort in life-history theory has been addressed to the dependence
 of life-history traits on age, especially the phenomenon of senescence and
 its evolution. Although senescent declines in survival are well documented
 in humans and in domestic and laboratory animals, evidence for their
 occurrence and importance in wild animal species remains limited and
 equivocal. Several recent papers have suggested that methodological issues
 may contribute to this problem, and have encouraged investigators to
 improve sampling designs and to analyse their data using recently
 developed approaches to modelling of capture-mark-recapture data. Here we
 report on a three-year, two-site, mark-recapture study of known-aged
 common terns (Sterna hirundo) in the north-eastern USA. The study was
 nested within a long-term ecological study in which large numbers of
 chicks had been banded in each year for &#x3e; 25 years. We used a range
 of models to test the hypothesis of an influence of age on survival
 probability. We also tested for a possible influence of sex on survival.
 The cross-sectional design of the study (one year's parameter estimates)
 avoided the possible confounding of effects of age and time. The study was
 conducted at a time when one of the study sites was being colonized and
 numbers were increasing rapidly. We detected two-way movements between the
 sites and estimated movement probabilities in the year for which they
 could be modelled. We also obtained limited data on emigration from our
 study area to more distant sites. We found no evidence that survival
 depended on either sex or age, except that survival was lower among the
 youngest birds (ages 2-3 years). Despite the large number of birds
 included in the study (1599 known-aged birds, 2367 total), confidence
 limits on estimates of survival probability were wide, especially for the
 oldest age-classes, so that a slight decline in survival late in life
 could not have been detected. In addition, the cross-sectional design of
 this study meant that a decline in survival probability within individuals
 (actuarial senescence) could have been masked by heterogeneity in survival
 probability among individuals (mortality selection). This emphasizes the
 need for the development of modelling tools permitting separation of these
 two phenomena, valid under field conditions in which the recapture
 probabilities are less than one. 
Journal: Journal of Applied Statistics 
Pages: 65-83 
Issue: 1-4 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120108467 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120108467
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:65-83




Template-Type: ReDIF-Article 1.0
Author-Name: M. Dolores Ugarte 
Author-X-Name-First: M. Dolores 
Author-X-Name-Last: Ugarte 
Title: Book Review 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 669-669 
Issue: 1-4 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120108881 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120108881
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:669-669




Template-Type: ReDIF-Article 1.0
Author-Name: Victoria Dreitz 
Author-X-Name-First: Victoria 
Author-X-Name-Last: Dreitz 
Author-Name: James Nichols 
Author-X-Name-First: James 
Author-X-Name-Last: Nichols 
Author-Name: James Hines 
Author-X-Name-First: James 
Author-X-Name-Last: Hines 
Author-Name: Robert Bennetts 
Author-X-Name-First: Robert 
Author-X-Name-Last: Bennetts 
Author-Name: Wiley Kitchens 
Author-X-Name-First: Wiley 
Author-X-Name-Last: Kitchens 
Author-Name: Donald Deangelis 
Author-X-Name-First: Donald 
Author-X-Name-Last: Deangelis 
Title: The use of resighting data to estimate the rate of population growth of the snail kite in Florida 
Abstract:
  The rate of population growth ( u ) is an important demographic parameter
 used to assess the viability of a population and to develop management and
 conservation agendas. We examined the use of resighting data to estimate u
 for the snail kite population in Florida from 1997-2000. The analyses
 consisted of (1) a robust design approach that derives an estimate of u
 from estimates of population size and (2) the Pradel (1996) temporal
 symmetry (TSM) approach that directly estimates u using an open-population
 capture-recapture model. Besides resighting data, both approaches required
 information on the number of unmarked individuals that were sighted during
 the sampling periods. The point estimates of u differed between the robust
 design and TSM approaches, but the 95% confidence intervals overlapped
 substantially. We believe the differences may be the result of sparse data
 and do not indicate the inappropriateness of either modelling technique.
 We focused on the results of the robust design because this approach
 provided estimates for all study years. Variation among these estimates
 was smaller than levels of variation among ad hoc estimates based on
 previously reported index statistics. We recommend that u of snail kites
 be estimated using capture-resighting methods rather than ad hoc counts. 
Journal: Journal of Applied Statistics 
Pages: 609-623 
Issue: 1-4 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120108854 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120108854
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:609-623




Template-Type: ReDIF-Article 1.0
Author-Name: David Otis 
Author-X-Name-First: David 
Author-X-Name-Last: Otis 
Author-Name: Gary White 
Author-X-Name-First: Gary 
Author-X-Name-Last: White 
Title: Re-analysis of a banding study to test the effects of an experimental increase in bag limits of mourning doves 
Abstract:
  In 1966-1971, eastern US states with hunting seasons on mourning doves (
 Zenaida macroura ) participated in a study designed to estimate the
 effects of bag limit increases on population survival rates. More than 400
 000 adult and juvenile birds were banded and released during this period,
 and subsequent harvest and return of bands, together with total harvest
 estimates from mail and telephone surveys of hunters, provided the
 database for analysis. The original analysis used an ANOVA framework, and
 resulted in inferences of no effect of bag limit increase on population
 parameters (Hayne 1975). We used a logistic regression analysis to infer
 that the bag limit increase did not cause a biologically significant
 increase in harvest rate and thus the experiment could not provide any
 insight into the relationship between harvest and annual survival rates.
 Harvest rate estimates of breeding populations from geographical
 subregions were used as covariates in a Program MARK analysis and revealed
 an association between annual survival and harvest rates, although this
 relationship is potentially confounded by a latitudinal gradient in
 survival rates of dove populations. We discuss methodological problems
 encountered in the analysis of these data, and provide recommendations for
 future studies of the relationship between harvest and annual survival
 rates of mourning dove populations. 
Journal: Journal of Applied Statistics 
Pages: 479-495 
Issue: 1-4 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120108539 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120108539
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:479-495




Template-Type: ReDIF-Article 1.0
Author-Name: Jean Clobert 
Author-X-Name-First: Jean 
Author-X-Name-Last: Clobert 
Title: Capture-recapture and evolutionary ecology: Further comments 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 53-56 
Issue: 1-4 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120108773 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120108773
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:53-56




Template-Type: ReDIF-Article 1.0
Author-Name: George Seber 
Author-X-Name-First: George 
Author-X-Name-Last: Seber 
Author-Name: Carl Schwarz 
Author-X-Name-First: Carl 
Author-X-Name-Last: Schwarz 
Title: Capture-recapture: Before and after EURING 2000 
Abstract:
  Capture-recapture studies and analyses have become an important tool for
 the study of bird populations. One reason for the rapid advancement in
 this area has been the EURING conferences where population biologists and
 statisticians meet to review recent progress, identify areas that require
 further work, and work collaborately to solve real world problems. In this
 paper, we forecast the needs for future research in this area and review
 the recent conference to try and identify what questions are yet unsolved.
 This EURING conference was dedicated to Dr George Seber who was the author
 of a number of key papers and whose name is synonymous with 'The
 estimation of animal abundance and related parameter' (Seber, 1982). He
 has retired from working in this field. 
Journal: Journal of Applied Statistics 
Pages: 5-18 
Issue: 1-4 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120108700 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120108700
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:5-18




Template-Type: ReDIF-Article 1.0
Author-Name: Anne Loison 
Author-X-Name-First: Anne 
Author-X-Name-Last: Loison 
Author-Name: Bernt-Erik S&#xe6;ther 
Author-X-Name-First: Bernt-Erik 
Author-X-Name-Last: S&#xe6;ther 
Author-Name: Kurt Jerstad 
Author-X-Name-First: Kurt 
Author-X-Name-Last: Jerstad 
Author-Name: Ole Wiggo R&#xf8;stad 
Author-X-Name-First: Ole Wiggo 
Author-X-Name-Last: R&#xf8;stad 
Title: Disentangling the sources of variation in the survival of the European dipper 
Abstract:
  The population growth rate of the European dipper has been shown to
 decrease with winter temperature and population size. We examine here the
 demographic mechanism for this effect by analysing how these factors
 affect the survival rate. Using more than 20 years of
 capture-mark-recapture data (1974-1997) based on more than 4000 marked
 individuals, we perform analyses using open capture-mark-recapture models.
 This allowed us to estimate the annual apparent survival rates
 (probability of surviving and staying on the study site from one year to
 the next one) and the recapture probabilities. We partitioned the variance
 of the apparent survival rates into sampling variance and process variance
 using random effects models, and investigated which variables best
 accounted for temporal process variation. Adult males and females had
 similar apparent survival rates, with an average of 0.52 and a coefficient
 of variation of 40%. Chick apparent survival was lower, averaging 0.06
 with a coefficient of variation of 42%. Eighty percent of the variance in
 apparent survival rates was explained by winter temperature and population
 size for adults and 48% by winter temperature for chicks. The process
 variance outweighed the sampling variance both for chick and adult
 survival rates, which explained that shrunk estimates obtained under
 random effects models were close to MLE estimates. A large proportion of
 the annual variation in the apparent survival rate of chicks appears to be
 explained by inter-year differences in dispersal rates. 
Journal: Journal of Applied Statistics 
Pages: 289-304 
Issue: 1-4 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120108665 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120108665
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:289-304




Template-Type: ReDIF-Article 1.0
Author-Name: J. D. Lebreton 
Author-X-Name-First: J. D. 
Author-X-Name-Last: Lebreton 
Author-Name: R. Pradel Cefe 
Author-X-Name-First: R. Pradel 
Author-X-Name-Last: Cefe 
Title: Multistate recapture models: Modelling incomplete individual histories 
Abstract:
  Multistate capture-recapture models are a natural generalization of the
 usual one-site recapture models. Similarly, individuals are sampled on
 discrete occasions, at which they may be captured or not. However,
 contrary to the one-site case, the individuals can move within a finite
 set of states between occasions. The growing interest in spatial aspects
 of population dynamics presently contributes to making multistate models a
 very promising tool for population biology. We review first the interest
 and the potential of multistate models, in particular when they are used
 with individual states as well as geographical sites. Multistate models
 indeed constitute canonical capture-recapture models for individual
 categorical covariates changing over time, and can be linked to
 longitudinal studies with missing data and models such as hidden Markov
 chains. Multistate models also provide a promising tool for handling
 heterogeneity of capture, provided states related to capturability can be
 defined and used. Such an approach could be relevant for population size
 estimation in closed populations. Multistate models also constitute a
 natural framework for mixtures of information in individual history data.
 Presently, most models can be fit using program MARK. As an example, we
 present a canonical model for multisite accession to reproduction, which
 fully generalizes a classical one-site model. In the generalization
 proposed, one can estimate simultaneously age-dependent rates of accession
 to reproduction, natal and breeding dispersal. Finally, we discuss further
 generalizations - such as a multistate generalization of growth rate
 models and models for data where the state in which an individual is
 detected is known with uncertainty - and prospects for software
 development. 
Journal: Journal of Applied Statistics 
Pages: 353-369 
Issue: 1-4 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120108638 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120108638
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:353-369




Template-Type: ReDIF-Article 1.0
Author-Name: James Hines 
Author-X-Name-First: James 
Author-X-Name-Last: Hines 
Author-Name: James Nichols 
Author-X-Name-First: James 
Author-X-Name-Last: Nichols 
Title: Investigations of potential bias in the estimation of u using Pradel's (1996) model for capture-recapture data 
Abstract:
  Pradel's (1996) temporal symmetry model permitting direct estimation and
 modelling of population growth rate, u i , provides a potentially useful
 tool for the study of population dynamics using marked animals. Because of
 its recent publication date, the approach has not seen much use, and there
 have been virtually no investigations directed at robustness of the
 resulting estimators. Here we consider several potential sources of bias,
 all motivated by specific uses of this estimation approach. We consider
 sampling situations in which the study area expands with time and present
 an analytic expression for the bias in u i We next consider trap response
 in capture probabilities and heterogeneous capture probabilities and
 compute large-sample and simulation-based approximations of resulting bias
 in u i . These approximations indicate that trap response is an especially
 important assumption violation that can produce substantial bias. Finally,
 we consider losses on capture and emphasize the importance of selecting
 the estimator for u i that is appropriate to the question being addressed.
 For studies based on only sighting and resighting data, Pradel's (1996) u
 i ' is the appropriate estimator. 
Journal: Journal of Applied Statistics 
Pages: 573-587 
Issue: 1-4 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120108872 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120108872
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:573-587




Template-Type: ReDIF-Article 1.0
Author-Name: James Nichols 
Author-X-Name-First: James 
Author-X-Name-Last: Nichols 
Title: Discussion comments on: 'Occam's shadow: Levels of analysis in evolutionary ecology-- where to next?' by Cooch, Cam and Link 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 49-52 
Issue: 1-4 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120108449 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120108449
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:49-52




Template-Type: ReDIF-Article 1.0
Author-Name: Michael Conroy 
Author-X-Name-First: Michael 
Author-X-Name-Last: Conroy 
Author-Name: Juan Carlos Senar 
Author-X-Name-First: Juan Carlos 
Author-X-Name-Last: Senar 
Author-Name: Jordi Domenech 
Author-X-Name-First: Jordi 
Author-X-Name-Last: Domenech 
Title: Analysis of individual- and time-specific covariate effects on survival of Serinus serinus in north-eastern Spain 
Abstract:
  We developed models for the analysis of recapture data for 2678 serins (
 Serinus serinus ) ringed in north-eastern Spain since 1985. We
 investigated several time- and individual-specific factors as potential
 predictors of overall mortality and dispersal patterns, and of gender and
 age differences in these patterns. Time-specific covariates included
 minimum daily temperature, days below freezing, and abundance of a strong
 competitor, siskins ( Carduelis spinus ) during winter, and maximum
 temperature and rainfall during summer. Individual covariates included
 body mass (i.e. body condition), and wing length (i.e. flying ability),
 and interactions between body mass and environmental factors. We found
 little support of a predictive relationship between environmental factors
 and survival, but good evidence of relationships between body mass and
 survival, especially for juveniles. Juvenile survival appears to vary in a
 curvilinear manner with increasing mass, suggesting that there may exist
 an optimal mass beyond which increases are detrimental. The mass-survival
 relationship does seem to be influenced by at least one environmental
 factor, namely the abundance of wintering siskins. When siskins are
 abundant, increases in body mass appear to relate strongly to increasing
 survival. When siskin numbers are average or low the relationship is
 largely reversed, suggesting that the presence of strong competition
 mitigates the otherwise largely negative aspects of greater body mass.
 Wing length in juveniles also appears to be related positively to
 survival, perhaps largely due to the influence of a few unusually large
 juveniles with adult-like survival. Further work is needed to test these
 relationships, ideally under experimentation. 
Journal: Journal of Applied Statistics 
Pages: 125-142 
Issue: 1-4 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120108674 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120108674
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:125-142




Template-Type: ReDIF-Article 1.0
Author-Name: Richard Barker 
Author-X-Name-First: Richard 
Author-X-Name-Last: Barker 
Author-Name: David Fletcher 
Author-X-Name-First: David 
Author-X-Name-Last: Fletcher 
Author-Name: Paul Scofield 
Author-X-Name-First: Paul 
Author-X-Name-Last: Scofield 
Title: Measuring density dependence in survival from mark-recapture data 
Abstract:
  We discuss the analysis of mark-recapture data when the aim is to
 quantify density dependence between survival rate and abundance. We
 describe an analysis for a random effects model that includes a linear
 relationship between abundance and survival using an errors-in-variables
 regression estimator with analytical adjustment for approximate bias. The
 analysis is illustrated using data from short-tailed shearwaters banded
 for 48 consecutive years at Fisher Island, Tasmania, and Hutton's
 shearwater banded at Kaikoura, New Zealand for nine consecutive years. The
 Fisher Island data provided no evidence of a density dependence
 relationship between abundance and survival, and confidence interval
 widths rule out anything but small density dependent effects. The Hutton's
 shearwater data were equivocal with the analysis unable to rule out
 anything but a very strong density dependent relationship between survival
 and abundance. 
Journal: Journal of Applied Statistics 
Pages: 305-313 
Issue: 1-4 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120108782 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120108782
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:305-313




Template-Type: ReDIF-Article 1.0
Author-Name: Jeffrey Spendelow 
Author-X-Name-First: Jeffrey 
Author-X-Name-Last: Spendelow 
Author-Name: James Nichols 
Author-X-Name-First: James 
Author-X-Name-Last: Nichols 
Author-Name: James Hines 
Author-X-Name-First: James 
Author-X-Name-Last: Hines 
Author-Name: Jean-Dominique Lebreton 
Author-X-Name-First: Jean-Dominique 
Author-X-Name-Last: Lebreton 
Author-Name: Roger Pradel 
Author-X-Name-First: Roger 
Author-X-Name-Last: Pradel 
Title: Modelling postfledging survival and age-specific breeding probabilities in species with delayed maturity: A case study of Roseate Terns at Falkner Island, Connecticut 
Abstract:
  We modelled postfledging survival and age-specific breeding probabilities
 in endangered Roseate Terns ( Sterna dougallii ) at Falkner Island,
 Connecticut, USA using capture-recapture data from 1988-1998 of birds
 ringed as chicks and as adults. While no individuals bred as 2-year-olds
 during this period, about three-quarters of the young that survived and
 returned as 3-year-olds nested, and virtually all surviving birds had
 begun breeding by the time they reached 5 years of age. We found no
 evidence of temporal variation age of first breeding of birds from
 different cohorts. There was significant temporal variation in the annual
 survival of adults and the survival over the typical 3-year maturation
 period of prebreeding birds, with extremely low values for both groups
 from the 1991 breeding season. The estimated overwinter survival rate
 (0.62) for adults from 1991-1992 was about three-quarters the usual rate
 of about 0.83, but the low survival of fledglings from 1991 resulted in
 less than 25% of the otherwise expected number of young from that cohort
 returning as breeding birds; this suggests that fledglings suffered a
 greater proportional decrease in survival than did adults. The survival
 estimates of young from 1989 and 1990 show that these cohorts were not
 negatively influenced by the events that decimated the young from 1991,
 and the young from 1992 and 1993 had above-average survival estimates. The
 apparent decrease since 1996 in development of fidelity of new recruits to
 this site is suspected to be due mainly to nocturnal disturbance and
 predation of chicks causing low productivity. 
Journal: Journal of Applied Statistics 
Pages: 385-405 
Issue: 1-4 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120108764 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120108764
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:385-405




Template-Type: ReDIF-Article 1.0
Author-Name: J. Andrew Royle 
Author-X-Name-First: J. Andrew 
Author-X-Name-Last: Royle 
Author-Name: William Link 
Author-X-Name-First: William 
Author-X-Name-Last: Link 
Title: Random effects and shrinkage estimation in capture-recapture models 
Abstract:
  We discuss the analysis of random effects in capture-recapture models,
 and outline Bayesian and frequentists approaches to their analysis. Under
 a normal model, random effects estimators derived from Bayesian or
 frequentist considerations have a common form as shrinkage estimators. We
 discuss some of the difficulties of analysing random effects using
 traditional methods, and argue that a Bayesian formulation provides a
 rigorous framework for dealing with these difficulties. In
 capture-recapture models, random effects may provide a parsimonious
 compromise between constant and completely time-dependent models for the
 parameters (e.g. survival probability). We consider application of random
 effects to band-recovery models, although the principles apply to more
 general situations, such as Cormack-Jolly-Seber models. We illustrate
 these ideas using a commonly analysed band recovery data set. 
Journal: Journal of Applied Statistics 
Pages: 329-351 
Issue: 1-4 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120108746 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120108746
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:329-351




Template-Type: ReDIF-Article 1.0
Author-Name: E. A. Catchpole 
Author-X-Name-First: E. A. 
Author-X-Name-Last: Catchpole 
Author-Name: B. J. T. Morgan 
Author-X-Name-First: B. J. T. 
Author-X-Name-Last: Morgan 
Author-Name: A. Viallefont 
Author-X-Name-First: A. 
Author-X-Name-Last: Viallefont 
Title: Solving problems in parameter redundancy using computer algebra 
Abstract:
  A model, involving a particular set of parameters, is said to be
 parameter redundant when the likelihood can be expressed in terms of a
 smaller set of parameters. In many important cases, the parameter
 redundancy of a model can be checked by evaluating the symbolic rank of a
 derivative matrix. We describe the main results, and show how to construct
 this matrix using the symbolic algebra package Maple. We apply the theory
 to examples from the mark-recapture field. General code is given which can
 be applied to other models. 
Journal: Journal of Applied Statistics 
Pages: 625-636 
Issue: 1-4 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120108601 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120108601
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:625-636




Template-Type: ReDIF-Article 1.0
Author-Name: Evan Cooch 
Author-X-Name-First: Evan 
Author-X-Name-Last: Cooch 
Author-Name: Emmanuelle Cam 
Author-X-Name-First: Emmanuelle 
Author-X-Name-Last: Cam 
Author-Name: William Link 
Author-X-Name-First: William 
Author-X-Name-Last: Link 
Title: Occam's shadow: Levels of analysis in evolutionary ecology--where to next? 
Abstract:
  Evolutionary ecology is the study of evolutionary processes, and the
 ecological conditions that influence them. A fundamental paradigm
 underlying the study of evolution is natural selection. Although there are
 a variety of operational definitions for natural selection in the
 literature, perhaps the most general one is that which characterizes
 selection as the process whereby heritable variation in fitness associated
 with variation in one or more phenotypic traits leads to intergenerational
 change in the frequency distribution of those traits. The past 20 years
 have witnessed a marked increase in the precision and reliability of our
 ability to estimate one or more components of fitness and characterize
 natural selection in wild populations, owing particularly to significant
 advances in methods for analysis of data from marked individuals. In this
 paper, we focus on several issues that we believe are important
 considerations for the application and development of these methods in the
 context of addressing questions in evolutionary ecology. First, our
 traditional approach to estimation often rests upon analysis of aggregates
 of individuals, which in the wild may reflect increasingly non-random
 (selected) samples with respect to the trait(s) of interest. In some
 cases, analysis at the aggregate level, rather than the individual level,
 may obscure important patterns. While there are a growing number of
 analytical tools available to estimate parameters at the individual level,
 and which can cope (to varying degrees) with progressive selection of the
 sample, the advent of new methods does not reduce the need to consider
 carefully the appropriate level of analysis in the first place. Estimation
 should be motivated a priori by strong theoretical analysis. Doing so
 provides clear guidance, in terms of both (i) assisting in the
 identification of realistic and meaningful models to include in the
 candidate model set, and (ii) providing the appropriate context under
 which the results are interpreted. Second, while it is true that selection
 (as defined) operates at the level of the individual, the selection
 gradient is often (if not generally) conditional on the abundance of the
 population. As such, it may be important to consider estimating transition
 rates conditional on both the parameter values of the other individuals in
 the population (or at least their distribution), and population abundance.
 This will undoubtedly pose a considerable challenge, for both single- and
 multi-strata applications. It will also require renewed consideration of
 the estimation of abundance, especially for open populations. Thirdly,
 selection typically operates on dynamic, individually varying traits. Such
 estimation may require characterizing fitness in terms of individual
 plasticity in one or more state variables, constituting analysis of the
 norms of reaction of individuals to variable environments. This can be
 quite complex, especially for traits that are under facultative control.
 Recent work has indicated that the pattern of selection on such traits is
 conditional on the relative rates of movement among and frequency of
 spatially heterogeneous habitats, suggesting analyses of evolution of life
 histories in open populations can be misleading in some cases. 
Journal: Journal of Applied Statistics 
Pages: 19-48 
Issue: 1-4 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120108421 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120108421
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:19-48




Template-Type: ReDIF-Article 1.0
Author-Name: Brett Sandercock 
Author-X-Name-First: Brett 
Author-X-Name-Last: Sandercock 
Author-Name: Steven Beissinger 
Author-X-Name-First: Steven 
Author-X-Name-Last: Beissinger 
Title: Estimating rates of population change for a neotropical parrot with ratio, mark-recapture and matrix methods 
Abstract:
  Robust methods for estimating rates of population change ( u ) are
 necessary for applied and theoretical goals in conservation and
 evolutionary biology. Traditionally, u has been calculated from either
 ratios of population counts (observed u or u obs ), or population models
 based on projection matrices (asymptotic u or u asy ,). New mark-recapture
 methods permit calculation of u from mark-resighting information alone
 (realized u or u rea ), but empirical comparisons with other methods are
 rare. In this paper, rates of population change were calculated for a
 population of green-rumped parrotlets ( Forpus passerinus ) that have been
 studied for more than a decade in central Venezuela. First, a ratio method
 based on counts of detected birds was used to calculate u obs. Next, a
 temporal symmetry method based on mark-recapture data (i.e. the u
 -parameterization introduced by Pradel, 1996) was used to calculate u rea
 . Finally, a stage-structured matrix model based on state-specific
 estimates of fecundity, immigration, local survival, and transition rates
 was used to calculate u asy . Analyses were conducted separately for
 females and males. Overall values of u &#x2041;from the three methods were
 consistent and all indicated that the finite rate of population change was
 not significantly different from 1. Annual values of u from the three
 methods were also in general agreement for a majority of years. However, u
 rea from the temporal symmetry method had the greatest precision, and
 apparently better accuracy than u asy . Unrealistic annual values of u asy
 could have been due to poor estimates of the transitional probability of
 becoming a breeder ( &#x201a; ) or to a mismatch between the actual and
 the asymptotic stable stage distribution. In this study, the trade-off
 between biological realism and accuracy was better met by the temporal
 symmetry than the matrix method. Our results suggest that the temporal
 symmetry models can be applied with confidence to populations where less
 information may be available. 
Journal: Journal of Applied Statistics 
Pages: 589-607 
Issue: 1-4 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120108818 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120108818
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:589-607




Template-Type: ReDIF-Article 1.0
Author-Name: Douglas Johnson 
Author-X-Name-First: Douglas 
Author-X-Name-Last: Johnson 
Title: Discussion comments on 'Evaluation of some random effects methodology applicable to bird ringing data' by Burnham & White 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 265-266 
Issue: 1-4 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120108728 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120108728
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:265-266




Template-Type: ReDIF-Article 1.0
Author-Name: Mijeom Joe 
Author-X-Name-First: Mijeom 
Author-X-Name-Last: Joe 
Author-Name: Kenneth H. Pollock Biomathematics 
Author-X-Name-First: Kenneth H. Pollock 
Author-X-Name-Last: Biomathematics 
Title: Separation of survival and movement rates in multi-state tag-return and capture-recapture models 
Abstract:
  There has been growing interest in the estimation of transition
 probabilities among stages (Hestbeck et al. , 1991; Brownie et al. , 1993;
 Schwarz et al. , 1993) in tag-return and capture-recapture models. This
 has been driven by the increasing interest in meta-population models in
 ecology and the need for parameter estimates to use in these models. These
 transition probabilities are composed of survival and movement rates,
 which can only be estimated separately when an additional assumption is
 made (Brownie et al. , 1993). Brownie et al. (1993) assumed that movement
 occurs at the end of the interval between time i and i + 1. We generalize
 this work to allow different movement patterns in the interval for
 multiple tag-recovery and capture-recapture experiments. The time of
 movement is a random variable with a known distribution. The model
 formulations can be viewed as matrix extensions to the model formulations
 of single open population capturerecapture and tag-recovery experiments
 (Jolly, 1965; Seber, 1965; Brownie et al. , 1985). We also present the
 results of a small simulation study for the tag-return model when movement
 time follows a beta distribution, and later another simulation study for
 the capture-recapture model when movement time follows a uniform
 distribution. The simulation studies use a modified program SURVIV (White,
 1983). The Relative Standard Errors (RSEs) of estimates according to high
 and low movement rates are presented. We show there are strong
 correlations between movement and survival estimates in the case that the
 movement rate is high. We also show that estimators of movement rates to
 different areas and estimators of survival rates in different areas have
 substantial correlations. 
Journal: Journal of Applied Statistics 
Pages: 373-384 
Issue: 1-4 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120108836 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120108836
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:373-384




Template-Type: ReDIF-Article 1.0
Author-Name: Marlina Nasution 
Author-X-Name-First: Marlina 
Author-X-Name-Last: Nasution 
Author-Name: Cavell Brownie 
Author-X-Name-First: Cavell 
Author-X-Name-Last: Brownie 
Author-Name: Kenneth Pollock 
Author-X-Name-First: Kenneth 
Author-X-Name-Last: Pollock 
Title: Optimal allocation of sample sizes between regular banding and radio-tagging for estimating annual survival and emigration rates 
Abstract:
  Many authors have shown that a combined analysis of data from two or more
 types of recapture survey brings advantages, such as the ability to
 provide more information about parameters of interest. For example, a
 combined analysis of annual resighting and monthly radio-telemetry data
 allows separate estimates of true survival and emigration rates, whereas
 only apparent survival can be estimated from the resighting data alone.
 For studies involving more than one type of survey, biologists should
 consider how to allocate the total budget to the surveys related to the
 different types of marks so that they will gain optimal information from
 the surveys. For example, since radio tags and subsequent monitoring are
 very costly, while leg bands are cheap, the biologists should try to
 balance costs with information obtained in deciding how many animals
 should receive radios. Given a total budget and specific costs, it is
 possible to determine the allocation of sample sizes to different types of
 marks in order to minimize the variance of parameters of interest, such as
 annual survival and emigration rates. In this paper, we propose a cost
 function for a study where all birds receive leg bands and a subset
 receives radio tags and all new releases occur at the start of the study.
 Using this cost function, we obtain the allocation of sample sizes to the
 two survey types that minimizes the standard error of survival rate
 estimates or, alternatively, the standard error of emigration rates. Given
 the proposed costs, we show that for high resighting probability, e.g.
 0.6, tagging roughly 10-40% of birds with radios will give survival
 estimates with standard errors within the minimum range. Lower resighting
 rates will require a higher percentage of radioed birds. In addition, the
 proposed costs require tagging the maximum possible percentage of radioed
 birds to minimize the standard error of emigration estimates. 
Journal: Journal of Applied Statistics 
Pages: 443-457 
Issue: 1-4 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120108863 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120108863
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:443-457




Template-Type: ReDIF-Article 1.0
Author-Name: Thierry Boulinier 
Author-X-Name-First: Thierry 
Author-X-Name-Last: Boulinier 
Author-Name: Nigel Yoccoz 
Author-X-Name-First: Nigel 
Author-X-Name-Last: Yoccoz 
Author-Name: Karen McCoy 
Author-X-Name-First: Karen 
Author-X-Name-Last: McCoy 
Author-Name: Kjell Einar Erikstad 
Author-X-Name-First: Kjell Einar 
Author-X-Name-Last: Erikstad 
Author-Name: Torkild Tveraa 
Author-X-Name-First: Torkild 
Author-X-Name-Last: Tveraa 
Title: Testing the effect of conspecific reproductive success on dispersal and recruitment decisions in a colonial bird: Design issues 
Abstract:
  Factors affecting dispersal and recruitment in animal populations will
 play a prominent role in the dynamics of populations. This is particularly
 the case for subdivided populations where the dispersal of individuals
 among patches may lead to local extinction and 'rescue effects'. A
 long-term observational study carried out in Brittany, France, and
 involving colour-ringed Black-legged Kittiwakes (Rissa tridactyla)
 suggested that the reproductive success of conspecifics (or some social
 correlate) could be one important factor likely to affect dispersal and
 recruitment. By dispersing from patches where the local reproductive
 success was low and recruiting to patches where the local reproductive
 success was high, individual birds could track spatio-temporal variations
 in the quality of breeding patches (the quality of breeding patches can be
 affected by different factors, such as food availability, the presence of
 predators or ectoparasites, which can vary in space and time at different
 scales). Such an observational study may nevertheless have confounded the
 role of conspecific reproductive success with the effect of a correlated
 factor (e.g. the local activities of a predator). In other words,
 individuals may have been influenced directly by the factor responsible
 for the low local reproductive success or indirectly by the low success of
 their neighbours. Thus, an experimental approach was needed to address
 this question. Estimates of demographic parameters (other than
 reproductive success) and studies of the response of marked individuals to
 changes in their environment usually face problems associated with
 variability in the probability of detecting individuals and with
 nonindependence among events occurring on a local scale. Further, very few
 studies on dispersal have attempted to address the causal nature of
 relationships by experimentally manipulating factors. Here we present an
 experiment designed to test for an effect of local reproductive success of
 conspecifics on behavioural decisions of individuals regarding dispersal
 and recruitment. The experiment was carried out on Kittiwakes within a
 large seabird colony in northern Norway. It involved (i) the colour
 banding of several hundreds of birds; (ii) the manipulation
 (increase/decrease) of the local reproductive success of breeding groups
 on cliffpatches; and (iii) the detailed survey of attendance and
 activities of birds on these patches. It also involved the manipulation of
 the nest content of marked individuals breeding within these patches
 (individuals failing at the egg stage were expected to respond in terms of
 dispersal to the success of their neighbours). This allowed us to test
 whether a lower local reproductive success would lower (1) the attendance
 of breeders at the end of the breeding season; (2) the presence of
 prospecting birds; and (3) the proportion of failed breeders that came
 back to breed on the same patch the year after. In this paper, we discuss
 how we dealt with (I) the use of return rates to infer differences in
 dispersal rates; (II) the trade-off between sample sizes and local
 treatment levels; and (III) potential differences in detection
 probabilities among locations. We also present some results to illustrate
 the design and implementation of the experiment. 
Journal: Journal of Applied Statistics 
Pages: 509-520 
Issue: 1-4 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120108566 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120108566
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:509-520




Template-Type: ReDIF-Article 1.0
Author-Name: James Nichols 
Author-X-Name-First: James 
Author-X-Name-Last: Nichols 
Author-Name: James Hines 
Author-X-Name-First: James 
Author-X-Name-Last: Hines 
Title: Approaches for the direct estimation of u , and demographic contributions to u , using capture-recapture data 
Abstract:
  We first consider the estimation of the finite rate of population
 increase or population growth rate, u i , using capture-recapture data
 from open populations. We review estimation and modelling of u i under
 three main approaches to modelling openpopulation data: the classic
 approach of Jolly (1965) and Seber (1965), the superpopulation approach of
 Crosbie & Manly (1985) and Schwarz & Arnason (1996), and the temporal
 symmetry approach of Pradel (1996). Next, we consider the contributions of
 different demographic components to u i using a probabilistic approach
 based on the composition of the population at time i + 1 (Nichols et al.,
 2000b). The parameters of interest are identical to the seniority
 parameters, n i , of Pradel (1996). We review estimation of n i under the
 classic, superpopulation, and temporal symmetry approaches. We then
 compare these direct estimation approaches for u i and n i with analogues
 computed using projection matrix asymptotics. We also discuss various
 extensions of the estimation approaches to multistate applications and to
 joint likelihoods involving multiple data types. 
Journal: Journal of Applied Statistics 
Pages: 539-568 
Issue: 1-4 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120108809 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120108809
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:539-568




Template-Type: ReDIF-Article 1.0
Author-Name: Nigel Yoccoz 
Author-X-Name-First: Nigel 
Author-X-Name-Last: Yoccoz 
Author-Name: Kjell Erikstad 
Author-X-Name-First: Kjell 
Author-X-Name-Last: Erikstad 
Author-Name: Jan Bustnes 
Author-X-Name-First: Jan 
Author-X-Name-Last: Bustnes 
Author-Name: Sveinn Hanssen 
Author-X-Name-First: Sveinn 
Author-X-Name-Last: Hanssen 
Author-Name: Torkild Tveraa 
Author-X-Name-First: Torkild 
Author-X-Name-Last: Tveraa 
Title: Costs of reproduction in common eiders ( Somateria mollissima ): An assessment of relationships between reproductive effort and future survival and reproduction based on observational and experimental studies 
Abstract:
  The two traditional approaches to the study of costs of reproduction,
 correlational and experimental, have been used in parallel in a breeding
 colony of common eiders ( Somateria mollissima ) and were compared in this
 paper. The analysis of the observational data was based on a two-strata
 capture-recapture model, the strata being defined on the basis of the
 clutch size laid by individual females in a given year. The best model
 according to AIC C indicated substantial variation in survival, recapture
 and transition rates, but overall a pattern emerged: females laying large
 clutches have a somewhat higher survival and much higher capture rate than
 females laying small clutches, and transition from large to small clutch
 size occurs much more frequently than the reverse transition. The analysis
 of the experimental data (adding/removing one egg) showed that no clear
 effect was found on either survival or transition rates. We conclude by
 suggesting (1) that condition should be included in multi-strata models in
 addition to reproductive effort; (2) that a specific study design for
 estimating the proportion of non-breeding females should be implemented,
 and (3) that non-breeding (a non-observable state in this study) may be
 influenced by previous reproduction events. 
Journal: Journal of Applied Statistics 
Pages: 57-64 
Issue: 1-4 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120108458 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120108458
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:57-64




Template-Type: ReDIF-Article 1.0
Author-Name: S. E. Piper 
Author-X-Name-First: S. E. 
Author-X-Name-Last: Piper 
Title: Survival of adult, territorial Longtailed Wagtails Motacilla clara : The effects of environmental factors and individual covariates 
Abstract:
  The Longtailed Wagtail is a non-migratory African passerine that is
 confined exclusively to small, fast-flowing rivers in a largely arboreal
 environment. The breeding adults hold permanent, life-long, linear
 territories in their riverine habitat and this makes it easy to locate
 colour-marked birds. They are confiding by nature and permit close
 approach, often to less than 10 m, and this allows their unique
 permutations of colourrings to be read. Using data from the 21 year
 period, 1 August 1978 to 31 July 1999, of a dozen territories it has been
 shown that the breeding territories have not changed at all, even though
 there has been a continual, but slow turnover of territory holders. A
 total of 109 territorial adult birds were monitored for a total of 1121
 bird-quarters and survival was estimated for each of four quarters in a
 year. The average survival rate is estimated at 68.8% yr -1 (95%
 confidence limits: 63.3% to 69.3%) and this is high for such a small bird
 (approximately 20 g) and there have been some remarkably long-lived
 individuals, e.g. 10 to 12 years. In this paper, a generalized linear
 model is built of the survival of territorial adults. It is shown that
 bigger birds have a higher survival rate and that there are seasonal
 differences in survival that are ascribable to the cost of breeding and
 possibly cost of moult. There is an underlying long-term quadratic trend
 in survival that is related to increasing environmental degradation and
 decreasing chemical pollution. 
Journal: Journal of Applied Statistics 
Pages: 107-124 
Issue: 1-4 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120108485 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120108485
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:107-124




Template-Type: ReDIF-Article 1.0
Author-Name: Evan Cooch 
Author-X-Name-First: Evan 
Author-X-Name-Last: Cooch 
Title: Fledging size and survival in snow geese: Timing is everything (or is it?) 
Abstract:
  In many birds, body size at fledging is assumed to predict accurately the
 probability of subsequent survival, and size at fledging is often used as
 a proxy variable in analyses attempting to assess the pattern of natural
 selection on body size. However, in some species, size at fledging can
 vary significantly as a function of variation in the environmental
 component of growth. Such developmental plasticity has been demonstrated
 in several species of Arctic-breeding geese. In many cases, slower growth
 and reduced size at fledging has been suggested as the most parsimonious
 explanation for reduced post-fledging survival in goslings reared under
 poor environmental conditions. However, simply quantifying a relationship
 between mean size at fledging and mean survival rate (Francis et al .,
 1992) may obscure the pattern of selection on the interaction of the
 genetic and environmental components of growth. The hypothesis that
 selection operates on the environmental component of body size at
 fledging, rather than the genetic component of size per se, was tested
 using data from the long-term study of Lesser Snow Geese ( Anser c.
 caerulescens ) breeding at La Perouse Bay, Manitoba, Canada. Using data
 from female goslings measured at fledging, post-fledging survival rates
 were estimated using combined live encounter and dead recovery data
 (Burnham, 1993). To control for the covariation between growth and
 environmental factors, survival rates were constrained to be functions of
 individual covariation of size at fledging, and various measures of the
 timing of hatch; in all Arctic-breeding geese studied to date, late
 hatching goslings grow significantly more slowly than do early hatching
 goslings. The slower growth of late-hatching goslings has been
 demonstrated to reflect systematic changes in the environmental component
 of growth, and thus controlling for hatch date controls for a significant
 proportion of variation in the environmental component of growth. The
 relationship between size at fledging, hatch date and survival was found
 to be significantly non-linear; among early hatching goslings, there was
 little indication of significant differences in survival rate among large
 and small goslings. However, with increasingly later hatch dates, there
 was progressively greater mortality selection against smaller, slower
 growing goslings in most years. This would appear to suggest that body
 size matters, but not absolutely; small size leads to reduced survival for
 late-hatching goslings only at La Perouse Bay. Since at least some of the
 variation in size among goslings for a given hatch date reflects genetic
 differences, this suggests selection may favour larger size at fledging,
 albeit only among late-hatching goslings. 
Journal: Journal of Applied Statistics 
Pages: 143-162 
Issue: 1-4 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120108494 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120108494
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:143-162




Template-Type: ReDIF-Article 1.0
Author-Name: S. M. Slattery 
Author-X-Name-First: S. M. 
Author-X-Name-Last: Slattery 
Author-Name: R. T. Alisauskas 
Author-X-Name-First: R. T. 
Author-X-Name-Last: Alisauskas 
Title: Use of the Barker model in an experiment examining covariate effects on first-year survival in Ross's Geese ( Chen rossii ): A case study 
Abstract:
  The Barker model provides researchers with an opportunity to use three
 types of data for mark-recapture analyses - recaptures, recoveries, and
 resightings. This model structure maximizes use of encounter data and
 increases the precision of parameter estimates, provided the researcher
 has large amounts of resighting data. However, to our knowledge, this
 model has not been used for any published ringing studies. Our objective
 here is to report our use of the Barker model in covariate-dependent
 analyses that we conducted in Program MARK. In particular, we wanted to
 describe our experimental study design and discuss our analytical approach
 plus some logistical constraints we encountered while conducting a study
 of the effects of growth and parasites on survival of juvenile Ross's
 Geese. Birds were marked just before fledging, alternately injected with
 antiparasite drugs or a control, and then were re-encountered during
 migration and breeding in following years. Although the Barker model
 estimates seven parameters, our objectives focused on annual survival
 only, thus we considered all other parameters as nuisance terms.
 Therefore, we simplified our model structures by maintaining biological
 complexity on survival, while retaining a very basic structure on nuisance
 parameters. These analyses were conducted in a two-step approach where we
 used the most parsimonious model from nuisance parameter analyses as our
 starting model for analyses of covariate effects. This analytical approach
 also allowed us to minimize the long CPU times associated with the use of
 covariates in earlier versions of Program MARK. Resightings made up about
 80% of our encounter history data, and simulations demonstrated that
 precision and bias of parameter estimates were minimally affected by this
 distribution. Overall, the main source of bias was that smaller goslings
 were too small to retain neckbands, yet were the birds that we predicted
 would have the lowest survival probability and highest probability for
 parasite effects. Consequently, we considered our results conservative.
 The largest constraint of our study design was the inability to partition
 survival into biologically meaningful periods to provide insight into the
 timing and mechanisms of mortality. 
Journal: Journal of Applied Statistics 
Pages: 497-508 
Issue: 1-4 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120108548 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120108548
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:497-508




Template-Type: ReDIF-Article 1.0
Author-Name: Gary White 
Author-X-Name-First: Gary 
Author-X-Name-Last: White 
Title: Discussion comments on: The use of auxiliary variables in capture-recapture modelling. An overview 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 103-106 
Issue: 1-4 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120108476 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120108476
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:103-106




Template-Type: ReDIF-Article 1.0
Author-Name: C. J. Schwarz 
Author-X-Name-First: C. J. 
Author-X-Name-Last: Schwarz 
Title: Discussion comments on 'Prior distributions for stratified capture-recapture models' 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 239-240 
Issue: 1-4 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120108647 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120108647
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:239-240




Template-Type: ReDIF-Article 1.0
Author-Name: Juan Carlos Senar 
Author-X-Name-First: Juan Carlos 
Author-X-Name-Last: Senar 
Author-Name: Michael Conroy 
Author-X-Name-First: Michael 
Author-X-Name-Last: Conroy 
Author-Name: Antoni Borras 
Author-X-Name-First: Antoni 
Author-X-Name-Last: Borras 
Title: Asymmetric exchange between populations differing in habitat quality: A metapopulation study on the citril finch 
Abstract:
  The citril finch ( Serinus citrinella ) is a Cardueline finch restricted
 to the high mountains of western Europe. Since 1991 we have
 captured-recaptured about 6000 birds in two contrasting subpopulations
 located on the same mountain but separated by 5 km in distance. Citril
 finches, at the north-facing locality (La Vansa), rely more on Pine trees
 ( Pinus uncinata ) as their main food source, than birds at the
 south-facing locality (La Bofia), which rely more on herb seeds, which are
 of lower energetic content. Birds at La Vansa had higher body mass and fat
 score than those at La Bofia, suggesting that La Vansa was a site of
 higher-quality than La Bofia. By the use of a metapopulation approach and
 multistate models, we found that citril finches at the high-quality
 locality (La Vansa) showed higher survival rates than those at the
 low-quality one (La Bofia) (Vansa adults: &#x3c6; = 0.42 - 0.04,
 juveniles: &#x3c6; = 0.34 - 0.05; Bofia adults: &#x3c6; = 0.35 - 0.04,
 juveniles: &#x3c6; = 0.28 - 0.05). Dispersal was also asymmetric and
 higher for juvenile birds, with movement rates for juvenile citril finches
 from the low-quality to the higher-quality locality (Bofia to Vansa:
 &#x201a; = 0.38 - 0.10) higher than the reverse (Vansa to Bofia: &#x201a;
 = 0.09 - 0.03). We also investigated time-specific factors (e.g.
 meteorological data and fructification rate of Pinus ) as potential
 predictors of overall mortality and dispersal patterns. The results do not
 allow strong conclusions regarding the impact of these factors on survival
 and movement rates. Patterns of movement found in the Citril Finch between
 localities document a new model for the dispersal of species from low to
 high quality habitats, which we label of 'sources and pools'. This
 contrasts with currently accepted models of 'sources and sinks', in which
 movement is from high to low quality habitats, and 'Ideal Free
 Distributions', in which there is a balanced dispersal between habitats of
 different quality. 
Journal: Journal of Applied Statistics 
Pages: 425-441 
Issue: 1-4 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120108791 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120108791
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:425-441




Template-Type: ReDIF-Article 1.0
Author-Name: Richard Barker 
Author-X-Name-First: Richard 
Author-X-Name-Last: Barker 
Author-Name: Evan Cooch 
Author-X-Name-First: Evan 
Author-X-Name-Last: Cooch 
Author-Name: Carl Schwarz 
Author-X-Name-First: Carl 
Author-X-Name-Last: Schwarz 
Title: Discussion comments on: 'Approaches for the direct estimation of u and demographic contributions to u using capture-recapture data' 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 569-572 
Issue: 1-4 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120108610 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120108610
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:569-572




Template-Type: ReDIF-Article 1.0
Author-Name: J. A. Dupuis 
Author-X-Name-First: J. A. 
Author-X-Name-Last: Dupuis 
Title: Prior distributions for stratified capture-recapture models 
Abstract:
  We consider the Arnason-Schwarz model, usually used to estimate survival
 and movement probabilities from capture-recapture data. A missing data
 structure of this model is constructed which allows a clear separation of
 information relative to capture and relative to movement. Extensions of
 the Arnason-Schwarz model are considered. For example, we consider a model
 that takes into account both the individual migration history and the
 individual reproduction history. Biological assumptions of these
 extensions are summarized via a directed graph. Owing to missing data, the
 posterior distribution of parameters is numerically intractable. To
 overcome those computational difficulties we advocate a Gibbs sampling
 algorithm that takes advantage of the missing data structure inherent in
 capture-recapture models. Prior information on survival, capture and
 movement probabilities typically consists of a prior mean and of a prior
 95% credible confidence interval. Dirichlet distributions are used to
 incorporate some prior information on capture, survival probabilities, and
 movement probabilities. Finally, the influence of the prior on the
 Bayesian estimates of movement probabilities is examined. 
Journal: Journal of Applied Statistics 
Pages: 225-237 
Issue: 1-4 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120108692 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120108692
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:225-237




Template-Type: ReDIF-Article 1.0
Author-Name: Shirley Pledger 
Author-X-Name-First: Shirley 
Author-X-Name-Last: Pledger 
Author-Name: Carl Schwarz 
Author-X-Name-First: Carl 
Author-X-Name-Last: Schwarz 
Title: Modelling heterogeneity of survival in band-recovery data using mixtures 
Abstract:
  Finite mixture methods are applied to bird band-recovery studies to allow
 for heterogeneity of survival. Birds are assumed to belong to one of
 finitely many groups, each of which has its own survival rate (or set of
 survival rates varying by time and/or age). The group to which a specific
 animal belongs is not known, so its survival probability is a random
 variable from a finite mixture. Heterogeneity is thus modelled as a latent
 effect. This gives a wide selection of likelihood-based models, which may
 be compared using likelihood ratio tests. These models are discussed with
 reference to real and simulated data, and compared with previous models. 
Journal: Journal of Applied Statistics 
Pages: 315-327 
Issue: 1-4 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120108737 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120108737
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:315-327




Template-Type: ReDIF-Article 1.0
Author-Name: A. Neil Arnason 
Author-X-Name-First: A. Neil 
Author-X-Name-Last: Arnason 
Author-Name: Carl Schwarz 
Author-X-Name-First: Carl 
Author-X-Name-Last: Schwarz 
Title: POPAN-6: Exploring convergence and estimate properties with SIMULATE 
Abstract:
  We describe some developments in the P OPAN system for the analysis of
 mark-recapture data from Jolly-Seber (JS) type experiments. The latest
 version, P OPAN-6, adopts the Design Matrix approach for specifying
 constraints and then uses it in the constrained maximization of the
 likelihood. We describe how this is done and the difference it makes to
 convergence and parameter identifiability over the constraint
 contrast-equation methods used in P OPAN-5. Then we show how the SIMULATE
 capabilities of P OPAN can be used to explore the properties of estimates,
 including their identifiability, precision, and robustness to model
 misspecification or capture heterogeneity. 
Journal: Journal of Applied Statistics 
Pages: 649-668 
Issue: 1-4 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120108593 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120108593
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:649-668




Template-Type: ReDIF-Article 1.0
Author-Name: Kenneth Pollock 
Author-X-Name-First: Kenneth 
Author-X-Name-Last: Pollock 
Title: The use of auxiliary variables in capture-recapture modelling: An overview 
Abstract:
  I review the use of auxiliary variables in capture-recapture models for
 estimation of demographic parameters (e.g. capture probability, population
 size, survival probability, and recruitment, emigration and immigration
 numbers). I focus on what has been done in current research and what still
 needs to be done. Typically in the literature, covariate modelling has
 made capture and survival probabilities functions of covariates, but there
 are good reasons also to make other parameters functions of covariates as
 well. The types of covariates considered include environmental covariates
 that may vary by occasion but are constant over animals, and individual
 animal covariates that are usually assumed constant over time. I also
 discuss the difficulties of using time-dependent individual animal
 covariates and some possible solutions. Covariates are usually assumed to
 be measured without error, and that may not be realistic. For closed
 populations, one approach to modelling heterogeneity in capture
 probabilities uses observable individual covariates and is thus related to
 the primary purpose of this paper. The now standard Huggins-Alho approach
 conditions on the captured animals and then uses a generalized
 Horvitz-Thompson estimator to estimate population size. This approach has
 the advantage of simplicity in that one does not have to specify a
 distribution for the covariates, and the disadvantage is that it does not
 use the full likelihood to estimate population size. Alternately one could
 specify a distribution for the covariates and implement a full likelihood
 approach to inference to estimate the capture function, the covariate
 probability distribution, and the population size. The general Jolly-Seber
 open model enables one to estimate capture probability, population sizes,
 survival rates, and birth numbers. Much of the focus on modelling
 covariates in program MARK has been for survival and capture probability
 in the Cormack-Jolly-Seber model and its generalizations (including
 tag-return models). These models condition on the number of animals marked
 and released. A related, but distinct, topic is radio telemetry survival
 modelling that typically uses a modified Kaplan-Meier method and Cox
 proportional hazards model for auxiliary variables. Recently there has
 been an emphasis on integration of recruitment in the likelihood, and
 research on how to implement covariate modelling for recruitment and
 perhaps population size is needed. The combined open and closed 'robust'
 design model can also benefit from covariate modelling and some important
 options have already been implemented into MARK. Many models are usually
 fitted to one data set. This has necessitated development of model
 selection criteria based on the AIC (Akaike Information Criteria) and the
 alternative of averaging over reasonable models. The special problems of
 estimating over-dispersion when covariates are included in the model and
 then adjusting for over-dispersion in model selection could benefit from
 further research. 
Journal: Journal of Applied Statistics 
Pages: 85-102 
Issue: 1-4 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120108430 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120108430
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:85-102




Template-Type: ReDIF-Article 1.0
Author-Name: Andrea. Dhondt 
Author-X-Name-First: Andrea. 
Author-X-Name-Last: Dhondt 
Title: Discussion comments 'Multistate recapture models: Modelling incomplete individual histories'--why are we doing all this? 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 371-372 
Issue: 1-4 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120108629 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120108629
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:371-372




Template-Type: ReDIF-Article 1.0
Author-Name: Alan Franklin 
Author-X-Name-First: Alan 
Author-X-Name-Last: Franklin 
Author-Name: David Anderson 
Author-X-Name-First: David 
Author-X-Name-Last: Anderson 
Author-Name: Kenneth Burnham 
Author-X-Name-First: Kenneth 
Author-X-Name-Last: Burnham 
Title: Estimation of long-term trends and variation in avian survival probabilities using random effects models 
Abstract:
  We obtained banding and recovery data from the Bird Banding Laboratory
 (operated by the Biological Resources Division of the US Geological
 Survey) for adults from 129 avian species that had been continuously
 banded for &#x3e; 24 years. Data were partitioned by gender, banding
 period (winter versus summer), and by states/provinces. Data sets were
 initially screened for adequacy based on specific criteria (e.g. minimum
 sample sizes). Fifty-nine data sets (11 waterfowl species, the Mourning
 Dove and Common Grackle) met our criteria of adequacy for further
 analysis. We estimated annual survival probabilities using the Brownie et
 al. recovery model {St, ft} in program MARK. Trends in annual survival and
 temporal process variation were estimated using random effects models
 based on shrinkage estimators. Waterfowl species had relatively little
 variation in annual survival probabilities (mean CV = 8.7% and 10% for
 males and females, respectively). The limited data for other species
 suggested similar low temporal variation for males, but higher temporal
 variation for females (CV = 40%). Evidence for long-term trends varied by
 species, banding period and sex, with no obvious spatial patterns for
 either positive or negative trends in survival probabilities. An exception
 was Mourning Doves banded in Illinois/Missouri and Arizona/New Mexico
 where both males (slope = -0.0122, se = 0.0019 and females (slope =
 -0.0109 to -0.0128, se = 0.0018 -0.0032) exhibited declining trends in
 survival probabilities. We believe our approach has application for
 large-scale monitoring. However, meaningful banding and recovery data for
 species other than waterfowl is very limited in North America. 
Journal: Journal of Applied Statistics 
Pages: 267-287 
Issue: 1-4 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120108719 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120108719
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:267-287




Template-Type: ReDIF-Article 1.0
Author-Name: Blandine Doligez 
Author-X-Name-First: Blandine 
Author-X-Name-Last: Doligez 
Author-Name: Jean Clobert 
Author-X-Name-First: Jean 
Author-X-Name-Last: Clobert 
Author-Name: Richard Pettifor 
Author-X-Name-First: Richard 
Author-X-Name-Last: Pettifor 
Author-Name: Marcus Rowcliffe 
Author-X-Name-First: Marcus 
Author-X-Name-Last: Rowcliffe 
Author-Name: Lars Gustafsson 
Author-X-Name-First: Lars 
Author-X-Name-Last: Gustafsson 
Author-Name: Christopher Perrins 
Author-X-Name-First: Christopher 
Author-X-Name-Last: Perrins 
Author-Name: Robin McCleery 
Author-X-Name-First: Robin 
Author-X-Name-Last: McCleery 
Title: Costs of reproduction: Assessing responses to brood size manipulation on life-history and behavioural traits using multi-state capture-recapture models 
Abstract:
  Costs of reproduction are fundamental trade-offs shaping the evolution of
 life histories. There has been much interest, discussion and controversy
 about the nature and type of reproductive costs. The manipulation of
 reproductive effort (e.g. brood size manipulation) may alter not only
 life-history traits such as future adult survival rate and future
 reproductive effort, but also behavioural decisions affecting
 recapture/resighting and dispersal probabilities. We argue that many
 previous studies of the costs of reproduction may have erroneously
 concluded the existence or non-existence of such costs because of their
 use of local return rates to assess survival. In this paper, we take
 advantage of the modern multistate capture-recapture methods to highlight
 how the accurate assessment of the costs of reproduction requires
 incorporating not only recapture probability, but also behavioural 'state'
 variables, for example dispersal status and current reproductive
 investment. The inclusion of state-dependent decisions can radically alter
 the conclusions drawn regarding the costs of reproduction on future
 survival or reproductive investment. We illustrate this point by
 re-analysing data collected to address the question of the costs of
 reproduction in the collared flycatcher and the great tit. We discuss in
 some detail the methodological issues and implications of the analytical
 techniques. 
Journal: Journal of Applied Statistics 
Pages: 407-423 
Issue: 1-4 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120108845 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120108845
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:407-423




Template-Type: ReDIF-Article 1.0
Author-Name: R. T. Alisauskas 
Author-X-Name-First: R. T. 
Author-X-Name-Last: Alisauskas 
Author-Name: M. S. Lindberg 
Author-X-Name-First: M. S. 
Author-X-Name-Last: Lindberg 
Title: Effects of neckbands on survival and fidelity of white-fronted and Canada geese captured as non-breeding adults 
Abstract:
  We conducted an experiment to examine the effect of neckbands,
 controlling for differences in sex, species and year of study (1991-1997),
 on probabilities of capture, survival, reporting, and fidelity in
 non-breeding small Canada ( Branta canadensis hutchinsi ) and
 white-fronted ( Anser albifrons frontalis ) geese. In Canada's central
 arctic, we systematically double-marked about half of the individuals from
 each species with neckbands and legbands, and we marked the other half
 only with legbands. We considered 48 a priori models that included
 combinations of sex, species, year, and neckband effects on the four
 population parameters produced by Burnham's (1993) model, using AIC for
 model selection. The four best approximating models each included a
 negative effect of neckbands on survival, and effect size varied among
 years. True survival probability of neckbanded birds annually ranged from
 0.006 to 0.23 and 0.039 to 0.22 (Canada and white-fronted geese,
 respectively) lower than for conspecifics without neckbands. Changes in
 estimates of survival probability in neckbanded birds appeared to
 attenuate more recently, particularly in Canada Geese, a result that we
 suspect was related to lower retention rates of neckbands. We urge extreme
 caution in use of neckbands for estimation of certain population
 parameters, and discourage their use for estimation of unbiased survival
 probability in these two species. 
Journal: Journal of Applied Statistics 
Pages: 521-537 
Issue: 1-4 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120108575 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120108575
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:521-537




Template-Type: ReDIF-Article 1.0
Author-Name: Michael Conroy 
Author-X-Name-First: Michael 
Author-X-Name-Last: Conroy 
Title: Real and quasi-experiments in capture-recapture studies: Suggestions for advancing the state of the art 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 475-477 
Issue: 1-4 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120108520 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120108520
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:475-477




Template-Type: ReDIF-Article 1.0
Author-Name: Carl James Schwarz 
Author-X-Name-First: Carl James 
Author-X-Name-Last: Schwarz 
Title: Real and quasi-experiments in capture-recapture studies 
Abstract:
  The three key elements of experimental design are randomization,
 replication, and variance identification and control. Capture-recapture
 experiments usually pay sufficient attention to the first two elements,
 but often do not pay sufficient attention to sources of variation. These
 include blocking factors and different sizes of experimental units. By
 casting capture-recapture studies in an experimental design framework, the
 various roles of these sources of variation become clear and the sources
 that are pooled when these experiments are analysed using existing
 software is also clear. This formulation also shows that care must be
 taken with pseudo-replication and different sized experimental units. 
Journal: Journal of Applied Statistics 
Pages: 459-473 
Issue: 1-4 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120108511 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120108511
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:459-473




Template-Type: ReDIF-Article 1.0
Author-Name: J. A. Dupuis 
Author-X-Name-First: J. A. 
Author-X-Name-Last: Dupuis 
Title: Response to Carl Schwarz 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 241-244 
Issue: 1-4 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120108656 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120108656
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:241-244




Template-Type: ReDIF-Article 1.0
Author-Name: S. P. Brooks 
Author-X-Name-First: S. P. 
Author-X-Name-Last: Brooks 
Author-Name: E. A. Catchpole 
Author-X-Name-First: E. A. 
Author-X-Name-Last: Catchpole 
Author-Name: B. J. T. Morgan 
Author-X-Name-First: B. J. T. 
Author-X-Name-Last: Morgan 
Author-Name: M. P. Harris 
Author-X-Name-First: M. P. 
Author-X-Name-Last: Harris 
Title: Bayesian methods for analysing ringing data 
Abstract:
  A major recent development in statistics has been the use of fast
 computational methods of Markov chain Monte Carlo. These procedures allow
 Bayesian methods to be used in quite complex modelling situations. In this
 paper, we shall use a range of real data examples involving lapwings,
 shags, teal, dippers, and herring gulls, to illustrate the power and range
 of Bayesian techniques. The topics include: prior sensitivity; the use of
 reversible-jump MCMC for constructing model probabilities and comparing
 models, with particular reference to models with random effects;
 model-averaging; and the construction of Bayesian measures of
 goodness-of-fit. Throughout, there will be discussion of the practical
 aspects of the work - for instance explaining when and when not to use the
 BUGS package. 
Journal: Journal of Applied Statistics 
Pages: 187-206 
Issue: 1-4 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120108683 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120108683
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:187-206




Template-Type: ReDIF-Article 1.0
Author-Name: Charles Francis 
Author-X-Name-First: Charles 
Author-X-Name-Last: Francis 
Author-Name: Pertti Saurola 
Author-X-Name-First: Pertti 
Author-X-Name-Last: Saurola 
Title: Estimating age-specific survival rates of tawny owls--recaptures versus recoveries 
Abstract:
  We compared estimates of annual survival rates of tawny owls ( Strix
 aluco ) ringed in southern Finland from several different sampling
 methods: recoveries of birds ringed as young; recaptures of birds ringed
 as young; recoveries of birds ringed as adults as well as young; combined
 recoveries and recaptures of birds ringed as young, and combined
 recoveries and recaptures of birds ringed as adults and young. From 1979
 to 1998, 18 040 young owls were ringed, of which 983 were recaptured as
 breeders in subsequent years during this period, and 1764 were recovered
 dead at various locations. In addition, 1751 owls were ringed as adults,
 of which 612 were later recaptured and 199 were recovered dead. First-year
 survival rates estimated using only recoveries of birds ringed as young
 averaged 48%, while apparent survival rates estimated using only
 recaptures from birds ringed as young averaged 10-13%. Use of combined
 recapture-recovery models, or supplementary information from recoveries of
 birds ringed as adults, produced survival estimates of 30-37%. Survival
 estimates from young-recoveries-only models were biased high, because of
 violation of the assumption of constant recovery rates with age: birds
 dying in their first-year were one-third less likely to be found and
 reported than older birds. In contrast, recaptures-only models confounded
 emigration with mortality. Despite these differences in mean values,
 annual fluctuations in estimated first-year survival rates were similar
 with all models. Estimates of adult survival rates were similar with all
 models, while those for second-year birds were similar for all models
 except recaptures-only. These results highlight the potential biases
 associated with analysing either recaptures or recoveries alone of birds
 ringed as young, and the benefits of using combined data. 
Journal: Journal of Applied Statistics 
Pages: 637-647 
Issue: 1-4 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120108584 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120108584
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:637-647




Template-Type: ReDIF-Article 1.0
Author-Name: David Harvey 
Author-X-Name-First: David 
Author-X-Name-Last: Harvey 
Author-Name: Terence Mills 
Author-X-Name-First: Terence 
Author-X-Name-Last: Mills 
Title: Unit roots and double smooth transitions 
Abstract:
  Techniques for testing the null hypothesis of difference stationarity
 against stationarity around some deterministic function have received much
 attention. In particular, unit root tests where the alternative is
 stationarity around a smooth transition in a linear trend have recently
 been proposed to permit the possibility of non-instantaneous structural
 change. In this paper we develop tests extending such an approach in order
 to admit more than one structural change. The analysis is motivated by
 time series that appear to undergo two smooth transitions in the linear
 trend, and the application of the new tests to two such series (average
 global temperature and US consumer prices) highlights the benefits of this
 double transition extension. 
Journal: Journal of Applied Statistics 
Pages: 675-683 
Issue: 5 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120098739 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120098739
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:5:p:675-683




Template-Type: ReDIF-Article 1.0
Author-Name: M. P. Diaz 
Author-X-Name-First: M. P. 
Author-X-Name-Last: Diaz 
Author-Name: A. H. Barchuk 
Author-X-Name-First: A. H. 
Author-X-Name-Last: Barchuk 
Author-Name: S. Luque 
Author-X-Name-First: S. 
Author-X-Name-Last: Luque 
Author-Name: C. Oviedo 
Author-X-Name-First: C. 
Author-X-Name-Last: Oviedo 
Title: Generalized linear models to study spatial distribution of tree species in Argentinean arid Chaco 
Abstract:
  This work adapts some generalized linear models in order to study the
 spatial pattern of an important tree species. The classical multivariate
 Ising model, which incorporates the dependence on neighbour individuals in
 a regular lattice, was adapted by setting a Poisson regression with an
 extra variation parameter to fit over-dispersion. Because the spatial
 pattern is only evident to a special reference scale, plots were sampled
 at two different scales. Two individual presence-absence matrices were
 analysed for each case through over-dispersion Poisson regression and
 log-linear models, including binary indicators for a neighbour in the four
 directions in the linear predictor. The results showed that the species,
 in the adult stage, has a spatial distribution in patches having no more
 than two adult individuals. 
Journal: Journal of Applied Statistics 
Pages: 685-694 
Issue: 5 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120098748 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120098748
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:5:p:685-694




Template-Type: ReDIF-Article 1.0
Author-Name: M. Dharmalingam 
Author-X-Name-First: M. 
Author-X-Name-Last: Dharmalingam 
Title: Construction of Partial Triallel Crosses based on Trojan Square Design 
Abstract:
  A systematic method of developing or raising the offsprings of parents or
 lines that are subjected to analysis to draw valid inferences about
 parents is called a Mating Design (MD). A Mating Design represents only a
 part of a genetic experiment. Diallel and the four North Carolina (NC)
 designs, Triallel and Double Crosses are notable examples of mating
 designs. In this paper, an attempt has been made to provide a systematic
 method of construction of Partial Triallel Crosses (PTC) using Trojan
 Square Design (TSD), which requires only the fraction of the number of
 crosses to be made compared with Triallel Crosses. 
Journal: Journal of Applied Statistics 
Pages: 695-702 
Issue: 5 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120098757 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120098757
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:5:p:695-702




Template-Type: ReDIF-Article 1.0
Author-Name: Chul Ahn 
Author-X-Name-First: Chul 
Author-X-Name-Last: Ahn 
Author-Name: Sin-Ho Jung 
Author-X-Name-First: Sin-Ho 
Author-X-Name-Last: Jung 
Author-Name: Seung-Ho Kang 
Author-X-Name-First: Seung-Ho 
Author-X-Name-Last: Kang 
Title: Modified regression coefficient analysis for repeated binary measurements 
Abstract:
  Myers & Broyles (2000a, 2000b) illustrate that regression coefficient
 analysis (RCA) is a viable alternative to a generalized estimating
 equation (GEE) in the analysis of correlated binomial data. Since the
 regression coefficients (b i ' s ) may have different precisions, we
 modify RCA by weighting b i ' s by the inverses of their variances for
 statistical optimality. We perform the simulation study to evaluate the
 performance of RCA, modified RCA and GEE in terms of empirical type I
 errors and empirical powers of the regression coefficients in repeated
 binary measurement designs with and without dropouts. Two thousand data
 sets are generated using autoregressive (AR(1)) and compound symmetry (CS)
 correlation structures. We compare the type I errors and powers of RCA,
 modified RCA and GEE for the analysis of repeated binary measurement data
 as affected by different dropout mechanisms such as random dropouts and
 treatment dependent dropouts. 
Journal: Journal of Applied Statistics 
Pages: 703-710 
Issue: 5 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120098766 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120098766
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:5:p:703-710




Template-Type: ReDIF-Article 1.0
Author-Name: Jorge Belaire-Franch 
Author-X-Name-First: Jorge 
Author-X-Name-Last: Belaire-Franch 
Author-Name: Dulce Contreras-Bayarri 
Author-X-Name-First: Dulce 
Author-X-Name-Last: Contreras-Bayarri 
Title: Improving cross-correlation tests through re-sampling techniques 
Abstract:
  In this paper, we show that type I and type II errors of the
 cross-correlation test between two autocorrelated time series can be
 reduced, in some cases, by means of tabulation of the empirical
 distribution of the sample cross-correlation coefficient, using
 alternative re-sampling techniques. 
Journal: Journal of Applied Statistics 
Pages: 711-720 
Issue: 5 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120098775 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120098775
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:5:p:711-720




Template-Type: ReDIF-Article 1.0
Author-Name: Moon Yul Huh 
Author-X-Name-First: Moon Yul 
Author-X-Name-Last: Huh 
Author-Name: Kiyeol Kim 
Author-X-Name-First: Kiyeol 
Author-X-Name-Last: Kim 
Title: Visualization of multidimensional data using modifications of the Grand Tour 
Abstract:
  Current implementations of Asimov's Grand Tour (for example in XLISP-STAT
 by Tierney, 1990, or in XGobi by Buja et al., 1996) do not remember the
 path of projections and show only the current state during the touring
 process. We propose a modification of the Grand Tour, named Tracking Grand
 Tour (TGT), that shows the trace of the touring process as small 'comet
 trails' of the projected points. The usefulness of the TGT is demonstrated
 with a simulated and a real data set. 
Journal: Journal of Applied Statistics 
Pages: 721-728 
Issue: 5 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120098784 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120098784
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:5:p:721-728




Template-Type: ReDIF-Article 1.0
Author-Name: Zbigniew Kominek 
Author-X-Name-First: Zbigniew 
Author-X-Name-Last: Kominek 
Title: Minimum chi-squared estimation of stable distributions parameters: An application to the Warsaw Stock Exchange 
Abstract:
  This paper derives an application of the minimum chi-squared (MCS)
 methodology to estimate the parameters of the unimodal symmetric stable
 distribution. The proposed method is especially suitable for large, both
 regular and non-standard, data sets. Monte Carlo simulations are performed
 to compare the efficiency of the MCS estimation with the efficiency of the
 McCulloch quantile algorithm. In the case of grouped observations,
 evidence in favour of the MCS method is reported. For the ungrouped data
 the MCS estimation generally performs better than McCulloch's quantile
 method for samples larger than 400 observations and for high alphas. The
 relative advantage of the MCS over the McCulloch estimators increases for
 larger samples. The empirical example analyses the highly irregular
 distributions of returns on the selected securities from the Warsaw Stock
 Exchange. The quantile and maximum likelihood estimates of characteristic
 exponents are generally smaller than the MCS ones. This reflects the bias
 in the traditional methods, which is due to a lack of adjustment for
 censored and clustered observations, and shows the flexibility of the
 proposed MCS approach. 
Journal: Journal of Applied Statistics 
Pages: 729-744 
Issue: 5 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120098793 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120098793
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:5:p:729-744




Template-Type: ReDIF-Article 1.0
Author-Name: Kelly Zou 
Author-X-Name-First: Kelly 
Author-X-Name-Last: Zou 
Author-Name: W. J. Hall 
Author-X-Name-First: W. J. 
Author-X-Name-Last: Hall 
Title: On estimating a transformation correlation coefficient 
Abstract:
  We consider a semiparametric and a parametric transformation-to-normality
 model for bivariate data. After an unstructured or structured monotone
 transformation of the measurement scales, the measurements are assumed to
 have a bivariate normal distribution with correlation coefficient &#x201e;
 , here termed the 'transformation correlation coefficient'. Under the
 semiparametric model with unstructured transformation, the principle of
 invariance leads to basing inference on the marginal ranks. The resulting
 rank-based likelihood function of &#x201e; is maximized via a Monte Carlo
 procedure. Under the parametric model, we consider Box-Cox type
 transformations and maximize the likelihood of &#x201e; along with the
 nuisance parameters. Efficiencies of competing methods are reported, both
 theoretically and by simulations. The methods are illustrated on a
 real-data example. 
Journal: Journal of Applied Statistics 
Pages: 745-760 
Issue: 5 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120098801 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120098801
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:5:p:745-760




Template-Type: ReDIF-Article 1.0
Author-Name: James Kepner 
Author-X-Name-First: James 
Author-X-Name-Last: Kepner 
Author-Name: Dennis Wackerly 
Author-X-Name-First: Dennis 
Author-X-Name-Last: Wackerly 
Title: Observations on the effect of the prior distribution on the predictive distribution in Bayesian inferences 
Abstract:
  Typically, in the brief discussion of Bayesian inferential methods
 presented at the beginning of calculus-based undergraduate or graduate
 mathematical statistics courses, little attention is paid to the process
 of choosing the parameter value(s) for the prior distribution. Even less
 attention is paid to the impact of these choices on the predictive
 distribution of the data. Reasons for this include that the posterior can
 be found by ignoring the predictive distribution thereby streamlining the
 derivation of the posterior and/or that computer software can be used to
 find the posterior distribution. In this paper, the binomial,
 negative-binomial and Poisson distributions along with their conjugate
 beta and gamma priors are utilized to obtain the resulting predictive
 distributions. It is then demonstrated that specific choices of the
 parameters of the priors can lead to predictive distributions with
 properties that might be surprising to a non-expert user of Bayesian
 methods. 
Journal: Journal of Applied Statistics 
Pages: 761-769 
Issue: 5 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120098810 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120098810
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:5:p:761-769




Template-Type: ReDIF-Article 1.0
Author-Name: Man-Suk Oh 
Author-X-Name-First: Man-Suk 
Author-X-Name-Last: Oh 
Author-Name: Dong Wan Shin 
Author-X-Name-First: Dong Wan 
Author-X-Name-Last: Shin 
Title: Bayesian model selection and parameter estimation for possibly asymmetric and non-stationary time series using a reversible jump Markov chain Monte Carlo approach 
Abstract:
  A Markov chain Monte Carlo (MCMC) approach, called a reversible jump
 MCMC, is employed in model selection and parameter estimation for possibly
 non-stationary and non-linear time series data. The non-linear structure
 is modelled by the asymmetric momentum threshold autoregressive process
 (MTAR) of Enders & Granger (1998) or by the asymmetric self-exciting
 threshold autoregressive process (SETAR) of Tong (1990). The
 non-stationary and non-linear feature is represented by the MTAR (or
 SETAR) model in which one ( &#x201e; 1 ) of the AR coefficients is greater
 than one, and the other ( &#x201e; 2 ) is smaller than one. The other
 non-stationary and linear, stationary and nonlinear, and stationary and
 linear features, represented respectively by ( &#x201e; 1 = &#x201e; 2 = 1
 ), ( &#x201e; 1 p &#x201e; 2 &#x3c; 1 ) and ( &#x201e; 1 = &#x201e; 2
 &#x3c; 1 ), are also considered as possible models. The reversible jump
 MCMC provides estimates of posterior probabilities for these four
 different models as well as estimates of the AR coefficients &#x201e; 1
 and &#x201e; 2 . The proposed method is illustrated by analysing six
 series of US interest rates in terms of model selection, parameter
 estimation, and forecasting. 
Journal: Journal of Applied Statistics 
Pages: 771-789 
Issue: 5 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760120098829 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760120098829
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:5:p:771-789




Template-Type: ReDIF-Article 1.0
Author-Name: Kelly Zou 
Author-X-Name-First: Kelly 
Author-X-Name-Last: Zou 
Author-Name: W. J. Hall 
Author-X-Name-First: W. J. 
Author-X-Name-Last: Hall 
Title: Semiparametric and parametric transformation models for comparing diagnostic markers with paired design 
Abstract:
  We develop semiparametric and parametric transformation models for
 estimation and comparison of ROC curves derived from measurements from two
 diagnostic tests on the same subjects. We assume the existence of
 transformed measurement scales, one for each test, on which the paired
 measurements have bivariate normal distributions. The resulting pair of
 ROC curves are estimated by maximum likelihood algorithms, using joint
 rank data in the semiparametric model with unspecified transformations and
 using Box-Cox transformations in the parametric transformation case.
 Several hypothesis tests for comparing the two ROC curves, or
 characteristics of them, are developed. Two clinical examples are
 presented and simulation results are provided. 
Journal: Journal of Applied Statistics 
Pages: 803-816 
Issue: 6 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760220136140 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760220136140
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:6:p:803-816




Template-Type: ReDIF-Article 1.0
Author-Name: Abdulnasser Hatemi-J 
Author-X-Name-First: Abdulnasser 
Author-X-Name-Last: Hatemi-J 
Author-Name: Ghazi Shukur 
Author-X-Name-First: Ghazi 
Author-X-Name-Last: Shukur 
Title: Multivariate-based causality tests of twin deficits in the US 
Abstract:
  This paper provides an alternative methodology for testing the causality
 direction between twin deficits in the US. Rao's multivariate F-test
 combined with the bootstrap simulation technique has appealing properties,
 especially when the data-generating process is characterized by unit
 roots. In addition the results show that the effect of structural breaks
 are of paramount importance when the causality tests are conducted. In
 much contemporary applied econometrics there is all-too-little attention
 given to the possibility of changes in the economic process. 
Journal: Journal of Applied Statistics 
Pages: 817-824 
Issue: 6 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760220136159 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760220136159
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:6:p:817-824




Template-Type: ReDIF-Article 1.0
Author-Name: C. Agostinelli 
Author-X-Name-First: C. 
Author-X-Name-Last: Agostinelli 
Title: Robust stepwise regression 
Abstract:
  The selection of an appropriate subset of explanatory variables to use in
 a linear regression model is an important aspect of a statistical
 analysis. Classical stepwise regression is often used with this aim but it
 could be invalidated by a few outlying observations. In this paper, we
 introduce a robust F-test and a robust stepwise regression procedure based
 on weighted likelihood in order to achieve robustness against the presence
 of outliers. The introduced methodology is asymptotically equivalent to
 the classical one when no contamination is present. Some examples and
 simulation are presented. 
Journal: Journal of Applied Statistics 
Pages: 825-840 
Issue: 6 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760220136168 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760220136168
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:6:p:825-840




Template-Type: ReDIF-Article 1.0
Author-Name: M. D. Jimenez Gamero 
Author-X-Name-First: M. D. Jimenez 
Author-X-Name-Last: Gamero 
Author-Name: J. M. Munoz Pichardo 
Author-X-Name-First: J. M. Munoz 
Author-X-Name-Last: Pichardo 
Author-Name: J. Munoz Garcia 
Author-X-Name-First: J. Munoz 
Author-X-Name-Last: Garcia 
Author-Name: A. Pascual Acosta 
Author-X-Name-First: A. Pascual 
Author-X-Name-Last: Acosta 
Title: Rao distance as a measure of influence in the multivariate linear model 
Abstract:
  Several methods have been suggested to detect influential observations in
 the linear regression model and a number of them have been extended for
 the multivariate regression model. In this article we consider the
 multivariate general linear model, Y = XB + k , which contains the linear
 regression model and the multivariate regression model as particular
 cases. Assuming that the random disturbances are normally distributed, the
 BLUE of v B is also normally distributed. Since the distribution of the
 BLUE of v B and the distribution of the BLUE of v B in the model with the
 omission of a set of observations differ, to study the influence that a
 set of observations has on the BLUE of v B , we propose to measure the
 distance between both distributions. To do this we use Rao distance. 
Journal: Journal of Applied Statistics 
Pages: 841-854 
Issue: 6 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760220136177 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760220136177
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:6:p:841-854




Template-Type: ReDIF-Article 1.0
Author-Name: E. Andres Houseman 
Author-X-Name-First: E. Andres 
Author-X-Name-Last: Houseman 
Author-Name: Louise Ryan 
Author-X-Name-First: Louise 
Author-X-Name-Last: Ryan 
Author-Name: Jonathan Levy 
Author-X-Name-First: Jonathan 
Author-X-Name-Last: Levy 
Author-Name: John Spengler 
Author-X-Name-First: John 
Author-X-Name-Last: Spengler 
Title: Autocorrelation in real-time continuous monitoring of microenvironments 
Abstract:
  Interpretation of continuous measurements in microenvironmental studies
 and exposure assessments can be complicated by autocorrelation, the
 implications of which are often not fully addressed. We discuss some
 statistical issues that arose in the analysis of microenvironmental
 particulate matter concentration data collected in 1998 by the Harvard
 School of Public Health. We present a simulation study that suggests that
 Generalized Estimating Equations, a technique often used to adjust for
 autocorrelation, may produce inflated Type I errors when applied to
 microenvironmental studies of small or moderate sample size, and that
 Linear Mixed Effects models may be more appropriate in small-sample
 settings. Environmental scientists often appeal to longer averaging times
 to reduce autocorrelation. We explore the functional relationship between
 averaging time, autocorrelation, and standard errors of both mean and
 variance, showing that longer averaging times impair statistical
 inferences about main effects. We conclude that, given widely available
 techniques that adjust for autocorrelation, longer averaging times may be
 inappropriate in microenvironmental studies. 
Journal: Journal of Applied Statistics 
Pages: 855-872 
Issue: 6 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760220136186A 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760220136186A
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:6:p:855-872




Template-Type: ReDIF-Article 1.0
Author-Name: Jixian Wang 
Author-X-Name-First: Jixian 
Author-X-Name-Last: Wang 
Author-Name: Peter Donnan 
Author-X-Name-First: Peter 
Author-X-Name-Last: Donnan 
Title: Adjusting for missing record linkage in outcome studies 
Abstract:
  Record linkage databases have been increasingly available and used in
 pharmacoepidemiology, pharmacoeconomic and outcome studies, where the
 relationship between drug exposure or intervention and outcome is the main
 concern. Sometimes the linkage between outcome data and exposure data may
 be missing so that only a proportion of patients in the outcome database
 can be linked to other databases. This paper proposes maximum likelihood
 (ML) and GEE procedures to obtain consistent estimates of parameters in
 the model relating the outcome and risk factors. Asymptotic variances of
 the estimates were derived for the situation where the missing rate is
 estimated from the same dataset. We show that using the estimated missing
 rate, rather than the known missing rate, may result in more accurate
 estimates of the parameters. The confidence interval of the predicted
 occurrence rate, when the missing rate was estimated, was derived.
 Simulations for different scenarios were performed in order to explore the
 small-sample behaviour of the ML procedure using the estimated missing
 rate. The results confirmed the greater efficiency of using the estimated
 missing rate instead of the true one for large sample sizes. However, this
 may not be true for small samples. The ML procedure was applied to an
 analysis of coronary artery bypass operations in patients with acute
 coronary syndrome. 
Journal: Journal of Applied Statistics 
Pages: 873-884 
Issue: 6 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760220136186 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760220136186
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:6:p:873-884




Template-Type: ReDIF-Article 1.0
Author-Name: Sung Park 
Author-X-Name-First: Sung 
Author-X-Name-Last: Park 
Author-Name: Kiho Kim 
Author-X-Name-First: Kiho 
Author-X-Name-Last: Kim 
Title: Construction of central composite designs for balanced orthogonal blocks 
Abstract:
  Box & Hunter (1957) recommended a set of orthogonally blocked central
 composite designs (CCD) when the region of interest is spherical. In order
 to achieve rotatability along with orthogonal blocking, the block size for
 those designs becomes unequal and it may not be attractive or practical to
 use such unequally blocked designs in many practical situations. In this
 paper, a construction method of orthogonally blocked CCD under the
 assumption of equal block size is proposed and an index of block
 orthogonality is introduced. 
Journal: Journal of Applied Statistics 
Pages: 885-893 
Issue: 6 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760220136195 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760220136195
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:6:p:885-893




Template-Type: ReDIF-Article 1.0
Author-Name: K. Hafeez 
Author-X-Name-First: K. 
Author-X-Name-Last: Hafeez 
Author-Name: H. Rowlands 
Author-X-Name-First: H. 
Author-X-Name-Last: Rowlands 
Author-Name: G. Kanji 
Author-X-Name-First: G. 
Author-X-Name-Last: Kanji 
Author-Name: S. Iqbal 
Author-X-Name-First: S. 
Author-X-Name-Last: Iqbal 
Title: Design optimization using ANOVA 
Abstract:
  This paper describes the design optimization of a robot sensor used for
 locating 3-D objects employing the Taguchi method in a computer simulation
 scenario. The location information from the sensor is to be utilized to
 control the movements of an industrial robot in a 'pick-and-place' or
 assembly operation. The Taguchi method, which is based on the
 Analysis-of-Variance (ANOVA) approach, is utilized to improve the
 performance of the sensor over a wider operating range. A review of the
 Taguchi method is presented along with step-by-step implementation details
 to identify and optimize the design parameters of the sensor. The method
 allows us to gauge the impact of various interactions present in the
 sensor system exclusively and permits us to single out those factors that
 have a dominant influence on the overall performance of the sensor. The
 investigation suggests that the Taguchi method is a more structured and
 efficient approach for achieving a robust design compared with the
 classical full factorial design approach. 
Journal: Journal of Applied Statistics 
Pages: 895-906 
Issue: 6 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760220136203 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760220136203
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:6:p:895-906




Template-Type: ReDIF-Article 1.0
Author-Name: Herbert Buning 
Author-X-Name-First: Herbert 
Author-X-Name-Last: Buning 
Title: Robustness and power of modified Lepage, Kolmogorov-Smirnov and Cramer-von Mises two-sample tests 
Abstract:
  For the two-sample problem with location and/or scale alternatives, as
 well as different shapes, several statistical tests are presented, such as
 of Kolmogorov-Smirnov and Cramer-von Mises type for the general
 alternative, and such as of Lepage type for location and scale
 alternatives. We compare these tests with the t-test and other location
 tests, such as the Welch test, and also the Levene test for scale. It
 turns out that there is, of course, no clear winner among the tests but,
 for symmetric distributions with the same shape, tests of Lepage type are
 the best ones whereas, for different shapes, Cramer-von Mises type tests
 are preferred. For extremely right-skewed distributions, a modification of
 the Kolmogorov-Smirnov test should be applied. 
Journal: Journal of Applied Statistics 
Pages: 907-924 
Issue: 6 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760220136212 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760220136212
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:6:p:907-924




Template-Type: ReDIF-Article 1.0
Author-Name: Beverley Causey 
Author-X-Name-First: Beverley 
Author-X-Name-Last: Causey 
Title: Parametric estimation of the number of classes in a population 
Abstract:
  This paper deals with the well-studied problem of how best to estimate
 the number of mutually exclusive and exhaustive classes in a population,
 based on a sample from it. Haas & Stokes review and provide non-parametric
 approaches, but there are associated difficulties especially for small
 sampling fractions and/or widely varying population class sizes. Sichel
 provided 'GIGP' methodology, for this problem and for other purposes; this
 paper utilizes the three-parameter GIGP distribution for this problem, and
 also for the estimation of the number of classes of size 1, as an
 alternative to the non-parametric approaches. Methodological and
 computational issues are considered, and examples indicate the potential
 for GIGP. 
Journal: Journal of Applied Statistics 
Pages: 925-934 
Issue: 6 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760220136221 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760220136221
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:6:p:925-934




Template-Type: ReDIF-Article 1.0
Author-Name: Vic Patrangenaru 
Author-X-Name-First: Vic 
Author-X-Name-Last: Patrangenaru 
Author-Name: Kanti Mardia 
Author-X-Name-First: Kanti 
Author-X-Name-Last: Mardia 
Title: A bootstrap approach to Pluto's origin 
Abstract:
  The solar nebula theory hypothesizes that planets are formed from an
 accretion disk of material that, over time, condenses into dust, small
 planetesimals, and that the planets should have, on average, coplanar,
 nearly circular orbits. If the orbit of Pluto has a different origin to
 the other planets in the solar system, then there will be tremendous
 repercussions on modelling the spacecrafts for a mission to Pluto. We test
 here the nebula theory for Pluto, using both parametric and non-parametric
 methods. We first develop asymptotic distributions of extrinsic means on a
 manifold, and then derive bootstrap and large sample distributions of the
 sample mean direction. Our parametric and non-parametric analyses provide
 very strong evidence that the solar nebula theory does not hold for Pluto. 
Journal: Journal of Applied Statistics 
Pages: 935-943 
Issue: 6 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/02664760220136230 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760220136230
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:6:p:935-943




Template-Type: ReDIF-Article 1.0
Author-Name: M. E. Ghitany 
Author-X-Name-First: M. E. 
Author-X-Name-Last: Ghitany 
Author-Name: S. Al-Awadhi 
Author-X-Name-First: S. 
Author-X-Name-Last: Al-Awadhi 
Title: Maximum likelihood estimation of Burr XII distribution parameters under random censoring 
Abstract:
  In this paper, we consider the maximum likelihood estimation of the
 parameters of Burr XII distribution using randomly right censored data. We
 provide necessary and sufficient conditions for the existence and
 uniqueness of the maximum likelihood estimates. Under such conditions, it
 is shown that the maximum likelihood estimates are strongly consistent for
 the true values of the parameters and are asymptotically bivariate normal.
 An application to leukemia free-survival times for allogeneic and
 autologous transplant patients is given. 
Journal: Journal of Applied Statistics 
Pages: 955-965 
Issue: 7 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/0266476022000006667 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000006667
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:7:p:955-965




Template-Type: ReDIF-Article 1.0
Author-Name: S. Balamurali 
Author-X-Name-First: S. 
Author-X-Name-Last: Balamurali 
Author-Name: M. Kalyanasundaram 
Author-X-Name-First: M. 
Author-X-Name-Last: Kalyanasundaram 
Title: Construction of a generalized robust Taguchi capability index 
Abstract:
  In this paper, a generalized Taguchi capability index is proposed and the
 construction method is also indicated. 
Journal: Journal of Applied Statistics 
Pages: 967-971 
Issue: 7 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/0266476022000006676 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000006676
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:7:p:967-971




Template-Type: ReDIF-Article 1.0
Author-Name: E. Andersson 
Author-X-Name-First: E. 
Author-X-Name-Last: Andersson 
Title: Monitoring cyclical processes. A non-parametric approach 
Abstract:
  Forecasting the turning points in business cycles is important to
 economic and political decisions. Time series of business indicators often
 exhibit cycles that cannot easily be modelled with a parametric function.
 This article presents a method for monitoring time-series with cycles in
 order to detect the turning points. A non-parametric estimation procedure
 that uses only monotonicity restrictions is used. The methodology of
 statistical surveillance is used for developing a system for early
 warnings of cycle turning points in monthly data. In monitoring, the
 inference situation is one of repeated decisions. Measurements of the
 performance of a method of surveillance are, for example, average run
 length and expected delay to a correct alarm. The properties of the
 proposed monitoring system are evaluated by means of a simulation study.
 The false alarms are controlled by a fixed median run length to the first
 false alarm. Results are given on the median delay time to a correct alarm
 for two situations: a peak after two and three years respectively . 
Journal: Journal of Applied Statistics 
Pages: 973-990 
Issue: 7 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/0266476022000006685 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000006685
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:7:p:973-990




Template-Type: ReDIF-Article 1.0
Author-Name: Steven Caudill 
Author-X-Name-First: Steven 
Author-X-Name-Last: Caudill 
Author-Name: Norman Godwin 
Author-X-Name-First: Norman 
Author-X-Name-Last: Godwin 
Title: Heterogeneous skewness in binary choice models: Predicting outcomes in the men's NCAA basketball tournament 
Abstract:
  Several authors have recently explored the estimation of binary choice
 models based on asymmetric error structures. One such family of skewed
 models is based on the exponential generalized beta type 2 (EGB2). One
 model in this family is the skewed logit. Recently, McDonald (1996, 2000)
 extended the work on the EGB2 family of skewed models to permit
 heterogeneity in the scale parameter. The aim of this paper is to extend
 the skewed logit model to allow for heterogeneity in the skewness
 parameter. By this we mean that, in the model developed, here the skewness
 parameter is permitted to vary from observation to observation by making
 it a function of exogenous variables. To demonstrate the usefulness of our
 model, we examine the issue of the predictive ability of sports seedings.
 We find that we are able to obtain better probability predictions using
 the skewed logit model with heterogeneous skewness than can be obtained
 with logit, probit, or skewed logit. 
Journal: Journal of Applied Statistics 
Pages: 991-1001 
Issue: 7 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/0266476022000006694 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000006694
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:7:p:991-1001




Template-Type: ReDIF-Article 1.0
Author-Name: Chung-Ho Chen 
Author-X-Name-First: Chung-Ho 
Author-X-Name-Last: Chen 
Author-Name: Chao-Yu Chou 
Author-X-Name-First: Chao-Yu 
Author-X-Name-Last: Chou 
Title: Economic design of continuous sampling plan under linear inspection cost 
Abstract:
  The article explores the problem of an economically based type I
 continuous sampling plan (CSP-1 plan) under linear inspection cost. By
 assuming that the per unit inspection cost is linearly proportional to the
 average number of inspections per inspection cycle, and by solving the
 modified Cassady et al.'s model, we not only have the required level of
 product quality but also obtain the minimum total expected cost per unit
 produced. 
Journal: Journal of Applied Statistics 
Pages: 1003-1009 
Issue: 7 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/0266476022000006702 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000006702
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:7:p:1003-1009




Template-Type: ReDIF-Article 1.0
Author-Name: Claudio Verzilli 
Author-X-Name-First: Claudio 
Author-X-Name-Last: Verzilli 
Author-Name: James Carpenter 
Author-X-Name-First: James 
Author-X-Name-Last: Carpenter 
Title: A Monte Carlo EM algorithm for random-coefficient-based dropout models 
Abstract:
  Longitudinal studies of neurological disorders suffer almost inevitably
 from non-compliance, which is likely to be non-ignorable. It is important
 in these cases to model the response variable and the dropout mechanism
 jointly. In this article we propose a Monte Carlo version of the EM
 algorithm that can be used to fit random-coefficient-based dropout models.
 A linear mixed model is assumed for the response variable and a
 discrete-time proportional hazards model for the dropout mechanism; these
 share a common set of random coefficients. The ideas are illustrated using
 data from a five-year trial assessing the efficacy of two drugs in the
 treatment of patients in the early stages of Parkinson's disease. 
Journal: Journal of Applied Statistics 
Pages: 1011-1021 
Issue: 7 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/0266476022000006711 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000006711
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:7:p:1011-1021




Template-Type: ReDIF-Article 1.0
Author-Name: James Mays 
Author-X-Name-First: James 
Author-X-Name-Last: Mays 
Author-Name: Jeffrey Birch 
Author-X-Name-First: Jeffrey 
Author-X-Name-Last: Birch 
Title: Smoothing for small samples with model misspecification: Nonparametric and semiparametric concerns 
Abstract:
  Our goal is to find a regression technique that can be used in a
 small-sample situation with possible model misspecification. The
 development of a new bandwidth selector allows nonparametric regression
 (in conjunction with least squares) to be used in this small-sample
 problem, where nonparametric procedures have previously proven to be
 inadequate. Considered here are two new semiparametric (model-robust)
 regression techniques that combine parametric and nonparametric techniques
 when there is partial information present about the underlying model. A
 general overview is given of how typical concerns for bandwidth selection
 in nonparametric regression extend to the model-robust procedures. A new
 penalized PRESS criterion (with a graphical selection strategy for
 applications) is developed that overcomes these concerns and is able to
 maintain the beneficial mean squared error properties of the new
 model-robust methods. It is shown that this new selector outperforms
 standard and recently improved bandwidth selectors. Comparisons of the
 selectors are made via numerous generated data examples and a small
 simulation study. 
Journal: Journal of Applied Statistics 
Pages: 1023-1045 
Issue: 7 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/0266476022000006720 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000006720
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:7:p:1023-1045




Template-Type: ReDIF-Article 1.0
Author-Name: Daniela Climov 
Author-X-Name-First: Daniela 
Author-X-Name-Last: Climov 
Author-Name: Michel Delecroix 
Author-X-Name-First: Michel 
Author-X-Name-Last: Delecroix 
Author-Name: Leopold Simar 
Author-X-Name-First: Leopold 
Author-X-Name-Last: Simar 
Title: Semiparametric estimation in single index Poisson regression: A practical approach 
Abstract:
  In a single index Poisson regression model with unknown link function,
 the index parameter can be root- n consistently estimated by the method of
 pseudo maximum likelihood. In this paper, we study, by simulation
 arguments, the practical validity of the asymptotic behaviour of the
 pseudo maximum likelihood index estimator and of some associated
 cross-validation bandwidths. A robust practical rule for implementing the
 pseudo maximum likelihood estimation method is suggested, which uses the
 bootstrap for estimating the variance of the index estimator and a variant
 of bagging for numerically stabilizing its variance. Our method gives
 reasonable results even for moderate sized samples; thus, it can be used
 for doing statistical inference in practical situations. The procedure is
 illustrated through a real data example. 
Journal: Journal of Applied Statistics 
Pages: 1047-1070 
Issue: 7 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/0266476022000006739 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000006739
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:7:p:1047-1070




Template-Type: ReDIF-Article 1.0
Author-Name: Yangxin Huang 
Author-X-Name-First: Yangxin 
Author-X-Name-Last: Huang 
Title: Robustness of interval estimation of the 90% effective dose: Bootstrap resampling and some large-sample parametric methods 
Abstract:
  Interval estimation of the x th effective dose (ED x ), where x is a
 prespecified percentage, has been the focus of interest of a number of
 recent studies, the majority of which have considered the case in which a
 logistic dose-response curve is correctly assumed. In this paper, we focus
 our attention upon the 90% effective dose (ED 90 ) and consider the
 situation in which the assumption of a logistic dose-response curve is
 incorrect. Specifically, we consider three classes of true model: the
 probit, the cubic logistic and the asymmetric Aranda-Ordaz models. We
 investigate the robustness of four large sample parametric methods of
 interval construction and four methods based upon bootstrap resampling. 
Journal: Journal of Applied Statistics 
Pages: 1071-1081 
Issue: 7 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/0266476022000006748 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000006748
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:7:p:1071-1081




Template-Type: ReDIF-Article 1.0
Author-Name: T. Antoniadou 
Author-X-Name-First: T. 
Author-X-Name-Last: Antoniadou 
Author-Name: D. Wallach 
Author-X-Name-First: D. 
Author-X-Name-Last: Wallach 
Title: Evaluating optimal fertilizer rates using plant measurements 
Abstract:
  Correctly adjusting the amount of nitrogen fertilizer to crop needs is
 important for both economic and environmental reasons. A recent
 development in nitrogen fertilization is the use of plant measurements to
 indicate plant nitrogen status. We present a theoretical treatment of this
 practice. We assume that yield response to nitrogen dose can be described
 using a random parameter model. The lack of precise knowledge of the
 parameter values leads to calculated nitrogen doses that are not optimal.
 The plant measurement allows one to calculate a conditional distribution
 of the parameter values, which leads to improved calculated nitrogen
 doses. We apply the treatment to a data set for wheat in northern France.
 It is shown that the use of a plant measurement, compared with no
 measurement, has only a minor effect on net profit, but achieves this with
 less nitrogen and, in particular, reduces the probability of large
 excesses of nitrogen beyond crop needs. 
Journal: Journal of Applied Statistics 
Pages: 1083-1099 
Issue: 7 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/0266476022000006757 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000006757
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:7:p:1083-1099




Template-Type: ReDIF-Article 1.0
Author-Name: Andre Khuri 
Author-X-Name-First: Andre 
Author-X-Name-Last: Khuri 
Title: Graphical evaluation of the adequacy of the method of unweighted means 
Abstract:
  A graphical technique is introduced to assess the adequacy of the method
 of unweighted means in providing approximate F -tests for an unbalanced
 random model. These tests are similar to those obtained under a balanced
 ANOVA. The proposed technique is simple and can easily be used to
 determine the effects of imbalance and values of the variance components
 on the adequacy of the approximation. The one-way and two-way random
 models are used to illustrate the proposed methodology. Extensions to
 higher-order models are also mentioned. 
Journal: Journal of Applied Statistics 
Pages: 1107-1119 
Issue: 8 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/0266476022000011193 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000011193
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:8:p:1107-1119




Template-Type: ReDIF-Article 1.0
Author-Name: Patrick Bourke 
Author-X-Name-First: Patrick 
Author-X-Name-Last: Bourke 
Title: A continuous sampling plan using CUSUMs 
Abstract:
  A Continuous Sampling Plan, CSP-CUSUM, is proposed based on the use of
 Cumulative Sums (CUSUMs) for deciding when to switch between the phases of
 sampling inspection and 100% inspection. The Geometric CUSUM, also termed
 the Run-length CUSUM, is chosen for this purpose, and two separate CUSUMs
 are to be operated, one for each inspection phase. The conventional
 measures of performance for CSPs such as average outgoing quality, average
 fraction inspected, and average proportion passed under sampling
 inspection are evaluated for CSP-CUSUM, and comparisons with some standard
 CSPs are presented. An additional performance-measure, Average Cycle
 Length, is proposed. A table is provided to aid the choice of parameters
 for the operation of CSP-CUSUM. It is recommended that a Geometric CUSUM
 control chart be maintained in parallel with CSP-CUSUM to detect
 significant upward shifts in the incoming fraction defective. 
Journal: Journal of Applied Statistics 
Pages: 1121-1133 
Issue: 8 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/0266476022000011201 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000011201
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:8:p:1121-1133




Template-Type: ReDIF-Article 1.0
Author-Name: N. David Yanez 
Author-X-Name-First: N. David 
Author-X-Name-Last: Yanez 
Author-Name: Richard Kronmal 
Author-X-Name-First: Richard 
Author-X-Name-Last: Kronmal 
Author-Name: Jennifer Nelson 
Author-X-Name-First: Jennifer 
Author-X-Name-Last: Nelson 
Author-Name: Todd Alonzo 
Author-X-Name-First: Todd 
Author-X-Name-Last: Alonzo 
Title: Analysing change in clinical trials using quasi-likelihoods 
Abstract:
  In clinical trials, investigations focus upon whether a treatment affects
 a measured outcome. Data often collected include pre- and post-treatment
 measurements on each patient and an analysis of the change in the outcome
 is typically performed to determine treatment efficacy. Absolute change
 and relative change are frequently selected as the outcome. In selecting
 from these two measures, the analyst makes implicit assumptions regarding
 the mean and variance-mean relationship of the data. Some have provided ad
 hoc guidelines for selecting between the two measures. We present a more
 rigorous means of investigating change using quasi-likelihoods. We show
 that both absolute change and relative change are special cases of the
 specified quasi-likelihood model. A cystic fibrosis example is provided. 
Journal: Journal of Applied Statistics 
Pages: 1135-1145 
Issue: 8 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/0266476022000011210 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000011210
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:8:p:1135-1145




Template-Type: ReDIF-Article 1.0
Author-Name: S. P. Singh 
Author-X-Name-First: S. P. 
Author-X-Name-Last: Singh 
Author-Name: A. K. Srivastava 
Author-X-Name-First: A. K. 
Author-X-Name-Last: Srivastava 
Author-Name: B. V. S. Sisodia 
Author-X-Name-First: B. V. S. 
Author-X-Name-Last: Sisodia 
Title: Evaluation of a synthetic method of estimation for small areas 
Abstract:
  A synthetic estimator is one of the simplest estimators for a small area,
 and it has several variants. In this paper, a ratio-synthetic estimator is
 proposed and compared with the existing synthetic estimator (Ghangurde &
 Singh, 1977) and it is observed that the gain due to stratification in the
 case of a synthetic estimator reduces proportional to the domain coverage. 
Journal: Journal of Applied Statistics 
Pages: 1147-1151 
Issue: 8 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/0266476022000011229 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000011229
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:8:p:1147-1151




Template-Type: ReDIF-Article 1.0
Author-Name: Loki Natarajan 
Author-X-Name-First: Loki 
Author-X-Name-Last: Natarajan 
Author-Name: John O'Quigley 
Author-X-Name-First: John 
Author-X-Name-Last: O'Quigley 
Title: Predictive capability of stratified proportional hazards models 
Abstract:
  Following on from the work of O'Quigley & Flandre (1994) and, more
 recently, O'Quigley & Xu (2000), we develop a measure, R2, of the
 predictive ability of a stratified proportional hazards regression model.
 The extension of this earlier work to the stratified case is relatively
 straightforward, both conceptually and in its practical implementation.
 The extension is nonetheless important in that the stratified model is
 making weaker assumptions than the full multivariate model. Formulae are
 given that can be readily incorporated into standard software routines,
 since the component parts of the calculations are routinely provided by
 most packages. We give examples on the predictability of survival in
 breast cancer data, modelled via proportional hazards and stratified
 proportional hazards models, the latter being necessary in view of the
 effects of a non-proportional hazards nature. 
Journal: Journal of Applied Statistics 
Pages: 1153-1163 
Issue: 8 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/0266476022000011238 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000011238
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:8:p:1153-1163




Template-Type: ReDIF-Article 1.0
Author-Name: Samuel Kotz 
Author-X-Name-First: Samuel 
Author-X-Name-Last: Kotz 
Author-Name: J. Renevan Dorp 
Author-X-Name-First: J. Renevan 
Author-X-Name-Last: Dorp 
Title: A versatile bivariate distribution on a bounded domain: Another look at the product moment correlation 
Abstract:
  The Farlie-Gumbel-Morgenstern (FGM) family has been investigated in
 detail for various continuous marginals such as Cauchy, normal,
 exponential, gamma, Weibull, lognormal and others. It has been a popular
 model for the bivariate distribution with mild dependence. However,
 bivariate FGMs with continuous marginals on a bounded support discussed in
 the literature are only those with uniform or power marginals. In this
 paper we study the bivariate FGM family with marginals given by the
 recently proposed two-sided power (TSP) distribution. Since this family of
 bounded continuous distributions is very flexible, the properties of the
 FGM family with TSP marginals could serve as an indication of the
 structure of the FGM distribution with arbitrary marginals defined on a
 compact set. A remarkable stability of the correlation between the
 marginals has been observed. 
Journal: Journal of Applied Statistics 
Pages: 1165-1179 
Issue: 8 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/0266476022000011247 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000011247
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:8:p:1165-1179




Template-Type: ReDIF-Article 1.0
Author-Name: Alan Hutson 
Author-X-Name-First: Alan 
Author-X-Name-Last: Hutson 
Author-Name: Lauren Fishbein 
Author-X-Name-First: Lauren 
Author-X-Name-Last: Fishbein 
Author-Name: Patricia O'Brien 
Author-X-Name-First: Patricia 
Author-X-Name-Last: O'Brien 
Author-Name: Peter Stacpoole 
Author-X-Name-First: Peter 
Author-X-Name-Last: Stacpoole 
Title: Accounting for plasma levels below detection limits in a one-compartment zero-order absorption pharmacokinetics model 
Abstract:
  We provide a simple method for fitting a one-compartment, zero-order
 absorption pharmacokinetics model in the presence of observations below
 the detection limit. This method may be extended to more complex
 pharmacokinetics models. We demonstrate, using a small simulation study,
 that the method provides accurate parameter estimates over a range of
 detection limits and we compare it to an ad hoc midpoint method. An
 applied example is provided from a pharmacokinetic investigation of a
 nicotine nasal spray. 
Journal: Journal of Applied Statistics 
Pages: 1181-1190 
Issue: 8 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/0266476022000011256 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000011256
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:8:p:1181-1190




Template-Type: ReDIF-Article 1.0
Author-Name: Manuel Galea 
Author-X-Name-First: Manuel 
Author-X-Name-Last: Galea 
Author-Name: Heleno Bolfarine 
Author-X-Name-First: Heleno 
Author-X-Name-Last: Bolfarine 
Author-Name: Filidor Vilcalabra 
Author-X-Name-First: Filidor 
Author-X-Name-Last: Vilcalabra 
Title: Influence diagnostics for the structural errors-in-variables model under the Student-t distribution 
Abstract:
  The influence of observations on the parameter estimates for the simple
 structural errors-in-variables model with no equation error, under the
 Student-t distribution, is investigated using the local influence
 approach. The main conclusion is that the Student-t model with small
 degrees of freedom is able to incorporate possible outliers and
 influential observations in the data. The likelihood displacement approach
 is useful for outlier detection, especially when a masking phenomenon is
 present and the degrees of freedom parameter is large. The diagnostics are
 illustrated with two examples. 
Journal: Journal of Applied Statistics 
Pages: 1191-1204 
Issue: 8 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/0266476022000011265 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000011265
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:8:p:1191-1204




Template-Type: ReDIF-Article 1.0
Author-Name: Herwig Friedl 
Author-X-Name-First: Herwig 
Author-X-Name-Last: Friedl 
Author-Name: Erwin Stampfer 
Author-X-Name-First: Erwin 
Author-X-Name-Last: Stampfer 
Title: Estimating general variable acceptance sampling plans by bootstrap methods 
Abstract:
  We consider variable acceptance sampling plans that control the lot or
 process fraction defective, where a specification limit defines acceptable
 quality. The problem is to find a sampling plan that fulfils some
 conditions, usually on the operation characteristic. Its calculation
 heavily depends on distributional properties that, in practice, might be
 doubtful. If prior data are already available, we propose to estimate the
 sampling plan by means of bootstrap methods. The bias and standard error
 of the estimated plan can be assessed easily by Monte Carlo approximation
 to the respective bootstrap moments. This resampling approach does not
 require strong assumptions and, furthermore, is a flexible method that can
 be extended to any statistic that might be informative for the fraction
 defective in a lot. 
Journal: Journal of Applied Statistics 
Pages: 1205-1217 
Issue: 8 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/0266476022000011274 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000011274
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:8:p:1205-1217




Template-Type: ReDIF-Article 1.0
Author-Name: Sangit Chatterjee 
Author-X-Name-First: Sangit 
Author-X-Name-Last: Chatterjee 
Author-Name: Frederick Wiseman 
Author-X-Name-First: Frederick 
Author-X-Name-Last: Wiseman 
Author-Name: Robert Perez 
Author-X-Name-First: Robert 
Author-X-Name-Last: Perez 
Title: Studying improved performance in golf 
Abstract:
  The topic of improved performances by athletes in both team and
 individual sports has shown that each sport has its own unique set of
 characteristics and these have to be analysed accordingly. This paper
 presents an extensive analysis of the nature and extent of improvement in
 golf by analysing the performances of the top players in the Masters
 tournament throughout the entire history of the event. The results
 indicate that golfers are obtaining lower scores over time and that the
 variation of the scores has declined. Further, the distributions of scores
 are symmetric and display a monotonic reduction of peakedness (kurtosis).
 These findings are indicative of rapid and improved performance and
 increased competition. 
Journal: Journal of Applied Statistics 
Pages: 1219-1227 
Issue: 8 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/0266476022000011283 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000011283
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:8:p:1219-1227




Template-Type: ReDIF-Article 1.0
Author-Name: Avner Bar-Hen 
Author-X-Name-First: Avner 
Author-X-Name-Last: Bar-Hen 
Title: Influence of missing data on compact designs for spacing experiments 
Abstract:
  Density optimization of a plantation is a classical task with important
 practical consequences. In this article, we present an adaptation of
 criss-cross design and an alternative analysis. If a tree is missing, the
 spacing of neighbouring trees is altered and considerable information is
 lost. We derive the estimate of the missing value that minimizes the
 residual sum of squares and obtain the analytical solution of the EM
 algorithm. The relationships between the two techniques are clarified. The
 method is applied to data from a plantation of Eucalyptus in the Congo. 
Journal: Journal of Applied Statistics 
Pages: 1229-1240 
Issue: 8 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/0266476022000011292 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000011292
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:8:p:1229-1240




Template-Type: ReDIF-Article 1.0
Author-Name: George Box 
Author-X-Name-First: George 
Author-X-Name-Last: Box 
Author-Name: Alberto Luceno 
Author-X-Name-First: Alberto 
Author-X-Name-Last: Luceno 
Title: Feedforward as a supplement to feedback adjustment in allowing for feedstock changes 
Abstract:
  Many industrial processes must be adjusted from time to time to maintain
 their mean continuously close to the target value. Compensations for
 deviations of the process mean from the target may be accomplished by
 feedback and/or by feedforward adjustment. Feedback adjustments are made
 in reaction to errors at the output; feedforward adjustments are made to
 compensate anticipated changes. This article considers the complementary
 use of feedback and feedforward adjustments to compensate for anticipated
 step changes in the process mean as may be necessary in a manufacturing
 process each time a new batch of feedstock material is introduced. We
 consider and compare five alternative control schemes: (1) feedforward
 adjustment alone, (2) feedback adjustment alone, (3) feedback- feedforward
 adjustment, (4) feedback and indirect feedforward to increase the
 sensitivity of the feedback scheme, and (5) feedback with both direct and
 indirect feedforward. 
Journal: Journal of Applied Statistics 
Pages: 1241-1254 
Issue: 8 
Volume: 29 
Year: 2002 
X-DOI: 10.1080/0266476022000011300 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000011300
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:29:y:2002:i:8:p:1241-1254




Template-Type: ReDIF-Article 1.0
Author-Name: Reiko Aoki 
Author-X-Name-First: Reiko 
Author-X-Name-Last: Aoki 
Author-Name: Jorge Achcar 
Author-X-Name-First: Jorge 
Author-X-Name-Last: Achcar 
Author-Name: Heleno Bolfarine 
Author-X-Name-First: Heleno 
Author-X-Name-Last: Bolfarine 
Author-Name: Julio Singer 
Author-X-Name-First: Julio 
Author-X-Name-Last: Singer 
Title: Bayesian analysis of null intercept errors-in-variables regression for pretest/post-test data 
Abstract:
  This article discusses a Bayesian analysis of repeated measures
 pretest/post-test data under null intercepts errors-in-variables
 regression models. For illustration we consider an example in the field of
 dentistry involving the comparison of two types of toothbrushes with
 respect to the efficacy in removing dental plaque. The proposed Bayesian
 approach accommodates the correlated measurements and incorporates the
 restriction that the slopes must lie in the [0,1] interval, a feature not
 considered in the analysis conducted by Singer & Andrade (1997). The
 observed values of the (repeated) response and explanatory variables are
 supposed to follow a Multivariate Student- t distribution. A Gibbs sampler
 is used to perform the computations. 
Journal: Journal of Applied Statistics 
Pages: 3-12 
Issue: 1 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476022000018466 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000018466
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:1:p:3-12




Template-Type: ReDIF-Article 1.0
Author-Name: Maria Carapeto 
Author-X-Name-First: Maria 
Author-X-Name-Last: Carapeto 
Author-Name: William Holt 
Author-X-Name-First: William 
Author-X-Name-Last: Holt 
Title: Testing for heteroscedasticity in regression models 
Abstract:
  A new test for heteroscedasticity in regression models is presented based
 on the Goldfeld-Quandt methodology. Its appeal derives from the fact that
 no further regressions are required, enabling widespread use across all
 types of regression models. The distribution of the test is computed using
 the Imhof method and its power is assessed by performing a Monte Carlo
 simulation. We compare our results with those of Griffiths & Surekha
 (1986) and show that our test is more powerful than the wide range of
 tests they examined. We introduce an estimation procedure using a neural
 network to correct the heteroscedastic disturbances. 
Journal: Journal of Applied Statistics 
Pages: 13-20 
Issue: 1 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476022000018475 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000018475
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:1:p:13-20




Template-Type: ReDIF-Article 1.0
Author-Name: D. K. Panda 
Author-X-Name-First: D. K. 
Author-X-Name-Last: Panda 
Author-Name: Rajender Parsad 
Author-X-Name-First: Rajender 
Author-X-Name-Last: Parsad 
Author-Name: V. K. Sharma 
Author-X-Name-First: V. K. 
Author-X-Name-Last: Sharma 
Title: Robustness of complete diallel crossing plans against exchange of one cross 
Abstract:
  The robustness aspects of block designs for complete diallel crossing
 plans against the exchange of one cross using connectedness and efficiency
 criteria have been investigated. The exchanged cross may have either no
 line in common or one line in common with the original cross. It has been
 found that randomized complete block (RCB) designs for complete diallel
 crosses and binary balanced block designs for complete diallel crosses are
 robust against the exchange of one cross in one observation. The RCB
 designs for diallel crosses have been shown to be robust against the
 exchange of one cross with another cross in all the blocks. The non-binary
 balanced block designs obtainable from Family 5 of Das et al. (1998) have
 also been found to be robust against the exchange of one cross. 
Journal: Journal of Applied Statistics 
Pages: 21-35 
Issue: 1 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476022000018484 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000018484
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:1:p:21-35




Template-Type: ReDIF-Article 1.0
Author-Name: JosE Eduardo Corrente 
Author-X-Name-First: JosE Eduardo 
Author-X-Name-Last: Corrente 
Author-Name: Liciana Chalita 
Author-X-Name-First: Liciana 
Author-X-Name-Last: Chalita 
Author-Name: Jeanete Alves Moreira 
Author-X-Name-First: Jeanete Alves 
Author-X-Name-Last: Moreira 
Title: Choosing between Cox proportional hazards and logistic models for interval- censored data via bootstrap 
Abstract:
  This work develops a new methodology in order to discriminate models for
 interval- censored data based on bootstrap residual simulation by
 observing the deviance difference from one model in relation to another,
 according to Hinde (1992). Generally, this sort of data can generate a
 large number of tied observations and, in this case, survival time can be
 regarded as discrete. Therefore, the Cox proportional hazards model for
 grouped data (Prentice & Gloeckler, 1978) and the logistic model (Lawless,
 1982) can be fitted by means of generalized linear models. Whitehead
 (1989) considered censoring to be an indicative variable with a binomial
 distribution and fitted the Cox proportional hazards model using
 complementary log-log as a link function. In addition, a logistic model
 can be fitted using logit as a link function. The proposed methodology
 arises as an alternative to the score tests developed by Colosimo et al.
 (2000), where such models can be obtained for discrete binary data as
 particular cases from the Aranda-Ordaz distribution asymmetric family.
 These tests are thus developed with a basis on link functions to generate
 such a fit. The example that motivates this study was the dataset from an
 experiment carried out on a flax cultivar planted on four substrata
 susceptible to the pathogen Fusarium oxysoprum . The response variable,
 which is the time until blighting, was observed in intervals during 52
 days. The results were compared with the model fit and the AIC values. 
Journal: Journal of Applied Statistics 
Pages: 37-47 
Issue: 1 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476022000018493 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000018493
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:1:p:37-47




Template-Type: ReDIF-Article 1.0
Author-Name: Penelope Vounatsou 
Author-X-Name-First: Penelope 
Author-X-Name-Last: Vounatsou 
Author-Name: Tom Smith 
Author-X-Name-First: Tom 
Author-X-Name-Last: Smith 
Author-Name: Alan Gelfand 
Author-X-Name-First: Alan 
Author-X-Name-Last: Gelfand 
Title: Spatial modelling of gene frequencies in the presence of undetectable alleles 
Abstract:
  Bayesian hierarchical models are developed to estimate the frequencies of
 the alleles at the HLA-C locus in the presence of non-identifiable alleles
 and possible spatial correlations in a large but sparse, spatially defined
 database from Papua New Guinea. Bayesian model selection methods are
 applied to investigate the effects of altitude and language on the genetic
 diversity of HLA-C alleles. The general model includes fixed altitudinal
 effects, random language effects and random spatially structured location
 effects. Conditional autoregressive priors are used to incorporate the
 geographical structure of the map, and Markov chain Monte Carlo simulation
 methods are applied for estimation and inference. The results show that
 HLA-C allele frequencies are explained more by linguistic than altitudinal
 differences, indicating that genetic diversity at this locus in Papua New
 Guinea probably tracks population movements and is less influenced by
 natural selection than is variation at HLA-A and HLA-B. 
Journal: Journal of Applied Statistics 
Pages: 49-62 
Issue: 1 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476022000018501 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000018501
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:1:p:49-62




Template-Type: ReDIF-Article 1.0
Author-Name: Dimitris Karlis 
Author-X-Name-First: Dimitris 
Author-X-Name-Last: Karlis 
Title: An EM algorithm for multivariate Poisson distribution and related models 
Abstract:
  Multivariate extensions of the Poisson distribution are plausible models
 for multivariate discrete data. The lack of estimation and inferential
 procedures reduces the applicability of such models. In this paper, an EM
 algorithm for Maximum Likelihood estimation of the parameters of the
 Multivariate Poisson distribution is described. The algorithm is based on
 the multivariate reduction technique that generates the Multivariate
 Poisson distribution. Illustrative examples are also provided. Extension
 to other models, generated via multivariate reduction, is discussed. 
Journal: Journal of Applied Statistics 
Pages: 63-77 
Issue: 1 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476022000018510 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000018510
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:1:p:63-77




Template-Type: ReDIF-Article 1.0
Author-Name: James Reed 
Author-X-Name-First: James 
Author-X-Name-Last: Reed 
Title: Adjusting for bias in randomized cluster trials 
Abstract:
  The randomized cluster design is typical in studies where the unit of
 randomization is a cluster of individuals rather than the individual.
 Evaluating various intervention strategies across medical care providers
 at either an institutional level or at a physician group practice level
 fits the randomized cluster model. Clearly, the analytical approach to
 such studies must take the unit of randomization and accompanying
 intraclass correlation into consideration. We review alternative methods
 to the typical Pearson's chi-square analysis and illustrate these
 alternatives. We have written and tested a Fortran program that produces
 the statistics outlined in this paper. The program, in an executable
 format is available from the author on request. 
Journal: Journal of Applied Statistics 
Pages: 79-85 
Issue: 1 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476022000018529 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000018529
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:1:p:79-85




Template-Type: ReDIF-Article 1.0
Author-Name: H&#xe5;kon Tjelmeland 
Author-X-Name-First: H&#xe5;kon 
Author-X-Name-Last: Tjelmeland 
Author-Name: Kjetill Vassmo Lund 
Author-X-Name-First: Kjetill Vassmo 
Author-X-Name-Last: Lund 
Title: Bayesian modelling of spatial compositional data 
Abstract:
  Compositional data are vectors of proportions, specifying fractions of a
 whole. Aitchison (1986) defines logistic normal distributions for
 compositional data by applying a logistic transformation and assuming the
 transformed data to be multi- normal distributed. In this paper we
 generalize this idea to spatially varying logistic data and thereby define
 logistic Gaussian fields. We consider the model in a Bayesian framework
 and discuss appropriate prior distributions. We consider both complete
 observations and observations of subcompositions or individual
 proportions, and discuss the resulting posterior distributions. In
 general, the posterior cannot be analytically handled, but the Gaussian
 base of the model allows us to define efficient Markov chain Monte Carlo
 algorithms. We use the model to analyse a data set of sediments in an
 Arctic lake. These data have previously been considered, but then without
 taking the spatial aspect into account. 
Journal: Journal of Applied Statistics 
Pages: 87-100 
Issue: 1 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476022000018547 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000018547
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:1:p:87-100




Template-Type: ReDIF-Article 1.0
Author-Name: Christopher Illert 
Author-X-Name-First: Christopher 
Author-X-Name-Last: Illert 
Title: Lexigenesis in ancestral south-east Australian Aboriginal language 
Abstract:
  The 1/x frequency distribution is known to researchers ranging from
 economists and biologists to electronic engineers. It is known to
 linguists as Zipf's Law (Zipf, 1949) and has recently been shown not to be
 a consequence of the Central Limit Theorem (Troll & Graben, 1998)--leaving
 an &#x22;unsolved problem' in information theory (Jones, 1999). This 1/x
 distribution, associated with scale-invariant physical systems (Machlup &
 Hoshiko, 1980), is a special case of the general power law x&#x3bb;
 arising from the Lagrangian L(x,F-super-&#x2d9;(x)) =
 &#xbd;x1-&#x3bb;F-super-&#x2d9;2 and, as &#x3bb; need not be an integer,
 some related research understandably involves fractals (Allison et al. ,
 2001). The present paper generalizes this Lagrangian to include a van der
 Waals effect. It is argued that ancestral Aboriginal language consisted of
 root-morphemes that were built up into, and often condensed within,
 subsequent words or lexemes. Using discrete-optimization techniques
 pioneered elsewhere (Illert, 1987; Reverberi, 1985), and the new
 morpho-statistics, this paper models lexeme-condensation in ancestral
 south-east Australian Aboriginal language. 
Journal: Journal of Applied Statistics 
Pages: 113-143 
Issue: 2 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476022000023703 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000023703
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:2:p:113-143




Template-Type: ReDIF-Article 1.0
Author-Name: Kaushik Ghosh 
Author-X-Name-First: Kaushik 
Author-X-Name-Last: Ghosh 
Author-Name: Rao Jammalamadaka 
Author-X-Name-First: Rao 
Author-X-Name-Last: Jammalamadaka 
Author-Name: Ram Tiwari 
Author-X-Name-First: Ram 
Author-X-Name-Last: Tiwari 
Title: Semiparametric Bayesian Techniques for Problems in Circular Data 
Abstract:
  In this paper, we consider the problems of prediction and tests of
 hypotheses for directional data in a semiparametric Bayesian set-up.
 Observations are assumed to be independently drawn from the von Mises
 distribution and uncertainty in the location parameter is modelled by a
 Dirichlet process. For the prediction problem, we present a method to
 obtain the predictive density of a future observation, and, for the
 testing problem, we present a method of computing the Bayes factor by
 obtaining the posterior probabilities of the hypotheses under
 consideration. The semiparametric model is seen to be flexible and robust
 against prior misspecifications. While analytical expressions are
 intractable, the methods are easily implemented using the Gibbs sampler.
 We illustrate the methods with data from two real-life examples. 
Journal: Journal of Applied Statistics 
Pages: 145-161 
Issue: 2 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476022000023712 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000023712
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:2:p:145-161




Template-Type: ReDIF-Article 1.0
Author-Name: Shuo-Jye Wu 
Author-X-Name-First: Shuo-Jye 
Author-X-Name-Last: Wu 
Author-Name: Chun-Tao Chang 
Author-X-Name-First: Chun-Tao 
Author-X-Name-Last: Chang 
Title: Inference in the Pareto distribution based on progressive Type II censoring with random removals 
Abstract:
  This study considers the estimation problem for the Pareto distribution
 based on progressive Type II censoring with random removals. The number of
 units removed at each failure time has a discrete uniform distribution. We
 are going to use the maximum likelihood method to obtain the estimator of
 parameter. The expectation and variance of the maximum likelihood
 estimator will be derived. The expected time required to complete such an
 experiment will be computed. Some numerical results of expected test times
 are carried out for this type of progressive censoring and other sampling
 schemes. 
Journal: Journal of Applied Statistics 
Pages: 163-172 
Issue: 2 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476022000023721 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000023721
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:2:p:163-172




Template-Type: ReDIF-Article 1.0
Author-Name: Alan Wan 
Author-X-Name-First: Alan 
Author-X-Name-Last: Wan 
Author-Name: Anoop Chaturvedi 
Author-X-Name-First: Anoop 
Author-X-Name-Last: Chaturvedi 
Author-Name: Guohuazou Zou 
Author-X-Name-First: Guohuazou 
Author-X-Name-Last: Zou 
Title: Unbiased estimation of the MSE matrices of improved estimators in linear regression 
Abstract:
  Stein-rule and other improved estimators have scarcely been used in
 empirical work. One major reason is that it is not easy to obtain
 precision measures for these estimators. In this paper, we derive unbiased
 estimators for both the mean squared error (MSE) and the scaled MSE
 matrices of a class of Stein-type estimators. Our derivation provides the
 basis for measuring the estimators' precision and constructing confidence
 bands. Comparisons are made between these MSE estimators and the least
 squares covariance estimator. For illustration, the methodology is applied
 to data on energy consumption. 
Journal: Journal of Applied Statistics 
Pages: 173-189 
Issue: 2 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476022000023730 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000023730
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:2:p:173-189




Template-Type: ReDIF-Article 1.0
Author-Name: Man-Suk Oh 
Author-X-Name-First: Man-Suk 
Author-X-Name-Last: Oh 
Author-Name: Jung Whan Choi 
Author-X-Name-First: Jung Whan 
Author-X-Name-Last: Choi 
Author-Name: Dai-Gyoung Kim 
Author-X-Name-First: Dai-Gyoung 
Author-X-Name-Last: Kim 
Title: Bayesian inference and model selection in latent class logit models with parameter constraints: An application to market segmentation 
Abstract:
  Latent class models have recently drawn considerable attention among many
 researchers and practitioners as a class of useful tools for capturing
 heterogeneity across different segments in a target market or population.
 In this paper, we consider a latent class logit model with parameter
 constraints and deal with two important issues in the latent class
 models--parameter estimation and selection of an appropriate number of
 classes--within a Bayesian framework. A simple Gibbs sampling algorithm is
 proposed for sample generation from the posterior distribution of unknown
 parameters. Using the Gibbs output, we propose a method for determining an
 appropriate number of the latent classes. A real-world marketing example
 as an application for market segmentation is provided to illustrate the
 proposed method. 
Journal: Journal of Applied Statistics 
Pages: 191-204 
Issue: 2 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476022000023749 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000023749
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:2:p:191-204




Template-Type: ReDIF-Article 1.0
Author-Name: Yuzhi Cai 
Author-X-Name-First: Yuzhi 
Author-X-Name-Last: Cai 
Author-Name: Neville Davies 
Author-X-Name-First: Neville 
Author-X-Name-Last: Davies 
Title: A simple diagnostic method of outlier detection for stationary Gaussian time series 
Abstract:
  In this paper we present a &#x22;model free' method of outlier detection
 for Gaussian time series by using the autocorrelation structure of the
 time series. We also present a graphic diagnostic method in order to
 distinguish an additive outlier (AO) from an innovation outlier (IO). The
 test statistic for detecting the outlier has a P &#xb2; distribution with
 one degree of freedom. We show that this method works well when the time
 series contain either one type of the outliers or both additive and
 innovation type outliers, and this method has the advantage that no time
 series model needs to be estimated from the data. Simulation evidence
 shows that different types of outliers can be graphically distinguished by
 using the techniques proposed. 
Journal: Journal of Applied Statistics 
Pages: 205-223 
Issue: 2 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476022000023758 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000023758
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:2:p:205-223




Template-Type: ReDIF-Article 1.0
Author-Name: Kepher Makambi 
Author-X-Name-First: Kepher 
Author-X-Name-Last: Makambi 
Title: Weighted inverse chi-square method for correlated significance tests 
Abstract:
  Fisher's inverse chi-square method for combining independent significance
 tests is extended to cover cases of dependence among the individual tests.
 A weighted version of the method and its approximate null distribution are
 presented. To illustrate the use of the proposed method, two tests for the
 overall treatment efficacy are combined, with the resulting test procedure
 exhibiting good control of the type I error probability. Two examples from
 clinical trials are given to illustrate the applicability of the
 procedures to real-life situations. 
Journal: Journal of Applied Statistics 
Pages: 225-234 
Issue: 2 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476022000023767 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000023767
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:2:p:225-234




Template-Type: ReDIF-Article 1.0
Author-Name: Didier Renard 
Author-X-Name-First: Didier 
Author-X-Name-Last: Renard 
Author-Name: Helena Geys 
Author-X-Name-First: Helena 
Author-X-Name-Last: Geys 
Author-Name: Geert Molenberghs 
Author-X-Name-First: Geert 
Author-X-Name-Last: Molenberghs 
Author-Name: Tomasz Burzykowski 
Author-X-Name-First: Tomasz 
Author-X-Name-Last: Burzykowski 
Author-Name: Marc Buyse 
Author-X-Name-First: Marc 
Author-X-Name-Last: Buyse 
Author-Name: Tony Vangeneugden 
Author-X-Name-First: Tony 
Author-X-Name-Last: Vangeneugden 
Author-Name: Luc Bijnens 
Author-X-Name-First: Luc 
Author-X-Name-Last: Bijnens 
Title: Validation of a longitudinally measured surrogate marker for a time-to-event endpoint 
Abstract:
  The objective of this paper is to extend the surrogate endpoint
 validation methodology proposed by Buyse et al. (2000) to the case of a
 longitudinally measured surrogate marker when the endpoint of interest is
 time to some key clinical event. A joint model for longitudinal and event
 time data is required. To this end, the model formulation of Henderson et
 al. (2000) is adopted. The methodology is applied to a set of two
 randomized clinical trials in advanced prostate cancer to evaluate the
 usefulness of prostate-specific antigen (PSA) level as a surrogate for
 survival. 
Journal: Journal of Applied Statistics 
Pages: 235-247 
Issue: 2 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476022000023776 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000023776
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:2:p:235-247




Template-Type: ReDIF-Article 1.0
Author-Name: Gang Zheng 
Author-X-Name-First: Gang 
Author-X-Name-Last: Zheng 
Author-Name: Mohammad Al-Saleh 
Author-X-Name-First: Mohammad 
Author-X-Name-Last: Al-Saleh 
Title: Improving the best linear unbiased estimator for the scale parameter of symmetric distributions by using the absolute value of ranked set samples 
Abstract:
  Ranked set sampling is a cost efficient sampling technique when actually
 measuring sampling units is difficult but ranking them is relatively easy.
 For a family of symmetric location-scale distributions with known location
 parameter, we consider a best linear unbiased estimator for the scale
 parameter. Instead of using original ranked set samples, we propose to use
 the absolute deviations of the ranked set samples from the location
 parameter. We demonstrate that this new estimator has smaller variance
 than the best linear unbiased estimator using original ranked set samples.
 Optimal allocation in the absolute value of ranked set samples is also
 discussed for the estimation of the scale parameter when the location
 parameter is known. Finally, we perform some sensitivity analyses for this
 new estimator when the location parameter is unknown but estimated using
 ranked set samples and when the ranking of sampling units is imperfect. 
Journal: Journal of Applied Statistics 
Pages: 253-265 
Issue: 3 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476022000030039 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000030039
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:3:p:253-265




Template-Type: ReDIF-Article 1.0
Author-Name: Steven Cook 
Author-X-Name-First: Steven 
Author-X-Name-Last: Cook 
Title: The stylized approach to unit root testing: Neglected contributions and the cost of simplicity 
Abstract:
  Following Dickey & Fuller (1979) (DF), a stylized approach to the testing
 of the unit root hypothesis has emerged. Based upon the combined use of
 the DF test in its augmented t -ratio form and MacKinnon (1991) critical
 values, the approach has received widespread adoption due to the ease with
 which it can be applied. In this paper a number of departures from this
 &#x22;stylized approach', which do not significantly reduce its ease of
 application, are examined. The results obtained from an empirical
 application to UK industrial production and Monte Carlo experimentation
 have clear methodological implications, showing that routine application
 of the stylized approach can lead to misleading inferences concerning the
 integrated nature of economic time series. 
Journal: Journal of Applied Statistics 
Pages: 267-272 
Issue: 3 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476022000030048 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000030048
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:3:p:267-272




Template-Type: ReDIF-Article 1.0
Author-Name: Tirthankar Dasgupta 
Author-X-Name-First: Tirthankar 
Author-X-Name-Last: Dasgupta 
Title: An economic inspection interval for control of defective items in a hot rolling mill 
Abstract:
  The article addresses a real-life problem on determining the optimum
 sampling interval for control of defective items in a hot rolling mill.
 Having observed that the pattern of appearance of mill defects indicates a
 geometric process failure mechanism, an economic model is developed in
 line with the method suggested by Taguchi and critically examined by
 Nayebpour & Woodall. An expression for the expected loss per product as a
 function of the sampling interval is derived and the optimum interval is
 obtained by minimizing this loss function. The practical issues involved
 in this exercise, such as estimation of various cost components, are also
 discussed and the effect of erroneous estimation of cost components is
 studied through a sensitivity analysis. 
Journal: Journal of Applied Statistics 
Pages: 273-282 
Issue: 3 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476022000030057 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000030057
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:3:p:273-282




Template-Type: ReDIF-Article 1.0
Author-Name: Jacobo De UNA-ALvarez 
Author-X-Name-First: Jacobo 
Author-X-Name-Last: De UNA-ALvarez 
Author-Name: M. Soledad Otero-GirALdez 
Author-X-Name-First: M. Soledad 
Author-X-Name-Last: Otero-GirALdez 
Author-Name: Gema ALvarez-Llorente 
Author-X-Name-First: Gema 
Author-X-Name-Last: ALvarez-Llorente 
Title: Estimation under length-bias and right-censoring: An application to unemployment duration analysis for married women 
Abstract:
  In this work we analyse unemployment duration for married women in Spain,
 using the Labour Force Survey (LFS) of the Spanish Institute for
 Statistics, 1987-1997. Consistent non-parametric estimation of the
 unemployment survival function is provided. Since the available data are
 length- biased, a suitable correction of the Kaplan-Meier product-limit
 estimator is motivated and used for the referred analysis. The accuracy of
 parametric models is checked by means of goodness-of-fit plots--a
 graphical tool that requires preliminary estimation of the survival.
 Structural features of the associated hazard (as monotonicity and
 unimodality) are explored. 
Journal: Journal of Applied Statistics 
Pages: 283-291 
Issue: 3 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476022000030066 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000030066
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:3:p:283-291




Template-Type: ReDIF-Article 1.0
Author-Name: Paul Gustafson 
Author-X-Name-First: Paul 
Author-X-Name-Last: Gustafson 
Author-Name: Lawrence Walker 
Author-X-Name-First: Lawrence 
Author-X-Name-Last: Walker 
Title: An extension of the Dirichlet prior for the analysis of longitudinal multinomial data 
Abstract:
  Studies producing longitudinal multinomial data arise in several subject
 areas. This article suggests a Bayesian approach to the analysis of such
 data. Rather than infusing a latent model structure, we develop a prior
 distribution for the multinomial parameters which reflects the
 longitudinal nature of the observations. This distribution is constructed
 by modifying the prior that posits independent Dirichlet distributions for
 the multinomial parameters across time. Posterior analysis, which is
 implemented using Monte Carlo methods, can then be used to assess the
 temporal behaviour of the multinomial parameters underlying the observed
 data. We test this methodology on simulated data, opinion polling data,
 and data from a study concerning the development of moral reasoning. 
Journal: Journal of Applied Statistics 
Pages: 293-310 
Issue: 3 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476022000030075 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000030075
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:3:p:293-310




Template-Type: ReDIF-Article 1.0
Author-Name: Saralees Nadarajah 
Author-X-Name-First: Saralees 
Author-X-Name-Last: Nadarajah 
Author-Name: Samuel Kotz 
Author-X-Name-First: Samuel 
Author-X-Name-Last: Kotz 
Title: Moments of some J-shaped distributions 
Abstract:
  This paper concerns a family of univariate distributions suggested by
 Topp & Leone in 1955. Topp & Leone provided no motivation for this new
 family and by way of properties they derived only the first four
 integer-order moments, i.e. E(Xn) for n=1, r 2, r 3, r 4 . In this paper
 we provide a motivation for the family of distributions and derive
 explicit algebraic expressions for: (1) hazard rate function; (2) E(Xn)
 when n &#xb1; 1 is any integer; (3) E(Xn) for n=1, r 2, r &#x2026; r , r
 10 , and (4) E[{X-E(X)} n] , n=2, r 3, r 4 . We also give an expression
 for the characteristic function and discuss issues on estimation and
 simulation. The main calculations of this paper use properties of the
 Gauss hypergeometric function. 
Journal: Journal of Applied Statistics 
Pages: 311-317 
Issue: 3 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476022000030084 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000030084
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:3:p:311-317




Template-Type: ReDIF-Article 1.0
Author-Name: Camino GonzALez 
Author-X-Name-First: Camino 
Author-X-Name-Last: GonzALez 
Author-Name: Gabriel Palomo 
Author-X-Name-First: Gabriel 
Author-X-Name-Last: Palomo 
Title: Bayesian acceptance sampling plans following economic criteria: An application to paper pulp manufacturing 
Abstract:
  The main purposes of this paper are to derive Bayesian acceptance
 sampling plans regarding the number of defects per unit of product, and to
 illustrate how to apply the methodology to the paper pulp industry. The
 sampling plans are obtained following an economic criterion: minimize the
 expected total cost of quality. It has been assumed that the number of
 defects per unit of product follows a Poisson distribution with process
 average 5 , whose prior information is described either for a gamma or for
 a non- informative distribution. The expected total cost of quality is
 composed of three independent components: inspection, acceptance and
 rejection. Both quadratic and step-loss functions have been used to
 quantify the cost incurred for the acceptance of a lot containing units
 with defects. Combining the prior information on 5 with the loss
 functions, four different sampling plans are obtained. When the
 quadratic-loss function is used, an analytical relation between the
 optimum settings of the sample size and the acceptance number is derived.
 The robustness analysis indicates that the sampling plans obtained are
 robust with respect to the prior distribution of the process average as
 well as to the misspecification of its mean and variance. 
Journal: Journal of Applied Statistics 
Pages: 319-333 
Issue: 3 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476022000030093 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000030093
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:3:p:319-333




Template-Type: ReDIF-Article 1.0
Author-Name: Sueli Mingoti 
Author-X-Name-First: Sueli 
Author-X-Name-Last: Mingoti 
Author-Name: Otaviano Neves 
Author-X-Name-First: Otaviano 
Author-X-Name-Last: Neves 
Title: A note on the Zhang omnibus test for normality based on the Q statistic 
Abstract:
  A discussion about the estimators proposed by Zhang (1999) for the true
 standard deviation C of a normal distribution is presented. Those
 estimators, called by Zhang q 1 and q 2 , are functions of the expected
 values of the order statistics from a standard normal distribution and
 they were the basis of the Q statistic used in the derivation of a new
 test for normality proposed by Zhang. Although the type I error and the
 power of the test was discussed by Zhang, no study was performed to test
 the reliability of q 1 and q 2 as estimators of C . In this paper, it is
 shown that q 1 is a very poor estimator for C especially when C is large.
 On the other hand, the estimator q 2 has a performance very similar to the
 well-known sample standard deviation S. When some correlation is
 introduced among the sample units it can be seen that the estimator q 1 is
 much more affected than the estimators q 2 and S. 
Journal: Journal of Applied Statistics 
Pages: 335-341 
Issue: 3 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476022000030101 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476022000030101
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:3:p:335-341




Template-Type: ReDIF-Article 1.0
Author-Name: Yoshinori Kawasaki 
Author-X-Name-First: Yoshinori 
Author-X-Name-Last: Kawasaki 
Author-Name: Philip Hans Franses 
Author-X-Name-First: Philip Hans 
Author-X-Name-Last: Franses 
Title: Detecting seasonal unit roots in a structural time series model 
Abstract:
  In this paper, we propose to detect seasonal unit roots within the
 context of a structural time series model. Such a model is often found to
 be useful in practice. Using Monte Carlo simulations, we show that our
 method works well. We illustrate our approach for several quarterly
 macroeconomic time series variables. 
Journal: Journal of Applied Statistics 
Pages: 373-387 
Issue: 4 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000035412 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000035412
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:4:p:373-387




Template-Type: ReDIF-Article 1.0
Author-Name: Thaddeus Tarpey 
Author-X-Name-First: Thaddeus 
Author-X-Name-Last: Tarpey 
Title: Estimating the average slope 
Abstract:
  The slope is usually the parameter of primary importance in a simple
 linear regression. If the straight line model gives a poor fit to the
 data, one can consider the average slope of the non-linear response. In
 this paper, we show that if the response is quadratic, then the average
 slope can be obtained by simply using the slope from a straight line fit.
 In fact, if the slope of the best fitting line to a smooth non-linear
 function equals the average slope of the function over an arbitrary
 interval, then the function must be quadratic. This paper illustrates the
 case where intentionally fitting a wrong model (in this case, a straight
 line) gives the correct result (the average slope). The example which
 motivated this study is used to illustrate the results. 
Journal: Journal of Applied Statistics 
Pages: 389-395 
Issue: 4 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000035412a 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000035412a
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:4:p:389-395




Template-Type: ReDIF-Article 1.0
Author-Name: Liming Xiang 
Author-X-Name-First: Liming 
Author-X-Name-Last: Xiang 
Author-Name: Andy Lee 
Author-X-Name-First: Andy 
Author-X-Name-Last: Lee 
Author-Name: Siu-Keung Tse 
Author-X-Name-First: Siu-Keung 
Author-X-Name-Last: Tse 
Title: Assessing local cluster influence in generalized linear mixed models 
Abstract:
  This paper investigates local influence measures for assessing cluster
 influence in generalized linear mixed models. Several cluster-specific
 perturbation schemes are considered. The proposed local influence
 diagnostics are applied to analyse maternity length of inpatient stay data
 where individual observations are nested within hospitals and the relative
 performance of hospitals is of interest. 
Journal: Journal of Applied Statistics 
Pages: 349-359 
Issue: 4 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000035395 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000035395
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:4:p:349-359




Template-Type: ReDIF-Article 1.0
Author-Name: Patrick Barrie 
Author-X-Name-First: Patrick 
Author-X-Name-Last: Barrie 
Title: A new sports ratings system: The tiddlywinks world ratings 
Abstract:
  A system for calculating relative playing strengths of tiddlywinks
 players is described. The method can also be used for other sports. It is
 specifically designed to handle cases where the number of games played in
 a season varies greatly between players, and thus the confidence that one
 can have in an assigned rating also varies greatly between players. In
 addition, the method is designed to handle situations in which some games
 in the tournament are played as individuals (&#x22;singles'), while others
 are played with a partner (&#x22;pairs'). These factors make application
 of some statistical treatments, such as the Elo rating system used in
 chess, difficult to apply. The new method characterizes each player's
 ability by a numerical rating together with an associated uncertainty in
 that player's rating. After each tournament, a &#x22;tournament rating' is
 calculated for each player based on how many points the player achieved
 and the relative strength of partner(s) and opponent(s). Statistical
 analysis is then used to estimate the likely error in the calculated
 tournament rating. Both the tournament rating and its estimated error are
 used in the calculation of new ratings. The method has been applied to
 calculate tiddlywinks world ratings based on over 13 r 000 national
 tournament games in Britain and the USA going back to 1985. 
Journal: Journal of Applied Statistics 
Pages: 361-372 
Issue: 4 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000035403 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000035403
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:4:p:361-372




Template-Type: ReDIF-Article 1.0
Author-Name: Hajaj Al-Oraini 
Author-X-Name-First: Hajaj 
Author-X-Name-Last: Al-Oraini 
Author-Name: M. A. Rahim 
Author-X-Name-First: M. A. 
Author-X-Name-Last: Rahim 
Title: Economic statistical design of x &#xa5; control charts for systems with gamma ( 5 ,2) in-control times 
Abstract:
  In this paper, gamma ( 5 ,2) distribution is considered as a failure
 model for the economic statistical design of x &#xa5; control charts. The
 study shows that the statistical performance of control charts can be
 improved significantly, with only a slight increase in the cost, by adding
 constraints to the optimization problem. The use of an economic
 statistical design instead of an economic design results in control charts
 that may be less expensive to implement, that have lower false alarm
 rates, and that have a higher probability of detecting process shifts.
 Numerical examples are presented to support this proposition. The results
 of economic statistical design are compared with those of a pure economic
 design. The effects of adding constraints for statistical performance
 measures, such as Type I error rate and the power of the chart, are
 extensively investigated. 
Journal: Journal of Applied Statistics 
Pages: 397-409 
Issue: 4 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000035430 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000035430
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:4:p:397-409




Template-Type: ReDIF-Article 1.0
Author-Name: Getachew Asfaw Dagne 
Author-X-Name-First: Getachew Asfaw 
Author-X-Name-Last: Dagne 
Title: The use of power transformations in small area estimation 
Abstract:
  Sample surveys are usually designed and analysed to produce estimates for
 larger areas. Nevertheless, sample sizes are often not large enough to
 give adequate precision for small area estimates of interest. To overcome
 such difficulties, borrowing strength from related small areas via
 modelling becomes essential. In line with this, we propose components of
 variance models with power transformations for small area estimation. This
 paper reports the results of a study aimed at incorporating the power
 transformation in small area estimation for improving the quality of small
 area predictions. The proposed methods are demonstrated on satellite data
 in conjunction with survey data to estimate mean acreage under a specified
 crop for counties in Iowa. 
Journal: Journal of Applied Statistics 
Pages: 411-423 
Issue: 4 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000035449 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000035449
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:4:p:411-423




Template-Type: ReDIF-Article 1.0
Author-Name: JosE Eduardo Corrente 
Author-X-Name-First: JosE Eduardo 
Author-X-Name-Last: Corrente 
Author-Name: Maria Del Pilar DIAz 
Author-X-Name-First: Maria Del Pilar 
Author-X-Name-Last: DIAz 
Title: Ordinal models and generalized estimating equations to evaluate disease severity 
Abstract:
  Many assays have been carried out in Capsicum spp. in order to evaluate
 its resistance to Phytophthora capsici , which causes blight and
 considerable yield loss. An assay aiming at the selection of resistant
 pepper and bell pepper genotypes to P. capsici was jointly performed in
 the laboratory of the Phytopathological Clinic of Entomology,
 Phytopathology and Agricultural Zoology and in the experimental area of
 the Plant Production Department, both located at ESALQ, University of Sao
 Paulo, Brazil. The data set for this assay comes from ordinal categorized
 random variables, whose analysis does not generally take into account the
 ordinal nature of the responses, but instead, builds indexes, among other
 measures, in order to evaluate the resistance of the studied genotypes.
 This work presents ordinal generalized linear fits in order to evaluate
 blight severity as well as to identify and select new resources to the
 pathogen. It also analyses the estimating equations proposed by Liang &
 Zeger (1986a, b) in order to obtain an infection pattern for the disease.
 From the fit of the cumulative logit models, valuable genotypes are
 identified for genetic breeding programs. 
Journal: Journal of Applied Statistics 
Pages: 425-439 
Issue: 4 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000035458 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000035458
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:4:p:425-439




Template-Type: ReDIF-Article 1.0
Author-Name: Shakir Hussain 
Author-X-Name-First: Shakir 
Author-X-Name-Last: Hussain 
Author-Name: Ghazi Shukur 
Author-X-Name-First: Ghazi 
Author-X-Name-Last: Shukur 
Title: Testing for autocorrelation in non-stationary dynamic systems of equations 
Abstract:
  Using Monte Carlo methods, the properties of systemwise generalizations
 of the Breusch-Godfrey test for autocorrelated errors are studied in
 integrated cointegrated systems of equations. Our analysis, regarding the
 size of the test, reveals that the corrected LR tests have been shown to
 perform satisfactorily even in cases when the exogenous variables follow a
 unit root process, whilst the commonly used TR2 test behaves badly even in
 single equations. All tests perform badly, however, when the number of
 equations increases and the exogenous variables are highly autocorrelated. 
Journal: Journal of Applied Statistics 
Pages: 441-454 
Issue: 4 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000035467 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000035467
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:4:p:441-454




Template-Type: ReDIF-Article 1.0
Author-Name: Steen Magnussen 
Author-X-Name-First: Steen 
Author-X-Name-Last: Magnussen 
Title: Stepwise estimators for three-phase sampling of categorical variables 
Abstract:
  Three-phase sampling can be a very effective design for the estimation of
 regional and national forest cover type frequencies. Simultaneous
 estimation of frequencies and sampling variances require estimation of a
 large number of parameters; often so many that consistency and robustness
 of results becomes an issue. A new stepwise estimation model, in which
 bias in phase one and two is corrected sequentially instead of
 simultaneously, requires fewer parameters. Simulated three-phase sampling
 tested the new model with 144 settings of sample sizes, the number of
 classes and classification accuracy. Relative mean absolute deviations and
 root mean square errors were, in most cases, about 8% lower with the
 stepwise method than with a simultaneous approach. Differences were a
 function of design parameters. Average expected relative root mean square
 errors, derived from the assumption of a Dirichlet distribution of
 cover-type frequencies, tracked the empirical root mean square errors
 obtained from repeated sampling with - 10%. Resampling results indicate
 that the relative bias of the most frequent cover types was slightly
 inflated by the stepwise method. For the least common cover type, the
 simultaneous method produced the largest relative bias. 
Journal: Journal of Applied Statistics 
Pages: 461-475 
Issue: 5 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000053628 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000053628
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:5:p:461-475




Template-Type: ReDIF-Article 1.0
Author-Name: Youngjo Lee 
Author-X-Name-First: Youngjo 
Author-X-Name-Last: Lee 
Author-Name: John Nelder 
Author-X-Name-First: John 
Author-X-Name-Last: Nelder 
Title: False parsimony and its detection with GLMs 
Abstract:
  A search for a good parsimonious model is often required in data
 analysis. However, unfortunately we may end up with a falsely parsimonious
 model. Misspecification of the variance structure causes a loss of
 efficiency in regression estimation and this can lead to large
 standard-error estimates, producing possibly false parsimony. With
 generalized linear models (GLMs) we can keep the link function fixed while
 changing the variance function, thus allowing us to recognize false
 parsimony caused by such increased standard errors. With data
 transformation, any change of transformation automatically changes the
 scale for additivity, making false parsimony hard to recognize. 
Journal: Journal of Applied Statistics 
Pages: 477-483 
Issue: 5 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000053637 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000053637
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:5:p:477-483




Template-Type: ReDIF-Article 1.0
Author-Name: Sifa Mvoi 
Author-X-Name-First: Sifa 
Author-X-Name-Last: Mvoi 
Author-Name: Yan-Xia Lin 
Author-X-Name-First: Yan-Xia 
Author-X-Name-Last: Lin 
Title: Analysis of experiments using the asymptotic quasi-likelihood approach 
Abstract:
  Comparison of treatment effects in an experiment is usually done through
 analysis of variance under the assumption that the errors are normally and
 independently distributed with zero mean and constant variance. The
 traditional approach in dealing with non-constant variance is to apply a
 variance stabilizing transformation and then run the analysis on the
 transformed data. In this approach, the conclusions of analysis of
 variance apply only to the transformed population. In this paper, the
 asymptotic quasi-likelihood method is introduced to the analysis of
 experimental designs. The weak assumptions of the asymptotic
 quasi-likelihood method make it possible to draw conclusions on
 heterogeneous populations without transforming them. This paper
 demonstrates how to apply the asymptotic quasi-likelihood technique to
 three commonly used models. This gives a possible way to analyse data
 given a complex experimental design. 
Journal: Journal of Applied Statistics 
Pages: 485-505 
Issue: 5 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000053646 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000053646
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:5:p:485-505




Template-Type: ReDIF-Article 1.0
Author-Name: Arthur Yeh 
Author-X-Name-First: Arthur 
Author-X-Name-Last: Yeh 
Author-Name: Dennis Lin 
Author-X-Name-First: Dennis 
Author-X-Name-Last: Lin 
Author-Name: Honghong Zhou 
Author-X-Name-First: Honghong 
Author-X-Name-Last: Zhou 
Author-Name: Chandramouliswaran Venkataramani 
Author-X-Name-First: Chandramouliswaran 
Author-X-Name-Last: Venkataramani 
Title: A multivariate exponentially weighted moving average control chart for monitoring process variability 
Abstract:
  This paper introduces a new multivariate exponentially weighted moving
 average (EWMA) control chart. The proposed control chart, called an EWMA
 V-chart, is designed to detect small changes in the variability of
 correlated multivariate quality characteristics. Through examples and
 simulations, it is demonstrated that the EWMA V-chart is superior to the
 &7CS&7C-chart in detecting small changes in process variability.
 Furthermore, a counterpart of the EWMA V-chart for monitoring process
 mean, called the EWMA M-chart is proposed. In detecting small changes in
 process variability, the combination of EWMA M-chart and EWMA V-chart is a
 better alternative to the combination of MEWMA control chart (Lowry et al.
 , 1992) and &7CS&7C-chart. Furthermore, the EWMA M- chart and V-chart can
 be plotted in one single figure. As for monitoring both process mean and
 process variability, the combined MEWMA and EWMA V-charts provide the best
 control procedure. 
Journal: Journal of Applied Statistics 
Pages: 507-536 
Issue: 5 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000053655 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000053655
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:5:p:507-536




Template-Type: ReDIF-Article 1.0
Author-Name: H. Wong 
Author-X-Name-First: H. 
Author-X-Name-Last: Wong 
Author-Name: Wai-Cheung Ip 
Author-X-Name-First: Wai-Cheung 
Author-X-Name-Last: Ip 
Author-Name: Zhongjie Xie 
Author-X-Name-First: Zhongjie 
Author-X-Name-Last: Xie 
Author-Name: Xueli Lui 
Author-X-Name-First: Xueli 
Author-X-Name-Last: Lui 
Title: Modelling and forecasting by wavelets, and the application to exchange rates 
Abstract:
  This paper investigates the modelling and forecasting method for
 non-stationary time series. Using wavelets, the authors propose a
 modelling procedure that decomposes the series as the sum of three
 separate components, namely trend, harmonic and irregular components. The
 estimates suggested in this paper are all consistent. This method has been
 used for the modelling of US dollar against DM exchange rate data, and ten
 steps ahead (2 weeks) forecasting are compared with several other methods.
 Under the Average Percentage of forecasting Error (APE) criterion, the
 wavelet approach is the best one. The results suggest that forecasting
 based on wavelets is a viable alternative to existing methods. 
Journal: Journal of Applied Statistics 
Pages: 537-553 
Issue: 5 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000053664 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000053664
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:5:p:537-553




Template-Type: ReDIF-Article 1.0
Author-Name: Christopher Weigand 
Author-X-Name-First: Christopher 
Author-X-Name-Last: Weigand 
Title: Economically optimal inspection policy with geometric adaptation 
Abstract:
  A process is considered whose quality deteriorates according to a
 constant failure intensity 5 . As, in practice, it can be difficult to
 estimate the true value of 5 , the purpose of this paper is to present a
 strategy that can be applied without knowing 5 . In order to maximize
 profit per item, perfect inspections and renewals are performed. The
 length of the inspection interval is described by a geometric sequence and
 changes in time, depending on perceived assignable causes. Optimal
 adaptive control plans provide nearly the same profit per item as in the
 case when 5 is known. 
Journal: Journal of Applied Statistics 
Pages: 555-569 
Issue: 5 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000053673 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000053673
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:5:p:555-569




Template-Type: ReDIF-Article 1.0
Author-Name: Abdullah Almasri 
Author-X-Name-First: Abdullah 
Author-X-Name-Last: Almasri 
Author-Name: Ghazi Shukur 
Author-X-Name-First: Ghazi 
Author-X-Name-Last: Shukur 
Title: An illustration of the causality relation between government spending and revenue using wavelet analysis on Finnish data 
Abstract:
  Quarterly data for the period 1960:1 to 1997:2, conventional tests, a
 bootstrap simulation approach and a multivariate Rao's F-test have been
 used to investigate if the causality between government spending and
 revenue in Finland was changed at the beginning of 1990 due to future
 plans to create the European Monetary Union (EMU). The results indicate
 that during the period before 1990, the government revenue Granger-caused
 spending, while the opposite happened after 1990, which agrees better with
 Barro's tax smoothing hypothesis. However, when using monthly data instead
 of quarterly data for almost the same sample period, totally different
 results have been noted. The general conclusion is that the relationship
 between spending and revenue in Finland is still not completely
 understood. The ambiguity of these results may well be due to the fact
 that there are several time scales involved in the relationship, and that
 the conventional analyses may be inadequate to separate out the time scale
 structured relationships between these variables. Therefore, to
 investigate empirically the relation between these variables we attempt to
 use the wavelets analysis that enables us to separate out different time
 scales of variation in the data. We find that time scale decomposition is
 important for analysing these economic variables. 
Journal: Journal of Applied Statistics 
Pages: 571-584 
Issue: 5 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000053682 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000053682
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:5:p:571-584




Template-Type: ReDIF-Article 1.0
Author-Name: Stephen Leary 
Author-X-Name-First: Stephen 
Author-X-Name-Last: Leary 
Author-Name: Atul Bhaskar 
Author-X-Name-First: Atul 
Author-X-Name-Last: Bhaskar 
Author-Name: Andy Keane 
Author-X-Name-First: Andy 
Author-X-Name-Last: Keane 
Title: Optimal orthogonal-array-based latin hypercubes 
Abstract:
  The use of optimal orthogonal array latin hypercube designs is proposed.
 Orthogonal arrays were proposed for constructing latin hypercube designs
 by Tang (1993). Such designs generally have better space filling
 properties than random latin hypercube designs. Even so, these designs do
 not necessarily fill the space particularly well. As a result, we consider
 orthogonal-array-based latin hypercube designs that try to achieve
 optimality in some sense. Optimization is performed by adapting strategies
 found in Morris & Mitchell (1995) and Ye et al. (2000). The strategies
 here search only orthogonal-array-based latin hypercube designs and, as a
 result, optimal designs are found in a more efficient fashion. The designs
 found are in general agreement with existing optimal designs reported
 elsewhere. 
Journal: Journal of Applied Statistics 
Pages: 585-598 
Issue: 5 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000053691 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000053691
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:5:p:585-598




Template-Type: ReDIF-Article 1.0
Author-Name: R. G. Aykroyd 
Author-X-Name-First: R. G. 
Author-X-Name-Last: Aykroyd 
Author-Name: K. V. Mardia 
Author-X-Name-First: K. V. 
Author-X-Name-Last: Mardia 
Title: A wavelet approach to shape analysis for spinal curves 
Abstract:
  We present a new method to describe shape change and shape differences in
 curves, by constructing a deformation function in terms of a wavelet
 decomposition. Wavelets form an orthonormal basis which allows
 representations at multiple resolutions. The deformation function is
 estimated, in a fully Bayesian framework, using a Markov chain Monte Carlo
 algorithm. This Bayesian formulation incorporates prior information about
 the wavelets and the deformation function. The flexibility of the MCMC
 approach allows estimation of complex but clinically important summary
 statistics, such as curvature in our case, as well as estimates of
 deformation functions with variance estimates, and allows thorough
 investigation of the posterior distribution. This work is motivated by
 multi-disciplinary research involving a large-scale longitudinal study of
 idiopathic scoliosis in UK children. This paper provides novel statistical
 tools to study this spinal deformity, from which 5% of UK children suffer.
 Using the data we consider statistical inference for shape differences
 between normals, scoliotics and developers of scoliosis, in particular for
 spinal curvature, and look at longitudinal deformations to describe shape
 changes with time. 
Journal: Journal of Applied Statistics 
Pages: 605-623 
Issue: 6 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000053718 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000053718
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:6:p:605-623




Template-Type: ReDIF-Article 1.0
Author-Name: Ren-Fen Lee 
Author-X-Name-First: Ren-Fen 
Author-X-Name-Last: Lee 
Author-Name: Deng-Yuan Huang 
Author-X-Name-First: Deng-Yuan 
Author-X-Name-Last: Huang 
Title: On some data oriented robust estimation procedures for means 
Abstract:
  Data oriented to estimate means is very important for large data sets.
 Since outliers usually occur, the trimmed mean is a robust estimator of
 locations. After building a reasonable linear model to explain the
 relationship between the suitably transformed symmetric data and the
 approximately standardized normal statistics, we find the trimmed
 proportion based on the smallest variance of trimmed means. The related
 statistical inference is also discussed. An empirical study based on an
 annual survey about inbound visitors in the Taiwan area is used to achieve
 our goal in deciding the trimmed proportion. In this study, we propose a
 complete procedure to attain the goal. 
Journal: Journal of Applied Statistics 
Pages: 625-634 
Issue: 6 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000053727 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000053727
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:6:p:625-634




Template-Type: ReDIF-Article 1.0
Author-Name: K. D. Patterson 
Author-X-Name-First: K. D. 
Author-X-Name-Last: Patterson 
Author-Name: S. M. Heravi 
Author-X-Name-First: S. M. 
Author-X-Name-Last: Heravi 
Title: The impact of fat-tailed distributions on some leading unit roots tests 
Abstract:
  There is substantial evidence that many time series associated with
 financial and insurance claim data are fat-tailed, with a (much) higher
 probability of &#x22; outliers' compared with the normal distribution.
 However, standard tests, or variants of them, for the presence of unit
 roots assume a normal distribution for the innovations driving the series.
 Application of the former to the latter therefore involves an
 inconsistency. We assess the impact of this inconsistency and provide
 information on its impact on inference when innovations are drawn from the
 Cauchy and sequence of t(v) distributions. A simple prediction that fat
 tails will uniformly lead to over-sizing of standard tests (because the
 fatness in the tail translates to the test distribution) turns out to be
 incorrect: we find that some tests are over-sized but some are
 under-sized. We also consider size retention and the power of the
 Dickey-Fuller pivotal and normalized bias test statistics and weighted
 symmetric versions of these tests. To make the unit root testing procedure
 feasible, we develop an entropy-based test for some fat-tailed
 distributions and apply it to share prices from the FTSE100. 
Journal: Journal of Applied Statistics 
Pages: 635-667 
Issue: 6 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000053736 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000053736
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:6:p:635-667




Template-Type: ReDIF-Article 1.0
Author-Name: S. Robin 
Author-X-Name-First: S. 
Author-X-Name-Last: Robin 
Author-Name: M. Lecomte 
Author-X-Name-First: M. 
Author-X-Name-Last: Lecomte 
Author-Name: H. Hofte 
Author-X-Name-First: H. 
Author-X-Name-Last: Hofte 
Author-Name: G. Mouille 
Author-X-Name-First: G. 
Author-X-Name-Last: Mouille 
Title: A procedure for the clustering of cell wall mutants in the model plant Arabidopsis based on Fourier-transform infrared (FT-IR) spectroscopy 
Abstract:
  FT-IR microspectroscopy can be used to study the global composition and
 architecture of plant cell walls and it allows cell wall mutants to be
 identified. Our aim is to define a distance between cell wall mutants in
 the model species Arabidopsis based on FT-IR spectra. Since the number of
 data points that constitute a spectrum exceeds the number of samples
 analysed, it is essential to reduce first the dimension of the dataset. We
 present a comparison of several compression methods, including linear
 discriminant analysis using a non-canonical covariance matrix. The
 calculated distances were used to define clusters of mutants that appeared
 to be biologically meaningful. 
Journal: Journal of Applied Statistics 
Pages: 669-681 
Issue: 6 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000053745 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000053745
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:6:p:669-681




Template-Type: ReDIF-Article 1.0
Author-Name: Daniel Wang 
Author-X-Name-First: Daniel 
Author-X-Name-Last: Wang 
Author-Name: Michael Conerly 
Author-X-Name-First: Michael 
Author-X-Name-Last: Conerly 
Title: Evaluation of three lack of fit tests in linear regression models 
Abstract:
  A key diagnostic in the analysis of linear regression models is whether
 the fitted model is appropriate for the observed data. The classical lack
 of fit test is used for testing the adequacy of a linear regression model
 when replicates are available. While many efforts have been made in
 finding alternative lack of fit tests for models without replicates, this
 paper focuses on studying the efficacy of three tests: the classical lack
 of fit test, Utts' (1982) test, Burn & Ryan's (1983) test. The powers of
 these tests are computed for a variety of situations. Comments and
 conclusions on the overall performance of these tests are made, including
 recommendations for future studies. 
Journal: Journal of Applied Statistics 
Pages: 683-696 
Issue: 6 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000053763 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000053763
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:6:p:683-696




Template-Type: ReDIF-Article 1.0
Author-Name: D. K. Ghosh 
Author-X-Name-First: D. K. 
Author-X-Name-Last: Ghosh 
Author-Name: P. C. Biswas 
Author-X-Name-First: P. C. 
Author-X-Name-Last: Biswas 
Title: Complete diallel crosses plans through balanced incomplete block designs 
Abstract:
  The present investigation involves the methods of construction of
 complete diallel cross plans using balanced incomplete block (BIB)
 designs. Furthermore, the analysis of complete diallel crosses plans are
 carried out to estimate the general combining ability of the ith line
 (i=1, r 2, r &#x2026;, r v) where the intra- block analysis of the
 adjusted sum of squares for GCA and the unadjusted block sum of squares
 are also obtained, thereafter the relationship between the estimates of
 BIB design and the estimates of the GCA effect of CDC plan has been
 established. Moreover, it has also been shown that the complete diallel
 crosses design obtained through two BIB designs satisfying v1=b1= 4 5
 1+3=v2=b2, r r1=2 5 1+1=r2=k1=k2 and 5 1= 5 2 are universally optimum.
 These results are further supported by a suitable example of each.
 However, the need of this study is to show that the analysis of the CDC
 plan is reducible to the analysis of generating the BIB design. 
Journal: Journal of Applied Statistics 
Pages: 697-708 
Issue: 6 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000053772 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000053772
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:6:p:697-708




Template-Type: ReDIF-Article 1.0
Author-Name: Siu-Keung Tse 
Author-X-Name-First: Siu-Keung 
Author-X-Name-Last: Tse 
Author-Name: Chunyan Yang 
Author-X-Name-First: Chunyan 
Author-X-Name-Last: Yang 
Title: Reliability sampling plans for the Weibull distribution under Type II progressive censoring with binomial removals 
Abstract:
  This paper presents reliability sampling plans for the Weibull
 distribution under Type II progressive censoring with random removals
 (PCR), where the number of units removed at each failure time follows a
 binomial distribution. To construct the sampling plans, the sample size n
 and the acceptance constant k are determined based on asymptotic
 distribution theory. The resulting sampling plans are tabulated for
 selected specifications under the proposed censoring scheme. Furthermore,
 a Monte Carlo simulation is conducted to validate the true probability of
 acceptance for the designed sampling plans. 
Journal: Journal of Applied Statistics 
Pages: 709-718 
Issue: 6 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000053781 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000053781
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:6:p:709-718




Template-Type: ReDIF-Article 1.0
Author-Name: Dimitrios Vougas 
Author-X-Name-First: Dimitrios 
Author-X-Name-Last: Vougas 
Title: Reconsidering LM unit root testing 
Abstract:
  Non-rejection of a unit root hypothesis by usual Dickey & Fuller (1979)
 (DF, hereafter) or Phillips & Perron (1988) (hereafter PP) tests should
 not be taken as strong evidence in favour of unit root presence. There are
 less popular, but more powerful, unit root tests that should be employed
 instead of DF-PP tests. A prime example of an alternative test is the LM
 unit root test developed by Schmidt & Phillips (1992) (hereafter SP) and
 Schmidt & Lee (1991) (hereafter SL). LM unit root tests are easy to
 calculate and invariant (similar); they employ optimal detrending and are
 more powerful than usual DF-PP tests. Asymptotic theory and finite sample
 critical values (with inaccuracies that we correct in this paper) are
 available for SP-SL tests. However, the usefulness of LM tests is not
 fully understood, due to ambiguity over test type recommendation, as well
 as potentially inefficient derivation of the test that might confuse
 applied researchers. In this paper, we reconsider LM unit root testing in
 a model with linear trend. We derive asymptotic distribution theory (in a
 new fashion), as well as accurate appropriate critical values. We
 undertake Monte Carlo investigation of finite sample properties of SP-SL
 LM tests, along with applications to the Nelson & Plosser (1982) time
 series and real quarterly UK GDP. 
Journal: Journal of Applied Statistics 
Pages: 727-741 
Issue: 7 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000076010 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000076010
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:7:p:727-741




Template-Type: ReDIF-Article 1.0
Author-Name: W. J. Krzanowski 
Author-X-Name-First: W. J. 
Author-X-Name-Last: Krzanowski 
Title: Non-parametric estimation of distance between groups 
Abstract:
  A numerical procedure is outlined for obtaining the distance between
 samples from two populations. First, the probability densities in the two
 populations are estimated by kernel methods, and then the distance is
 derived by numerical integration of a suitable function of these
 densities. Various such functions have been proposed in the past; they are
 all implemented and compared with each other and with Mahalanobis D 2 on
 several real and simulated data sets. The results show the method to be
 viable, and to perform well against the Mahalanobis D 2 standard. 
Journal: Journal of Applied Statistics 
Pages: 743-750 
Issue: 7 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000076029 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000076029
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:7:p:743-750




Template-Type: ReDIF-Article 1.0
Author-Name: J. Kowalski 
Author-X-Name-First: J. 
Author-X-Name-Last: Kowalski 
Author-Name: X. M. Tu 
Author-X-Name-First: X. M. 
Author-X-Name-Last: Tu 
Title: Trend analysis with response incompatible formats and measurement error 
Abstract:
  The increasing popularity of longitudinal studies, along with the rapid
 advances in science and technology, has created a potential
 incompatibility between data formats, which leads to an inference problem
 when applying conventional statistical methods. This inference problem is
 further compounded by measurement error, since incompatible data format
 often arise in the context of measuring latent constructs. Without a
 systematic study of the impact of scale differences, ad-hoc approaches
 generally lead to inconsistent estimates and thus, invalid statistical
 inferences. In this paper, we examine the asymptotic properties and
 identify conditions that guarantee consistent estimation within the
 context of a trend analysis with response incompatible formats and
 measurement error. For model estimation, we introduce two competing
 methods that use a generalized estimating equation approach to provide
 inferences for the parameters of interest, and highlight the relative
 strengths of each method. The approach is illustrated by data obtained
 from a multi-centre AIDS cohort study (MACS), where a trend analysis of an
 immunologic marker of HIV infection is of interest. 
Journal: Journal of Applied Statistics 
Pages: 751-770 
Issue: 7 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000076038 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000076038
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:7:p:751-770




Template-Type: ReDIF-Article 1.0
Author-Name: Jian-Xin Pan 
Author-X-Name-First: Jian-Xin 
Author-X-Name-Last: Pan 
Author-Name: Peng Bai 
Author-X-Name-First: Peng 
Author-X-Name-Last: Bai 
Title: Local influence analysis in the growth curve model with Rao's simple covariance structure 
Abstract:
  In this paper we discuss the likelihood-based local influence in a growth
 curve model with Rao's simple covariance structure. Under an abstract
 perturbation, the Hessian matrix is provided in which the eigenvector
 corresponding to the maximum absolute eigenvalue is used to assess the
 influence of observations. Specifically, we employ covariance-weighted
 perturbation to demonstrate the use of the proposed approach. A practical
 example is analysed using the proposed local influence approach. 
Journal: Journal of Applied Statistics 
Pages: 771-781 
Issue: 7 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000076047 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000076047
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:7:p:771-781




Template-Type: ReDIF-Article 1.0
Author-Name: Chien-Tai Lin 
Author-X-Name-First: Chien-Tai 
Author-X-Name-Last: Lin 
Author-Name: N. Balakrishnan 
Author-X-Name-First: N. 
Author-X-Name-Last: Balakrishnan 
Title: Exact prediction intervals for exponential distributions based on doubly Type-II censored samples 
Abstract:
  In this paper, we make use of an algorithm of Huffer & Lin (2001) in
 order to develop exact prediction intervals for failure times from
 one-parameter and two- parameter exponential distributions based on doubly
 Type-II censored samples. We show that this method yields the same results
 as those of Lawless (1971, 1977) and Like w (1974) in the case when the
 available sample is Type-II right censored. We present a computational
 algorithm for the determination of the exact percentage points of the
 pivotal quantities used in the construction of these prediction intervals.
 We also present some tables of these percentage points for the prediction
 of the ' th order statistic in a sample of size n for both one- and
 two-parameter exponential distributions, assuming that the available
 sample is doubly Type-II censored. Finally, we present two examples to
 illustrate the methods of inference developed here. 
Journal: Journal of Applied Statistics 
Pages: 783-801 
Issue: 7 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000076056 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000076056
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:7:p:783-801




Template-Type: ReDIF-Article 1.0
Author-Name: Nicolaus Tideman 
Author-X-Name-First: Nicolaus 
Author-X-Name-Last: Tideman 
Author-Name: Reza Kheirandish 
Author-X-Name-First: Reza 
Author-X-Name-Last: Kheirandish 
Title: Structurally consistent probabilities of selecting answers 
Abstract:
  This paper offers a procedure for specifying probabilities for students
 to select answers on a multiple-choice test that, unlike previous
 procedures, satisfies all three of the following structural consistency
 conditions: (1) for any student, the sum over questions of the
 probabilities that the student will use the correct answers is the
 student's score on the test; (2) for any student, the sum over possible
 answers of the probabilities of using the answers is 1.0; and (3) for any
 answer to any question, the sum over students of the probabilities of
 using that answer is the number of students who used that answer. When
 applied to an exam, these fully consistent probabilities had the same
 power to identify cheaters as the probabilities proposed by Wesolowsky,
 and noticeably better power than the probabilities suggested by Frary et
 al. 
Journal: Journal of Applied Statistics 
Pages: 803-811 
Issue: 7 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000076065 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000076065
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:7:p:803-811




Template-Type: ReDIF-Article 1.0
Author-Name: Duolao Wang 
Author-X-Name-First: Duolao 
Author-X-Name-Last: Wang 
Author-Name: Panuwat Lertsithichai 
Author-X-Name-First: Panuwat 
Author-X-Name-Last: Lertsithichai 
Author-Name: Kiran Nanchahal 
Author-X-Name-First: Kiran 
Author-X-Name-Last: Nanchahal 
Author-Name: Mohammed Yousufuddin 
Author-X-Name-First: Mohammed 
Author-X-Name-Last: Yousufuddin 
Title: Risk factors of coronary heart disease: A Bayesian model averaging approach 
Abstract:
  To analyse the risk factors of coronary heart disease (CHD), we apply the
 Bayesian model averaging approach that formalizes the model selection
 process and deals with model uncertainty in a discrete-time survival model
 to the data from the Framingham Heart Study. We also use the Alternating
 Conditional Expectation algorithm to transform the risk factors, such that
 their relationships with CHD are best described, overcoming the problem of
 coding such variables subjectively. For the Framingham Study, the Bayesian
 model averaging approach, which makes inferences about the effects of
 covariates on CHD based on an average of the posterior distributions of
 the set of identified models, outperforms the stepwise method in
 predictive performance. We also show that age, cholesterol, and smoking
 are nonlinearly associated with the occurrence of CHD and that P-values
 from models selected from stepwise methods tend to overestimate the
 evidence for the predictive value of a risk factor and ignore model
 uncertainty. 
Journal: Journal of Applied Statistics 
Pages: 813-826 
Issue: 7 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000076074 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000076074
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:7:p:813-826




Template-Type: ReDIF-Article 1.0
Author-Name: A. H. M. Rahmatullah Imon 
Author-X-Name-First: A. H. M. Rahmatullah 
Author-X-Name-Last: Imon 
Title: Residuals from deletion in added variable plots 
Abstract:
  An added variable plot is a commonly used plot in regression diagnostics.
 The rationale for this plot is to provide information about the addition
 of a further explanatory variable to the model. In addition, an added
 variable plot is most often used for detecting high leverage points and
 influential data. So far as we know, this type of plot involves the least
 squares residuals which, we suspect, could produce a confusing picture
 when a group of unusual cases are present in the data. In this situation,
 added variable plots may not only fail to detect the unusual cases but
 also may fail to focus on the need for adding a further regressor to the
 model. We suggest that residuals from deletion should be more convincing
 and reliable in this type of plot. The usefulness of an added variable
 plot based on residuals from deletion is investigated through a few
 examples and a Monte Carlo simulation experiment in a variety of
 situations. 
Journal: Journal of Applied Statistics 
Pages: 827-841 
Issue: 7 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000076083 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000076083
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:7:p:827-841




Template-Type: ReDIF-Article 1.0
Author-Name: Youngjo Lee 
Author-X-Name-First: Youngjo 
Author-X-Name-Last: Lee 
Author-Name: John Nelder 
Author-X-Name-First: John 
Author-X-Name-Last: Nelder 
Title: Extended-REML estimators 
Abstract:
  Restricted likelihood was originally introduced as the criterion for the
 estimation of dispersion components in normal mixed linear models. Lee &
 Nelder (2001a) showed that it can be extended to a much wider class of
 models via double extended quasi-likelihood. We give a detailed
 description of the new method and show that it gives an efficient
 estimation procedure for dispersion components. 
Journal: Journal of Applied Statistics 
Pages: 845-856 
Issue: 8 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000075930 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000075930
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:8:p:845-856




Template-Type: ReDIF-Article 1.0
Author-Name: Kelvin Yau 
Author-X-Name-First: Kelvin 
Author-X-Name-Last: Yau 
Author-Name: Karen Yip 
Author-X-Name-First: Karen 
Author-X-Name-Last: Yip 
Author-Name: H. K. Yuen 
Author-X-Name-First: H. K. 
Author-X-Name-Last: Yuen 
Title: Modelling repeated insurance claim frequency data using the generalized linear mixed model 
Abstract:
  Most of the methods used to estimate claim frequency rates in general
 insurance have assumed that data are independent. However, it is not
 uncommon for information stored in the database of an insurance company to
 contain previous years' claim data from each policyholder. We consider the
 application of the generalized linear mixed model approach to the analysis
 of repeated insurance claim frequency data in which a conditionally fixed
 random effect vector is incorporated explicitly into the linear predictor
 to model the inherent correlation. A motor insurance data set is used as
 the basis for simulation to demonstrate the advantages of the method.
 Ignoring the underlying association for observations within the same
 policyholder results in an underestimation of the standard error of the
 parameter estimates and a remarkable reduction in the prediction accuracy.
 The method provides a viable alternative for incorporating repeated claim
 experience that enables the revision of rates in general insurance. 
Journal: Journal of Applied Statistics 
Pages: 857-865 
Issue: 8 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000075949 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000075949
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:8:p:857-865




Template-Type: ReDIF-Article 1.0
Author-Name: V. K. Sharma 
Author-X-Name-First: V. K. 
Author-X-Name-Last: Sharma 
Author-Name: Seema Jaggi 
Author-X-Name-First: Seema 
Author-X-Name-Last: Jaggi 
Author-Name: Cini Varghese 
Author-X-Name-First: Cini 
Author-X-Name-Last: Varghese 
Title: Minimal balanced repeated measurements designs 
Abstract:
  Experimental designs in which treatments are applied to the experimental
 units, one at a time, in sequences over a number of periods, have been
 used in several scientific investigations and are known as repeated
 measurements designs. Besides direct effects, these designs allow
 estimation of residual effects of treatments along with adjustment for
 them. Assuming the existence of first-order residual effects of
 treatments, Hedayat & Afsarinejad (1975) gave a method of constructing
 minimal balanced repeated measurements [RM(v,n,p)] design for v treatments
 using n=2v experimental units for p [=(v+1)/2] periods when v is a prime
 or prime power. Here, a general method of construction of these designs
 for all odd v has been given along with an outline for their analysis. In
 terms of variances of estimated elementary contrasts between treatment
 effects (direct and residual), these designs are seen to be partially
 variance balanced based on the circular association scheme. 
Journal: Journal of Applied Statistics 
Pages: 867-872 
Issue: 8 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000075958 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000075958
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:8:p:867-872




Template-Type: ReDIF-Article 1.0
Author-Name: Sueli Mingoti 
Author-X-Name-First: Sueli 
Author-X-Name-Last: Mingoti 
Title: A note on the sample size required in sequential tests for the generalized binomial distribution 
Abstract:
  In this paper, we discuss the sample size needed to perform Wald's
 sequential statistical test for the proportion of non-conforming items
 generated by a process when the results of the inspections are correlated
 and the generalized binomial distribution proposed by Madsen (1993) is
 used. It will be shown that, in the presence of correlation, the sample
 size increases as the value of the coefficient of correlation
 increases--being much higher for processes with small failure rates. 
Journal: Journal of Applied Statistics 
Pages: 873-879 
Issue: 8 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000075967 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000075967
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:8:p:873-879




Template-Type: ReDIF-Article 1.0
Author-Name: Hyun Sook Oh 
Author-X-Name-First: Hyun Sook 
Author-X-Name-Last: Oh 
Author-Name: Seoung-gon Ko 
Author-X-Name-First: Seoung-gon 
Author-X-Name-Last: Ko 
Author-Name: Man-Suk Oh 
Author-X-Name-First: Man-Suk 
Author-X-Name-Last: Oh 
Title: A Bayesian approach to assessing population bioequivalence in a 2 2 2 crossover design 
Abstract:
  A Bayesian testing procedure is proposed for assessment of the
 bioequivalence in both mean and variance, which ensures population
 bioequivalence under the normality assumption. We derive the joint
 posterior distribution of the means and variances in a standard 2 2 2
 crossover experimental design and propose a Bayesian testing procedure for
 bioequivalence based on a Markov chain Monte Carlo method. The proposed
 method is applied to a real data set. 
Journal: Journal of Applied Statistics 
Pages: 881-891 
Issue: 8 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000117131 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000117131
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:8:p:881-891




Template-Type: ReDIF-Article 1.0
Author-Name: R. Bellio 
Author-X-Name-First: R. 
Author-X-Name-Last: Bellio 
Author-Name: E. Gori 
Author-X-Name-First: E. 
Author-X-Name-Last: Gori 
Title: Impact evaluation of job training programmes: Selection bias in multilevel models 
Abstract:
  This paper focuses on the evaluation of a job training programme composed
 of several different courses. The aim is to evaluate the impact of the
 programme for the participants with respect to non-participants, paying
 attention to possible differences in the effectiveness between the
 courses. The analysis is based on discrete data with a hierarchical
 structure. Multilevel modelling is the natural choice in this setting, but
 the results may be severely affected by selection bias. We propose a
 two-step procedure, which suits both the hierarchical structure and the
 observational nature of data. The method selects the appropriate control
 group, using standard results of the propensity score methodology. A
 suitable multilevel model is formulated, and the dependence of the results
 on the amount of non-random sample selection is analysed within a
 likelihood-based framework. As a result, rankings for comparative
 performances are obtained, adjusted for the amount of plausible selection
 bias. The procedure is illustrated with reference to a data set about a
 job training programme organized in Italy in the late 1990s. 
Journal: Journal of Applied Statistics 
Pages: 893-907 
Issue: 8 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000075976 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000075976
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:8:p:893-907




Template-Type: ReDIF-Article 1.0
Author-Name: Peter Austin 
Author-X-Name-First: Peter 
Author-X-Name-Last: Austin 
Author-Name: Michael Escobar 
Author-X-Name-First: Michael 
Author-X-Name-Last: Escobar 
Title: The use of finite mixture models to estimate the distribution of the health utilities index in the presence of a ceiling effect 
Abstract:
  Finite mixture models are flexible parametric models that allow one to
 describe complex probability distributions as a mixture of a small number
 of simple probability distributions. Measures of health status are often
 used to reflect a person's overall health. Such measures may be subject to
 a ceiling effect, in that the measure is unable to discern gradations in
 health status above the ceiling. The purpose of this paper is to
 illustrate the use of finite mixture models to describe the probability
 distribution of the Health Utilities Index, under the assumption that the
 HUI is subject to a ceiling effect. Mixture models with two through six
 components are fit to the HUI. Bayes factors were used to compare the
 evidence that the Canadian population of non-institutionalized residents
 is composed of four distinct subpopulations, and that a mixture of six
 Normal components is required to describe these four subpopulations. 
Journal: Journal of Applied Statistics 
Pages: 909-923 
Issue: 8 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000075985 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000075985
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:8:p:909-923




Template-Type: ReDIF-Article 1.0
Author-Name: Rhonda Magel 
Author-X-Name-First: Rhonda 
Author-X-Name-Last: Magel 
Author-Name: Li Qin 
Author-X-Name-First: Li 
Author-X-Name-Last: Qin 
Title: A non-parametric test for umbrella alternatives based on ranked-set sampling 
Abstract:
  A test is proposed that extends the Chen-Wolfe (1990) test for umbrella
 alternatives with an unknown peak to use with ranked-set samples data.
 This follows from ideas in Bohn & Wolfe (1992), Magel (1994), and Hartlaub
 & Wolfe (1999). Critical values are simulated for the proposed test based
 on ranked-set samples of size 2 for 3, 4 and 5 populations. A power study
 is conducted comparing the proposed test using ranked-set samples with the
 Chen-Wolfe and Mack-Wolfe tests using simple random samples. Results are
 given. 
Journal: Journal of Applied Statistics 
Pages: 925-937 
Issue: 8 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000075994 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000075994
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:8:p:925-937




Template-Type: ReDIF-Article 1.0
Author-Name: Goran Arnoldsson 
Author-X-Name-First: Goran 
Author-X-Name-Last: Arnoldsson 
Title: Optimal designs for beta-binomial logistic regression models 
Abstract:
  Optimal designs for a logistic regression model with over-dispersion
 introduced by a beta-binomial distribution are characterized. Designs are
 defined by a set of design points and design weights as usual but, in
 addition, the experimenter must also make a choice of a sub-sampling
 design specifying the distribution of observations on sample sizes. In an
 earlier work it has been shown that Ds-optimal sampling designs for
 estimation of the parameters of the beta-binomial distribution are
 supported on at most two design points. This admits a simplified approach
 using single sample sizes. Linear predictor values for Ds-optimal designs
 using a common sample size are tabulated for different levels of
 over-dispersion and choice of subsets of parameters. 
Journal: Journal of Applied Statistics 
Pages: 939-951 
Issue: 8 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000076001 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000076001
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:8:p:939-951




Template-Type: ReDIF-Article 1.0
Author-Name: Vernon Farewell 
Author-X-Name-First: Vernon 
Author-X-Name-Last: Farewell 
Author-Name: Agnes Herzberg 
Author-X-Name-First: Agnes 
Author-X-Name-Last: Herzberg 
Title: Plaid designs for the evaluation of training for medical practitioners 
Abstract:
  The training of medical practitioners to improve the practitioner/patient
 relationship may be difficult, as limitations often exist on the choice of
 patients included in the study. A specific study of this type of training
 is given. It is proposed that a simple modification and generalization of
 Yates' plaid-square designs be used. It is shown that a replicated
 plaid-design incorporates as a special case the criss-cross or strip-plot
 design. The usefulness of these designs in studies of the training of
 medical practitioners is illustrated. The basic characteristics of their
 analysis are outlined. 
Journal: Journal of Applied Statistics 
Pages: 957-965 
Issue: 9 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000076092 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000076092
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:9:p:957-965




Template-Type: ReDIF-Article 1.0
Author-Name: Richard Stevens 
Author-X-Name-First: Richard 
Author-X-Name-Last: Stevens 
Title: Evaluation of methods for interval estimation of model outputs, with application to survival models 
Abstract:
  When a published statistical model is also distributed as computer
 software, it will usually be desirable to present the outputs as interval,
 as well as point, estimates. The present paper compares three methods for
 approximate interval estimation about a model output, for use when the
 model form does not permit an exact interval estimate. The methods
 considered are first-order asymptotics, using second derivatives of the
 log-likelihood to estimate variance information; higher-order asymptotics
 based on the signed-root transformation; and the non-parametric bootstrap.
 The signed-root method is Bayesian, and uses an approximation for
 posterior moments that has not previously been tested in a real-world
 application. Use of the three methods is illustrated with reference to a
 software project arising in medical decision-making, the UKPDS Risk
 Engine. Intervals from the first-order and signed-root methods are near-
 identical, and typically 1% wider to 7% narrower than those from the
 non-parametric bootstrap. The asymptotic methods are markedly faster than
 the bootstrap method. 
Journal: Journal of Applied Statistics 
Pages: 967-981 
Issue: 9 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000076100 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000076100
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:9:p:967-981




Template-Type: ReDIF-Article 1.0
Author-Name: Yuzhi Cai 
Author-X-Name-First: Yuzhi 
Author-X-Name-Last: Cai 
Author-Name: Neville Davies 
Author-X-Name-First: Neville 
Author-X-Name-Last: Davies 
Title: Monitoring the parameter changes in general ARIMA time series models 
Abstract:
  We propose methods for monitoring the residuals of a fitted ARIMA or an
 autoregressive fractionally integrated moving average (ARFIMA) model in
 order to detect changes of the parameters in that model. We extend the
 procedures of Box & Ramirez (1992) and Ramirez (1992) and allow the
 differencing parameter, d to be fractional or integer. Test statistics are
 approximated by Wiener processes. We carry out simulations and also apply
 our method to several real time series. The results show that our method
 is effective for monitoring all parameters in ARFIMA models. 
Journal: Journal of Applied Statistics 
Pages: 983-1001 
Issue: 9 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000076119 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000076119
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:9:p:983-1001




Template-Type: ReDIF-Article 1.0
Author-Name: David Thomson 
Author-X-Name-First: David 
Author-X-Name-Last: Thomson 
Author-Name: Arie van Noordwijk 
Author-X-Name-First: Arie 
Author-X-Name-Last: van Noordwijk 
Author-Name: Ward Hagemeijer 
Author-X-Name-First: Ward 
Author-X-Name-Last: Hagemeijer 
Title: Estimating avian dispersal distances from data on ringed birds 
Abstract:
  Data from birds ringed as chicks and recaptured during subsequent
 breeding seasons provide information on avian natal dispersal distances.
 However, national patterns of ring reports are influenced by recapture
 rates as well as by dispersal rates. While an extensive methodology has
 been developed to study survival rates using models that correct for
 recapture rates, the same is not true for dispersal. Here, we present such
 a method, showing how corrections for spatial heterogeneity in recapture
 rate can be built into estimates of dispersal rates if detailed atlas data
 and ringing totals can be combined with extensive data on birds ringed as
 chicks and recaptured as breeding adults. We show how the method can be
 implemented in the software package SURVIV (White, 1992). 
Journal: Journal of Applied Statistics 
Pages: 1003-1008 
Issue: 9 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000076128 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000076128
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:9:p:1003-1008




Template-Type: ReDIF-Article 1.0
Author-Name: Seppo Laaksonen 
Author-X-Name-First: Seppo 
Author-X-Name-Last: Laaksonen 
Title: Alternative imputation techniques for complex metric variables 
Abstract:
  This paper deals with imputation techniques and strategies. Usually,
 imputation truly commences after the first data editing, but many
 preceding operations are needed before that. In this editing step, the
 missing or deficient items are to be recognized and coded, and then it is
 decided which of these, if any, should be substituted by imputing. There
 are a number of imputation methods and their specifications. Consequently,
 it is not clear what method finally should be chosen, especially when an
 imputation method may be best in one respect, and another method in the
 other. In this paper, we consider these questions through the following
 four imputation methods: (i) random hot decking, (ii) logistic regression
 imputation, (iii) linear regression imputation, and (iv) regression-based
 nearest neighbour hot decking. The last two methods are applied with the
 two different specifications. The two metric variables have been used in
 empirical tests. The first is very complex, but the second is more
 ordinary, and thus easier to handle. The empirical examples are based on
 simulations, which clearly show the biases of the various methods and
 their specifications. In general, it seems that method (iv) is
 recommendable although the results from it are not perfect either. 
Journal: Journal of Applied Statistics 
Pages: 1009-1020 
Issue: 9 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000076137 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000076137
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:9:p:1009-1020




Template-Type: ReDIF-Article 1.0
Author-Name: Luis Gil-Alana 
Author-X-Name-First: Luis 
Author-X-Name-Last: Gil-Alana 
Title: Estimation of the degree of dependence in the temperatures in the northern hemisphere using semi-parametric techniques 
Abstract:
  We are concerned in this article with the estimation of the degree of
 dependence between the observations of the monthly temperatures in the
 northern hemisphere from 1854 to 1989 by means of using fractionally
 integrated semi-parametric techniques. We use several estimation
 procedures proposed by P. M. Robinson in a number of papers, and the
 results indicate that the order of integration of the series is around
 0.37, implying that the time series is stationary but with long memory
 behaviour. Separating the data in two subsamples (1854-1921 and 1922-89),
 the results show that there has been an increase in the degree of
 dependence across time by about 0.05-0.10, the order of integration
 oscillating around 0.3 (0.35) for the time period 1854-1921, and around
 0.35 (0.40) for the period 1922-89. 
Journal: Journal of Applied Statistics 
Pages: 1021-1031 
Issue: 9 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000076146 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000076146
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:9:p:1021-1031




Template-Type: ReDIF-Article 1.0
Author-Name: J. F. Walhin 
Author-X-Name-First: J. F. 
Author-X-Name-Last: Walhin 
Title: Bivariate Hofmann distributions 
Abstract:
  The aim of this paper is to develop some bivariate generalizations of the
 Hofmann distribution. The Hofmann distribution is known to give nice fits
 for overdispersed data sets. Two bivariate models are proposed. Recursive
 formulae are given for the evaluation of the probability function.
 Moments, conditional distributions and marginal distributions are studied.
 Two data sets are fitted based on the proposed models. Parameters are
 estimated by maximum likelihood. 
Journal: Journal of Applied Statistics 
Pages: 1033-1046 
Issue: 9 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000076155 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000076155
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:9:p:1033-1046




Template-Type: ReDIF-Article 1.0
Author-Name: H. Oztay Ayhan 
Author-X-Name-First: H. Oztay 
Author-X-Name-Last: Ayhan 
Title: Models of response error components in supervised interview-reinterview surveys 
Abstract:
  The current work deals with modelling of response error components in
 supervised interview-reinterview surveys. The model considers several
 stages of an interactive process to obtain and record a response. The
 response process is evaluated as,
 controller-interviewer-respondent-interviewer-controller interaction
 setting under a supervised interviewing process. The allocation of
 controllers, interviewers and respondents is made by a hierarchical design
 for the interview-reinterview process. In addition, a coder error
 component is also added to the above proposed model. The proposed model
 operates under two major sub-models, namely an error detection model and
 response model. 
Journal: Journal of Applied Statistics 
Pages: 1047-1054 
Issue: 9 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000076164 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000076164
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:9:p:1047-1054




Template-Type: ReDIF-Article 1.0
Author-Name: Hassen Muttlak 
Author-X-Name-First: Hassen 
Author-X-Name-Last: Muttlak 
Author-Name: Walid Al-Sabah 
Author-X-Name-First: Walid 
Author-X-Name-Last: Al-Sabah 
Title: Statistical quality control based on ranked set sampling 
Abstract:
  Different quality control charts for the sample mean are developed using
 ranked set sampling (RSS), and two of its modifications, namely median
 ranked set sampling (MRSS) and extreme ranked set sampling (ERSS). These
 new charts are compared to the usual control charts based on simple random
 sampling (SRS) data. The charts based on RSS or one of its modifications
 are shown to have smaller average run length (ARL) than the classical
 chart when there is a sustained shift in the process mean. The MRSS and
 ERSS methods are compared with RSS and SRS data, it turns out that MRSS
 dominates all other methods in terms of the out-of-control ARL
 performance. Real data are collected using the RSS, MRSS, and ERSS in
 cases of perfect and imperfect ranking. These data sets are used to
 construct the corresponding control charts. These charts are compared to
 usual SRS chart. Throughout this study we are assuming that the underlying
 distribution is normal. A check of the normality for our example data set
 indicated that the normality assumption is reasonable. 
Journal: Journal of Applied Statistics 
Pages: 1055-1078 
Issue: 9 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000076173 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000076173
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:9:p:1055-1078




Template-Type: ReDIF-Article 1.0
Author-Name: Saralees Nadarajah 
Author-X-Name-First: Saralees 
Author-X-Name-Last: Nadarajah 
Author-Name: Kosto Mitov 
Author-X-Name-First: Kosto 
Author-X-Name-Last: Mitov 
Author-Name: Samuel Kotz 
Author-X-Name-First: Samuel 
Author-X-Name-Last: Kotz 
Title: Local dependence functions for extreme value distributions 
Abstract:
  Kotz & Nadarajah (2002) introduced a measure of local dependence which is
 a localized version of the Pearson's correlation coefficient. In this
 paper we provide detailed analyses (both algebraic and numerical) of the
 form of the measure for the class of bivariate extreme value
 distributions. We consider, in particular, five families of bivariate
 extreme value distributions. We also discuss two applications of the new
 measure. In the first application we introduce an overall measure of
 correlation and produce evidence to suggest that it is superior than the
 usual Pearson's correlation coefficient. The second application introduces
 two new concepts for ordering of bivariate dependence. 
Journal: Journal of Applied Statistics 
Pages: 1081-1100 
Issue: 10 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000107123 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000107123
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:10:p:1081-1100




Template-Type: ReDIF-Article 1.0
Author-Name: Michael Greenacre 
Author-X-Name-First: Michael 
Author-X-Name-Last: Greenacre 
Title: Singular value decomposition of matched matrices 
Abstract:
  We consider the joint analysis of two matched matrices which have common
 rows and columns, for example multivariate data observed at two time
 points or split according to a dichotomous variable. Methods of interest
 include principal components analysis for interval-scaled data,
 correspondence analysis for frequency data, log-ratio analysis of
 compositional data and linear biplots in general, all of which depend on
 the singular value decomposition. A simple result in matrix algebra shows
 that by setting up two matched matrices in a particular block format,
 matrix sum and difference components can be analysed using a single
 application of the singular value decomposition algorithm. The methodology
 is applied to data from the International Social Survey Program comparing
 male and female attitudes on working wives across eight countries. The
 resulting biplots optimally display the overall cross-cultural differences
 as well as the male-female differences. The case of more than two matched
 matrices is also discussed. 
Journal: Journal of Applied Statistics 
Pages: 1101-1113 
Issue: 10 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000107132 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000107132
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:10:p:1101-1113




Template-Type: ReDIF-Article 1.0
Author-Name: Christian Sonesson 
Author-X-Name-First: Christian 
Author-X-Name-Last: Sonesson 
Title: Evaluations of some Exponentially Weighted Moving Average methods 
Abstract:
  The need for statistical surveillance has been noted in many different
 areas, and examples of applications include the detection of an increased
 incidence of a disease, the detection of an increased radiation level and
 the detection of a turning point in a leading index for a business cycle.
 In all cases, preventive actions are possible if the alarm is made early.
 Several versions of the EWMA (Exponentially Weighted Moving Average)
 method for monitoring a process with the aim of detecting a shift in the
 mean are studied both for the one-sided and the two-sided case. The
 effects of using barriers for the one-sided alarm statistic are also
 studied. One important issue is the effect of different types of alarm
 limits. Different measures of evaluation, suitable in different types of
 applications, are considered such as the expected delay, the ARL&#xb9;,
 the probability of successful detection and the predictive value of an
 alarm, to give a broad picture of the features of the methods. Results
 from a large-scale simulation study are presented both for a fixed ARL0
 and a fixed probability of a false alarm. It appears that important
 differences from an inferential point of view exist between the one- and
 two-sided versions of the methods. It is demonstrated that the method,
 usually considered as a convenient approximation, is to be preferred over
 the exact version in the overwhelming majority of applications. 
Journal: Journal of Applied Statistics 
Pages: 1115-1133 
Issue: 10 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000107141 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000107141
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:10:p:1115-1133




Template-Type: ReDIF-Article 1.0
Author-Name: Yangxin Huang 
Author-X-Name-First: Yangxin 
Author-X-Name-Last: Huang 
Title: Selection of number of dose levels and its robustness for binary response data 
Abstract:
  Muller & Schmitt (1990) have considered the question of how to choose the
 number of doses for estimating the median effective dose (ED50) when a
 probit dose-response curve is correctly assumed. However, they restricted
 their investigation to designs with doses symmetrical about the true ED50.
 In this paper, we investigate how the conclusions of Muller & Schmitt may
 change as the dose designs become slightly asymmetric about the true ED50.
 In addition, we address the question of the robustness of the number of
 doses chosen for an incorrectly assumed logistic model, when the dose
 designs are asymmetric about the assumed ED50. The underlying true
 dose-response curves considered here include the probit, cubic logistic
 and Aranda- Ordaz asymmetric models. The simulation results show that, for
 various underlying true dose-response curves and the uniform design
 density with doses spaced asymmetrically around the assumed ED50, the
 choice of as many doses as possible is almost optimal. This agrees with
 the results obtained for a correctly assumed probit or logistic
 dose-response curve when the dose designs are symmetric or slightly
 asymmetric about the ED50. 
Journal: Journal of Applied Statistics 
Pages: 1135-1146 
Issue: 10 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000107150 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000107150
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:10:p:1135-1146




Template-Type: ReDIF-Article 1.0
Author-Name: L. A. Gil-Alana 
Author-X-Name-First: L. A. 
Author-X-Name-Last: Gil-Alana 
Title: A fractional integration analysis of the population in some OECD countries 
Abstract:
  In this article we examine the degree of persistence of the population
 series in 19 OECD countries during the period 1948-2000 by means of using
 fractionally integrated techniques. We use a parametric procedure due to
 Robinson (1994) that permits us to test I(d) statistical models. The
 results show that the order of integration of the series substantially
 varies across countries and also depending on how we specify the I(0)
 disturbances. Overall, Germany and Portugal present the smallest degrees
 of integration while population in Japan appears as the most
 non-stationary series. 
Journal: Journal of Applied Statistics 
Pages: 1147-1159 
Issue: 10 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000107169 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000107169
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:10:p:1147-1159




Template-Type: ReDIF-Article 1.0
Author-Name: Manuel Salvador 
Author-X-Name-First: Manuel 
Author-X-Name-Last: Salvador 
Author-Name: Pilar Gargallo 
Author-X-Name-First: Pilar 
Author-X-Name-Last: Gargallo 
Title: Automatic selective intervention in dynamic linear models 
Abstract:
  In this paper we propose an algorithm to carry out the monitoring and
 retrospective intervention process in Dynamic Linear Models, both
 selectively and automatically. The algorithm is illustrated by analysing
 several series taken from the literature, in which the proposed procedure
 is shown to perform better than the scheme proposed by West & Harrison
 (1997, Chapter 11). 
Journal: Journal of Applied Statistics 
Pages: 1161-1184 
Issue: 10 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000107178 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000107178
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:10:p:1161-1184




Template-Type: ReDIF-Article 1.0
Author-Name: Herve Cardot 
Author-X-Name-First: Herve 
Author-X-Name-Last: Cardot 
Author-Name: Robert Faivre 
Author-X-Name-First: Robert 
Author-X-Name-Last: Faivre 
Author-Name: Michel Goulard 
Author-X-Name-First: Michel 
Author-X-Name-Last: Goulard 
Title: Functional approaches for predicting land use with the temporal evolution of coarse resolution remote sensing data 
Abstract:
  The sensor SPOT 4/Vegetation gives every day satellite images of Europe
 with medium spatial resolution, each pixel corresponding to an area of 1 r
 km 2 1 r km. Such data are useful to characterize the development of the
 vegetation at a large scale. The pixels, named &#x22;mixed' pixels,
 aggregate information of different crops and thus different themes of
 interest (wheat, corn, forest, &#x2026;). We aim at estimating the land
 use when observing the temporal evolution of reflectances of mixed pixels.
 The statistical problem is to predict proportions with longitudinal
 covariates. We compared two functional approaches. The first relies on
 varying-time regression models and the second is an extension of the
 multilogit model for functional data. The comparison is achieved on a
 small area on which the land use is known. Satellite data were collected
 between March and August 1998. The functional multilogit model gives
 better predictions and the use of composite vegetation index is more
 efficient. 
Journal: Journal of Applied Statistics 
Pages: 1185-1199 
Issue: 10 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000107187 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000107187
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:10:p:1185-1199




Template-Type: ReDIF-Article 1.0
Author-Name: Robert Till 
Author-X-Name-First: Robert 
Author-X-Name-Last: Till 
Author-Name: David Hand 
Author-X-Name-First: David 
Author-X-Name-Last: Hand 
Title: Behavioural models of credit card usage 
Abstract:
  Behavioural models characterize the way customers behave in their use of
 a credit product. In this paper, we examine repayment and transaction
 behaviour with credit cards. In particular, we describe the development of
 Markov chain models for late repayment, investigate the extent to which
 there are different classes of behaviour pattern, and explore the extent
 to which distinct behaviours can be predicted. We also develop overall
 models for transaction time distributions. Once such models have been
 built to summarize the data, they can be used to predict likely future
 behaviour, and can also serve as the basis of predictions of what one
 might expect when economic circumstances change. 
Journal: Journal of Applied Statistics 
Pages: 1201-1220 
Issue: 10 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000107196 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000107196
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:10:p:1201-1220




Template-Type: ReDIF-Article 1.0
Author-Name: Dejian Lai 
Author-X-Name-First: Dejian 
Author-X-Name-Last: Lai 
Author-Name: Rakesh Sharma 
Author-X-Name-First: Rakesh 
Author-X-Name-Last: Sharma 
Author-Name: Jerry Wolinsky 
Author-X-Name-First: Jerry 
Author-X-Name-Last: Wolinsky 
Author-Name: Ponnada Narayana 
Author-X-Name-First: Ponnada 
Author-X-Name-Last: Narayana 
Title: A comparative study of correlation coefficients in spatially MRSI-observed neurochemicals from multiple sclerosis patients 
Abstract:
  In measuring the association between magnetic resonance spectroscopic
 imaging (MRSI)-observed neurochemicals from multiple sclerosis (MS)
 patients, the classic correlation coefficients such as the Pearson's r,
 Spearman's A and Kendall's F do not take into account the spatial
 dependence of the observations. This paper reports a comparative study on
 these classic correlation coefficients (Pearson's r, Spearman's A and
 Kendall's F ) and some more recent correlation coefficients (Tjostheim's
 a, the modified t) that take into account the spatial dependence of the
 intensities of the concentrations of several neurochemicals in MS
 patients. Our study indicates that the use of the classic correlation
 coefficients that ignores the spatial dependence of the observations may
 overestimate the statistical significance of the results. 
Journal: Journal of Applied Statistics 
Pages: 1221-1229 
Issue: 10 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000107204 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000107204
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:10:p:1221-1229




Template-Type: ReDIF-Article 1.0
Author-Name: Rand Wilcox 
Author-X-Name-First: Rand 
Author-X-Name-Last: Wilcox 
Title: Multiple comparisons based on a modified one-step M-estimator 
Abstract:
  Although many methods are available for performing multiple comparisons
 based on some measure of location, most can be unsatisfactory in at least
 some situations, in simulations when sample sizes are small, say less than
 or equal to twenty. That is, the actual Type I error probability can
 substantially exceed the nominal level, and for some methods the actual
 Type I error probability can be well below the nominal level, suggesting
 that power might be relatively poor. In addition, all methods based on
 means can have relatively low power under arbitrarily small departures
 from normality. Currently, a method based on 20% trimmed means and a
 percentile bootstrap method performs relatively well (Wilcox, in press).
 However, symmetric trimming was used, even when sampling from a highly
 skewed distribution and a rigid adherence to 20% trimming can result in
 low efficiency when a distribution is sufficiently heavy-tailed. Robust
 M-estimators are more flexible but they can be unsatisfactory in terms of
 Type I errors when sample sizes are small. This paper describes an
 alternative approach based on a modified one-step M-estimator that
 introduces more flexibility than a trimmed mean but provides better
 control over Type I error probabilities compared with using a one-step
 M-estimator. 
Journal: Journal of Applied Statistics 
Pages: 1231-1241 
Issue: 10 
Volume: 30 
Year: 2003 
X-DOI: 10.1080/0266476032000137463 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000137463
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:30:y:2003:i:10:p:1231-1241




Template-Type: ReDIF-Article 1.0
Author-Name: L. Di Scala 
Author-X-Name-First: L. Di 
Author-X-Name-Last: Scala 
Author-Name: L. La Rocca 
Author-X-Name-First: L. 
Author-X-Name-Last: La Rocca 
Author-Name: G. Consonni 
Author-X-Name-First: G. 
Author-X-Name-Last: Consonni 
Title: A Bayesian Hierarchical Model for the Evaluation of a Website 
Abstract:
  Consider a website and the surfers visiting its pages. A typical issue of
 interest, for example while monitoring an advertising campaign, concerns
 whether a specific page has been designed successfully, i.e. is able to
 attract surfers or address them to other pages within the site. We assume
 that the surfing behaviour is fully described by the transition
 probabilities from one page to another, so that a clickstream (sequence of
 consecutively visited pages) can be viewed as a finite-state-space Markov
 chain. We then implement a variety of hierarchical prior distributions on
 the multivariate logits of the transition probabilities and define, for
 each page, a content effect and a link effect. The former measures the
 attractiveness of the page due to its contents, while the latter signals
 its ability to suggest further interesting links within the site.
 Moreover, we define an additional effect, representing overall page
 success, which incorporates both effects previously described. Using
 WinBUGS, we provide estimates and credible intervals for each of the above
 effects and rank pages accordingly. 
Journal: Journal of Applied Statistics 
Pages: 15-27 
Issue: 1 
Volume: 31 
Year: 2004 
Keywords: Clickstream analysis, multilevel models, multivariate logits, ranking, transition counts, 
X-DOI: 10.1080/0266476032000148920 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000148920
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:1:p:15-27




Template-Type: ReDIF-Article 1.0
Author-Name: L. Pace 
Author-X-Name-First: L. 
Author-X-Name-Last: Pace 
Author-Name: A. Salvan 
Author-X-Name-First: A. 
Author-X-Name-Last: Salvan 
Author-Name: L. Ventura 
Author-X-Name-First: L. 
Author-X-Name-Last: Ventura 
Title: The Effects of Rounding on Likelihood Procedures 
Abstract:
  The aim of this paper is to investigate the robustness properties of
 likelihood inference with respect to rounding effects. Attention is
 focused on exponential families and on inference about a scalar parameter
 of interest, also in the presence of nuisance parameters. A summary value
 of the influence function of a given statistic, the local-shift
 sensitivity, is considered. It accounts for small fluctuations in the
 observations. The main result is that the local-shift sensitivity is
 bounded for the usual likelihood-based statistics, i.e. the directed
 likelihood, the Wald and score statistics. It is also bounded for the
 modified directed likelihood, which is a higher-order adjustment of the
 directed likelihood. The practical implication is that likelihood
 inference is expected to be robust with respect to rounding effects.
 Theoretical analysis is supplemented and confirmed by a number of Monte
 Carlo studies, performed to assess the coverage probabilities of
 confidence intervals based on likelihood procedures when data are rounded.
 In addition, simulations indicate that the directed likelihood is less
 sensitive to rounding effects than the Wald and score statistics. This
 provides another criterion for choosing among first-order equivalent
 likelihood procedures. The modified directed likelihood shows the same
 robustness as the directed likelihood, so that its gain in inferential
 accuracy does not come at the price of an increase in instability with
 respect to rounding. 
Journal: Journal of Applied Statistics 
Pages: 29-48 
Issue: 1 
Volume: 31 
Year: 2004 
Keywords: Directed likelihood, exponential family, higher-order asymptotics, influence function, maximum likelihood estimator, modified directed likelihood, robustness, Wald test, 
X-DOI: 10.1080/0266476032000148939 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000148939
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:1:p:29-48




Template-Type: ReDIF-Article 1.0
Author-Name: A. Martin Andres 
Author-X-Name-First: A. Martin 
Author-X-Name-Last: Andres 
Author-Name: I. Herranz Tejedor 
Author-X-Name-First: I. Herranz 
Author-X-Name-Last: Tejedor 
Title: The Equivalence of Two Proportions Revisited 
Abstract:
  The classic conditional test for checking that the difference between two
 independent proportions is not null may not be appropriate in many
 circumstances. Dunnett & Gent (1977) showed that in clinical trials, in
 studies of drugs, etc, the aim is to prove the practical equality
 (equivalence) of both proportions. On other occasions the aim may be the
 opposite: i.e. to prove that the two proportions are substantially
 different (biologically significant). Both cases are usually solved by two
 one-sided tests (TOST test). In this article, this procedure is shown to
 be conservative and two true two-sided tests for each case are proposed. 
Journal: Journal of Applied Statistics 
Pages: 61-72 
Issue: 1 
Volume: 31 
Year: 2004 
Keywords: Confidence intervals, comparisons of two proportions, conditional test, equivalence test, Z test, &#x3c9;&#xb2; test, 2&#xd7;2 tables, 
X-DOI: 10.1080/0266476032000148957 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000148957
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:1:p:61-72




Template-Type: ReDIF-Article 1.0
Author-Name: C. Illert 
Author-X-Name-First: C. 
Author-X-Name-Last: Illert 
Author-Name: A. Allison 
Author-X-Name-First: A. 
Author-X-Name-Last: Allison 
Title: Phono-genesis and the Origin of Accusative Syntax in Proto-Australian Language 
Abstract:
  It is claimed that a set of 62 known (Illert, 2003) ancient Aboriginal
 words constitute a representative sample of the original proto-Australian
 lexicon whose maximum likelihood (Fisher, 1912) 'power law signature' is
 determined and shown to precisely fit genetically related 'modern'
 lexicons from south-eastern-Australia. This measure of 'sameness' builds
 the confidence required to justify inter-lexicon diachronic word-
 frequency comparisons which provide a powerful new statistical tool
 capable of revealing important features of ancestral grammar. This paper
 supplies the first ever published modern translations of authentic
 traditional language documented in obscure literary and archival sources
 which have, until recently, been lost (Dawes, 1790b; Wood, 1924; Troy,
 1992) or overlooked (Everitt et al., 1900; Illert, 2001) for centuries.
 These newly found examples of accusative syntax supported by word-
 frequency data may come as quite a surprise to some linguists (Dixon,
 1980; Osmond, 1989; Troy, 1992; Nichols, 1993) who, in the absence of
 adequate evidence, seem to have long-imagined that language from this
 region&#x2014;if not the entire continent&#x2014; simply had to be
 inherently and at the core ergative. On the contrary we find that changing
 word-frequencies, from proto-Australian to modern times, supply
 overwhelming evidence of the emergence of ancient accusative prefixes
 which have even survived into recent centuries in the Sydney region.
 Additionally it is found that, over millennia, words die-off in a lexicon,
 replaced by others, according to the famous &#x22;mortality law' of
 Gompertz (1825) which also describes the likelihood of death of biological
 organisms within populations and is the basis for modern actuarial science
 (Bowers et al., 1997). Just as disease and epidemics can wipe out entire
 cohorts of creatures from a population, so too can syntactic change
 annihilate word-classes in an evolving lexicon. 
Journal: Journal of Applied Statistics 
Pages: 73-104 
Issue: 1 
Volume: 31 
Year: 2004 
Keywords: Maximum-likelihood, frequency analysis, Gompertz law, morpho-statistics, linguistics, 
X-DOI: 10.1080/0266476032000148966 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000148966
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:1:p:73-104




Template-Type: ReDIF-Article 1.0
Author-Name: T. Nummi 
Author-X-Name-First: T. 
Author-X-Name-Last: Nummi 
Author-Name: J. Mottonen 
Author-X-Name-First: J. 
Author-X-Name-Last: Mottonen 
Title: Prediction of Stem Measurements of Scots Pine 
Abstract:
  The aim of this study was to investigate prediction of stem measurements
 of Scots pine(Pinus sylvestris L.) for a modern computerized forest
 harvester. We are interested in the prediction of stem curve measurements
 when measurements of stems already processed and a short section of the
 stem under process are known. The techniques presented here are based on
 cubic smoothing splines and on multivariate regression models. One
 advantage of these methods is that they do not assume any special
 functional form of the stem curve. They can also be applied to the
 prediction of branch limits and stem height of pine stems. 
Journal: Journal of Applied Statistics 
Pages: 105-114 
Issue: 1 
Volume: 31 
Year: 2004 
Keywords: Cubic smoothing splines, forest harvesting, mixed models, 
X-DOI: 10.1080/0266476032000148975 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000148975
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:1:p:105-114




Template-Type: ReDIF-Article 1.0
Author-Name: Pedro Gouveia 
Author-X-Name-First: Pedro 
Author-X-Name-Last: Gouveia 
Author-Name: Paulo Rodrigues 
Author-X-Name-First: Paulo 
Author-X-Name-Last: Rodrigues 
Title: Threshold Cointegration and the PPP Hypothesis 
Abstract:
  Self-Exciting Threshold Autoregressive (SETAR) models are a non-linear
 variant of conventional linear Autoregressive (AR) models. One advantage
 of SETAR models over conventional AR models lies in its flexible nature in
 dealing with possible asymmetric behaviour of economic variables. The
 concept of threshold cointegration implies that the Error Correction
 Mechanism (ECM) at a particular interval is inactive as a result of
 adjustment costs, and active when deviations from equilibrium exceed
 certain thresholds. For instance, the presence of adjustment costs can, in
 many circumstances, justify the fact that economic agents intervene to
 recalibrate back to a tolerable limit, as in the case when the benefits of
 adjustment are superior to its costs. We introduce an approach that
 accounts for potential asymmetry and we investigate the presence of the
 relative version of the purchasing power parity (PPP) hypothesis for 14
 countries. Based on a threshold cointegration adaptation of the unit root
 test procedure suggested by Caner & Hansen (2001), we find evidence of an
 asymmetric adjustment for the relative version of PPP for eight pairs of
 countries. 
Journal: Journal of Applied Statistics 
Pages: 115-127 
Issue: 1 
Volume: 31 
Year: 2004 
Keywords: Nonlinearity, cointegration, Setar models, 
X-DOI: 10.1080/0266476032000148984 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000148984
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:1:p:115-127




Template-Type: ReDIF-Article 1.0
Author-Name: Rand Wilcox 
Author-X-Name-First: Rand 
Author-X-Name-Last: Wilcox 
Title: Inferences Based on a Skipped Correlation Coefficient 
Abstract:
  The most popular method for trying to detect an association between two
 random variables is to test H0 : &#x3c1;=0, the hypothesis that Pearson's
 correlation is equal to zero. It is well known, however, that Pearson's
 correlation is not robust, roughly meaning that small changes in any
 distribution, including any bivariate normal distribution as a special
 case, can alter its value. Moreover, the usual estimate of &#x3c1;, r, is
 sensitive to only a few outliers which can mask a true association. A
 simple alternative to testing H0 : &#x3c1; =0 is to switch to a measure of
 association that guards against outliers among the marginal distributions
 such as Kendall's tau, Spearman's rho, a Winsorized correlation, or a
 so-called percentage bend correlation. But it is known that these methods
 fail to take into account the overall structure of the data. Many measures
 of association that do take into account the overall structure of the data
 have been proposed, but it seems that nothing is known about how they
 might be used to detect dependence. One such measure of association is
 selected, which is designed so that under bivariate normality, its
 estimator gives a reasonably accurate estimate of &#x3c1;. Then methods
 for testing the hypothesis of a zero correlation are studied. 
Journal: Journal of Applied Statistics 
Pages: 131-143 
Issue: 2 
Volume: 31 
Year: 2004 
Keywords: Skipped correlation coefficient, inferences, random variables, Pearson's correlation, 
X-DOI: 10.1080/0266476032000148821 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000148821
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:2:p:131-143




Template-Type: ReDIF-Article 1.0
Author-Name: Markus Neuhauser 
Author-X-Name-First: Markus 
Author-X-Name-Last: Neuhauser 
Author-Name: Herbert Buning 
Author-X-Name-First: Herbert 
Author-X-Name-Last: Buning 
Author-Name: Ludwig Hothorn 
Author-X-Name-First: Ludwig 
Author-X-Name-Last: Hothorn 
Title: Maximum Test versus Adaptive Tests for the Two-Sample Location Problem 
Abstract:
  For the non-parametric two-sample location problem, adaptive tests based
 on a selector statistic are compared with a maximum and a sum test,
 respectively. When the class of all continuous distributions is not
 restricted, the sum test is not a robust test, i.e. it does not have a
 relatively high power across the different possible distributions.
 However, according to our simulation results, the adaptive tests as well
 as the maximum test are robust. For a small sample size, the maximum test
 is preferable, whereas for a large sample size the comparison between the
 adaptive tests and the maximum test does not show a clear winner.
 Consequently, one may argue in favour of the maximum test since it is a
 useful test for all sample sizes. Furthermore, it does not need a selector
 and the specification of which test is to be performed for which values of
 the selector. When the family of possible distributions is restricted, the
 maximin efficiency robust test may be a further robust alternative.
 However, for the family of t distributions this test is not as powerful as
 the corresponding maximum test. 
Journal: Journal of Applied Statistics 
Pages: 215-227 
Issue: 2 
Volume: 31 
Year: 2004 
Keywords: Location-shift model, measures of skewness and tailweight, maximin efficiency robust test, non-parametric tests, two-sample location problem, selector statistic, 
X-DOI: 10.1080/0266476032000148876 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000148876
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:2:p:215-227




Template-Type: ReDIF-Article 1.0
Author-Name: Theodore Karrison 
Author-X-Name-First: Theodore 
Author-X-Name-Last: Karrison 
Author-Name: Peter O'Brien 
Author-X-Name-First: Peter 
Author-X-Name-Last: O'Brien 
Title: A Rank-Sum-Type Test for Paired Data with Multiple Endpoints 
Abstract:
  Clinical trials and other types of studies often examine the effects of a
 particular treatment or experimental condition on a number of different
 response variables. Although the usual approach for analysing such data is
 to examine each variable separately, this can increase the chance of false
 positive findings. Bonferroni's inequality or Hotelling's T2 statistic can
 be employed to control the overall type I error rate, but these tests
 generally lack power for alternatives in which the treatment improves the
 outcome on most or all of the endpoints. For the comparison of independent
 groups, O'Brien (1984) developed a rank-sum type test that has greater
 power than the Bonferroni and T2 procedures when one treatment is
 uniformly better (i.e. for all endpoints) than the other treatment(s). In
 this paper we adapt the rank-sum test to studies involving paired data and
 demonstrate that it, too, has power advantages for such alternatives.
 Simulation results are described, and an example from a study measuring
 the effects of sleep loss on glucose metabolism is presented to illustrate
 the methodology. 
Journal: Journal of Applied Statistics 
Pages: 229-238 
Issue: 2 
Volume: 31 
Year: 2004 
Keywords: Multiple endpoints, paired data, non-parametric tests, statistical power, 
X-DOI: 10.1080/0266476032000148885 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000148885
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:2:p:229-238




Template-Type: ReDIF-Article 1.0
Author-Name: Tom Benton 
Author-X-Name-First: Tom 
Author-X-Name-Last: Benton 
Author-Name: David Hand 
Author-X-Name-First: David 
Author-X-Name-Last: Hand 
Author-Name: Martin Crowder 
Author-X-Name-First: Martin 
Author-X-Name-Last: Crowder 
Title: Two zs are Better than One 
Abstract:
  Given only a random sample of observations, the usual estimator for the
 population mean is the sample mean. If additional information is provided
 it might be possible in some situations to obtain a better estimator. The
 situation considered here is when the variable whose mean is sought is
 composed of factors that are themselves observable. In the basic case, the
 variable can be expressed as the product of two, independent, more basic
 variables, but we also consider the case of more than two, the effect of
 correlation, and when there are observation costs. 
Journal: Journal of Applied Statistics 
Pages: 239-247 
Issue: 2 
Volume: 31 
Year: 2004 
Keywords: Sample mean, component observations, product estimators, 
X-DOI: 10.1080/0266476032000148894 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000148894
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:2:p:239-247




Template-Type: ReDIF-Article 1.0
Author-Name: Guillermo Miro-Quesada 
Author-X-Name-First: Guillermo 
Author-X-Name-Last: Miro-Quesada 
Author-Name: Enrique Del Castillo 
Author-X-Name-First: Enrique Del 
Author-X-Name-Last: Castillo 
Author-Name: John Peterson 
Author-X-Name-First: John 
Author-X-Name-Last: Peterson 
Title: A Bayesian Approach for Multiple Response Surface Optimization in the Presence of Noise Variables 
Abstract:
  An approach for the multiple response robust parameter design problem
 based on a methodology by Peterson (2000) is presented. The approach is
 Bayesian, and consists of maximizing the posterior predictive probability
 that the process satisfies a set of constraints on the responses. In order
 to find a solution robust to variation in the noise variables, the
 predictive density is integrated not only with respect to the response
 variables but also with respect to the assumed distribution of the noise
 variables. The maximization problem involves repeated Monte Carlo
 integrations, and two different methods to solve it are evaluated. A
 Matlab code was written that rapidly finds an optimal (robust) solution in
 case it exists. Two examples taken from the literature are used to
 illustrate the proposed method. 
Journal: Journal of Applied Statistics 
Pages: 251-270 
Issue: 3 
Volume: 31 
Year: 2004 
Keywords: Response surface methodology, robust parameter design, Bayesian statistics, Monte Carlo integration, 
X-DOI: 10.1080/0266476042000184019 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476042000184019
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:3:p:251-270




Template-Type: ReDIF-Article 1.0
Author-Name: Sadao Tomizawa 
Author-X-Name-First: Sadao 
Author-X-Name-Last: Tomizawa 
Author-Name: Nobuko Miyamoto 
Author-X-Name-First: Nobuko 
Author-X-Name-Last: Miyamoto 
Author-Name: Ryo Funato 
Author-X-Name-First: Ryo 
Author-X-Name-Last: Funato 
Title: Conditional Difference Asymmetry Model for Square Contingency Tables with Nominal Categories 
Abstract:
  This paper proposes a model, which is an extension-of-symmetry model, for
 square contingency tables with the same nominal row and column
 classifications. The model states that the absolute values of difference
 between the conditional probability that an observation will fall in cell
 (i, j) on condition that it falls in cell (i, j) or (j, i) and the
 conditional probability that it falls in cell (j, i) on the same
 condition, are constant for every i&#x2260;j. The model describes a
 structure of asymmetry (not symmetry), and it is applied to the data on a
 nominal scale. An example is given. 
Journal: Journal of Applied Statistics 
Pages: 271-277 
Issue: 3 
Volume: 31 
Year: 2004 
Keywords: Asymmetry, conditional distribution, nominal category, model, square table, symmetry, 
X-DOI: 10.1080/0266476042000184028 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476042000184028
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:3:p:271-277




Template-Type: ReDIF-Article 1.0
Author-Name: H. Bayo Lawal 
Author-X-Name-First: H. Bayo 
Author-X-Name-Last: Lawal 
Title: Using a GLM to Decompose the Symmetry Model in Square Contingency Tables with Ordered Categories 
Abstract:
  In this paper, we are employing the generalized linear model (GLM) in the
 form lij=X&#x3bb; to decompose the symmetry model into the class of models
 discussed in Tomizawa (1992). In this formulation, the random component
 would be the observed counts fij with an underlying Poisson distribution.
 This approach utilizes the non-standard log-linear model and our focus in
 this paper therefore relates to models that are decompositions of the
 complete symmetry model. That is, models that are implied by the symmetry
 models. We develop factor and regression variables required for the
 implementation of these models in SAS PROC GENMOD and SPSS PROC GENLOG. We
 apply this methodology to analyse the three 4&#xd7;4 contingency table,
 one of which is the Japanese Unaided distance vision data. Results
 obtained in this study are consistent with those from the numerous
 literature on the subject. We further extend our applications to the
 6&#xd7;6 Brazilian social mobility data. We found that both the quasi
 linear diagonal-parameters symmetry (QLDPS) and the quasi 2-ratios
 parameter symmetry (Q2RPS) models fit the Brazilian data very well.
 Parsimonious models being the QLDPS and the quasi-conditional symmetry
 (QCS) models. The SAS and SPSS programs for implementing the models
 discussed in this paper are presented in Appendices A, B and C. 
Journal: Journal of Applied Statistics 
Pages: 279-303 
Issue: 3 
Volume: 31 
Year: 2004 
Keywords: Poisson, factor, regression, quasi-diagonal symmetry model, 
X-DOI: 10.1080/0266476042000184037 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476042000184037
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:3:p:279-303




Template-Type: ReDIF-Article 1.0
Author-Name: Yves Berger 
Author-X-Name-First: Yves 
Author-X-Name-Last: Berger 
Title: A Simple Variance Estimator for Unequal Probability Sampling without Replacement 
Abstract:
  Survey sampling textbooks often refer to the Sen-Yates-Grundy variance
 estimator for use with without-replacement unequal probability designs.
 This estimator is rarely implemented because of the complexity of
 determining joint inclusion probabilities. In practice, the variance is
 usually estimated by simpler variance estimators such as the
 Hansen-Hurwitz with replacement variance estimator; which often leads to
 overestimation of the variance for large sampling fractions that are
 common in business surveys. We will consider an alternative estimator: the
 Hajek (1964) variance estimator that depends on the first-order inclusion
 probabilities only and is usually more accurate than the Hansen-Hurwitz
 estimator. We review this estimator and show its practical value. We
 propose a simple alternative expression; which is as simple as the Hansen-
 Hurwitz estimator. We also show how the Hajek estimator can be easily
 implemented with standard statistical packages. 
Journal: Journal of Applied Statistics 
Pages: 305-315 
Issue: 3 
Volume: 31 
Year: 2004 
Keywords: Design-based inference, Hansen-Hurwitz variance estimator, inclusion probabilities, &#x3c0;-estimator, Sen-Yates-Grundy variance estimator, 
X-DOI: 10.1080/0266476042000184046 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476042000184046
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:3:p:305-315




Template-Type: ReDIF-Article 1.0
Author-Name: A. A. Kalgonda 
Author-X-Name-First: A. A. 
Author-X-Name-Last: Kalgonda 
Author-Name: S. R. Kulkarni 
Author-X-Name-First: S. R. 
Author-X-Name-Last: Kulkarni 
Title: Multivariate Quality Control Chart for Autocorrelated Processes 
Abstract:
  Traditional multivariate statistical process control (SPC) techniques are
 based on the assumption that the successive observation vectors are
 independent. In recent years, due to automation of measurement and data
 collection systems, a process can be sampled at higher rates, which
 ultimately leads to autocorrelation. Consequently, when the
 autocorrelation is present in the data, it can have a serious impact on
 the performance of classical control charts. This paper considers the
 problem of monitoring the mean vector of a process in which observations
 can be modelled as a first-order vector autoregressive VAR (1) process. We
 propose a control chart called Z-chart which is based on the single step
 finite intersection test (Timm, 1996). An important feature of the
 proposed method is that it not only detects an out of control status but
 also helps in identifying variable(s) responsible for the out of control
 situation. The proposed method is illustrated with the help of suitable
 illustrations. 
Journal: Journal of Applied Statistics 
Pages: 317-327 
Issue: 3 
Volume: 31 
Year: 2004 
Keywords: Multivariate statistical process control, autocorrelation, 
X-DOI: 10.1080/0266476042000184000 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476042000184000
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:3:p:317-327




Template-Type: ReDIF-Article 1.0
Author-Name: R. Deardon 
Author-X-Name-First: R. 
Author-X-Name-Last: Deardon 
Author-Name: S. G. Gilmour 
Author-X-Name-First: S. G. 
Author-X-Name-Last: Gilmour 
Author-Name: N. A. Butler 
Author-X-Name-First: N. A. 
Author-X-Name-Last: Butler 
Author-Name: K. Phelps 
Author-X-Name-First: K. 
Author-X-Name-Last: Phelps 
Author-Name: R. Kennedy 
Author-X-Name-First: R. 
Author-X-Name-Last: Kennedy 
Title: A Method for Ascertaining and Controlling Representation Bias in Field Trials for Airborne Plant Pathogens 
Abstract:
  The basic premise of running a field trial is that the estimates of
 treatment effects obtained are representative of how the different
 treatments will perform in the field. The disparities between the
 treatment effects observed experimentally, and those that would be
 observed were the treatments applied to the field, we term 'representation
 bias.' When looking at field trials testing the efficacies of treatment
 sprays on plant pathogens, representation bias can be caused by positive
 and negative inter-plot interference. The potential for such effects will
 be greatest when looking at pathogens that are dispersed by wind. In this
 paper, a computer simulation that simulates plant disease dispersal under
 such conditions is described. This program is used to quantify the amount
 of representation bias occurring in various experimental situations.
 Through this, the relationships between field design parameters and
 representation bias are explored, and the importance of plot dimension and
 spacing, as well as treatment to plot allocation, emphasized. 
Journal: Journal of Applied Statistics 
Pages: 329-343 
Issue: 3 
Volume: 31 
Year: 2004 
Keywords: Inter-plot interference, experimental design, plant pathology, simulation of plant disease dispersal, 
X-DOI: 10.1080/0266476042000184073 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476042000184073
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:3:p:329-343




Template-Type: ReDIF-Article 1.0
Author-Name: Willem Albers 
Author-X-Name-First: Willem 
Author-X-Name-Last: Albers 
Author-Name: Wilbert Kallenberg 
Author-X-Name-First: Wilbert 
Author-X-Name-Last: Kallenberg 
Title: Empirical Non-Parametric Control Charts: Estimation Effects and Corrections 
Abstract:
  Owing to the extreme quantiles involved, standard control charts are very
 sensitive to the effects of parameter estimation and non-normality. More
 general parametric charts have been devised to deal with the latter
 complication and corrections have been derived to compensate for the
 estimation step, both under normal and parametric models. The resulting
 procedures offer a satisfactory solution over a broad range of underlying
 distributions. However, situations do occur where even such a large model
 is inadequate and nothing remains but to consider non- parametric charts.
 In principle, these form ideal solutions, but the problem is that huge
 sample sizes are required for the estimation step. Otherwise the resulting
 stochastic error is so large that the chart is very unstable, a
 disadvantage that seems to outweigh the advantage of avoiding the model
 error from the parametric case. Here we analyse under what conditions
 non-parametric charts actually become feasible alternatives for their
 parametric counterparts. In particular, corrected versions are suggested
 for which a possible change point is reached at sample sizes that are
 markedly less huge (but still larger than the customary range). These
 corrections serve to control the behaviour during in-control (markedly
 wrong outcomes of the estimates only occur sufficiently rarely). The price
 for this protection will clearly be some loss of detection power during
 out-of-control. A change point comes in view as soon as this loss can be
 made sufficiently small. 
Journal: Journal of Applied Statistics 
Pages: 345-360 
Issue: 3 
Volume: 31 
Year: 2004 
Keywords: Statistical process control, Phase II control limits, exceedance probability, empirical quantiles, 
X-DOI: 10.1080/0266476042000184055 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476042000184055
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:3:p:345-360




Template-Type: ReDIF-Article 1.0
Author-Name: Thaddeus Tarpey 
Author-X-Name-First: Thaddeus 
Author-X-Name-Last: Tarpey 
Author-Name: Richard Sanders 
Author-X-Name-First: Richard 
Author-X-Name-Last: Sanders 
Title: Linear Conditional Expectation for Discretized Distributions 
Abstract:
  Many statistical methods for continuous distributions assume a linear
 conditional expectation. Components of multivariate distributions are
 often measured on a discrete ordinal scale based on a discretization of an
 underlying continuous latent variable. The results in this paper show that
 common examples of discretized bivariate and trivariate distributions will
 have a linear conditional expectation. Examples and simulations are
 provided to illustrate the results. 
Journal: Journal of Applied Statistics 
Pages: 361-372 
Issue: 3 
Volume: 31 
Year: 2004 
Keywords: Conditional expectation, biserial/polyserial correlations, elliptical distributions, polychoric correlations, tetrachoric correlations, 
X-DOI: 10.1080/0266476042000184064 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476042000184064
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:3:p:361-372




Template-Type: ReDIF-Article 1.0
Author-Name: H. E. T. Holgersson 
Author-X-Name-First: H. E. T. 
Author-X-Name-Last: Holgersson 
Title: Testing for Multivariate Autocorrelation 
Abstract:
  This paper concerns the problem of assessing autocorrelation of
 multivariate (i.e. systemwise) models. It is well known that systemwise
 diagnostic tests for autocorrelation often suffers from poor small sample
 properties in the sense that the true size overstates the nominal size.
 The failure of keeping control of the size usually stems from the fact
 that the critical values (used to decide the rejection area) originate
 from the slowly converging asymptotic null distribution. Another drawback
 of existing tests is that the power may be rather low if the deviation
 from the null is not symmetrical over the marginal models. In this paper
 we consider four quite different test techniques for autocorrelation.
 These are (i) Pillai's trace, (ii) Roy's largest root, (iii) the maximum
 F-statistic and (iv) the maximum t2 test. We show how to obtain control of
 the size of the tests, and then examine the true (small sample) size and
 power properties by means of Monte Carlo simulations. 
Journal: Journal of Applied Statistics 
Pages: 379-395 
Issue: 4 
Volume: 31 
Year: 2004 
Keywords: Autocorrelation Test, Multivariate Analysis, Linear Hypothesis, Residuals, 
X-DOI: 10.1080/02664760410001681693 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760410001681693
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:4:p:379-395




Template-Type: ReDIF-Article 1.0
Author-Name: Xia Pan 
Author-X-Name-First: Xia 
Author-X-Name-Last: Pan 
Author-Name: Jeffrey Jarrett 
Author-X-Name-First: Jeffrey 
Author-X-Name-Last: Jarrett 
Title: Applying State Space to SPC: Monitoring Multivariate Time Series 
Abstract:
  Monitoring cross-sectional and serially interdependent processes has
 become a new issue in statistical process control (SPC). In up-to-date SPC
 literature, Kalman filtering was reported to monitor univariate
 autocorrelated processes. This paper applies a Kalman filter or
 state-space method for SPC to monitoring multivariate time series. We use
 Aoki's approach to estimate the parameter matrices of a state-space model.
 Multivariate Hotelling T2 control charts are employed to monitor the
 residuals of the state-space. Examples of this approach are illustrated. 
Journal: Journal of Applied Statistics 
Pages: 397-418 
Issue: 4 
Volume: 31 
Year: 2004 
Keywords: Quality Control Charts, Spc, State-space, Multivariate Time Series, Aoki's Approach, 
X-DOI: 10.1080/02664760410001681701 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760410001681701
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:4:p:397-418




Template-Type: ReDIF-Article 1.0
Author-Name: Ping Sa 
Author-X-Name-First: Ping 
Author-X-Name-Last: Sa 
Author-Name: Luminita Razaila 
Author-X-Name-First: Luminita 
Author-X-Name-Last: Razaila 
Title: One-sided Continuous Tolerance Limits and their Accompanying Sample Size Problem 
Abstract:
  Tolerance limits are those limits that contain a certain proportion of
 the distribution of a characteristic with a given probability. 'They are
 used to make sure that the production will not be outside of
 specifications' (Amin & Lee, 1999). Usually, tolerance limits are
 constructed at the beginning of the monitoring of the process. Since they
 are calculated just one time, these tolerance limits cannot reflect
 changes of tolerance level over the lifetime of the process. This research
 proposes an algorithm to construct tolerance limits continuously over time
 for any given distribution. This algorithm makes use of the exponentially
 weighted moving average (EWMA) technique. It can be observed that the
 sample size required by this method is reduced over time. 
Journal: Journal of Applied Statistics 
Pages: 419-434 
Issue: 4 
Volume: 31 
Year: 2004 
Keywords: Tolerance Limits, Exponentially Weighted Moving Average Technique, Order Statistics, 
X-DOI: 10.1080/02664760410001681710 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760410001681710
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:4:p:419-434




Template-Type: ReDIF-Article 1.0
Author-Name: Sanjoy Roy Chowdhury 
Author-X-Name-First: Sanjoy Roy 
Author-X-Name-Last: Chowdhury 
Title: Catalogue of Group Structures for Three-level Fractional Factorial Designs 
Abstract:
  Taguchi (1959) introduced the concept of split-unit design to sort the
 factors into different groups depending upon the difficulties involved in
 changing the levels of factors. Li et al. (1991) renamed it as split-plot
 design. Chen et al. (1993) have given a catalogue of small designs for
 two- and three-level fractional factorial designs pertaining to a single
 type of factors. Aggarwal et al. (1997) have given a catalogue of group
 structure for two-level fractional factorial designs developed under the
 concept of split-plot design. In this paper, an algorithm has been
 developed for generating group structure and possible allocations for
 various 3n-k fractional factorial designs. 
Journal: Journal of Applied Statistics 
Pages: 435-444 
Issue: 4 
Volume: 31 
Year: 2004 
Keywords: Interaction Graphs, Orthogonal Arrays, Split Plot Designs, Group Structures, Word Length Patterns, 
X-DOI: 10.1080/02664760410001681729 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760410001681729
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:4:p:435-444




Template-Type: ReDIF-Article 1.0
Author-Name: Petros Maravelakis 
Author-X-Name-First: Petros 
Author-X-Name-Last: Maravelakis 
Author-Name: John Panaretos 
Author-X-Name-First: John 
Author-X-Name-Last: Panaretos 
Author-Name: Stelios Psarakis 
Author-X-Name-First: Stelios 
Author-X-Name-Last: Psarakis 
Title: EWMA Chart and Measurement Error 
Abstract:
  Measurement error is a usually met distortion factor in real-world
 applications that influences the outcome of a process. In this paper, we
 examine the effect of measurement error on the ability of the EWMA control
 chart to detect out-of-control situations. The model used is the one
 involving linear covariates. We investigate the ability of the EWMA chart
 in the case of a shift in mean. The effect of taking multiple measurements
 on each sampled unit and the case of linearly increasing variance are also
 examined. We prove that, in the case of measurement error, the performance
 of the chart regarding the mean is significantly affected. 
Journal: Journal of Applied Statistics 
Pages: 445-455 
Issue: 4 
Volume: 31 
Year: 2004 
Keywords: Exponentially Weighted Moving Average Control Chart, Average Run Length, Average Time To Signal, Measurement Error, Markov Chain, Statistical Process Control, 
X-DOI: 10.1080/02664760410001681738 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760410001681738
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:4:p:445-455




Template-Type: ReDIF-Article 1.0
Author-Name: Rose Baker 
Author-X-Name-First: Rose 
Author-X-Name-Last: Baker 
Title: A Modified Knox Test of Space-Time Clustering 
Abstract:
  Tests of space-time clustering such as the Knox test are used by
 epidemiologists in the preliminary analysis of datasets where an
 infectious aetiology is suspected. The Knox test statistic is the number
 of cases close in both space and time to another case. The test statistic
 proposed here is the excess number of such cases over that expected under
 H0 of no infection. It is argued that this modified test is more powerful
 than the Knox test, because the test statistic is not heavily tied as is
 the Knox test statistic. The use of the test is illustrated with examples. 
Journal: Journal of Applied Statistics 
Pages: 457-463 
Issue: 4 
Volume: 31 
Year: 2004 
Keywords: Epidemiology, Permutation Test, Monte Carlo, Fortran95 Program, 
X-DOI: 10.1080/02664760410001681747 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760410001681747
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:4:p:457-463




Template-Type: ReDIF-Article 1.0
Author-Name: K. V. Mardia 
Author-X-Name-First: K. V. 
Author-X-Name-Last: Mardia 
Author-Name: J. Kirkbride 
Author-X-Name-First: J. 
Author-X-Name-Last: Kirkbride 
Author-Name: F. L. Bookstein 
Author-X-Name-First: F. L. 
Author-X-Name-Last: Bookstein 
Title: Statistics of Shape, Direction and Cylindrical Variables 
Abstract:
  In statistical shape analysis, the shape of an object is understood to be
 what remains after the effects of location, scale and rotation are
 removed. We consider the distributional problem of triangular shape and an
 associated direction; motivated by a data set of microscopic fossils. We
 begin by constructing a parallel transport system such that the data
 transform onto the space S2&#xd7;S2. A joint shape distribution on
 S2&#xd7;S1 is proposed based on Jupp & Mardia's bivariate distribution on
 S2&#xd7;S1. For concentrated data, an approximation to the distribution on
 S2&#xd7;S1 is given by a distribution on &#xe045;1&#xd7;S1, and we explore
 a distribution on this space by extending Mardia & Sutton's distribution
 on &#xe045;2&#xd7;S1. In this distribution, the expected edgel direction
 varies linearly in the shape coordinates. This is found to be a useful
 model for the microfossil data. 
Journal: Journal of Applied Statistics 
Pages: 465-479 
Issue: 4 
Volume: 31 
Year: 2004 
Keywords: Bookstein Coordinates, Edgel, Fisher Distribution, Kendall Coordinates, Microfossil Data, Triangle Shape, Von Mises Distribution, 
X-DOI: 10.1080/02664760410001681756 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760410001681756
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:4:p:465-479




Template-Type: ReDIF-Article 1.0
Author-Name: S. Balamurali 
Author-X-Name-First: S. 
Author-X-Name-Last: Balamurali 
Author-Name: K. Subramani 
Author-X-Name-First: K. 
Author-X-Name-Last: Subramani 
Title: Modified CSP-C Continuous Sampling Plan for Consumer Protection 
Abstract:
  Dodge (1943) introduced a single level attribute continuous sampling plan
 designated as CSP-1 for the application of continuous production
 processes. Govindaraju & Kandasamy (2000) developed a new single level
 continuous sampling plan whose sampling inspection phase is characterized
 by a maximum allowable number of non-conforming units c, and a constant
 sampling rate f and was designated as CSP-C. In this paper, a modification
 is proposed on the CSP-C continuous sampling plan. In this modified plan,
 sampling inspection is continued until the occurrence of c+1
 non-conforming units, provided the first m sampled units have been found
 conforming during the sampling phase. Using a Markov chain model,
 expressions for the performance measures of the modified CSP-C plan are
 derived. The main advantage of the modified plan is that it is possible to
 lower the average outgoing quality limit. 
Journal: Journal of Applied Statistics 
Pages: 481-494 
Issue: 4 
Volume: 31 
Year: 2004 
Keywords: Csp-C Continuous Sampling, Production Processes, Consumer Protection, 
X-DOI: 10.1080/02664760410001681765A 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760410001681765A
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:4:p:481-494




Template-Type: ReDIF-Article 1.0
Author-Name: B. M. Colosimo 
Author-X-Name-First: B. M. 
Author-X-Name-Last: Colosimo 
Author-Name: R. Pan 
Author-X-Name-First: R. 
Author-X-Name-Last: Pan 
Author-Name: E. del Castillo 
Author-X-Name-First: E. del 
Author-X-Name-Last: Castillo 
Title: A Sequential Markov Chain Monte Carlo Approach to Set-up Adjustment of a Process over a Set of Lots 
Abstract:
  We consider the problem of adjusting a machine that manufactures parts in
 batches or lots and experiences random offsets or shifts whenever a set-up
 operation takes place between lots. The existing procedures for adjusting
 set-up errors in a production process over a set of lots are based on the
 assumption of known process parameters. In practice, these parameters are
 usually unknown, especially in short-run production. Due to this lack of
 knowledge, adjustment procedures such as Grubbs' (1954, 1983) rules and
 discrete integral controllers (also called EWMA controllers) aimed at
 adjusting for the initial offset in each single lot, are typically used.
 This paper presents an approach for adjusting the initial machine offset
 over a set of lots when the process parameters are unknown and are
 iteratively estimated using Markov Chain Monte Carlo (MCMC). As each
 observation becomes available, a Gibbs Sampler is run to estimate the
 parameters of a hierarchical normal means model given the observations up
 to that point in time. The current lot mean estimate is then used for
 adjustment. If used over a series of lots, the proposed method allows one
 eventually to start adjusting the offset before producing the first part
 in each lot. The method is illustrated with application to two examples
 reported in the literature. It is shown how the proposed MCMC adjusting
 procedure can outperform existing rules based on a quadratic off-target
 criterion. 
Journal: Journal of Applied Statistics 
Pages: 499-520 
Issue: 5 
Volume: 31 
Year: 2004 
Keywords: Process Adjustment, Gibbs Sampling, Bayesian Hierarchical Models, Random Effects Model, Normal Means Model, Process Control, 
X-DOI: 10.1080/02664760410001681765 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760410001681765
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:5:p:499-520




Template-Type: ReDIF-Article 1.0
Author-Name: Darryl Holden 
Author-X-Name-First: Darryl 
Author-X-Name-Last: Holden 
Title: Testing the Normality Assumption in the Tobit Model 
Abstract:
  This paper examines a number of statistics that have been proposed to
 test the normality assumption in the tobit (censored regression) model. It
 argues that a number of commonly proposed statistics can be interpreted as
 different versions of the Lagrange multiplier, or score, test for a common
 null hypothesis. This observation is useful in examining the Monte Carlo
 results presented in the paper. The Monte Carlo results suggest that the
 computational convenience of a number of statistics is obtained at the
 cost of poor finite sample performance under the null hypothesis. 
Journal: Journal of Applied Statistics 
Pages: 521-532 
Issue: 5 
Volume: 31 
Year: 2004 
Keywords: Tobit (Censored Regression) And Probit Models, Normality, Language Multiplier (score) Tests, Hours Of Work Equations, 
X-DOI: 10.1080/02664760410001681783 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760410001681783
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:5:p:521-532




Template-Type: ReDIF-Article 1.0
Author-Name: Kui-Yin Cheung 
Author-X-Name-First: Kui-Yin 
Author-X-Name-Last: Cheung 
Author-Name: Elspeth Thomson 
Author-X-Name-First: Elspeth 
Author-X-Name-Last: Thomson 
Title: The Demand for Gasoline in China: A Cointegration Analysis 
Abstract:
  The economic reforms in China since 1979 and consequent increases in
 disposable income have caused total gasoline consumption to soar nearly
 240% between 1980 and 1999. As the growth rate of gasoline consumption is
 expected to be high due to the increased economic activity resulting from
 China's re-accession to the WTO, the government must understand the
 implications for economic growth and balance of payments. Using
 cointegration techniques, it was found that, between 1980 and 1999, demand
 for gasoline was relatively inelastic to price changes, both in the short
 and long terms. The long-run income elasticity was 0.97, implying that the
 future growth rate of gasoline consumption will be close to the growth
 rate of the economy, which is predicted to be about 7% per annum from 2001
 to 2005, and 5-6% over the decade thereafter. 
Journal: Journal of Applied Statistics 
Pages: 533-544 
Issue: 5 
Volume: 31 
Year: 2004 
Keywords: Gasoline Consumption, Price And Income Elasticities, Cointegration Analysis, 
X-DOI: 10.1080/02664760410001681837 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760410001681837
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:5:p:533-544




Template-Type: ReDIF-Article 1.0
Author-Name: K. G. Russell 
Author-X-Name-First: K. G. 
Author-X-Name-Last: Russell 
Author-Name: S. M. Lewis 
Author-X-Name-First: S. M. 
Author-X-Name-Last: Lewis 
Author-Name: A. Dean 
Author-X-Name-First: A. 
Author-X-Name-Last: Dean 
Title: Fractional Factorial Designs for the Detection of Interactions between Design and Noise Factors 
Abstract:
  In industrial experiments on both design (control) factors and noise
 factors aimed at improving the quality of manufactured products, designs
 are needed which afford independent estimation of all design&#xd7;noise
 interactions in as few runs as possible, while allowing aliasing between
 those factorial effects of less interest. An algorithm for generating
 orthogonal fractional factorial designs of this type is described for
 factors at two levels. The generated designs are appropriate for
 experimenting on individual factors or for experimentation involving group
 screening of factors. 
Journal: Journal of Applied Statistics 
Pages: 545-552 
Issue: 5 
Volume: 31 
Year: 2004 
Keywords: Algorithms, Alias Count Vectors, Regular Fractional Factorials, Tables Of Designs, 
X-DOI: 10.1080/02664760410001681800 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760410001681800
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:5:p:545-552




Template-Type: ReDIF-Article 1.0
Author-Name: P. H. Chau 
Author-X-Name-First: P. H. 
Author-X-Name-Last: Chau 
Author-Name: Paul Yip 
Author-X-Name-First: Paul 
Author-X-Name-Last: Yip 
Title: Non-parametric Back-projection of HIV Positive Tests using Multinomial and Poisson Settings 
Abstract:
  Back-projection is a commonly used method in reconstructing HIV
 incidence. Instead of using AIDS incidence data in back-projection, this
 paper uses HIV positive tests data. Both multinomial and Poisson settings
 are used. The two settings give similar results when a parametric form or
 step function is assumed for the infection curve. However, this may not be
 true when the HIV infection in each year is characterized by a different
 parameter. This paper attempts to use simulation studies to compare these
 two settings by constructing various scenarios for the infection curve.
 Results show that both methods give approximately the same estimates of
 the number of HIV infections in the past, whilst the estimates for HIV
 infections in the recent past differ a lot. The multinomial setting always
 gives a levelling-off pattern for the recent past, while the Poisson
 setting is more sensitive to the change in the shape of the HIV infection
 curve. Nonetheless, the multinomial setting gives a relatively narrower
 point-wise probability interval. When the size of the epidemic is large,
 the narrow probability interval may be under-estimating the true
 underlying variation. 
Journal: Journal of Applied Statistics 
Pages: 553-564 
Issue: 5 
Volume: 31 
Year: 2004 
Keywords: Back-calculation, Back-projection, Diagnoses, Hiv/AIDS, Hong Kong, Incidence, Multinomial, Poisson, Simulation, 
X-DOI: 10.1080/02664760410001681792 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760410001681792
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:5:p:553-564




Template-Type: ReDIF-Article 1.0
Author-Name: Horng-Jinh Chang 
Author-X-Name-First: Horng-Jinh 
Author-X-Name-Last: Chang 
Author-Name: Chih-Li Wang 
Author-X-Name-First: Chih-Li 
Author-X-Name-Last: Wang 
Author-Name: Kuo-Chung Huang 
Author-X-Name-First: Kuo-Chung 
Author-X-Name-Last: Huang 
Title: Using Randomized Response to Estimate the Proportion and Truthful Reporting Probability in a Dichotomous Finite Population 
Abstract:
  In this paper, an alternative randomized response procedure is given that
 allows us to estimate the population proportion in addition to the
 probability of providing a truthful answer. It overcomes a difficulty
 associated with traditional randomized response techniques. Properties of
 the proposed estimators as well as sample size allocations are studied. In
 addition, an efficiency comparison is carried out to investigate the
 performance of the proposed technique. 
Journal: Journal of Applied Statistics 
Pages: 565-573 
Issue: 5 
Volume: 31 
Year: 2004 
Keywords: Binomial Distribution, Estimation Of Proportion, Randomized Response, 
X-DOI: 10.1080/02664760410001681819 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760410001681819
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:5:p:565-573




Template-Type: ReDIF-Article 1.0
Author-Name: Arthur Pewsey 
Author-X-Name-First: Arthur 
Author-X-Name-Last: Pewsey 
Title: Testing for Circular Reflective Symmetry about a Known Median Axis 
Abstract:
  Circular data arise in many contexts, a particularly rich source being
 animal orientation experiments. Often, in the analysis of such data, a
 fundamental question of scientific interest is whether the underlying
 distribution is reflectively symmetric about some specific axis. In this
 paper, the situation in which the axis of interest is known to be a median
 axis is considered and a simple, asymptotically distribution- free test
 for circular reflective symmetry against skew alternatives is developed.
 The results from a simulation study lead to a testing strategy
 incorporating the new test and the circular analogue of the modified runs
 test of Modarres & Gastwirth (1996). The application of the testing
 strategy is illustrated using circular data arising from two animal
 orientation experiments. 
Journal: Journal of Applied Statistics 
Pages: 575-585 
Issue: 5 
Volume: 31 
Year: 2004 
Keywords: Circular Data, Hybrid Testing Strategy, Modified Runs Test, Skew Alternates, 
X-DOI: 10.1080/02664760410001681828 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760410001681828
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:5:p:575-585




Template-Type: ReDIF-Article 1.0
Author-Name: Key-Il Shin 
Author-X-Name-First: Key-Il 
Author-X-Name-Last: Shin 
Title: A Multivariate Unit Root Test Based on the Modified Weighted Symmetric Estimator for VAR(p) 
Abstract:
  Multivariate unit root tests for the VAR model have been commonly used in
 time series analysis. Several unit root tests were developed. Most of the
 estimators of coefficient matrices developed in the VAR model are obtained
 using ordinary least squares estimators. In this paper, we suggest a
 multivariate unit root test based on a modified weighted symmetric
 estimator. Using a limited Monte Carlo simulation, we compare the powers
 of the new test statistic and the test statistic suggested in Fuller
 (1996). 
Journal: Journal of Applied Statistics 
Pages: 587-596 
Issue: 5 
Volume: 31 
Year: 2004 
Keywords: Vector Autoregressive Process, Cointegration, 
X-DOI: 10.1080/02664760410001681774 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760410001681774
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:5:p:587-596




Template-Type: ReDIF-Article 1.0
Author-Name: Peter Congdon 
Author-X-Name-First: Peter 
Author-X-Name-Last: Congdon 
Title: Modelling Trends and Inequality in Small Area Mortality 
Abstract:
  This paper considers the modelling of mortality rates classified by age,
 time, and small area with a view to developing life table parameters
 relevant to assessing trends in inequalities in life chances. In
 particular, using a fully Bayes perspective, one may assess the stochastic
 variation in small area life table parameters, such as life expectancies,
 and also formally assess whether trends in indices of inequality in
 mortality are significant. Modelling questions include choice between
 random walk priors for age and time effects as against non-linear
 regression functions, questions of identifiability when several random
 effects are present in the death rates model, and the choice of model when
 both within and out-of-sample performance may be important. A case study
 application involves 44 small areas in North East London and mortality in
 five sub-periods (1986-88, 1989-91, 1992-94, 1995-97, 1998-2000) between
 1986 and 2000, with the final period used for assessing out-of-sample
 performance. 
Journal: Journal of Applied Statistics 
Pages: 603-622 
Issue: 6 
Volume: 31 
Year: 2004 
Keywords: Apc Models, Mortality, Life Tables, Random Effects Model, Cohort, Bayesian, 
X-DOI: 10.1080/1478881042000214695 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/1478881042000214695
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:6:p:603-622




Template-Type: ReDIF-Article 1.0
Author-Name: Wei-Ming Luh 
Author-X-Name-First: Wei-Ming 
Author-X-Name-Last: Luh 
Author-Name: Jiin-Huarng Guo 
Author-X-Name-First: Jiin-Huarng 
Author-X-Name-Last: Guo 
Title: Improved Robust Test Statistic Based on Trimmed Means and Hall's Transformation for Two-way ANOVA Models Under Non-normality 
Abstract:
  For the two-way fixed effects ANOVA, under assumption violations, the
 present study employs trimmed means and Hall's transformation to correct
 asymmetry, and an approximate test, such as the Alexander-Govern or
 Welch-James test, to correct heterogeneity. The unweighted as well as
 weighted means analyses of omnibus effects in unbalanced designs were
 considered. A simulated data set was presented and computer simulations
 were performed to investigate the small-sample properties of the methods.
 The simulation results show that the proposed technique is valid and
 powerful compared with the conventional methods. 
Journal: Journal of Applied Statistics 
Pages: 623-643 
Issue: 6 
Volume: 31 
Year: 2004 
Keywords: Alexander-Govern Test, Computer Simulation, Non-orthogonal, Robustness, Welch-James Type, Winsorized Variance, 
X-DOI: 10.1080/1478881042000214622 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/1478881042000214622
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:6:p:623-643




Template-Type: ReDIF-Article 1.0
Author-Name: D. K. Al-Mutairi 
Author-X-Name-First: D. K. 
Author-X-Name-Last: Al-Mutairi 
Title: Bayesian Computations for Random Environment Models 
Abstract:
  This paper deals with the analysis of reliability data from a Bayesian
 perspective for Random Environment (RE) models. We give an overview of
 current literature on RE models. We also study the computational problems
 associated with the implementations of RE models in a Bayesian setting.
 Then, we present the Markov Chain Monte Carlo technique to solve such
 problems. These problems arise in posterior and predictive analysis and
 their relevant quantities such as mean, variance, and median. The
 suggested methodology is incorporated with an illustration. 
Journal: Journal of Applied Statistics 
Pages: 645-659 
Issue: 6 
Volume: 31 
Year: 2004 
Keywords: Bayesian Computation, Bayesian Inference, Gibbs Sampling, Joint Prior Distribution, Random Environment, 
X-DOI: 10.1080/1478881042000214631 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/1478881042000214631
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:6:p:645-659




Template-Type: ReDIF-Article 1.0
Author-Name: Gang Zheng 
Author-X-Name-First: Gang 
Author-X-Name-Last: Zheng 
Title: Maximizing a Family of Optimal Statistics over a Nuisance Parameter with Applications to Genetic Data Analysis 
Abstract:
  In this article, a simple algorithm is used to maximize a family of
 optimal statistics for hypothesis testing with a nuisance parameter not
 defined under the null hypothesis. This arises from genetic linkage and
 association studies and other hypothesis testing problems. The maximum of
 optimal statistics over the nuisance parameter space can be used as a
 robust test in this situation. Here, we use the maximum and minimum
 statistics to examine the sensitivity of testing results with respect to
 the unknown nuisance parameter. Examples from genetic linkage analysis
 using affected sub pairs and a candidate-gene association study in
 case-parents trio design are studied. 
Journal: Journal of Applied Statistics 
Pages: 661-671 
Issue: 6 
Volume: 31 
Year: 2004 
Keywords: Genetic Analysis, Maximal Statistics, Nuisance Parameter, Robust Test, 
X-DOI: 10.1080/1478881042000214640 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/1478881042000214640
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:6:p:661-671




Template-Type: ReDIF-Article 1.0
Author-Name: Alan Hutson 
Author-X-Name-First: Alan 
Author-X-Name-Last: Hutson 
Title: Utilizing the Flexibility of the Epsilon-Skew-Normal Distribution for Common Regression Problems 
Abstract:
  In this paper we illustrate the properties of the epsilon-skew-normal
 (ESN) distribution with respect to developing more flexible regression
 models. The ESN model is a simple one-parameter extension of the standard
 normal model. The additional parameter ~ corresponds to the degree of
 skewness in the model. In the fitting process we take advantage of
 relatively new powerful routines that are now available in standard
 software packages such as SAS. It is illustrated that even if the true
 underlying error distribution is exactly normal there is no practical loss
 n power with respect to testing for non-zero regression coefficients. If
 the true underlying error distribution is slightly skewed, the ESN model
 is superior in terms of statistical power for tests about the regression
 coefficient. This model has good asymptotic properties for samples of size
 n&#x3e;50. 
Journal: Journal of Applied Statistics 
Pages: 673-683 
Issue: 6 
Volume: 31 
Year: 2004 
Keywords: Robust Regression, Epsilon-skew-normal Distribution, 
X-DOI: 10.1080/1478881042000214659 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/1478881042000214659
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:6:p:673-683




Template-Type: ReDIF-Article 1.0
Author-Name: Cynthia Tojeiro 
Author-X-Name-First: Cynthia 
Author-X-Name-Last: Tojeiro 
Author-Name: Francisco Louzada-Neto 
Author-X-Name-First: Francisco 
Author-X-Name-Last: Louzada-Neto 
Author-Name: Heleno Bolfarine 
Author-X-Name-First: Heleno 
Author-X-Name-Last: Bolfarine 
Title: A Bayesian Analysis for Accelerated Lifetime Tests Under an Exponential Power Law Model with Threshold Stress 
Abstract:
  In this paper, we present a Bayesian methodology for modelling
 accelerated lifetime tests under a stress response relationship with a
 threshold stress. Both Laplace and MCMC methods are considered. The
 methodology is described in detail for the case when an exponential
 distribution is assumed to express the behaviour of lifetimes, and a power
 law model with a threshold stress is assumed as the stress response
 relationship. We assume vague but proper priors for the parameters of
 interest. The methodology is illustrated by a accelerated failure test on
 an electrical insulation film. 
Journal: Journal of Applied Statistics 
Pages: 685-691 
Issue: 6 
Volume: 31 
Year: 2004 
Keywords: Accelerated Life Tests, Threshold Stress, Bayesian Approach, Mcmc, Laplace Approxiation, 
X-DOI: 10.1080/1478881042000214668 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/1478881042000214668
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:6:p:685-691




Template-Type: ReDIF-Article 1.0
Author-Name: Chung-Ho Chen 
Author-X-Name-First: Chung-Ho 
Author-X-Name-Last: Chen 
Title: Determining the Optimum Process Mean of a One-sided Specification Limit with the Linear Quality Loss Function of Product 
Abstract:
  Wen & Mergen (1999) proposed a method for setting the optimal process
 mean when a process was not capable of meeting specifications in the short
 term. However, they neglected to consider the quality loss for a product
 within specifications in the model. Chen & Chou (2002) presented a
 modified Wen & Mergen's (1999) model, including the quadratic quality loss
 function for a one-sided specification limit. In this paper, we propose
 the modified Wen & Mergen (1999) cost model including the linear quality
 loss function of a product for determining the optimal process mean of a
 one-sided specification limit. 
Journal: Journal of Applied Statistics 
Pages: 693-703 
Issue: 6 
Volume: 31 
Year: 2004 
Keywords: Quality Loss Function, Specification Limits, Process Mean, Process Standard Deviation, 
X-DOI: 10.1080/1478881042000214677 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/1478881042000214677
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:6:p:693-703




Template-Type: ReDIF-Article 1.0
Author-Name: Seung-Hoon Yoo 
Author-X-Name-First: Seung-Hoon 
Author-X-Name-Last: Yoo 
Title: A Note on an Approximation of the Mobile Communications Expenditures Distribution Function Using a Mixture Model 
Abstract:
  Approximating the distribution of mobile communications expenditures
 (MCE) is complicated by zero observations in the sample. To deal with the
 zero observations by allowing a point mass at zero, a mixture model of MCE
 distributions is proposed and applied. The MCE distribution is specified
 as a mixture of two distributions, one with a point mass at zero and the
 other with full support on the positive half of the real line. The model
 is empirically verified for individual MCE survey data collected in Seoul,
 Korea. The mixture model can easily capture the common bimodality feature
 of the MCE distribution. In addition, when covariates were added to the
 model, it was found that the probability that an individual has
 non-expenditure significantly varies with some variables. Finally, the
 goodness-of-fit test suggests that the data are well represented by the
 mixture model. 
Journal: Journal of Applied Statistics 
Pages: 747-752 
Issue: 7 
Volume: 31 
Year: 2004 
Keywords: Mobile Communications Expenditures, Zero Observations, Mixture Model, Weibull Distribution, 
X-DOI: 10.1080/0266476042000214475 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476042000214475
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:7:p:747-752




Template-Type: ReDIF-Article 1.0
Author-Name: Haobo Ren 
Author-X-Name-First: Haobo 
Author-X-Name-Last: Ren 
Author-Name: Xiao-Hua Zhou 
Author-X-Name-First: Xiao-Hua 
Author-X-Name-Last: Zhou 
Author-Name: Hua Liang 
Author-X-Name-First: Hua 
Author-X-Name-Last: Liang 
Title: A Flexible Method for Estimating the ROC Curve 
Abstract:
  In this paper we propose a flexible method for estimating a receiver
 operating characteristic (ROC) curve that is based on a continuous-scale
 test. The approach is easily understood and efficiently computed, and
 robust to the smooth parameter selection, which needs intensive
 computation when using local polynomial and smoothing spline techniques.
 The results from our simulation experiment indicate that the
 moderate-sample numerical performance of our estimator is better than the
 empirical ROC curve estimator and comparable to the local linear
 estimator. The availability of easy implementation is also illustrated by
 our simulation. We apply the proposed method to two real data sets. 
Journal: Journal of Applied Statistics 
Pages: 773-784 
Issue: 7 
Volume: 31 
Year: 2004 
Keywords: Penalized Spline, Kernel Smoothing, Local Polynomial, Bandwidth Selection, 
X-DOI: 10.1080/0266476042000214493 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476042000214493
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:7:p:773-784




Template-Type: ReDIF-Article 1.0
Author-Name: Philip Shively 
Author-X-Name-First: Philip 
Author-X-Name-Last: Shively 
Title: Testing for a Unit Root in ARIMA Processes 
Abstract:
  A unit root has important long-run implications for many time series in
 economics and finance. This paper develops a unit-root test of an
 ARIMA(p-1, 1, q) with drift null process against a trend-stationary
 ARMA(p, q) alternative process, where the order of the time series is
 assumed known through previous statistical testing or relevant theory.
 This test uses a point-optimal test statistic, but it estimates the null
 and alternative variance-covariance matrices that are used in the test
 statistic. Consequently, this test approximates a point-optimal test.
 Simulations show that its small-sample size is close to the nominal test
 level for a variety of unit-root processes, that it has a robust power
 curve against a variety of stationary alternatives, that its combined
 small-sample size and power properties are highly competitive with
 previous unit-root tests, and that it is robust to conditional
 heteroskedasticity. An application to post-Second World War real per
 capita gross domestic product is provided. 
Journal: Journal of Applied Statistics 
Pages: 785-798 
Issue: 7 
Volume: 31 
Year: 2004 
Keywords: Point-optimal, Invariant, Unit Root, ARIMA, Gross Domestic Product, 
X-DOI: 10.1080/0266476042000214547 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476042000214547
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:7:p:785-798




Template-Type: ReDIF-Article 1.0
Author-Name: Silvia Ferrari 
Author-X-Name-First: Silvia 
Author-X-Name-Last: Ferrari 
Author-Name: Francisco Cribari-Neto 
Author-X-Name-First: Francisco 
Author-X-Name-Last: Cribari-Neto 
Title: Beta Regression for Modelling Rates and Proportions 
Abstract:
  This paper proposes a regression model where the response is beta
 distributed using a parameterization of the beta law that is indexed by
 mean and dispersion parameters. The proposed model is useful for
 situations where the variable of interest is continuous and restricted to
 the interval (0, 1) and is related to other variables through a regression
 structure. The regression parameters of the beta regression model are
 interpretable in terms of the mean of the response and, when the logit
 link is used, of an odds ratio, unlike the parameters of a linear
 regression that employs a transformed response. Estimation is performed by
 maximum likelihood. We provide closed-form expressions for the score
 function, for Fisher's information matrix and its inverse. Hypothesis
 testing is performed using approximations obtained from the asymptotic
 normality of the maximum likelihood estimator. Some diagnostic measures
 are introduced. Finally, practical applications that employ real data are
 presented and discussed. 
Journal: Journal of Applied Statistics 
Pages: 799-815 
Issue: 7 
Volume: 31 
Year: 2004 
Keywords: Beta Distribution, Maximum Likelihood Estimation, Leverage, Proportions, Residuals, 
X-DOI: 10.1080/0266476042000214501 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476042000214501
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:7:p:799-815




Template-Type: ReDIF-Article 1.0
Author-Name: Rosalba Miceli 
Author-X-Name-First: Rosalba 
Author-X-Name-Last: Miceli 
Author-Name: Lara Lusa 
Author-X-Name-First: Lara 
Author-X-Name-Last: Lusa 
Author-Name: Luigi Mariani 
Author-X-Name-First: Luigi 
Author-X-Name-Last: Mariani 
Title: Revising a Prognostic Index Developed for Classification Purposes: An Application to Gastric Cancer Data 
Abstract:
  A prognostic index (PI) is usually derived from a regression model as a
 weighted mean of the covariates, with weights (partial scores)
 proportional to the parameter estimates. When a PI is applied to patients
 other than those considered for its development, the issue of assessing
 its validity on the new case series is crucial. For this purpose, Van
 Houwelingen (2000) proposed a method of validation by calibration, which
 limits overfitting by embedding the original model into a new one, so that
 only a few parameters will have to be estimated. Here we address the
 problem of PI validation and revision with the above approach when the PI
 has classification purposes and it represents the linear predictor of a
 Weibull model, derived from an accelerated failure time parameterization
 instead of a proportional hazards one, as originally described by Van
 Houwelingen. We show that the Van Houwelingen method can be applied in a
 straightforward manner, provided that the parameterization originally used
 in the PI model is appropriately taken into account. We also show that
 model validation and revision can be carried out by modifying the cut-off
 values used for prognostic grouping without affecting the partial scores
 of the original PI. This procedure can be applied to simplify the
 clinician's use of an established PI for classification purposes. 
Journal: Journal of Applied Statistics 
Pages: 817-830 
Issue: 7 
Volume: 31 
Year: 2004 
Keywords: Prognostic Index, Survival Analysis, Weibull Model, Gastric Cancer, 
X-DOI: 10.1080/0266476042000214510 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476042000214510
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:7:p:817-830




Template-Type: ReDIF-Article 1.0
Author-Name: James Reed 
Author-X-Name-First: James 
Author-X-Name-Last: Reed 
Author-Name: David Stark 
Author-X-Name-First: David 
Author-X-Name-Last: Stark 
Title: Robust Two-Sample Statistics for Testing Equality of Means: A Simulation Study 
Abstract:
  When testing the equality of the means from two independent normally
 distributed populations given that the variances of the two populations
 are unknown but assumed equal, the classical two-sample t-test is
 recommended. If the underlying population distributions are normal with
 unequal and unknown variances, either Welch's t-statistic or
 Satterthwaite's Approximate F-test is suggested. However, Welch's
 procedure is non-robust under most non-normal distributions. There is a
 variable tolerance level around the strict assumptions of data
 independence, homogeneity of variances and normality of the distributions.
 Few textbooks offer alternatives when one or more of the underlying
 assumptions are not defensible. 
Journal: Journal of Applied Statistics 
Pages: 831-854 
Issue: 7 
Volume: 31 
Year: 2004 
Keywords: Behrens-Fisher Problem, Two Sample t-tests, Adaptive Two-sample Robust Tests, 
X-DOI: 10.1080/0266476042000214529 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476042000214529
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:7:p:831-854




Template-Type: ReDIF-Article 1.0
Author-Name: G. J. M. Rosa 
Author-X-Name-First: G. J. M. 
Author-X-Name-Last: Rosa 
Author-Name: D. Gianola 
Author-X-Name-First: D. 
Author-X-Name-Last: Gianola 
Author-Name: C. R. Padovani 
Author-X-Name-First: C. R. 
Author-X-Name-Last: Padovani 
Title: Bayesian Longitudinal Data Analysis with Mixed Models and Thick-tailed Distributions using MCMC 
Abstract:
  Linear mixed effects models are frequently used to analyse longitudinal
 data, due to their flexibility in modelling the covariance structure
 between and within observations. Further, it is easy to deal with
 unbalanced data, either with respect to the number of observations per
 subject or per time period, and with varying time intervals between
 observations. In most applications of mixed models to biological sciences,
 a normal distribution is assumed both for the random effects and for the
 residuals. This, however, makes inferences vulnerable to the presence of
 outliers. Here, linear mixed models employing thick-tailed distributions
 for robust inferences in longitudinal data analysis are described.
 Specific distributions discussed include the Student-t, the slash and the
 contaminated normal. A Bayesian framework is adopted, and the Gibbs
 sampler and the Metropolis-Hastings algorithms are used to carry out the
 posterior analyses. An example with data on orthodontic distance growth in
 children is discussed to illustrate the methodology. Analyses based on
 either the Student-t distribution or on the usual Gaussian assumption are
 contrasted. The thick-tailed distributions provide an appealing robust
 alternative to the Gaussian process for modelling distributions of the
 random effects and of residuals in linear mixed models, and the MCMC
 implementation allows the computations to be performed in a flexible
 manner. 
Journal: Journal of Applied Statistics 
Pages: 855-873 
Issue: 7 
Volume: 31 
Year: 2004 
Keywords: Robust-inference, Longitudinal Study, Mixed Model, Thick-tailed Distribution, Heteroscedasticity, Bayesian Inference, 
X-DOI: 10.1080/0266476042000214538 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476042000214538
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:7:p:855-873




Template-Type: ReDIF-Article 1.0
Author-Name: David Hand 
Author-X-Name-First: David 
Author-X-Name-Last: Hand 
Title: Pattern Discovery 
Abstract:
  This article does not have an abstract 
Journal: Journal of Applied Statistics 
Pages: 883-884 
Issue: 8 
Volume: 31 
Year: 2004 
X-DOI: 10.1080/0266476042000270509 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476042000270509
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:8:p:883-884




Template-Type: ReDIF-Article 1.0
Author-Name: David Hand 
Author-X-Name-First: David 
Author-X-Name-Last: Hand 
Author-Name: Richard Bolton 
Author-X-Name-First: Richard 
Author-X-Name-Last: Bolton 
Title: Pattern Discovery and Detection: A Unified Statistical Methodology 
Abstract:
  Modern statistical data analysis is predominantly model-driven, seeking
 to decompose an observed data distribution in terms of major underlying
 descriptive features modified by some stochastic variation. A large part
 of data mining is also concerned with this exercise. However, another
 fundamental part of data mining is concerned with detecting anomalies
 amongst the vast mass of the data: the small deviations, unusual
 observations, unexpected clusters of observations, or surprising blips in
 the data, which the model does not explain. We call such anomalies
 patterns. For sound reasons, which are outlined in the paper, the data
 mining community has tended to focus on the algorithmic aspects of pattern
 discovery, and has not developed any general underlying theoretical base.
 However, such a base is important for any technology: it helps to steer
 the direction in which the technology develops, as well as serving to
 provide a basis from which algorithms can be compared, and to indicate
 which problems are the important ones waiting to be solved. This paper
 attempts to provide such a theoretical base, linking the ideas to
 statistical work in spatial epidemiology, scan statistics, outlier
 detection, and other areas. One of the striking characteristics of work on
 pattern discovery is that the ideas have been developed in several
 theoretical arenas, and also in several application domains, with little
 apparent awareness of the fundamentally common nature of the problem. Like
 model building, pattern discovery is fundamentally an inferential
 activity, and is an area in which statisticians can make very significant
 contributions. 
Journal: Journal of Applied Statistics 
Pages: 885-924 
Issue: 8 
Volume: 31 
Year: 2004 
Keywords: Patterns, pattern discovery, data mining, association analysis, bioinformatics, technical analysis, market basket analysis, configural frequency analysis, scan statistics, spatial epidemiology, 
X-DOI: 10.1080/0266476042000270518 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476042000270518
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:8:p:885-924




Template-Type: ReDIF-Article 1.0
Author-Name: Gonzalo Navarro 
Author-X-Name-First: Gonzalo 
Author-X-Name-Last: Navarro 
Title: Pattern Matching 
Abstract:
  An important subtask of the pattern discovery process is pattern
 matching, where the pattern sought is already known and we want to
 determine how often and where it occurs in a sequence. In this paper we
 review the most practical techniques to find patterns of different kinds.
 We show how regular expressions can be searched for with general
 techniques, and how simpler patterns can be dealt with more simply and
 efficiently. We consider exact as well as approximate pattern matching.
 Also we cover both sequential searching, where the sequence cannot be
 preprocessed, and indexed searching, where we have a data structure built
 over the sequence to speed up the search. 
Journal: Journal of Applied Statistics 
Pages: 925-949 
Issue: 8 
Volume: 31 
Year: 2004 
Keywords: Regular expressions, automata, back tracking, suffix trees and arrays, approximate string matching, bit parallelism, 
X-DOI: 10.1080/0266476042000270527 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476042000270527
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:8:p:925-949




Template-Type: ReDIF-Article 1.0
Author-Name: Andrew Lawson 
Author-X-Name-First: Andrew 
Author-X-Name-Last: Lawson 
Author-Name: Allan Clark 
Author-X-Name-First: Allan 
Author-X-Name-Last: Clark 
Author-Name: Carmen Vidal Rodeiro 
Author-X-Name-First: Carmen Vidal 
Author-X-Name-Last: Rodeiro 
Title: Developments in General and Syndromic Surveillance for Small Area Health Data 
Abstract:
  In this paper we examine a range of issues related to the analysis of
 health surveillance data when it is spatially-referenced. The importance
 of considering alarm functions derived from likelihood or Bayesian models
 is stressed. In addition, we focus on some new developments in predictive
 distribution residuals in the analysis. 
Journal: Journal of Applied Statistics 
Pages: 951-966 
Issue: 8 
Volume: 31 
Year: 2004 
Keywords: Syndromic, surveillance, statistics, small area, health, 
X-DOI: 10.1080/0266476042000270568 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476042000270568
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:8:p:951-966




Template-Type: ReDIF-Article 1.0
Author-Name: Joseph Glaz 
Author-X-Name-First: Joseph 
Author-X-Name-Last: Glaz 
Author-Name: Zhenkui Zhang 
Author-X-Name-First: Zhenkui 
Author-X-Name-Last: Zhang 
Title: Multiple Window Discrete Scan Statistics 
Abstract:
  In this article, multiple scan statistics of variable window sizes are
 derived for independent and identically distributed 0-1 Bernoulli trials.
 Both one and two dimensional, as well as, conditional and unconditional
 cases are treated. The advantage in using multiple scan statistics, as
 opposed to single fixed window scan statistics, is that they are more
 sensitive in detecting a change in the underlying distribution of the
 observed data. We show how to derive simple approximations for the
 significance level of these testing procedures and present numerical
 results to evaluate their performance. 
Journal: Journal of Applied Statistics 
Pages: 967-980 
Issue: 8 
Volume: 31 
Year: 2004 
Keywords: Combining test statistics, one-dimensional scan statistics, p-values, two-dimensional scan statistics, variable windows, 
X-DOI: 10.1080/0266476042000270536 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476042000270536
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:8:p:967-980




Template-Type: ReDIF-Article 1.0
Author-Name: Alexander Von Eye 
Author-X-Name-First: Alexander 
Author-X-Name-Last: Von Eye 
Author-Name: Eduardo Gutierrez Pena 
Author-X-Name-First: Eduardo Gutierrez 
Author-X-Name-Last: Pena 
Title: Configural Frequency Analysis: The Search for Extreme Cells 
Abstract:
  Configural Frequency Analysis (CFA) asks whether a cell in a
 cross-classification contains more or fewer cases than expected with
 respect to some base model. This base model is specified such that cells
 with more cases than expected (also called types) can be interpreted from
 a substantive perspective. The same applies to cells with fewer cases than
 expected (antitypes). This article gives an introduction to both
 frequentist and Bayesian approaches to CFA. Specification of base models,
 testing, and protection are discussed. In an example, Prediction CFA and
 two-sample CFA are illustrated. The discussion focuses on the differences
 between CFA and modelling. 
Journal: Journal of Applied Statistics 
Pages: 981-997 
Issue: 8 
Volume: 31 
Year: 2004 
Keywords: Configural frequency analysis (CFA), extreme cells, types, antitypes, base models, protection, frequentist CFA, Bayesian CFA, Dirichlet distribution, contingency table, 
X-DOI: 10.1080/0266476042000270545 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476042000270545
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:8:p:981-997




Template-Type: ReDIF-Article 1.0
Author-Name: Cheolwoo Park 
Author-X-Name-First: Cheolwoo 
Author-X-Name-Last: Park 
Author-Name: J. S. Marron 
Author-X-Name-First: J. S. 
Author-X-Name-Last: Marron 
Author-Name: Vitaliana Rondonotti 
Author-X-Name-First: Vitaliana 
Author-X-Name-Last: Rondonotti 
Title: Dependent SiZer: Goodness-of-Fit Tests for Time Series Models 
Abstract:
  In this paper, we extend SiZer (SIgnificant ZERo crossing of the
 derivatives) to dependent data for the purpose of goodness-of-fit tests
 for time series models. Dependent SiZer compares the observed data with a
 specific null model being tested by adjusting the statistical inference
 using an assumed autocovariance function. This new approach uses a SiZer
 type visualization to flag statistically significant differences between
 the data and a given null model. The power of this approach is
 demonstrated through some examples of time series of Internet traffic
 data. It is seen that such time series can have even more burstiness than
 is predicted by the popular, long- range dependent, Fractional Gaussian
 Noise model. 
Journal: Journal of Applied Statistics 
Pages: 999-1017 
Issue: 8 
Volume: 31 
Year: 2004 
Keywords: Autocovariance function, dependent SiZer, fractional Gaussian noise, Internet traffic data, goodness-of-fit test, SiZer, time series, 
X-DOI: 10.1080/0266476042000270554 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476042000270554
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:8:p:999-1017




Template-Type: ReDIF-Article 1.0
Author-Name: Balaji Padmanabhan 
Author-X-Name-First: Balaji 
Author-X-Name-Last: Padmanabhan 
Title: The Interestingness Paradox in Pattern Discovery 
Abstract:
  Noting that several rule discovery algorithms in data mining can produce
 a large number of irrelevant or obvious rules from data, there has been
 substantial research in data mining that addressed the issue of what makes
 rules truly 'interesting'. This resulted in the development of a number of
 interestingness measures and algorithms that find all interesting rules
 from data. However, these approaches have the drawback that many of the
 discovered rules, while supposed to be interesting by definition, may
 actually (1) be obvious in that they logically follow from other
 discovered rules or (2) be expected given some of the other discovered
 rules and some simple distributional assumptions. In this paper we argue
 that this is a paradox since rules that are supposed to be interesting, in
 reality are uninteresting for the above reason. We show that this paradox
 exists for various popular interestingness measures and present an
 abstract characterization of an approach to alleviate the paradox. We
 finally discuss existing work in data mining that addresses this issue and
 show how these approaches can be viewed with respect to the
 characterization presented here. 
Journal: Journal of Applied Statistics 
Pages: 1019-1035 
Issue: 8 
Volume: 31 
Year: 2004 
Keywords: Interestingness measures, rule discovery, minimality, 
X-DOI: 10.1080/0266476042000270563 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476042000270563
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:8:p:1019-1035




Template-Type: ReDIF-Article 1.0
Author-Name: Steven Gilmour 
Author-X-Name-First: Steven 
Author-X-Name-Last: Gilmour 
Title: Irregular Four-level Response Surface Designs 
Abstract:
  Four-level response surface designs based on regular two-level fractional
 factorial designs were introduced by Edmondson (1991). Here, the methods
 are extended to include designs based on irregular two-level fractional
 factorials. These designs allow orthogonal blocking and require fewer
 experimental units than the regular designs. 
Journal: Journal of Applied Statistics 
Pages: 1043-1048 
Issue: 9 
Volume: 31 
Year: 2004 
Keywords: Agricultural experimentation, experimental design, polynomial regression, pseudo-factors, 
X-DOI: 10.1080/0266476042000280391 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476042000280391
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:9:p:1043-1048




Template-Type: ReDIF-Article 1.0
Author-Name: Manuel Galea 
Author-X-Name-First: Manuel 
Author-X-Name-Last: Galea 
Author-Name: Victor Leiva-Sanchez 
Author-X-Name-First: Victor 
Author-X-Name-Last: Leiva-Sanchez 
Author-Name: Gilberto Paula 
Author-X-Name-First: Gilberto 
Author-X-Name-Last: Paula 
Title: Influence Diagnostics in log-Birnbaum-Saunders Regression Models 
Abstract:
  In this paper we present various diagnostic methods for a linear
 regression model under a logarithmic Birnbaum-Saunders distribution for
 the errors, which may be applied for accelerated life testing or to
 compare the median lives of several populations. Some influence methods,
 such as the local influence, total local influence of an individual and
 generalized leverage are derived, analysed and discussed. We also present
 a connection between the local influence and generalized leverage methods.
 A discussion of the computation of the likelihood displacement as well as
 the normal curvature in the local influence method are presented. Finally,
 an example with real data is given for illustration. 
Journal: Journal of Applied Statistics 
Pages: 1049-1064 
Issue: 9 
Volume: 31 
Year: 2004 
Keywords: Birnbaum- Saunders distribution, life distributions, sinh-normal distribution, fatigue life, log-linear models, influence diagnostic, generalized leverage, local influence, maximum likelihood estimator, 
X-DOI: 10.1080/0266476042000280409 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476042000280409
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:9:p:1049-1064




Template-Type: ReDIF-Article 1.0
Author-Name: M. L. Aggarwal 
Author-X-Name-First: M. L. 
Author-X-Name-Last: Aggarwal 
Author-Name: Lih-Yuan Deng 
Author-X-Name-First: Lih-Yuan 
Author-X-Name-Last: Deng 
Author-Name: Mithilesh Kumar Jha 
Author-X-Name-First: Mithilesh Kumar 
Author-X-Name-Last: Jha 
Title: Some New Residual Treatment Effects Designs for Comparing Test Treatments with a Control 
Abstract:
  Pigeon & Raghavarao (1987) introduced control balanced residual treatment
 effects designs for the situation where one treatment is a control or
 standard and is to be compared with the v test treatments, and they have
 also given methods of construction of control balanced residual treatment
 effects designs and have investigated their efficiencies. In this paper we
 have developed some new families of control balanced residual treatment
 effects designs, which are Schur-optimal. 
Journal: Journal of Applied Statistics 
Pages: 1065-1081 
Issue: 9 
Volume: 31 
Year: 2004 
Keywords: Residual treatment effects designs, control treatment, test treatments, Schur-optimality, 
X-DOI: 10.1080/0266476042000280382 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476042000280382
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:9:p:1065-1081




Template-Type: ReDIF-Article 1.0
Author-Name: Hafzullah Aksoy 
Author-X-Name-First: Hafzullah 
Author-X-Name-Last: Aksoy 
Title: Using Markov Chains for Non-perennial Daily Streamflow Data Generation 
Abstract:
  The use of Markov chains to simulate non-perennial streamflow data is
 considered. A non-perennial stream may be thought as having three states,
 namely zero flow, increasing flow and decreasing flow, for which a
 three-state Markov chain can be constructed. Alternatively, two two-state
 Markov chains can be used, the first of which represents the existence and
 non-existence of flow, whereas the second deals with the increment and
 decrement in the flow for periods with flow. Probabilistic relationships
 between the two alternatives are derived. Their performances in simulating
 the state of the stream are compared on the basis of data from two
 different geographical regions in Turkey. It is concluded that both
 alternatives are capable of simulating the state of the stream. 
Journal: Journal of Applied Statistics 
Pages: 1083-1094 
Issue: 9 
Volume: 31 
Year: 2004 
Keywords: Daily streamflow, data generation, Markov chain, simulation, 
X-DOI: 10.1080/0266476042000280418 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476042000280418
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:9:p:1083-1094




Template-Type: ReDIF-Article 1.0
Author-Name: M. L. Menendez 
Author-X-Name-First: M. L. 
Author-X-Name-Last: Menendez 
Author-Name: J. A. Pardo 
Author-X-Name-First: J. A. 
Author-X-Name-Last: Pardo 
Author-Name: L. Pardo 
Author-X-Name-First: L. 
Author-X-Name-Last: Pardo 
Title: Tests of Symmetry in Three-dimensional Contingency Tables Based on Phi-divergence Statistics 
Abstract:
  In this paper we introduce a family of test statistics for testing
 complete symmetry in three-dimensional contingency tables based on phi-
 divergence families. These test statistics yield the likelihood ratio test
 and the Pearson test statistics as special cases. Asymptotic distribution
 for the new test statistics are derived under both the null and the
 alternative hypotheses. A simulation study is presented to show that some
 new statistics offer an attractive alternative to the classical Pearson
 and likelihood ratio test statistics for this problem of complete
 symmetry. 
Journal: Journal of Applied Statistics 
Pages: 1095-1114 
Issue: 9 
Volume: 31 
Year: 2004 
Keywords: Three dimensional contingency table, complete symmetry, &#x3c6;-divergence statistic, 
X-DOI: 10.1080/0266476042000280373 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476042000280373
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:9:p:1095-1114




Template-Type: ReDIF-Article 1.0
Author-Name: W. L. Pearn 
Author-X-Name-First: W. L. 
Author-X-Name-Last: Pearn 
Author-Name: Y. C. Chang 
Author-X-Name-First: Y. C. 
Author-X-Name-Last: Chang 
Author-Name: Chien-Wei Wu 
Author-X-Name-First: Chien-Wei 
Author-X-Name-Last: Wu 
Title: Distributional and Inferential Properties of the Process Loss Indices 
Abstract:
  Johnson (1992) developed the process loss index Le, which is defined as
 the ratio of the expected quadratic loss to the square of half
 specification width. Tsui (1997) expressed the index LeasLe=Lpe+Lot, which
 provides an uncontaminated separation between information concerning the
 potential relative expected loss (Lpe) and the relative off-target squared
 (Lot), as the ratio of the process variance and the square of the half
 specification width, and the square of the ratio of the deviation of mean
 from the target and the half specification width, respectively. In this
 paper, we consider these three loss function indices, and investigate the
 statistical properties of their natural estimators. For the three indices,
 we obtain their UMVUEs and MLEs, and compare the reliability of the two
 estimators based on the relative mean squared errors. In addition, we
 construct 90%, 95%, and 99% upper confidence limits, and the maximum
 values of L^e for which the process is capable, 90%, 95%, and 99% of the
 time. The results obtained in this paper are useful to the practitioners
 in choosing good estimators and making reliable decisions on judging
 process capability. 
Journal: Journal of Applied Statistics 
Pages: 1115-1135 
Issue: 9 
Volume: 31 
Year: 2004 
Keywords: MLE, potential relative expected loss, relative expected loss, relative mean squared error, relative off-target squared, UMVUE, 
X-DOI: 10.1080/0266476042000280364 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476042000280364
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:9:p:1115-1135




Template-Type: ReDIF-Article 1.0
Author-Name: Genming Shi 
Author-X-Name-First: Genming 
Author-X-Name-Last: Shi 
Author-Name: N. Rao Chaganty 
Author-X-Name-First: N. Rao 
Author-X-Name-Last: Chaganty 
Title: Application of Quasi-Least Squares to Analyse Replicated Autoregressive Time Series Regression Models 
Abstract:
  Time series regression models have been widely studied in the literature
 by several authors. However, statistical analysis of replicated time
 series regression models has received little attention. In this paper, we
 study the application of the quasi-least squares method to estimate the
 parameters in a replicated time series model with errors that follow an
 autoregressive process of order p. We also discuss two other established
 methods for estimating the parameters: maximum likelihood assuming
 normality and the Yule-Walker method. When the number of repeated
 measurements is bounded and the number of replications n goes to infinity,
 the regression and the autocorrelation parameters are consistent and
 asymptotically normal for all three methods of estimation. Basically, the
 three methods estimate the regression parameter efficiently and differ in
 how they estimate the autocorrelation. When p=2, for normal data we use
 simulations to show that the quasi-least squares estimate of the
 autocorrelation is undoubtedly better than the Yule-Walker estimate. And
 the former estimate is as good as the maximum likelihood estimate almost
 over the entire parameter space. 
Journal: Journal of Applied Statistics 
Pages: 1147-1156 
Issue: 10 
Volume: 31 
Year: 2004 
Keywords: Autoregression, quasi-least squares, relative efficiency, repeated measurements, time series regression models, 
X-DOI: 10.1080/0266476042000285530 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476042000285530
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:10:p:1147-1156




Template-Type: ReDIF-Article 1.0
Author-Name: Jin-Guan Lin 
Author-X-Name-First: Jin-Guan 
Author-X-Name-Last: Lin 
Author-Name: Bo-Cheng Wei 
Author-X-Name-First: Bo-Cheng 
Author-X-Name-Last: Wei 
Author-Name: Nan-Song Zhang 
Author-X-Name-First: Nan-Song 
Author-X-Name-Last: Zhang 
Title: Varying Dispersion Diagnostics for Inverse Gaussian Regression Models 
Abstract:
  Homogeneity of dispersion parameters is a standard assumption in inverse
 Gaussian regression analysis. However, this assumption is not necessarily
 appropriate. This paper is devoted to the test for varying dispersion in
 general inverse Gaussian linear regression models. Based on the modified
 profile likelihood (Cox & Reid, 1987), the adjusted score test for varying
 dispersion is developed and illustrated with Consumer- Product Sales data
 (Whitmore, 1986) and Gas vapour data (Weisberg, 1985). The effectiveness
 of orthogonality transformation and the properties of a score statistic
 and its adjustment are investigated through Monte Carlo simulations. 
Journal: Journal of Applied Statistics 
Pages: 1157-1170 
Issue: 10 
Volume: 31 
Year: 2004 
Keywords: Adjusted score test, dispersion parameter, inverse Gaussian models, orthogonality transformation, simulation study, varying dispersion, 
X-DOI: 10.1080/0266476042000285512 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476042000285512
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:10:p:1157-1170




Template-Type: ReDIF-Article 1.0
Author-Name: A. F. B. Costa 
Author-X-Name-First: A. F. B. 
Author-X-Name-Last: Costa 
Author-Name: M. A. Rahim 
Author-X-Name-First: M. A. 
Author-X-Name-Last: Rahim 
Title: Monitoring Process Mean and Variability with One Non-central Chi-square Chart 
Abstract:
  Traditionally, an X-chart is used to control the process mean and an
 R-chart to control the process variance. However, these charts are not
 sensitive to small changes in process parameters. A good alternative to
 these charts is the exponentially weighted moving average (EWMA) control
 chart for controlling the process mean and variability, which is very
 effective in detecting small process disturbances. In this paper, we
 propose a single chart that is based on the non-central chi-square
 statistic, which is more effective than the joint X and R charts in
 detecting assignable cause(s) that change the process mean and/or increase
 variability. It is also shown that the EWMA control chart based on a
 non-central chi-square statistic is more effective in detecting both
 increases and decreases in mean and/or variability. 
Journal: Journal of Applied Statistics 
Pages: 1171-1183 
Issue: 10 
Volume: 31 
Year: 2004 
Keywords: Monitoring process mean and variance, X chart, EWMA chart, non-central chi-square chart, 
X-DOI: 10.1080/0266476042000285503 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476042000285503
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:10:p:1171-1183




Template-Type: ReDIF-Article 1.0
Author-Name: Mu'azu Abujiya 
Author-X-Name-First: Mu'azu 
Author-X-Name-Last: Abujiya 
Author-Name: Hassen Muttlak 
Author-X-Name-First: Hassen 
Author-X-Name-Last: Muttlak 
Title: Quality Control Chart for the Mean using Double Ranked Set Sampling 
Abstract:
  In this paper, an attempt is made to develop Quality Control Charts for
 monitoring the process mean based on Double Ranked Set Sampling (DRSS)
 rather than the traditional Simple Random Sampling (SRS). Considering a
 normal population and several shift values, the performance of the Average
 Run Length (ARL) of these new charts was compared with the control charts
 based on Ranked Set Sampling (RSS) and SRS with the same number of
 observations. It is shown that the new charts do a better job of detecting
 changes in process mean compared with SRS and RSS. 
Journal: Journal of Applied Statistics 
Pages: 1185-1201 
Issue: 10 
Volume: 31 
Year: 2004 
Keywords: Average run length, double median ranked set sampling, lower central limit, median double ranked set sampling, median ranked set sampling, ranked set sampling and upper central limit, 
X-DOI: 10.1080/0266476042000285549 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476042000285549
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:10:p:1185-1201




Template-Type: ReDIF-Article 1.0
Author-Name: Seung-Hoon Yoo 
Author-X-Name-First: Seung-Hoon 
Author-X-Name-Last: Yoo 
Title: A Note on a Bayesian Approach to a Dichotomous Choice Environmental Valuation Model 
Abstract:
  As an alternative to the classical approach for analysing dichotomous
 choice environmental valuation data, this note develops a Bayesian
 approach by using the idea of Gibbs sampling and data augmentation. A
 by-product from the approach is a welfare measure, such as the mean
 willingness to pay, and its confidence interval, which can be used for
 policy analysis. 
Journal: Journal of Applied Statistics 
Pages: 1203-1209 
Issue: 10 
Volume: 31 
Year: 2004 
Keywords: Bayesian approach, dichotomous choice environmental valuation, Gibbs sampling, data augmentation, 
X-DOI: 10.1080/0266476042000285558 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476042000285558
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:10:p:1203-1209




Template-Type: ReDIF-Article 1.0
Author-Name: David Reineke 
Author-X-Name-First: David 
Author-X-Name-Last: Reineke 
Author-Name: John Crown 
Author-X-Name-First: John 
Author-X-Name-Last: Crown 
Title: Estimation of Hazard, Density and Survivor Functions for Randomly Censored Data 
Abstract:
  Maximum likelihood estimation and goodness-of-fit techniques are used
 within a competing risks framework to obtain maximum likelihood estimates
 of hazard, density, and survivor functions for randomly right-censored
 variables. Goodness-of- fit techniques are used to fit distributions to
 the crude lifetimes, which are used to obtain an estimate of the hazard
 function, which, in turn, is used to construct the survivor and density
 functions of the net lifetime of the variable of interest. If only one of
 the crude lifetimes can be adequately characterized by a parametric model,
 then semi-parametric estimates may be obtained using a maximum likelihood
 estimate of one crude lifetime and the empirical distribution function of
 the other. Simulation studies show that the survivor function estimates
 from crude lifetimes compare favourably with those given by the
 product-limit estimator when crude lifetimes are chosen correctly. Other
 advantages are discussed. 
Journal: Journal of Applied Statistics 
Pages: 1211-1225 
Issue: 10 
Volume: 31 
Year: 2004 
Keywords: Randomly censored data, competing risks, net and crude lifetimes, maximum likelihood estimation, goodness-of-fit, semi-parametric models, 
X-DOI: 10.1080/0266476042000285521 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476042000285521
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:10:p:1211-1225




Template-Type: ReDIF-Article 1.0
Author-Name: Clive Granger 
Author-X-Name-First: Clive 
Author-X-Name-Last: Granger 
Author-Name: Yongil Jeon 
Author-X-Name-First: Yongil 
Author-X-Name-Last: Jeon 
Title: Forecasting Performance of Information Criteria with Many Macro Series 
Abstract:
  Stock & Watson (1999) consider the relative quality of different
 univariate forecasting techniques. This paper extends their study on
 forecasting practice, comparing the forecasting performance of two popular
 model selection procedures, the Akaike information criterion (AIC) and the
 Bayesian information criterion (BIC). This paper considers several topics:
 how AIC and BIC choose lags in autoregressive models on actual series, how
 models so selected forecast relative to an AR(4) model, the effect of
 using a maximum lag on model selection, and the forecasting performance of
 combining AR(4), AIC, and BIC models with an equal weight. 
Journal: Journal of Applied Statistics 
Pages: 1227-1240 
Issue: 10 
Volume: 31 
Year: 2004 
Keywords: Large macro model, information criterion, AIC, BIC, 
X-DOI: 10.1080/0266476042000285495 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476042000285495
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:10:p:1227-1240




Template-Type: ReDIF-Article 1.0
Author-Name: Gordana Derado 
Author-X-Name-First: Gordana 
Author-X-Name-Last: Derado 
Author-Name: Kanti Mardia 
Author-X-Name-First: Kanti 
Author-X-Name-Last: Mardia 
Author-Name: Vic Patrangenaru 
Author-X-Name-First: Vic 
Author-X-Name-Last: Patrangenaru 
Author-Name: Hilary Thompson 
Author-X-Name-First: Hilary 
Author-X-Name-Last: Thompson 
Title: A Shape-based Glaucoma Index for Tomographic Images 
Abstract:
  We examine the use of Confocal Laser Tomographic images for detecting
 glaucoma. From the clinical aspect, the optic nerve head's (ONH) area
 contains all the relevant information on glaucoma. The shape of ONH is
 approximately a skewed cup. We summarize its shape by three biological
 landmarks on the neural-rim and the fourth landmark as the point of the
 maximum depth, which is approximately the point where the optic nerve
 enters this eye cup. These four landmarks are extracted from the images
 related to some Rhesus monkeys before and after inducing glaucoma.
 Previous analysis on Bookstein shape coordinates of these four landmarks
 revealed only marginally significant findings. From clinical experience,
 it is believed that the ratio depth to diameter of the eye cup provides a
 useful measure of the shape change. We consider the bootstrap distribution
 of this normalized 'depth' (G) and give evidence that it provides an
 appropriate measure of the shape change. This measure G is labelled as the
 glaucoma index. Further experiments are in progress to validate its use
 for glaucoma in humans. 
Journal: Journal of Applied Statistics 
Pages: 1241-1248 
Issue: 10 
Volume: 31 
Year: 2004 
Keywords: Glaucoma index, medical imaging, high level image analysis, anatomical landmarks, non- parametric bootstrap, 
X-DOI: 10.1080/0266476042000285486 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476042000285486
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:31:y:2004:i:10:p:1241-1248




Template-Type: ReDIF-Article 1.0
Author-Name: Don Amos 
Author-X-Name-First: Don 
Author-X-Name-Last: Amos 
Author-Name: T. Randolph Beard 
Author-X-Name-First: T. Randolph 
Author-X-Name-Last: Beard 
Author-Name: Steven Caudill 
Author-X-Name-First: Steven 
Author-X-Name-Last: Caudill 
Title: A statistical analysis of the handling characteristics of certain sporting arms: frontier regression, the moment of inertia, and the radius of gyration 
Abstract:
  This article applies composed error frontier regression techniques to
 estimate the minimal moments of inertia and radii of gyration for a unique
 and varied sample of shotguns. We find that minimum inertia depends on
 weight, center of gravity, length of pull, and barrel length, but not on
 gauge, action type, or number of barrels. Curiously, minimal radii of
 gyration does not depend on barrel length, suggesting that the constraints
 on these two related but non-identical measures of handling are
 significantly different despite their high correlation. We also provide
 evidence in support of G. T. Garwood's claim that a lower inertia, other
 things equal, is a market-validated characteristic associated with
 quality. 
Journal: Journal of Applied Statistics 
Pages: 3-16 
Issue: 1 
Volume: 32 
Year: 2005 
Keywords: Stochastic frontier, best practice, moment of inertia, 
X-DOI: 10.1080/0266476042000305113 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476042000305113
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:1:p:3-16




Template-Type: ReDIF-Article 1.0
Author-Name: Neil Marks 
Author-X-Name-First: Neil 
Author-X-Name-Last: Marks 
Title: Estimation of Weibull parameters from common percentiles 
Abstract:
  Estimation of Weibull distribution shape and scale parameters is
 accomplished through use of symmetrically located percentiles from a
 sample. The process requires algebraic solution of two equations derived
 from the cumulative distribution function. Three alternatives examined are
 compared for precision and variability with maximum likelihood (MLE) and
 least squares (LS) estimators. The best percentile estimator (using the
 10th and 90th) is inferior to MLE in variability and to one least squares
 estimator in accuracy and variability to a small degree. However,
 application of a correction factor related to sample size improves the
 percentile estimator substantially, making it more accurate than LS. 
Journal: Journal of Applied Statistics 
Pages: 17-24 
Issue: 1 
Volume: 32 
Year: 2005 
Keywords: Parameter estimation, Weibull distribution, percentiles, 
X-DOI: 10.1080/0266476042000305122 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476042000305122
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:1:p:17-24




Template-Type: ReDIF-Article 1.0
Author-Name: Chao-Yu Chou 
Author-X-Name-First: Chao-Yu 
Author-X-Name-Last: Chou 
Author-Name: CHung-Ho Chen 
Author-X-Name-First: CHung-Ho 
Author-X-Name-Last: Chen 
Author-Name: Hui-Rong Liu 
Author-X-Name-First: Hui-Rong 
Author-X-Name-Last: Liu 
Title: Acceptance control charts for non-normal data 
Abstract:
  Control charts are one of the most important methods in industrial
 process control. The acceptance control chart is generally applied in
 situations when an X-super-&#xaf; chart is used to control the fraction of
 conforming units produced by the process and where 6-sigma spread of the
 process is smaller than the spread in the specification limits.
 Traditionally, when designing control charts, one usually assumes that the
 data or measurements are normally distributed. However, this assumption
 may not be true in some processes. In this paper, we use the Burr
 distribution, which is employed to represent various non-normal
 distributions, to determine the appropriate control limits or sample size
 for the acceptance control chart under non-normality. Some numerical
 examples are given for illustration. From the presented examples, ignoring
 the effect of non-normality in the data leads to a higher type I or type
 II error probability. 
Journal: Journal of Applied Statistics 
Pages: 25-36 
Issue: 1 
Volume: 32 
Year: 2005 
Keywords: Control chart, non-normality, skewness, kurtosis, the Burr distribution, 
X-DOI: 10.1080/0266476042000305131 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476042000305131
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:1:p:25-36




Template-Type: ReDIF-Article 1.0
Author-Name: James Reed 
Author-X-Name-First: James 
Author-X-Name-Last: Reed 
Title: Contributions to two-sample statistics 
Abstract:
  When testing the equality of the means from two independent normally
 distributed populations given that the variances of the two populations
 are unknown but assumed equal, the classical Student's two-sample t-test
 is recommended. If the underlying population distributions are normal with
 unequal and unknown variances, either Welch's t-statistic or
 Satterthwaite's approximate F test is suggested. However, Welch's
 procedure is non-robust under most non-normal distributions. There is a
 variable tolerance level around the strict assumptions of data
 independence, homogeneity of variances, and identical and normal
 distributions. Few textbooks offer alternatives when one or more of the
 underlying assumptions are not defensible. While there are more than a few
 non-parametric (rank) procedures that provide alternatives to Student's
 t-test, we restrict this review to the promising alternatives to Student's
 two-sample t-test in non-normal models. 
Journal: Journal of Applied Statistics 
Pages: 37-44 
Issue: 1 
Volume: 32 
Year: 2005 
Keywords: Robust two-sample t-tests, symmetric trimmed means, asymmetric trimmed means, linear rank statistics, transformation statistics, 
X-DOI: 10.1080/0266476042000305140 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476042000305140
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:1:p:37-44




Template-Type: ReDIF-Article 1.0
Author-Name: George Halkos 
Author-X-Name-First: George 
Author-X-Name-Last: Halkos 
Author-Name: Ilias Kevork 
Author-X-Name-First: Ilias 
Author-X-Name-Last: Kevork 
Title: A comparison of alternative unit root tests 
Abstract:
  In this paper we evaluate the performance of three methods for testing
 the existence of a unit root in a time series, when the models under
 consideration in the null hypothesis do not display autocorrelation in the
 error term. In such cases, simple versions of the Dickey-Fuller test
 should be used as the most appropriate ones instead of the known augmented
 Dickey-Fuller or Phillips-Perron tests. Through Monte Carlo simulations we
 show that, apart from a few cases, testing the existence of a unit root we
 obtain actual type I error and power very close to their nominal levels.
 Additionally, when the random walk null hypothesis is true, by gradually
 increasing the sample size, we observe that p-values for the drift in the
 unrestricted model fluctuate at low levels with small variance and the
 Durbin-Watson (DW) statistic is approaching 2 in both the unrestricted and
 restricted models. If, however, the null hypothesis of a random walk is
 false, taking a larger sample, the DW statistic in the restricted model
 starts to deviate from 2 while in the unrestricted model it continues to
 approach 2. It is also shown that the probability not to reject that the
 errors are uncorrelated, when they are indeed not correlated, is higher
 when the DW test is applied at 1% nominal level of significance. 
Journal: Journal of Applied Statistics 
Pages: 45-60 
Issue: 1 
Volume: 32 
Year: 2005 
Keywords: Unit root tests, type I error, power of the test, Monte Carlo simulations, 
X-DOI: 10.1080/0266476052000330286 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476052000330286
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:1:p:45-60




Template-Type: ReDIF-Article 1.0
Author-Name: Alex Riba 
Author-X-Name-First: Alex 
Author-X-Name-Last: Riba 
Author-Name: Josep Ginebra 
Author-X-Name-First: Josep 
Author-X-Name-Last: Ginebra 
Title: Change-point estimation in a multinomial sequence and homogeneity of literary style 
Abstract:
  To help settle the debate around the authorship of Tirant lo Blanc, all
 the words in each chapter of that book are categorized according to their
 length and the appearance of various words is counted. The graphical
 exploration of the sequences of multinomial observations obtained reveals
 a clear single sudden change point that is consistently estimated to be
 between chapters 371 and 382 and might indicate a switch of author.
 Correspondence analysis indicates that at the end of the book the words
 tend to be longer and the frequency of various words changes
 significantly. By doing a cluster analysis of the multinomial
 observations, the evidence in favor of the existence of that stylistic
 boundary is strengthened, because the two clusters obtained match very
 closely the before and after change-point groups; only a few chapters at
 the end of the book appear to be misclassified by the change point. 
Journal: Journal of Applied Statistics 
Pages: 61-74 
Issue: 1 
Volume: 32 
Year: 2005 
Keywords: Correspondence analysis, multinomial cluster analysis, stylometry, word length, 
X-DOI: 10.1080/0266476052000330295 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476052000330295
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:1:p:61-74




Template-Type: ReDIF-Article 1.0
Author-Name: Jens Nielsen 
Author-X-Name-First: Jens 
Author-X-Name-Last: Nielsen 
Author-Name: Henrik Jensen 
Author-X-Name-First: Henrik 
Author-X-Name-Last: Jensen 
Author-Name: Per Andersen 
Author-X-Name-First: Per 
Author-X-Name-Last: Andersen 
Title: Creating a reference to a complex emergency situation using time series methods: war in Guinea-Bissau 1998-1999 
Abstract:
  Impacts of complex emergencies or relief interventions have often been
 evaluated by absolute mortality compared to international standardized
 mortality rates. A better evaluation would be to compare with local
 baseline mortality of the affected populations. A projection of
 population-based survival data into time of emergency or intervention
 based on information from before the emergency may create a local baseline
 reference. We find a log-transformed Gaussian time series model where
 standard errors of the estimated rates are included in the variance to
 have the best forecasting capacity. However, if time-at-risk during the
 forecasted period is known then forecasting might be done using a Poisson
 time series model with overdispersion. Whatever, the standard error of the
 estimated rates must be included in the variance of the model either in an
 additive form in a Gaussian model or in a multiplicative form by
 overdispersion in a Poisson model. Data on which the forecasting is based
 must be modelled carefully concerning not only calendar-time trends but
 also periods with excessive frequency of events (epidemics) and seasonal
 variations to eliminate residual autocorrelation and to make a proper
 reference for comparison, reflecting changes over time during the
 emergency. Hence, when modelled properly it is possible to predict a
 reference to an emergency-affected population based on local conditions.
 We predicted childhood mortality during the war in Guinea-Bissau
 1998-1999. We found an increased mortality in the first half-year of the
 war and a mortality corresponding to the expected one in the last
 half-year of the war. 
Journal: Journal of Applied Statistics 
Pages: 75-86 
Issue: 1 
Volume: 32 
Year: 2005 
Keywords: Time series, forecasting, Poisson regression, mixed models, complex emergency, mortality, 
X-DOI: 10.1080/0266476042000305168 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476042000305168
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:1:p:75-86




Template-Type: ReDIF-Article 1.0
Author-Name: Jitendra Singh Tomar 
Author-X-Name-First: Jitendra Singh 
Author-X-Name-Last: Tomar 
Author-Name: Seema Jaggi 
Author-X-Name-First: Seema 
Author-X-Name-Last: Jaggi 
Author-Name: Cini Varghese 
Author-X-Name-First: Cini 
Author-X-Name-Last: Varghese 
Title: On totally balanced block designs for competition effects 
Abstract:
  Competition between neighbouring units in field experiments is a serious
 source of bias. The study of a competing situation needs construction of
 an environment in which it can happen and the competing units have to
 appear in a predetermined pattern. This paper describes methods of
 constructing incomplete block designs balanced for neighbouring
 competition effects. The designs obtained are totally balanced in the
 sense that all the effects, direct and neighbours, are estimated with the
 same variance. The efficiency of these designs has been computed as
 compared to a complete block design balanced for neighbours and a
 catalogue has also been prepared. 
Journal: Journal of Applied Statistics 
Pages: 87-97 
Issue: 1 
Volume: 32 
Year: 2005 
Keywords: Competition effects, circular design, totally balanced design, MOLS, 
X-DOI: 10.1080/0266476042000305177 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/0266476042000305177
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:1:p:87-97




Template-Type: ReDIF-Article 1.0
Author-Name: Athanasios Micheas 
Author-X-Name-First: Athanasios 
Author-X-Name-Last: Micheas 
Author-Name: Dipak Dey 
Author-X-Name-First: Dipak 
Author-X-Name-Last: Dey 
Title: Assessing shape differences in populations of shapes using the complex watson shape distribution 
Abstract:
  This paper presents a novel Bayesian method based on the complex Watson
 shape distribution that is used in detecting shape differences between the
 second thoracic vertebrae for two groups of mice, small and large,
 categorized according to their body weight. Considering the data provided
 in Johnson et al. (1988), we provide Bayesian methods of estimation as
 well as highest posterior density (HPD) estimates for modal vertebrae
 shapes within each group. Finally, we present a classification procedure
 that can be used in any shape classification experiment, and apply it for
 categorizing new vertebrae shapes in small or large groups. 
Journal: Journal of Applied Statistics 
Pages: 105-116 
Issue: 2 
Volume: 32 
Year: 2005 
Keywords: Average shape difference, complex Watson shape distribution, HPD credible set, modal shape, 
X-DOI: 10.1080/02664760500054137 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500054137
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:2:p:105-116




Template-Type: ReDIF-Article 1.0
Author-Name: Matthew Hall 
Author-X-Name-First: Matthew 
Author-X-Name-Last: Hall 
Author-Name: Matthew Mayo 
Author-X-Name-First: Matthew 
Author-X-Name-Last: Mayo 
Title: The impact of correlated readings on the estimation of the average area under readers' ROC curves 
Abstract:
  Receiver operating characteristic (ROC) analysis has been used in a
 variety of settings since it was first declassified by the United States
 government over 60 years ago. One venue in which it has received
 particular attention is in the field of radiology. In radiology, as in
 other areas of application, ROC analysis is used to assess the ability of
 a diagnostic test to distinguish between two opposing states. One useful
 descriptor in ROC analysis is the area under the ROC curve. At times, it
 is useful and insightful to average ROC curves in order to create a single
 curve that summarizes all of the data from multiple readers. In this
 paper, we investigate the impact of correlated readings on the average
 area under two readers' ROC curves using several common averaging
 strategies, and then apply the results to a radiologic study. 
Journal: Journal of Applied Statistics 
Pages: 117-125 
Issue: 2 
Volume: 32 
Year: 2005 
Keywords: Receiver operating characteristic curve, correlated data, 
X-DOI: 10.1080/02664760500054152 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500054152
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:2:p:117-125




Template-Type: ReDIF-Article 1.0
Author-Name: J. A. Amaral 
Author-X-Name-First: J. A. 
Author-X-Name-Last: Amaral 
Author-Name: E. P. Pereira 
Author-X-Name-First: E. P. 
Author-X-Name-Last: Pereira 
Author-Name: M. T. Paixao 
Author-X-Name-First: M. T. 
Author-X-Name-Last: Paixao 
Title: Data and projections of HIV/AIDS cases in Portugal: an unstoppable epidemic? 
Abstract:
  The size of the affected population with HIV/AIDS is a vital question
 asked by healthcare providers. A statistical procedure called
 Back-calculation has been the most widely used method to answer that
 question. Recent discussions suggest that this method is gradually
 becoming less appropriate for reliable incidence and prevalence estimates,
 as it does not take into account the effect of treatment. In spite of
 this, in the current paper that method and a worst-case scenario are used
 to assess the quality of previous projections and obtain new ones. The
 first problem faced was the need to account for reporting delays, no
 reporting and underreporting. The adjusted AIDS incidence data were then
 used to obtain lower bounds on the size of the AIDS epidemic, using the
 back-calculation methodology. A Weibull and Gamma distribution was
 considered for the latency period distribution. The EM algorithm was
 applied to obtain maximum likelihood estimates of the HIV incidence. The
 density of infection times was parameterized as a step function. The
 methodology is applied to AIDS incidence in Portugal for four different
 transmission categories (injecting drug users, heterosexual, homo/bisexual
 and other) to obtain short-term projections (2002-2005) and an estimate of
 the minimum size of the epidemic. 
Journal: Journal of Applied Statistics 
Pages: 127-140 
Issue: 2 
Volume: 32 
Year: 2005 
Keywords: HIV/AIDS, back-calculation, projections, Portugal, 
X-DOI: 10.1080/02664760500054160 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500054160
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:2:p:127-140




Template-Type: ReDIF-Article 1.0
Author-Name: Paul Zhang 
Author-X-Name-First: Paul 
Author-X-Name-Last: Zhang 
Title: Multiple imputation of missing data with ante-dependence covariance structure 
Abstract:
  A controlled clinical trial was conducted to investigate the efficacy
 effect of a chemical compound in the treatment of Premenstrual Dysphoric
 Disorder (PMDD). The data from the trial showed a non-monotone pattern of
 missing data and an ante-dependence covariance structure. A new analytical
 method for imputing the missing data with the ante-dependence covariance
 is proposed. The PMDD data are analysed by the non-imputation method and
 two imputation methods: the proposed method and the MCMC method. 
Journal: Journal of Applied Statistics 
Pages: 141-155 
Issue: 2 
Volume: 32 
Year: 2005 
Keywords: Missing data, multiple imputation, ante-dependence covariance, 
X-DOI: 10.1080/02664760500054178 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500054178
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:2:p:141-155




Template-Type: ReDIF-Article 1.0
Author-Name: Victor Guerrero 
Author-X-Name-First: Victor 
Author-X-Name-Last: Guerrero 
Title: Restricted estimation of an adjusted time series: application to Mexico's industrial production index 
Abstract:
  The inclusion of linear deterministic effects in a time series model is
 important to get an appropriate specification. Such effects may be due to
 calendar variation, outlying observations or interventions. This article
 proposes a two-step method for estimating an adjusted time series and the
 parameters of its linear deterministic effects simultaneously. Although
 the main goal when applying this method in practice might only be to
 estimate the adjusted series, an important by-product is a substantial
 increase in efficiency in the estimates of the deterministic effects. Some
 theoretical examples are presented to demonstrate the intuitive appeal of
 this proposal. Then the methodology is applied on two real datasets. One
 of these applications investigates the importance of the 1995 economic
 crisis on Mexico's industrial production index. 
Journal: Journal of Applied Statistics 
Pages: 157-177 
Issue: 2 
Volume: 32 
Year: 2005 
Keywords: Deterministic effects, intervention analysis, minimum mean square error, precision share, 
X-DOI: 10.1080/02664760500054186 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500054186
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:2:p:157-177




Template-Type: ReDIF-Article 1.0
Author-Name: Jan Graffelman 
Author-X-Name-First: Jan 
Author-X-Name-Last: Graffelman 
Title: Enriched biplots for canonical correlation analysis 
Abstract:
  This paper discusses biplots of the between-set correlation matrix
 obtained by canonical correlation analysis. It is shown that these biplots
 can be enriched with the representation of the cases of the original data
 matrices. A representation of the cases that is optimal in the generalized
 least squares sense is obtained by the superposition of a scatterplot of
 the canonical variates on the biplot of the between-set correlation
 matrix. Goodness of fit statistics for all correlation and data matrices
 involved in canonical correlation analysis are discussed. It is shown that
 adequacy and redundancy coefficients are in fact statistics that express
 the goodness of fit of the original data matrices in the biplot. The
 within-set correlation matrix that is represented in standard coordinates
 always has a better goodness of fit than the within-set correlation matrix
 that is represented in principal coordinates. Given certain scalings, the
 scalar products between variable vectors approximate correlations better
 than the cosines of angles between variable vectors. Several data sets are
 used to illustrate the results. 
Journal: Journal of Applied Statistics 
Pages: 173-188 
Issue: 2 
Volume: 32 
Year: 2005 
Keywords: Canonical weights, canonical loadings, supplementary variables, generalized least squares, goodness of fit, 
X-DOI: 10.1080/02664760500054202 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500054202
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:2:p:173-188




Template-Type: ReDIF-Article 1.0
Author-Name: S. Hussain 
Author-X-Name-First: S. 
Author-X-Name-Last: Hussain 
Author-Name: R. Harrison 
Author-X-Name-First: R. 
Author-X-Name-Last: Harrison 
Author-Name: J. Ayres 
Author-X-Name-First: J. 
Author-X-Name-Last: Ayres 
Author-Name: S. Walter 
Author-X-Name-First: S. 
Author-X-Name-Last: Walter 
Author-Name: J. Hawker 
Author-X-Name-First: J. 
Author-X-Name-Last: Hawker 
Author-Name: R. Wilson 
Author-X-Name-First: R. 
Author-X-Name-Last: Wilson 
Author-Name: G. Shukur 
Author-X-Name-First: G. 
Author-X-Name-Last: Shukur 
Title: Estimation and forecasting hospital admissions due to Influenza: Planning for winter pressure. The case of the West Midlands, UK 
Abstract:
  Winters are a difficult period for the National Health Service (NHS) in
 the United Kingdom (UK), due to the combination of cold weather and the
 increased likelihood of respiratory infections, especially influenza. In
 this article we present a proper statistical time series approach for
 modelling and analysing weekly hospital admissions in the West Midlands in
 the UK during the period week 15/1990 to week 14/1999. We consider three
 variables, namely, hospital admissions, general practitioner consultants,
 and minimum temperature. The autocorrelations of each series are shown to
 decay hyperbolically. The correlations of hospital admission and the lag
 of other series also decay hyperbolically but with different speed and
 directions. One of the main objectives of this paper is to show that each
 of the three series can be represented by a Fractional Differenced
 Autoregressive integrated moving average model, (FDA). Further, the
 hospital admission winter and summer residuals shows significant
 interdependency, which may be interpreted as hidden periodicities within
 the last 10-years time interval. The short-range (8 weeks) forecasting of
 hospital admission of the FDA model and a fourth-order AutoRegressive
 AR(4) model are quite similar. However, our results reveal that the
 long-range forecasting of FDA is more realistic. This implies that, using
 the FDA approach, the respective authority can plan for winter pressure
 properly. 
Journal: Journal of Applied Statistics 
Pages: 191-205 
Issue: 3 
Volume: 32 
Year: 2005 
Keywords: Hospital admissions, long-range dependence, periodicity, fractional forecasting, 
X-DOI: 10.1080/02664760500054384 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500054384
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:3:p:191-205




Template-Type: ReDIF-Article 1.0
Author-Name: Sung Park 
Author-X-Name-First: Sung 
Author-X-Name-Last: Park 
Author-Name: Bum Lee 
Author-X-Name-First: Bum 
Author-X-Name-Last: Lee 
Author-Name: Hyang Jung 
Author-X-Name-First: Hyang 
Author-X-Name-Last: Jung 
Title: Joint impact of multiple observations on a subset of variables in multiple linear regression 
Abstract:
  In multiple linear regression analysis, each observation affects the
 fitted regression equation differently and has varying influences on the
 regression coefficients of the different variables. Chatterjee & Hadi
 (1988) have proposed some measures such as DSSEij (Impact on Residual Sum
 of Squares of simultaneously omitting the ith observation and the jth
 variable), Fj (Partial F-test for the jth variable) and Fj(i) (Partial
 F-test for the jth variable omitting the ith observation) to show the
 joint impact and the interrelationship that exists among a variable and an
 observation. In this paper we have proposed more extended form of those
 measures DSSEIJ, FJ and FJ(I) to deal with the interrelationships that
 exist among the multiple observations and a subset of variables by
 monitoring the effects of the simultaneous omission of multiple variables
 and multiple observations. 
Journal: Journal of Applied Statistics 
Pages: 207-219 
Issue: 3 
Volume: 32 
Year: 2005 
Keywords: Subset of variables, multiple linear regression, joint impact, regression diagnostics, 
X-DOI: 10.1080/02664760500054418 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500054418
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:3:p:207-219




Template-Type: ReDIF-Article 1.0
Author-Name: Zhang Wu 
Author-X-Name-First: Zhang 
Author-X-Name-Last: Wu 
Author-Name: Yu Tian 
Author-X-Name-First: Yu 
Author-X-Name-Last: Tian 
Author-Name: Sheng Zhang 
Author-X-Name-First: Sheng 
Author-X-Name-Last: Zhang 
Title: Adjusted-loss-function charts with variable sample sizes and sampling intervals 
Abstract:
  Recent research has shown that the control charts with adaptive features
 are quicker than the traditional static Shewhart charts in detecting
 process shifts. This article presents the design and implementation of a
 control chart based on Adjusted Loss Function (AL) with Variable Sample
 Sizes and Sampling Intervals (VSSI). This single chart (called the VSSI AL
 chart) is able to monitor the process shifts in mean and variance
 simultaneously. Our studies show that the VSSI AL chart is not only easier
 to design and implement than the VSSI X & S (or X & R) charts, but is also
 10% more effective than the latter in detecting the process shifts from an
 overall viewpoint. 
Journal: Journal of Applied Statistics 
Pages: 221-242 
Issue: 3 
Volume: 32 
Year: 2005 
Keywords: Statistical Process Control, loss function, adaptive control chart, 
X-DOI: 10.1080/02664760500054475 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500054475
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:3:p:221-242




Template-Type: ReDIF-Article 1.0
Author-Name: Duolao Wang 
Author-X-Name-First: Duolao 
Author-X-Name-Last: Wang 
Author-Name: Michael Murphy 
Author-X-Name-First: Michael 
Author-X-Name-Last: Murphy 
Title: Identifying Nonlinear Relationships in Regression using the ACE Algorithm 
Abstract:
  This paper introduces an alternating conditional expectation (ACE)
 algorithm: a non-parametric approach for estimating the transformations
 that lead to the maximal multiple correlation of a response and a set of
 independent variables in regression and correlation analysis. These
 transformations can give the data analyst insight into the relationships
 between these variables so that this can be best described and non-linear
 relationships uncovered. Using the Bayesian information criterion (BIC),
 we show how to find the best closed-form approximations for the optimal
 ACE transformations. By means of ACE and BIC, the model fit can be
 considerably improved compared with the conventional linear model as
 demonstrated in the two simulated and two real datasets in this paper. 
Journal: Journal of Applied Statistics 
Pages: 243-258 
Issue: 3 
Volume: 32 
Year: 2005 
Keywords: Alternating Conditional Expectation (ACE) algorithm, transformation, non-parametric regression, smoothing, Bayesian Information Criterion (BIC), 
X-DOI: 10.1080/02664760500054517 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500054517
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:3:p:243-258




Template-Type: ReDIF-Article 1.0
Author-Name: Ulysses Brown 
Author-X-Name-First: Ulysses 
Author-X-Name-Last: Brown 
Author-Name: Dharam Rana 
Author-X-Name-First: Dharam 
Author-X-Name-Last: Rana 
Title: Generalized exchange and propensity for military service: The moderating effect of prior military exposure 
Abstract:
  The propensity for military service (PMS) of young Americans is an
 important issue for our Armed Forces. Since the 1990s, the PMS of young
 Americans has steadily declined. Overtime, a declining PMS may cause
 military mission degradation, lowering of military recruitment standards,
 base closures, and reinstatement of the unpopular military draft system.
 This paper investigates the moderator effect of prior military service on
 the Generalized Exchange-PMS relationship. Generalized exchange is when
 indirect benefits such as preserving freedom and the American way of life
 accrue to the larger society because of an individual's military service.
 This paper uses a structural equation modelling approach to analyse the
 moderating effect of prior military exposure on prospective recruits
 regarding their PMS. Findings indicate that the group of prospective
 recruits with prior military exposure had higher levels of PMS than the
 group without such exposure, that is, the young people with prior military
 exposure are more likely to enlist in the military than the young
 Americans with no prior military exposure. 
Journal: Journal of Applied Statistics 
Pages: 259-270 
Issue: 3 
Volume: 32 
Year: 2005 
Keywords: Propensity, structural equation modelling, military, exchange theory, 
X-DOI: 10.1080/02664760500054590 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500054590
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:3:p:259-270




Template-Type: ReDIF-Article 1.0
Author-Name: Ronald Tracy 
Author-X-Name-First: Ronald 
Author-X-Name-Last: Tracy 
Author-Name: David Doane 
Author-X-Name-First: David 
Author-X-Name-Last: Doane 
Title: Using the studentized range to assess kurtosis 
Abstract:
  Because it is easy to compute from three common statistics (minimum,
 maximum, standard deviation) the studentized range is a useful test for
 non-normality when the original data are unavailable. For samples from
 symmetric populations, the studentized range allows an assessment of
 kurtosis with Type I and II error rates similar to those obtained from the
 moment coefficients. 
Journal: Journal of Applied Statistics 
Pages: 271-280 
Issue: 3 
Volume: 32 
Year: 2005 
Keywords: EDA, Studentized range, kurtosis, skewness, normality, 
X-DOI: 10.1080/02664760500054632 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500054632
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:3:p:271-280




Template-Type: ReDIF-Article 1.0
Author-Name: Andres Alonso 
Author-X-Name-First: Andres 
Author-X-Name-Last: Alonso 
Author-Name: Juan Romo 
Author-X-Name-First: Juan 
Author-X-Name-Last: Romo 
Title: Forecast of the expected non-epidemic morbidity of acute diseases using resampling methods 
Abstract:
  In epidemiological surveillance it is important that any unusual increase
 of reported cases be detected as rapidly as possible. Reliable forecasting
 based on a suitable time series model for an epidemiological indicator is
 necessary for estimating the expected non-epidemic indicator and to
 elaborate an alert threshold. Time series analyses of acute diseases often
 use Gaussian autoregressive integrated moving average models. However,
 these approaches can be adversely affected by departures from the true
 underlying distribution. The objective of this paper is to introduce a
 bootstrap procedure for obtaining prediction intervals in linear models in
 order to avoid the normality assumption. We present a Monte Carlo study
 comparing the finite sample properties of bootstrap prediction intervals
 with those of alternative methods. Finally, we illustrate the performance
 of the proposed method with a meningococcal disease incidence series. 
Journal: Journal of Applied Statistics 
Pages: 281-295 
Issue: 3 
Volume: 32 
Year: 2005 
Keywords: Morbidity prediction, epidemiological time series, sieve bootstrap, 
X-DOI: 10.1080/02664760500054780 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500054780
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:3:p:281-295




Template-Type: ReDIF-Article 1.0
Author-Name: Guillermo De Leon Adams 
Author-X-Name-First: Guillermo De Leon 
Author-X-Name-Last: Adams 
Author-Name: Pere Grima Cintas 
Author-X-Name-First: Pere Grima 
Author-X-Name-Last: Cintas 
Author-Name: Xavier Tort-Martorell Llabres 
Author-X-Name-First: Xavier Tort-Martorell 
Author-X-Name-Last: Llabres 
Title: Experimentation order in factorial designs with 8 or 16 runs 
Abstract:
  Randomizing the order of experimentation in a factorial design does not
 always achieve the desired effect of neutralizing the influence of unknown
 factors. In fact, with some very reasonable assumptions, an important
 proportion of random orders afford the same degree of protection as that
 obtained by experimenting in the design matrix standard order. In
 addition, randomization can induce a big number of changes in factor
 levels and thus make experimentation expensive and difficult. This paper
 discusses this subject and suggests experimentation orders for designs
 with 8 or 16 runs that combine an excellent level of protection against
 the influence of unknown factors, with the minimum number of changes in
 factor levels. 
Journal: Journal of Applied Statistics 
Pages: 297-313 
Issue: 3 
Volume: 32 
Year: 2005 
Keywords: Randomization, experimentation order, factorial design, bias protection, minimum number of level changes, 
X-DOI: 10.1080/02664760500054731 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500054731
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:3:p:297-313




Template-Type: ReDIF-Article 1.0
Author-Name: Belen Zalba 
Author-X-Name-First: Belen 
Author-X-Name-Last: Zalba 
Author-Name: Belen Sanchez-valverde 
Author-X-Name-First: Belen 
Author-X-Name-Last: Sanchez-valverde 
Author-Name: Jose Marin 
Author-X-Name-First: Jose 
Author-X-Name-Last: Marin 
Title: An experimental study of thermal energy storage with phase change materials by design of experiments 
Abstract:
  Accurate theoretical modelling and simulation of thermal energy storage
 (TES) by means of phase change materials (PCM) is very complex and its
 results are not close enough to experimental values. This paper presents
 the empirical study of a thermal storage unit operating with a commercial
 PCM called RT25. The study is carried out by means of the statistical
 procedure, Design of Experiments. This methodology has rarely been used in
 the analysis of heat transfer problems. The present study has allowed us
 to investigate the phenomena involved and to design an actual system. We
 show the whole procedure followed in order to design the set-up, to run
 the experiments with a 23 factorial design, to compare its results with a
 numerical simulation and to get the empirical model by regression. Its
 results have been used to design actual installations aimed at
 free-cooling or maintaining the temperature constant in rooms where
 thermal security is necessary. 
Journal: Journal of Applied Statistics 
Pages: 321-332 
Issue: 4 
Volume: 32 
Year: 2005 
Keywords: Factorial design, simulation, thermal energy storage, phase change materials, 
X-DOI: 10.1080/02664760500078920 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500078920
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:4:p:321-332




Template-Type: ReDIF-Article 1.0
Author-Name: Rolf Aaberge 
Author-X-Name-First: Rolf 
Author-X-Name-Last: Aaberge 
Author-Name: Li-chun Zhang 
Author-X-Name-First: Li-chun 
Author-X-Name-Last: Zhang 
Title: A class of exact UMP unbiased tests for conditional symmetry in small-sample square contingency tables 
Abstract:
  Testing conditional symmetry against various alternative
 diagonals-parameter symmetry models often provides a point of departure in
 studies of square contingency tables with ordered categories. Typically,
 chi-square or likelihood-ratio tests are used for such purposes. Since
 these tests depend on the validity of asymptotic approximation, they may
 be inappropriate in small-sample situations where exact tests are
 required. In this paper, we apply the theory of UMP unbiased tests to
 develop a class of exact tests for conditional symmetry in small samples.
 Oesophageal cancer and longitudinal income data are used to illustrate the
 approach. 
Journal: Journal of Applied Statistics 
Pages: 333-340 
Issue: 4 
Volume: 32 
Year: 2005 
Keywords: Multinomial distribution, conditional symmetry, diagonals-parameter symmetry, 
X-DOI: 10.1080/02664760500078953 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500078953
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:4:p:333-340




Template-Type: ReDIF-Article 1.0
Author-Name: Francois Husson 
Author-X-Name-First: Francois 
Author-X-Name-Last: Husson 
Author-Name: Jerome Pages 
Author-X-Name-First: Jerome 
Author-X-Name-Last: Pages 
Title: Scatter Plot and Additional Variables 
Abstract:
  We often want to complete the interpretation of the usual graphs (x, y)
 with additional quantitative variables. The Prefmap method (vectorial
 model) proposes a representation of these additional variables but this
 representation has some drawbacks when the variables x and y are
 correlated. To solve this problem, we propose to substitute the
 coefficients of the linear regression by the coefficient of the PLS
 regression in the Prefmap method. The graph obtained is made operational
 thanks to contour lines of quality of representation and it becomes richer
 than the Prefmap one. 
Journal: Journal of Applied Statistics 
Pages: 341-350 
Issue: 4 
Volume: 32 
Year: 2005 
Keywords: Scatter plot, Prefmap, Pls, additional variables, 
X-DOI: 10.1080/02664760500079043 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500079043
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:4:p:341-350




Template-Type: ReDIF-Article 1.0
Author-Name: Natasha Yakovchuk 
Author-X-Name-First: Natasha 
Author-X-Name-Last: Yakovchuk 
Author-Name: Thomas Willemain 
Author-X-Name-First: Thomas 
Author-X-Name-Last: Willemain 
Title: Monte carlo comparison of estimation methods for additive two-way tables 
Abstract:
  We considered the problem of estimating effects in the following linear
 model for data arranged in a two-way table: Response = Common effect + Row
 effect + Column effect + Residual. This work was occasioned by a project
 to analyse Federal Aviation Administration (FAA) data on daily temporal
 deviations from flight plans for commercial US flights, with rows and
 columns representing origin and destination airports, respectively. We
 conducted a large Monte Carlo study comparing the accuracy of three
 methods of estimation: classical least squares, median polish and least
 absolute deviations (LAD). The experiments included a wide spectrum of
 tables of different sizes and shapes, with different levels of
 non-linearity, noise variance, and percentages of empty cells and
 outliers. We based our comparison on the accuracy of the estimates and on
 computational speed. We identified factors that significantly affect
 accuracy and speed, and compared the methods based on their sensitivity to
 these factors. We concluded that there is no dominant method of estimation
 and identified conditions under which each method is most attractive. 
Journal: Journal of Applied Statistics 
Pages: 351-374 
Issue: 4 
Volume: 32 
Year: 2005 
Keywords: Additive model, least squares, least absolute deviations, Monte Carlo, robust estimation, two-way tables, 
X-DOI: 10.1080/02664760500079118 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500079118
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:4:p:351-374




Template-Type: ReDIF-Article 1.0
Author-Name: Michael Zeifman 
Author-X-Name-First: Michael 
Author-X-Name-Last: Zeifman 
Author-Name: Dov Ingman 
Author-X-Name-First: Dov 
Author-X-Name-Last: Ingman 
Title: Modelling of unexpected shift in SPC 
Abstract:
  Optimal statistical process control (SPC) requires models of both
 in-control and out-of-control process states. Whereas a normal
 distribution is the generally accepted model for the in-control state,
 there is a doubt as to the existence of reliable models for out-of-control
 cases. Various process models, available in the literature, for discrete
 manufacturing systems (parts industry) can be treated as bounded
 discrete-space Markov chains, completely characterized by the original
 in-control state and a transition matrix for shifts to an out-of-control
 state. The present work extends these models by using a continuous-state
 Markov chain, incorporating non-random corrective actions. These actions
 are to be realized according to the SPC technique and should substantially
 affect the model. The developed stochastic model yields a Laplace
 distribution of a process mean. An alternative approach, based on the
 Information theory, also results in a Laplace distribution. Real-data
 tests confirm the applicability of a Laplace distribution for the parts
 industry and show that the distribution parameter is mainly controlled by
 the SPC sample size. 
Journal: Journal of Applied Statistics 
Pages: 375-386 
Issue: 4 
Volume: 32 
Year: 2005 
Keywords: Control charts, Markov chain, mixture distribution, information distance, 
X-DOI: 10.1080/02664760500079175 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500079175
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:4:p:375-386




Template-Type: ReDIF-Article 1.0
Author-Name: Jadran Dobric 
Author-X-Name-First: Jadran 
Author-X-Name-Last: Dobric 
Author-Name: Friedrich Schmid 
Author-X-Name-First: Friedrich 
Author-X-Name-Last: Schmid 
Title: Nonparametric estimation of the lower tail dependence &#x3bb;L in bivariate copulas 
Abstract:
  The lower tail dependence &#x3bb;L is a measure that characterizes the
 tendency of extreme co-movements in the lower tails of a bivariate
 distribution. It is invariant with respect to strictly increasing
 transformations of the marginal distribution and is therefore a function
 of the copula of the bivariate distribution. &#x3bb;L plays an important
 role in modelling aggregate financial risk with copulas. This paper
 introduces three non-parametric estimators for &#x3bb;L. They are weakly
 consistent under mild regularity conditions on the copula and under the
 assumption that the number k = k(n) of observations in the lower tail,
 used for estimation, is asymptotically k &#x2248; &#x221a;n. The finite
 sample properties of the estimators are investigated using a Monte Carlo
 simulation in special cases. It turns out that these estimators are
 biased, where amount and sign of the bias depend on the underlying copula,
 on the sample size n, on k, and on the true value of &#x3bb;L. 
Journal: Journal of Applied Statistics 
Pages: 387-407 
Issue: 4 
Volume: 32 
Year: 2005 
Keywords: Copula, lower tail dependence, non-parametric estimation, empirical copula process, consistency of estimators, small sample properties of estimators, 
X-DOI: 10.1080/02664760500079217 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500079217
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:4:p:387-407




Template-Type: ReDIF-Article 1.0
Author-Name: Dogan Argac 
Author-X-Name-First: Dogan 
Author-X-Name-Last: Argac 
Title: Testing hypotheses on coefficients of variation from a series of two-armed experiments 
Abstract:
  We consider the problem of testing hypotheses on the difference of the
 coefficients of variation from several two-armed experiments with normally
 distributed outcomes. In particular, we deal with testing the homogeneity
 of the difference of the coefficients of variation and testing the
 equality of the difference of the coefficients of variation to a specified
 value. The test statistics proposed are derived in a limiting one-way
 classification with fixed effects and heteroscedastic error variances,
 using results from analysis of variance. By way of simulation, the
 performance of these test statistics is compared for both testing problems
 considered. 
Journal: Journal of Applied Statistics 
Pages: 409-419 
Issue: 4 
Volume: 32 
Year: 2005 
Keywords: Analysis of variance, heteroscedastic variances, homogeneity, one-way classification, two-armed experiments, 
X-DOI: 10.1080/02664760500079225 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500079225
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:4:p:409-419




Template-Type: ReDIF-Article 1.0
Author-Name: Takafumi Isogai 
Author-X-Name-First: Takafumi 
Author-X-Name-Last: Isogai 
Title: Applications of a new power normal family 
Abstract:
  The main purpose of this paper is to give an algorithm to attain joint
 normality of non-normal multivariate observations through a new power
 normal family introduced by the author (Isogai, 1999). The algorithm tries
 to transform each marginal variable simultaneously to joint normality, but
 due to a large number of parameters it repeats a maximization process with
 respect to the conditional normal density of one transformed variable
 given the other transformed variables. A non-normal data set is used to
 examine performance of the algorithm, and the degree of achievement of
 joint normality is evaluated by measures of multivariate skewness and
 kurtosis. Besides the above topic, making use of properties of our power
 normal family, we discuss not only a normal approximation formula of
 non-central F distributions in the frame of regression analysis but also
 some decomposition formulas of a power parameter, which appear in a
 Wilson-Hilferty power transformation setting. 
Journal: Journal of Applied Statistics 
Pages: 421-436 
Issue: 4 
Volume: 32 
Year: 2005 
Keywords: Power normal family, non-normality, joint normality, measures of multivariate skewness and kurtosis, 
X-DOI: 10.1080/02664760500079233 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500079233
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:4:p:421-436




Template-Type: ReDIF-Article 1.0
Author-Name: Diego Kuonen 
Author-X-Name-First: Diego 
Author-X-Name-Last: Kuonen 
Title: Studentized bootstrap confidence intervals based on M-estimates 
Abstract:
  This article reviews and applies saddlepoint approximations to
 studentized confidence intervals based on robust M-estimates. The latter
 are known to be very accurate without needing standard theory assumptions.
 As examples, the classical studentized statistic, the studentized versions
 of Huber's M-estimate of location, of its initially MAD scaled version and
 of Huber's proposal 2 are considered. The aim is to know whether the
 studentized statistics yield robust confidence intervals with coverages
 close to nominal, with short intervals. The results of an extensive
 simulation study and the recommendations for practical use given in this
 article may fill gaps in the current literature and stimulate further
 discussion and research. 
Journal: Journal of Applied Statistics 
Pages: 443-460 
Issue: 5 
Volume: 32 
Year: 2005 
Keywords: Bootstrap, confidence interval, <i>M</i>-estimation, resampling, robust inference, saddlepoint, studentized bootstrap, 
X-DOI: 10.1080/02664760500079340 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500079340
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:5:p:443-460




Template-Type: ReDIF-Article 1.0
Author-Name: Thomas Ryan 
Author-X-Name-First: Thomas 
Author-X-Name-Last: Ryan 
Author-Name: William Woodall 
Author-X-Name-First: William 
Author-X-Name-Last: Woodall 
Title: The most-cited statistical papers 
Abstract:
  We attempt to identify the 25 most-cited statistical papers, providing
 some brief commentary on each paper on our list. This list consists, to a
 great extent, of papers that are on non-parametric methods, have
 applications in the life sciences, or deal with the multiple comparisons
 problem. We also list the most-cited papers published in 1993 or later. In
 contrast to the overall most-cited papers, these are predominately papers
 on Bayesian methods and wavelets. We briefly discuss some of the issues
 involved in the use of citation counts. 
Journal: Journal of Applied Statistics 
Pages: 461-474 
Issue: 5 
Volume: 32 
Year: 2005 
Keywords: Citations, history of statistics, 
X-DOI: 10.1080/02664760500079373 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500079373
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:5:p:461-474




Template-Type: ReDIF-Article 1.0
Author-Name: Mike Nicholson 
Author-X-Name-First: Mike 
Author-X-Name-Last: Nicholson 
Author-Name: Jon Barry 
Author-X-Name-First: Jon 
Author-X-Name-Last: Barry 
Title: Target detection from a classical and a Bayesian viewpoint 
Abstract:
  Two approaches have been used for designing spatial surveys to detect a
 target. The classical approach controls the probability of missing a
 target that exists; a Bayesian approach controls the probability that a
 target exists given that none was seen. In both cases, information about
 the likely size of the target can reduce sampling requirements. In this
 paper, previous results are summarized and then used to assess the risk
 that Roman remains could be present at sites scheduled for development in
 Greater London. 
Journal: Journal of Applied Statistics 
Pages: 475-482 
Issue: 5 
Volume: 32 
Year: 2005 
Keywords: Target detection, statistical archaeology, spatial surveys, 
X-DOI: 10.1080/02664760500079407 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500079407
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:5:p:475-482




Template-Type: ReDIF-Article 1.0
Author-Name: Diane Dancer 
Author-X-Name-First: Diane 
Author-X-Name-Last: Dancer 
Author-Name: Andrew Tremayne 
Author-X-Name-First: Andrew 
Author-X-Name-Last: Tremayne 
Title: R-squared and prediction in regression with ordered quantitative response 
Abstract:
  This paper is concerned with the use of regression methods to predict
 values of a response variable when that variable is naturally ordered. An
 application to the prediction of student examination performance is
 provided and it is argued that, although individual scores are unlikely to
 be well predicted at the extremes of the range using the conditional mean,
 conditional on covariates, it is possible to usefully predict where an
 individual is likely to feature in the rank order of performance. 
Journal: Journal of Applied Statistics 
Pages: 483-493 
Issue: 5 
Volume: 32 
Year: 2005 
Keywords: Regression prediction, prediction error, rank correlation, 
X-DOI: 10.1080/02664760500079423 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500079423
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:5:p:483-493




Template-Type: ReDIF-Article 1.0
Author-Name: Rand Wilcox 
Author-X-Name-First: Rand 
Author-X-Name-Last: Wilcox 
Title: Estimating the conditional variance of Y, given X, in a simple regression model 
Abstract:
  Consider the regression model [image omitted]&#xa0;. In a variety of
 situations, an estimate of VAR (Y &7C X) = &#x3bb; (X) is needed. The
 paper compares the small-sample accuracy of five estimators of &#x3bb;
 (X). The results suggest that the optimal estimator is a somewhat complex
 function of the underlying distributions. In terms of mean squared error,
 one of the estimators, which is based in part on a non-robust version of
 Cleveland's smoother, performed about as well as a bagged version of the
 so-called running interval smoother, but the running interval smoother was
 found to be preferable in terms of bias. A modification of Cleveland's
 smoother, stemming from Ruppert et al. (1997), achieves its intended goal
 of reducing bias when the error term is homoscedastic, but under
 heteroscedasticity, bias can be high, and in terms of mean squared error
 it does not compete well with the kernel method considered in the paper.
 When &#x3b5; has a heavy-tailed distribution, a robust version of
 Cleveland's smoother performed particularly well except in some situations
 where X has a heavy-tailed distribution as well. A negative feature of
 using Cleveland's robust smoother is relatively high bias, and when there
 is heteroscedasticity and X has a heavy-tailed distribution, a kernel-type
 method and the running interval smoother give superior results in terms of
 both mean squared error and bias. 
Journal: Journal of Applied Statistics 
Pages: 495-502 
Issue: 5 
Volume: 32 
Year: 2005 
Keywords: Strength of association, heteroscedasticity, 
X-DOI: 10.1080/02664760500079480 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500079480
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:5:p:495-502




Template-Type: ReDIF-Article 1.0
Author-Name: Gabriel Huerta 
Author-X-Name-First: Gabriel 
Author-X-Name-Last: Huerta 
Title: Multivariate Bayes Wavelet shrinkage and applications 
Abstract:
  In recent years, wavelet shrinkage has become a very appealing method for
 data de-noising and density function estimation. In particular, Bayesian
 modelling via hierarchical priors has introduced novel approaches for
 Wavelet analysis that had become very popular, and are very competitive
 with standard hard or soft thresholding rules. In this sense, this paper
 proposes a hierarchical prior that is elicited on the model parameters
 describing the wavelet coefficients after applying a Discrete Wavelet
 Transformation (DWT). In difference to other approaches, the prior
 proposes a multivariate Normal distribution with a covariance matrix that
 allows for correlations among Wavelet coefficients corresponding to the
 same level of detail. In addition, an extra scale parameter is
 incorporated that permits an additional shrinkage level over the
 coefficients. The posterior distribution for this shrinkage procedure is
 not available in closed form but it is easily sampled through Markov chain
 Monte Carlo (MCMC) methods. Applications on a set of test signals and two
 noisy signals are presented. 
Journal: Journal of Applied Statistics 
Pages: 529-542 
Issue: 5 
Volume: 32 
Year: 2005 
Keywords: Bayes shrinkage, wavelets, discrete wavelet transformation, data de-noising, MCMC methods, 
X-DOI: 10.1080/02664760500079662 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500079662
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:5:p:529-542




Template-Type: ReDIF-Article 1.0
Author-Name: Jason Dietrich 
Author-X-Name-First: Jason 
Author-X-Name-Last: Dietrich 
Title: The effects of sampling strategies on the small sample properties of the logit estimator 
Abstract:
  Empirical researchers face a trade-off between the lower resource costs
 associated with smaller samples and the increased confidence in the
 results gained from larger samples. Choice of sampling strategy is one
 tool researchers can use to reduce costs yet still attain desired
 confidence levels. This study uses Monte Carlo simulation to examine the
 impact of nine sampling strategies on the finite sample performance of the
 maximum likelihood logit estimator. The results show stratified random
 sampling with balanced strata sizes and a bias correction for choice-based
 sampling outperforms all other sampling strategies with respect to four
 small-sample performance measures. 
Journal: Journal of Applied Statistics 
Pages: 543-554 
Issue: 6 
Volume: 32 
Year: 2005 
Keywords: Sampling, Logit, Monte Carlo, 
X-DOI: 10.1080/02664760500078888 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500078888
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:6:p:543-554




Template-Type: ReDIF-Article 1.0
Author-Name: Paulo Rodrigues 
Author-X-Name-First: Paulo 
Author-X-Name-Last: Rodrigues 
Author-Name: Philip Hans Franses 
Author-X-Name-First: Philip Hans 
Author-X-Name-Last: Franses 
Title: A sequential approach to testing seasonal unit roots in high frequency data 
Abstract:
  In this paper we introduce a sequential seasonal unit root testing
 approach which explicitly addresses its application to high frequency
 data. The main idea is to see which unit roots at higher frequency data
 can also be found in temporally aggregated data. We illustrate our
 procedure to the analysis of monthly data, and we find, upon analysing the
 aggregated quarterly data, that a smaller amount of test statistics can
 sometimes be considered. Monte Carlo simulation and empirical
 illustrations emphasize the practical relevance of our method. 
Journal: Journal of Applied Statistics 
Pages: 555-569 
Issue: 6 
Volume: 32 
Year: 2005 
Keywords: Seasonal unit roots, temporal aggregation, 
X-DOI: 10.1080/02664760500078912 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500078912
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:6:p:555-569




Template-Type: ReDIF-Article 1.0
Author-Name: John Zhang 
Author-X-Name-First: John 
Author-X-Name-Last: Zhang 
Author-Name: Mahmud Ibrahim 
Author-X-Name-First: Mahmud 
Author-X-Name-Last: Ibrahim 
Title: A simulation study on SPSS ridge regression and ordinary least squares regression procedures for multicollinearity data 
Abstract:
  This study compares the SPSS ordinary least squares (OLS) regression and
 ridge regression procedures in dealing with multicollinearity data. The LS
 regression method is one of the most frequently applied statistical
 procedures in application. It is well documented that the LS method is
 extremely unreliable in parameter estimation while the independent
 variables are dependent (multicollinearity problem). The Ridge Regression
 procedure deals with the multicollinearity problem by introducing a small
 bias in the parameter estimation. The application of Ridge Regression
 involves the selection of a bias parameter and it is not clear if it works
 better in applications. This study uses a Monte Carlo method to compare
 the results of OLS procedure with the Ridge Regression procedure in SPSS. 
Journal: Journal of Applied Statistics 
Pages: 571-588 
Issue: 6 
Volume: 32 
Year: 2005 
Keywords: Ridge regression, least squares regression, eigenvalues, eigenvectors, simulation, 
X-DOI: 10.1080/02664760500078946 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500078946
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:6:p:571-588




Template-Type: ReDIF-Article 1.0
Author-Name: E. Schrevens 
Author-X-Name-First: E. 
Author-X-Name-Last: Schrevens 
Author-Name: H. Coppenolle 
Author-X-Name-First: H. 
Author-X-Name-Last: Coppenolle 
Author-Name: K. M. Portier 
Author-X-Name-First: K. M. 
Author-X-Name-Last: Portier 
Title: A comparative study between latent class binomial segmentation and mixed-effects logistic regression to explore between-respondent variability in visual preference for horticultural products 
Abstract:
  A methodological concept is proposed to study between-respondent
 variability in visual preference for horticultural products using
 quantitative imaging techniques. Chicory, a typical Belgian vegetable,
 serves as a model product. Eight image sequences of high-quality chicory,
 each representing a different combination of two factor levels of length,
 width and ovality, were constructed to satisfy a 23 factorial design by
 using quantitative imaging techniques. The image sequences were pair-wise
 visualized using a computer-based image system to study visual preference.
 Twenty respondents chose which of two samples was preferred in all 28
 pair-wise combinations of the eight constructed image sequences. The
 consistency of the respondents and the agreement between respondents was
 evaluated. The poor fit of a traditional binomial logit model that relates
 preference with quality descriptors was due to the low agreement in
 preference between respondents. Therefore, latent class binomial
 segmentation is compared to mixed-effects logistic regression. Both
 approaches relax the traditional assumption that the same model holds for
 all respondents by recognizing the typical between-respondent variability
 inherent in preference studies. Where the latent class model
 simultaneously estimates different logit models for different consumer
 segments, the mixed-effects model recognizes between-respondent
 variability by incorporating random effects varying by respondent in model
 formulation. 
Journal: Journal of Applied Statistics 
Pages: 589-605 
Issue: 6 
Volume: 32 
Year: 2005 
Keywords: Chicory, latent class, logistic, mixed model, pair-wise comparison, variability, 
X-DOI: 10.1080/02664760500078987 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500078987
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:6:p:589-605




Template-Type: ReDIF-Article 1.0
Author-Name: Mark Greer 
Author-X-Name-First: Mark 
Author-X-Name-Last: Greer 
Title: Combination forecasting for directional accuracy: An application to survey interest rate forecasts 
Abstract:
  Using published interest rates forecasts issued by professional
 economists, two combination forecasts designed to improve the directional
 accuracy of interest rate forecasting are constructed. The first
 combination forecast takes a weighted average of the individual
 forecasters' predictions. The more successful the forecaster was in past
 forecasts at predicting the direction of change in interest rates, the
 greater is the weight given to his/her current forecast. The second
 combination forecast is simply the forecast issued by the forecaster who
 had the greatest success rate at predicting the direction of change in
 interest rates in previous forecasts. In cases where two or more
 forecasters tie for best historic directional accuracy track record, the
 arithmetic mean of these forecasters is used. The study finds that neither
 combination forecasting method performs better than coin-flipping at
 predicting the direction of change in interest rates. Nor does either
 method beat the simple arithmetic mean of the predictions of all the
 forecasters surveyed at predicting the direction of change in interest
 rates. 
Journal: Journal of Applied Statistics 
Pages: 607-615 
Issue: 6 
Volume: 32 
Year: 2005 
Keywords: Forecasting, directional accuracy, combination forecasting, interest rates, 
X-DOI: 10.1080/02664760500079027 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500079027
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:6:p:607-615




Template-Type: ReDIF-Article 1.0
Author-Name: Man Lai Tang 
Author-X-Name-First: Man Lai 
Author-X-Name-Last: Tang 
Author-Name: Ka Ho Wu 
Author-X-Name-First: Ka Ho 
Author-X-Name-Last: Wu 
Title: A unified sequential test procedure for simultaneous testing the equality of several binomial proportions to a specified standard 
Abstract:
  In this article, we propose a unified sequentially rejective test
 procedure for testing simultaneously the equality of several independent
 binomial proportions to a specified standard. The proposed test procedure
 is general enough to include some well-known multiple testing procedures
 such as the Ordinary Bonferroni procedure, Hochberg procedure and Rom
 procedure. It involves multiple tests of significance based on the simple
 binomial tests (exact or approximate) which can be easily found in many
 elementary standard statistics textbooks. Unlike the traditional
 Chi-square test of the overall hypothesis, the procedure can identify the
 subset of the binomial proportions, which are different from the
 prespecified standard with the control of the familywise type I error
 rate. Moreover, the power computation of the procedure is provided and the
 procedure is illustrated by two real examples from an ecological study and
 a carcinogenicity study. 
Journal: Journal of Applied Statistics 
Pages: 617-624 
Issue: 6 
Volume: 32 
Year: 2005 
Keywords: Multiple test procedure, Binomial proportions, 
X-DOI: 10.1080/02664760500079100 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500079100
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:6:p:617-624




Template-Type: ReDIF-Article 1.0
Author-Name: Christopher Illert 
Author-X-Name-First: Christopher 
Author-X-Name-Last: Illert 
Title: Origins of linguistic zonation in the Australian Alps. part 1 - Huygens' principle 
Abstract:
  The hitherto poorly recorded boundaries of extinct traditional
 south-east-Australian Aboriginal languages can now be redetermined with
 greatly improved precision using an entropy-maximizing phonetic-signature
 calculated from existing data sources, including old word-lists and census
 forms, that have, until now, largely been considered informationally
 worthless. Having thus determined traditional Aboriginal language zones to
 a previously unimaginable degree of geographical precision, it is argued
 that these boundaries should not be viewed merely as a static 'snapshot'
 but, instead, as the end-product of a knowable dynamic process (Gillieron
 wave propagation) governed by well-known physical rules (such as Huygens'
 principle and Snell's Law) and operating over 'deep' time-scales more
 familiar to the archaeologist than the linguist. Although this initial
 study is limited to south-eastern Australia, the new methodology provides
 the first real hope of obtaining a detailed understanding of language
 dispersal throughout the entire continent over the past 60,000 years. 
Journal: Journal of Applied Statistics 
Pages: 625-659 
Issue: 6 
Volume: 32 
Year: 2005 
Keywords: Lexical signature, deep linguistics, Gillieron wave propagation, Huygens', Principle, 
X-DOI: 10.1080/02664760500079258 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500079258
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:6:p:625-659




Template-Type: ReDIF-Article 1.0
Author-Name: Mariam Mahfouz 
Author-X-Name-First: Mariam 
Author-X-Name-Last: Mahfouz 
Author-Name: Pascale Giraudet 
Author-X-Name-First: Pascale 
Author-X-Name-Last: Giraudet 
Author-Name: Michel Chaput 
Author-X-Name-First: Michel 
Author-X-Name-Last: Chaput 
Title: Strategy for a statistical analysis of odour influence on the mammalian olfactory bulb responsiveness 
Abstract:
  This paper proposes a global strategy for statistical analysis of odour
 influence on the responsiveness of the mammalian olfactory bulb, the first
 relay of the olfactory pathway. Experiments were performed on 86 mitral
 cells recorded in 17 anaesthetized freely breathing rats. Five pure odours
 and their binary mixture were used. The spontaneous activity and
 odour-evoked responses of the cells were characterized by their temporal
 distribution of activity along the respiratory cycle, i.e. by
 cycle-triggered histograms. Several statistical analyses were performed to
 describe the influence of binary odour mixtures and, especially, to detect
 a possible dominance of one component of the mixture. 
Journal: Journal of Applied Statistics 
Pages: 661-679 
Issue: 6 
Volume: 32 
Year: 2005 
Keywords: Olfaction, odour responses, statistical analyses, temporal patterns, response comparison, 
X-DOI: 10.1080/02664760500079357 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500079357
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:6:p:661-679




Template-Type: ReDIF-Article 1.0
Author-Name: Saralees Nadarajah 
Author-X-Name-First: Saralees 
Author-X-Name-Last: Nadarajah 
Title: A generalized normal distribution 
Abstract:
  Undoubtedly, the normal distribution is the most popular distribution in
 statistics. In this paper, we introduce a natural generalization of the
 normal distribution and provide a comprehensive treatment of its
 mathematical properties. We derive expressions for the nth moment, the nth
 central moment, variance, skewness, kurtosis, mean deviation about the
 mean, mean deviation about the median, Renyi entropy, Shannon entropy, and
 the asymptotic distribution of the extreme order statistics. We also
 discuss estimation by the methods of moments and maximum likelihood and
 provide an expression for the Fisher information matrix. 
Journal: Journal of Applied Statistics 
Pages: 685-694 
Issue: 7 
Volume: 32 
Year: 2005 
Keywords: Estimation, entropy, generalized normal distribution, moments, normal distribution, order statistics, 
X-DOI: 10.1080/02664760500079464 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500079464
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:7:p:685-694




Template-Type: ReDIF-Article 1.0
Author-Name: Xia Pan 
Author-X-Name-First: Xia 
Author-X-Name-Last: Pan 
Title: An alternative approach to multivariate EWMA control chart 
Abstract:
  This Paper proposes a multivariate EWMA scheme that is alternative to the
 traditional EWMA-M. The distribution of the chart statistic is derived
 from Box quadratic form and the sensitivity of the chart is examined. The
 average run lengths of the M-EWMA scheme are numerically computed with the
 integral equation method. The exponential weight of 0.2 is found to be the
 optimal choice for the sensitive chart to detect assignable causes in the
 mean vector of processes. 
Journal: Journal of Applied Statistics 
Pages: 695-705 
Issue: 7 
Volume: 32 
Year: 2005 
Keywords: Quality control, multivariate EWMA chart, box quadratic form, ARL, integral equation for numerical analysis, 
X-DOI: 10.1080/02664760500079522 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500079522
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:7:p:695-705




Template-Type: ReDIF-Article 1.0
Author-Name: Norah Al-Ballaa 
Author-X-Name-First: Norah 
Author-X-Name-Last: Al-Ballaa 
Title: Test for cointegration based on two-stage least squares 
Abstract:
  A residual-based test for cointegration is proposed. The method of
 two-stage least squares is used to estimate the cointegration model
 parameters. The residuals are then tested for the existence of a unit root
 using the augmented Dickey-Fuller test. 
Journal: Journal of Applied Statistics 
Pages: 707-713 
Issue: 7 
Volume: 32 
Year: 2005 
Keywords: Single-equation approach, residual-based test, two-stage least squares, Monte Carlo, 
X-DOI: 10.1080/02664760500079571 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500079571
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:7:p:707-713




Template-Type: ReDIF-Article 1.0
Author-Name: M. E. Ghitany 
Author-X-Name-First: M. E. 
Author-X-Name-Last: Ghitany 
Author-Name: S. Kotz 
Author-X-Name-First: S. 
Author-X-Name-Last: Kotz 
Author-Name: M. Xie 
Author-X-Name-First: M. 
Author-X-Name-Last: Xie 
Title: On some reliability measures and their stochastic orderings for the Topp-Leone distribution 
Abstract:
  Topp-Leone distribution is a continuous unimodal distribution with
 bounded support (recently rediscovered) which is useful for modelling
 life-time phenomena. In this paper we study some reliability measures of
 this distribution such as the hazard rate, mean residual life, reversed
 hazard rate, expected inactivity time, and their stochastic orderings. 
Journal: Journal of Applied Statistics 
Pages: 715-722 
Issue: 7 
Volume: 32 
Year: 2005 
Keywords: Expected inactivity time, hazard rate, mean residual life, reversed hazard rate, stochastic orders, Topp-Leone distribution, 
X-DOI: 10.1080/02664760500079613 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500079613
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:7:p:715-722




Template-Type: ReDIF-Article 1.0
Author-Name: Filidor Labra 
Author-X-Name-First: Filidor 
Author-X-Name-Last: Labra 
Author-Name: Reiko Aoki 
Author-X-Name-First: Reiko 
Author-X-Name-Last: Aoki 
Author-Name: Heleno Bolfarine 
Author-X-Name-First: Heleno 
Author-X-Name-Last: Bolfarine 
Title: Local influence in null intercept measurement error regression under a student_t model 
Abstract:
  In this paper we discuss the application of local influence in a
 measurement error regression model with null intercepts under a Student_t
 model with dependent populations. The Student_t distribution is a robust
 alternative to modelling data sets involving errors with longer than
 Normal tails. We derive the appropriate matrices for assessing the local
 influence for different perturbation schemes and use real data as an
 illustration of the usefulness of the application. 
Journal: Journal of Applied Statistics 
Pages: 723-740 
Issue: 7 
Volume: 32 
Year: 2005 
Keywords: Influence diagnostic, student_t model, likelihood displacement, pretest/post-test data, measurement error models, 
X-DOI: 10.1080/02664760500079639 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500079639
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:7:p:723-740




Template-Type: ReDIF-Article 1.0
Author-Name: M. A. Alkhamisi 
Author-X-Name-First: M. A. 
Author-X-Name-Last: Alkhamisi 
Author-Name: Ghazi Shukur 
Author-X-Name-First: Ghazi 
Author-X-Name-Last: Shukur 
Title: Bayesian analysis of a linear mixed model with AR(p) errors via MCMC 
Abstract:
  We develop Bayesian procedures to make inference about parameters of a
 statistical design with autocorrelated error terms. Modelling treatment
 effects can be complex in the presence of other factors such as time; for
 example in longitudinal data. In this paper, Markov chain Monte Carlo
 methods (MCMC), the Metropolis-Hastings algorithm and Gibbs sampler are
 used to facilitate the Bayesian analysis of real life data when the error
 structure can be expressed as an autoregressive model of order p. We
 illustrate our analysis with real data. 
Journal: Journal of Applied Statistics 
Pages: 741-755 
Issue: 7 
Volume: 32 
Year: 2005 
Keywords: Linear mixed model, autoregressive process, Metropolis-Hastings algorithm, Gibbs sampling, Bayesian statistics, autocorrelation, repeated measurement designs, 
X-DOI: 10.1080/02664760500079688 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500079688
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:7:p:741-755




Template-Type: ReDIF-Article 1.0
Author-Name: Donna Mohr 
Author-X-Name-First: Donna 
Author-X-Name-Last: Mohr 
Title: Confidence limits for estimates of totals from stratified samples, with application to medicare Part B overpayment audits 
Abstract:
  Superpopulation models are proposed that should be appropriate for
 modelling sample-based audits of Medicare payments and other overpayment
 situations. Simulations are used to estimate the coverage probabilities of
 confidence intervals formed using the standard Stratified Expansion and
 Combined Ratio estimators of the total. Despite severe departures from the
 usual model of normal deviations, these methods have actual coverage
 probabilities reasonably close to the nominal level specified by the US
 government's sampling guidelines. An exception occurs when all claims from
 a single sampling unit are either completely allowed, or completely
 denied, and for this situation an alternative is explored. A balanced
 sampling design is also examined, but shown to make no improvement over
 ordinary stratified samples used in conjunction with ratio estimates. 
Journal: Journal of Applied Statistics 
Pages: 757-769 
Issue: 7 
Volume: 32 
Year: 2005 
Keywords: Stratified samples, ratio estimators stratified expansion estimators, coverage probability, audit, overpayment, 
X-DOI: 10.1080/02664760500079712 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500079712
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:7:p:757-769




Template-Type: ReDIF-Article 1.0
Author-Name: Emilio Gomez-deniz 
Author-X-Name-First: Emilio 
Author-X-Name-Last: Gomez-deniz 
Author-Name: Francisco Vazquez-polo 
Author-X-Name-First: Francisco 
Author-X-Name-Last: Vazquez-polo 
Title: Modelling uncertainty in insurance Bonus-Malus premium principles by using a Bayesian robustness approach 
Abstract:
  When Bayesian models are implemented for a Bonus-Malus System (BMS), a
 parametric structure, &#x3c0;0 (&#x3bb;), is normally included in the
 insurer's portfolio. Following Bayesian sensitivity analysis, it is
 possible to model the structure function by specifying a class &#x393; of
 priors instead of a single prior. This paper examines the ranges of the
 relativities of the form, [image omitted]&#xa0; Standard and robust
 Bayesian tools are combined to show how the choice of the prior can affect
 the relative premiums. As an extension of the paper by Gomez et al.
 (2002b), our model is developed to the variance premium principle and the
 class of prior densities extended to ones that are more realistic in an
 actuarial setting, i.e. classes of generalized moments conditions. The
 proposed method is illustrated with data from Lemaire (1979). The main aim
 of the paper is to demonstrate an appropriate methodology to perform a
 Bayesian sensitivity analysis of the Bonus-Malus of loaded premiums. 
Journal: Journal of Applied Statistics 
Pages: 771-784 
Issue: 7 
Volume: 32 
Year: 2005 
Keywords: Bonus-malus, Bayesian robustness, &#x3b5;-contamination class, generalized moments conditions, 
X-DOI: 10.1080/02664760500079746 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500079746
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:7:p:771-784




Template-Type: ReDIF-Article 1.0
Author-Name: Tsung-Shan Tsou 
Author-X-Name-First: Tsung-Shan 
Author-X-Name-Last: Tsou 
Title: Inferences of variance function - a parametric robust way 
Abstract:
  Tsou (2003a) proposed a parametric procedure for making robust inference
 for mean regression parameters in the context of generalized linear
 models. This robust procedure is extended to model variance heterogeneity.
 The normal working model is adjusted to become asymptotically robust for
 inference about regression parameters of the variance function for
 practically all continuous response variables. The connection between the
 novel robust variance regression model and the estimating equations
 approach is also provided. 
Journal: Journal of Applied Statistics 
Pages: 785-796 
Issue: 8 
Volume: 32 
Year: 2005 
Keywords: Generalized linear models, variance function, robust profile likelihood, normal regression, 
X-DOI: 10.1080/02664760500079803 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500079803
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:8:p:785-796




Template-Type: ReDIF-Article 1.0
Author-Name: Juan Miguel Marin 
Author-X-Name-First: Juan Miguel 
Author-X-Name-Last: Marin 
Author-Name: Lluis Pla 
Author-X-Name-First: Lluis 
Author-X-Name-Last: Pla 
Author-Name: David Rios-Insua 
Author-X-Name-First: David 
Author-X-Name-Last: Rios-Insua 
Title: Forecasting for some stochastic process models related to sow farm management 
Abstract:
  Sow farm management requires appropriate methods to forecast the sow
 population structure evolution. We describe two models for such purpose.
 The first is a semi-Markov process model, used for long-term predictions
 and strategic management. The second is a state-space model for continuous
 proportions, used for short-term predictions and operational management. 
Journal: Journal of Applied Statistics 
Pages: 797-812 
Issue: 8 
Volume: 32 
Year: 2005 
Keywords: Sow herd management, semi-Markov models, dynamic linear models, Bayesian inference and forecasting, 
X-DOI: 10.1080/02664760500079845 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500079845
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:8:p:797-812




Template-Type: ReDIF-Article 1.0
Author-Name: Yuehjen Shao 
Author-X-Name-First: Yuehjen 
Author-X-Name-Last: Shao 
Author-Name: John Fowler 
Author-X-Name-First: John 
Author-X-Name-Last: Fowler 
Author-Name: George Runger 
Author-X-Name-First: George 
Author-X-Name-Last: Runger 
Title: A note on determining an optimal target by considering the dependence of holding costs and the quality characteristics 
Abstract:
  Products that do not meet the specification criteria of an intended buyer
 represent a challenge to the producer in maximizing profits. To understand
 the value of the optimal process target (OPT) set at a profit-maximizing
 level, a model was developed by Shao et al. (1999) involving multiple
 markets and finished products having holding costs independent from their
 quality. Investigation in cases considered previously has involved holding
 costs as a fixed amount or as a normal random variable independent of the
 quality characteristic (QC) of the product. Less specific in nature, this
 study considers more general cases in which the HC can be a truncated
 normal random variable, which is dependent on the QC of the product. 
Journal: Journal of Applied Statistics 
Pages: 813-822 
Issue: 8 
Volume: 32 
Year: 2005 
Keywords: Optimal process target, dependence, profit function, quality characteristics, 
X-DOI: 10.1080/02664760500080066 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500080066
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:8:p:813-822




Template-Type: ReDIF-Article 1.0
Author-Name: Arturo Fernandez 
Author-X-Name-First: Arturo 
Author-X-Name-Last: Fernandez 
Title: Progressively censored variables sampling plans for two-parameter exponential distributions 
Abstract:
  Progressive censoring is quite useful in many practical situations where
 budget constraints are in place or there is a demand for rapid testing.
 Balasooriya & Saw (1998) present reliability sampling plans for the
 two-parameter exponential distribution, based on progressively censored
 samples. However, the operating characteristic (OC) curve derived in their
 paper does not depend on the sample size. This seems to be because, in
 their computations, they forget to consider the proportion of uncensored
 data, which also has an important influence on the subsequent
 developments. In consequence, their OC curve is only valid when there is
 no censoring. In this paper, some modifications are proposed. These are
 needed to obtain a proper design of the above sampling plan. Whenever at
 least two uncensored observations are available, the OC curve is derived
 in closed form and a procedure for determining progressively censored
 reliability sampling plans is also presented. Finally, the example
 considered by Balasooriya & Saw is used to illustrate the results
 developed in this paper for several censoring levels. 
Journal: Journal of Applied Statistics 
Pages: 823-829 
Issue: 8 
Volume: 32 
Year: 2005 
Keywords: Reliability sampling plans, operating characteristic curve, acceptable and rejectable quality levels, 
X-DOI: 10.1080/02664760500080074 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500080074
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:8:p:823-829




Template-Type: ReDIF-Article 1.0
Author-Name: C. Mante 
Author-X-Name-First: C. 
Author-X-Name-Last: Mante 
Author-Name: J. P. Durbec 
Author-X-Name-First: J. P. 
Author-X-Name-Last: Durbec 
Author-Name: J. C. Dauvin 
Author-X-Name-First: J. C. 
Author-X-Name-Last: Dauvin 
Title: A functional data-analytic approach to the classification of species according to their spatial dispersion. Application to a marine macrobenthic community from the Bay of Morlaix (Western English Channel) 
Abstract:
  We investigate with multivariate methods the behaviour of species
 collected in a sequence of ecological surveys. The behaviour of the sth
 species is first characterized by a classical dispersion index. Under the
 hypothesis (H) of spatial randomness, the probability distribution vs of
 this index obeys a reference law &#x3bc;. All the sampled species are then
 compared through a Principal Components Analysis whose metric structure
 depends on &#x3bc;. More precisely, the distance between two species s and
 s' is an approximation of the &#x3bc;-centred chi-square distance
 (Benzecri 1976) between vs and vs'. Thus, while Correspondence Analysis
 displays departures from independence or homogeneity, the proposed
 analysis displays departures of the species from (H). As an application, a
 macrobenthic data time series is analysed, and the obtained species
 typology is described and discussed. The method enabled us to separate
 rare species from random ones while rare species could easily be confused
 with random ones. All the aggregated species were common (or even
 dominant), and most random ones were moderately abundant. Finally, a group
 of 23 species showed a mixed random-aggregated behaviour. The repulsive
 (uniform) behaviour was extremely rare. 
Journal: Journal of Applied Statistics 
Pages: 831-840 
Issue: 8 
Volume: 32 
Year: 2005 
Keywords: Index of dispersion, quadrat method, principal components analysis, density approximation, marine ecology, 
X-DOI: 10.1080/02664760500080124 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500080124
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:8:p:831-840




Template-Type: ReDIF-Article 1.0
Author-Name: Yoon Young Jung 
Author-X-Name-First: Yoon Young 
Author-X-Name-Last: Jung 
Author-Name: Dong Wan Shin 
Author-X-Name-First: Dong Wan 
Author-X-Name-Last: Shin 
Author-Name: Man-Suk Oh 
Author-X-Name-First: Man-Suk 
Author-X-Name-Last: Oh 
Title: Bayesian analysis of panel data using an MTAR model 
Abstract:
  Bayesian analysis of panel data using a class of momentum threshold
 autoregressive (MTAR) models is considered. Posterior estimation of
 parameters of the MTAR models is done by using a simple Markov Chain Monte
 Carlo (MCMC) algorithm. Selection of appropriate differenced variables,
 test for asymmetry and unit roots are recast as model selections and a
 simple way of computing posterior probabilities of the candidate models is
 proposed. The proposed method is applied to the yearly unemployment rates
 of 51 US states and the results show strong evidence of stationarity and
 asymmetry. 
Journal: Journal of Applied Statistics 
Pages: 841-854 
Issue: 8 
Volume: 32 
Year: 2005 
Keywords: MTAR, panel data, MCMC, model selection, 
X-DOI: 10.1080/02664760500080132 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500080132
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:8:p:841-854




Template-Type: ReDIF-Article 1.0
Author-Name: Dejian Lai 
Author-X-Name-First: Dejian 
Author-X-Name-Last: Lai 
Author-Name: Shyang-Yun Pamela Shiao 
Author-X-Name-First: Shyang-Yun Pamela 
Author-X-Name-Last: Shiao 
Title: Comparing two clinical measurements: a linear mixed model approach 
Abstract:
  In this article, we extended the widely used Bland-Altman graphical
 technique of comparing two measurements in clinical studies to include an
 analytical approach using a linear mixed model. The proposed statistical
 inferences can be conducted easily by commercially available statistical
 software such as SAS. The linear mixed model approach was illustrated
 using a real example in a clinical nursing study of oxygen saturation
 measurements, when functional oxygen saturation was compared against
 fractional oxy-hemoglobin. 
Journal: Journal of Applied Statistics 
Pages: 855-860 
Issue: 8 
Volume: 32 
Year: 2005 
Keywords: Accuracy, Bland-Altman method, linear mixed model, oxygen saturation, 
X-DOI: 10.1080/02664760500080157 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500080157
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:8:p:855-860




Template-Type: ReDIF-Article 1.0
Author-Name: Joseph Brian Adams 
Author-X-Name-First: Joseph Brian 
Author-X-Name-Last: Adams 
Author-Name: Yijin Wert 
Author-X-Name-First: Yijin 
Author-X-Name-Last: Wert 
Title: Logistic and neural network models for predicting a hospital admission 
Abstract:
  Feedforward neural networks are often used in a similar manner as
 logistic regression models; that is, to estimate the probability of the
 occurrence of an event. In this paper, a probabilistic model is developed
 for the purpose of estimating the probability that a patient who has been
 admitted to the hospital with a medical back diagnosis will be released
 after only a short stay or will remain hospitalized for a longer period of
 time. As the purpose of the analysis is to determine if hospital
 characteristics influence the decision to retain a patient, the inputs to
 this model are a set of demographic variables that describe the various
 hospitals. The output is the probability of either a short or long term
 hospital stay. In order to compare the ability of each method to model the
 data, a hypothesis test is performed to test for an improvement resulting
 from the use of the neural network model. 
Journal: Journal of Applied Statistics 
Pages: 861-869 
Issue: 8 
Volume: 32 
Year: 2005 
Keywords: Neural networks, logistic regression, prediction, hospital admissions, medical informatics, 
X-DOI: 10.1080/02664760500080207 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500080207
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:8:p:861-869




Template-Type: ReDIF-Article 1.0
Author-Name: Duolao Wang 
Author-X-Name-First: Duolao 
Author-X-Name-Last: Wang 
Author-Name: Pengjun Lu 
Author-X-Name-First: Pengjun 
Author-X-Name-Last: Lu 
Title: Modelling and forecasting mortality distributions in England and Wales using the Lee-Carter model 
Abstract:
  Lee and Carter proposed in 1992 a non-linear model mxt = exp (ax + bx kt
 + &#x3b5;xt) for fitting and forecasting age-specific mortality rates at
 age x and time t. For the model parameter estimation, they employed the
 singular value decomposition method to find a least squares solution.
 However, the singular value decomposition algorithm does not provide the
 standard errors of estimated parameters, making it impossible to assess
 the accuracy of model parameters. This article describes the Lee-Carter
 model and the technical procedures to fit and extrapolate this model. To
 estimate the precision of the parameter estimates of the Lee-Carter model,
 we propose a binomial framework, whose parameter point estimates can be
 obtained by the maximum likelihood approach and interval estimates by a
 bootstrap approach. This model is used to fit mortality data in England
 and Wales from 1951 to 1990 and to forecast mortality change from 1991 to
 2020. The Lee-Carter model fits these mortality data very well with R2
 being 0.9980. The estimated overall age pattern of mortality ax is very
 robust whereas there is considerable uncertainty in bx (changes in the age
 pattern over time) and kt (overall change in mortality). The fitted log
 age-specific mortality rates have been declining linearly from 1951 to
 1990 at different paces and the projected rates will continue to decline
 in such a way in the 30 years prediction period. 
Journal: Journal of Applied Statistics 
Pages: 873-885 
Issue: 9 
Volume: 32 
Year: 2005 
Keywords: Lee-Carter model, single value decomposition, binomial distribution, bootstrap, mortality forecasting, 
X-DOI: 10.1080/02664760500163441 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500163441
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:9:p:873-885




Template-Type: ReDIF-Article 1.0
Author-Name: Andre Khuri 
Author-X-Name-First: Andre 
Author-X-Name-Last: Khuri 
Title: Slack-variable models versus Scheffe's mixture models 
Abstract:
  Slack-variable models are compared against Scheffe's polynomial model for
 mixture experiments. The notion of model equivalence and the use of
 various diagnostic measures provide effective tools in making such
 comparisons, particularly when the experimental region is highly
 constrained. It is demonstrated that the choice of the best fitting model,
 through variable selection, depends on which mixture component is selected
 as a slack variable, and on the size of the fitted model. In addition, the
 equivalence of two well-known representations of a complete mixture model
 is shown to be valid. Two numerical examples are presented. 
Journal: Journal of Applied Statistics 
Pages: 887-908 
Issue: 9 
Volume: 32 
Year: 2005 
Keywords: Collinearity, column space, condition number, constrained mixture region, mixture components, model equivalence, L-pseudocomponents, variable selection, variance-decomposition proportions, variance inflation factors, well-formulated model, 
X-DOI: 10.1080/02664760500163466 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500163466
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:9:p:887-908




Template-Type: ReDIF-Article 1.0
Author-Name: Yoeng-Kuan Chang 
Author-X-Name-First: Yoeng-Kuan 
Author-X-Name-Last: Chang 
Author-Name: Deng-Yuan Huang 
Author-X-Name-First: Deng-Yuan 
Author-X-Name-Last: Huang 
Title: On some robust estimation procedures for quantiles based on data 
Abstract:
  This paper introduces some robust estimation procedures to estimate
 quantiles of a continuous random variable based on data, without any other
 assumptions of probability distribution. We construct a reasonable linear
 regression model to connect the relationship between a suitable symmetric
 data transformation and the approximate standard normal statistics.
 Statistical properties of this linear regression model and its
 applications are studied, including estimators of quantiles, quartile
 mean, quartile deviation, correlation coefficient of quantiles and
 standard errors of these estimators. We give some empirical examples to
 illustrate the statistical properties and apply our estimators to grouping
 data. 
Journal: Journal of Applied Statistics 
Pages: 909-927 
Issue: 9 
Volume: 32 
Year: 2005 
Keywords: Quantile estimation, symmetric data transformations, quartile mean, quartile deviation, correlation coefficient of quantiles grouping data, 
X-DOI: 10.1080/02664760500163532 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500163532
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:9:p:909-927




Template-Type: ReDIF-Article 1.0
Author-Name: A. H. M. Rahmatullah Imon 
Author-X-Name-First: A. H. M. Rahmatullah 
Author-X-Name-Last: Imon 
Title: Identifying multiple influential observations in linear regression 
Abstract:
  The identification of influential observations has drawn a great deal of
 attention in regression diagnostics. Most of these identification
 techniques are based on single case deletion and among them DFFITS has
 become very popular with the statisticians. But this technique along with
 all other single case diagnostics may be ineffective in the presence of
 multiple influential observations. In this paper we develop a generalized
 version of DFFITS based on group deletion and then propose a new technique
 to identify multiple influential observations using this. The advantage of
 using the proposed method in the identification of multiple influential
 cases is then investigated through several well-referred data sets. 
Journal: Journal of Applied Statistics 
Pages: 929-946 
Issue: 9 
Volume: 32 
Year: 2005 
Keywords: Influential observations, high leverage points, outliers, masking, swamping, group deletion, generalized Cook's distance, generalized DFFITS, index plot, 
X-DOI: 10.1080/02664760500163599 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500163599
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:9:p:929-946




Template-Type: ReDIF-Article 1.0
Author-Name: Jacques Benasseni 
Author-X-Name-First: Jacques 
Author-X-Name-Last: Benasseni 
Title: A concentration study of principal components 
Abstract:
  Influence functions are commonly used as diagnostic tools in order to
 investigate sensitivity aspects in principal component analysis. This
 paper suggests a practical alternative for the eigenvalues by introducing
 a sensitivity measure derived from the classical Lorenz curve and
 associated Gini index. The results are illustrated by analysing an
 example. 
Journal: Journal of Applied Statistics 
Pages: 947-957 
Issue: 9 
Volume: 32 
Year: 2005 
Keywords: Gini index of concentration, influence function, Lorenz curve, principal component analysis, sensitivity, 
X-DOI: 10.1080/02664760500163664 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500163664
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:9:p:947-957




Template-Type: ReDIF-Article 1.0
Author-Name: Edward Bedrick 
Author-X-Name-First: Edward 
Author-X-Name-Last: Bedrick 
Title: Graphical modelling and the Mahalanobis distance 
Abstract:
  I consider the problem of estimating the Mahalanobis distance between
 multivariate normal populations when the population covariance matrix
 satisfies a graphical model. In addition to providing a clear
 understanding of the dependencies in a multivariate data set, the use of
 graphical models can reduce the variability of the estimated distances and
 improve inferences. I derive the asymptotic distribution of the estimated
 Mahalanobis distance under a general covariance model, which includes
 graphical models as a special case. Two examples are discussed. 
Journal: Journal of Applied Statistics 
Pages: 959-967 
Issue: 9 
Volume: 32 
Year: 2005 
Keywords: Discriminant analysis, distance between populations, 
X-DOI: 10.1080/02664760500163680 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500163680
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:9:p:959-967




Template-Type: ReDIF-Article 1.0
Author-Name: Hugh Chipman 
Author-X-Name-First: Hugh 
Author-X-Name-Last: Chipman 
Author-Name: Hong Gu 
Author-X-Name-First: Hong 
Author-X-Name-Last: Gu 
Title: Interpretable dimension reduction 
Abstract:
  The analysis of high-dimensional data often begins with the
 identification of lower dimensional subspaces. Principal component
 analysis is a dimension reduction technique that identifies linear
 combinations of variables along which most variation occurs or which best
 &#x201c;reconstruct&#x201d; the original variables. For example, many
 temperature readings may be taken in a production process when in fact
 there are just a few underlying variables driving the process. A problem
 with principal components is that the linear combinations can seem quite
 arbitrary. To make them more interpretable, we introduce two classes of
 constraints. In the first, coefficients are constrained to equal a small
 number of values (homogeneity constraint). The second constraint attempts
 to set as many coefficients to zero as possible (sparsity constraint). The
 resultant interpretable directions are either calculated to be close to
 the original principal component directions, or calculated in a stepwise
 manner that may make the components more orthogonal. A small dataset on
 characteristics of cars is used to introduce the techniques. A more
 substantial data mining application is also given, illustrating the
 ability of the procedure to scale to a very large number of variables. 
Journal: Journal of Applied Statistics 
Pages: 969-987 
Issue: 9 
Volume: 32 
Year: 2005 
Keywords: Principal component, interpretable, homogeneity, sparsity, stepwise algorithm, dimension reduction, data mining, 
X-DOI: 10.1080/02664760500168648 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500168648
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:9:p:969-987




Template-Type: ReDIF-Article 1.0
Author-Name: Gabriel Nunez-Antonio 
Author-X-Name-First: Gabriel 
Author-X-Name-Last: Nunez-Antonio 
Author-Name: Eduardo Gutierrez-Pena 
Author-X-Name-First: Eduardo 
Author-X-Name-Last: Gutierrez-Pena 
Title: A Bayesian analysis of directional data using the projected normal distribution 
Abstract:
  This paper presents a Bayesian analysis of the projected normal
 distribution, which is a flexible and useful distribution for the analysis
 of directional data. We obtain samples from the posterior distribution
 using the Gibbs sampler after the introduction of suitably chosen latent
 variables. The procedure is illustrated using simulated data as well as a
 real data set previously analysed in the literature. 
Journal: Journal of Applied Statistics 
Pages: 995-1001 
Issue: 10 
Volume: 32 
Year: 2005 
Keywords: Circular data, Gibbs sampler, latent variables, radial projection, spherical data, 
X-DOI: 10.1080/02664760500164886 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500164886
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:10:p:995-1001




Template-Type: ReDIF-Article 1.0
Author-Name: W. L. Pearn 
Author-X-Name-First: W. L. 
Author-X-Name-Last: Pearn 
Author-Name: M. H. Shu 
Author-X-Name-First: M. H. 
Author-X-Name-Last: Shu 
Author-Name: B. M. Hsu 
Author-X-Name-First: B. M. 
Author-X-Name-Last: Hsu 
Title: Testing process capability based on Cpm in the presence of random measurement errors 
Abstract:
  Process capability indices have been widely used in the manufacturing
 industry providing numerical measures on process performance. The index Cp
 provides measures on process precision (or product consistency). The index
 Cpm, sometimes called the Taguchi index, meditates on process centring
 ability and process loss. Most research work related to Cp and Cpm assumes
 no gauge measurement errors. This assumption insufficiently reflects real
 situations even with highly advanced measuring instruments. Conclusions
 drawn from process capability analysis are therefore unreliable and
 misleading. In this paper, we conduct sensitivity investigation on process
 capability Cp and Cpm in the presence of gauge measurement errors. Due to
 the randomness of variations in the data, we consider capability testing
 for Cp and Cpm to obtain lower confidence bounds and critical values for
 true process capability when gauge measurement errors are unavoidable. The
 results show that the estimator with sample data contaminated by the
 measurement errors severely underestimates the true capability, resulting
 in imperceptible smaller test power. To obtain the true process
 capability, adjusted confidence bounds and critical values are presented
 to practitioners for their factory applications. 
Journal: Journal of Applied Statistics 
Pages: 1003-1024 
Issue: 10 
Volume: 32 
Year: 2005 
Keywords: Gauge measurement error, lower confidence bound, critical value, process capability analysis, 
X-DOI: 10.1080/02664760500164951 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500164951
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:10:p:1003-1024




Template-Type: ReDIF-Article 1.0
Author-Name: M. E. Ghitany 
Author-X-Name-First: M. E. 
Author-X-Name-Last: Ghitany 
Author-Name: E. K. Al-Hussaini 
Author-X-Name-First: E. K. 
Author-X-Name-Last: Al-Hussaini 
Author-Name: R. A. Al-Jarallah 
Author-X-Name-First: R. A. 
Author-X-Name-Last: Al-Jarallah 
Title: Marshall-Olkin extended weibull distribution and its application to censored data 
Abstract:
  In this paper we show that the Marshall-Olkin extended Weibull
 distribution can be obtained as a compound distribution with mixing
 exponential distribution. In addition, we provide simple sufficient
 conditions for the shape of the hazard rate function of the distribution.
 Moreover, we extend the considered distribution to accommodate randomly
 right censored data. Finally, application of the extended distribution to
 a data set representing the remission times of bladder cancer patients is
 given and its goodness-of-fit is demonstrated. 
Journal: Journal of Applied Statistics 
Pages: 1025-1034 
Issue: 10 
Volume: 32 
Year: 2005 
Keywords: Akiake information criterion, Bayesian information criterion, censored data, compound distribution, hazard rate, likelihood ratio test, maximum likelihood, Weibull distribution, 
X-DOI: 10.1080/02664760500165008 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500165008
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:10:p:1025-1034




Template-Type: ReDIF-Article 1.0
Author-Name: Marc Callens 
Author-X-Name-First: Marc 
Author-X-Name-Last: Callens 
Author-Name: Christophe Croux 
Author-X-Name-First: Christophe 
Author-X-Name-Last: Croux 
Title: The impact of education on third births. A multilevel discrete-time hazard analysis 
Abstract:
  We propose to use multilevel discrete-time hazard models to assess the
 impact of societal and individual level covariates on the timing and
 occurrence of third births. We focus mainly on the impact of educational
 attainment on third births across 15 European countries. From the analysis
 in this paper, the effect of education on the propensity to have a third
 child is found to be negative. This education effect is not significantly
 weakened by the Nordic countries, but living in Scandinavia does increase
 the hazard for a third birth. 
Journal: Journal of Applied Statistics 
Pages: 1035-1050 
Issue: 10 
Volume: 32 
Year: 2005 
Keywords: Multilevel analysis, discrete-time hazard analysis, multilevel hazard analysis, life course events, 
X-DOI: 10.1080/02664760500165040 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500165040
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:10:p:1035-1050




Template-Type: ReDIF-Article 1.0
Author-Name: Bi-super-&#x2d9;rdal Seno&#x11f;lu 
Author-X-Name-First: Bi-super-&#x2d9;rdal 
Author-X-Name-Last: Seno&#x11f;lu 
Title: Robust 2k factorial design with Weibull error distributions 
Abstract:
  It is well known that the least squares method is optimal only if the
 error distributions are normally distributed. However, in practice,
 non-normal distributions are more prevalent. If the error terms have a
 non-normal distribution, then the efficiency of least squares estimates
 and tests is very low. In this paper, we consider the 2k factorial design
 when the distribution of error terms are Weibull W(p,&#x3c3;). From the
 methodology of modified likelihood, we develop robust and efficient
 estimators for the parameters in 2k factorial design. F statistics based
 on modified maximum likelihood estimators (MMLE) for testing the main
 effects and interaction are defined. They are shown to have high powers
 and better robustness properties as compared to the normal theory
 solutions. A real data set is analysed. 
Journal: Journal of Applied Statistics 
Pages: 1051-1066 
Issue: 10 
Volume: 32 
Year: 2005 
Keywords: Least squares, modified maximum likelihood, robustness, experimental design, Weibull distribution, 
X-DOI: 10.1080/02664760500165099 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500165099
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:10:p:1051-1066




Template-Type: ReDIF-Article 1.0
Author-Name: D. T. Shirke 
Author-X-Name-First: D. T. 
Author-X-Name-Last: Shirke 
Author-Name: R. R. Kumbhar 
Author-X-Name-First: R. R. 
Author-X-Name-Last: Kumbhar 
Author-Name: D. Kundu 
Author-X-Name-First: D. 
Author-X-Name-Last: Kundu 
Title: Tolerance intervals for exponentiated scale family of distributions 
Abstract:
  In this article we provide an asymptotic upper &#x3b2;-expectation and
 &#x3b2;-content &#x3b3;-level tolerance intervals for a new family of
 distributions, namely the Exponentiated Scale family of distributions.
 Expected coverage of a proposed &#x3b2;-expectation Tolerance Interval is
 obtained. Bootstrap-based tolerance limits are obtained for data arising
 from an exponentiated exponential distribution. 
Journal: Journal of Applied Statistics 
Pages: 1067-1074 
Issue: 10 
Volume: 32 
Year: 2005 
Keywords: <i>&#x3b2;</i>-expectation tolerance interval, <i>&#x3b2;</i>-content <i>&#x3b3;</i>-level tolerance interval, expected coverage, exponentiated scale family, exponentiated exponential distribution, 
X-DOI: 10.1080/02664760500165297 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500165297
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:10:p:1067-1074




Template-Type: ReDIF-Article 1.0
Author-Name: J. Lopez-Fidalgo 
Author-X-Name-First: J. 
Author-X-Name-Last: Lopez-Fidalgo 
Author-Name: J. M. Rodriguez-Diaz 
Author-X-Name-First: J. M. 
Author-X-Name-Last: Rodriguez-Diaz 
Author-Name: G. Sanchez 
Author-X-Name-First: G. 
Author-X-Name-Last: Sanchez 
Author-Name: M. T. Santos-Martin 
Author-X-Name-First: M. T. 
Author-X-Name-Last: Santos-Martin 
Title: Optimal designs for compartmental models with correlated observations 
Abstract:
  The flow of internally deposited radioisotope particles inside the body
 of people exposed to inhalation, ingestion, injection or other ways is
 usually evaluated using compartmental models (see Sanchez & Lopez-Fidalgo,
 (2003, and Lopez-Fidalgo & Sanchez, 2005). The International Commission on
 Radiological Protection (ICRP, 1994) describes the model of the human
 respiratory tract, represented by two main regions. One of these, the
 thoracic region (lungs) is divided into different compartments. The
 retention in the lungs is given by a large combination of ratios of
 exponential sums depending on time. The aim of this work is to provide
 optimal times for making bioassays when there has been an accidental
 radioactivity intake and there is interest in estimating it. In this
 paper, a large two-parameter model is studied and a simplified model is
 proposed in order to obtain optimal designs in a more suitable way. Local
 c-optimal designs for the main parameters are obtained using the results
 of Lopez-Fidalgo & Rodriguez-Diaz, 2004). Efficiencies for all the
 computed designs are provided and compared. 
Journal: Journal of Applied Statistics 
Pages: 1075-1088 
Issue: 10 
Volume: 32 
Year: 2005 
Keywords: Bioassays, biokinetic models, design efficiencies, initial deposition factors, radioactivity retention, 
X-DOI: 10.1080/02664760500165313 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500165313
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:10:p:1075-1088




Template-Type: ReDIF-Article 1.0
Author-Name: Douglas Bonett 
Author-X-Name-First: Douglas 
Author-X-Name-Last: Bonett 
Title: Robust confidence interval for a residual standard deviation 
Abstract:
  The residual standard deviation of a general linear model provides
 information about predictive accuracy that is not revealed by the multiple
 correlation or regression coefficients. The classic confidence interval
 for a residual standard deviation is hypersensitive to minor violations of
 the normality assumption and its robustness does not improve with
 increasing sample size. An approximate confidence interval for the
 residual standard deviation is proposed and shown to be robust to moderate
 violations of the normality assumption with robustness to extreme
 non-normality that improves with increasing sample size. 
Journal: Journal of Applied Statistics 
Pages: 1089-1094 
Issue: 10 
Volume: 32 
Year: 2005 
Keywords: Dispersion, regression, model fit, 
X-DOI: 10.1080/02664760500165339 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500165339
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:32:y:2005:i:10:p:1089-1094




Template-Type: ReDIF-Article 1.0
Author-Name: Patrick Bourke 
Author-X-Name-First: Patrick 
Author-X-Name-Last: Bourke 
Title: The RL2 chart versus the np chart for detecting upward shifts in fraction defective 
Abstract:
  The use of the np chart for monitoring fraction-defective is
 well-established, but there are a number of relatively simple alternatives
 based on run-lengths of conforming items. Here, the RL2 chart, based on
 the moving sum of two successive conforming run-lengths, is investigated
 in order to provide SPC practitioners with clear-cut guidance on the
 comparative performance of these competing charts. Both sampling
 inspection and 100% inspection are considered here, and it is shown that
 the RL2 chart can often be considerably more efficient than the np chart,
 but the comparative performance depends on the false-alarm rate used for
 the comparison. Graphs to aid parameter-choice for the RL2 chart are also
 provided. 
Journal: Journal of Applied Statistics 
Pages: 1-15 
Issue: 1 
Volume: 33 
Year: 2006 
Keywords: Conforming run-length, control chart, statistical process control, 
X-DOI: 10.1080/02664760500389400 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500389400
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:1:p:1-15




Template-Type: ReDIF-Article 1.0
Author-Name: Yoshio Komori 
Author-X-Name-First: Yoshio 
Author-X-Name-Last: Komori 
Title: Properties of the Weibull cumulative exposure model 
Abstract:
  This article is aimed at the investigation of some properties of the
 Weibull cumulative exposure model on multiple-step step-stress accelerated
 life test data. Although the model includes a probabilistic idea of
 Miner's rule in order to express the effect of cumulative damage in
 fatigue, our result shows that the application of only this is not
 sufficient to express degradation of specimens and the shape parameter
 must be larger than 1. For a random variable obeying the model, its
 average and standard deviation are investigated on a various sets of
 parameter values. In addition, a way of checking the validity of the model
 is illustrated through an example of the maximum likelihood estimation on
 an actual data set, which is about time to breakdown of cross-linked
 polyethylene-insulated cables. 
Journal: Journal of Applied Statistics 
Pages: 17-34 
Issue: 1 
Volume: 33 
Year: 2006 
Keywords: Residual lifetime estimation, step-stress accelerated life test, maximum likelihood estimation, 
X-DOI: 10.1080/02664760500389475 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500389475
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:1:p:17-34




Template-Type: ReDIF-Article 1.0
Author-Name: Daniel Hunt 
Author-X-Name-First: Daniel 
Author-X-Name-Last: Hunt 
Author-Name: Dale Bowman 
Author-X-Name-First: Dale 
Author-X-Name-Last: Bowman 
Title: Modeling developmental data using U-shaped threshold dose-response curves 
Abstract:
  This paper develops threshold models for developmental toxicity data. The
 distinguishing feature of these threshold models is their flexibility in
 modeling data below threshold with a U-shaped function if the data
 warrants. The method is applied to actual data from a developmental study
 which exhibits U-shaped behavior in early dose groups. Results from a
 simulation study demonstrate the flexibility of the threshold model to
 pick up on U-shaped trends in the data. In addition, the simulation study
 reveals important considerations in design of developmental studies. 
Journal: Journal of Applied Statistics 
Pages: 35-47 
Issue: 1 
Volume: 33 
Year: 2006 
Keywords: Beta-binomial, dose-response curves, threshold models, U-shaped models, 
X-DOI: 10.1080/02664760500389525 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500389525
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:1:p:35-47




Template-Type: ReDIF-Article 1.0
Author-Name: Lee-Ing Tong 
Author-X-Name-First: Lee-Ing 
Author-X-Name-Last: Tong 
Author-Name: Chien-Hui Yang 
Author-X-Name-First: Chien-Hui 
Author-X-Name-Last: Yang 
Title: Analyzing type II censored data obtained from repetitious experiments 
Abstract:
  Experimental design and Taguchi's parameter design are widely employed by
 industry to optimize the process/product. However, censored data are often
 observed in product lifetime testing during the experiments. After
 implementing a repetitious experiment with type II censored data, the
 censored data are usually estimated by establishing a complex statistical
 model. However, using the incomplete data to fit a model may not
 accurately estimates the censored data. Moreover, the model fitting
 process is complicated for a practitioner who has only limited statistical
 training. This study proposes a less complex approach to analyze censored
 data, using the least square estimation method and Torres's analysis of
 unreplicated factorials with possible abnormalities. This study also
 presents an effective method to analyze the censored data from Taguchi's
 parameter design using least square estimation method. Finally, examples
 are given to illustrate the effectiveness of the proposed methods. 
Journal: Journal of Applied Statistics 
Pages: 49-63 
Issue: 1 
Volume: 33 
Year: 2006 
Keywords: Type II censored data, least square estimation, Torres's method, experimental design, Taguchi's parameter design, 
X-DOI: 10.1080/02664760500389673 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500389673
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:1:p:49-63




Template-Type: ReDIF-Article 1.0
Author-Name: Li-Ping Zhu 
Author-X-Name-First: Li-Ping 
Author-X-Name-Last: Zhu 
Author-Name: Li-Xing Zhu 
Author-X-Name-First: Li-Xing 
Author-X-Name-Last: Zhu 
Author-Name: Shi-Song Mao 
Author-X-Name-First: Shi-Song 
Author-X-Name-Last: Mao 
Title: A non-iterative approach to estimating parameters in a linear structural equation model 
Abstract:
  The research described herein was motivated by a study of the
 relationship between the performance of students in senior high schools
 and at universities in China. A special linear structural equation model
 is established, in which some parameters are known and both the responses
 and the covariables are measured with errors. To explore the relationship
 between the true responses and latent covariables and to estimate the
 parameters, we suggest a non-iterative estimation approach that can
 account for the external dependence between the true responses and latent
 covariables. This approach can also deal with the collinearity problem
 because the use of dimension-reduction techniques can remove redundant
 variables. Combining further with the information that some of parameters
 are given, we can perform estimation for the other unknown parameters. An
 easily implemented algorithm is provided. A simulation is carried out to
 provide evidence of the performance of the approach and to compare it with
 existing methods. The approach is applied to the education example for
 illustration, and it can be readily extended to more general models. 
Journal: Journal of Applied Statistics 
Pages: 65-78 
Issue: 1 
Volume: 33 
Year: 2006 
Keywords: Linear structural equation model, collinearity, canonical correlation analysis, partial least squares, 
X-DOI: 10.1080/02664760500389723 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500389723
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:1:p:65-78




Template-Type: ReDIF-Article 1.0
Author-Name: Markus Neuhauser 
Author-X-Name-First: Markus 
Author-X-Name-Last: Neuhauser 
Title: An exact test for trend among binomial proportions based on a modified Baumgartner-Weiss-Schindler statistic 
Abstract:
  The Cochran-Armitage test is the most frequently used test for trend
 among binomial proportions. This test can be performed based on the
 asymptotic normality of its test statistic or based on an exact null
 distribution. As an alternative, a recently introduced modification of the
 Baumgartner-Weiss-Schindler statistic, a novel nonparametric statistic,
 can be used. Simulation results indicate that the exact test based on this
 modification is preferable to the Cochran-Armitage test. This exact test
 is less conservative and more powerful than the exact Cochran-Armitage
 test. The power comparison to the asymptotic Cochran-Armitage test does
 not show a clear winner, but the difference in power is usually small. The
 exact test based on the modification is recommended here because, in
 contrast to the asymptotic Cochran-Armitage test, it guarantees a type I
 error rate less than or equal to the significance level. Moreover, an
 exact test is often more appropriate than an asymptotic test because
 randomization rather than random sampling is the norm, for example in
 biomedical research. The methods are illustrated with an example data set. 
Journal: Journal of Applied Statistics 
Pages: 79-88 
Issue: 1 
Volume: 33 
Year: 2006 
Keywords: Binomial data, Cochran-Armitage test, exact conditional test, randomization model, 
X-DOI: 10.1080/02664760500389756 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500389756
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:1:p:79-88




Template-Type: ReDIF-Article 1.0
Author-Name: Enrique De Alba 
Author-X-Name-First: Enrique 
Author-X-Name-Last: De Alba 
Author-Name: Juan Fernandez-Duran 
Author-X-Name-First: Juan 
Author-X-Name-Last: Fernandez-Duran 
Author-Name: M. Mercedes Gregorio-Dominguez 
Author-X-Name-First: M. Mercedes 
Author-X-Name-Last: Gregorio-Dominguez 
Title: Bayesian inference for the mean and standard deviation of a normal population when only the sample size, mean and range are observed 
Abstract:
  Consider a random sample X1, X2,&#x2026;, Xn, from a normal population
 with unknown mean and standard deviation. Only the sample size, mean and
 range are recorded and it is necessary to estimate the unknown population
 mean and standard deviation. In this paper the estimation of the mean and
 standard deviation is made from a Bayesian perspective by using a Markov
 Chain Monte Carlo (MCMC) algorithm to simulate samples from the
 intractable joint posterior distribution of the mean and standard
 deviation. The proposed methodology is applied to simulated and real data.
 The real data refers to the sugar content (oBRIX level) of orange juice
 produced in different countries. 
Journal: Journal of Applied Statistics 
Pages: 89-99 
Issue: 1 
Volume: 33 
Year: 2006 
Keywords: Bayesian estimation, range, order statistics, MCMC, 
X-DOI: 10.1080/02664760500389913 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500389913
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:1:p:89-99




Template-Type: ReDIF-Article 1.0
Author-Name: D. Muthuraj 
Author-X-Name-First: D. 
Author-X-Name-Last: Muthuraj 
Author-Name: D. Senthilkumar 
Author-X-Name-First: D. 
Author-X-Name-Last: Senthilkumar 
Title: Designing and construction of tightened-normal-tightened variables sampling scheme 
Abstract:
  The tightened-normal-tightened (TNT) attributes sampling scheme was
 devised by Calvin (1977). In this paper, a TNT Scheme with variables
 sampling plan as the reference plan, designated as TNTVSS (n&#x3c3;; kT,
 kN) is introduced, where n&#x3c3; is the sample size under the reference
 plan, and kT and kN are the acceptance constants corresponding to
 tightened and normal plans respectively. The behaviour of OC curves of the
 TNTVSS (n&#x3c3;; kT, kN) is studied. The efficiency of TNTVSS (n&#x3c3;;
 kT, kN) with respect to smaller sample sizes has been established over the
 attributes scheme. The TNTVSS is matched with the TNT (n; cN, cT) of
 Vijayaraghavan and Soundararajan (1996), for the specified points on the
 OC curves, namely (p1, &#x3b1;) and (p2, &#x3b2;) and it is shown that the
 sample size of the variables scheme is much smaller than that of the
 attributes scheme. The TNT scheme with an unknown &#x3c3; variables plan
 as the reference plan is also introduced along with the procedure of
 selection of the parameters. The method of designing the scheme based on
 the given AQL (Acceptable Quality level), &#x3b1; (producer's risk), LQL
 (Limiting Quality Level) and &#x3b2; (consumer's risk) is indicated. Among
 the class of TNTVSS which exists, for a given (p1,&#x3b1;) and (p2,
 &#x3b2;), a scheme, which will have a more steeper OC curve than that of
 any other scheme, is identified and given. 
Journal: Journal of Applied Statistics 
Pages: 101-111 
Issue: 1 
Volume: 33 
Year: 2006 
Keywords: Variables plan, tightened-normal-tightened plan, AQL, LQL, switching rules, producer's risk, consumer's risk, 
X-DOI: 10.1080/02664760500389582 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500389582
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:1:p:101-111




Template-Type: ReDIF-Article 1.0
Author-Name: Teo Jasic 
Author-X-Name-First: Teo 
Author-X-Name-Last: Jasic 
Author-Name: Douglas Wood 
Author-X-Name-First: Douglas 
Author-X-Name-Last: Wood 
Title: Testing for efficiency and non-linearity in market and natural time series 
Abstract:
  Time series in traded markets such as currencies and securities involve
 supply/demand interaction, so they might be expected to contain
 distinctive and identifiable structures in comparison with data based on
 natural phenomena such as river flows or sunspots. This paper tests this
 proposition using standard econometric tests including variance ratios,
 modified rescaled range (R/S) ratios and BDS statistics together with
 non-linear prediction models. Four time series of each type (market or
 natural) are subject to a battery of tests for random walk and non-linear
 dependence. Surprisingly, the tests provide no reliable discrimination
 between the two types of series or reveal any embedded specification
 differences. 
Journal: Journal of Applied Statistics 
Pages: 113-138 
Issue: 2 
Volume: 33 
Year: 2006 
Keywords: Efficiency tests, non-linearity, neural networks, market versus natural data, 
X-DOI: 10.1080/02664760500250370 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500250370
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:2:p:113-138




Template-Type: ReDIF-Article 1.0
Author-Name: S. Balamurali 
Author-X-Name-First: S. 
Author-X-Name-Last: Balamurali 
Author-Name: Chi-Hyuck Jun 
Author-X-Name-First: Chi-Hyuck 
Author-X-Name-Last: Jun 
Title: Average outgoing quality of CSP-C continuous sampling plan under short run production processes 
Abstract:
  A CSP-C continuous sampling plan is a new single-level continuous
 sampling procedure developed by Govindaraju & Kandasamy (2000) by
 incorporating the concept of acceptance number to the CSP-1 plan for the
 application of continuous production processes. In this new plan, the
 sampling inspection phase is characterized by a maximum allowable number
 of non-conforming units, c, and a constant sampling rate, f. Govindaraju &
 Kandasamy (2000) derived the performance measures such as average outgoing
 quality (AOQ), average fraction inspected (AFI) etc, of the CSP-C plan
 using a Markov chain model for long run production processes. Yang (1983)
 has observed that the AOQ and AFI, being long run average measures, are
 not satisfactory measures of performance for short run production
 processes. Hence, formulas are derived in this paper, using the renewal
 theory approach enabling one to compute AOQ and AFI for both long run and
 short run production processes. Numerical illustrations are also given. By
 simulation, the accuracy of the short run measures is studied. 
Journal: Journal of Applied Statistics 
Pages: 139-154 
Issue: 2 
Volume: 33 
Year: 2006 
Keywords: Average outgoing quality, renewal theory, short run production process, 
X-DOI: 10.1080/02664760500250537 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500250537
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:2:p:139-154




Template-Type: ReDIF-Article 1.0
Author-Name: Yangxin Huang 
Author-X-Name-First: Yangxin 
Author-X-Name-Last: Huang 
Author-Name: Hulin Wu 
Author-X-Name-First: Hulin 
Author-X-Name-Last: Wu 
Title: A Bayesian approach for estimating antiviral efficacy in HIV dynamic models 
Abstract:
  The study of HIV dynamics is one of the most important developments in
 recent AIDS research. It has led to a new understanding of the
 pathogenesis of HIV infection. Although important findings in HIV dynamics
 have been published in prestigious scientific journals, the statistical
 methods for parameter estimation and model-fitting used in those papers
 appear surprisingly crude and have not been studied in more detail. For
 example, the unidentifiable parameters were simply imputed by mean
 estimates from previous studies, and important pharmacological/clinical
 factors were not considered in the modelling. In this paper, a viral
 dynamic model is developed to evaluate the effect of pharmacokinetic
 variation, drug resistance and adherence on antiviral responses. In the
 context of this model, we investigate a Bayesian modelling approach under
 a non-linear mixed-effects (NLME) model framework. In particular, our
 modelling strategy allows us to estimate time-varying antiviral efficacy
 of a regimen during the whole course of a treatment period by
 incorporating the information of drug exposure and drug susceptibility.
 Both simulated and real clinical data examples are given to illustrate the
 proposed approach. The Bayesian approach has great potential to be used in
 many aspects of viral dynamics modelling since it allow us to fit complex
 dynamic models and identify all the model parameters. Our results suggest
 that Bayesian approach for estimating parameters in HIV dynamic models is
 flexible and powerful. 
Journal: Journal of Applied Statistics 
Pages: 155-174 
Issue: 2 
Volume: 33 
Year: 2006 
Keywords: Bayesian mixed-effects models, drug efficacy, drug resistance, HIV, MCMC, viral dynamics, 
X-DOI: 10.1080/02664760500250552 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500250552
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:2:p:155-174




Template-Type: ReDIF-Article 1.0
Author-Name: Ashis Sengupta 
Author-X-Name-First: Ashis 
Author-X-Name-Last: Sengupta 
Author-Name: Fidelis Ugwuowo 
Author-X-Name-First: Fidelis 
Author-X-Name-Last: Ugwuowo 
Title: Modelling multi-stage processes through multivariate distributions 
Abstract:
  A new model combining parametric and semi-parametric approaches and
 following the lines of a semi-Markov model is developed for multi-stage
 processes. A Bivariate sojourn time distribution derived from the
 bivariate exponential distribution of Marshall & Olkin (1967) is adopted.
 The results compare favourably with the usual semi-parametric approaches
 that have been in use. Our approach also has several advantages over the
 models in use including its amenability to statistical inference. For
 example, the tests for symmetry and also for independence of the marginals
 of the sojourn time distributions, which were not available earlier, can
 now be conveniently derived and are enhanced in elegant forms. A unified
 Goodness-of-Fit test procedure for our proposed model is also presented.
 An application to the human resource planning involving real-life data
 from University of Nigeria is given. 
Journal: Journal of Applied Statistics 
Pages: 175-188 
Issue: 2 
Volume: 33 
Year: 2006 
Keywords: Bivariate exponential, multi-stage processes, semi-Markov, semi-parametric, human resource planning, 
X-DOI: 10.1080/02664760500250586 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500250586
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:2:p:175-188




Template-Type: ReDIF-Article 1.0
Author-Name: Alberto Luceno 
Author-X-Name-First: Alberto 
Author-X-Name-Last: Luceno 
Author-Name: Jaime Puig-Pey 
Author-X-Name-First: Jaime 
Author-X-Name-Last: Puig-Pey 
Title: The random intrinsic fast initial response of one-sided CUSUM charts 
Abstract:
  This article analyses the performance of a one-sided cumulative sum
 (CUSUM) chart that is initialized using a random starting point following
 the natural or intrinsic probability distribution of the CUSUM statistic.
 By definition, this probability distribution remains stable as the chart
 is used. The probability that the chart starts at zero according to this
 intrinsic distribution is always smaller than one, which confers on the
 chart a fast initial response feature. The article provides a fast and
 accurate algorithm to compute the in-control and out-of-control average
 run lengths and run-length probability distributions for one-sided CUSUM
 charts initialized using this random intrinsic fast initial response
 (RIFIR) scheme. The algorithm also computes the intrinsic distribution of
 the CUSUM statistic and random samples extracted from this distribution.
 Most importantly, no matter how the chart was initialized, if no level
 shifts and no alarms have occurred before time &#x3c4; &#x3e; 0, the
 distribution of the run length remaining after &#x3c4; is provided by this
 algorithm very accurately, provided that &#x3c4; is not too small. 
Journal: Journal of Applied Statistics 
Pages: 189-201 
Issue: 2 
Volume: 33 
Year: 2006 
Keywords: Average run length, cumulative sum charts, Gaussian quadrature, Markov chains, run-length distribution, statistical process control, 
X-DOI: 10.1080/02664760500250610 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500250610
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:2:p:189-201




Template-Type: ReDIF-Article 1.0
Author-Name: Nobuko Miyamoto 
Author-X-Name-First: Nobuko 
Author-X-Name-Last: Miyamoto 
Author-Name: Kouji Tahata 
Author-X-Name-First: Kouji 
Author-X-Name-Last: Tahata 
Author-Name: Hirokazu Ebie 
Author-X-Name-First: Hirokazu 
Author-X-Name-Last: Ebie 
Author-Name: Sadao Tomizawa 
Author-X-Name-First: Sadao 
Author-X-Name-Last: Tomizawa 
Title: Marginal inhomogeneity models for square contingency tables with nominal categories 
Abstract:
  For the analysis of square contingency tables with nominal categories,
 this paper proposes two kinds of models that indicate the structure of
 marginal inhomogeneity. One model states that the absolute values of log
 odds of the row marginal probability to the corresponding column marginal
 probability for each category i are constant for every i. The other model
 states that, on the condition that an observation falls in one of the
 off-diagonal cells in the square table, the absolute values of log odds of
 the conditional row marginal probability to the corresponding conditional
 column marginal probability for each category i are constant for every i.
 These models are used when the marginal homogeneity model does not hold,
 and the values of parameters in the models are useful for seeing the
 degree of departure from marginal homogeneity for the data on a nominal
 scale. Examples are given. 
Journal: Journal of Applied Statistics 
Pages: 203-215 
Issue: 2 
Volume: 33 
Year: 2006 
Keywords: Asymmetry, conditional probability, nominal category, marginal homogeneity, marginal inhomogeneity, model, square table, 
X-DOI: 10.1080/02664760500251576 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500251576
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:2:p:203-215




Template-Type: ReDIF-Article 1.0
Author-Name: D. J. Spiegelhalter 
Author-X-Name-First: D. J. 
Author-X-Name-Last: Spiegelhalter 
Author-Name: E. C. Marshall 
Author-X-Name-First: E. C. 
Author-X-Name-Last: Marshall 
Title: Strategies for inference robustness in focused modelling 
Abstract:
  Advances in computation mean that it is now possible to fit a wide range
 of complex models to data, but there remains the problem of selecting a
 model on which to base reported inferences. Following an early suggestion
 of Box & Tiao, it seems reasonable to seek 'inference robustness' in
 reported models, so that alternative assumptions that are reasonably well
 supported would not lead to substantially different conclusions. We
 propose a four-stage modelling strategy in which we iteratively assess and
 elaborate an initial model, measure the support for each of the resulting
 family of models, assess the influence of adopting alternative models on
 the conclusions of primary interest, and identify whether an approximate
 model can be reported. The influence-support plot is then introduced as a
 tool to aid model comparison. The strategy is semi-formal, in that it
 could be embedded in a decision-theoretic framework but requires
 substantive input for any specific application. The one restriction of the
 strategy is that the quantity of interest, or 'focus', must retain its
 interpretation across all candidate models. It is, therefore, applicable
 to analyses whose goal is prediction, or where a set of common model
 parameters are of interest and candidate models make alternative
 distributional assumptions. The ideas are illustrated by two examples.
 Technical issues include the calibration of the Kullback-Leibler
 divergence between marginal distributions, and the use of alternative
 measures of support for the range of models fitted. 
Journal: Journal of Applied Statistics 
Pages: 217-232 
Issue: 2 
Volume: 33 
Year: 2006 
Keywords: Influence diagnostics, hierarchical models, model choice, prediction, institutional comparisons, Markov chain Monte Carlo, Kullback-Leibler divergence, 
X-DOI: 10.1080/02664760500251618 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500251618
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:2:p:217-232




Template-Type: ReDIF-Article 1.0
Author-Name: Steven Cook 
Author-X-Name-First: Steven 
Author-X-Name-Last: Cook 
Title: A finite-sample sensitivity analysis of the Dickey-Fuller test under local-to-unity detrending 
Abstract:
  In recent research, Elliott et al. (1996) have shown the use of
 local-to-unity detrending via generalized least squares (GLS) to
 substantially increase the power of the Dickey-Fuller (1979) unit root
 test. In this paper the relationship between the extent of detrending
 undertaken, determined by the detrending parameter [image omitted], and
 the power of the resulting GLS-based Dickey-Fuller (DF-GLS) test is
 examined. Using Monte Carlo simulation it is shown that the values of
 [image omitted] suggested by Elliott et al. (1996) on the basis of a
 limiting power function seldom maximize the power of the DF-GLS test for
 the finite samples encountered in applied research. This result is found
 to hold for the DF-GLS test including either an intercept or an intercept
 and a trend term. An empirical examination of the order of integration of
 the UK household savings ratio illustrates these findings, with the unit
 root hypothesis rejected using values of [image omitted] other than that
 proposed by Elliott et al. (1996). 
Journal: Journal of Applied Statistics 
Pages: 233-240 
Issue: 2 
Volume: 33 
Year: 2006 
Keywords: Dickey-Fuller test, unit roots, local-to-unity detrending, savings ratio, 
X-DOI: 10.1080/02664760500251725 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500251725
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:2:p:233-240




Template-Type: ReDIF-Article 1.0
Author-Name: Richard Woodhouse 
Author-X-Name-First: Richard 
Author-X-Name-Last: Woodhouse 
Title: Graphical solutions for structural regression assist errors-in-variables modelling 
Abstract:
  Structural regression attempts to reveal an underlying relationship by
 compensating for errors in the variables. Ordinary least-squares
 regression has an entirely different purpose and provides a relationship
 between error-included variables. Structural model solutions, also known
 as the errors-in-variables and measurement-error solutions, use various
 inputs such as the error-variance ratio and x-error variance. This paper
 proposes that more accurate structural line gradient (coefficient)
 solutions will result from using the several solutions together as a
 system of equations. The known data scatter, as measured by the
 correlation coefficient, should always be used in choosing legitimate
 combinations of x- and y-error terms. However, this is difficult using
 equations. Chart solutions are presented to assist users to understand the
 structural regression process, to observe the correlation coefficient
 constraint, to assess the impact of their error estimates and, therefore,
 to provide better quality estimates of the structural regression gradient. 
Journal: Journal of Applied Statistics 
Pages: 241-255 
Issue: 3 
Volume: 33 
Year: 2006 
Keywords: Correlation coefficient constraint, error compensation, error-variance ratio, line fitting, measurement-error model,, 
X-DOI: 10.1080/02664760500445483 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500445483
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:3:p:241-255




Template-Type: ReDIF-Article 1.0
Author-Name: E. Andersson 
Author-X-Name-First: E. 
Author-X-Name-Last: Andersson 
Author-Name: D. Bock 
Author-X-Name-First: D. 
Author-X-Name-Last: Bock 
Author-Name: M. Frisen 
Author-X-Name-First: M. 
Author-X-Name-Last: Frisen 
Title: Some statistical aspects of methods for detection of turning points in business cycles 
Abstract:
  Methods for online turning point detection in business cycles are
 discussed. The statistical properties of three likelihood-based methods
 are compared. One is based on a Hidden Markov Model, another includes a
 non-parametric estimation procedure and the third combines features of the
 other two. The methods are illustrated by monitoring a period of the
 Swedish industrial production. Evaluation measures that reflect timeliness
 are used. The effects of smoothing, seasonal variation, autoregression and
 multivariate issues on methods for timely detection are discussed. 
Journal: Journal of Applied Statistics 
Pages: 257-278 
Issue: 3 
Volume: 33 
Year: 2006 
Keywords: Monitoring, surveillance, early warning system, regime switching, 
X-DOI: 10.1080/02664760500445517 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500445517
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:3:p:257-278




Template-Type: ReDIF-Article 1.0
Author-Name: Fong-jung Yu 
Author-X-Name-First: Fong-jung 
Author-X-Name-Last: Yu 
Author-Name: Jiang-liang Hou 
Author-X-Name-First: Jiang-liang 
Author-X-Name-Last: Hou 
Title: Optimization of design parameters for [image omitted] control charts with multiple assignable causes 
Abstract:
  Duncan's economic model of Shewhart's original x-super-&#xaf; chart has
 established its optimal and economic application for processes with the
 Markovian failure characteristic. As the sample statistics show some
 indications of process variations, the variable-sampling-interval (VSI)
 control charts perform more effectively than the fixed sampling interval
 (FSI) ones due to a higher frequency in the sampling rate. Regarding the
 economic design of control charts, most studies have been dedicated to the
 FSI scheme. In 1998, Bai & Lee considered the production process with a
 single assignable cause and proposed an economic VSI design for a general
 x-super-&#xaf; control chart. However, in real cases, there are multiple
 assignable causes in the production process. Therefore, concerning the
 operation characteristics of the real industry, this research develops an
 economic model for the VSI control chart with multiple assignable causes
 based on stochastic and statistics theory and determines the optimal
 design parameters of the chart. A numerical example is also provided to
 demonstrate the effectiveness of the proposed model and the result
 indicates that VSI performs more effectively than a FSI control chart. 
Journal: Journal of Applied Statistics 
Pages: 279-290 
Issue: 3 
Volume: 33 
Year: 2006 
Keywords: Economic design, Variable Sampling Interval (VSI), x-super-&#xaf; control charts, multiple assignable causes, Statistical Process Control (SPC), 
X-DOI: 10.1080/02664760500445541 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500445541
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:3:p:279-290




Template-Type: ReDIF-Article 1.0
Author-Name: R. R. L. Kantam 
Author-X-Name-First: R. R. L. 
Author-X-Name-Last: Kantam 
Author-Name: G. Srinivasa Rao 
Author-X-Name-First: G. Srinivasa 
Author-X-Name-Last: Rao 
Author-Name: B. Sriram 
Author-X-Name-First: B. 
Author-X-Name-Last: Sriram 
Title: An economic reliability test plan: Log-logistic distribution 
Abstract:
  Sampling plans in which items that are put to test, to collect the life
 of the items in order to decide upon accepting or rejecting a submitted
 lot, are called reliability test plans. The basic probability model of the
 life of the product is specified as the well-known log-logistic
 distribution with a known shape parameter. For a given producer's risk,
 sample size, termination number, and waiting time to terminate the test
 plan are computed. The preferability of the test plan over similar plans
 existing in the literature is established with respect to cost and time of
 the experiment. 
Journal: Journal of Applied Statistics 
Pages: 291-296 
Issue: 3 
Volume: 33 
Year: 2006 
Keywords: Log-logistic distribution, reliability test plan, producer's risk, acceptance sample number, 
X-DOI: 10.1080/02664760500445681 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500445681
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:3:p:291-296




Template-Type: ReDIF-Article 1.0
Author-Name: A. M. Wade 
Author-X-Name-First: A. M. 
Author-X-Name-Last: Wade 
Author-Name: K. Lawrence 
Author-X-Name-First: K. 
Author-X-Name-Last: Lawrence 
Author-Name: W. Mandy 
Author-X-Name-First: W. 
Author-X-Name-Last: Mandy 
Author-Name: D. Skuse 
Author-X-Name-First: D. 
Author-X-Name-Last: Skuse 
Title: Charting the development of emotion recognition from 6 years of age 
Abstract:
  Recognition of emotions within others is a necessary life skill. We know
 that this is a learnt skill, which develops throughout childhood and is
 deficient in some individuals. To put individual development in context,
 it is necessary to understand the nature of development amongst the normal
 population. Age-related centiles can be used to add this context. The
 level of emotion recognition is assessed using an ordinal outcome scale,
 and hence establishing age-related centiles for these measures creates
 particular analytical problems. In this paper, we use methodology
 previously developed by us for monitoring the development of visual acuity
 during childhood to calculate age-related centiles for emotion recognition
 ratings. The ratings do not consistently improve with age and appear to be
 affected by hormonal developments. A comparison of ability to rate
 emotions according to the stage of pubertal development is used to
 illustrate how the conversion of ordinal assessments to continuous centile
 scores facilitates the investigation. The specific issues relating to the
 application of the methodology to data that are not consistent in the
 direction of change with age and where large amounts of data can be
 gathered electronically are discussed. 
Journal: Journal of Applied Statistics 
Pages: 297-315 
Issue: 3 
Volume: 33 
Year: 2006 
Keywords: Ordinal, age-related centiles, emotion recognition, ekman-friesen test, proportional odds models, 
X-DOI: 10.1080/02664760500445756 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500445756
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:3:p:297-315




Template-Type: ReDIF-Article 1.0
Author-Name: Rand Wilcox 
Author-X-Name-First: Rand 
Author-X-Name-Last: Wilcox 
Title: Confidence intervals for prediction intervals 
Abstract:
  When working with a single random variable, the simplest and most obvious
 approach when estimating a 1 - &#x3b3; prediction interval, is to estimate
 the &#x3b3;/2 and 1 - &#x3b3;/2 quantiles. The paper compares the
 small-sample properties of several methods aimed at estimating an interval
 that contains the 1 - &#x3b3; prediction interval with probability 1 -
 &#x3b1;. In effect, the goal is to compute a 1 - &#x3b1; confidence
 interval for the true 1 - &#x3b3; prediction interval. The only successful
 method when the sample size is small is based in part on an adaptive
 kernel estimate of the underlying density. Some simulation results are
 reported on how an extension to non-parametric regression performs, based
 on a so-called running interval smoother. 
Journal: Journal of Applied Statistics 
Pages: 317-326 
Issue: 3 
Volume: 33 
Year: 2006 
Keywords: Quantile estimation, kernel density estimators, non-parametric regression, 
X-DOI: 10.1080/02664760500445962 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500445962
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:3:p:317-326




Template-Type: ReDIF-Article 1.0
Author-Name: S. Balamurali 
Author-X-Name-First: S. 
Author-X-Name-Last: Balamurali 
Author-Name: Chi-hyuck Jun 
Author-X-Name-First: Chi-hyuck 
Author-X-Name-Last: Jun 
Title: Repetitive group sampling procedure for variables inspection 
Abstract:
  This paper introduces the concept of repetitive group sampling (RGS) for
 variables inspection. The repetitive group sampling plan for variables
 inspection will be useful when testing is costly and destructive. The
 advantages of the variables RGS plan over variables single sampling plan,
 variables double sampling plan and attributes RGS plan are discussed.
 Tables are also constructed for the selection of parameters of known and
 unknown standard deviation variables repetitive group sampling plan
 indexed by acceptable quality level and limiting quality level. 
Journal: Journal of Applied Statistics 
Pages: 327-338 
Issue: 3 
Volume: 33 
Year: 2006 
Keywords: Acceptable quality level, average sample number, limiting quality level, repetitive group sampling, sampling by variables, 
X-DOI: 10.1080/02664760500446010 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500446010
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:3:p:327-338




Template-Type: ReDIF-Article 1.0
Author-Name: Kanti Mardia 
Author-X-Name-First: Kanti 
Author-X-Name-Last: Mardia 
Author-Name: Paul McDonnell 
Author-X-Name-First: Paul 
Author-X-Name-Last: McDonnell 
Author-Name: Alf Linney 
Author-X-Name-First: Alf 
Author-X-Name-Last: Linney 
Title: Penalized image averaging and discrimination with facial and fishery applications 
Abstract:
  In this paper we use a penalized likelihood approach to image warping in
 the context of discrimination and averaging. The choice of average image
 is formulated statistically by minimizing a penalized likelihood, where
 the likelihood measures the similarity between images after warping and
 the penalty is a measure of distortion of a warping. The notions of
 measures of similarity are given in terms of normalized image information.
 The measures of distortion are landmark based. Thus we use a combination
 of landmark and normalized image information. The average defined in the
 paper is also extended by allowing random perturbation of the landmarks.
 This strategy improves averages for discrimination purposes. We give here
 real applications from medical and biological areas. 
Journal: Journal of Applied Statistics 
Pages: 339-371 
Issue: 3 
Volume: 33 
Year: 2006 
Keywords: Female and male faces, Fisher discriminant analysis, haddock and whiting fish, laser images, normalized images, penalized likelihood, 
X-DOI: 10.1080/02664760500163649 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500163649
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:3:p:339-371




Template-Type: ReDIF-Article 1.0
Author-Name: Stefano Barone 
Author-X-Name-First: Stefano 
Author-X-Name-Last: Barone 
Author-Name: Alberto Lombardo 
Author-X-Name-First: Alberto 
Author-X-Name-Last: Lombardo 
Title: Balanced Asymmetrical Nearly Orthogonal Designs for first and second order effect estimation 
Abstract:
  A method for constructing asymmetrical (mixed-level) designs, satisfying
 the balancing and interaction estimability requirements with a number of
 runs as small as possible, is proposed in this paper. The method, based on
 a heuristic procedure, uses a new optimality criterion formulated here.
 The proposed method demonstrates efficiency in terms of searching time and
 optimality of the attained designs. A complete collection of such
 asymmetrical designs with two- and three-level factors is available. A
 technological application is also presented. 
Journal: Journal of Applied Statistics 
Pages: 373-386 
Issue: 4 
Volume: 33 
Year: 2006 
Keywords: Balancing, asymmetrical (mixed-level) designs, nearly orthogonal arrays, optimality, two- and three-level designs, 
X-DOI: 10.1080/02664760500448917 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500448917
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:4:p:373-386




Template-Type: ReDIF-Article 1.0
Author-Name: Murat Kulahci 
Author-X-Name-First: Murat 
Author-X-Name-Last: Kulahci 
Author-Name: S&#xf8;ren Bisgaard 
Author-X-Name-First: S&#xf8;ren 
Author-X-Name-Last: Bisgaard 
Title: A generalization of the alias matrix 
Abstract:
  The investigation of aliases or biases is important for the
 interpretation of the results from factorial experiments. For two-level
 fractional factorials this can be facilitated through their group
 structure. For more general arrays the alias matrix can be used. This tool
 is traditionally based on the assumption that the error structure is that
 associated with ordinary least squares. For situations where that is not
 the case, we provide in this article a generalization of the alias matrix
 applicable under the generalized least squares assumptions. We also show
 that for the special case of split plot error structure, the generalized
 alias matrix simplifies to the ordinary alias matrix. 
Journal: Journal of Applied Statistics 
Pages: 387-395 
Issue: 4 
Volume: 33 
Year: 2006 
X-DOI: 10.1080/02664760500449014 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500449014
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:4:p:387-395




Template-Type: ReDIF-Article 1.0
Author-Name: Greg Piepel 
Author-X-Name-First: Greg 
Author-X-Name-Last: Piepel 
Title: A note comparing component-slope, Scheffe and Cox parameterizations of the linear mixture experiment model 
Abstract:
  A mixture experiment involves combining two or more components in various
 proportions and collecting data on one or more responses. A linear mixture
 model may adequately represent the relationship between a response and
 mixture component proportions and be useful in screening the mixture
 components. The Scheffe and Cox parameterizations of the linear mixture
 model are commonly used for analyzing mixture experiment data. With the
 Scheffe parameterization, the fitted coefficient for a component is the
 predicted response at that pure component (i.e. single-component mixture).
 With the Cox parameterization, the fitted coefficient for a mixture
 component is the predicted difference in response at that pure component
 and at a pre-specified reference composition. This article presents a new
 component-slope parameterization, in which the fitted coefficient for a
 mixture component is the predicted slope of the linear response surface
 along the direction determined by that pure component and at a
 pre-specified reference composition. The component-slope, Scheffe, and Cox
 parameterizations of the linear mixture model are compared and their
 advantages and disadvantages are discussed. 
Journal: Journal of Applied Statistics 
Pages: 397-403 
Issue: 4 
Volume: 33 
Year: 2006 
Keywords: Mixture component effects, Scheffe, linear mixture model, Cox linear mixture model, component-slope linear mixture model, 
X-DOI: 10.1080/02664760500449170 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500449170
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:4:p:397-403




Template-Type: ReDIF-Article 1.0
Author-Name: Ron Kenett 
Author-X-Name-First: Ron 
Author-X-Name-Last: Kenett 
Title: On the planning and design of sample surveys 
Abstract:
  Surveys rely on structured questions used to map out reality, using
 sample observations from a population frame, into data that can be
 statistically analyzed. This paper focuses on the planning and design of
 surveys, making a distinction between individual surveys, household
 surveys and establishment surveys. Knowledge from cognitive science is
 used to provide guidelines on questionnaire design. Non-standard, but
 simple, statistical methods are described for analyzing survey results.
 The paper is based on experience gained by conducting over 150 customer
 satisfaction surveys in Europe, America and the Far East. 
Journal: Journal of Applied Statistics 
Pages: 405-415 
Issue: 4 
Volume: 33 
Year: 2006 
Keywords: Questionnaire design, cognitive science, individual surveys, household surveys, establishment surveys, control charts analysis of survey data, 
X-DOI: 10.1080/02664760500448974 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500448974
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:4:p:405-415




Template-Type: ReDIF-Article 1.0
Author-Name: Sungcheol Yun 
Author-X-Name-First: Sungcheol 
Author-X-Name-Last: Yun 
Author-Name: So Young Sohn 
Author-X-Name-First: So Young 
Author-X-Name-Last: Sohn 
Author-Name: Youngjo Lee 
Author-X-Name-First: Youngjo 
Author-X-Name-Last: Lee 
Title: Modelling and estimating heavy-tailed non-homogeneous correlated queues: Pareto-inverse gamma HGLM with covariates 
Abstract:
  Evidence of communication traffic complexity reveals correlation in a
 within-queue and heterogeneity among queues. We show how a random-effect
 model can be used to accommodate these kinds of phenomena. We apply a
 Pareto distribution for arrival (service) time of individual queue for
 given arrival (service) rate. For modelling potential correlation in
 arrival (service) times within a queue and heterogeneity of the arrival
 (service) rates among queues, we use an inverse gamma distribution. This
 modelling approach is then applied to the cache access log data processed
 through an Internet server. We believe that our approach is potentially
 useful in the area of network resource management. 
Journal: Journal of Applied Statistics 
Pages: 417-425 
Issue: 4 
Volume: 33 
Year: 2006 
Keywords: Within-queue correlation, between-queue variability, internet traffic, random effects linear model, hierarchical generalized linear model, 
X-DOI: 10.1080/02664760500449311 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500449311
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:4:p:417-425




Template-Type: ReDIF-Article 1.0
Author-Name: Issam Samarah 
Author-X-Name-First: Issam 
Author-X-Name-Last: Samarah 
Author-Name: Gamal Weheba 
Author-X-Name-First: Gamal 
Author-X-Name-Last: Weheba 
Author-Name: Thomas Lacy 
Author-X-Name-First: Thomas 
Author-X-Name-Last: Lacy 
Title: Response surface characterization of the mechanical behavior of impact-damaged sandwich composites 
Abstract:
  In this research, Response Surface Methodology (RSM) is employed to
 characterize the influence of material configuration on the damage
 tolerance and residual strength characteristics of sandwich composites.
 Test specimens used were comprised of carbon-epoxy woven fabric
 facesheets, and Nomex honeycomb cores. The ranges of the material
 configuration used are typical of those employed in aircraft applications.
 A series of carefully selected tests were used to isolate the coupled
 influence of various combinations of the number of facesheet plies, core
 density, and core thickness on the damage formation and residual strength
 degradation due to normal impact. Response surface estimates suggest that
 impact damage development and residual strength degradation are highly
 material and lay-up configuration dependent. Increasing the core thickness
 for a specific number of facesheet plies resulted in decreasing the impact
 damage, whereas increasing the number of facesheet plies for a given core
 thickness resulted in enhancing the residual strength. The derived damage
 tolerance and residual strength models can lead to a better understanding
 of the mechanical behavior of the impact-damaged sandwich composites, and
 hence improve their design and expand their applications. 
Journal: Journal of Applied Statistics 
Pages: 427-437 
Issue: 4 
Volume: 33 
Year: 2006 
Keywords: Sandwich composites, damage tolerance, response surface methods, Box-Behnken design, 
X-DOI: 10.1080/02664760500449295 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500449295
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:4:p:427-437




Template-Type: ReDIF-Article 1.0
Author-Name: Subha Chakraborti 
Author-X-Name-First: Subha 
Author-X-Name-Last: Chakraborti 
Title: Parameter estimation and design considerations in prospective applications of the X chart 
Abstract:
  The effects of parameter estimation on the in-control performance of the
 Shewhart X chart are studied in prospective (phase 2 or stage 2)
 applications via a thorough examination of the attained false alarm rate
 (AFAR), the conditional false alarm rate (CFAR), the conditional and the
 unconditional run-length distributions, some run-length characteristics
 such as the ARL, the conditional ARL (CARL), some selected percentiles
 including the median, and cumulative run-length probabilities. The
 examination involves both numerical evaluations and graphical displays.
 The effects of parameter estimation need to be accounted for in designing
 the chart. To this end, as an application of the exact formulations, chart
 constants are provided for a specified in-control average run-length of
 370 and 500 for a number of subgroups and subgroup sizes. These will be
 useful in the implementation of the X chart in practice. 
Journal: Journal of Applied Statistics 
Pages: 439-459 
Issue: 4 
Volume: 33 
Year: 2006 
Keywords: Shewhart chart for the mean, run-length, false alarm rate, conditional distribution, phase 1, phase 2, average run-length, median run-length, chart constant, 
X-DOI: 10.1080/02664760500163516 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500163516
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:4:p:439-459




Template-Type: ReDIF-Article 1.0
Author-Name: Christopher Pomory 
Author-X-Name-First: Christopher 
Author-X-Name-Last: Pomory 
Title: A note on calculating P values from 0.15-0.005 for the Anderson-Darling normality test using the F distribution 
Abstract:
  Exact P values in the range 0.15-0.005 for the Anderson-Darling statistic
 can be calculated using the F distribution by modifying the asymptotic
 statistic A* with a simple formula. The formula calculates F* and P is
 calculated using [image omitted]&#xa0;. 
Journal: Journal of Applied Statistics 
Pages: 461-462 
Issue: 4 
Volume: 33 
Year: 2006 
Keywords: EDF test, 
X-DOI: 10.1080/02664760600677720 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600677720
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:4:p:461-462




Template-Type: ReDIF-Article 1.0
Author-Name: L. A. McSweeney 
Author-X-Name-First: L. A. 
Author-X-Name-Last: McSweeney 
Title: Monitoring paper production using a spectral control chart designed to detect in the presence of multiple cycles 
Abstract:
  In this paper we introduce a spectral control chart that is designed to
 detect the onset of cyclic behaviour in a process, even in the presence of
 multiple cycles. This new spectral control chart is based on the
 periodogram test proposed by B&#xf8;lviken (1983a, b). While no more
 difficult to implement than the traditional spectral control based on
 Fisher's test statistic, this new control chart shows improvement in
 detecting the presence of compound periodicity, which the chart based on
 Fisher's test is not designed to handle. This is assessed using Monte
 Carlo simulations to estimate and compare the average run lengths of
 several spectral control charts. In addition, the spectral control charts
 are applied to paper production data, published by Pandit & Wu (1993), in
 which the stock flow and paper thickness are monitored. The application of
 the new spectral control chart to the stock flow process detects
 out-of-control behaviour that is not found using standard control charts.
 This behaviour, in turn, appears to be related to out-of-control behaviour
 that is observed in the paper thickness measurements later in the
 production process. 
Journal: Journal of Applied Statistics 
Pages: 467-480 
Issue: 5 
Volume: 33 
Year: 2006 
Keywords: Periodogram, Fourier frequency, quality control, Monte Carlo methods, average run length, 
X-DOI: 10.1080/02664760500446333 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500446333
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:5:p:467-480




Template-Type: ReDIF-Article 1.0
Author-Name: Roberta Zizza 
Author-X-Name-First: Roberta 
Author-X-Name-Last: Zizza 
Title: A measure of output gap for Italy through structural time series models 
Abstract:
  The aim of this paper is to achieve a reliable estimate of the output gap
 for Italy through the development of several models within the class of
 the unobserved component time series models. These formulations imply the
 decomposition of output into a trend component (the 'potential output')
 and a cycle component (the 'output gap'). Both univariate and multivariate
 methods will be explored. In the former, only one measure of aggregate
 activity, such as GDP, is considered; in the latter, unemployment and
 industrial production are introduced. A comparison with alternative
 measures of output gap, mainly those published by international
 organisations, will conclude. 
Journal: Journal of Applied Statistics 
Pages: 481-496 
Issue: 5 
Volume: 33 
Year: 2006 
Keywords: Output gap, potential output, trend and cycle decomposition, unobserved component models, 
X-DOI: 10.1080/02664760500448875 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500448875
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:5:p:481-496




Template-Type: ReDIF-Article 1.0
Author-Name: Chang Dorea 
Author-X-Name-First: Chang 
Author-X-Name-Last: Dorea 
Author-Name: Sueli Mingoti 
Author-X-Name-First: Sueli 
Author-X-Name-Last: Mingoti 
Title: Estimating the total number of distinct species using quadrat sampling and under-dependence structure 
Abstract:
  To estimate the total number of distinct species in a given region,
 Bayesian methods along with quadrat sampling procedures have been used by
 several authors. A key underlying assumption relies on the independence
 among the species. In this note, we analyse these estimates allowing a
 generalized binomial dependence between species. 
Journal: Journal of Applied Statistics 
Pages: 497-512 
Issue: 5 
Volume: 33 
Year: 2006 
Keywords: Estimating the number of species, quadrat sampling, generalized binomial distribution, 
X-DOI: 10.1080/02664760600585535 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600585535
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:5:p:497-512




Template-Type: ReDIF-Article 1.0
Author-Name: S. S. Ganguly 
Author-X-Name-First: S. S. 
Author-X-Name-Last: Ganguly 
Title: Cumulative logit models for matched pairs case-control design: Studies with covariates 
Abstract:
  Binary as well as polytomous logistic models have been found useful for
 estimating odds ratios when the exposure of prime interest assumes
 unordered multiple levels under matched pair case-control design. In our
 earlier studies, we have shown the use of a polytomous logistic model for
 estimating cumulative odds ratios when the exposure of prime interest
 assumes multiple ordered levels under matched pair case-control design. In
 this paper, using the above model, we estimate the covariate adjusted
 cumulative odds ratios, in the case of an ordinal multiple level exposure
 variable under a pairwise matched case-control retrospective design. An
 approach, based on asymptotic distributional results, is also described to
 investigate whether or not the response categories are distinguishable
 with respect to the cumulative odds ratios after adjusting the effect of
 covariates. An illustrative example is presented and discussed. 
Journal: Journal of Applied Statistics 
Pages: 513-522 
Issue: 5 
Volume: 33 
Year: 2006 
Keywords: Logistic model, polytomous logistic model, matched pairs, odds ratio, cumulative odds ratio, deviance statistic, 
X-DOI: 10.1080/02664760600585576 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600585576
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:5:p:513-522




Template-Type: ReDIF-Article 1.0
Author-Name: Kaliappa Kalirajan 
Author-X-Name-First: Kaliappa 
Author-X-Name-Last: Kalirajan 
Author-Name: Shashanka Bhide 
Author-X-Name-First: Shashanka 
Author-X-Name-Last: Bhide 
Title: Bias free measurement of technical efficiency 
Abstract:
  Technical efficiency, which is a measure of production performance of a
 firm, has been estimated generally using a primal production frontier.
 Since the estimation is carried out for a given level of inputs, the
 efficiency measure includes the effect of 'input-mix' or
 'input-allocation' and consequently, the technical efficiency estimate is
 biased. The objectives of this paper are to gauge the magnitude of
 'input-mix' bias in technical efficiency estimate and to suggest a method
 to measure technical efficiency eliminating the bias. The workability of
 the suggested method is demonstrated through an empirical analysis using
 agricultural data from India covering the period 1970-1993. 
Journal: Journal of Applied Statistics 
Pages: 523-533 
Issue: 5 
Volume: 33 
Year: 2006 
Keywords: Technical efficiency, input-mix bias, frontier production function, India, 
X-DOI: 10.1080/02664760600585592 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600585592
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:5:p:523-533




Template-Type: ReDIF-Article 1.0
Author-Name: Jennifer Mooney 
Author-X-Name-First: Jennifer 
Author-X-Name-Last: Mooney 
Author-Name: Ian Jolliffe 
Author-X-Name-First: Ian 
Author-X-Name-Last: Jolliffe 
Author-Name: Peter Helms 
Author-X-Name-First: Peter 
Author-X-Name-Last: Helms 
Title: Modelling seasonally varying data: A case study for Sudden Infant Death Syndrome (SIDS) 
Abstract:
  Many time series are measured monthly, either as averages or totals, and
 such data often exhibit seasonal variability - the values of the series
 are consistently larger for some months of the year than for others. A
 typical series of this type is the number of deaths each month attributed
 to SIDS (Sudden Infant Death Syndrome). Seasonality can be modelled in a
 number of ways. This paper describes and discusses various methods for
 modelling seasonality in SIDS data, though much of the discussion is
 relevant to other seasonally varying data. There are two main approaches,
 either fitting a circular probability distribution to the data, or using
 regression-based techniques to model the mean seasonal behaviour. Both are
 discussed in this paper. 
Journal: Journal of Applied Statistics 
Pages: 535-547 
Issue: 5 
Volume: 33 
Year: 2006 
Keywords: Cardioid distribution, circular data, cosinor analysis, regression, seasonality, SIDS, von Mises distribution, 
X-DOI: 10.1080/2664760600585642 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/2664760600585642
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:5:p:535-547




Template-Type: ReDIF-Article 1.0
Author-Name: J. Peng 
Author-X-Name-First: J. 
Author-X-Name-Last: Peng 
Author-Name: C. I. C. Lee 
Author-X-Name-First: C. I. C. 
Author-X-Name-Last: Lee 
Author-Name: L. Liu 
Author-X-Name-First: L. 
Author-X-Name-Last: Liu 
Title: Max-min multiple comparison procedure for comparing several dose levels with a zero dose control 
Abstract:
  The comparison of increasing doses of a compound to a zero dose control
 is of interest in medical and toxicological studies. Assume that the mean
 dose effects are non-decreasing among the non-zero doses of the compound.
 A simple procedure that modifies Dunnett's procedure is proposed to
 construct simultaneous confidence intervals for pairwise comparisons of
 each dose group with the zero dose control by utilizing the ordering of
 the means. The simultaneous lower bounds and upper bounds by the new
 procedure are monotone, which is not the case with Dunnett's procedure.
 This is useful to categorize dose levels. The expected gains of the new
 procedure over Dunnett's procedure are studied. The procedure is shown by
 real data to compare well with its predecessor. 
Journal: Journal of Applied Statistics 
Pages: 549-555 
Issue: 5 
Volume: 33 
Year: 2006 
Keywords: Dunnett's procedure, simultaneous confidence intervals, 
X-DOI: 10.1080/02664760600585675 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600585675
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:5:p:549-555




Template-Type: ReDIF-Article 1.0
Author-Name: M. Khazaee 
Author-X-Name-First: M. 
Author-X-Name-Last: Khazaee 
Author-Name: K. Shafie 
Author-X-Name-First: K. 
Author-X-Name-Last: Shafie 
Title: Regression models for Boolean random sets 
Abstract:
  In this paper we consider the regression problem for random sets of the
 Boolean-model type. Regression modeling of the Boolean random sets using
 some explanatory variables are classified according to the type of these
 variables as propagation, growth or propagation-growth models. The maximum
 likelihood estimation of the parameters for the propagation model is
 explained in detail for some specific link functions using three methods.
 These three methods of estimation are also compared in a simulation study. 
Journal: Journal of Applied Statistics 
Pages: 557-567 
Issue: 5 
Volume: 33 
Year: 2006 
Keywords: Random closed set, Boolean model, generalized linear model, 
X-DOI: 10.1080/02664760600585683 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600585683
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:5:p:557-567




Template-Type: ReDIF-Article 1.0
Author-Name: Adelaide Figueiredo 
Author-X-Name-First: Adelaide 
Author-X-Name-Last: Figueiredo 
Title: Two-way analysis of variance for data from a concentrated bipolar Watson distribution 
Abstract:
  The bipolar Watson distribution is frequently used for modeling axial
 data. We extend the one-way analysis of variance based on this
 distribution to a two-way layout. We illustrate the method with
 directional data in three dimensions 
Journal: Journal of Applied Statistics 
Pages: 575-581 
Issue: 6 
Volume: 33 
Year: 2006 
Keywords: Axial data, ANOVA, directional data, Watson distribution, 
X-DOI: 10.1080/02664760600679619 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600679619
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:6:p:575-581




Template-Type: ReDIF-Article 1.0
Author-Name: James Stamey 
Author-X-Name-First: James 
Author-X-Name-Last: Stamey 
Author-Name: Dean Young 
Author-X-Name-First: Dean 
Author-X-Name-Last: Young 
Author-Name: Tom Bratcher 
Author-X-Name-First: Tom 
Author-X-Name-Last: Bratcher 
Title: Bayesian sample-size determination for one and two Poisson rate parameters with applications to quality control 
Abstract:
  We formulate Bayesian approaches to the problems of determining the
 required sample size for Bayesian interval estimators of a predetermined
 length for a single Poisson rate, for the difference between two Poisson
 rates, and for the ratio of two Poisson rates. We demonstrate the efficacy
 of our Bayesian-based sample-size determination method with two real-data
 quality-control examples and compare the results to frequentist
 sample-size determination methods. 
Journal: Journal of Applied Statistics 
Pages: 583-594 
Issue: 6 
Volume: 33 
Year: 2006 
Keywords: Average coverage criterion, interval estimators, HPD intervals, coverage probability, 
X-DOI: 10.1080/02664760600679643 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600679643
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:6:p:583-594




Template-Type: ReDIF-Article 1.0
Author-Name: Tzong-Ru Tsai 
Author-X-Name-First: Tzong-Ru 
Author-X-Name-Last: Tsai 
Author-Name: Shuo-Jye Wu 
Author-X-Name-First: Shuo-Jye 
Author-X-Name-Last: Wu 
Title: Acceptance sampling based on truncated life tests for generalized Rayleigh distribution 
Abstract:
  This paper considers the problem of an acceptance sampling plan for a
 truncated life test when the lifetime follows the generalized Rayleigh
 distribution. For different acceptance numbers, confidence levels, and
 values of the ratio of the fixed experiment time to the specified mean
 life, the minimum sample sizes necessary to ensure the specified mean life
 are found. The operating characteristic values of the sampling plans and
 producer's risk are discussed. Some tables are presented and the use of
 the tables is illustrated by a numerical example. 
Journal: Journal of Applied Statistics 
Pages: 595-600 
Issue: 6 
Volume: 33 
Year: 2006 
Keywords: Consumer's risk, generalized Rayleigh distribution, operating characteristic curve, producer's risk, truncated life tests, 
X-DOI: 10.1080/02664760600679700 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600679700
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:6:p:595-600




Template-Type: ReDIF-Article 1.0
Author-Name: Ashish Das 
Author-X-Name-First: Ashish 
Author-X-Name-Last: Das 
Author-Name: Sudhir Gupta 
Author-X-Name-First: Sudhir 
Author-X-Name-Last: Gupta 
Author-Name: Sanpei Kageyama 
Author-X-Name-First: Sanpei 
Author-X-Name-Last: Kageyama 
Title: A-optimal diallel crosses for test versus control comparisons 
Abstract:
  A-optimality of block designs for control versus test comparisons in
 diallel crosses is investigated. A sufficient condition for designs to be
 A-optimal is derived. Type S0 designs are defined and A-optimal type S0
 designs are characterized. A lower bound to the A-efficiency of type S0
 designs is also given. Using the lower bound to A-efficiency, type S0
 designs are shown to yield efficient designs for test versus control
 comparisons. 
Journal: Journal of Applied Statistics 
Pages: 601-608 
Issue: 6 
Volume: 33 
Year: 2006 
Keywords: Diallel cross, type S design, A-optimality, A-efficiency, 
X-DOI: 10.1080/02664760600679726 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600679726
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:6:p:601-608




Template-Type: ReDIF-Article 1.0
Author-Name: Ian Dryden 
Author-X-Name-First: Ian 
Author-X-Name-Last: Dryden 
Author-Name: Rahman Farnoosh 
Author-X-Name-First: Rahman 
Author-X-Name-Last: Farnoosh 
Author-Name: Charles Taylor 
Author-X-Name-First: Charles 
Author-X-Name-Last: Taylor 
Title: Image segmentation using voronoi polygons and MCMC, with application to muscle fibre images 
Abstract:
  We investigate a Bayesian method for the segmentation of muscle fibre
 images. The images are reasonably well approximated by a Dirichlet
 tessellation, and so we use a deformable template model based on Voronoi
 polygons to represent the segmented image. We consider various prior
 distributions for the parameters and suggest an appropriate likelihood.
 Following the Bayesian paradigm, the mathematical form for the posterior
 distribution is obtained (up to an integrating constant). We introduce a
 Metropolis-Hastings algorithm and a reversible jump Markov chain Monte
 Carlo algorithm (RJMCMC) for simulation from the posterior when the number
 of polygons is fixed or unknown. The particular moves in the RJMCMC
 algorithm are birth, death and position/colour changes of the point
 process which determines the location of the polygons. Segmentation of the
 true image was carried out using the estimated posterior mode and
 posterior mean. A simulation study is presented which is helpful for
 tuning the hyperparameters and to assess the accuracy. The algorithms work
 well on a real image of a muscle fibre cross-section image, and an
 additional parameter, which models the boundaries of the muscle fibres, is
 included in the final model. 
Journal: Journal of Applied Statistics 
Pages: 609-622 
Issue: 6 
Volume: 33 
Year: 2006 
Keywords: Coloured tessellation, Markov chain Monte Carlo, point pattern, regularity, reversible jump, Strauss process, 
X-DOI: 10.1080/02664760600679825 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600679825
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:6:p:609-622




Template-Type: ReDIF-Article 1.0
Author-Name: Martin Arvidsson 
Author-X-Name-First: Martin 
Author-X-Name-Last: Arvidsson 
Author-Name: Ida Gremyr 
Author-X-Name-First: Ida 
Author-X-Name-Last: Gremyr 
Author-Name: Bo Bergman 
Author-X-Name-First: Bo 
Author-X-Name-Last: Bergman 
Title: Interpretation of dispersion effects in a robust design context 
Abstract:
  The purpose of this paper is to discuss the interpretation of dispersion
 effects in un-replicated fractional factorials from a robust design
 perspective. We propose an interpretation of dispersion effects as
 manifested interactions between control factors and unobserved and
 uncontrolled factors, an interpretation shown to be useful in achieving
 robust designs. Further, we show the consequences this interpretation has
 on the identification of dispersion effects. 
Journal: Journal of Applied Statistics 
Pages: 623-627 
Issue: 6 
Volume: 33 
Year: 2006 
Keywords: Dispersion effects, robust design, split-plot experiments, control factors, noise factors, random noise factors, 
X-DOI: 10.1080/02664760600679874 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600679874
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:6:p:623-627




Template-Type: ReDIF-Article 1.0
Author-Name: Petros Hadjicostas 
Author-X-Name-First: Petros 
Author-X-Name-Last: Hadjicostas 
Title: Maximizing proportions of correct classifications in binary logistic regression 
Abstract:
  In this paper, we give simple mathematical results that allow us to get
 all cut-off points that maximize the overall proportion of correct
 classifications in any binary classification method (and, in particular,
 in binary logistic regression). In addition, we give results that allow us
 to get all cut-off points that maximize a weighted combination of
 specificity and sensitivity. In addition, we discuss measures of
 association between predicted probabilities and observed responses, and,
 in particular, we discuss the calculation of the overall percentages of
 concordant, discordant, and tied pairs of input observations with
 different responses. We mention that the calculation of these quantities
 by SAS and Minitab is sometimes incorrect. The concepts and methods of the
 paper are illustrated by a hypothetical example of school retention data. 
Journal: Journal of Applied Statistics 
Pages: 629-640 
Issue: 6 
Volume: 33 
Year: 2006 
Keywords: Classification, concordant pairs, cut-off points, discordant pairs, logistic regression, maximization of proportions, sensitivity, specificity, 
X-DOI: 10.1080/02664760600723367 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600723367
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:6:p:629-640




Template-Type: ReDIF-Article 1.0
Author-Name: G. Yi 
Author-X-Name-First: G. 
Author-X-Name-Last: Yi 
Author-Name: S. Coleman 
Author-X-Name-First: S. 
Author-X-Name-Last: Coleman 
Author-Name: Q. Ren 
Author-X-Name-First: Q. 
Author-X-Name-Last: Ren 
Title: CUSUM method in predicting regime shifts and its performance in different stock markets allowing for transaction fees 
Abstract:
  Statistical Process Control (SPC) is a scientific approach to quality
 improvement in which data are collected and used as evidence of the
 performance of a process, organisation or set of equipment. One of the SPC
 techniques, the cumulative sum (CUSUM) method, first developed by E.S.
 Page (1961), uses a series of cumulative sums of sample data for online
 process control. This paper reviews CUSUM techniques applied to financial
 markets in several different ways. The performance of the CUSUM method in
 predicting regime shifts in stock market indices is then studied in
 detail. Research in this field so far does not take the transaction fees
 of buying and selling into consideration. As the study in this paper
 shows, the performances of the CUSUM when taking account of transaction
 fees are quite different to those not taking transaction fees into
 account. The CUSUM plan is defined by parameters h and k. Choosing the
 parameters of the method should be based on studies that take transaction
 fees into account. The performances of the CUSUM in different stock
 markets are also compared in this paper. The results show that the same
 CUSUM plan has remarkably different performances in different stock
 markets. 
Journal: Journal of Applied Statistics 
Pages: 647-661 
Issue: 7 
Volume: 33 
Year: 2006 
Keywords: SPC, CUSUM, regime shifts, financial markets, transaction fees, 
X-DOI: 10.1080/02664760600708590 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600708590
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:7:p:647-661




Template-Type: ReDIF-Article 1.0
Author-Name: Rob Deardon 
Author-X-Name-First: Rob 
Author-X-Name-Last: Deardon 
Author-Name: Steven Gilmour 
Author-X-Name-First: Steven 
Author-X-Name-Last: Gilmour 
Author-Name: Neil Butler 
Author-X-Name-First: Neil 
Author-X-Name-Last: Butler 
Author-Name: Kath Phelps 
Author-X-Name-First: Kath 
Author-X-Name-Last: Phelps 
Author-Name: Roy Kennedy 
Author-X-Name-First: Roy 
Author-X-Name-Last: Kennedy 
Title: Designing field experiments which are subject to representation bias 
Abstract:
  The term 'representation bias' is used to describe the disparities that
 exist between treatment effects estimated from field experiments, and
 those effects that would be seen if treatments were used in the field. In
 this paper we are specifically concerned with representation bias caused
 by disease inoculum travelling between plots, or out of the experimental
 area altogether. The scope for such bias is maximized in the case of
 airborne spread diseases. This paper extends the work of Deardon et al.
 (2004), using simulation methods to explore the relationship between
 design and representation bias. In doing so, we illustrate the importance
 of plot size and spacing, as well as treatment-to-plot allocation. We
 examine a novel class of designs, incomplete column designs, to develop an
 understanding of the mechanisms behind representation bias. We also
 introduce general methods of designing field trials, which can be used to
 limit representation bias by carefully controlling treatment to block
 allocation in both incomplete column and incomplete randomized block
 designs. Finally, we show how the commonly used practice of sampling from
 the centres of plots, rather than entire plots, can also help to control
 representation bias. 
Journal: Journal of Applied Statistics 
Pages: 663-678 
Issue: 7 
Volume: 33 
Year: 2006 
Keywords: Experimental design, inter-plot interference, plant pathology, plant disease dispersal simulation, 
X-DOI: 10.1080/02664760600708681 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600708681
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:7:p:663-678




Template-Type: ReDIF-Article 1.0
Author-Name: Alan Hutson 
Author-X-Name-First: Alan 
Author-X-Name-Last: Hutson 
Title: Modifying the exact test for a binomial proportion and comparisons with other approaches 
Abstract:
  In this note we provide a simple continuity and tail-corrected approach
 to the standard exact test for a single binomial proportion commonly used
 in practice. We redefine the p-value for the two-sided alternative by
 noting the skewed distribution of the sample proportion under the null
 hypothesis. We illustrate that for both one and two-sided alternatives the
 coverage probabilities of the new methodology approaches more closely the
 desired type I error &#x3b1; and thus recommend these modifications to the
 applied statistician for consideration. 
Journal: Journal of Applied Statistics 
Pages: 679-690 
Issue: 7 
Volume: 33 
Year: 2006 
Keywords: Binomial confidence interval, exact test, 
X-DOI: 10.1080/02664760600708723 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600708723
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:7:p:679-690




Template-Type: ReDIF-Article 1.0
Author-Name: Shih-Chou Kao 
Author-X-Name-First: Shih-Chou 
Author-X-Name-Last: Kao 
Author-Name: Chuan-Ching Ho 
Author-X-Name-First: Chuan-Ching 
Author-X-Name-Last: Ho 
Author-Name: Ying-Chin Ho 
Author-X-Name-First: Ying-Chin 
Author-X-Name-Last: Ho 
Title: Transforming the exponential by minimizing the sum of the absolute differences 
Abstract:
  This work presents an optimal value to be used in the power
 transformation to transform the exponential to normality for statistical
 process control (SPC) applications. The optimal value is found by
 minimizing the sum of absolute differences between two distinct cumulative
 probability functions. Based on this criterion, a numerical search yields
 a proposed value of 3.5142, so the transformed distribution is well
 approximated by the normal distribution. Two examples are presented to
 demonstrate the effectiveness of using the transformation method and its
 applications in SPC. The transformed data are almost normally distributed
 and the performance of the individual charts is satisfactory. Compared to
 charts that use the original exponential data and probability control
 limits, the individual charts constructed using the transformed
 distribution are superior in appearance, ease of interpretation and
 implementation by practitioners. 
Journal: Journal of Applied Statistics 
Pages: 691-702 
Issue: 7 
Volume: 33 
Year: 2006 
Keywords: Weibull distribution, exponential distribution, normal distribution, individual chart, probability control limits, 
X-DOI: 10.1080/02664760600708780 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600708780
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:7:p:691-702




Template-Type: ReDIF-Article 1.0
Author-Name: Michael Martin 
Author-X-Name-First: Michael 
Author-X-Name-Last: Martin 
Author-Name: Steven Roberts 
Author-X-Name-First: Steven 
Author-X-Name-Last: Roberts 
Title: An evaluation of bootstrap methods for outlier detection in least squares regression 
Abstract:
  Outlier detection is a critical part of data analysis, and the use of
 Studentized residuals from regression models fit using least squares is a
 very common approach to identifying discordant observations in linear
 regression problems. In this paper we propose a bootstrap approach to
 constructing critical points for use in outlier detection in the context
 of least-squares Studentized residuals, and find that this approach allows
 naturally for mild departures in model assumptions such as non-Normal
 error distributions. We illustrate our methodology through both a real
 data example and simulated data. 
Journal: Journal of Applied Statistics 
Pages: 703-720 
Issue: 7 
Volume: 33 
Year: 2006 
Keywords: Case-based resampling, error distribution, externally Studentized residuals, internally Studentized residuals, jackknife-after-bootstrap, residual-based resampling, RSTUDENT, 
X-DOI: 10.1080/02664760600708863 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600708863
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:7:p:703-720




Template-Type: ReDIF-Article 1.0
Author-Name: Markus Neuhauser 
Author-X-Name-First: Markus 
Author-X-Name-Last: Neuhauser 
Author-Name: Ludwig Hothorn 
Author-X-Name-First: Ludwig 
Author-X-Name-Last: Hothorn 
Title: A robust modification of the ordered-heterogeneity test 
Abstract:
  An ordered heterogeneity (OH) test is a test for a trend that combines a
 non-directional heterogeneity test with the rank-order information
 specified under the alternative. We propose two modifications of the OH
 test procedure: (1) to use the mean ranks of the groups rather than the
 sample means to determine the observed ordering of the groups, and (2) to
 use the maximum correlation out of the 2k - 1 - 1 possibilities under the
 alternative rather than the single ordering (1, 2, &#x2026;&#xa0;, k),
 where k is the number of independent groups. A simulation study indicates
 that these two changes increase the power of the ordered heterogeneity
 test when, as common in practice, the underlying distribution may deviate
 from a normal distribution and the trend pattern is a priori unknown. In
 contrast to the original OH test, the modified OH test can detect all
 possible patterns under the alternative with a relatively high power. 
Journal: Journal of Applied Statistics 
Pages: 721-727 
Issue: 7 
Volume: 33 
Year: 2006 
Keywords: Comparing more than two groups, <i>k</i>-sample test, tests for trend, non-parametric tests, Spearman's rank correlation, 
X-DOI: 10.1080/02664760600708954 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600708954
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:7:p:721-727




Template-Type: ReDIF-Article 1.0
Author-Name: Alex Riba 
Author-X-Name-First: Alex 
Author-X-Name-Last: Riba 
Author-Name: Josep Ginebra 
Author-X-Name-First: Josep 
Author-X-Name-Last: Ginebra 
Title: Diversity of vocabulary and homogeneity of literary style 
Abstract:
  To help settle the debate around the authorship of Tirant lo Blanc, we
 analyse the evolution of the diversity of the vocabulary used in that
 book, as measured through eight different diversity indices. The
 exploratory analysis reveals a clear single shift in diversity, that is
 estimated through change-point techniques to be in chapter 382, and might
 indicate the existence of one main author writing about four fifths of the
 book, and of a second author finishing the last one fifth of the book.
 Before chapter 382, the language is richer and more diverse than after it. 
Journal: Journal of Applied Statistics 
Pages: 729-741 
Issue: 7 
Volume: 33 
Year: 2006 
Keywords: Change-point analysis, inverse Gaussian-Poisson mixture, Sichel distribution, Simpson index, stylometry, 
X-DOI: 10.1080/02664760600708970 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600708970
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:7:p:729-741




Template-Type: ReDIF-Article 1.0
Author-Name: Farid Zayeri 
Author-X-Name-First: Farid 
Author-X-Name-Last: Zayeri 
Author-Name: Anoshirvan Kazemnejad 
Author-X-Name-First: Anoshirvan 
Author-X-Name-Last: Kazemnejad 
Title: A latent variable regression model for asymmetric bivariate ordered categorical data 
Abstract:
  In many areas of medical research, especially in studies that involve
 paired organs, a bivariate ordered categorical response should be
 analyzed. Using a bivariate continuous distribution as the latent variable
 is an interesting strategy for analyzing these data sets. In this context,
 the bivariate standard normal distribution, which leads to the bivariate
 cumulative probit regression model, is the most common choice. In this
 paper, we introduce another latent variable regression model for modeling
 bivariate ordered categorical responses. This model may be an appropriate
 alternative for the bivariate cumulative probit regression model, when
 postulating a symmetric form for marginal or joint distribution of
 response data does not appear to be a valid assumption. We also develop
 the necessary numerical procedure to obtain the maximum likelihood
 estimates of the model parameters. To illustrate the proposed model, we
 analyze data from an epidemiologic study to identify some of the most
 important risk indicators of periodontal disease among students 15-19
 years in Tehran, Iran. 
Journal: Journal of Applied Statistics 
Pages: 743-753 
Issue: 7 
Volume: 33 
Year: 2006 
Keywords: Latent variable, paired organs, bivariate cumulative model, asymmetric distribution, 
X-DOI: 10.1080/02664760600709010 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600709010
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:7:p:743-753




Template-Type: ReDIF-Article 1.0
Author-Name: Wen-Den Chen 
Author-X-Name-First: Wen-Den 
Author-X-Name-Last: Chen 
Title: Testing for spurious regression in a panel data model with the individual number and time length growing 
Abstract:
  This article shows a test for the spurious regression problem in a panel
 data model with a growing individual number and time series length. In the
 estimation, tapers are used and the integrated order for the remainder
 disturbance is extended to a real number; at the same time, the spurious
 regression problem can be detected without prior knowledge. Through Monte
 Carlo experiments, we examine the consistent estimators by various sizes
 of time length and individual number, in which the remainder disturbance
 is assumed to be either stationary or non-stationary. In addition, the
 asymptotic normality properties are discussed with a quasi log-likelihood
 function. From the power tests we can see that the estimators are quite
 successful and powerful. 
Journal: Journal of Applied Statistics 
Pages: 759-772 
Issue: 8 
Volume: 33 
Year: 2006 
Keywords: Spurious regression, Whittle method, panel data model, pseudo spectral density function, tapering, 
X-DOI: 10.1080/02664760600741989 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600741989
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:8:p:759-772




Template-Type: ReDIF-Article 1.0
Author-Name: Tony Cooper 
Author-X-Name-First: Tony 
Author-X-Name-Last: Cooper 
Author-Name: Mary Leitnaker 
Author-X-Name-First: Mary 
Author-X-Name-Last: Leitnaker 
Title: Further exploratory analysis of split-plot experiments to study certain stratified effects 
Abstract:
  Designed experiments are a key component in many companies' improvement
 strategies. Because completely randomized experiments are not always
 reasonable from a cost or physical perspective, split-plot experiments are
 prevalent. The recommended analysis accounts for the different sources of
 variation affecting whole-plot and split-plot error. However experiments
 on industrial processes must be run and, consequently analyzed quite
 differently from ones run in a controlled environment. Such experiments
 are typically subject to a wide array of uncontrolled, and barely
 understood, variation. In particular, it is important to examine the
 experimental results for additional, unanticipated sources of variation.
 In this paper, we consider how unanticipated, stratified effects may
 influence a split-plot experiment and discuss further exploratory analysis
 to indicate the presence of stratified effects. Examples of such
 experiments are provided, additional tests are suggested and discussed in
 light of their power, and recommendations given. 
Journal: Journal of Applied Statistics 
Pages: 773-786 
Issue: 8 
Volume: 33 
Year: 2006 
Keywords: Designed experiment, split-plot error, source of variation, 
X-DOI: 10.1080/02664760600742201 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600742201
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:8:p:773-786




Template-Type: ReDIF-Article 1.0
Author-Name: Sanaa Ismail 
Author-X-Name-First: Sanaa 
Author-X-Name-Last: Ismail 
Author-Name: Hesham Auda 
Author-X-Name-First: Hesham 
Author-X-Name-Last: Auda 
Title: Bayesian and fiducial inference for the inverse gaussian distribution via Gibbs sampler 
Abstract:
  This paper presents a kernel estimation of the distribution of the scale
 parameter of the inverse Gaussian distribution under type II censoring
 together with the distribution of the remaining time. Estimation is
 carried out via the Gibbs sampling algorithm combined with a missing data
 approach. Estimates and confidence intervals for the parameters of
 interest are also presented. 
Journal: Journal of Applied Statistics 
Pages: 787-805 
Issue: 8 
Volume: 33 
Year: 2006 
Keywords: Gibbs sampler, Bayesian inference, Fiducial inference, 
X-DOI: 10.1080/02664760600742268 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600742268
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:8:p:787-805




Template-Type: ReDIF-Article 1.0
Author-Name: Yuang-Chin Chiang 
Author-X-Name-First: Yuang-Chin 
Author-X-Name-Last: Chiang 
Author-Name: Lin-An Chen 
Author-X-Name-First: Lin-An 
Author-X-Name-Last: Chen 
Author-Name: Hsien-Chueh Peter Yang 
Author-X-Name-First: Hsien-Chueh Peter 
Author-X-Name-Last: Yang 
Title: Symmetric quantiles and their applications 
Abstract:
  To develop estimators with stronger efficiencies than the trimmed means
 which use the empirical quantile, Kim (1992) and Chen & Chiang (1996),
 implicitly or explicitly used the symmetric quantile, and thus introduced
 new trimmed means for location and linear regression models, respectively.
 This study further investigates the properties of the symmetric quantile
 and extends its application in several aspects. (a) The symmetric quantile
 is more efficient than the empirical quantiles in asymptotic variances
 when quantile percentage &#x3b1; is either small or large. This reveals
 that for any proposal involving the &#x3b1; th quantile of small or large
 &#x3b1; s, the symmetric quantile is the right choice; (b) a trimmed mean
 based on it has asymptotic variance achieving a Cramer-Rao lower bound in
 one heavy tail distribution; (c) an improvement of the quantiles-based
 control chart by Grimshaw & Alt (1997) is discussed; (d) Monte Carlo
 simulations of two new scale estimators based on symmetric quantiles also
 support this new quantile. 
Journal: Journal of Applied Statistics 
Pages: 807-817 
Issue: 8 
Volume: 33 
Year: 2006 
Keywords: Regression quantile, scale estimator, trimmed mean, 
X-DOI: 10.1080/02664760600743464 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600743464
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:8:p:807-817




Template-Type: ReDIF-Article 1.0
Author-Name: E. Ayuga Tellez 
Author-X-Name-First: E. Ayuga 
Author-X-Name-Last: Tellez 
Author-Name: A.J. Martin Fernandez 
Author-X-Name-First: A.J. Martin 
Author-X-Name-Last: Fernandez 
Author-Name: C. Gonzalez Garcia 
Author-X-Name-First: C. Gonzalez 
Author-X-Name-Last: Garcia 
Author-Name: E. Martinez Falero 
Author-X-Name-First: E. Martinez 
Author-X-Name-Last: Falero 
Title: Estimation of non-parametric regression for dasometric measures 
Abstract:
  The aim of this paper is to describe a simulation procedure to compare
 parametric regression against a non-parametric regression method, for
 different functions and sets of information. The proposed methodology
 improves lack of fit at the edges of the regression curves, and an
 acceptable result is obtained for the no-parametric estimation in all
 studied cases. Larger differences appear at the edges of the estimation.
 The results are applied to the study of dasometric variables, which do not
 fulfil the normality hypothesis needed for parametric estimation. The
 kernel regression shows the relationship between the studied variables,
 which would not be detected with more rigid parametric models. 
Journal: Journal of Applied Statistics 
Pages: 819-836 
Issue: 8 
Volume: 33 
Year: 2006 
Keywords: Regression kernel, edge effect, simulation, comparison, dasometric variables, 
X-DOI: 10.1080/02664760600743472 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600743472
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:8:p:819-836




Template-Type: ReDIF-Article 1.0
Author-Name: Jixian Wang 
Author-X-Name-First: Jixian 
Author-X-Name-Last: Wang 
Title: Optimal parametric design with applications to pharmacokinetic and pharmacodynamic trials 
Abstract:
  This paper considers optimal parametric designs, i.e. designs represented
 by probability measures determined by a set of parameters, for nonlinear
 models and illustrates their use in designs for pharmacokinetic (PK) and
 pharmacokinetic/pharmacodynamic (PK/PD) trials. For some practical
 problems, such as designs for modelling PK/PD relationship, this is often
 the only feasible type of design, as the design points follow a PK model
 and cannot be directly controlled. Even for ordinary design problems the
 parametric designs have some advantages over the traditional designs,
 which often have too few design points for model checking and may not be
 robust to model and parameter misspecifications. We first describe methods
 and algorithms to construct the parametric design for ordinary nonlinear
 design problems and show that the parametric designs are robust to
 parameter misspecification and have good power for model discrimination.
 Then we extend this design method to construct optimal repeated
 measurement designs for nonlinear mixed models. We also use this
 parametric design for modelling a PK/PD relationship and propose a
 simulation based algorithm. The application of parametric designs is
 illustrated with a three-parameter open one-compartment PK model for the
 ordinary design and repeated measurement design, and an Emax model for the
 phamacokinetic/pharmacodynamic trial design. 
Journal: Journal of Applied Statistics 
Pages: 837-852 
Issue: 8 
Volume: 33 
Year: 2006 
Keywords: D-optimal design, model discrimination, pharmacokinetic models, repeated measure design, parametric design, PK/PD models, 
X-DOI: 10.1080/02664760600743571 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600743571
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:8:p:837-852




Template-Type: ReDIF-Article 1.0
Author-Name: Seong-Keon Lee 
Author-X-Name-First: Seong-Keon 
Author-X-Name-Last: Lee 
Author-Name: Seohoon Jin 
Author-X-Name-First: Seohoon 
Author-X-Name-Last: Jin 
Title: Decision tree approaches for zero-inflated count data 
Abstract:
  There have been many methodologies developed about zero-inflated data in
 the field of statistics. However, there is little literature in the data
 mining fields, even though zero-inflated data could be easily found in
 real application fields. In fact, there is no decision tree method that is
 suitable for zero-inflated responses. To analyze continuous target
 variable with decision trees as one of data mining techniques, we use
 F-statistics (CHAID) or variance reduction (CART) criteria to find the
 best split. But these methods are only appropriate to a continuous target
 variable. If the target variable is rare events or zero-inflated count
 data, the above criteria could not give a good result because of its
 attributes. In this paper, we will propose a decision tree for
 zero-inflated count data, using a maximum of zero-inflated Poisson
 likelihood as the split criterion. In addition, using well-known data sets
 we will compare the performance of the split criteria. In the case when
 the analyst is interested in lower value groups (e.g. no defect areas,
 customers who do not claim), the suggested ZIP tree would be more
 efficient. 
Journal: Journal of Applied Statistics 
Pages: 853-865 
Issue: 8 
Volume: 33 
Year: 2006 
Keywords: Data mining, decision tree, homogeneity, maximum likelihood, zero-inflated Poisson (ZIP), 
X-DOI: 10.1080/02664760600743613 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600743613
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:8:p:853-865




Template-Type: ReDIF-Article 1.0
Author-Name: Drgabriel Escarela 
Author-X-Name-First: Drgabriel 
Author-X-Name-Last: Escarela 
Author-Name: Jacques Carriere 
Author-X-Name-First: Jacques 
Author-X-Name-Last: Carriere 
Title: A bivariate model of claim frequencies and severities 
Abstract:
  Bivariate claim data come from a population that consists of insureds who
 may claim either one, both or none of the two types of benefits covered by
 a policy. In the present paper, we develop a statistical procedure to fit
 bivariate distributions of claims in presence of covariates. This allows
 for a more accurate study of insureds' choice and size in the frequency
 and severity of the two types of claims. A generalised logistic model is
 employed to examine the frequency probabilities, whilst the three
 parameter Burr distribution is suggested to model the underlying severity
 distributions. The bivariate copula model is exploited in such a way that
 it allows us to adjust for a range of frequency dependence structures; a
 method for assessing the adequacy of the fitted severity model is
 outlined. A health claims dataset illustrates the methods; we describe the
 use of orthogonal polynomials for characterising the relationship between
 age and the frequency and severity models. 
Journal: Journal of Applied Statistics 
Pages: 867-883 
Issue: 8 
Volume: 33 
Year: 2006 
Keywords: Bivariate loss distribution, Frank's copula, Survival copula, Burr regression, Diagnostics, 
X-DOI: 10.1080/02664760600743969 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600743969
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:8:p:867-883




Template-Type: ReDIF-Article 1.0
Author-Name: Ayoe Hoff 
Author-X-Name-First: Ayoe 
Author-X-Name-Last: Hoff 
Title: Bootstrapping Malmquist Indices for Danish Seiners in the North Sea and Skagerrak 
Abstract:
  In connection with assessing how an ongoing development in fisheries
 management may change fishing activity, evaluation of Total Factor
 Productivity (TFP) change over a period, including efficiency, scale and
 technology changes, is an important tool. The Malmquist index, based on
 distance functions evaluated with Data Envelopment Analysis (DEA), is
 often employed to estimate TFP changes. DEA is generally gaining attention
 for evaluating efficiency and capacity in fisheries. One main criticism of
 DEA is that it does not have any statistical foundation, i.e. that it is
 not possible to make inference about DEA scores or related parameters. The
 bootstrap method for estimating confidence intervals of deterministic
 parameters can however be applied to estimate confidence intervals for DEA
 scores. This method is applied in the present paper for assessing TFP
 changes between 1987 and 1999 for the fleet of Danish seiners operating in
 the North Sea and the Skagerrak. 
Journal: Journal of Applied Statistics 
Pages: 891-907 
Issue: 9 
Volume: 33 
Year: 2006 
Keywords: Total factor productivity change, Malmquist index, data envelopment analysis, bootstrap, 
X-DOI: 10.1080/02664760600742151 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600742151
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:9:p:891-907




Template-Type: ReDIF-Article 1.0
Author-Name: Masakazu Iwasaki 
Author-X-Name-First: Masakazu 
Author-X-Name-Last: Iwasaki 
Author-Name: Hiroe Tsubaki 
Author-X-Name-First: Hiroe 
Author-X-Name-Last: Tsubaki 
Title: Bivariate Negative Binomial Generalized Linear Models for Environmental Count Data 
Abstract:
  We propose a new bivariate negative binomial model with constant
 correlation structure, which was derived from a contagious bivariate
 distribution of two independent Poisson mass functions, by mixing the
 proposed bivariate gamma type density with constantly correlated
 covariance structure (Iwasaki & Tsubaki, 2005), which satisfies the
 integrability condition of McCullagh & Nelder (1989, p. 334). The proposed
 bivariate gamma type density comes from a natural exponential family. Joe
 (1997) points out the necessity of a multivariate gamma distribution to
 derive a multivariate distribution with negative binomial margins, and the
 luck of a convenient form of multivariate gamma distribution to get a
 model with greater flexibility in a dependent structure with indices of
 dispersion. In this paper we first derive a new bivariate negative
 binomial distribution as well as the first two cumulants, and, secondly,
 formulate bivariate generalized linear models with a constantly correlated
 negative binomial covariance structure in addition to the moment estimator
 of the components of the matrix. We finally fit the bivariate negative
 binomial models to two correlated environmental data sets. 
Journal: Journal of Applied Statistics 
Pages: 909-923 
Issue: 9 
Volume: 33 
Year: 2006 
Keywords: Bivariate negative binomial generalized linear models (BIVARNB GLM), bivariate negative binomial distribution, bivariate gamma type GLM, bivariate count data analysis, 
X-DOI: 10.1080/02664760600744157 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600744157
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:9:p:909-923




Template-Type: ReDIF-Article 1.0
Author-Name: Mak Kaboudan 
Author-X-Name-First: Mak 
Author-X-Name-Last: Kaboudan 
Title: Computational Forecasting of Wavelet-converted Monthly Sunspot Numbers 
Abstract:
  Monthly average sunspot numbers follow irregular cycles with complex
 nonlinear dynamics. Statistical linear models constructed to forecast them
 are therefore inappropriate, while nonlinear models produce solutions
 sensitive to initial conditions. Two computational techniques - neural
 networks and genetic programming - that have their advantages are applied
 instead to the monthly numbers and their wavelet-transformed and
 wavelet-denoised series. The objective is to determine if modeling
 wavelet-conversions produces better forecasts than those from modeling
 series' observed values. Because sunspot numbers are indicators of
 geomagnetic activity their forecast is important. Geomagnetic storms
 endanger satellites and disrupt communications and power systems on Earth. 
Journal: Journal of Applied Statistics 
Pages: 925-941 
Issue: 9 
Volume: 33 
Year: 2006 
Keywords: Wavelets, thresholding, neural networks, genetic programming, sunspot numbers, 
X-DOI: 10.1080/02664760600744215 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600744215
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:9:p:925-941




Template-Type: ReDIF-Article 1.0
Author-Name: Murat Kucuk 
Author-X-Name-First: Murat 
Author-X-Name-Last: Kucuk 
Author-Name: Necati A&#x11f;irali-super-&#x2d9;o&#x11f;lu 
Author-X-Name-First: Necati 
Author-X-Name-Last: A&#x11f;irali-super-&#x2d9;o&#x11f;lu 
Title: Wavelet Regression Technique for Streamflow Prediction 
Abstract:
  In order to explain many secret events of natural phenomena, analyzing
 non-stationary series is generally an attractive issue for various
 research areas. The wavelet transform technique, which has been widely
 used last two decades, gives better results than former techniques for the
 analysis of earth science phenomena and for feature detection of real
 measurements. In this study, a new technique is offered for streamflow
 modeling by using the discrete wavelet transform. This new technique
 depends on the feature detection characteristic of the wavelet transform.
 The model was applied to two geographical locations with different
 climates. The results were compared with energy variation and error values
 of models. The new technique offers a good advantage through a physical
 interpretation. This technique is applied to streamflow regression models,
 because they are simple and widely used in practical applications.
 However, one can apply this technique to other models. 
Journal: Journal of Applied Statistics 
Pages: 943-960 
Issue: 9 
Volume: 33 
Year: 2006 
Keywords: Streamflow prediction, discrete wavelet transform, hydrological modeling, 
X-DOI: 10.1080/02664760600744298 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600744298
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:9:p:943-960




Template-Type: ReDIF-Article 1.0
Author-Name: Kuo-Yuan Liang 
Author-X-Name-First: Kuo-Yuan 
Author-X-Name-Last: Liang 
Author-Name: Jack Lee 
Author-X-Name-First: Jack 
Author-X-Name-Last: Lee 
Author-Name: Kurt Shao 
Author-X-Name-First: Kurt 
Author-X-Name-Last: Shao 
Title: On the Distribution of the Inverted Linear Compound of Dependent F-Variates and its Application to the Combination of Forecasts 
Abstract:
  This paper establishes a sampling theory for an inverted linear
 combination of two dependent F-variates. It is found that the random
 variable is approximately expressible in terms of a mixture of weighted
 beta distributions. Operational results, including rth-order raw moments
 and critical values of the density are subsequently obtained by using the
 Pearson Type I approximation technique. As a contribution to the
 probability theory, our findings extend Lee & Hu's (1996) recent
 investigation on the distribution of the linear compound of two
 independent F-variates. In terms of relevant applied works, our results
 refine Dickinson's (1973) inquiry on the distribution of the optimal
 combining weights estimates based on combining two independent rival
 forecasts, and provide a further advancement to the general case of
 combining three independent competing forecasts. Accordingly, our
 conclusions give a new perception of constructing the confidence intervals
 for the optimal combining weights estimates studied in the literature of
 the linear combination of forecasts. 
Journal: Journal of Applied Statistics 
Pages: 961-973 
Issue: 9 
Volume: 33 
Year: 2006 
Keywords: Combining weights, critical values, error-variance minimizing criterion, inverted F-variates, Pearson Type I approximation, 
X-DOI: 10.1080/02664760600744330 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600744330
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:9:p:961-973




Template-Type: ReDIF-Article 1.0
Author-Name: Ho-Seog Kang 
Author-X-Name-First: Ho-Seog 
Author-X-Name-Last: Kang 
Author-Name: Kee-Hoon Kang 
Author-X-Name-First: Kee-Hoon 
Author-X-Name-Last: Kang 
Author-Name: Sung Park 
Author-X-Name-First: Sung 
Author-X-Name-Last: Park 
Title: Minimax Designs for the Stability of Slope Estimation on Second-order Response Surfaces 
Abstract:
  In this paper, designs for the stability of the slope estimation on a
 second-order response surface are considered. Minimization of the point
 dispersion measure, which is maximized over all points in the region of
 interest is taken as the optimality criterion, and the minimax properties
 in some class of designs are derived in spherical and cubic regions of
 interest. We study the efficiencies of the minimax designs relative to
 other optimal designs with various criteria. 
Journal: Journal of Applied Statistics 
Pages: 975-988 
Issue: 9 
Volume: 33 
Year: 2006 
Keywords: Point dispersion measure, minimax criterion, slope variance, stable design, stability measure, 
X-DOI: 10.1080/02664760600744447 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600744447
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:9:p:975-988




Template-Type: ReDIF-Article 1.0
Author-Name: Christopher Illert 
Author-X-Name-First: Christopher 
Author-X-Name-Last: Illert 
Title: Origins of Linguistic Zonation in the Australian Alps. Part 2 - Snell's Law 
Abstract:
  In this second paper, analysing archival SE-Australian Aboriginal
 word/name lists, Snell's Law is used to deduce the likely minimal
 sound-systems of pre Ice-Age language superfamilies - some probably dating
 back beyond the first occupation of Australia by humans. The deduced
 'Turuwal-like' ancestral sound-system is then used as a basis for
 reconstructing deictic forms apparently so ancient that they seem to even
 unify 'PamaNyungan' and 'non-PamaNyungan' language within a single system
 of formal logic which, having apparently provided the semantic basis for
 at least 60,000 years of speech throughout the entire Australian
 continent, deserves to be called proto-Australian regardless of whether or
 not it arose in SE-Asia tens of millennia before. Whatever the exact age
 of this reconstructed proto-Australian, presented here for the first time,
 it is an order of magnitude older than any known human language and, as
 such, a 'Rosetta Stone' for human languages worldwide. It also provides an
 unprecedented window into human consciousness and perception of the world
 up to 75,000 years ago, which is especially significant given that humans
 can only have engaged in finely controlled speech and fully modern
 language since chance mutation of our FOXP2 gene about 120,000 years ago.
 These truly ancient deictic forms dating halfway back to the beginning of
 modern human speech, retrieved only through modern statistical analysis,
 provide insight into our very origins and as such are perhaps amongst the
 most precious cultural treasures that humanity currently possesses.1 
Journal: Journal of Applied Statistics 
Pages: 989-1030 
Issue: 9 
Volume: 33 
Year: 2006 
Keywords: Phonotactic signatures, archaeo-linguistics, proto-Australian, Snell's Law, 
X-DOI: 10.1080/02664760500450160 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760500450160
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:9:p:989-1030




Template-Type: ReDIF-Article 1.0
Author-Name: Saralees Nadarajah 
Author-X-Name-First: Saralees 
Author-X-Name-Last: Nadarajah 
Title: Acknowledgement of Priority: the Generalized Normal Distribution 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 1031-1032 
Issue: 9 
Volume: 33 
Year: 2006 
X-DOI: 10.1080/02664760600938494 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600938494
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:9:p:1031-1032




Template-Type: ReDIF-Article 1.0
Author-Name: Pradeep George 
Author-X-Name-First: Pradeep 
Author-X-Name-Last: George 
Author-Name: Madara Ogot 
Author-X-Name-First: Madara 
Author-X-Name-Last: Ogot 
Title: A Compromise Experimental Design Method for Parametric Polynomial Response Surface Approximations 
Abstract:
  This study presents a compromise approach to augmentation of experimental
 designs, necessitated by the expense of performing each experiment
 (computational or physical), that yields higher quality parametric
 polynomial response surface approximations than traditional augmentation.
 Based on the D-optimality criterion as a measure of experimental design
 quality, the method simultaneously considers several polynomial models
 during the experimental design, resulting in good quality designs for all
 models under consideration, as opposed to good quality designs only for
 lower-order models, as in the case of traditional augmentation. Several
 numerical examples and an engineering example are presented to illustrate
 the efficacy of the approach. 
Journal: Journal of Applied Statistics 
Pages: 1037-1050 
Issue: 10 
Volume: 33 
Year: 2006 
Keywords: Response surface method, surrogate models, 
X-DOI: 10.1080/02664760600746533 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600746533
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:10:p:1037-1050




Template-Type: ReDIF-Article 1.0
Author-Name: Steven Cook 
Author-X-Name-First: Steven 
Author-X-Name-Last: Cook 
Author-Name: Alan Speight 
Author-X-Name-First: Alan 
Author-X-Name-Last: Speight 
Title: International Business Cycle Asymmetry and Time Irreversible Nonlinearities 
Abstract:
  Using tests of time reversibility, this paper provides further
 statistical evidence on the long-standing conjecture in economics
 concerning the potentially asymmetric behaviour of output over the
 expansionary and contractionary phases of the business cycle. A particular
 advantage of this approach is that it provides a discriminating test that
 is instructive as to whether any asymmetries detected are due to
 asymmetric shocks to a linear model, or an underlying non-linear model
 with symmetric shocks, and in the latter case is informative as to the
 potential form of that nonlinear model. Using a long span of international
 per capita output growth data, the asymmetry detected is overwhelmingly
 consistent with the long standing perception that the output business
 cycle is characterized by steeper recessions and longer more gentle
 expansions, but the evidence for this form of business cycle asymmetry is
 weaker in the data adjusted for the influence of outliers associated with
 wars and other extreme events. Statistically significant time
 irreversibility is reported for the output growth rates of almost all of
 the countries considered in the full sample data, and there is evidence
 that this time irreversibility is of a form implying an underlying
 nonlinear model with symmetrically distributed innovations for 15 of the
 22 countries considered. However, the time irreversibility test results
 for the outlier-trimmed full sample data reveal significant time
 irreversibility in output growth for around one half of the countries
 considered, predominantly in Northern Europe and North America, and of a
 form implying a nonlinear underlying model in only a further half of those
 cases. 
Journal: Journal of Applied Statistics 
Pages: 1051-1065 
Issue: 10 
Volume: 33 
Year: 2006 
Keywords: Time reversibility, time irreversibility, nonlinearity, per capita output growth, 
X-DOI: 10.1080/02664760600746582 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600746582
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:10:p:1051-1065




Template-Type: ReDIF-Article 1.0
Author-Name: Kamziah Abd Kudus 
Author-X-Name-First: Kamziah Abd 
Author-X-Name-Last: Kudus 
Author-Name: A. C. Kimber 
Author-X-Name-First: A. C. 
Author-X-Name-Last: Kimber 
Author-Name: J. Lapongan 
Author-X-Name-First: J. 
Author-X-Name-Last: Lapongan 
Title: A Parametric Model for the Interval Censored Survival Times of Acacia Mangium Plantation in a Spacing Trial 
Abstract:
  Survival times for the Acacia mangium plantation in the Segaliud Lokan
 Project, Sabah, East Malaysia were analysed based on 20 permanent sample
 plots (PSPs) established in 1988 as a spacing experiment. The PSPs were
 established following a complete randomized block design with five levels
 of spacing randomly assigned to units within four blocks at different
 sites. The survival times of trees in years are of interest. Since the
 inventories were only conducted annually, the actual survival time for
 each tree was not observed. Hence, the data set comprises censored
 survival times. Initial analysis of the survival of the Acacia mangium
 plantation suggested there is block by spacing interaction; a Weibull
 model gives a reasonable fit to the replicate survival times within each
 PSP; but a standard Weibull regression model is inappropriate because the
 shape parameter differs between PSPs. In this paper we investigate the
 form of the non-constant Weibull shape parameter. Parsimonious models for
 the Weibull survival times have been derived using maximum likelihood
 methods. The factor selection for the parameters is based on a backward
 elimination procedure. The models are compared using likelihood ratio
 statistics. The results suggest that both Weibull parameters depend on
 spacing and block. 
Journal: Journal of Applied Statistics 
Pages: 1067-1074 
Issue: 10 
Volume: 33 
Year: 2006 
Keywords: Survival times, interval censored, Weibull distribution, maximum likelihood, backward elimination, 
X-DOI: 10.1080/02664760600746616 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600746616
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:10:p:1067-1074




Template-Type: ReDIF-Article 1.0
Author-Name: Ian McHale 
Author-X-Name-First: Ian 
Author-X-Name-Last: McHale 
Author-Name: Patrick Laycock 
Author-X-Name-First: Patrick 
Author-X-Name-Last: Laycock 
Title: Applications of a General Stable Law Regression Model 
Abstract:
  In this paper we present a method for performing regression with stable
 disturbances. The method of maximum likelihood is used to estimate both
 distribution and regression parameters. Our approach utilises a numerical
 integration procedure to calculate the stable density, followed by
 sequential quadratic programming optimisation procedures to obtain
 estimates and standard errors. A theoretical justification for the use of
 stable law regression is given followed by two real world practical
 examples of the method. First, we fit the stable law multiple regression
 model to housing price data and examine how the results differ from normal
 linear regression. Second, we calculate the beta coefficients for 26
 companies from the Financial Times Ordinary Shares Index. 
Journal: Journal of Applied Statistics 
Pages: 1075-1084 
Issue: 10 
Volume: 33 
Year: 2006 
Keywords: Stable distribution, heavy-tails, extreme values, regression, 
X-DOI: 10.1080/02664760600746699 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600746699
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:10:p:1075-1084




Template-Type: ReDIF-Article 1.0
Author-Name: Eiji Minemura 
Author-X-Name-First: Eiji 
Author-X-Name-Last: Minemura 
Title: An Interest-rate Model Analysis Based on Data Augmentation Bayesian Forecasting 
Abstract:
  In this paper, the author presents an efficient method of analyzing an
 interest-rate model using a new approach called 'data augmentation
 Bayesian forecasting.' First, a dynamic linear model estimation was
 constructed with a hierarchically-incorporated model. Next, an
 observational replication was generated based on the one-step forecast
 distribution derived from the model. A Markov-chain Monte Carlo sampling
 method was conducted on it as a new observation and unknown parameters
 were estimated. At that time, the EM algorithm was applied to establish
 initial values of unknown parameters while the 'quasi Bayes factor' was
 used to appreciate parameter candidates. 'Data augmentation Bayesian
 forecasting' is a method of evaluating the transition and history of
 'future,' 'present' and 'past' of an arbitrary stochastic process by which
 an appropriate evaluation is conducted based on the probability measure
 that has been sequentially modified with additional information. It would
 be possible to use future prediction results for modifying the model to
 grasp the present state or re-evaluate the past state. It would be also
 possible to raise the degree of precision in predicting the future through
 the modification of the present and the past. Thus, 'data augmentation
 Bayesian forecasting' is applicable not only in the field of financial
 data analysis but also in forecasting and controlling the stochastic
 process. 
Journal: Journal of Applied Statistics 
Pages: 1085-1104 
Issue: 10 
Volume: 33 
Year: 2006 
Keywords: Bayesian inference, dynamic linear model, Markov-chain Monte Carlo, computational simulation, probability measure transformation, 
X-DOI: 10.1080/02664760600746756 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600746756
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:10:p:1085-1104




Template-Type: ReDIF-Article 1.0
Author-Name: W. L. Pearn 
Author-X-Name-First: W. L. 
Author-X-Name-Last: Pearn 
Author-Name: Y. C. Chang 
Author-X-Name-First: Y. C. 
Author-X-Name-Last: Chang 
Author-Name: Chien-Wei Wu 
Author-X-Name-First: Chien-Wei 
Author-X-Name-Last: Wu 
Title: Measuring Process Performance Based on Expected Loss with Asymmetric Tolerances 
Abstract:
  By approaching capability from the point of view of process loss similar
 to Cpm , Johnson (1992) provided the expected relative loss Le to consider
 the proximity of the target value. Putting the loss in relative terms, a
 user needs only to specify the target and the distance from the target at
 which the product would have zero worth to quantify the process loss. Tsui
 (1997) expressed the index Le as Le = Lot + Lpe , which provides an
 uncontaminated separation between information concerning the process
 relative off-target loss (Lot) and the process relative inconsistency loss
 (Lpe). Unfortunately, the index Le inconsistently measures process
 capability in many cases, particularly for processes with asymmetric
 tolerances, and thus reflects process potential and performance
 inaccurately. In this paper, we consider a generalization, which we refer
 to as [image omitted]&#xa0;, to deal with processes with asymmetric
 tolerances. The generalization is shown to be superior to the original
 index Le. In the cases of symmetric tolerances, the new generalization of
 process loss indices [image omitted]&#xa0;, [image omitted]&#xa0; and
 [image omitted]&#xa0; reduces to the original index Le, Lot, and Lpe ,
 respectively. We investigate the statistical properties of a natural
 estimator of [image omitted]&#xa0; [image omitted]&#xa0; and [image
 omitted]&#xa0; when the underlying process is normally distributed. We
 obtained the rth moment, expected value, and the variance of the natural
 estimator [image omitted]&#xa0;, [image omitted]&#xa0;, and [image
 omitted]&#xa0;. We also analyzed the bias and the mean squared error in
 each case. The new generalization [image omitted]&#xa0; measures process
 loss more accurately than the original index Le. 
Journal: Journal of Applied Statistics 
Pages: 1105-1120 
Issue: 10 
Volume: 33 
Year: 2006 
Keywords: Asymmetric tolerances, bias, mean squared error, process capability indices, process loss indices, 
X-DOI: 10.1080/02664760600746871 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600746871
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:10:p:1105-1120




Template-Type: ReDIF-Article 1.0
Author-Name: Bradley Ewing 
Author-X-Name-First: Bradley 
Author-X-Name-Last: Ewing 
Author-Name: Teresa Kerr 
Author-X-Name-First: Teresa 
Author-X-Name-Last: Kerr 
Author-Name: Mark Thompson 
Author-X-Name-First: Mark 
Author-X-Name-Last: Thompson 
Title: Do Flow Rates Respond Asymmetrically to Water Level? Evidence from the Edwards Aquifer 
Abstract:
  This research examines the time series relationship between the Comal
 Springs flow rate and the water level in the Edwards Aquifer (Well J-17).
 The empirical methodology utilizes threshold autoregression (TAR) and
 momentum-TAR models that allow for asymmetry in responses and adjustments
 to a disequilibrium in the long-run cointegrating relationship. Based on
 the results, an asymmetric error-correction model (AECM) is proposed to
 characterize the short-run and long-run dynamic relationship between
 spring flow and water level. The results have implications for the
 management of water resources, water demand, and ecosystems. 
Journal: Journal of Applied Statistics 
Pages: 1121-1129 
Issue: 10 
Volume: 33 
Year: 2006 
Keywords: Threshold cointegration, asymmetric adjustment, spring flow, water level, Edwards Aquifer, 
X-DOI: 10.1080/02664760600746905 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600746905
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:10:p:1121-1129




Template-Type: ReDIF-Article 1.0
Author-Name: Kenneth Rice 
Author-X-Name-First: Kenneth 
Author-X-Name-Last: Rice 
Author-Name: David Spiegelhalter 
Author-X-Name-First: David 
Author-X-Name-Last: Spiegelhalter 
Title: A Simple Diagnostic Plot Connecting Robust Estimation, Outlier Detection, and False Discovery Rates 
Abstract:
  Robust estimation of parameters, and identification of specific data
 points that are discordant with an assumed model, are often treated as
 different statistical problems. The two aims are, however, closely
 inter-related and in many cases the two analyses are required
 simultaneously. We present a simple diagnostic plot that connects existing
 robust estimators with simultaneous outlier detection, and uses the
 concept of false discovery rates to allow for the multiple comparisons
 induced by considering each point as a potential outlier. It is
 straightforward to implement, and applicable in any situation for which
 robust estimation procedures exist. Several examples are given. 
Journal: Journal of Applied Statistics 
Pages: 1131-1147 
Issue: 10 
Volume: 33 
Year: 2006 
Keywords: Robust estimation, Outlier detection, False discovery rate, 
X-DOI: 10.1080/02664760600747002 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600747002
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:10:p:1131-1147




Template-Type: ReDIF-Article 1.0
Author-Name: M. C. Jones 
Author-X-Name-First: M. C. 
Author-X-Name-Last: Jones 
Title: Book Review 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 1149-1151 
Issue: 10 
Volume: 33 
Year: 2006 
X-DOI: 10.1080/02664760600747424 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600747424
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:33:y:2006:i:10:p:1149-1151




Template-Type: ReDIF-Article 1.0
Author-Name: Alice Whittemore 
Author-X-Name-First: Alice 
Author-X-Name-Last: Whittemore 
Title: A Bayesian False Discovery Rate for Multiple Testing 
Abstract:
  Case-control studies of genetic polymorphisms and gene-environment
 interactions are reporting large numbers of statistically significant
 associations, many of which are likely to be spurious. This problem
 reflects the low prior probability that any one null hypothesis is false,
 and the large number of test results reported for a given study. In a
 Bayesian approach to the low prior probabilities, Wacholder et al. (2004)
 suggest supplementing the p-value for a hypothesis with its posterior
 probability given the study data. In a frequentist approach to the test
 multiplicity problem, Benjamini & Hochberg (1995) propose a
 hypothesis-rejection rule that provides greater statistical power by
 controlling the false discovery rate rather than the family-wise error
 rate controlled by the Bonferroni correction. This paper defines a Bayes
 false discovery rate and proposes a Bayes-based rejection rule for
 controlling it. The method, which combines the Bayesian approach of
 Wacholder et al. with the frequentist approach of Benjamini & Hochberg, is
 used to evaluate the associations reported in a case-control study of
 breast cancer risk and genetic polymorphisms of genes involved in the
 repair of double-strand DNA breaks. 
Journal: Journal of Applied Statistics 
Pages: 1-9 
Issue: 1 
Volume: 34 
Year: 2007 
Keywords: Bayes, breast cancer, false discovery rate, false positive report probability, haplotypes, multiple comparisons, single nucleotide polymorphism, 
X-DOI: 10.1080/02664760600994745 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600994745
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:1:p:1-9




Template-Type: ReDIF-Article 1.0
Author-Name: Amy Ming-Fang Yen 
Author-X-Name-First: Amy Ming-Fang 
Author-X-Name-Last: Yen 
Author-Name: Tony Hsiu-Hsi Chen 
Author-X-Name-First: Tony Hsiu-Hsi 
Author-X-Name-Last: Chen 
Title: Mixture Multi-state Markov Regression Model 
Abstract:
  Although heterogeneity across individuals may be reduced when a two-state
 process is extended into a multi-state process, the discrepancy between
 the observed and the predicted for some states may still exist owing to
 two possibilities, unobserved mixture distribution in the initial state
 and the effect of measured covariates on subsequent multi-state disease
 progression. In the present study, we developed a mixture Markov
 exponential regression model to take account of the above-mentioned
 heterogeneity across individuals (subject-to-subject variability) with a
 systematic model selection based on the likelihood ratio test. The model
 was successfully demonstrated by an empirical example on surveillance of
 patients with small hepatocellular carcinoma treated by non-surgical
 methods. The estimated results suggested that the model with the
 incorporation of unobserved mixture distribution behaves better than the
 one without. Complete and partial effects regarding risk factors on
 different subsequent multi-state transitions were identified using a
 homogeneous Markov model. The combination of both initial mixture
 distribution and homogeneous Markov exponential regression model makes a
 significant contribution to reducing heterogeneity across individuals and
 over time for disease progression. 
Journal: Journal of Applied Statistics 
Pages: 11-21 
Issue: 1 
Volume: 34 
Year: 2007 
Keywords: Markov mixture model, multi-state, model selection, 
X-DOI: 10.1080/02664760600994711 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600994711
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:1:p:11-21




Template-Type: ReDIF-Article 1.0
Author-Name: K. D. Patterson 
Author-X-Name-First: K. D. 
Author-X-Name-Last: Patterson 
Title: Bias Reduction through First-order Mean Correction, Bootstrapping and Recursive Mean Adjustment 
Abstract:
  Standard methods of estimation for autoregressive models are known to be
 biased in finite samples, which has implications for estimation,
 hypothesis testing, confidence interval construction and forecasting.
 Three methods of bias reduction are considered here: first-order bias
 correction, FOBC, where the total bias is approximated by the O(T-1) bias;
 bootstrapping; and recursive mean adjustment, RMA. In addition, we show
 how first-order bias correction is related to linear bias correction. The
 practically important case where the AR model includes an unknown linear
 trend is considered in detail. The fidelity of nominal to actual coverage
 of confidence intervals is also assessed. A simulation study covers the
 AR(1) model and a number of extensions based on the empirical AR(p) models
 fitted by Nelson & Plosser (1982). Overall, which method dominates depends
 on the criterion adopted: bootstrapping tends to be the best at reducing
 bias, recursive mean adjustment is best at reducing mean squared error,
 whilst FOBC does particularly well in maintaining the fidelity of
 confidence intervals. 
Journal: Journal of Applied Statistics 
Pages: 23-45 
Issue: 1 
Volume: 34 
Year: 2007 
Keywords: Autoregressive model, bias, first-order correction, bootstrap bias correction, recursive mean adjustment, 
X-DOI: 10.1080/02664760600994638 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600994638
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:1:p:23-45




Template-Type: ReDIF-Article 1.0
Author-Name: Noriah Al-Kandari 
Author-X-Name-First: Noriah 
Author-X-Name-Last: Al-Kandari 
Author-Name: Sana Buhamra 
Author-X-Name-First: Sana 
Author-X-Name-Last: Buhamra 
Author-Name: S. E. Ahmed 
Author-X-Name-First: S. E. 
Author-X-Name-Last: Ahmed 
Title: Testing and Merging Information for Effect Size Estimation 
Abstract:
  A large-sample test for testing the equality of two effect sizes is
 presented. The null and non-null distributions of the proposed test
 statistic are derived. Further, the problem of estimating the effect size
 is considered when it is a priori suspected that two effect sizes may be
 close to each other. The combined data from all the samples leads to more
 efficient estimator of the effect size. We propose a basis for optimally
 combining estimation problems when there is uncertainty concerning the
 appropriate statistical model-estimator to use in representing the
 sampling process. The objective here is to produce natural adaptive
 estimators with some good statistical properties. In the context of two
 bivariate statistical models, the expressions for the asymptotic mean
 squared error of the proposed estimators are derived and compared with the
 parallel expressions for the benchmark estimators. We demonstrate that the
 suggested preliminary test estimator has superior asymptotic mean squared
 error performance relative to the benchmark and pooled estimators. A
 simulation study and application of the methodology to real data are
 presented. 
Journal: Journal of Applied Statistics 
Pages: 47-60 
Issue: 1 
Volume: 34 
Year: 2007 
Keywords: Effect size, pooling, preliminary test estimator, large-sample properties, 
X-DOI: 10.1080/02664760600994604 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600994604
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:1:p:47-60




Template-Type: ReDIF-Article 1.0
Author-Name: Agnes Herzberg 
Author-X-Name-First: Agnes 
Author-X-Name-Last: Herzberg 
Author-Name: Richard Jarrett 
Author-X-Name-First: Richard 
Author-X-Name-Last: Jarrett 
Title: A-Optimal Block Designs with Additional Singly Replicated Treatments 
Abstract:
  Block designs to which have been added a number of singly-replicated
 treatments, known as secondary treatments, are particularly useful for
 experiments where only small amounts of material are available for some
 treatments, for example new plant varieties. The designs are of particular
 use in the microarray situation. Such designs are known as 'augmented
 designs'. This paper obtains the properties of these designs and shows
 that, with an equal number of secondary treatments in each block, the
 A-optimal design is obtained by using the A-optimal design for the
 original block design. It develops formulae for the variance of treatment
 comparisons, for both the primary and the secondary treatments. A number
 of examples are used to illustrate the results. 
Journal: Journal of Applied Statistics 
Pages: 61-70 
Issue: 1 
Volume: 34 
Year: 2007 
Keywords: Augmented designs, chain-block designs, coat-of-mail designs, block designs, efficiency, microarray designs, optimality, secondary treatments, singly-linked block designs, 
X-DOI: 10.1080/02664760600744512 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600744512
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:1:p:61-70




Template-Type: ReDIF-Article 1.0
Author-Name: Housila Singh 
Author-X-Name-First: Housila 
Author-X-Name-Last: Singh 
Author-Name: Mariano Ruiz Espejo 
Author-X-Name-First: Mariano Ruiz 
Author-X-Name-Last: Espejo 
Title: Double Sampling Ratio-product Estimator of a Finite Population Mean in Sample Surveys 
Abstract:
  It is well known that two-phase (or double) sampling is of significant
 use in practice when the population parameter(s) (say, population mean
 X-super-&#xaf;) of the auxiliary variate x is not known. Keeping this in
 view, we have suggested a class of ratio-product estimators in two-phase
 sampling with its properties. The asymptotically optimum estimators (AOEs)
 in the class are identified in two different cases with their variances.
 Conditions for the proposed estimator to be more efficient than the
 two-phase sampling ratio, product and mean per unit estimator are
 investigated. Comparison with single phase sampling is also discussed. An
 empirical study is carried out to demonstrate the efficiency of the
 suggested estimator over conventional estimators. 
Journal: Journal of Applied Statistics 
Pages: 71-85 
Issue: 1 
Volume: 34 
Year: 2007 
Keywords: Auxiliary variate, double sampling ratio and product estimators, finite population mean, study variate, 
X-DOI: 10.1080/02664760600994562 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600994562
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:1:p:71-85




Template-Type: ReDIF-Article 1.0
Author-Name: Thorsten Thadewald 
Author-X-Name-First: Thorsten 
Author-X-Name-Last: Thadewald 
Author-Name: Herbert Buning 
Author-X-Name-First: Herbert 
Author-X-Name-Last: Buning 
Title: Jarque-Bera Test and its Competitors for Testing Normality - A Power Comparison 
Abstract:
  For testing normality we investigate the power of several tests, first of
 all, the well-known test of Jarque & Bera (1980) and furthermore the tests
 of Kuiper (1960) and Shapiro & Wilk (1965) as well as tests of
 Kolmogorov-Smirnov and Cramer-von Mises type. The tests on normality are
 based, first, on independent random variables (model I) and, second, on
 the residuals in the classical linear regression (model II). We
 investigate the exact critical values of the Jarque-Bera test and the
 Kolmogorov-Smirnov and Cramer-von Mises tests, in the latter case for the
 original and standardized observations where the unknown parameters
 &#x3bc; and &#x3c3; have to be estimated. The power comparison is carried
 out via Monte Carlo simulation assuming the model of contaminated normal
 distributions with varying parameters &#x3bc; and &#x3c3; and different
 proportions of contamination. It turns out that for the Jarque-Bera test
 the approximation of critical values by the chi-square distribution does
 not work very well. The test is superior in power to its competitors for
 symmetric distributions with medium up to long tails and for slightly
 skewed distributions with long tails. The power of the Jarque-Bera test is
 poor for distributions with short tails, especially if the shape is
 bimodal - sometimes the test is even biased. In this case a modification
 of the Cramer-von Mises test or the Shapiro-Wilk test may be recommended. 
Journal: Journal of Applied Statistics 
Pages: 87-105 
Issue: 1 
Volume: 34 
Year: 2007 
Keywords: Goodness-of-fit tests, tests of Kolmogorov-Smirnov and Cramer-von Mises type, Shapiro-Wilk test, Kuiper test, skewness, kurtosis, contaminated normal distribution, Monte Carlo simulation, critical values, power comparison, 
X-DOI: 10.1080/02664760600994539 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600994539
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:1:p:87-105




Template-Type: ReDIF-Article 1.0
Author-Name: Zhiguo Wang 
Author-X-Name-First: Zhiguo 
Author-X-Name-Last: Wang 
Author-Name: Jinde Wang 
Author-X-Name-First: Jinde 
Author-X-Name-Last: Wang 
Author-Name: Xue Liang 
Author-X-Name-First: Xue 
Author-X-Name-Last: Liang 
Title: Non-parametric Estimation for NHPP Software Reliability Models 
Abstract:
  The non-homogeneous Poisson process (NHPP) model is a very important
 class of software reliability models and is widely used in software
 reliability engineering. NHPPs are characterized by their intensity
 functions. In the literature it is usually assumed that the functional
 forms of the intensity functions are known and only some parameters in
 intensity functions are unknown. The parametric statistical methods can
 then be applied to estimate or to test the unknown reliability models.
 However, in realistic situations it is often the case that the functional
 form of the failure intensity is not very well known or is completely
 unknown. In this case we have to use functional (non-parametric)
 estimation methods. The non-parametric techniques do not require any
 preliminary assumption on the software models and then can reduce the
 parameter modeling bias. The existing non-parametric methods in the
 statistical methods are usually not applicable to software reliability
 data. In this paper we construct some non-parametric methods to estimate
 the failure intensity function of the NHPP model, taking the
 particularities of the software failure data into consideration. 
Journal: Journal of Applied Statistics 
Pages: 107-119 
Issue: 1 
Volume: 34 
Year: 2007 
Keywords: Software reliability, NHPP model, intensity function, non-parametric estimation, 
X-DOI: 10.1080/02664760600994497 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600994497
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:1:p:107-119




Template-Type: ReDIF-Article 1.0
Author-Name: Howard D. Bondell 
Author-X-Name-First: Howard D. 
Author-X-Name-Last: Bondell 
Author-Name: Aiyi Liu 
Author-X-Name-First: Aiyi 
Author-X-Name-Last: Liu 
Author-Name: Enrique F. Schisterman 
Author-X-Name-First: Enrique F. 
Author-X-Name-Last: Schisterman 
Title: Statistical Inference Based on Pooled Data: A Moment-Based Estimating Equation Approach 
Abstract:
  We consider statistical inference on parameters of a distribution when
 only pooled data are observed. A moment-based estimating equation approach
 is proposed to deal with situations where likelihood functions based on
 pooled data are difficult to work with. We outline the method to obtain
 estimates and test statistics of the parameters of interest in the general
 setting. We demonstrate the approach on the family of distributions
 generated by the Box-Cox transformation model, and, in the process,
 construct tests for goodness of fit based on the pooled data. 
Journal: Journal of Applied Statistics 
Pages: 129-140 
Issue: 2 
Volume: 34 
Year: 2007 
Keywords: Pooling biospecimens, set-based observations, moments, Box-Cox transformation, goodness-of-fit, lognormal distribution, 
X-DOI: 10.1080/02664760600994844 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600994844
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:2:p:129-140




Template-Type: ReDIF-Article 1.0
Author-Name: Birdal Seno&#x11f;lu 
Author-X-Name-First: Birdal 
Author-X-Name-Last: Seno&#x11f;lu 
Title: Robust Estimation and Hypothesis Testing of Linear Contrasts in Analysis of Covariance with Stochastic Covariates 
Abstract:
  Estimators of parameters are derived by using the method of modified
 maximum likelihood (MML) estimation when the distribution of covariate X
 and the error e are both non-normal in a simple analysis of covariance
 (ANCOVA) model. We show that our estimators are efficient. We also develop
 a test statistic for testing a linear contrast and show that it is robust.
 We give a real life example. 
Journal: Journal of Applied Statistics 
Pages: 141-151 
Issue: 2 
Volume: 34 
Year: 2007 
Keywords: Generalized logistic, linear contrasts, modified likelihood, non-normality, robustness, stochastic covariates, 
X-DOI: 10.1080/02664760600994869 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600994869
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:2:p:141-151




Template-Type: ReDIF-Article 1.0
Author-Name: A. F. Militino 
Author-X-Name-First: A. F. 
Author-X-Name-Last: Militino 
Author-Name: M. D. Ugarte 
Author-X-Name-First: M. D. 
Author-X-Name-Last: Ugarte 
Author-Name: T. Goicoa 
Author-X-Name-First: T. 
Author-X-Name-Last: Goicoa 
Title: A BLUP Synthetic Versus an EBLUP Estimator: An Empirical Study of a Small Area Estimation Problem 
Abstract:
  Model-based estimators are becoming very popular in statistical offices
 because Governments require accurate estimates for small domains that were
 not planned when the study was designed, as their inclusion would have
 produced an increase in the cost of the study. The sample sizes in these
 domains are very small or even zero; consequently, traditional direct
 design-based estimators lead to unacceptably large standard errors. In
 this regard, model-based estimators that 'borrow information' from related
 areas by using auxiliary information are appropriate. This paper reviews,
 under the model-based approach, a BLUP synthetic and an EBLUP estimator.
 The goal is to obtain estimators of domain totals when there are several
 domains with very small sample sizes or without sampled units. We also
 provide detailed expressions of the mean squared error at different levels
 of aggregation. The results are illustrated with real data from the Basque
 Country Business Survey. 
Journal: Journal of Applied Statistics 
Pages: 153-165 
Issue: 2 
Volume: 34 
Year: 2007 
Keywords: Finite population, prediction theory, mixed models, mean squared error, business survey, 
X-DOI: 10.1080/02664760600994893 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600994893
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:2:p:153-165




Template-Type: ReDIF-Article 1.0
Author-Name: E. Kolaiti 
Author-X-Name-First: E. 
Author-X-Name-Last: Kolaiti 
Author-Name: C. Koukouvinos 
Author-X-Name-First: C. 
Author-X-Name-Last: Koukouvinos 
Title: A Comparison of Three-level Orthogonal Arrays in the Presence of a Possible Correlation in Observations 
Abstract:
  When we want to compare two designs we usually assume the standard linear
 model with uncorrelated observations. In this paper we use the comparison
 method proposed by Ghosh & Shen (2006) to compare three level orthogonal
 arrays with 18, 27 and 36 runs under a possible presence of correlation in
 observations. 
Journal: Journal of Applied Statistics 
Pages: 167-175 
Issue: 2 
Volume: 34 
Year: 2007 
Keywords: Correlation in observations, linear model, orthogonal arrays, optimal design, change of variance functions, 
X-DOI: 10.1080/02664760600995056 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600995056
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:2:p:167-175




Template-Type: ReDIF-Article 1.0
Author-Name: Yi-Ting Hwang 
Author-X-Name-First: Yi-Ting 
Author-X-Name-Last: Hwang 
Author-Name: Peir-Feng Wei 
Author-X-Name-First: Peir-Feng 
Author-X-Name-Last: Wei 
Title: A Remark on the Zhang Omnibus Test for Normality 
Abstract:
  Zhang (1999) proposed a novel test statistic Q for testing normality
 based on the ratio of two unbiased standard deviation estimators, q1 and
 q2, for the true population standard deviation &#x3c3;. Mingoti & Neves
 (2003) discussed some properties of q1 and q2 and showed that the variance
 of q1 increases as the true population variance increases. In this paper,
 we show that the distribution of q1 is not normal. As a result, normality
 percentage points for Q are not appropriate. In this paper, percentage
 points of Q are obtained using simulations. Monte Carlo simulations are
 provided to evaluate the performance of the new method and Zhang's method. 
Journal: Journal of Applied Statistics 
Pages: 177-184 
Issue: 2 
Volume: 34 
Year: 2007 
Keywords: Empirical distribution, Monte Carlo simulation, Normality test, <i>Q</i> statistic, 
X-DOI: 10.1080/02664760600995064 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600995064
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:2:p:177-184




Template-Type: ReDIF-Article 1.0
Author-Name: Saralees Nadarajah 
Author-X-Name-First: Saralees 
Author-X-Name-Last: Nadarajah 
Author-Name: Samuel Kotz 
Author-X-Name-First: Samuel 
Author-X-Name-Last: Kotz 
Title: On the Linear Combination of Laplace and Logistic Random Variables 
Abstract:
  The distribution of linear combinations of random variables arises
 explicitly in many areas of engineering. This has increased the need to
 have available the widest possible range of statistical results on linear
 combinations of random variables. In this note, the exact distribution of
 the linear combination &#x3b1; X+&#x3b2; Y is derived when X and Y are
 Laplace and logistic random variables distributed independently of each
 other. Extensive tabulations of the associated percentage points obtained
 by inverting the derived distribution are also given. 
Journal: Journal of Applied Statistics 
Pages: 185-194 
Issue: 2 
Volume: 34 
Year: 2007 
X-DOI: 10.1080/02664760600995072 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600995072
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:2:p:185-194




Template-Type: ReDIF-Article 1.0
Author-Name: Zeinab H. Amin 
Author-X-Name-First: Zeinab H. 
Author-X-Name-Last: Amin 
Title: Tests for the Validity of the Assumption that the Underlying Distribution of Life is Pareto 
Abstract:
  This article considers the problem of testing the validity of the
 assumption that the underlying distribution of life is Pareto. For
 complete and censored samples, the relationship between the Pareto and the
 exponential distributions could be of vital importance to test for the
 validity of this assumption. For grouped uncensored data the classical
 Pearson &#x3c7;2 test based on the multinomial model can be used.
 Attention is confined in this article to handle grouped data with
 withdrawals within intervals. Graphical as well as analytical procedures
 will be presented. Maximum likelihood estimators for the parameters of the
 Pareto distribution based on grouped data will be derived. 
Journal: Journal of Applied Statistics 
Pages: 195-201 
Issue: 2 
Volume: 34 
Year: 2007 
Keywords: Goodness of fit tests, Pareto distribution, grouped data, Types I and II censoring, hazard rate, maximum likelihood estimator, likelihood ratio statistic, 
X-DOI: 10.1080/02664760600995098 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600995098
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:2:p:195-201




Template-Type: ReDIF-Article 1.0
Author-Name: L. Corain 
Author-X-Name-First: L. 
Author-X-Name-Last: Corain 
Author-Name: L. Salmaso 
Author-X-Name-First: L. 
Author-X-Name-Last: Salmaso 
Title: A Non-parametric Method for Defining a Global Preference Ranking of Industrial Products 
Abstract:
  Although experimentation is a crucial stage in the process of research
 and development of industrial products, no satisfactory procedure is
 available to deal with the common but rather important industrial problem
 of defining a preference ranking among all the studied product prototypes
 on the basis of performances. In this paper we propose a two-stage
 non-parametric procedure in which we firstly perform a set of C-sample
 testing procedures, followed by multiple comparisons, in this way
 evaluating a set of partial preference rankings, and secondly synthesise
 the partial rankings by combining them into a global ranking that provides
 a general product preference rule. The proposed method is particularly
 useful in the context of industrial experimentation and offers several
 advantages such as effectiveness, high flexibility and practical adherence
 to real problems where preference ranking is a natural goal. 
Journal: Journal of Applied Statistics 
Pages: 203-216 
Issue: 2 
Volume: 34 
Year: 2007 
Keywords: Dependent rankings, industrial products, non-parametric combination, permutation tests, research and development, 
X-DOI: 10.1080/02664760600995122 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600995122
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:2:p:203-216




Template-Type: ReDIF-Article 1.0
Author-Name: C. A. Glasbey 
Author-X-Name-First: C. A. 
Author-X-Name-Last: Glasbey 
Author-Name: G. W. A. M. Van Der Heijden 
Author-X-Name-First: G. W. A. M. 
Author-X-Name-Last: Van Der Heijden 
Title: Alignment and Sub-pixel Interpolation of Images using Fourier Methods 
Abstract:
  A method is proposed for both estimating and correcting a translational
 mis-alignment between digital images, taking account of aliasing of
 high-frequency information. A parametric model is proposed for the power-
 and cross-spectra of the multivariate stochastic process that is assumed
 to have generated a continuous-space version of the images. Parameters,
 including those that specify misalignment, are estimated by numerical
 maximum likelihood. The effectiveness of the interpolant is confirmed by
 simulation and illustrated using multi-band Landsat images. 
Journal: Journal of Applied Statistics 
Pages: 217-230 
Issue: 2 
Volume: 34 
Year: 2007 
Keywords: Aliasing, coherency, complex Gaussian distribution, cross-spectrum, landsat image, phase spectrum, power spectrum, sub-pixel, 
X-DOI: 10.1080/02664760600995155 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600995155
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:2:p:217-230




Template-Type: ReDIF-Article 1.0
Author-Name: Arup Kumar Das 
Author-X-Name-First: Arup Kumar 
Author-X-Name-Last: Das 
Title: Application of Multivariate Analysis to Increase the Yield of Dry Cell Batteries 
Abstract:
  In an organization, the manufacturing process of a dry cell battery was
 suffering from the problem of frequent stoppages in the assembly line. The
 complete battery manufacturing operation is highly automated and
 mechanized. It was suspected that excessive variation in overall height of
 bobbin was the major reason for such stoppages. The bobbin, the inside
 part of a dry cell battery acting as cathode, is formed by the battery
 extrusion process. A planned experiment was carried out on the extrusion
 process that identified the setting of extrusion machines and the amount
 of water content in the cathode mixture as the parameters causing
 variation in the bobbin characteristics. The problem of frequent stoppages
 was eliminated when appropriate action was taken on these two parameters.
 Finally, multivariate and univariate control schemes were developed for
 online control of the bobbin characteristics. 
Journal: Journal of Applied Statistics 
Pages: 239-248 
Issue: 3 
Volume: 34 
Year: 2007 
Keywords: Battery extrusion process, cathode mixture, Bartlett's test, Duncan's multiple range test, MANOVA, multivariate control chart, 
X-DOI: 10.1080/02664760601004619 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760601004619
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:3:p:239-248




Template-Type: ReDIF-Article 1.0
Author-Name: Indranil Mukhopadhyay 
Author-X-Name-First: Indranil 
Author-X-Name-Last: Mukhopadhyay 
Author-Name: Sudipta Chatterjee 
Author-X-Name-First: Sudipta 
Author-X-Name-Last: Chatterjee 
Author-Name: Aditya Chatterjee 
Author-X-Name-First: Aditya 
Author-X-Name-Last: Chatterjee 
Title: Towards Enhancement of the Economy of a Thermal Power Generating System through Prediction of Plant Efficiency 
Abstract:
  The plant 'Heat Rate' (HR) is a measure of overall efficiency of a
 thermal power generating system. It depends on a large number of factors,
 some of which are non-measurable, while data relating to others are seldom
 available and recorded. However, coal quality (expressed in terms of
 'effective heat value' (EHV) as kcal/kg) transpires to be one of the
 important factors that influences HR values and data on EHV are available
 in any thermal power generating system. In the present work, we propose a
 prediction interval of the HR values on the basis of only EHV, keeping in
 mind that coal quality is one of the important (but not the only) factors
 that have a pronounced effect on the combustion process and hence on HR.
 The underlying theory borrows the idea of providing simultaneous
 confidence interval (SCI) to the coefficients of a p-th p(&#x2265;1) order
 autoregressive model (AR(p)). The theory has been substantiated with the
 help of real life data from a power utility (after suitable base and scale
 transformation of the data to maintain the confidentiality of the
 classified document). Scope for formulating strategies to enhance the
 economy of a thermal power generating system has also been explored. 
Journal: Journal of Applied Statistics 
Pages: 249-259 
Issue: 3 
Volume: 34 
Year: 2007 
Keywords: Plant heat rate, effective heat value, dependence analysis, autoregressive process, prediction interval, 
X-DOI: 10.1080/02664760601004767 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760601004767
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:3:p:249-259




Template-Type: ReDIF-Article 1.0
Author-Name: Mohamed Boutahar 
Author-X-Name-First: Mohamed 
Author-X-Name-Last: Boutahar 
Author-Name: Velayoudom Marimoutou 
Author-X-Name-First: Velayoudom 
Author-X-Name-Last: Marimoutou 
Author-Name: Leila Nouira 
Author-X-Name-First: Leila 
Author-X-Name-Last: Nouira 
Title: Estimation Methods of the Long Memory Parameter: Monte Carlo Analysis and Application 
Abstract:
  Since the seminal paper of Granger & Joyeux (1980), the concept of a long
 memory has focused the attention of many statisticians and econometricians
 trying to model and measure the persistence of stationary processes. Many
 methods for estimating d, the long-range dependence parameter, have been
 suggested since the work of Hurst (1951). They can be summarized in three
 classes: the heuristic methods, the semi-parametric methods and the
 maximum likelihood methods. In this paper, we try by simulation, to verify
 the two main properties of d-super-&#x2c6;: the consistency and the
 asymptotic normality. Hence, it is very important for practitioners to
 compare the performance of the various classes of estimators. The results
 indicate that only the semi-parametric and the maximum likelihood methods
 can give good estimators. They also suggest that the AR component of the
 ARFIMA (1, d, 0) process has an important impact on the properties of the
 different estimators and that the Whittle method is the best one, since it
 has the small mean squared error. We finally carry out an empirical
 application using the monthly seasonally adjusted US Inflation series, in
 order to illustrate the usefulness of the different estimation methods in
 the context of using real data. 
Journal: Journal of Applied Statistics 
Pages: 261-301 
Issue: 3 
Volume: 34 
Year: 2007 
Keywords: Long memory, ARFIMA (<i>p d q</i>) process, fractional Gaussian noise, Monte Carlo study, 
X-DOI: 10.1080/02664760601004874 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760601004874
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:3:p:261-301




Template-Type: ReDIF-Article 1.0
Author-Name: Lilian M. De Menezes 
Author-X-Name-First: Lilian M. 
Author-X-Name-Last: De Menezes 
Author-Name: Ana Lasaosa 
Author-X-Name-First: Ana 
Author-X-Name-Last: Lasaosa 
Title: Comparing Fits of Latent Trait and Latent Class Models Applied to Sparse Binary Data: An Illustration with Human Resource Management Data 
Abstract:
  This paper addresses the problem of comparing the fit of latent class and
 latent trait models when the indicators are binary and the contingency
 table is sparse. This problem is common in the analysis of data from large
 surveys, where many items are associated with an unobservable variable. A
 study of human resource data illustrates: (1) how the usual
 goodness-of-fit tests, model selection and cross-validation criteria can
 be inconclusive; (2) how model selection and evaluation procedures from
 time series and economic forecasting can be applied to extend residual
 analysis in this context. 
Journal: Journal of Applied Statistics 
Pages: 303-319 
Issue: 3 
Volume: 34 
Year: 2007 
Keywords: Multivariate statistics, latent variable models, forecast encompassing, human resource management, 
X-DOI: 10.1080/02664760601004908 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760601004908
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:3:p:303-319




Template-Type: ReDIF-Article 1.0
Author-Name: Andreas Diekmann 
Author-X-Name-First: Andreas 
Author-X-Name-Last: Diekmann 
Title: Not the First Digit! Using Benford's Law to Detect Fraudulent Scientif ic Data 
Abstract:
  Digits in statistical data produced by natural or social processes are
 often distributed in a manner described by 'Benford's law'. Recently, a
 test against this distribution was used to identify fraudulent accounting
 data. This test is based on the supposition that first, second, third, and
 other digits in real data follow the Benford distribution while the digits
 in fabricated data do not. Is it possible to apply Benford tests to detect
 fabricated or falsified scientific data as well as fraudulent financial
 data? We approached this question in two ways. First, we examined the use
 of the Benford distribution as a standard by checking the frequencies of
 the nine possible first and ten possible second digits in published
 statistical estimates. Second, we conducted experiments in which subjects
 were asked to fabricate statistical estimates (regression coefficients).
 The digits in these experimental data were scrutinized for possible
 deviations from the Benford distribution. There were two main findings.
 First, both digits of the published regression coefficients were
 approximately Benford distributed or at least followed a pattern of
 monotonic decline. Second, the experimental results yielded new insights
 into the strengths and weaknesses of Benford tests. Surprisingly, first
 digits of faked data also exhibited a pattern of monotonic decline, while
 second, third, and fourth digits were distributed less in accordance with
 Benford's law. At least in the case of regression coefficients, there were
 indications that checks for digit-preference anomalies should focus less
 on the first (i.e. leftmost) and more on later digits. 
Journal: Journal of Applied Statistics 
Pages: 321-329 
Issue: 3 
Volume: 34 
Year: 2007 
Keywords: Benford, first digit law, digital analysis, data fabrication, distribution of digits from regression coefficients, 
X-DOI: 10.1080/02664760601004940 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760601004940
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:3:p:321-329




Template-Type: ReDIF-Article 1.0
Author-Name: Kang-Mo Jung 
Author-X-Name-First: Kang-Mo 
Author-X-Name-Last: Jung 
Title: Least Trimmed Squares Estimator in the Errors-in-Variables Model 
Abstract:
  We propose a robust estimator in the errors-in-variables model using the
 least trimmed squares estimator. We call this estimator the orthogonal
 least trimmed squares (OLTS) estimator. We show that the OLTS estimator
 has the high breakdown point and appropriate equivariance properties. We
 develop an algorithm for the OLTS estimate. Simulations are performed to
 compare the efficiencies of the OLTS estimates with the total least
 squares (TLS) estimates and a numerical example is given to illustrate the
 effectiveness of the estimate. 
Journal: Journal of Applied Statistics 
Pages: 331-338 
Issue: 3 
Volume: 34 
Year: 2007 
Keywords: Breakdown point, equivariance, errors-in-variables model, least trimmed squares estimator, orthogonal regression, 
X-DOI: 10.1080/02664760601004973 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760601004973
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:3:p:331-338




Template-Type: ReDIF-Article 1.0
Author-Name: Jabu S. Sithole 
Author-X-Name-First: Jabu S. 
Author-X-Name-Last: Sithole 
Author-Name: Peter W. Jones 
Author-X-Name-First: Peter W. 
Author-X-Name-Last: Jones 
Title: Bivariate Longitudinal Model for Detecting Prescribing Change in Two Drugs Simultaneously with Correlated Errors 
Abstract:
  Bivariate responses of repeated measures data are usually analysed as two
 separate responses in the literature by several authors. The two responses
 usually tend to be related in some way and analysing this data jointly
 presents an opportunity to account for the joint movement, which may
 impact on the conclusions reached compared to analysing the responses
 separately. In this paper, a bivariate regression model with random
 effects (linear mixed model) is used to detect a change if any in the
 prescribing habits in the UK at the general practice (family medicine)
 level due to an educational intervention given repeated measures data
 before and after the intervention and a control group. The message was to
 increase the prescribing of one drug while simultaneously decreasing the
 prescribing of another. The effects of modelling a bivariate
 auto-regressive process are evaluated. 
Journal: Journal of Applied Statistics 
Pages: 339-352 
Issue: 3 
Volume: 34 
Year: 2007 
Keywords: Bivariate response, repeated measures data, linear mixed model, bivariate first order auto-regressive process, SAS proc mixed, educational intervention, prescribing analysis, 
X-DOI: 10.1080/02664760601005020 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760601005020
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:3:p:339-352




Template-Type: ReDIF-Article 1.0
Author-Name: Rand R. Wilcox 
Author-X-Name-First: Rand R. 
Author-X-Name-Last: Wilcox 
Title: Robust ANCOVA: Some Small-sample Results when there are Multiple Groups and Multiple Covariates 
Abstract:
  Numerous methods have been proposed for dealing with the serious
 practical problems associated with the conventional analysis of covariance
 method, with an emphasis on comparing two groups when there is a single
 covariate. Recently, Wilcox (2005a: section 11.8.2) outlined a method for
 handling multiple covariates that allows nonlinearity and
 heteroscedasticity. The method is readily extended to multiple groups, but
 nothing is known about its small-sample properties. This paper compares
 three variations of the method, each method based on one of three measures
 of location: means, medians and 20% trimmed means. The methods based on a
 20% trimmed mean or median are found to avoid Type I error probabilities
 well above the nominal level, but the method based on medians can be too
 conservative in various situations; using a 20% trimmed mean gave the best
 results in terms of Type I errors. The methods are based in part on a
 running interval smoother approximation of the regression surface.
 Included are comments on required sample sizes that are relevant to the
 so-called curse of dimensionality. 
Journal: Journal of Applied Statistics 
Pages: 353-364 
Issue: 3 
Volume: 34 
Year: 2007 
Keywords: Robust methods, smoothers, heteroscedasticity, curse of dimensionality, 
X-DOI: 10.1080/02664760601005053 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760601005053
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:3:p:353-364




Template-Type: ReDIF-Article 1.0
Author-Name: Jan G. De Gooijer 
Author-X-Name-First: Jan G. 
Author-X-Name-Last: De Gooijer 
Title: Power of the Neyman Smooth Test for Evaluating Multivariate Forecast Densities 
Abstract:
  We compare and investigate Neyman's smooth test, its components, and the
 Kolmogorov-Smirnov (KS) goodness-of-fit test for testing the uniformity of
 multivariate forecast densities. Simulations indicate that the KS test
 lacks power when the forecast distributions are misspecified, especially
 for correlated sequences of random variables. Neyman's smooth test and its
 components work well in samples of size typically available, although
 there sometimes are size distortions. The components provide directed
 diagnosis regarding the kind of departure from the null. For illustration,
 the tests are applied to forecast densities obtained from a bivariate
 threshold model fitted to high-frequency financial data. 
Journal: Journal of Applied Statistics 
Pages: 371-381 
Issue: 4 
Volume: 34 
Year: 2007 
Keywords: Goodness-of-fit, multivariate density forecasts, uniform distribution, 
X-DOI: 10.1080/02664760701231526 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701231526
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:4:p:371-381




Template-Type: ReDIF-Article 1.0
Author-Name: Mark Evans 
Author-X-Name-First: Mark 
Author-X-Name-Last: Evans 
Author-Name: Richard E. Johnston 
Author-X-Name-First: Richard E. 
Author-X-Name-Last: Johnston 
Title: Stochastic Modelling of Times to Temperature for Furnaces Supplying Titanium Blooms to a Rolling Mill at TIMET 
Abstract:
  In conjunction with TIMET at Waunarlwydd (Swansea, UK) a model has been
 developed that will optimise the scheduling of various blooms to their
 eight furnaces so as to minimise the time taken to roll these blooms into
 the finished mill products. This production scheduling model requires
 reliable data on times taken for the various furnaces that heat the slabs
 and blooms to reach the temperatures required for rolling. These times to
 temperature are stochastic in nature and this paper identifies the
 distributional form for these times using the generalised F distribution
 as a modelling framework. The times to temperature were found to be
 similarly distributed over all furnaces. The identified distributional
 forms were incorporated into the scheduling model to optimise a particular
 campaign that was run at TIMET Swansea. Amongst other conclusion it was
 found that, compared to the actual campaign, the model produced a schedule
 that reduced the makespan by some 35%. 
Journal: Journal of Applied Statistics 
Pages: 383-397 
Issue: 4 
Volume: 34 
Year: 2007 
Keywords: Titanium, scheduling, generalised F distribution, 
X-DOI: 10.1080/02664760701231575 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701231575
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:4:p:383-397




Template-Type: ReDIF-Article 1.0
Author-Name: Haritini Tsangari 
Author-X-Name-First: Haritini 
Author-X-Name-Last: Tsangari 
Title: An Alternative Methodology for Combining Different Forecasting Models 
Abstract:
  Many economic and financial time series exhibit heteroskedasticity, where
 the variability changes are often based on recent past shocks, which cause
 large or small fluctuations to cluster together. Classical ways of
 modelling the changing variance include the use of Generalized
 Autoregressive Conditional Heteroskedasticity (GARCH) models and Neural
 Networks models. The paper starts with a comparative study of these two
 models, both in terms of capturing the non-linear or heteroskedastic
 structure and forecasting performance. Monthly and daily exchange rates
 for three different countries are implemented. The paper continues with
 different methods for combining forecasts of the volatility from the
 competing models, in order to improve forecasting accuracy. Traditional
 methods for combining the predicted values from different models, using
 various weighting schemes are considered, such as the simple average or
 methods that find the best weights in terms of minimizing the squared
 forecast error. The main purpose of the paper is, however, to propose an
 alternative methodology for combining forecasts effectively. The new,
 hereby-proposed non-linear, non-parametric, kernel-based method, is shown
 to have the basic advantage of not being affected by outliers, structural
 breaks or shocks to the system and it does not require a specific
 functional form for the combination. 
Journal: Journal of Applied Statistics 
Pages: 403-421 
Issue: 4 
Volume: 34 
Year: 2007 
Keywords: GARCH models, neural networks, heteroskedasticity, combination methods, non-parametric methods, kernel regression, forecasting criteria, 
X-DOI: 10.1080/02664760701231633 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701231633
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:4:p:403-421




Template-Type: ReDIF-Article 1.0
Author-Name: Larry W. Taylor 
Author-X-Name-First: Larry W. 
Author-X-Name-Last: Taylor 
Title: Nonparametric Estimation of Duration Dependence in Militarized Interstate Disputes 
Abstract:
  A militarized interstate dispute (MID) involves military conflict between
 states with diplomatic ties and exists because two or more states have
 failed to resolve their differences through diplomatic channels. Jones et
 al. (1996) characterize an MID as the threat, display or use of military
 force short of war. They analyze over 2000 disputes spanning two centuries
 across the globe and conclude that disputes tend to be persistent once
 established. In this paper, I find that the passage of time can be a
 favorable factor in dispute resolution, and thus historical mechanisms for
 dispute resolution favor ending, not extending, militarized disputes. I
 emphasize the use of non-parametric procedures first to estimate the
 hazard function and then to estimate the benefits of negotiated
 settlements. 
Journal: Journal of Applied Statistics 
Pages: 423-441 
Issue: 4 
Volume: 34 
Year: 2007 
Keywords: Non-parametric estimation, militarized interstate dispute, duration dependence, continuous time, trimming, stochastic dominance, benefits of diplomacy, 
X-DOI: 10.1080/02664760701231690 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701231690
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:4:p:423-441




Template-Type: ReDIF-Article 1.0
Author-Name: Shashibhushan B. Mahadik 
Author-X-Name-First: Shashibhushan B. 
Author-X-Name-Last: Mahadik 
Author-Name: Digambar T. Shirke 
Author-X-Name-First: Digambar T. 
Author-X-Name-Last: Shirke 
Title: On the Superiority of a Variable Sampling Interval Control Chart 
Abstract:
  The paper establishes the analytical grounds of the uniform superiority
 of a variable sampling interval (VSI) Shewhart control chart over the
 conventional fixed sampling interval (FSI) control chart, with respect to
 the zero-time performance, for a wide class of process distributions. We
 provide a sufficient condition on the distribution of a control chart
 statistic, and propose a criterion to determine the control limits and the
 regions in the in-control area of the VSI chart, corresponding to the
 different sampling intervals used by it. The condition and the criterion
 together ensure the uniform zero-time superiority of the VSI chart over
 the matched FSI chart, in detecting a process shift of any magnitude. It
 is shown that normal, Student's t and Laplace distributions satisfy the
 sufficient condition. In addition, chi-square, F and beta distributions
 satisfy it, provided that these are not extremely skewed. Further, it is
 illustrated that the superiority of the VSI feature is not trivial and
 cannot be assured if the sufficient condition is not satisfied or the
 control limits and the regions are not determined according to the
 proposed criterion. An application of the result to confirm the
 superiority of the VSI feature is demonstrated for the control chart for
 individual observations used to monitor a milk-pouch filling process. 
Journal: Journal of Applied Statistics 
Pages: 443-458 
Issue: 4 
Volume: 34 
Year: 2007 
Keywords: Adaptive control chart, average time to signal, average number of samples to signal, zero-time performance, statistical process control, 
X-DOI: 10.1080/02664760701231765 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701231765
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:4:p:443-458




Template-Type: ReDIF-Article 1.0
Author-Name: Jeffrey E. Jarrett 
Author-X-Name-First: Jeffrey E. 
Author-X-Name-Last: Jarrett 
Author-Name: Xia Pan 
Author-X-Name-First: Xia 
Author-X-Name-Last: Pan 
Title: Monitoring Variability and Analyzing Multivariate Autocorrelated Processes 
Abstract:
  Traditional multivariate quality control charts are based on independent
 observations. In this paper, we explain how to extend univariate residual
 charts to multivariate cases and how to combine the traditional
 statistical process control (SPC) approaches to monitor changes in process
 variability in a dynamic environment. We propose using Alt's (1984) W
 chart on vector autoregressive (VAR) residuals to monitor the variability
 for multivariate processes in the presence of autocorrelation. We study
 examples jointly using the Hotelling T2 chart on VAR residuals, the W
 chart, and the Portmanteau test to diagnose the types of shift in process
 parameters. 
Journal: Journal of Applied Statistics 
Pages: 459-469 
Issue: 4 
Volume: 34 
Year: 2007 
Keywords: SPC, variability shift, quality control for multivariate and serially correlated processes, vector autoregressive (VAR) residuals, diagnosing types of parameter shift, 
X-DOI: 10.1080/02664760701231849 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701231849
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:4:p:459-469




Template-Type: ReDIF-Article 1.0
Author-Name: Perla Subbaiah 
Author-X-Name-First: Perla 
Author-X-Name-Last: Subbaiah 
Author-Name: George Xia 
Author-X-Name-First: George 
Author-X-Name-Last: Xia 
Title: Robustness of Inference for One-sample Problem with Correlated Observations 
Abstract:
  The inference about the population mean based on the standard t-test
 involves the assumption of normal population as well as independence of
 the observations. In this paper we examine the robustness of the inference
 in the presence of correlations among the observations. We consider the
 simplest correlation structure AR(1) and its impact on the t-test. A
 modification of the t-test suitable for this structure is suggested, and
 its effect on the inference is investigated using Monte Carlo simulation. 
Journal: Journal of Applied Statistics 
Pages: 471-486 
Issue: 4 
Volume: 34 
Year: 2007 
Keywords: Repeated measurements, AR(1) correlation structure, 
X-DOI: 10.1080/02664760701231906 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701231906
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:4:p:471-486




Template-Type: ReDIF-Article 1.0
Author-Name: Andriy Andreev 
Author-X-Name-First: Andriy 
Author-X-Name-Last: Andreev 
Author-Name: Antti Kanto 
Author-X-Name-First: Antti 
Author-X-Name-Last: Kanto 
Author-Name: Pekka Malo 
Author-X-Name-First: Pekka 
Author-X-Name-Last: Malo 
Title: Computational Examples of a New Method for Distribution Selection in the Pearson System 
Abstract:
  A considerable problem in statistics and risk management is finding
 distributions that capture the complex behaviour exhibited by financial
 data. The importance of higher order moments in decision making has been
 well recognized and there is increasing interest in modelling with
 distributions that are able to account for these effects. The Pearson
 system can be used to model a wide scale of distributions with various
 skewness and kurtosis. This paper provides computational examples of a new
 easily implemented method for selecting probability density functions from
 the Pearson family of distributions. We apply this method to daily,
 monthly, and annual series using a range of data from commodity markets to
 macroeconomic variables. 
Journal: Journal of Applied Statistics 
Pages: 487-506 
Issue: 4 
Volume: 34 
Year: 2007 
Keywords: Pearson system, block bootstrap, selection criteria, 
X-DOI: 10.1080/02664760701231922 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701231922
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:4:p:487-506




Template-Type: ReDIF-Article 1.0
Author-Name: Prasun Das 
Author-X-Name-First: Prasun 
Author-X-Name-Last: Das 
Author-Name: Sasadhar Bera 
Author-X-Name-First: Sasadhar 
Author-X-Name-Last: Bera 
Title: Standardization of Process Norms in Baker's Yeast Fermentation through Statistical Models in Comparison with Neural Networks 
Abstract:
  Achieving consistency of growth pattern for commercial yeast fermentation
 over batches through addition of water, molasses and other chemicals is
 often very complex in nature due to its bio-chemical reactions in
 operation. Regression models in statistical methods play a very important
 role in modeling the underlying mechanism, provided it is known. On the
 contrary, artificial neural networks provide a wide class of
 general-purpose, flexible non-linear architectures to explain any complex
 industrial processes. In this paper, an attempt has been made to find a
 robust control system for a time varying yeast fermentation process
 through statistical means, and in comparison to non-parametric neural
 network techniques. The data used in this context are obtained from an
 industry producing baker's yeast through a fed-batch fermentation process.
 The model accuracy for predicting the growth pattern of commercial yeast,
 when compared among the various techniques used, reveals the best
 performance capability with the backpropagation neural network. The
 statistical model used through projection pursuit regression also shows
 higher prediction accuracy. The models, thus developed, would also help to
 find an optimum combination of parameters for minimizing the variability
 of yeast production. 
Journal: Journal of Applied Statistics 
Pages: 511-527 
Issue: 5 
Volume: 34 
Year: 2007 
Keywords: Generalized linear model (GLM), multisample bootstrapping, projection pursuit regression, artificial neural network (ANN), yeast, fed-batch fermentation, 
X-DOI: 10.1080/02664760701234793 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701234793
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:5:p:511-527




Template-Type: ReDIF-Article 1.0
Author-Name: Daniel Gembris 
Author-X-Name-First: Daniel 
Author-X-Name-Last: Gembris 
Author-Name: John G. Taylor 
Author-X-Name-First: John G. 
Author-X-Name-Last: Taylor 
Author-Name: Dieter Suter 
Author-X-Name-First: Dieter 
Author-X-Name-Last: Suter 
Title: Evolution of Athletic Records: Statistical Effects versus Real Improvements 
Abstract:
  Athletic records represent the best results in a given discipline, thus
 improving monotonically with time. As has already been shown, this should
 not be taken as an indication that the athletes' capabilities keep
 improving. In other words, a new record is not noteworthy just because it
 is a new record, instead it is necessary to assess by how much the record
 has improved. In this paper we derive formulae that can be used to show
 that athletic records continue to improve with time, even if athletic
 performance remains constant. We are considering two specific examples,
 the German championships and the world records in several athletic
 disciplines. The analysis shows that, for the latter, true improvements
 occur in 20-50% of the disciplines. The analysis is supplemented by an
 application of our record estimation approach to the prediction of the
 maximum body length of humans for a specified size of a population
 respectively population group from a representative sample. 
Journal: Journal of Applied Statistics 
Pages: 529-545 
Issue: 5 
Volume: 34 
Year: 2007 
Keywords: Records, athletics, estimation of maxima and minima, 
X-DOI: 10.1080/02664760701234850 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701234850
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:5:p:529-545




Template-Type: ReDIF-Article 1.0
Author-Name: S. Magnussen 
Author-X-Name-First: S. 
Author-X-Name-Last: Magnussen 
Author-Name: R. Reeves 
Author-X-Name-First: R. 
Author-X-Name-Last: Reeves 
Title: Sample-based Maximum Likelihood Estimation of the Autologistic Model 
Abstract:
  New recursive algorithms for fast computation of the normalizing constant
 for the autologistic model on the lattice make feasible a sample-based
 maximum likelihood estimation (MLE) of the autologistic parameters. We
 demonstrate by sampling from 12 simulated 420&#xd7;420 binary lattices
 with square lattice plots of size 4&#xd7;4, &#x2026;, 7&#xd7;7 and sample
 sizes between 20 and 600. Sample-based results are compared with
 'benchmark' MCMC estimates derived from all binary observations on a
 lattice. Sample-based estimates are, on average, biased systematically by
 3%-7%, a bias that can be reduced by more than half by a set of
 calibrating equations. MLE estimates of sampling variances are large and
 usually conservative. The variance of the parameter of spatial association
 is about 2-10 times higher than the variance of the parameter of
 abundance. Sample distributions of estimates were mostly non-normal. We
 conclude that sample-based MLE estimation of the autologistic parameters
 with an appropriate sample size and post-estimation calibration will
 furnish fully acceptable estimates. Equations for predicting the expected
 sampling variance are given. 
Journal: Journal of Applied Statistics 
Pages: 547-561 
Issue: 5 
Volume: 34 
Year: 2007 
Keywords: Markov Chain Monte Carlo, bias, sample size, cluster sampling, calibration, sampling variance, 
X-DOI: 10.1080/02664760701234967 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701234967
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:5:p:547-561




Template-Type: ReDIF-Article 1.0
Author-Name: Li-Chu Chien 
Author-X-Name-First: Li-Chu 
Author-X-Name-Last: Chien 
Author-Name: Tsung-Shan Tsou 
Author-X-Name-First: Tsung-Shan 
Author-X-Name-Last: Tsou 
Title: Regression Diagnostic under Model Misspecification 
Abstract:
  We propose two novel diagnostic measures for the detection of influential
 observations for regression parameters in linear regression. Traditional
 diagnostic statistics focus on the effect of deletion of data points
 either on parameter estimates, or on predicted values. A data point is
 regarded as influential by the new methods if its inclusion determines a
 significantly different likelihood function for the parameter of interest.
 The concerned likelihood function is asymptotically valid for practically
 all underlying distributions whose second moments exist. 
Journal: Journal of Applied Statistics 
Pages: 563-575 
Issue: 5 
Volume: 34 
Year: 2007 
Keywords: Influential diagnostic, robust likelihood, robust normal regression, <i>DFBETAS</i>, <i>DFFITS</i>, Cook's distance, 
X-DOI: 10.1080/02664760701235014 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701235014
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:5:p:563-575




Template-Type: ReDIF-Article 1.0
Author-Name: Seema Jaggi 
Author-X-Name-First: Seema 
Author-X-Name-Last: Jaggi 
Author-Name: Cini Varghese 
Author-X-Name-First: Cini 
Author-X-Name-Last: Varghese 
Author-Name: V.K. Gupta 
Author-X-Name-First: V.K. 
Author-X-Name-Last: Gupta 
Title: Optimal Circular Block Designs for Neighbouring Competition Effects 
Abstract:
  Competition or interference occurs when the responses to treatments in
 experimental units are affected by the treatments in neighbouring units.
 This may contribute to variability in experimental results and lead to
 substantial losses in efficiency. The study of a competing situation needs
 designs in which the competing units appear in a predetermined pattern.
 This paper deals with optimality aspects of circular block designs for
 studying the competition among treatments applied to neighbouring
 experimental units. The model considered is a four-way classified model
 consisting of direct effect of the treatment applied to a particular plot,
 the effect of those treatments applied to the immediate left and right
 neighbouring units and the block effect. Conditions have been obtained for
 the block design to be universally optimal for estimating direct and
 neighbour effects. Some classes of balanced and strongly balanced complete
 block designs have been identified to be universally optimal for the
 estimation of direct, left and right neighbour effects and a list of
 universally optimal designs for v&#x3c;20 and r&#x3c;100 has been
 prepared. 
Journal: Journal of Applied Statistics 
Pages: 577-584 
Issue: 5 
Volume: 34 
Year: 2007 
Keywords: Circular block design, universal optimality, direct effects, neighbour effects, balanced and strongly balanced design, 
X-DOI: 10.1080/02664760701235089 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701235089
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:5:p:577-584




Template-Type: ReDIF-Article 1.0
Author-Name: Paul Gustafson 
Author-X-Name-First: Paul 
Author-X-Name-Last: Gustafson 
Author-Name: S. Siddarth 
Author-X-Name-First: S. 
Author-X-Name-Last: Siddarth 
Title: Describing the Dynamics of Attention to TV Commercials: A Hierarchical Bayes Analysis of the Time to Zap an Ad 
Abstract:
  This paper provides insights into the dynamics of attention to TV
 commercials via an analysis of the length of time that commercials are
 viewed before being 'zapped'. The model, which incorporates a flexible
 baseline hazard rate and captures unobserved heterogeneity across both
 consumers and commercials using a hierarchical Bayes approach, is
 estimated on two datasets in which commercial viewing is captured by a
 passive online device that continually monitors a household's TV viewing.
 Consistent with previous findings in psychology about the nature of
 attentional engagement in TV viewing, baseline hazard rates are found to
 be non-monotonic. In addition, the data show considerable ad-to-ad and
 household-to-household heterogeneity in zapping behavior. While one of the
 datasets contains some information on characteristics of the ads, these
 data do not reveal any firm links between the ad heterogeneity and the ad
 characteristics. A number of methodological and computational issues arise
 in the hierarchical Bayes analysis. 
Journal: Journal of Applied Statistics 
Pages: 585-609 
Issue: 5 
Volume: 34 
Year: 2007 
Keywords: Heterogeneity, hierarchical Bayes, marketing, 
X-DOI: 10.1080/02664760701235279 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701235279
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:5:p:585-609




Template-Type: ReDIF-Article 1.0
Author-Name: A. Felipe 
Author-X-Name-First: A. 
Author-X-Name-Last: Felipe 
Author-Name: M. L. Menendez 
Author-X-Name-First: M. L. 
Author-X-Name-Last: Menendez 
Author-Name: L. Pardo 
Author-X-Name-First: L. 
Author-X-Name-Last: Pardo 
Title: Order-restricted Dose-related Trend Phi-divergence Tests for Generalized Linear Models 
Abstract:
  In this paper a new family of test statistics is presented for testing
 the independence between the binary response Y and an ordered categorical
 explanatory variable X (doses) against the alternative hypothesis of an
 increase dose-response relationship between a response variable Y and X
 (doses). The properties of these test statistics are studied. This new
 family of test statistics is based on the family of &#x3c6;-divergence
 measures and contains as a particular case the likelihood ratio test. We
 pay special attention to the family of test statistics associated with the
 power divergence family. A simulation study is included in order to
 analyze the behavior of the power divergence family of test statistics. 
Journal: Journal of Applied Statistics 
Pages: 611-623 
Issue: 5 
Volume: 34 
Year: 2007 
Keywords: Phi-divergence test statistic, isotonic regression, response variable, explanatory variable, 
X-DOI: 10.1080/02664760701235303 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701235303
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:5:p:611-623




Template-Type: ReDIF-Article 1.0
Author-Name: Shannon E. Allen 
Author-X-Name-First: Shannon E. 
Author-X-Name-Last: Allen 
Author-Name: Burt Holland 
Author-X-Name-First: Burt 
Author-X-Name-Last: Holland 
Title: Expected Mean Squares for Hierarchical Factorial Layouts with Population Imbalance 
Abstract:
  We introduce an analysis of variance usable for two-factor hierarchical
 models where observations are incompletely sampled from unbalanced
 populations of finite effects. Our new approach enables unbiased
 estimation of the variance components for this type of model and allows
 hypothesis testing to identify significant effects/sub-class effects. An
 explanation of how these results can be generalized to factorial layouts
 with more than two factors is given. 
Journal: Journal of Applied Statistics 
Pages: 625-637 
Issue: 5 
Volume: 34 
Year: 2007 
X-DOI: 10.1080/02664760701235352 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701235352
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:5:p:625-637




Template-Type: ReDIF-Article 1.0
Author-Name: Patrick Roger 
Author-X-Name-First: Patrick 
Author-X-Name-Last: Roger 
Author-Name: Marie-Helene Broihanne 
Author-X-Name-First: Marie-Helene 
Author-X-Name-Last: Broihanne 
Title: Efficiency of Betting Markets and Rationality of Players: Evidence from the French 6/49 Lotto 
Abstract:
  We analyse the existence of preferred numbers on the French Lotto market
 and prove that this market is not strongly efficient in the sense of
 Thaler & Ziemba (1988). The preference for low numbers is investigated by
 means of stochastic dominance tests. The specific features of the French
 Lotto game allow us to build a simple estimate of the probability
 distribution of numbers actually played. The results are compared with the
 (highly time-consuming) maximum likelihood estimator used by Farrell et
 al. (2000). It is shown that the two methods give very close results. Our
 conclusions stress the perspectives of this study in various domains. 
Journal: Journal of Applied Statistics 
Pages: 645-662 
Issue: 6 
Volume: 34 
Year: 2007 
Keywords: Lotto, pari-mutuel, information efficiency, maximum likelihood estimation, 
X-DOI: 10.1080/02664760701236889 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701236889
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:6:p:645-662




Template-Type: ReDIF-Article 1.0
Author-Name: R.B. Arellano-Valle 
Author-X-Name-First: R.B. 
Author-X-Name-Last: Arellano-Valle 
Author-Name: H. Bolfarine 
Author-X-Name-First: H. 
Author-X-Name-Last: Bolfarine 
Author-Name: V.H. Lachos 
Author-X-Name-First: V.H. 
Author-X-Name-Last: Lachos 
Title: Bayesian Inference for Skew-normal Linear Mixed Models 
Abstract:
  Linear mixed models (LMM) are frequently used to analyze repeated
 measures data, because they are more flexible to modelling the correlation
 within-subject, often present in this type of data. The most popular LMM
 for continuous responses assumes that both the random effects and the
 within-subjects errors are normally distributed, which can be an
 unrealistic assumption, obscuring important features of the variations
 present within and among the units (or groups). This work presents
 skew-normal liner mixed models (SNLMM) that relax the normality assumption
 by using a multivariate skew-normal distribution, which includes the
 normal ones as a special case and provides robust estimation in mixed
 models. The MCMC scheme is derived and the results of a simulation study
 are provided demonstrating that standard information criteria may be used
 to detect departures from normality. The procedures are illustrated using
 a real data set from a cholesterol study. 
Journal: Journal of Applied Statistics 
Pages: 663-682 
Issue: 6 
Volume: 34 
Year: 2007 
Keywords: Bayesian inference, MCMC, Gibbs sampler, multivariate skew-normal distribution, skewness, 
X-DOI: 10.1080/02664760701236905 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701236905
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:6:p:663-682




Template-Type: ReDIF-Article 1.0
Author-Name: Lourdes Pozueta 
Author-X-Name-First: Lourdes 
Author-X-Name-Last: Pozueta 
Author-Name: Xavier Tort-Martorell 
Author-X-Name-First: Xavier 
Author-X-Name-Last: Tort-Martorell 
Author-Name: Lluis Marco 
Author-X-Name-First: Lluis 
Author-X-Name-Last: Marco 
Title: Identifying Dispersion Effects in Robust Design Experiments&#x2014;Issues and Improvements 
Abstract:
  The two experimental methods most commonly used for reducing the effect
 of noise factors on a response of interest Y aim either to estimate a
 model of the variability (V(Y), or an associated function), that is
 transmitted by the noise factors, or to estimate a model of the ratio
 between the response (Y) and all the control and noise factors involved
 therein. Both methods aim to determine which control factor conditions
 minimise the noise factors' effect on the response of interest, and a
 series of analytical guidelines are established to reach this end. Product
 array designs allow robustness problems to be solved in both ways, but
 require a large number of experiments. Thus, practitioners tend to choose
 more economical designs that only allow them to model the surface response
 for Y. The general assumption is that both methods would lead to similar
 conclusions. In this article we present a case that utilises a design
 based on a product design and for which the conclusions yielded by the two
 analytical methods are quite different. This example casts doubt on the
 guidelines that experimental practice follows when using either of the two
 methods. Based on this example, we show the causes behind these
 discrepancies and we propose a number of guidelines to help researchers in
 the design and interpretation of robustness problems when using either of
 the two methods. 
Journal: Journal of Applied Statistics 
Pages: 683-699 
Issue: 6 
Volume: 34 
Year: 2007 
Keywords: Robust conditions, noise factors, product array, full data array, dispersion effects, Taguchi methods, transmitted variation, quality improvement, interactions, 
X-DOI: 10.1080/02664760701236947 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701236947
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:6:p:683-699




Template-Type: ReDIF-Article 1.0
Author-Name: Manoj Chacko 
Author-X-Name-First: Manoj 
Author-X-Name-Last: Chacko 
Author-Name: P. Yageen Thomas 
Author-X-Name-First: P. Yageen 
Author-X-Name-Last: Thomas 
Title: Estimation of a Parameter of Bivariate Pareto Distribution by Ranked Set Sampling 
Abstract:
  Ranked set sampling is applicable whenever ranking of a set of sampling
 units can be done easily by a judgement method or based on the measurement
 of an auxiliary variable on the units selected. In this work, we derive
 different estimators of a parameter associated with the distribution of
 the study variate Y, based on a ranked-set sample obtained by using an
 auxiliary variable X correlated with Y for ranking the sample units, when
 (X, Y) follows a bivariate Pareto distribution. Efficiency comparisons
 among these estimators are also made. Real-life data have been used to
 illustrate the application of the results obtained. 
Journal: Journal of Applied Statistics 
Pages: 703-714 
Issue: 6 
Volume: 34 
Year: 2007 
Keywords: Ranked set sampling, bivariate Pareto distribution, best linear unbiased estimator, 
X-DOI: 10.1080/02664760701236954 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701236954
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:6:p:703-714




Template-Type: ReDIF-Article 1.0
Author-Name: Chien-Tai Lin 
Author-X-Name-First: Chien-Tai 
Author-X-Name-Last: Lin 
Author-Name: Cheng-Chieh Chou 
Author-X-Name-First: Cheng-Chieh 
Author-X-Name-Last: Chou 
Title: Empirical-distribution-function Tests for the Beta-Binomial Model 
Abstract:
  Empirical-distribution-function (EDF) goodness-of-fit tests are
 considered for the beta-binomial model. The testing procedures based on
 EDF statistics are given. A Monte Carlo study is conducted to investigate
 the accuracy and power of the tests against various alternative
 distributions. Our method is found to produce considerably greater power
 than that of Garren et al. (2001) in most cases. The tests are applied to
 data sets of the foraging behavior of herons and environmental toxicity
 studies. 
Journal: Journal of Applied Statistics 
Pages: 715-724 
Issue: 6 
Volume: 34 
Year: 2007 
Keywords: Beta-binomial distribution, goodness-of-fit, parametric bootstrap, power, simulation, 
X-DOI: 10.1080/02664760701236970 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701236970
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:6:p:715-724




Template-Type: ReDIF-Article 1.0
Author-Name: Hongmei Zhang 
Author-X-Name-First: Hongmei 
Author-X-Name-Last: Zhang 
Title: Inferences on the Number of Unseen Species and the Number of Abundant/Rare Species 
Abstract:
  This paper focuses on estimating the number of species and the number of
 abundant species in a specific geographic region and, consequently, draw
 inferences on the number of rare species. The word 'species' is generic
 referring to any objects in a population that can be categorized. In the
 areas of biology, ecology, literature, etc, the species frequency
 distributions are usually severely skewed, in which case the population
 contains a few very abundant species and many rare ones. To model a such
 situation, we develop an asymmetric multinomial-Dirichlet probability
 model using species frequency data. Posterior distributions on the number
 of species and the number of abundant species are obtained and posterior
 inferences are induced using MCMC simulations. Simulations are used to
 demonstrate and evaluate the developed methodology. We apply the method to
 a DNA segment data set and a butterfly data set. Comparisons among
 different approaches to inferring the number of species are also discussed
 in this paper. 
Journal: Journal of Applied Statistics 
Pages: 725-740 
Issue: 6 
Volume: 34 
Year: 2007 
Keywords: Generalized multinomial model, Bayesian hierarchical model, Markov Chain Monte Carlo (MCMC), Dirichlet distribution, rare species, 
X-DOI: 10.1080/02664760701237010 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701237010
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:6:p:725-740




Template-Type: ReDIF-Article 1.0
Author-Name: Juan Sun 
Author-X-Name-First: Juan 
Author-X-Name-Last: Sun 
Author-Name: Lifu Bi 
Author-X-Name-First: Lifu 
Author-X-Name-Last: Bi 
Author-Name: Yaojun Chi 
Author-X-Name-First: Yaojun 
Author-X-Name-Last: Chi 
Author-Name: Guowei Huang 
Author-X-Name-First: Guowei 
Author-X-Name-Last: Huang 
Author-Name: Chun Fan 
Author-X-Name-First: Chun 
Author-X-Name-Last: Fan 
Author-Name: Kazuo Aoki 
Author-X-Name-First: Kazuo 
Author-X-Name-Last: Aoki 
Author-Name: Akihiro Kono 
Author-X-Name-First: Akihiro 
Author-X-Name-Last: Kono 
Author-Name: Tian Hui 
Author-X-Name-First: Tian 
Author-X-Name-Last: Hui 
Author-Name: Junichi Misumi 
Author-X-Name-First: Junichi 
Author-X-Name-Last: Misumi 
Title: The Impact of Ovarian Cancer on Life Expectancy in Japan 
Abstract:
  The purpose of this study was to determine how life expectancy is
 modified by ovarian cancer from 1950-2000. The contributions of ovarian
 cancer to life expectancy were estimated. The age characteristics of
 ovarian cancer were detected using the Gompertz relational mortality
 model. The patterns between years of potential life lost (YPLL) and
 mortality were obtained by fitting a linear regression equation to the
 natural logarithm of their ratios. YPLLs are substantially higher in
 Ireland than in Japan. However, the rates of change were much higher in
 Japan than in Ireland. YPLLs changed from 0.02 year in 1950 to 0.12 year
 in 2000. In Japan, there was a sixfold increase in the proportion of YPLLs
 for death from ovarian cancer relative to those for death from
 gynaecological cancers during the last half century. The impact of ovarian
 cancer on life expectancy clearly increased and the age-specific mortality
 tend to ageing. 
Journal: Journal of Applied Statistics 
Pages: 741-747 
Issue: 6 
Volume: 34 
Year: 2007 
Keywords: Ovarian cancer, life expectancy, YPLLs, Gompertz, 
X-DOI: 10.1080/02664760701237036 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701237036
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:6:p:741-747




Template-Type: ReDIF-Article 1.0
Author-Name: Juan Antonio Cuesta-Albertos 
Author-X-Name-First: Juan Antonio 
Author-X-Name-Last: Cuesta-Albertos 
Author-Name: Ricardo Fraiman 
Author-X-Name-First: Ricardo 
Author-X-Name-Last: Fraiman 
Author-Name: Antonio Galves 
Author-X-Name-First: Antonio 
Author-X-Name-Last: Galves 
Author-Name: Jesus Garcia 
Author-X-Name-First: Jesus 
Author-X-Name-Last: Garcia 
Author-Name: Marcela Svarc 
Author-X-Name-First: Marcela 
Author-X-Name-Last: Svarc 
Title: Classifying Speech Sonority Functional Data using a Projected Kolmogorov-Smirnov Approach 
Abstract:
  This paper addresses a linguistically motivated question of
 classification of functional data, namely the statistical classification
 of languages according to their rhythmic features. This is an important
 open problem in phonology. The analysis is based on the information
 provided by the sonority, which is an index of local regularity of the
 speech signal. Our main tool is the projected Kolmogorov-Smirnov test.
 This is a new goodness of fit test for functional data. The result
 obtained supports the linguistic conjecture of the existence of three
 rhythmic classes. 
Journal: Journal of Applied Statistics 
Pages: 749-761 
Issue: 6 
Volume: 34 
Year: 2007 
Keywords: Classification of languages, rhythmic classes, functional data, projected Kolmogorov-Smirnov test, 
X-DOI: 10.1080/02664760701237077 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701237077
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:6:p:749-761




Template-Type: ReDIF-Article 1.0
Author-Name: Trevor Park 
Author-X-Name-First: Trevor 
Author-X-Name-Last: Park 
Title: Alternative Penalty Functions for Penalized Likelihood Principal Components 
Abstract:
  The penalized likelihood principal component method of Park (2005) offers
 flexibility in the choice of the penalty function. This flexibility allows
 the method to be tailored to enhance interpretation in special cases. Of
 particular interest is a penalty function in the style of the Lasso that
 can be used to produce exactly zero loadings. Also of interest is a
 penalty function for cases in which interpretability is best represented
 by alignment with orthogonal subspaces, rather than with axis directions.
 In each case, a data example is presented. 
Journal: Journal of Applied Statistics 
Pages: 767-777 
Issue: 7 
Volume: 34 
Year: 2007 
Keywords: Interpretation, Lasso penalty, multivariate exploratory analysis, principal component rotation, varimax, 
X-DOI: 10.1080/02664760701239859 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701239859
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:7:p:767-777




Template-Type: ReDIF-Article 1.0
Author-Name: Man Yu Wong 
Author-X-Name-First: Man Yu 
Author-X-Name-Last: Wong 
Author-Name: D.R. Cox 
Author-X-Name-First: D.R. 
Author-X-Name-Last: Cox 
Title: On the Screening of Large Numbers of Significance Tests 
Abstract:
  A brief review is given of procedures for the collective analysis of a
 large number of significance tests. A simple procedure previously supplied
 for isolating 'real' effects on the basis of a large number of
 significance tests is generalized to deal with two-sided tests and is also
 related more explicitly to the false discovery rate. 
Journal: Journal of Applied Statistics 
Pages: 779-783 
Issue: 7 
Volume: 34 
Year: 2007 
Keywords: False discovery rate, mixture of distributions, Bayes factor, multiple testing, 
X-DOI: 10.1080/02664760701240014 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701240014
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:7:p:779-783




Template-Type: ReDIF-Article 1.0
Author-Name: Mohamed El Ghourabi 
Author-X-Name-First: Mohamed El 
Author-X-Name-Last: Ghourabi 
Author-Name: Mohamed Limam 
Author-X-Name-First: Mohamed 
Author-X-Name-Last: Limam 
Title: Residual Responses to Change Patterns of Autocorrelated Processes 
Abstract:
  This article studies the residual behaviour of various stationary
 processes in the presence of change patterns. Three types of change
 patterns are considered, Additive Outliers, Innovative Outliers and Level
 Shift. The knowledge of the residual behaviour is important for monitoring
 production processes. A new method of residual process control is
 proposed, the patterns chart. In addition to the advantage of detecting
 change patterns, it distinguishes their nature. The patterns chart's
 performance is compared to the performance of the special causes control
 (SCC) chart based on average run length. The results show that the
 proposed method performs better than a SCC chart. A real case study
 illustrates that the patterns chart has all the desirable properties of a
 SCC chart and it overcomes the negative ones. 
Journal: Journal of Applied Statistics 
Pages: 785-798 
Issue: 7 
Volume: 34 
Year: 2007 
Keywords: Autocorrelation, outliers, residual responses, control chart, ARL, 
X-DOI: 10.1080/02664760701240063 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701240063
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:7:p:785-798




Template-Type: ReDIF-Article 1.0
Author-Name: R. Vijayaraghavan 
Author-X-Name-First: R. 
Author-X-Name-Last: Vijayaraghavan 
Title: Minimum Size Double Sampling Plans for Large Isolated Lots 
Abstract:
  A common approach to the design of an acceptance sampling plan is to
 require that the operating characteristic (OC) curve should pass through
 two designated points that would fix the curve in accordance with a
 desired degree of discrimination. This paper presents a search procedure
 for the selection of double sampling inspection plans of type DSP - (0, 1)
 for specified two points on the OC curve, namely acceptance quality limit,
 producer's risk, limiting quality and consumer's risk. Selection of the
 plans is discussed for both the cases of fraction non-conforming and the
 number of non-conformities per unit. 
Journal: Journal of Applied Statistics 
Pages: 799-806 
Issue: 7 
Volume: 34 
Year: 2007 
Keywords: Acceptance quality limit, limiting quality, double sampling plan, operating characteristic curve, single sampling plan, 
X-DOI: 10.1080/02664760701240287 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701240287
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:7:p:799-806




Template-Type: ReDIF-Article 1.0
Author-Name: Carlos Diaz Avalos 
Author-X-Name-First: Carlos Diaz 
Author-X-Name-Last: Avalos 
Title: Spatial Modeling of Habitat Preferences of Biological Species using Markov Random Fields 
Abstract:
  Spatial modeling has gained interest in ecology during the past two
 decades, especially in the area of biodiversity, where reliable
 distribution maps are required. Several methods have been proposed to
 construct distribution maps, most of them acknowledging the presence of
 spatial interactions. In many cases, a key problem is the lack of true
 absence data. We present here a model suitable for use when true absence
 data are missing. The quality of the estimates obtained from the model is
 evaluated using ROC curve analysis as well as a quadratic cost function,
 computed from the false positive and false negative error rates. The model
 is also tested under random and clustered scattering of the presence
 records. We also present an application of the model to the construction
 of distribution maps of two endemic bird species in Mexico. 
Journal: Journal of Applied Statistics 
Pages: 807-821 
Issue: 7 
Volume: 34 
Year: 2007 
Keywords: Biodiversity maps, Markov random fields, spatial modeling, autologistic model, species distribution, 
X-DOI: 10.1080/02664760701240782 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701240782
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:7:p:807-821




Template-Type: ReDIF-Article 1.0
Author-Name: Jurate saltyte Benth 
Author-X-Name-First: Jurate saltyte 
Author-X-Name-Last: Benth 
Author-Name: Fred Espen Benth 
Author-X-Name-First: Fred Espen 
Author-X-Name-Last: Benth 
Author-Name: Paulius Jalinskas 
Author-X-Name-First: Paulius 
Author-X-Name-Last: Jalinskas 
Title: A Spatial-temporal Model for Temperature with Seasonal Variance 
Abstract:
  We propose a spatial-temporal stochastic model for daily average surface
 temperature data. First, we build a model for a single spatial location,
 independently on the spatial information. The model includes trend,
 seasonality, and mean reversion, together with a seasonally dependent
 variance of the residuals. The spatial dependency is modelled by a
 Gaussian random field. Empirical fitting to data collected in 16
 measurement stations in Lithuania over more than 40 years shows that our
 model captures the seasonality in the autocorrelation of the squared
 residuals, a property of temperature data already observed by other
 authors. We demonstrate through examples that our spatial-temporal model
 is applicable for prediction and classification. 
Journal: Journal of Applied Statistics 
Pages: 823-841 
Issue: 7 
Volume: 34 
Year: 2007 
Keywords: Spatial-temporal random field, temperature, seasonally dependent variance, 
X-DOI: 10.1080/02664760701511398 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701511398
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:7:p:823-841




Template-Type: ReDIF-Article 1.0
Author-Name: Zhang Wu 
Author-X-Name-First: Zhang 
Author-X-Name-Last: Wu 
Author-Name: Qinan Wang 
Author-X-Name-First: Qinan 
Author-X-Name-Last: Wang 
Title: An NP Control Chart Using Double Inspections 
Abstract:
  The np control chart is used widely in Statistical Process Control (SPC)
 for attributes. It is difficult to design an np chart that simultaneously
 satisfies a requirement on false alarm rate and has high detection
 effectiveness. This is mainly because one is often unable to make the
 in-control Average Run Length ARL0 of an np chart close to a specified or
 desired value. This article proposes a new np control chart which is able
 to overcome the problems suffered by the conventional np chart. It is
 called the Double Inspection (DI) np chart, because it uses a double
 inspection scheme to decide the process status (in control or out of
 control). The first inspection decides the process status according to the
 number of non-conforming units found in a sample; and the second
 inspection makes a decision based on the location of a particular
 non-conforming unit in the sample. The double inspection scheme makes the
 in-control ARL0 very close to a specified value and the out-of-control
 Average Run Length ARL1 quite small. As a result, the requirement on a
 false alarm rate is satisfied and the detection effectiveness also
 achieves a high level. Moreover, the DI np chart retains the operational
 simplicity of the np chart to a large degree and achieves the performance
 improvement without requiring extra inspection (testing whether a unit is
 conforming or not). 
Journal: Journal of Applied Statistics 
Pages: 843-855 
Issue: 7 
Volume: 34 
Year: 2007 
Keywords: Quality control, statistical process control, control chart, double inspection, average run length, 
X-DOI: 10.1080/02664760701523492 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701523492
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:7:p:843-855




Template-Type: ReDIF-Article 1.0
Author-Name: Tee Chin Chang 
Author-X-Name-First: Tee Chin 
Author-X-Name-Last: Chang 
Author-Name: Fah Fatt Gan 
Author-X-Name-First: Fah Fatt 
Author-X-Name-Last: Gan 
Title: Modified Shewhart Charts for High Yield Processes 
Abstract:
  The conventional Shewhart p or np chart is not effective for monitoring a
 high yield process, a process in which the defect level is close to zero.
 An improved Shewhart np chart for monitoring high yield processes is
 proposed. A review of control charts for monitoring high yield processes
 is first given. The run length performance of the proposed Shewhart chart
 is then compared with other high yield control charts. A simple procedure
 for designing the chart for processes subjected to sampling or 100%
 continuous inspection is provided and this allows the chart to be
 implemented easily on the factory floor. The practical aspects of
 implementation of the Shewhart chart are discussed. An application of the
 Shewhart chart based on a real data set is demonstrated. 
Journal: Journal of Applied Statistics 
Pages: 857-877 
Issue: 7 
Volume: 34 
Year: 2007 
Keywords: Average run length, binomial counts, parts-per-million non-conforming items, supplementary runs rules, statistical process control, 
X-DOI: 10.1080/02664760701546279 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701546279
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:7:p:857-877




Template-Type: ReDIF-Article 1.0
Author-Name: J. A. Roldan Nofuentes 
Author-X-Name-First: J. A. Roldan 
Author-X-Name-Last: Nofuentes 
Author-Name: J. D. Luna Del Castillo 
Author-X-Name-First: J. D. Luna Del 
Author-X-Name-Last: Castillo 
Title: Risk of Error and the Kappa Coefficient of a Binary Diagnostic Test in the Presence of Partial Verification 
Abstract:
  The accuracy of a binary diagnostic test is usually measured in terms of
 its sensitivity and its specificity, or through positive and negative
 predictive values. Another way to describe the validity of a binary
 diagnostic test is the risk of error and the kappa coefficient of the risk
 of error. The risk of error is the average loss that is caused when
 incorrectly classifying a non-diseased or a diseased patient, and the
 kappa coefficient of the risk of error is a measure of the agreement
 between the diagnostic test and the gold standard. In the presence of
 partial verification of the disease, the disease status of some patients
 is unknown, and therefore the evaluation of a diagnostic test cannot be
 carried out through the traditional method. In this paper, we have deduced
 the maximum likelihood estimators and variances of the risk of error and
 of the kappa coefficient of the risk of error in the presence of partial
 verification of the disease. Simulation experiments have been carried out
 to study the effect of the verification probabilities on the coverage of
 the confidence interval of the kappa coefficient. 
Journal: Journal of Applied Statistics 
Pages: 887-898 
Issue: 8 
Volume: 34 
Year: 2007 
Keywords: Covariates, Kappa, partial verification, risk, sensitivity, specificity, verification bias, 
X-DOI: 10.1080/02664760701590681 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701590681
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:8:p:887-898




Template-Type: ReDIF-Article 1.0
Author-Name: Neil Marks 
Author-X-Name-First: Neil 
Author-X-Name-Last: Marks 
Title: Kolmogorov-Smirnov Test Statistic and Critical Values for the Erlang-3 and Erlang-4 Distributions 
Abstract:
  Following a procedure applied to the Erlang-2 distribution in a recent
 paper, an adjusted Kolmogorov-Smirnov statistic and critical values are
 developed for the Erlang-3 and -4 cases using data from Monte Carlo
 simulations. The test statistic produced features of compactness and ease
 of implementation. It is quite accurate for sample sizes as low as ten. 
Journal: Journal of Applied Statistics 
Pages: 899-906 
Issue: 8 
Volume: 34 
Year: 2007 
Keywords: Goodness-of-fit, Kolmogorov-Smirnov test, Erlang-k distribution, 
X-DOI: 10.1080/02664760701590640 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701590640
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:8:p:899-906




Template-Type: ReDIF-Article 1.0
Author-Name: Feng-Chang Xie 
Author-X-Name-First: Feng-Chang 
Author-X-Name-Last: Xie 
Author-Name: Bo-Cheng Wei 
Author-X-Name-First: Bo-Cheng 
Author-X-Name-Last: Wei 
Author-Name: Jin-Guan Lin 
Author-X-Name-First: Jin-Guan 
Author-X-Name-Last: Lin 
Title: Case-deletion Influence Measures for the Data from Multivariate t Distributions 
Abstract:
  For the data from multivariate t distributions, it is very hard to make
 an influence analysis based on the probability density function since its
 expression is intractable. In this paper, we present a technique for
 influence analysis based on the mixture distribution and EM algorithm. In
 fact, the multivariate t distribution can be considered as a particular
 Gaussian mixture by introducing the weights from the Gamma distribution.
 We treat the weights as the missing data and develop the influence
 analysis for the data from multivariate t distributions based on the
 conditional expectation of the complete-data log-likelihood function in
 the EM algorithm. Several case-deletion measures are proposed for
 detecting influential observations from multivariate t distributions. Two
 numerical examples are given to illustrate our methodology. 
Journal: Journal of Applied Statistics 
Pages: 907-921 
Issue: 8 
Volume: 34 
Year: 2007 
Keywords: Multivariate t distribution, influence analysis, EM algorithm, case-deletion, generalized Cook distance, 
X-DOI: 10.1080/02664760701590574 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701590574
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:8:p:907-921




Template-Type: ReDIF-Article 1.0
Author-Name: Harriet Namata 
Author-X-Name-First: Harriet 
Author-X-Name-Last: Namata 
Author-Name: Ziv Shkedy 
Author-X-Name-First: Ziv 
Author-X-Name-Last: Shkedy 
Author-Name: Christel Faes 
Author-X-Name-First: Christel 
Author-X-Name-Last: Faes 
Author-Name: Marc Aerts 
Author-X-Name-First: Marc 
Author-X-Name-Last: Aerts 
Author-Name: Geert Molenberghs 
Author-X-Name-First: Geert 
Author-X-Name-Last: Molenberghs 
Author-Name: Heide Theeten 
Author-X-Name-First: Heide 
Author-X-Name-Last: Theeten 
Author-Name: Pierre Van Damme 
Author-X-Name-First: Pierre 
Author-X-Name-Last: Van Damme 
Author-Name: Philippe Beutels 
Author-X-Name-First: Philippe 
Author-X-Name-Last: Beutels 
Title: Estimation of the Force of Infection from Current Status Data Using Generalized Linear Mixed Models 
Abstract:
  Based on sero-prevalence data of rubella, mumps in the UK and varicella
 in Belgium, we show how the force of infection, the age-specific rate at
 which susceptible individuals contract infection, can be estimated using
 generalized linear mixed models (McCulloch & Searle, 2001). Modelling the
 dependency of the force of infection on age by penalized splines, which
 involve fixed and random effects, allows us to use generalized linear
 mixed models techniques to estimate both the cumulative probability of
 being infected before a given age and the force of infection. Moreover,
 these models permit an automatic selection of the smoothing parameter. The
 smoothness of the estimated force of infection can be influenced by the
 number of knots and the degree of the penalized spline used. To determine
 these, a different number of knots and different degrees are used and the
 results are compared to establish this sensitivity. Simulations with a
 different number of knots and polynomial spline bases of different degrees
 suggest - for estimating the force of infection from serological data -
 the use of a quadratic penalized spline based on about 10 knots. 
Journal: Journal of Applied Statistics 
Pages: 923-939 
Issue: 8 
Volume: 34 
Year: 2007 
Keywords: Prevalence data, penalized splines, generalized linear mixed models, smoothing parameter, force of infection, 
X-DOI: 10.1080/02664760701590525 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701590525
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:8:p:923-939




Template-Type: ReDIF-Article 1.0
Author-Name: W. L. Pearn 
Author-X-Name-First: W. L. 
Author-X-Name-Last: Pearn 
Author-Name: F. K. Wang 
Author-X-Name-First: F. K. 
Author-X-Name-Last: Wang 
Author-Name: C. H. Yen 
Author-X-Name-First: C. H. 
Author-X-Name-Last: Yen 
Title: Multivariate Capability Indices: Distributional and Inferential Properties 
Abstract:
  Process capability indices have been widely used in the manufacturing
 industry for measuring process reproduction capability according to
 manufacturing specifications. Properties of the univariate processes have
 been investigated extensively, but are comparatively neglected for
 multivariate processes where multiple dependent characteristics are
 involved in quality measurement. In this paper, we consider two commonly
 used multivariate capability indices MCp and MCpm, to evaluate
 multivariate process capability. We investigate the statistical properties
 of the estimated MCp and obtain the lower confidence bound for MCp. We
 also consider testing MCp, and provide critical values for testing if a
 multivariate process meets the preset capability requirement. In addition,
 an approximate confidence interval for MCpm is derived. A simulation study
 is conducted to ascertain the accuracy of the approximation. Three
 examples are presented to illustrate the applicability of the obtained
 results. 
Journal: Journal of Applied Statistics 
Pages: 941-962 
Issue: 8 
Volume: 34 
Year: 2007 
Keywords: Multivariate capability index, lower confidence bound, hypothesis testing, critical value, 
X-DOI: 10.1080/02664760701590475 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701590475
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:8:p:941-962




Template-Type: ReDIF-Article 1.0
Author-Name: Terence Mills 
Author-X-Name-First: Terence 
Author-X-Name-Last: Mills 
Title: A Note on Trend Decomposition: The 'Classical' Approach Revisited with an Application to Surface Temperature Trends 
Abstract:
  This note reconsiders the 'classical' approach to trend estimation and
 presents a modern treatment of this technique that enables trend filters
 which incorporate end-effects to be constructed easily and efficiently.
 The approach is illustrated by estimating recent Northern Hemispheric
 temperature trends. In so doing, it shows how classical trend models may
 be selected in empirical applications and indicates how this choice
 determines the properties of the latest trend estimates. 
Journal: Journal of Applied Statistics 
Pages: 963-972 
Issue: 8 
Volume: 34 
Year: 2007 
Keywords: Trend estimation, local polynomial trend, temperature trends, 
X-DOI: 10.1080/02664760701590418 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701590418
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:8:p:963-972




Template-Type: ReDIF-Article 1.0
Author-Name: Edgardo Escalante-Vazquez 
Author-X-Name-First: Edgardo 
Author-X-Name-Last: Escalante-Vazquez 
Title: SPC Study of a Brewing Process 
Abstract:
  The process of brewing is a complex one, in which several biological and
 chemical reactions occur that involve many variables and their
 interactions. This pilot study is an attempt to understand and to control
 the chemical and biological nature of the process of 'beer cooking'.
 Through data collection and analysis the measurement system was initially
 evaluated and improved to allow the assessment of the stability of the
 analysed response variable: wort's F (F is a fictitious name for this
 variable due to confidentiality). Next, a deeper analysis was carried out
 to characterize, improve and control the behaviour of this factor by means
 of confidence intervals and several regression analyses. The way to
 control F is by adding a certain amount of element X according to a
 previously empirically developed table. After the analyses, this table was
 questioned and a new one was developed. This study is the outcome of the
 willingness of a group of people in this company to incorporate into its
 traditional and, at some stages, artisan way of producing beer, the
 utilization of statistical techniques for analysing and improving its
 processes and products. 
Journal: Journal of Applied Statistics 
Pages: 973-984 
Issue: 8 
Volume: 34 
Year: 2007 
Keywords: SPC, brewing process, quality improvement, 
X-DOI: 10.1080/02664760701590699 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701590699
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:8:p:973-984




Template-Type: ReDIF-Article 1.0
Author-Name: Olena Babak 
Author-X-Name-First: Olena 
Author-X-Name-Last: Babak 
Author-Name: Birgir Hrafnkelsson 
Author-X-Name-First: Birgir 
Author-X-Name-Last: Hrafnkelsson 
Author-Name: Olafur Palsson 
Author-X-Name-First: Olafur 
Author-X-Name-Last: Palsson 
Title: Estimation of the Length Distribution of Marine Populations in the Gaussian-multinomial Setting using the Method of Moments 
Abstract:
  In this paper, the problem of estimation of the length distribution of
 marine populations in the Gaussian-multinomial model is considered. For
 the purpose of the mean and covariance parameter estimation, the method of
 moments estimators are developed. That is, minimum variance linear
 unbiased estimator for the mean frequency vector is derived and a
 consistent estimator for the covariance matrix of the length observations
 is presented. The usefulness of the proposed estimators is illustrated
 with an analysis of real cod length measurement data. 
Journal: Journal of Applied Statistics 
Pages: 985-991 
Issue: 8 
Volume: 34 
Year: 2007 
Keywords: Gaussian-multinomial model, method of moments estimators, length distribution, 
X-DOI: 10.1080/02664760701590376 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701590376
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:8:p:985-991




Template-Type: ReDIF-Article 1.0
Author-Name: Wai-Yin Poon 
Author-X-Name-First: Wai-Yin 
Author-X-Name-Last: Poon 
Author-Name: Yat Sun Poon 
Author-X-Name-First: Yat Sun 
Author-X-Name-Last: Poon 
Title: Local Conditional Influence 
Abstract:
  Through an investigation of normal curvature functions for influence
 graphs of a family of perturbed models, we develop the concept of local
 conditional influence. This concept can be used to study masking and
 boosting effects in local influence. We identify the situation under which
 the influence graph of the unperturbed model contains all the information
 on these effects. The linear regression model is used for illustration and
 it is shown that the concept developed is consistent with Lawrance's
 (1995) approach of conditional influence in Cook's distance. 
Journal: Journal of Applied Statistics 
Pages: 997-1009 
Issue: 8 
Volume: 34 
Year: 2007 
Keywords: Normal curvature, curvature function, local conditional influence, masking, linear regression, 
X-DOI: 10.1080/02664760600744371 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600744371
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:8:p:997-1009




Template-Type: ReDIF-Article 1.0
Author-Name: Dianxu Ren 
Author-X-Name-First: Dianxu 
Author-X-Name-Last: Ren 
Author-Name: Roslyn Stone 
Author-X-Name-First: Roslyn 
Author-X-Name-Last: Stone 
Title: A Bayesian Adjustment for Covariate Misclassification with Correlated Binary Outcome Data 
Abstract:
  Estimated associations between an outcome variable and misclassified
 covariates tend to be biased when the methods of estimation that ignore
 the classification error are applied. Available methods to account for
 misclassification often require the use of a validation sample (i.e. a
 gold standard). In practice, however, such a gold standard may be
 unavailable or impractical. We propose a Bayesian approach to adjust for
 misclassification in a binary covariate in the random effect logistic
 model when a gold standard is not available. This Markov Chain Monte Carlo
 (MCMC) approach uses two imperfect measures of a dichotomous exposure
 under the assumptions of conditional independence and non-differential
 misclassification. A simulated numerical example and a real clinical
 example are given to illustrate the proposed approach. Our results suggest
 that the estimated log odds of inpatient care and the corresponding
 standard deviation are much larger in our proposed method compared with
 the models ignoring misclassification. Ignoring misclassification produces
 downwardly biased estimates and underestimate uncertainty. 
Journal: Journal of Applied Statistics 
Pages: 1019-1034 
Issue: 9 
Volume: 34 
Year: 2007 
Keywords: Bayesian approach, misclassification, logistic model, random effect logistic model, MCMC, 
X-DOI: 10.1080/02664760701591895 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701591895
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:9:p:1019-1034




Template-Type: ReDIF-Article 1.0
Author-Name: Pasquale Sarnacchiaro 
Author-X-Name-First: Pasquale 
Author-X-Name-Last: Sarnacchiaro 
Author-Name: Antonello D'ambra 
Author-X-Name-First: Antonello 
Author-X-Name-Last: D'ambra 
Title: Explorative Data Analysis and CATANOVA for Ordinal Variables: An Integrated Approach 
Abstract:
  Categorical analysis of variance (CATANOVA) is a statistical method
 designed to analyse variability between treatments of interest to the
 researcher. There are well-established links between CATANOVA and the
 &#x3c4; statistic of Goodman and Kruskal which, for the purpose of the
 graphical identification of this variation, is partitioned using singular
 value decomposition for Non-Symmetrical Correspondence Analysis (NSCA)
 (D'Ambra & Lauro, 1989). The aim of this paper is to show a decomposition
 of the Between Sum of Squares (BSS), measured both in CATANOVA framework
 and in the statistic &#x3c4;, into location, dispersion and higher order
 components. This decomposition has been developed using Emerson's
 orthogonal polynomials. Starting from this decomposition, a statistical
 test and a confidence circle have been calculated for each component and
 for each modality in which the BSS was decomposed, respectively. A
 Customer Satisfaction study has been considered to explain the
 methodology. 
Journal: Journal of Applied Statistics 
Pages: 1035-1050 
Issue: 9 
Volume: 34 
Year: 2007 
Keywords: Categorical analysis of variance, Goodman & Kruskal &#x3c4;, Emerson Orthogonal polynomials, customer satisfaction, non-symmetrical correspondence analysis, confidence circle, statistical test, Andrews curve, 
X-DOI: 10.1080/02664760701591937 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701591937
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:9:p:1035-1050




Template-Type: ReDIF-Article 1.0
Author-Name: Jonggun Lim 
Author-X-Name-First: Jonggun 
Author-X-Name-Last: Lim 
Author-Name: Sangun Park 
Author-X-Name-First: Sangun 
Author-X-Name-Last: Park 
Title: Censored Kullback-Leibler Information and Goodness-of-Fit Test with Type II Censored Data 
Abstract:
  The Kulback-Leibler information has been considered for establishing
 goodness-of-fit test statistics, which have been shown to perform very
 well (Arizono & Ohta, 1989; Ebrahimi et al., 1992, etc). In this paper, we
 propose censored Kullback-Leibler information to generalize the discussion
 of the Kullback-Leibler information to the censored case. Then we
 establish a goodness-of-fit test statistic based on the censored
 Kullback-Leibler information with the type 2 censored data, and compare
 the test statistics with some existing test statistics for the exponential
 and normal distributions. 
Journal: Journal of Applied Statistics 
Pages: 1051-1064 
Issue: 9 
Volume: 34 
Year: 2007 
Keywords: Entropy difference, maximum entropy distribution, minimum discrimination information loss estimation, order statistics, sample entropy, 
X-DOI: 10.1080/02664760701592000 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701592000
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:9:p:1051-1064




Template-Type: ReDIF-Article 1.0
Author-Name: Seunggeun Hyun 
Author-X-Name-First: Seunggeun 
Author-X-Name-Last: Hyun 
Author-Name: Yanqing Sun 
Author-X-Name-First: Yanqing 
Author-X-Name-Last: Sun 
Title: Hypotheses Tests of Strain-specific Vaccine Efficacy Adjusted for Covariate Effects 
Abstract:
  In the evaluation of efficacy of a vaccine to protect against disease
 caused by finitely many diverse infectious pathogens, it is often
 important to assess if vaccine protection depends on variations of the
 exposing pathogen. This problem can be formulated under a competing risks
 model where the endpoint event is the infection and the cause of failure
 is the infecting strain type determined after the infection is diagnosed.
 The strain-specific vaccine efficacy is defined as one minus the
 cause-specific hazard ratio (vaccine/placebo). This paper develops some
 simple procedures for testing if the vaccine affords protection against
 various strains and if and how the strain-specific vaccine efficacy
 depends on the type of exposing strain, adjusting for covariate effects.
 The Cox proportional hazards model is used to relate the cause-specific
 outcomes to explanatory variables. The finite sample properties of
 proposed tests are studied through simulations and are shown to have good
 performances. The tests developed are applied to the data collected from
 an oral cholera vaccine trial. 
Journal: Journal of Applied Statistics 
Pages: 1065-1073 
Issue: 9 
Volume: 34 
Year: 2007 
Keywords: Competing risks model, cause-specific hazard function, Cox proportional hazards model, 
X-DOI: 10.1080/02664760701592083 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701592083
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:9:p:1065-1073




Template-Type: ReDIF-Article 1.0
Author-Name: J. D. Bermudez 
Author-X-Name-First: J. D. 
Author-X-Name-Last: Bermudez 
Author-Name: J. V. Segura 
Author-X-Name-First: J. V. 
Author-X-Name-Last: Segura 
Author-Name: E. Vercher 
Author-X-Name-First: E. 
Author-X-Name-Last: Vercher 
Title: Holt-Winters Forecasting: An Alternative Formulation Applied to UK Air Passenger Data 
Abstract:
  This paper provides a formulation for the additive Holt-Winters
 forecasting procedure that simplifies both obtaining maximum likelihood
 estimates of all unknowns, smoothing parameters and initial conditions,
 and the computation of point forecasts and reliable predictive intervals.
 The stochastic component of the model is introduced by means of additive,
 uncorrelated, homoscedastic and Normal errors, and then the joint
 distribution of the data vector, a multivariate Normal distribution, is
 obtained. In the case where a data transformation was used to improve the
 fit of the model, cumulative forecasts are obtained here using a
 Monte-Carlo approximation. This paper describes the method by applying it
 to the series of monthly total UK air passengers collected by the Civil
 Aviation Authority, a long time series from 1949 to the present day, and
 compares the resulting forecasts with those obtained in previous studies. 
Journal: Journal of Applied Statistics 
Pages: 1075-1090 
Issue: 9 
Volume: 34 
Year: 2007 
Keywords: Exponential smoothing, time series forecasting, prediction intervals, linear model, additive error, Monte-Carlo methods, 
X-DOI: 10.1080/02664760701592125 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701592125
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:9:p:1075-1090




Template-Type: ReDIF-Article 1.0
Author-Name: Edoardo Otrano 
Author-X-Name-First: Edoardo 
Author-X-Name-Last: Otrano 
Author-Name: Umberto Triacca 
Author-X-Name-First: Umberto 
Author-X-Name-Last: Triacca 
Title: Testing for Equal Predictability of Stationary ARMA Processes 
Abstract:
  In this work we use a measure of predictability of a time series
 following a stationary ARMA process to develop a test of equal
 predictability of two or more time series. The test is derived by a set of
 propositions which links the structure of the AR and MA coefficients to
 the predictability measure. A particular case of this general approach is
 constituted by time series having a Wold decomposition with weights having
 the same sign; in this framework the equal predictability is equivalent to
 parallelism among ARMA models and the null hypothesis of equal
 predictability is simply a set of linear restrictions. The ARMA
 representation of the GARCH models presents non-negative weights, so that
 this test can be extended to verify the equal predictability of squared
 time series following GARCH structures. 
Journal: Journal of Applied Statistics 
Pages: 1091-1108 
Issue: 9 
Volume: 34 
Year: 2007 
Keywords: Forecasts, parallelism, Wold decomposition, GARCH models, 
X-DOI: 10.1080/02664760701592158 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701592158
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:9:p:1091-1108




Template-Type: ReDIF-Article 1.0
Author-Name: Chien-Wei Wu 
Author-X-Name-First: Chien-Wei 
Author-X-Name-Last: Wu 
Author-Name: M. H. Shu 
Author-X-Name-First: M. H. 
Author-X-Name-Last: Shu 
Title: A Bayesian Procedure for Assessing Process Performance Based on Expected Relative Loss with Asymmetric Tolerances 
Abstract:
  Taguchi has introduced the loss function approach to quality improvement
 by focusing on the reduction of variation around the target value. This
 concept pays attention to the product designer's original intent; that is,
 values of a critical characteristic at a target lead to maximum product
 performance. To address this concept, Johnson (1992) proposed the concept
 of expected relative squared error loss Le for symmetric cases, by
 approaching capability in terms of loss functions. Unfortunately, the
 index Le inconsistently measures process capability for processes with
 asymmetric tolerances, and thus reflects process potential and performance
 inaccurately. To remedy this, Pearn et al. (2006) proposed a modification
 of expected loss index, which is referred to as [image omitted]&#xa0;, to
 handle processes with both symmetric and asymmetric tolerances. The
 majority of the researches for assessing process performance based on the
 process loss indices are investigated using the traditional frequentist
 approach. However, the sampling distribution of the estimated [image
 omitted]&#xa0; is intractable, this makes establishing the exact
 confidence interval and testing process performance difficult. In the
 paper, we consider an alternative Bayesian approach to assess process
 performance based on the loss index for processes with asymmetric
 tolerances. Based on the derived posterior probability, a simple but
 practical procedure is proposed for practitioners to assess process
 performance on their shop floor, whether the manufacturing tolerance is
 symmetric or asymmetric. 
Journal: Journal of Applied Statistics 
Pages: 1109-1123 
Issue: 9 
Volume: 34 
Year: 2007 
Keywords: Asymmetric tolerances, Bayesian approach, credible interval, expected relative loss, 
X-DOI: 10.1080/02664760701592190 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701592190
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:9:p:1109-1123




Template-Type: ReDIF-Article 1.0
Author-Name: C. Xu 
Author-X-Name-First: C. 
Author-X-Name-Last: Xu 
Author-Name: P. A. Dowd 
Author-X-Name-First: P. A. 
Author-X-Name-Last: Dowd 
Author-Name: K. V. Mardia 
Author-X-Name-First: K. V. 
Author-X-Name-Last: Mardia 
Author-Name: R. J. Fowell 
Author-X-Name-First: R. J. 
Author-X-Name-Last: Fowell 
Author-Name: C. C. Taylor 
Author-X-Name-First: C. C. 
Author-X-Name-Last: Taylor 
Title: Simulating Correlated Marked-point Processes 
Abstract:
  The area of marked-point processes is well developed but simulation is
 still a challenging problem when mark correlations are to be included. In
 this paper we propose the use of simulated annealing to incorporate the
 spatial mark correlation into the simulations of correlated marked-point
 processes. Such a simulation has wide applications in areas such as
 inference and goodness-of-fit investigations of proposed models. The
 technique is applied to a forest dataset for which the results are
 extremely encouraging. 
Journal: Journal of Applied Statistics 
Pages: 1125-1134 
Issue: 9 
Volume: 34 
Year: 2007 
Keywords: Marked-point process, spatial mark correlation, point process simulation, simulated annealing, 
X-DOI: 10.1080/02664760701597231 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701597231
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:9:p:1125-1134




Template-Type: ReDIF-Article 1.0
Author-Name: Gopal Kanji 
Author-X-Name-First: Gopal 
Author-X-Name-Last: Kanji 
Author-Name: Parvesh Chopra 
Author-X-Name-First: Parvesh 
Author-X-Name-Last: Chopra 
Title: Poverty as a System: Human Contestability Approach to Poverty Measurement 
Abstract:
  Since Sen's (1976) paper on poverty measurement, a substantial
 literature, both theoretical and empirical, has emerged. There have been
 several recent efforts to drive poverty measures based on different
 approaches and axioms. These poverty indices are based on head count
 ratio, poverty gaps and distribution of income. These are very narrow in
 approach and suffer from several drawbacks. However, the purpose of the
 present paper is to introduce a new poverty measure based on a holistic
 and system modelling approach. Based on Chopra's human contestability
 (Chopra, 2003, 2007) approach to poverty, this new approach to measuring
 poverty has been developed using a structure equation model based on
 Kanji's business excellence model (Kanji, 2002) to create the proposed
 poverty model. We construct a latent variable structural equation model to
 measure the contestability excellence within certain boundaries of the
 societal system. It will provide us with a measurement of poverty in a
 society or community in terms of human contestability. A higher human
 contestability index will indicate the lower poverty within the society.
 Strengths and weakness as of various components will also indicate that a
 characteristic of the individual requires extra society or government
 support to remove poverty. However, there remains considerable
 disagreement on the best way to achieve this. 
Journal: Journal of Applied Statistics 
Pages: 1135-1158 
Issue: 9 
Volume: 34 
Year: 2007 
Keywords: Human contestability, system approach, poverty model, poverty dimensions, Kanji-Chopra poverty model, poverty measurement, 
X-DOI: 10.1080/02664760701619142 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701619142
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:9:p:1135-1158




Template-Type: ReDIF-Article 1.0
Author-Name: Ignacio Vidal 
Author-X-Name-First: Ignacio 
Author-X-Name-Last: Vidal 
Author-Name: Pilar Iglesias 
Author-X-Name-First: Pilar 
Author-X-Name-Last: Iglesias 
Author-Name: Manuel Galea 
Author-X-Name-First: Manuel 
Author-X-Name-Last: Galea 
Title: Influential Observations in the Functional Measurement Error Model 
Abstract:
  In this work we propose Bayesian measures to quantify the influence of
 observations on the structural parameters of the simple measurement error
 model (MEM). Different influence measures, like those based on
 q-divergence between posterior distributions and Bayes risk, are studied
 to evaluate the influence. A strategy based on the perturbation function
 and MCMC samples is used to compute these measures. The samples from the
 posterior distributions are obtained by using the Metropolis-Hastings
 algorithm and assuming specific proper prior distributions. The results
 are illustrated with an application to a real example modeled with MEM in
 the literature. 
Journal: Journal of Applied Statistics 
Pages: 1165-1183 
Issue: 10 
Volume: 34 
Year: 2007 
Keywords: MEM, Influence measures, Bayes risk, <i>q</i>-divergence, Perturbation function, Metropolis-Hastings, Gibbs sampling, 
X-DOI: 10.1080/02664760701592703 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701592703
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:10:p:1165-1183




Template-Type: ReDIF-Article 1.0
Author-Name: Pilar Olave 
Author-X-Name-First: Pilar 
Author-X-Name-Last: Olave 
Author-Name: Manuel Salvador 
Author-X-Name-First: Manuel 
Author-X-Name-Last: Salvador 
Title: Semi-parametric Bayesian Analysis of the Proportional Hazard Rate Model An Application to the Effect of Training Programs on Graduate Unemployment 
Abstract:
  In this paper, we introduce a semi-parametric Bayesian methodology based
 on the proportional hazard model that assumes that the baseline hazard
 function is constant over segments but, by contrast to what is usually
 assumed in the literature, with the periods at which the function changes
 not being specified in advance. The methodology is applied to explore the
 impact of Vocational Training courses offered by the University of
 Zaragoza (Spain) on the duration of the initial periods of unemployment
 experienced by graduate leavers. The framework is very flexible and allows
 us, in particular, to capture the presence of seasonality in the job
 insertion of graduates. 
Journal: Journal of Applied Statistics 
Pages: 1185-1205 
Issue: 10 
Volume: 34 
Year: 2007 
Keywords: Bayesian survival analysis, semi-parametric models, proportional hazard, training programs, labor market, 
X-DOI: 10.1080/02664760701592752 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701592752
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:10:p:1185-1205




Template-Type: ReDIF-Article 1.0
Author-Name: Alberto Luceno 
Author-X-Name-First: Alberto 
Author-X-Name-Last: Luceno 
Title: A Universal QQ-Plot for Continuous Non-homogeneous Populations 
Abstract:
  This article presents a universal quantile-quantile (QQ) plot that may be
 used to assess the fit of a family of absolutely continuous distribution
 functions in a possibly non-homogeneous population. This plot is more
 general than probability plotting papers because it may be used for
 distributions having more than two parameters. It is also more general
 than standard quantile-quantile plots because it may be used for families
 of not-necessarily identical distributions. In particular, the universal
 QQ plot may be used in the context of non-homogeneous Poisson processes,
 generalized linear models, and other general models. 
Journal: Journal of Applied Statistics 
Pages: 1207-1223 
Issue: 10 
Volume: 34 
Year: 2007 
Keywords: Generalized linear model, goodness of fit, Kolmogorov-Smirnov, non-homogeneous Poisson process, plot points, probability plotting papers, 
X-DOI: 10.1080/02664760701592786 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701592786
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:10:p:1207-1223




Template-Type: ReDIF-Article 1.0
Author-Name: David Clark 
Author-X-Name-First: David 
Author-X-Name-Last: Clark 
Author-Name: Louise Ryan 
Author-X-Name-First: Louise 
Author-X-Name-Last: Ryan 
Author-Name: F. L. Lucas 
Author-X-Name-First: F. L. 
Author-X-Name-Last: Lucas 
Title: A Multi-state Piecewise Exponential Model of Hospital Outcomes after Injury 
Abstract:
  To allow more accurate prediction of hospital length of stay (LOS) after
 serious injury or illness, a multi-state model is proposed, in which
 transitions from the hospitalized state to three possible outcome states
 (home, long-term care, or death) are assumed to follow constant rates for
 each of a limited number of time periods. This results in a piecewise
 exponential (PWE) model for each outcome. Transition rates may be affected
 by time-varying covariates, which can be estimated from a reference
 database using standard statistical software and Poisson regression. A PWE
 model combining the three outcomes allows prediction of LOS. Records of
 259,941 injured patients from the US Nationwide Inpatient Sample were used
 to create such a multi-state PWE model with four time periods. Hospital
 mortality and LOS for patient subgroups were calculated from this model,
 and time-varying covariate effects were estimated. Early mortality was
 increased by anatomic injury severity or penetrating mechanism, but these
 effects diminished with time; age and male sex remained strong predictors
 of mortality in all time periods. Rates of discharge home decreased
 steadily with time, while rates of transfer to long-term care peaked at
 five days. Predicted and observed LOS and mortality were similar for
 multiple subgroups. Conceptual background and methods of calculation are
 discussed and demonstrated. Multi-state PWE models may be useful to
 describe hospital outcomes, especially when many patients are not
 discharged home. 
Journal: Journal of Applied Statistics 
Pages: 1225-1239 
Issue: 10 
Volume: 34 
Year: 2007 
Keywords: LOS, injury, model, multi-state, piecewise exponential, competing risks, 
X-DOI: 10.1080/02664760701592836 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701592836
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:10:p:1225-1239




Template-Type: ReDIF-Article 1.0
Author-Name: Chrys Caroni 
Author-X-Name-First: Chrys 
Author-X-Name-Last: Caroni 
Author-Name: Nedret Billor 
Author-X-Name-First: Nedret 
Author-X-Name-Last: Billor 
Title: Robust Detection of Multiple Outliers in Grouped Multivariate Data 
Abstract:
  Many methods have been developed for detecting multiple outliers in a
 single multivariate sample, but very few for the case where there may be
 groups in the data. We propose a method of simultaneously determining
 groups (as in cluster analysis) and detecting outliers, which are points
 that are distant from every group. Our method is an adaptation of the
 BACON algorithm proposed by Billor, Hadi and Velleman for the robust
 detection of multiple outliers in a single group of multivariate data.
 There are two versions of our method, depending on whether or not the
 groups can be assumed to have equal covariance matrices. The effectiveness
 of the method is illustrated by its application to two real data sets and
 further shown by a simulation study for different sample sizes and
 dimensions for 2 and 3 groups, with and without planted outliers in the
 data. When the number of groups is not known in advance, the algorithm
 could be used as a robust method of cluster analysis, by running it for
 various numbers of groups and choosing the best solution. 
Journal: Journal of Applied Statistics 
Pages: 1241-1250 
Issue: 10 
Volume: 34 
Year: 2007 
Keywords: Multivariate data, outliers, robust methods, BACON, cluster analysis, 
X-DOI: 10.1080/02664760701592877 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701592877
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:10:p:1241-1250




Template-Type: ReDIF-Article 1.0
Author-Name: Paramjit Gill 
Author-X-Name-First: Paramjit 
Author-X-Name-Last: Gill 
Author-Name: Tim Swartz 
Author-X-Name-First: Tim 
Author-X-Name-Last: Swartz 
Author-Name: Michael Treschow 
Author-X-Name-First: Michael 
Author-X-Name-Last: Treschow 
Title: A Stylometric Analysis of King Alfred's Literary Works 
Abstract:
  For centuries, Alfred the Great was judged to have translated several
 Latin texts into Old English. Many scholars, however, have expressed doubt
 whether Alfred could have done all of this work. With the availability of
 the Old English Corpus in electronic form, it is feasible to subject the
 texts to statistical stylometric analysis. We approach the problem from a
 Bayesian perspective where key words are identified and frequencies of the
 key words are tabulated for seven relevant texts. The question of
 authorship falls into the general statistical problem of classification
 where several simple innovations to classical agglomerative procedures are
 introduced. Our results suggest that one translation that has been
 traditionally attributed to Alfred (The First Fifty Prose Psalms) tends to
 distinguish itself from texts that are known to be Alfredian. 
Journal: Journal of Applied Statistics 
Pages: 1251-1258 
Issue: 10 
Volume: 34 
Year: 2007 
Keywords: Agglomerative techniques, Bayesian methods, classification, Dirichlet distribution, disputed authorship, entropy, hierarchical clustering, multinomial distribution, Old English, 
X-DOI: 10.1080/02664760701592992 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701592992
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:10:p:1251-1258




Template-Type: ReDIF-Article 1.0
Author-Name: Daniel Yasumasa Takahashi 
Author-X-Name-First: Daniel Yasumasa 
Author-X-Name-Last: Takahashi 
Author-Name: Luiz Antonio Baccal 
Author-X-Name-First: Luiz Antonio 
Author-X-Name-Last: Baccal 
Author-Name: Koichi Sameshima 
Author-X-Name-First: Koichi 
Author-X-Name-Last: Sameshima 
Title: Connectivity Inference between Neural Structures via Partial Directed Coherence 
Abstract:
  This paper describes the rigorous asymptotic distributions of the
 recently introduced partial directed coherence (PDC) - a frequency domain
 description of Granger causality between multivariate time series
 represented by vector autoregressive models. We show that, when not zero,
 PDC is asymptotically normally distributed and therefore provides means of
 comparing different strengths of connection between observed time series.
 Zero PDC indicates an absence of a direct connection between time series,
 and its otherwise asymptotically normal behavior degenerates into that of
 a mixture of [image omitted]&#xa0; variables allowing the computation of
 rigorous thresholds for connectivity tests using either numerical
 integration or approximate numerical methods. A Monte Carlo study
 illustrates the power of the test under PDC nullity. An analysis of
 electroencephalographic data, before and during an epileptic seizure
 episode, is used to portray the usefulness of the test in a real
 application. 
Journal: Journal of Applied Statistics 
Pages: 1259-1273 
Issue: 10 
Volume: 34 
Year: 2007 
Keywords: Partial directed coherence, epilepsy, Granger causality, connectivity, 
X-DOI: 10.1080/02664760701593065 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701593065
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:10:p:1259-1273




Template-Type: ReDIF-Article 1.0
Author-Name: &#xd8;yvind Langsrud 
Author-X-Name-First: &#xd8;yvind 
Author-X-Name-Last: Langsrud 
Author-Name: Kjetil J&#xf8;rgensen 
Author-X-Name-First: Kjetil 
Author-X-Name-Last: J&#xf8;rgensen 
Author-Name: Ragni Ofstad 
Author-X-Name-First: Ragni 
Author-X-Name-Last: Ofstad 
Author-Name: Tormod N&#xe6;s 
Author-X-Name-First: Tormod 
Author-X-Name-Last: N&#xe6;s 
Title: Analyzing Designed Experiments with Multiple Responses 
Abstract:
  This paper is an overview of a unified framework for analyzing designed
 experiments with univariate or multivariate responses. Both categorical
 and continuous design variables are considered. To handle unbalanced data,
 we introduce the so-called Type II* sums of squares. This means that the
 results are independent of the scale chosen for continuous design
 variables. Furthermore, it does not matter whether two-level variables are
 coded as categorical or continuous. Overall testing of all responses is
 done by 50-50 MANOVA, which handles several highly correlated responses.
 Univariate p-values for each response are adjusted by using rotation
 testing. To illustrate multivariate effects, mean values and mean
 predictions are illustrated in a principal component score plot or
 directly as curves. For the unbalanced cases, we introduce a new variant
 of adjusted means, which are independent to the coding of two-level
 variables. The methodology is exemplified by case studies from cheese and
 fish pudding production. 
Journal: Journal of Applied Statistics 
Pages: 1275-1296 
Issue: 10 
Volume: 34 
Year: 2007 
Keywords: 50-50 MANOVA, general linear model, least-squares means, multiple testing, principal component, rotation test, unbalanced factorial design, 
X-DOI: 10.1080/02664760701594246 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701594246
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:34:y:2007:i:10:p:1275-1296




Template-Type: ReDIF-Article 1.0
Author-Name: Rand Wilcox 
Author-X-Name-First: Rand 
Author-X-Name-Last: Wilcox 
Title: Post-hoc analyses in multiple regression based on prediction error 
Abstract:
  A well-known problem in multiple regression is that it is possible to
 reject the hypothesis that all slope parameters are equal to zero, yet
 when applying the usual Student's T-test to the individual parameters, no
 significant differences are found. An alternative strategy is to estimate
 prediction error via the 0.632 bootstrap method for all models of interest
 and declare the parameters associated with the model that yields the
 smallest prediction error to differ from zero. The main results in this
 paper are that this latter strategy can have practical value versus
 Student's T; replacing squared error with absolute error can be beneficial
 in some situations and replacing least squares with an extension of the
 Theil-Sen estimator can substantially increase the probability of
 identifying the correct model under circumstances that are described. 
Journal: Journal of Applied Statistics 
Pages: 9-17 
Issue: 1 
Volume: 35 
Year: 2008 
Keywords: multiple comparisons, prediction error, bootstrap methods, robust regression, 
X-DOI: 10.1080/02664760701683288 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701683288
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:1:p:9-17




Template-Type: ReDIF-Article 1.0
Author-Name: Erik M&#xf8;nness 
Author-X-Name-First: Erik 
Author-X-Name-Last: M&#xf8;nness 
Author-Name: Kim Pearce 
Author-X-Name-First: Kim 
Author-X-Name-Last: Pearce 
Author-Name: Shirley Coleman 
Author-X-Name-First: Shirley 
Author-X-Name-Last: Coleman 
Title: Comparing a survey and a conjoint study: the future vision of water intermediaries 
Abstract:
  This paper compares and contrasts two methods of obtaining opinions using
 questionnaires. As the name suggests, a conjoint study makes it possible
 to consider several attributes jointly. Conjoint analysis is a statistical
 method to analyse preferences. However, conjoint analysis requires a
 certain amount of effort by the respondent. The alternative is ordinary
 survey questions, answered one at a time. Survey questions are easier to
 grasp mentally, but they do not challenge the respondent to prioritize.
 This investigation has utilized both methods, survey and conjoint, making
 it possible to compare them on real data. Attribute importance, attribute
 correlations, case clustering and attribute grouping are evaluated by both
 methods. Correspondence between how the two methods measure the attribute
 in question is also given. Overall, both methods yield the same picture
 concerning the relative importance of the attributes. Taken one attribute
 at a time, the correspondence between the methods varies from good to no
 correspondence. Considering all attributes together by cluster analysis of
 the cases, the conjoint and survey data yield different cluster
 structures. The attributes are grouped by factor analysis, and there is
 reasonable correspondence. The data originate from the EU project 'New
 Intermediary services and the transformation of urban water supply and
 wastewater disposal systems in Europe'. 
Journal: Journal of Applied Statistics 
Pages: 19-30 
Issue: 1 
Volume: 35 
Year: 2008 
Keywords: questionnaire, conjoint analysis, survey methods, 
X-DOI: 10.1080/02664760701683379 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701683379
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:1:p:19-30




Template-Type: ReDIF-Article 1.0
Author-Name: Hongying Dai 
Author-X-Name-First: Hongying 
Author-X-Name-Last: Dai 
Author-Name: Richard Charnigo 
Author-X-Name-First: Richard 
Author-X-Name-Last: Charnigo 
Title: Omnibus testing and gene filtration in microarray data analysis 
Abstract:
  When thousands of tests are performed simultaneously to detect
 differentially expressed genes in microarray analysis, the number of Type
 I errors can be immense if a multiplicity adjustment is not made. However,
 due to the large scale, traditional adjustment methods require very
 stringen significance levels for individual tests, which yield low power
 for detecting alterations. In this work, we describe how two omnibus tests
 can be used in conjunction with a gene filtration process to circumvent
 difficulties due to the large scale of testing. These two omnibus tests,
 the D-test and the modified likelihood ratio test (MLRT), can be used to
 investigate whether a collection of P-values has arisen from the
 Uniform(0,1) distribution or whether the Uniform(0,1) distribution
 contaminated by another Beta distribution is more appropriate. In the
 former case, attention can be directed to a smaller part of the genome; in
 the latter event, parameter estimates for the contamination model provide
 a frame of reference for multiple comparisons. Unlike the likelihood ratio
 test (LRT), both the D-test and MLRT enjoy simple limiting distributions
 under the null hypothesis of no contamination, so critical values can be
 obtained from standard tables. Simulation studies demonstrate that the
 D-test and MLRT are superior to the AIC, BIC, and Kolmogorov-Smirnov test.
 A case study illustrates omnibus testing and filtration. 
Journal: Journal of Applied Statistics 
Pages: 31-47 
Issue: 1 
Volume: 35 
Year: 2008 
Keywords: multiple comparisons, <i>P</i>-values, <i>Beta</i> contamination model, MMLEs, <i>D</i>-test, MLRT, 
X-DOI: 10.1080/02664760701683528 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701683528
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:1:p:31-47




Template-Type: ReDIF-Article 1.0
Author-Name: Xiaomo Jiang 
Author-X-Name-First: Xiaomo 
Author-X-Name-Last: Jiang 
Author-Name: Sankaran Mahadevan 
Author-X-Name-First: Sankaran 
Author-X-Name-Last: Mahadevan 
Title: Bayesian validation assessment of multivariate computational models 
Abstract:
  Multivariate model validation is a complex decision-making problem
 involving comparison of multiple correlated quantities, based upon the
 available information and prior knowledge. This paper presents a Bayesian
 risk-based decision method for validation assessment of multivariate
 predictive models under uncertainty. A generalized likelihood ratio is
 derived as a quantitative validation metric based on Bayes' theorem and
 Gaussian distribution assumption of errors between validation data and
 model prediction. The multivariate model is then assessed based on the
 comparison of the likelihood ratio with a Bayesian decision threshold, a
 function of the decision costs and prior of each hypothesis. The
 probability density function of the likelihood ratio is constructed using
 the statistics of multiple response quantities and Monte Carlo simulation.
 The proposed methodology is implemented in the validation of a transient
 heat conduction model, using a multivariate data set from experiments. The
 Bayesian methodology provides a quantitative approach to facilitate
 rational decisions in multivariate model assessment under uncertainty. 
Journal: Journal of Applied Statistics 
Pages: 49-65 
Issue: 1 
Volume: 35 
Year: 2008 
Keywords: Bayesian statistics, decision making, risk, reliability, model validation, multivariate statistics, 
X-DOI: 10.1080/02664760701683577 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701683577
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:1:p:49-65




Template-Type: ReDIF-Article 1.0
Author-Name: Gulser Koksal 
Author-X-Name-First: Gulser 
Author-X-Name-Last: Koksal 
Author-Name: Burcu Kantar 
Author-X-Name-First: Burcu 
Author-X-Name-Last: Kantar 
Author-Name: Taylan Ali Ula 
Author-X-Name-First: Taylan Ali 
Author-X-Name-Last: Ula 
Author-Name: Murat Caner Testik 
Author-X-Name-First: Murat Caner 
Author-X-Name-Last: Testik 
Title: The effect of Phase I sample size on the run length performance of control charts for autocorrelated data 
Abstract:
  Traditional control charts assume independence of observations obtained
 from the monitored process. However, if the observations are
 autocorrelated, these charts often do not perform as intended by the
 design requirements. Recently, several control charts have been proposed
 to deal with autocorrelated observations. The residual chart, modified
 Shewhart chart, EWMAST chart, and ARMA chart are such charts widely used
 for monitoring the occurrence of assignable causes in a process when the
 process exhibits inherent autocorrelation. Besides autocorrelation, one
 other issue is the unknown values of true process parameters to be used in
 the control chart design, which are often estimated from a reference
 sample of in-control observations. Performances of the above-mentioned
 control charts for autocorrelated processes are significantly affected by
 the sample size used in a Phase I study to estimate the control chart
 parameters. In this study, we investigate the effect of Phase I sample
 size on the run length performance of these four charts for monitoring the
 changes in the mean of an autocorrelated process, namely an AR(1) process.
 A discussion of the practical implications of the results and suggestions
 on the sample size requirements for effective process monitoring are
 provided. 
Journal: Journal of Applied Statistics 
Pages: 67-87 
Issue: 1 
Volume: 35 
Year: 2008 
Keywords: autocorrelated data, sample size, residual chart, EWMAST chart, modified Shewhart chart, ARMA chart, run length, 
X-DOI: 10.1080/02664760701683619 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701683619
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:1:p:67-87




Template-Type: ReDIF-Article 1.0
Author-Name: S. Chakraborti 
Author-X-Name-First: S. 
Author-X-Name-Last: Chakraborti 
Author-Name: S. W. Human 
Author-X-Name-First: S. W. 
Author-X-Name-Last: Human 
Title: Properties and performance of the c-chart for attributes data 
Abstract:
  The effects of parameter estimation are examined for the well-known
 c-chart for attributes data. The exact run length distribution is obtained
 for Phase II applications, when the true average number of
 non-conformities, c, is unknown, by conditioning on the observed number of
 non-conformities in a set of reference data (from Phase I). Expressions
 for various chart performance characteristics, such as the average run
 length (ARL), the standard deviation of the run length (SDRL) and the
 median run length (MDRL) are also obtained. Examples show that the actual
 performance of the chart, both in terms of the false alarm rate (FAR) and
 the in-control ARL, can be substantially different from what might be
 expected when c is known, in that an exceedingly large number of false
 alarms are observed, unless the number of inspection units (the size of
 the reference dataset) used to estimate c is very large, much larger than
 is commonly used or recommended in practice. In addition, the actual FAR
 and the in-control ARL values can be very different from the nominally
 expected values such as 0.0027 (or ARL0=370), particularly when c is
 small, even with large amounts of reference data. A summary and
 conclusions are offered. 
Journal: Journal of Applied Statistics 
Pages: 89-100 
Issue: 1 
Volume: 35 
Year: 2008 
Keywords: non-conformities, defects, Shewhart, statistical process control, Phase I, Phase II, parameter estimation, Poisson distribution, run length, average run length, percentiles, in-control, out-of-control, 
X-DOI: 10.1080/02664760701683643 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701683643
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:1:p:89-100




Template-Type: ReDIF-Article 1.0
Author-Name: Mahdi Alkhamisi 
Author-X-Name-First: Mahdi 
Author-X-Name-Last: Alkhamisi 
Author-Name: Ghadban Khalaf 
Author-X-Name-First: Ghadban 
Author-X-Name-Last: Khalaf 
Author-Name: Ghazi Shukur 
Author-X-Name-First: Ghazi 
Author-X-Name-Last: Shukur 
Title: The effect of fat-tailed error terms on the properties of systemwise RESET test 
Abstract:
  The small sample properties of the systemwise RESET (Regression
 Specification Error Test) test for functional misspecification are
 investigated using normal and non-normal error terms. When using normally
 distributed or less heavy tailed error terms, we find the Rao's
 multivariate F-test to be best among all other alternative test methods
 (i.e. Wald, Lagrange Multiplier and Likelihood Ratio). Using the bootstrap
 critical values, however, all test methods perform satisfactorily in
 almost all situations. However, the test methods perform extremely badly
 (even the RAO test) when the error terms are very heavy tailed. 
Journal: Journal of Applied Statistics 
Pages: 101-113 
Issue: 1 
Volume: 35 
Year: 2008 
Keywords: systemwise test of functional misspecification, non-normal error terms, small sample properties, 
X-DOI: 10.1080/02664760701683676 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701683676
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:1:p:101-113




Template-Type: ReDIF-Article 1.0
Author-Name: Daniel Wang 
Author-X-Name-First: Daniel 
Author-X-Name-Last: Wang 
Author-Name: Michael Conerly 
Author-X-Name-First: Michael 
Author-X-Name-Last: Conerly 
Title: Evaluating the power of Minitab's data subsetting lack of fit test in multiple linear regression 
Abstract:
  Minitab's data subsetting lack of fit test (denoted XLOF) is a
 combination of Burn and Ryan's test and Utts' test for testing lack of fit
 in linear regression models. As an alternative to the classical or pure
 error lack of fit test, it does not require replicates of predictor
 variables. However, due to the uncertainty about its performance, XLOF
 still remains unfamiliar to regression users while the well-known
 classical lack of fit test is not applicable to regression data without
 replicates. So far this procedure has not been mentioned in any textbooks
 and has not been included in any other software packages. This study
 assesses the performance of XLOF in detecting lack of fit in linear
 regressions without replicates by comparing the power with the classic
 test. The power of XLOF is simulated using Minitab macros for variables
 with several forms of curvature. These comparisons lead to pragmatic
 suggestions on the use of XLOF. The performance of XLOF was shown to be
 superior to the classical test based on the results. It should be noted
 that the replicates required for the classical test made itself
 unavailable for most of the regression data while XLOF can still be as
 powerful as the classic test even without replicates. 
Journal: Journal of Applied Statistics 
Pages: 115-124 
Issue: 1 
Volume: 35 
Year: 2008 
Keywords: Minitab XLOF, lack of fit test, linear regression, diagnosis, power, simulation, 
X-DOI: 10.1080/02664760701775381 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701775381
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:1:p:115-124




Template-Type: ReDIF-Article 1.0
Author-Name: R. L. J. Coetzer 
Author-X-Name-First: R. L. J. 
Author-X-Name-Last: Coetzer 
Author-Name: D. H. Morgan 
Author-X-Name-First: D. H. 
Author-X-Name-Last: Morgan 
Author-Name: H. Maumela 
Author-X-Name-First: H. 
Author-X-Name-Last: Maumela 
Title: Optimization of a catalyst system through the sequential application of experimental design techniques 
Abstract:
  The selective oligomerisation of ethylene to higher alpha olefins is an
 area of much recent interest. In this regard, Sasol Technology R{&}D has
 developed a homogeneous catalyst system based on bis-sulfanylamine (SNS)
 complexes of chromium for the selective trimerisation of ethylene to
 1-hexene. It is activated by methylaluminoxane (MAO), which is an
 extremely expensive activator. This paper discusses how, through the
 sequential application of experimental design and response surface
 techniques, the activator requirements of the catalyst system were reduced
 12 times, whilst improving the catalyst activity on a g/g Cr/h basis ca.
 three times and the activity on a g/g MAO basis ca. nine times. This
 reduction in the amount of MAO required led to economically attractive
 catalyst activities for the production of 1-hexene, and would not have
 been possible without the use of experimental design techniques. This
 paper will demonstrate the process of investigation through the use of
 sequential experimental design in practice. 
Journal: Journal of Applied Statistics 
Pages: 131-147 
Issue: 2 
Volume: 35 
Year: 2008 
Keywords: experimental design, methylaluminoxane, oligomerisation, response surface modelling, 
X-DOI: 10.1080/02664760701775613 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701775613
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:2:p:131-147




Template-Type: ReDIF-Article 1.0
Author-Name: R. Vijayaraghavan 
Author-X-Name-First: R. 
Author-X-Name-Last: Vijayaraghavan 
Author-Name: K. Rajagopal 
Author-X-Name-First: K. 
Author-X-Name-Last: Rajagopal 
Author-Name: A. Loganathan 
Author-X-Name-First: A. 
Author-X-Name-Last: Loganathan 
Title: A procedure for selection of a gamma-Poisson single sampling plan by attributes 
Abstract:
  Design and evaluation of sampling plans by attributes and by variables
 are important aspects in the area of acceptance sampling research. Various
 procedures for the selection of conventional single sampling by attributes
 have been developed and are available in the literature. This paper
 presents a design methodology and tables for the selection of parameters
 of single sampling plans for specified requirements (strengths) under the
 conditions of a gamma prior and Poisson sampling distribution. The
 relative efficiency of gamma-Poisson single sampling plans over
 conventional plans is discussed through empirical illustrations. 
Journal: Journal of Applied Statistics 
Pages: 149-160 
Issue: 2 
Volume: 35 
Year: 2008 
Keywords: sampling inspection by attributes, Bayesian acceptance sampling plan, consumer's risk, gamma-Poisson single sampling plan, gamma prior, operating characteristic curve, Poisson distribution, producer's risk, 
X-DOI: 10.1080/02664760701775654 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701775654
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:2:p:149-160




Template-Type: ReDIF-Article 1.0
Author-Name: Kim Pearce 
Author-X-Name-First: Kim 
Author-X-Name-Last: Pearce 
Author-Name: Shirley Coleman 
Author-X-Name-First: Shirley 
Author-X-Name-Last: Coleman 
Title: Modern-day perception of historic footwear and its links to preference 
Abstract:
  The importance of emotion in consumer preference is explored in the
 subject of Kansei Engineering. The Kansei methodology has been
 successfully adopted by many large companies in recent years. Currently, a
 European Union Fifth framework project called 'Kensys' (Kansei Engineering
 System) is being implemented to look at the application of Kansei
 engineering in the field of footwear. The Kensys project is being
 conducted in collaboration with several SMEs and this paper reports a
 study that has been carried out with one of the SMEs who designs and makes
 reproduction historic and specialist footwear. In addition, respondent
 views on 'real' products from history and reproduction footwear are
 compared. We report on the views of respondents in general and look at
 gender differences, the comparison of non-experts' views versus experts'
 views and we also look at differences due to age. The study was carried
 out in the UK and in Spain. The views in both counties are compared. 
Journal: Journal of Applied Statistics 
Pages: 161-178 
Issue: 2 
Volume: 35 
Year: 2008 
Keywords: Kansei Engineering, emotional response, design, 
X-DOI: 10.1080/02664760701775498 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701775498
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:2:p:161-178




Template-Type: ReDIF-Article 1.0
Author-Name: Manuel Galea 
Author-X-Name-First: Manuel 
Author-X-Name-Last: Galea 
Author-Name: Jose Diaz-Garcia 
Author-X-Name-First: Jose 
Author-X-Name-Last: Diaz-Garcia 
Author-Name: Filidor Vilca 
Author-X-Name-First: Filidor 
Author-X-Name-Last: Vilca 
Title: Influence diagnostics in the capital asset pricing model under elliptical distributions 
Abstract:
  In this paper we consider the Capital Asset Pricing Model under
 Elliptical (symmetric) Distributions. This class of distributions, which
 contains the normal distribution, t, contaminated normal and power
 exponential, among others, offers a more flexible framework for modelling
 asset prices or returns. In order to analyze the sensibility to possible
 outliers and/or atypical returns of the maximum likelihood estimators, the
 local influence method was implemented. The results are illustrated by
 using a set of shares from companies who trade in the Chilean Stock
 Market. Our main conclusion is that symmetric distributions having heavier
 tails than those of the normal distribution, especially the t distribution
 with small degrees of freedom, show a better fit and allow the reduction
 of the influence of atypical returns in the maximum likelihood estimators. 
Journal: Journal of Applied Statistics 
Pages: 179-192 
Issue: 2 
Volume: 35 
Year: 2008 
Keywords: robust estimation, diagnostics, local influence, elliptical distributions, 
X-DOI: 10.1080/02664760701775712 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701775712
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:2:p:179-192




Template-Type: ReDIF-Article 1.0
Author-Name: Emil Berendt 
Author-X-Name-First: Emil 
Author-X-Name-Last: Berendt 
Title: Contracts, livestock, and the Bernoulli process: an application of statistics to B. Traven's 'Cattle Drive' 
Abstract:
  One of the pivotal devices B. Traven employs in his short story 'The
 Cattle Drive' is a contract between the cattle owner and the trail boss
 who brings the livestock to market. By specifying a per-diem rate, the
 contract appears to encourage a wage-maximizing trail boss to delay the
 delivery of the cattle. However, a statistical model of the contract
 demonstrates that a rational trail boss has an incentive to maintain a
 rapid rate of travel. The article concludes that statistics can be applied
 in non-traditional ways such as to the analysis of the plot of a fictional
 story. The statistical model suggests plausible alternative endings to the
 story based on various parameter assumptions. Finally, it demonstrates
 that a well-crafted story can provide an excellent case study of how
 contracts create incentives and influence decision-making. 
Journal: Journal of Applied Statistics 
Pages: 193-202 
Issue: 2 
Volume: 35 
Year: 2008 
Keywords: B. Traven, contract, principal-agent problem, binomial, Cattle Drive, wage, literature, 
X-DOI: 10.1080/02664760701775571 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701775571
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:2:p:193-202




Template-Type: ReDIF-Article 1.0
Author-Name: Pankaj Sinha 
Author-X-Name-First: Pankaj 
Author-X-Name-Last: Sinha 
Author-Name: Ashok Bansal 
Author-X-Name-First: Ashok 
Author-X-Name-Last: Bansal 
Title: Bayesian optimization analysis with ML-II &#x3b5;-contaminated prior 
Abstract:
  In this paper we derive the predictive density function of a future
 observation when prior distribution for unknown mean of a normal
 population is a Type-II maximum likelihood &#x3b5;-contaminated prior. The
 derived predictive distribution is applied to the problem of optimization
 of a regression nature in the decisive prediction framework. 
Journal: Journal of Applied Statistics 
Pages: 203-211 
Issue: 2 
Volume: 35 
Year: 2008 
Keywords: &#x3b5;-contaminated prior, type II maximum likelihood technique, optimization analysis; decisive prediction, 
X-DOI: 10.1080/02664760701775415 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701775415
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:2:p:203-211




Template-Type: ReDIF-Article 1.0
Author-Name: David Bock 
Author-X-Name-First: David 
Author-X-Name-Last: Bock 
Title: Aspects on the control of false alarms in statistical surveillance and the impact on the return of financial decision systems 
Abstract:
  In systems for online detection of regime shifts, a process is
 continually observed. Based on the data available an alarm is given when
 there is enough evidence of a change. There is a risk of a false alarm and
 here two different ways of controlling the false alarms are compared: a
 fixed average run length until the first false alarm and a fixed
 probability of any false alarm (fixed size). The two approaches are
 evaluated in terms of the timeliness of alarms. A system with a fixed size
 is found to have a drawback: the ability to detect a change deteriorates
 with the time of the change. Consequently, the probability of successful
 detection will tend to zero and the expected delay of a motivated alarm
 tends to infinity. This drawback is present even when the size is set to
 be very large (close to one). Utility measures expressing the costs for a
 false or a too late alarm are used in the comparison. How the choice of
 the best approach can be guided by the parameters of the process and the
 different costs of alarms is demonstrated. The technique is illustrated by
 financial transactions of the Hang Seng Index. 
Journal: Journal of Applied Statistics 
Pages: 213-227 
Issue: 2 
Volume: 35 
Year: 2008 
Keywords: monitoring, surveillance, repeated decisions, moving average, Shewhart method, 
X-DOI: 10.1080/02664760701775431 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701775431
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:2:p:213-227




Template-Type: ReDIF-Article 1.0
Author-Name: Rafael Pino-Mejias 
Author-X-Name-First: Rafael 
Author-X-Name-Last: Pino-Mejias 
Author-Name: Mercedes Carrasco-Mairena 
Author-X-Name-First: Mercedes 
Author-X-Name-Last: Carrasco-Mairena 
Author-Name: Antonio Pascual-Acosta 
Author-X-Name-First: Antonio 
Author-X-Name-Last: Pascual-Acosta 
Author-Name: Maria-Dolores Cubiles-De-La-Vega 
Author-X-Name-First: Maria-Dolores 
Author-X-Name-Last: Cubiles-De-La-Vega 
Author-Name: Joaquin Munoz-Garcia 
Author-X-Name-First: Joaquin 
Author-X-Name-Last: Munoz-Garcia 
Title: A comparison of classification models to identify the Fragile X Syndrome 
Abstract:
  The main models of machine learning are briefly reviewed and considered
 for building a classifier to identify the Fragile X Syndrome (FXS). We
 have analyzed 172 patients potentially affected by FXS in Andalusia
 (Spain) and, by means of a DNA test, each member of the data set is known
 to belong to one of two classes: affected, not affected. The whole
 predictor set, formed by 40 variables, and a reduced set with only nine
 predictors significantly associated with the response are considered. Four
 alternative base classification models have been investigated: logistic
 regression, classification trees, multilayer perceptron and support vector
 machines. For both predictor sets, the best accuracy, considering both the
 mean and the standard deviation of the test error rate, is achieved by the
 support vector machines, confirming the increasing importance of this
 learning algorithm. Three ensemble methods - bagging, random forests and
 boosting - were also considered, amongst which the bagged versions of
 support vector machines stand out, especially when they are constructed
 with the reduced set of predictor variables. The analysis of the
 sensitivity, the specificity and the area under the ROC curve agrees with
 the main conclusions extracted from the accuracy results. All of these
 models can be fitted by free R programs. 
Journal: Journal of Applied Statistics 
Pages: 233-244 
Issue: 3 
Volume: 35 
Year: 2008 
Keywords: fragile X syndrome, support vector machines, multilayer perceptron, classification trees, logistic regression, ensemble methods, R system, 
X-DOI: 10.1080/02664760701832976 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701832976
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:3:p:233-244




Template-Type: ReDIF-Article 1.0
Author-Name: Raghu Nandan Sengupta 
Author-X-Name-First: Raghu Nandan 
Author-X-Name-Last: Sengupta 
Title: Use of asymmetric loss functions in sequential estimation problems for multiple linear regression 
Abstract:
  When estimating in a practical situation, asymmetric loss functions are
 preferred over squared error loss functions, as the former is more
 appropriate than the latter in many estimation problems. We consider here
 the problem of fixed precision point estimation of a linear parametric
 function in beta for the multiple linear regression model using asymmetric
 loss functions. Due to the presence of nuissance parameters, the sample
 size for the estimation problem is not known beforehand and hence we take
 the recourse of adaptive multistage sampling methodologies. We discuss
 here some multistage sampling techniques and compare the performances of
 these methodologies using simulation runs. The implementation of the codes
 for our proposed models is accomplished utilizing MATLAB 7.0.1 program run
 on a Pentium IV machine. Finally, we highlight the significance of such
 asymmetric loss functions with few practical examples. 
Journal: Journal of Applied Statistics 
Pages: 245-261 
Issue: 3 
Volume: 35 
Year: 2008 
Keywords: loss function, risk, bounded risk, asymmetric loss function, LINEX loss function, relative LINEX loss function, stopping rule, multistage sampling procedure, purely sequential sampling procedure, batch sequential sampling procedure, 
X-DOI: 10.1080/02664760701833388 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701833388
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:3:p:245-261




Template-Type: ReDIF-Article 1.0
Author-Name: Francesco Pauli 
Author-X-Name-First: Francesco 
Author-X-Name-Last: Pauli 
Author-Name: Laura Rizzi 
Author-X-Name-First: Laura 
Author-X-Name-Last: Rizzi 
Title: Summer temperature effects on deaths and hospital admissions among the elderly population in two Italian cities 
Abstract:
  In developed countries the effects of climate on health status are mainly
 due to temperature. Our analysis is aimed to deepen statistically the
 relationship between summer climate conditions and daily frequency of
 health episodes: deaths or hospital admissions. We expect to find a
 U-shaped relationship between temperature and frequencies of events
 occurring in summer regarding the elderly population resident in Milano
 and Brescia. We use as covariates hourly records of temperature recorded
 at observation sites located in Milano and Brescia. The analysis is
 performed using Generalized Additive Models (GAM), where the response
 variable is the daily number of events, which varies as a possibly
 non-linear function of meteorological variables measured on the same or
 previous day. We consider separate models for Milano and Brescia and then
 we compare temperature effects among the two towns and among different age
 classes. Moreover we consider separate models for all diagnosed events,
 for those due to respiratory disease and those due to circulatory
 pathologies. Model selection is a central problem, the basic methods used
 are the UBRE and GCV criteria but, instead of conditioning all final
 conclusions on the best model according to the chosen criterion, we
 investigated the effect of model selection by implementing a bootstrap
 procedure. 
Journal: Journal of Applied Statistics 
Pages: 263-276 
Issue: 3 
Volume: 35 
Year: 2008 
Keywords: temperature, deaths, hospital admissions, generalized additive models, model selection criteria, bootstrap, 
X-DOI: 10.1080/02664760701833354 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701833354
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:3:p:263-276




Template-Type: ReDIF-Article 1.0
Author-Name: P. Angelopoulos 
Author-X-Name-First: P. 
Author-X-Name-Last: Angelopoulos 
Author-Name: C. Koukouvinos 
Author-X-Name-First: C. 
Author-X-Name-Last: Koukouvinos 
Title: Detecting active effects in unreplicated designs 
Abstract:
  Unreplicated factorial designs pose a difficult problem in analysis
 because there are no degrees of freedom left to estimate the error. Daniel
 [Technometrics 1 (1959), pp. 311-341] proposed an ingenious graphical
 method that does not require &#x3c3; to be estimated. Here we try to put
 Daniel's method into a formal framework and lift the subjectiveness that
 carries. A simulation study has been conducted that shows that the
 proposed method behaves better than Lenth's [Technometrics 31 (1989), pp.
 469-473] popular method. 
Journal: Journal of Applied Statistics 
Pages: 277-281 
Issue: 3 
Volume: 35 
Year: 2008 
Keywords: unreplicated design, factorial, effect, outliers, 
X-DOI: 10.1080/02664760701833008 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701833008
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:3:p:277-281




Template-Type: ReDIF-Article 1.0
Author-Name: Sung-Soo Kim 
Author-X-Name-First: Sung-Soo 
Author-X-Name-Last: Kim 
Author-Name: Sung Park 
Author-X-Name-First: Sung 
Author-X-Name-Last: Park 
Author-Name: W. J. Krzanowski 
Author-X-Name-First: W. J. 
Author-X-Name-Last: Krzanowski 
Title: Simultaneous variable selection and outlier identification in linear regression using the mean-shift outlier model 
Abstract:
  We provide a method for simultaneous variable selection and outlier
 identification using the mean-shift outlier model. The procedure consists
 of two steps: the first step is to identify potential outliers, and the
 second step is to perform all possible subset regressions for the
 mean-shift outlier model containing the potential outliers identified in
 step 1. This procedure is helpful for model selection while simultaneously
 considering outlier identification, and can be used to identify multiple
 outliers. In addition, we can evaluate the impact on the regression model
 of simultaneous omission of variables and interesting observations. In an
 example, we provide detailed output from the R system, and compare the
 results with those using posterior model probabilities as proposed by
 Hoeting et al. [Comput. Stat. Data Anal. 22 (1996), pp. 252-270] for
 simultaneous variable selection and outlier identification. 
Journal: Journal of Applied Statistics 
Pages: 283-291 
Issue: 3 
Volume: 35 
Year: 2008 
Keywords: multiple outliers, variable selection, mean-shift outlier model, all-subset regressions, 
X-DOI: 10.1080/02664760701833040 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701833040
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:3:p:283-291




Template-Type: ReDIF-Article 1.0
Author-Name: Tonglin Zhang 
Author-X-Name-First: Tonglin 
Author-X-Name-Last: Zhang 
Author-Name: Ge Lin 
Author-X-Name-First: Ge 
Author-X-Name-Last: Lin 
Title: Identification of local clusters for count data: a model-based Moran's I test 
Abstract:
  We set out IDR as a loglinear-model-based Moran's I test for Poisson
 count data that resembles the Moran's I residual test for Gaussian data.
 We evaluate its type I and type II error probabilities via simulations,
 and demonstrate its utility via a case study. When population sizes are
 heterogeneous, IDR is effective in detecting local clusters by local
 association terms with an acceptable type I error probability. When used
 in conjunction with local spatial association terms in loglinear models,
 IDR can also indicate the existence of first-order global cluster that can
 hardly be removed by local spatial association terms. In this situation,
 IDR should not be directly applied for local cluster detection. In the
 case study of St. Louis homicides, we bridge loglinear model methods for
 parameter estimation to exploratory data analysis, so that a uniform
 association term can be defined with spatially varied contributions among
 spatial neighbors. The method makes use of exploratory tools such as
 Moran's I scatter plots and residual plots to evaluate the magnitude of
 deviance residuals, and it is effective to model the shape, the elevation
 and the magnitude of a local cluster in the model-based test. 
Journal: Journal of Applied Statistics 
Pages: 293-306 
Issue: 3 
Volume: 35 
Year: 2008 
Keywords: cluster and clustering, deviance residual, Moran's <i>I</i>, permutation test, spatial autocorrelation, type I error probability, 
X-DOI: 10.1080/02664760701833248 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701833248
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:3:p:293-306




Template-Type: ReDIF-Article 1.0
Author-Name: Anuradha Roy 
Author-X-Name-First: Anuradha 
Author-X-Name-Last: Roy 
Title: Computation aspects of the parameter estimates of linear mixed effects model in multivariate repeated measures set-up 
Abstract:
  The number of parameters mushrooms in a linear mixed effects (LME) model
 in the case of multivariate repeated measures data. Computation of these
 parameters is a real problem with the increase in the number of response
 variables or with the increase in the number of time points. The problem
 becomes more intricate and involved with the addition of additional random
 effects. A multivariate analysis is not possible in a small sample
 setting. We propose a method to estimate these many parameters in bits and
 pieces from baby models, by taking a subset of response variables at a
 time, and finally using these bits and pieces at the end to get the
 parameter estimates for the mother model, with all variables taken
 together. Applying this method one can calculate the fixed effects, the
 best linear unbiased predictions (BLUPs) for the random effects in the
 model, and also the BLUPs at each time of observation for each response
 variable, to monitor the effectiveness of the treatment for each subject.
 The proposed method is illustrated with an example of multiple response
 variables measured over multiple time points arising from a clinical trial
 in osteoporosis. 
Journal: Journal of Applied Statistics 
Pages: 307-320 
Issue: 3 
Volume: 35 
Year: 2008 
Keywords: best linear unbiased prediction, covariance structures, linear mixed effects model, multivariate repeated measures data, 
X-DOI: 10.1080/02664760701833271 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701833271
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:3:p:307-320




Template-Type: ReDIF-Article 1.0
Author-Name: Murari Singh 
Author-X-Name-First: Murari 
Author-X-Name-Last: Singh 
Author-Name: Michael Jones 
Author-X-Name-First: Michael 
Author-X-Name-Last: Jones 
Title: Modelling spatial-temporal covariance structures in monocropping barley trials 
Abstract:
  In long-term trials, not only are individual plot errors correlated over
 time but there is also a consistent underlying spatial variability in
 field conditions. The current study sought the most appropriate covariance
 structure of errors correlated in three dimensions for evaluating the
 productivity and time-trends in the barley yield data from the
 monocropping system established in northern Syria. The best
 spatial-temporal model found reflected the contribution of
 autocorrelations in spatial and temporal dimensions with estimates varying
 with the yield variable and location. Compared with a control structure
 based on independent errors, this covariance structure improved the
 significance of the fertilizer effect and the interaction with year.
 Time-trends were estimated in two ways: by accounting the seasonal
 variable contribution in annual variability (Method 1), which is suitable
 for detecting significant trends in short data series; and by using the
 linear component of the orthogonal polynomial on time (year), which is
 appropriate for long series (Method 2). Method 1 strengthened time-trend
 detection compared with the method of Jones and Singh [J. Agri. Sci.,
 Cambridge 135 (2000), pp. 251-259] which assumed independence of temporal
 errors. Most estimates of yield trends over time from fertilizer
 application were numerically greater than the corresponding linear trends
 estimated from orthogonal polynomials in time (Method 2), reflecting the
 effect of accounting for seasonal variables. Grain yield declined over
 time at the drier site in the absence of nitrogen or phosphorus
 application, but positive trends were observed fairly generally for straw
 yield and for grain yield under higher levels of fertilizer inputs. It is
 suggested that analyses of long-term trials on other crops and cropping
 systems in other agro-ecological zones could be improved by taking spatial
 and temporal variability into account in the data evaluation. 
Journal: Journal of Applied Statistics 
Pages: 321-333 
Issue: 3 
Volume: 35 
Year: 2008 
Keywords: barley monocropping, long-term trials, REML, spatial-temporal covariance, time-trend, 
X-DOI: 10.1080/02664760701832992 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701832992
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:3:p:321-333




Template-Type: ReDIF-Article 1.0
Author-Name: Man-Lai Tang 
Author-X-Name-First: Man-Lai 
Author-X-Name-Last: Tang 
Author-Name: Maozai Tian 
Author-X-Name-First: Maozai 
Author-X-Name-Last: Tian 
Author-Name: Ping-Shing Chan 
Author-X-Name-First: Ping-Shing 
Author-X-Name-Last: Chan 
Title: On the bootstrap quantile-treatment-effect test 
Abstract:
  Let {X1, &#x2026;, Xn} and {Y1, &#x2026;, Ym} be two samples of
 independent and identically distributed observations with common
 continuous cumulative distribution functions F(x)=P(X&#x2264;x) and
 G(y)=P(Y&#x2264;y), respectively. In this article, we would like to test
 the no quantile treatment effect hypothesis H0: F=G. We develop a
 bootstrap quantile-treatment-effect test procedure for testing H0 under
 the location-scale shift model. Our test procedure avoids the calculation
 of the check function (which is non-differentiable at the origin and makes
 solving the quantile effects difficult in typical quantile regression
 analysis). The limiting null distribution of the test procedure is derived
 and the procedure is shown to be consistent against a broad family of
 alternatives. Simulation studies show that our proposed test procedure
 attains its type I error rate close to the pre-chosen significance level
 even for small sample sizes. Our test procedure is illustrated with two
 real data sets on the lifetimes of guinea pigs from a treatment-control
 experiment. 
Journal: Journal of Applied Statistics 
Pages: 335-350 
Issue: 3 
Volume: 35 
Year: 2008 
Keywords: Brownian bridge, bootstrap, Monte Carlo simulation, order statistics, two-sample case, quantile-treatment-effects, 
X-DOI: 10.1080/02664760701834725 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701834725
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:3:p:335-350




Template-Type: ReDIF-Article 1.0
Author-Name: Enrique Gonzalez-Davila 
Author-X-Name-First: Enrique 
Author-X-Name-Last: Gonzalez-Davila 
Author-Name: Josep Ginebra 
Author-X-Name-First: Josep 
Author-X-Name-Last: Ginebra 
Author-Name: Roberto Dorta-Guerra 
Author-X-Name-First: Roberto 
Author-X-Name-Last: Dorta-Guerra 
Title: Sample size determination for 2k-r experiments with a binomial response 
Abstract:
  This paper provides closed form expressions for the sample size for
 two-level factorial experiments when the response is the number of
 defectives. The sample sizes are obtained by approximating the two-sided
 test for no effect through tests for the mean of a normal distribution,
 and borrowing the classical sample size solution for that problem. The
 proposals are appraised relative to the exact sample sizes computed
 numerically, without appealing to any approximation to the binomial
 distribution, and the use of the sample size tables provided is
 illustrated through an example. 
Journal: Journal of Applied Statistics 
Pages: 357-367 
Issue: 4 
Volume: 35 
Year: 2008 
Keywords: factorial experiments, binary data, sample size, deviance, 
X-DOI: 10.1080/02664760701833669 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701833669
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:4:p:357-367




Template-Type: ReDIF-Article 1.0
Author-Name: M. Ruiz 
Author-X-Name-First: M. 
Author-X-Name-Last: Ruiz 
Author-Name: F. J. Giron 
Author-X-Name-First: F. J. 
Author-X-Name-Last: Giron 
Author-Name: C. J. Perez 
Author-X-Name-First: C. J. 
Author-X-Name-Last: Perez 
Author-Name: J. Martin 
Author-X-Name-First: J. 
Author-X-Name-Last: Martin 
Author-Name: C. Rojano 
Author-X-Name-First: C. 
Author-X-Name-Last: Rojano 
Title: A Bayesian model for multinomial sampling with misclassified data 
Abstract:
  In this paper the issue of making inferences with misclassified data from
 a noisy multinomial process is addressed. A Bayesian model for making
 inferences about the proportions and the noise parameters is developed.
 The problem is reformulated in a more tractable form by introducing
 auxiliary or latent random vectors. This allows for an easy-to-implement
 Gibbs sampling-based algorithm to generate samples from the distributions
 of interest. An illustrative example related to elections is also
 presented. 
Journal: Journal of Applied Statistics 
Pages: 369-382 
Issue: 4 
Volume: 35 
Year: 2008 
Keywords: Bayesian inference, Gibbs sampling, misclassified data, noisy multinomial process, 
X-DOI: 10.1080/02664760701834832 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701834832
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:4:p:369-382




Template-Type: ReDIF-Article 1.0
Author-Name: Jose Manuel Pavia-Miralles 
Author-X-Name-First: Jose Manuel 
Author-X-Name-Last: Pavia-Miralles 
Author-Name: Beatriz Larraz-Iribas 
Author-X-Name-First: Beatriz 
Author-X-Name-Last: Larraz-Iribas 
Title: Quick counts from non-selected polling stations 
Abstract:
  Countless examples of misleading forecasts on behalf of both campaign and
 exit polls affecting, among others, British, French, and Spanish elections
 could be found. This has seriously damaged their image. Therefore,
 procedures should be used that minimize errors, especially on election
 night when errors are more noticeable, in order to maintain people's trust
 in surveys. This paper proposes a method to obtain quick and early outcome
 forecasts on the election night. The idea is to partly sample some
 (whatever) polling stations and use the consistency that polling stations
 show between elections to predict the final results. Model accuracy is
 analysed through simulation using seven different types of samples in four
 elections. The efficacy of the technique is also tested predicting the
 2005 Eusko Legebiltzarra elections from real data. Results confirm that
 the procedure generates highly reliable and accurate forecasts.
 Furthermore, compared with the classical quick count strategy, the method
 is revealed as much more robust and precise. 
Journal: Journal of Applied Statistics 
Pages: 383-405 
Issue: 4 
Volume: 35 
Year: 2008 
Keywords: election forecasts, error observation, generalized linear regression, pseudodata augmentation, Spanish elections, 
X-DOI: 10.1080/02664760701834881 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701834881
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:4:p:383-405




Template-Type: ReDIF-Article 1.0
Author-Name: Patricia Espinheira 
Author-X-Name-First: Patricia 
Author-X-Name-Last: Espinheira 
Author-Name: Silvia Ferrari 
Author-X-Name-First: Silvia 
Author-X-Name-Last: Ferrari 
Author-Name: Francisco Cribari-Neto 
Author-X-Name-First: Francisco 
Author-X-Name-Last: Cribari-Neto 
Title: On beta regression residuals 
Abstract:
  We propose two new residuals for the class of beta regression models, and
 numerically evaluate their behaviour relative to the residuals proposed by
 Ferrari and Cribari-Neto. Monte Carlo simulation results and empirical
 applications using real and simulated data are provided. The results
 favour one of the residuals we propose. 
Journal: Journal of Applied Statistics 
Pages: 407-419 
Issue: 4 
Volume: 35 
Year: 2008 
Keywords: beta distribution, beta regression, maximum likelihood estimation, proportions, residuals, 
X-DOI: 10.1080/02664760701834931 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701834931
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:4:p:407-419




Template-Type: ReDIF-Article 1.0
Author-Name: Arup Ranjan Mukhopadhyay 
Author-X-Name-First: Arup Ranjan 
Author-X-Name-Last: Mukhopadhyay 
Title: Multivariate attribute control chart using Mahalanobis D2 statistic 
Abstract:
  Process control involves repeated hypothesis testing based on several
 samples. However, process control is not exactly hypothesis testing as
 such since it deals with detection of non-random patterns of variation as
 well in a fleeting kind of population. Compare this with hypothesis
 testing which is principally meant for a stagnant population. Dr Walter A.
 Shewhart introduced a graphic method for doing this testing in a fleeting
 population in 1924. This graphic method came to be known as control chart
 and is widely used throughout the world today for process management
 purposes. Subsequently there was much advancement in process control
 techniques. In particular, when more than one variable was involved,
 process control techniques were developed mainly by Hicks (1955), Jackson
 (1956 and 1959) and Montgomery and Wadsworth (1972) based on the
 pioneering work of Hotelling in 1931. Most of them have worked in the area
 of multivariate variable control chart with the underlying distribution as
 multivariate normal. When more than one attribute variables are involved
 some works relating to test of hypothesis was done by Mahalanobis (1946).
 These works were also based on the Hotelling T2 test. This paper expands
 the concept of 'Mahalanobis Distance' in case of a multinomial
 distribution and thereby proposes a multivariate attribute control chart. 
Journal: Journal of Applied Statistics 
Pages: 421-429 
Issue: 4 
Volume: 35 
Year: 2008 
Keywords: Euclidean distance, Mahalanobis distance, multinomial distribution, correlation matrix, variance covariance matrix, 
X-DOI: 10.1080/02664760701834980 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701834980
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:4:p:421-429




Template-Type: ReDIF-Article 1.0
Author-Name: Abdullah Almasri 
Author-X-Name-First: Abdullah 
Author-X-Name-Last: Almasri 
Author-Name: H&#xe5;kan Locking 
Author-X-Name-First: H&#xe5;kan 
Author-X-Name-Last: Locking 
Author-Name: Ghazi Shukur 
Author-X-Name-First: Ghazi 
Author-X-Name-Last: Shukur 
Title: Testing for climate warming in Sweden during 1850-1999, using wavelets analysis 
Abstract:
  This paper describes an alternative approach for testing for the
 existence of trend among time series. The test method has been constructed
 using wavelet analysis which has the ability of decomposing a time series
 into low frequencies (trend) and high-frequency (noise) components. Under
 the normality assumption, the test is distributed as F. However, using
 generated empirical critical values, the properties of the test statistic
 have been investigated under different conditions and different types of
 wavelet. The Harr wavelet has shown to exhibit the highest power among the
 other wavelet types. The methodology here has been applied to real
 temperature data in Sweden for the period 1850-1999. The results indicate
 a significant increasing trend which agrees with the 'global warming'
 hypothesis during the last 100 years. 
Journal: Journal of Applied Statistics 
Pages: 431-443 
Issue: 4 
Volume: 35 
Year: 2008 
Keywords: wavelet analysis, trend, global warming, 
X-DOI: 10.1080/02664760701835011 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701835011
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:4:p:431-443




Template-Type: ReDIF-Article 1.0
Author-Name: Viswanathan Shankar 
Author-X-Name-First: Viswanathan 
Author-X-Name-Last: Shankar 
Author-Name: Shrikant Bangdiwala 
Author-X-Name-First: Shrikant 
Author-X-Name-Last: Bangdiwala 
Title: Behavior of agreement measures in the presence of zero cells and biased marginal distributions 
Abstract:
  Kappa and B assess agreement between two observers independently
 classifying N units into k categories. We study their behavior under zero
 cells in the contingency table and unbalanced asymmetric marginal
 distributions. Zero cells arise when a cross-classification is never
 endorsed by both observers; biased marginal distributions occur when some
 categories are preferred differently between the observers. Simulations
 studied the distributions of the unweighted and weighted statistics for
 k=4, under fixed proportions of diagonal agreement and different patterns
 off-diagonal, with various sample sizes, and under various zero cell count
 scenarios. Marginal distributions were first uniform and homogeneous, and
 then unbalanced asymmetric distributions. Results for unweighted kappa and
 B statistics were comparable to work of Munoz and Bangdiwala, even with
 zero cells. A slight increased variation was observed as the sample size
 decreased. Weighted statistics did show greater variation as the number of
 zero cells increased, with weighted kappa increasing substantially more
 than weighted B. Under biased marginal distributions, weighted kappa with
 Cicchetti weights were higher than with squared weights. Both statistics
 for observer agreement behaved well under zero cells. The weighted B was
 less variable than the weighted kappa under similar circumstances and
 different weights. In general, B's performance and graphical
 interpretation make it preferable to kappa under the studied scenarios. 
Journal: Journal of Applied Statistics 
Pages: 445-464 
Issue: 4 
Volume: 35 
Year: 2008 
Keywords: Cohen's kappa, Bangdiwala's B, observer bias, zero cell, 
X-DOI: 10.1080/02664760701835052 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701835052
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:4:p:445-464




Template-Type: ReDIF-Article 1.0
Author-Name: Hsiuying Wang 
Author-X-Name-First: Hsiuying 
Author-X-Name-Last: Wang 
Title: Ranking responses in multiple-choice questions 
Abstract:
  In many studies, the questionnaire is a common tool for surveying. There
 are two kinds of questions designed: single-choice questions and
 multiple-choice questions. For single-choice questions, the methodology
 for analyzing it has been provided in the literature. However, the
 analyses of multiple-choice questions are not established as in depth as
 those for single-choice questions. Recently, there has been a lot of
 literature published about testing the marginal independence between two
 questions involving at least one multiple-choice question. However,
 another important problem regarding this topic is to rank the responses in
 a multiple-choice question. The issue is whether there are significant
 differences in the popularity of particular responses within the same
 question. In this paper, methodologies for ranking responses are proposed. 
Journal: Journal of Applied Statistics 
Pages: 465-474 
Issue: 4 
Volume: 35 
Year: 2008 
Keywords: single-choice question, multiple-choice question, survey, likelihood ratio test, Wald test, ranking consistency, 
X-DOI: 10.1080/02664760801924533 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760801924533
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:4:p:465-474




Template-Type: ReDIF-Article 1.0
Author-Name: D. J. Best 
Author-X-Name-First: D. J. 
Author-X-Name-Last: Best 
Author-Name: J. C. W. Rayner 
Author-X-Name-First: J. C. W. 
Author-X-Name-Last: Rayner 
Author-Name: O. Thas 
Author-X-Name-First: O. 
Author-X-Name-Last: Thas 
Title: X2 and its components as tests of normality for grouped data 
Abstract:
  We consider testing for an unobservable normal distribution with
 unspecified mean and variance. It is only possible to observe the counts
 in groups with boundaries specified before sighting the data. On the basis
 of a small power study, we recommend the usual X2 test be used as an
 omnibus test, augmented by informal examination of the first two non-zero
 components of X2. We also recommend use of maximum likelihood and method
 of moments estimation. 
Journal: Journal of Applied Statistics 
Pages: 481-492 
Issue: 5 
Volume: 35 
Year: 2008 
Keywords: critical values, improved grouped normal models, maximum-likelihood estimation, method of moments estimation, power study, 
X-DOI: 10.1080/02664760701835219 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701835219
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:5:p:481-492




Template-Type: ReDIF-Article 1.0
Author-Name: Dilip Nachane 
Author-X-Name-First: Dilip 
Author-X-Name-Last: Nachane 
Author-Name: Jose Clavel 
Author-X-Name-First: Jose 
Author-X-Name-Last: Clavel 
Title: Forecasting interest rates: a comparative assessment of some second-generation nonlinear models 
Abstract:
  Modeling and forecasting of interest rates has traditionally proceeded in
 the framework of linear stationary methods such as ARMA and VAR, but only
 with moderate success. We examine here three methods, which account for
 several specific features of the real world asset prices such as
 nonstationarity and nonlinearity. Our three candidate methods are based,
 respectively, on a combined wavelet artificial neural network (WANN)
 analysis, a mixed spectrum (MS) analysis and nonlinear ARMA models with
 Fourier coefficients (FNLARMA). These models are applied to weekly data on
 interest rates in India and their forecasting performance is evaluated
 vis-a-vis three GARCH models [GARCH (1,1), GARCH-M (1,1) and EGARCH (1,1)]
 as well as the random walk model. Both the WANN and MS methods show marked
 improvement over other benchmark models, and may thus hold out several
 potentials for real world modeling and forecasting of financial data. 
Journal: Journal of Applied Statistics 
Pages: 493-514 
Issue: 5 
Volume: 35 
Year: 2008 
Keywords: interest rates, wavelets, artificial neural networks, mixed spectra, nonlinear ARMA, GARCH, forecast comparisons, 
X-DOI: 10.1080/02664760701835243 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701835243
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:5:p:493-514




Template-Type: ReDIF-Article 1.0
Author-Name: Takafumi Isogai 
Author-X-Name-First: Takafumi 
Author-X-Name-Last: Isogai 
Author-Name: Hiroaki Uchida 
Author-X-Name-First: Hiroaki 
Author-X-Name-Last: Uchida 
Author-Name: Susumu Miyama 
Author-X-Name-First: Susumu 
Author-X-Name-Last: Miyama 
Author-Name: Sadao Nishiyama 
Author-X-Name-First: Sadao 
Author-X-Name-Last: Nishiyama 
Title: Statistical modeling of enamel rater value data 
Abstract:
  Enamel rater value (shortly, ERV) of a quick stress test is usually used
 to evaluate the integrity of an organic coating for the inside of an
 aluminum (denoted by Al shortly) can. A large positive value of ERV is
 supposed to indicate the degree of imperfect coating coverage, i.e. the
 size of an exposed Al area. An Al can filled with some drink, if there is
 an exposed Al area due to imperfect coating coverage, has Al dissolution
 brought by corrosion. Thus a smaller value of ERV is desirable to prevent
 Al dissolution. However, quantitative evaluations of ERV data as well as
 an accumulated quantity of Al dissolution have never been published,
 because ERV is involved in complicated anode dissolution of an exposed Al
 area. Recently our experimental study has found out a relationship between
 ERV and sizes of exposed Al areas. This relationship enables us to
 construct a descriptive statistical model for ERV data as well as to
 evaluate coating effects for Al cans. Furthermore, empirical implications
 suggest that an instantaneous quantity of Al dissolution is proportional
 to ERV. Using this fact, we can derive a predictive statistical model for
 an accumulated quantity of Al dissolution in an Al can. 
Journal: Journal of Applied Statistics 
Pages: 515-535 
Issue: 5 
Volume: 35 
Year: 2008 
Keywords: enamel rater value, aluminum cans, corrosion, aluminum dissolution, generalized gamma distribution, new power normal family, 
X-DOI: 10.1080/02664760701835342 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701835342
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:5:p:515-535




Template-Type: ReDIF-Article 1.0
Author-Name: Stephen Duffy 
Author-X-Name-First: Stephen 
Author-X-Name-Last: Duffy 
Author-Name: Anne-Helene Olsen 
Author-X-Name-First: Anne-Helene 
Author-X-Name-Last: Olsen 
Author-Name: Rhian Gabe 
Author-X-Name-First: Rhian 
Author-X-Name-Last: Gabe 
Author-Name: Laszlo Tabar 
Author-X-Name-First: Laszlo 
Author-X-Name-Last: Tabar 
Author-Name: Jane Warwick 
Author-X-Name-First: Jane 
Author-X-Name-Last: Warwick 
Author-Name: Hilary Fielder 
Author-X-Name-First: Hilary 
Author-X-Name-Last: Fielder 
Author-Name: Laufey Tryggvadottir 
Author-X-Name-First: Laufey 
Author-X-Name-Last: Tryggvadottir 
Author-Name: Olorunsola Agbaje 
Author-X-Name-First: Olorunsola 
Author-X-Name-Last: Agbaje 
Title: Screening opportunity bias in case-control studies of cancer screening 
Abstract:
  In case-control evaluations of cancer screening, subjects who have died
 from the cancer in question (cases) are compared with those who have not
 (controls) with respect to screening histories. This method is subject to
 a rather subtle bias, among others, whereby the cases have greater
 opportunity to have been screened than the controls. In this paper, we
 propose a method of correction for this bias. We demonstrate its use on
 two case-control studies of mammographic screening for breast cancer. 
Journal: Journal of Applied Statistics 
Pages: 537-546 
Issue: 5 
Volume: 35 
Year: 2008 
Keywords: cancer screening, case-control study, opportunity bias, 
X-DOI: 10.1080/02664760701835755 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701835755
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:5:p:537-546




Template-Type: ReDIF-Article 1.0
Author-Name: Steven Cook 
Author-X-Name-First: Steven 
Author-X-Name-Last: Cook 
Title: The sensitivity of robust unit root tests 
Abstract:
  The power properties of the rank-based Dickey-Fuller (DF) unit root test
 of Granger and Hallman [C. Granger and J. Hallman, Nonlinear
 transformations of integrated time series, J. Time Ser. Anal. 12 (1991),
 pp. 207-218] and the range unit root tests of Aparicio et al. [F.
 Aparicio, A. Escribano, and A. Siplos, Range unit root (RUR) tests: Robust
 against non-linearities, error distributions, structural breaks and
 outliers, J. Time Ser. Anal. 27 (2006), pp. 545-576] are considered when
 applied to near-integrated time series processes with differing initial
 conditions. The results obtained show the empirical powers of the tests to
 be generally robust to smaller deviations of the initial condition of the
 time series from its underlying deterministic component, particularly for
 more highly stationary processes. However, dramatic decreases in power are
 observed when either the mean or variance of the deviation of the initial
 condition is increased. The robustness of the rank- and range-based unit
 root tests and their higher power results relative to the seminal DF test
 have both been noted previously in the econometrics literature. These
 results are questioned by the findings of the present paper. 
Journal: Journal of Applied Statistics 
Pages: 547-557 
Issue: 5 
Volume: 35 
Year: 2008 
Keywords: unit roots, range-based tests, range unit root tests, initial conditions, Monte Carlo simulation, 
X-DOI: 10.1080/02664760701835797 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701835797
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:5:p:547-557




Template-Type: ReDIF-Article 1.0
Author-Name: Sat Gupta 
Author-X-Name-First: Sat 
Author-X-Name-Last: Gupta 
Author-Name: Javid Shabbir 
Author-X-Name-First: Javid 
Author-X-Name-Last: Shabbir 
Title: On improvement in estimating the population mean in simple random sampling 
Abstract:
  Kadilar and Cingi [Ratio estimators in simple random sampling, Appl.
 Math. Comput. 151 (3) (2004), pp. 893-902] introduced some ratio-type
 estimators of finite population mean under simple random sampling.
 Recently, Kadilar and Cingi [New ratio estimators using correlation
 coefficient, Interstat 4 (2006), pp. 1-11] have suggested another form of
 ratio-type estimators by modifying the estimator developed by Singh and
 Tailor [Use of known correlation coefficient in estimating the finite
 population mean, Stat. Transit. 6 (2003), pp. 655-560]. Kadilar and Cingi
 [Improvement in estimating the population mean in simple random sampling,
 Appl. Math. Lett. 19 (1) (2006), pp. 75-79] have suggested yet another
 class of ratio-type estimators by taking a weighted average of the two
 known classes of estimators referenced above. In this article, we propose
 an alternative form of ratio-type estimators which are better than the
 competing ratio, regression, and other ratio-type estimators considered
 here. The results are also supported by the analysis of three real data
 sets that were considered by Kadilar and Cingi. 
Journal: Journal of Applied Statistics 
Pages: 559-566 
Issue: 5 
Volume: 35 
Year: 2008 
Keywords: ratio-type estimators, mean square error (MSE), transformation, efficiency, 
X-DOI: 10.1080/02664760701835839 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701835839
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:5:p:559-566




Template-Type: ReDIF-Article 1.0
Author-Name: Adam Branscum 
Author-X-Name-First: Adam 
Author-X-Name-Last: Branscum 
Author-Name: Timothy Hanson 
Author-X-Name-First: Timothy 
Author-X-Name-Last: Hanson 
Author-Name: Ian Gardner 
Author-X-Name-First: Ian 
Author-X-Name-Last: Gardner 
Title: Bayesian non-parametric models for regional prevalence estimation 
Abstract:
  We developed a flexible non-parametric Bayesian model for regional
 disease-prevalence estimation based on cross-sectional data that are
 obtained from several subpopulations or clusters such as villages, cities,
 or herds. The subpopulation prevalences are modeled with a mixture
 distribution that allows for zero prevalence. The distribution of
 prevalences among diseased subpopulations is modeled as a mixture of
 finite Polya trees. Inferences can be obtained for (1) the proportion of
 diseased subpopulations in a region, (2) the distribution of regional
 prevalences, (3) the mean and median prevalence in the region, (4) the
 prevalence of any sampled subpopulation, and (5) predictive distributions
 of prevalences for regional subpopulations not included in the study,
 including the predictive probability of zero prevalence. We focus on
 prevalence estimation using data from a single diagnostic test, but we
 also briefly discuss the scenario where two conditionally dependent (or
 independent) diagnostic tests are used. Simulated data demonstrate the
 utility of our non-parametric model over parametric analysis. An example
 involving brucellosis in cattle is presented. 
Journal: Journal of Applied Statistics 
Pages: 567-582 
Issue: 5 
Volume: 35 
Year: 2008 
Keywords: disease-prevalence estimation, Polya trees, prediction, 
X-DOI: 10.1080/02664760701835862 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760701835862
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:5:p:567-582




Template-Type: ReDIF-Article 1.0
Author-Name: Bahadur Singh 
Author-X-Name-First: Bahadur 
Author-X-Name-Last: Singh 
Author-Name: Susan Halabi 
Author-X-Name-First: Susan 
Author-X-Name-Last: Halabi 
Author-Name: Michael Schell 
Author-X-Name-First: Michael 
Author-X-Name-Last: Schell 
Title: Sample size selection in clinical trials when population means are subject to a partial order: one-sided ordered alternatives 
Abstract:
  The statistical methodology under order restriction is very mathematical
 and complex. Thus, we provide a brief methodological background of
 order-restricted likelihood ratio tests for the normal theoretical case
 for the basic understanding of its applications, and relegate more
 technical details to the appendices. For data analysis, algorithms for
 computing the order-restricted estimates and computation of p-values are
 described. A two-step procedure is presented for obtaining the sample size
 in clinical trials when the minimum power, say 0.80 or 0.90 is specified,
 and the normal means satisfy an order restriction. Using this approach
 will result in reduction of 14-24% in the sample size required when
 one-sided ordered alternatives are used, as illustrated by several
 examples. 
Journal: Journal of Applied Statistics 
Pages: 583-600 
Issue: 5 
Volume: 35 
Year: 2008 
Keywords: likelihood ratio tests, minimum power, simple order, simple tree ordering, simple loop ordering, two-step procedure, 
X-DOI: 10.1080/02664760801924780 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760801924780
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:5:p:583-600




Template-Type: ReDIF-Article 1.0
Author-Name: Teresa Alpuim 
Author-X-Name-First: Teresa 
Author-X-Name-Last: Alpuim 
Author-Name: Abdel El-Shaarawi 
Author-X-Name-First: Abdel 
Author-X-Name-Last: El-Shaarawi 
Title: On the efficiency of regression analysis with AR(p) errors 
Abstract:
  In this paper we will consider a linear regression model with the
 sequence of error terms following an autoregressive stationary process.
 The statistical properties of the maximum likelihood and least squares
 estimators of the regression parameters will be summarized. Then, it will
 be proved that, for some typical cases of the design matrix, both methods
 produce asymptotically equivalent estimators. These estimators are also
 asymptotically efficient. Such cases include the most commonly used models
 to describe trend and seasonality like polynomial trends, dummy variables
 and trigonometric polynomials. Further, a very convenient asymptotic
 formula for the covariance matrix will be derived. It will be illustrated
 through a brief simulation study that, for the simple linear trend model,
 the result applies even for sample sizes as small as 20. 
Journal: Journal of Applied Statistics 
Pages: 717-737 
Issue: 7 
Volume: 35 
Year: 2008 
Keywords: linear regression, autoregressive stationary process, maximum likelihood, least squares, trend, seasonality, linear difference equation, 
X-DOI: 10.1080/02664760600679775 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600679775
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:7:p:717-737




Template-Type: ReDIF-Article 1.0
Author-Name: Philip Prescott 
Author-X-Name-First: Philip 
Author-X-Name-Last: Prescott 
Author-Name: Norman Draper 
Author-X-Name-First: Norman 
Author-X-Name-Last: Draper 
Title: D-optimal mixture component-amount designs for quadratic and cubic models 
Abstract:
  When the total amount of a mixture of ingredients needs to be taken into
 account (in addition to the composition of its ingredients), an
 experimental design requires several levels of the amount. Designs for
 such situations are discussed, and D-optimal choices are made for fitting
 quadratic and cubic models, for various numbers of experimental units. 
Journal: Journal of Applied Statistics 
Pages: 739-749 
Issue: 7 
Volume: 35 
Year: 2008 
Keywords: component amounts, <i>D</i>-optimality, mixtures, Scheffe models, Scheffe designs, 
X-DOI: 10.1080/02664760801997133 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760801997133
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:7:p:739-749




Template-Type: ReDIF-Article 1.0
Author-Name: Simos Meintanis 
Author-X-Name-First: Simos 
Author-X-Name-Last: Meintanis 
Title: New inference procedures for generalized Poisson distributions 
Abstract:
  A common feature for compound Poisson and Katz distributions is that both
 families may be viewed as generalizations of the Poisson law. In this
 paper, we present a unified approach in testing the fit to any
 distribution belonging to either of these families. The test involves the
 probability generating function, and it is shown to be consistent under
 general alternatives. The asymptotic null distribution of the test
 statistic is obtained, and an effective bootstrap procedure is employed in
 order to investigate the performance of the proposed test with real and
 simulated data. Comparisons with classical methods based on the empirical
 distribution function are also included. 
Journal: Journal of Applied Statistics 
Pages: 751-762 
Issue: 7 
Volume: 35 
Year: 2008 
Keywords: empirical probability generating function, compound Poisson distribution, goodness-of-fit test, Katz laws, 
X-DOI: 10.1080/02664760801997174 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760801997174
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:7:p:751-762




Template-Type: ReDIF-Article 1.0
Author-Name: Sueli Mingoti 
Author-X-Name-First: Sueli 
Author-X-Name-Last: Mingoti 
Author-Name: Julia De Carvalho 
Author-X-Name-First: Julia 
Author-X-Name-Last: De Carvalho 
Author-Name: Joab De Oliveira Lima 
Author-X-Name-First: Joab 
Author-X-Name-Last: De Oliveira Lima 
Title: On the estimation of serial correlation in Markov-dependent production processes 
Abstract:
  In this paper, we present a study about the estimation of the serial
 correlation for Markov chain models which is used often in the quality
 control of autocorrelated processes. Two estimators, non-parametric and
 multinomial, for the correlation coefficient are discussed. They are
 compared with the maximum likelihood estimator [U.N. Bhat and R. Lal,
 Attribute control charts for Markov dependent production process, IIE
 Trans. 22 (2) (1990), pp. 181-188.] by using some theoretical facts and
 the Monte Carlo simulation under several scenarios that consider large and
 small correlations as well a range of fractions (p) of non-conforming
 items. The theoretical results show that for any value of p&#x2260;0.5 and
 processes with autocorrelation higher than 0.5, the multinomial is more
 precise than maximum likelihood. However, the maximum likelihood is better
 when the autocorrelation is smaller than 0.5. The estimators are similar
 for p=0.5. Considering the average of all simulated scenarios, the
 multinomial estimator presented lower mean error values and higher
 precision, being, therefore, an alternative to estimate the serial
 correlation. The performance of the non-parametric estimator was
 reasonable only for correlation higher than 0.5, with some improvement for
 p=0.5. 
Journal: Journal of Applied Statistics 
Pages: 763-771 
Issue: 7 
Volume: 35 
Year: 2008 
Keywords: Markov chain, serial correlation estimation, autocorrelated processes, 
X-DOI: 10.1080/02664760802005688 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802005688
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:7:p:763-771




Template-Type: ReDIF-Article 1.0
Author-Name: Sigyn Mark 
Author-X-Name-First: Sigyn 
Author-X-Name-Last: Mark 
Author-Name: Sture Holm 
Author-X-Name-First: Sture 
Author-X-Name-Last: Holm 
Title: Test and prediction in factorial models with independent variance estimates 
Abstract:
  The multiple inference character of several tests in the same application
 is usually taken into consideration by requiring that the tests have a
 multiple level of significance. Also, a prediction problem in an
 application with several possible predictor variables requires that the
 multiple inference character of the problem be considered. This is not
 being done in the methods commonly used to choose predictor variables.
 Here, we discuss both the test and prediction methods in two-level
 factorial designs and suggest a principle for choosing variables which is
 based on multiple inference thinking. By an example use demonstrated that
 the principle proposed leads to the use of fewer prediction variables than
 does the Akaike method. 
Journal: Journal of Applied Statistics 
Pages: 773-782 
Issue: 7 
Volume: 35 
Year: 2008 
Keywords: prediction, multiple inference, factorial design, Akaike's method, 
X-DOI: 10.1080/02664760802005852 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802005852
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:7:p:773-782




Template-Type: ReDIF-Article 1.0
Author-Name: Mahmoud Mahmoud 
Author-X-Name-First: Mahmoud 
Author-X-Name-Last: Mahmoud 
Author-Name: William Woodall 
Author-X-Name-First: William 
Author-X-Name-Last: Woodall 
Author-Name: Robert Davis 
Author-X-Name-First: Robert 
Author-X-Name-Last: Davis 
Title: Performance comparison of some likelihood ratio-based statistical surveillance methods 
Abstract:
  Using Markov chain representations, we evaluate and compare the
 performance of cumulative sum (CUSUM) and Shiryayev-Roberts methods in
 terms of the zero- and steady-state average run length and worst-case
 signal resistance measures. We also calculate the signal resistance values
 from the worst- to the best-case scenarios for both the methods. Our
 results support the recommendation that Shewhart limits be used with CUSUM
 and Shiryayev-Roberts methods, especially for low values of the size of
 the shift in the process mean for which the methods are designed to detect
 optimally. 
Journal: Journal of Applied Statistics 
Pages: 783-798 
Issue: 7 
Volume: 35 
Year: 2008 
Keywords: CUSUM chart, likelihood ratio, Shiryayev-Roberts chart, Shewhart chart, statistical process control, statistical surveillance, 
X-DOI: 10.1080/02664760802005878 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802005878
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:7:p:783-798




Template-Type: ReDIF-Article 1.0
Author-Name: Edward Boone 
Author-X-Name-First: Edward 
Author-X-Name-Last: Boone 
Author-Name: Susan Simmons 
Author-X-Name-First: Susan 
Author-X-Name-Last: Simmons 
Author-Name: Haikun Bao 
Author-X-Name-First: Haikun 
Author-X-Name-Last: Bao 
Author-Name: Ann Stapleton 
Author-X-Name-First: Ann 
Author-X-Name-Last: Stapleton 
Title: Bayesian hierarchical regression models for detecting QTLs in plant experiments 
Abstract:
  Quantitative trait loci (QTL) mapping is a growing field in statistical
 genetics. In plants, QTL detection experiments often feature replicates or
 clones within a specific genetic line. In this work, a Bayesian
 hierarchical regression model is applied to simulated QTL data and to a
 dataset from the Arabidopsis thaliana plants for locating the QTL mapping
 associated with cotyledon opening. A conditional model search strategy
 based on Bayesian model averaging is utilized to reduce the computational
 burden. 
Journal: Journal of Applied Statistics 
Pages: 799-808 
Issue: 7 
Volume: 35 
Year: 2008 
Keywords: hierarchical models, Bayesian statistics, quantitative trait loci, Bayesian model averaging, recombinant inbred Lines, 
X-DOI: 10.1080/02664760802005910 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802005910
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:7:p:799-808




Template-Type: ReDIF-Article 1.0
Author-Name: A. F. B. Costa 
Author-X-Name-First: A. F. B. 
Author-X-Name-Last: Costa 
Author-Name: M. A. G. Machado 
Author-X-Name-First: M. A. G. 
Author-X-Name-Last: Machado 
Title: Bivariate control charts with double sampling 
Abstract:
  In this article, we consider the T2 chart with double sampling to control
 bivariate processes (BDS chart). During the first stage of the sampling,
 n1 items of the sample are inspected and two quality characteristics (x;
 y) are measured. If the Hotelling statistic [image omitted]&#xa0; for the
 mean vector of (x; y) is less than w, the sampling is interrupted. If the
 Hotelling statistic [image omitted]&#xa0; is greater than CL1, where
 CL1&#x3e;w, the control chart signals an out-of-control condition. If
 [image omitted]&#xa0;, the sampling goes on to the second stage, where the
 remaining n2 items of the sample are inspected and [image omitted]&#xa0;
 for the mean vector of the whole sample is computed. During the second
 stage of the sampling, the control chart signals an out-of-control
 condition when the statistic [image omitted]&#xa0; is larger than CL2. A
 comparative study shows that the BDS chart detects process disturbances
 faster than the standard bivariate T2 chart and the adaptive bivariate T2
 charts with variable sample size and/or variable sampling interval. 
Journal: Journal of Applied Statistics 
Pages: 809-822 
Issue: 7 
Volume: 35 
Year: 2008 
Keywords: the Hotelling statistic <i>T</i>-super-2, bivariate processes, double sampling, 
X-DOI: 10.1080/02664760802061939 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802061939
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:7:p:809-822




Template-Type: ReDIF-Article 1.0
Author-Name: Andrew Robinson 
Author-X-Name-First: Andrew 
Author-X-Name-Last: Robinson 
Title: BOOK REVIEW 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 823-824 
Issue: 7 
Volume: 35 
Year: 2008 
X-DOI: 10.1080/02664760802066615 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802066615
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:7:p:823-824




Template-Type: ReDIF-Article 1.0
Author-Name: Herve Cardot 
Author-X-Name-First: Herve 
Author-X-Name-Last: Cardot 
Author-Name: Philippe Maisongrande 
Author-X-Name-First: Philippe 
Author-X-Name-Last: Maisongrande 
Author-Name: Robert Faivre 
Author-X-Name-First: Robert 
Author-X-Name-Last: Faivre 
Title: Varying-time random effects models for longitudinal data: unmixing and temporal interpolation of remote-sensing data 
Abstract:
  Remote sensing is a helpful tool for crop monitoring or vegetation-growth
 estimation at a country or regional scale. However, satellite images
 generally have to cope with a compromise between the time frequency of
 observations and their resolution (i.e. pixel size). When concerned with
 high temporal resolution, we have to work with information on the basis of
 kilometric pixels, named mixed pixels, that represent aggregated responses
 of multiple land cover. Disaggreggation or unmixing is then necessary to
 downscale from the square kilometer to the local dynamic of each theme
 (crop, wood, meadows, etc.). Assuming the land use is known, that is to
 say the proportion of each theme within each mixed pixel, we propose to
 address the downscaling issue through the generalization of varying-time
 regression models for longitudinal data and/or functional data by
 introducing random individual effects. The estimators are built by
 expanding the mixed pixels trajectories with B-splines functions and
 maximizing the log-likelihood with a backfitting-ECME algorithm. A BLUP
 formula allows then to get the 'best possible' estimations of the local
 temporal responses of each crop when observing mixed pixels trajectories.
 We show that this model has many potential applications in remote sensing,
 and an interesting one consists of coupling high and low spatial
 resolution images in order to perform temporal interpolation of high
 spatial resolution images (20&#xa0;m), increasing the knowledge on
 particular crops in very precise locations. The unmixing and temporal
 high-resolution interpolation approaches are illustrated on remote-sensing
 data obtained on the South-Western France during the year 2002. 
Journal: Journal of Applied Statistics 
Pages: 827-846 
Issue: 8 
Volume: 35 
Year: 2008 
Keywords: backfitting, BLUP, covariance function, downscaling, ECME, functional data, mixed effects, mixed pixels, splines, SPOT/VGT, SPOT/HRVIR, remote sensing, 
X-DOI: 10.1080/02664760802061970 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802061970
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:8:p:827-846




Template-Type: ReDIF-Article 1.0
Author-Name: Cody Hamilton 
Author-X-Name-First: Cody 
Author-X-Name-Last: Hamilton 
Author-Name: Tom Bratcher 
Author-X-Name-First: Tom 
Author-X-Name-Last: Bratcher 
Author-Name: James Stamey 
Author-X-Name-First: James 
Author-X-Name-Last: Stamey 
Title: Bayesian subset selection approach to ranking normal means 
Abstract:
  In this, article we consider a Bayesian approach to the problem of
 ranking the means of normal distributed populations, which is a common
 problem in the biological sciences. We use a decision-theoretic approach
 with a straightforward loss function to determine a set of candidate
 rankings. This loss function allows the researcher to balance the risk of
 not including the correct ranking with the risk of increasing the number
 of rankings selected. We apply our new procedure to an example regarding
 the effect of zinc on the diversity of diatom species. 
Journal: Journal of Applied Statistics 
Pages: 847-851 
Issue: 8 
Volume: 35 
Year: 2008 
Keywords: ranking, multiple comparisons, posterior approximation, Gibbs sampler, 
X-DOI: 10.1080/02664760802124174 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802124174
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:8:p:847-851




Template-Type: ReDIF-Article 1.0
Author-Name: Herbert Buning 
Author-X-Name-First: Herbert 
Author-X-Name-Last: Buning 
Author-Name: Michael Rietz 
Author-X-Name-First: Michael 
Author-X-Name-Last: Rietz 
Title: Adaptive bootstrap tests and its competitors in the c-sample scale problem 
Abstract:
  This paper deals with a study of different types of tests for the
 two-sided c-sample scale problem. We consider the classical parametric
 test of Bartlett [M.S. Bartlett, Properties of sufficiency and statistical
 tests, Proc. R. Stat. Soc. Ser. A. 160 (1937), pp. 268-282] several
 nonparametric tests, especially the test of Fligner and Killeen [M.A.
 Fligner and T.J. Killeen, Distribution-free two-sample tests for scale, J.
 Amer. Statist. Assoc. 71 (1976), pp. 210-213], the test of Levene [H.
 Levene, Robust tests for equality of variances, in Contribution to
 Probability and Statistics, I. Olkin, ed., Stanford University Press, Palo
 Alto, 1960, pp. 278-292] and a robust version of it introduced by Brown
 and Forsythe [M.B. Brown and A.B. Forsythe, Robust tests for the equality
 of variances, J. Amer. Statist. Assoc. 69 (1974), pp. 364-367] as well as
 two adaptive tests proposed by Buning [H. Buning, Adaptive tests for the
 c-sample location problem - the case of two-sided alternatives, Comm.
 Statist.Theory Methods. 25 (1996), pp. 1569-1582] and Buning [H. Buning,
 An adaptive test for the two sample scale problem, Nr. 2003/10,
 Diskussionsbeitrage des Fachbereich Wirtschaftswissenschaft der Freien
 Universitat Berlin, Volkswirtschaftliche Reihe, 2003]. which are based on
 the principle of Hogg [R.V. Hogg, Adaptive robust procedures. A partial
 review and some suggestions for future applications and theory, J. Amer.
 Statist. Assoc. 69 (1974), pp. 909-927]. For all the tests we use
 Bootstrap sampling strategies, too. We compare via Monte Carlo Methods all
 the tests by investigating level &#x3b1; and power &#x3b2; of the tests
 for distributions with different strength of tailweight and skewness and
 for various sample sizes. It turns out that the test of Fligner and
 Killeen in combination with the bootstrap is the best one among all tests
 considered. 
Journal: Journal of Applied Statistics 
Pages: 853-866 
Issue: 8 
Volume: 35 
Year: 2008 
Keywords: bootstrap, sampling strategies, parametric, nonparametric, robustified and adaptive tests, tailweight skewness, nonnormality, &#x3b1;-robustness, power comparison, 
X-DOI: 10.1080/02664760802124257 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802124257
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:8:p:853-866




Template-Type: ReDIF-Article 1.0
Author-Name: P. S. Chan 
Author-X-Name-First: P. S. 
Author-X-Name-Last: Chan 
Author-Name: H. K. T. Ng 
Author-X-Name-First: H. K. T. 
Author-X-Name-Last: Ng 
Author-Name: N. Balakrishnan 
Author-X-Name-First: N. 
Author-X-Name-Last: Balakrishnan 
Title: Statistical inference for start-up demonstration tests with rejection of units upon observing d failures 
Abstract:
  In this paper, we consider the statistical inference for the success
 probability in the case of start-up demonstration tests in which rejection
 of units is possible when a pre-fixed number of failures is observed
 before the required number of consecutive successes are achieved for
 acceptance of the unit. Since the expected value of the stopping time is
 not a monotone function of the unknown parameter, the method of moments is
 not useful in this situation. Therefore, we discuss two estimation methods
 for the success probability: (1) the maximum likelihood estimation (MLE)
 via the expectation-maximization (EM) algorithm and (2) Bayesian
 estimation with a beta prior. We examine the small-sample properties of
 the MLE and Bayesian estimator. Finally, we present an example to
 illustrate the method of inference discussed here. 
Journal: Journal of Applied Statistics 
Pages: 867-878 
Issue: 8 
Volume: 35 
Year: 2008 
Keywords: start-up demonstration test, maximum likelihood estimator, EM-algorithm, runs, Bayesian estimation, probability generating function, 
X-DOI: 10.1080/02664760802124455 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802124455
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:8:p:867-878




Template-Type: ReDIF-Article 1.0
Author-Name: Wen-Den Chen 
Author-X-Name-First: Wen-Den 
Author-X-Name-Last: Chen 
Title: Detecting and identifying interventions with the Whittle spectral approach in a long memory panel data model 
Abstract:
  This article provides a procedure for the detection and identification of
 outliers in the spectral domain where the Whittle maximum likelihood
 estimator of the panel data model proposed by Chen [W.D. Chen, Testing for
 spurious regression in a panel data model with the individual number and
 time length growing, J. Appl. Stat. 33(88) (2006b), pp. 759-772] is
 implemented. We extend the approach of Chang and co-workers [I. Chang,
 G.C. Tiao, and C. Chen, Estimation of time series parameters in the
 presence of outliers, Technometrics 30 (2) (1988), pp. 193-204] to the
 spectral domain and through the Whittle approach we can quickly detect and
 identify the type of outliers. A fixed effects panel data model is used,
 in which the remainder disturbance is assumed to be a fractional
 autoregressive integrated moving-average (ARFIMA) process and the
 likelihood ratio criterion is obtained directly through the modified
 inverse Fourier transform. This saves much time, especially when the
 estimated model implements a huge data-set. Through Monte Carlo
 experiments, the consistency of the estimator is examined by growing the
 individual number N and time length T, in which the long memory remainder
 disturbances are contaminated with two types of outliers: additive outlier
 and innovation outlier. From the power tests, we see that the estimators
 are quite successful and powerful. In the empirical study, we apply the
 model on Taiwan's computer motherboard industry. Weekly data from 1
 January 2000 to 31 October 2006 of nine familiar companies are used. The
 proposed model has a smaller mean square error and shows more distinctive
 aggressive properties than the raw data model does. 
Journal: Journal of Applied Statistics 
Pages: 879-892 
Issue: 8 
Volume: 35 
Year: 2008 
Keywords: long memory, intervention, additive outlier, innovation outlier, Whittle approach, spectral density function, panel data model, 
X-DOI: 10.1080/02664760802125213 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802125213
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:8:p:879-892




Template-Type: ReDIF-Article 1.0
Author-Name: Gemechis Djira 
Author-X-Name-First: Gemechis 
Author-X-Name-Last: Djira 
Author-Name: Volker Guiard 
Author-X-Name-First: Volker 
Author-X-Name-Last: Guiard 
Author-Name: Frank Bretz 
Author-X-Name-First: Frank 
Author-X-Name-Last: Bretz 
Title: Efficient and easy-to-use sample size formulas in ratio-based non-inferiority tests 
Abstract:
  In many biomedical applications, tests for the classical hypotheses based
 on the difference of treatment means in a one-way layout can be replaced
 by tests for ratios (or tests for relative changes). This approach is well
 noted for its simplicity in defining the margins, as for example in tests
 for non-inferiority. Here, we derive approximate and efficient sample size
 formulas in a multiple testing situation and then thoroughly investigate
 the relative performance of hypothesis testing based on the ratios of
 treatment means when compared with differences of means. The results will
 be illustrated with an example on simultaneous tests for non-inferiority. 
Journal: Journal of Applied Statistics 
Pages: 893-900 
Issue: 8 
Volume: 35 
Year: 2008 
Keywords: relative margin, sample size, multivariate t, normal approximation, 
X-DOI: 10.1080/02664760802125544 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802125544
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:8:p:893-900




Template-Type: ReDIF-Article 1.0
Author-Name: Sermin Elevli 
Author-X-Name-First: Sermin 
Author-X-Name-Last: Elevli 
Author-Name: Nevin Uzgoren 
Author-X-Name-First: Nevin 
Author-X-Name-Last: Uzgoren 
Author-Name: Birol Elevli 
Author-X-Name-First: Birol 
Author-X-Name-Last: Elevli 
Title: Correspondence analysis of repair data: a case study for electric cable shovels 
Abstract:
  In mining operation, effective maintenance scheduling is very important
 because of its effect on the performance of equipment and production
 costs. Classifying equipment on the basis of repair durations is
 considered one of the essential works to schedule maintenance activities
 effectively. In this study, repair data of electric cable shovels used in
 the Western Coal Company, Turkey, has been analyzed using correspondence
 analysis to classify shovels in terms of repair durations. Correspondence
 analysis, particularly helpful in analysing cross-tabular data in the form
 of numerical frequencies, has provided a graphical display that permitted
 more rapid interpretation and understanding of the repair data. The
 results indicated that there are five groups of shovels according to their
 repair duration. Especially, shovels numbered 2, 3, 7, 10 and 11 required
 a repair duration of&#x3c;1&#xa0;h and maintained relatively good service
 condition when compared with others. Thus, priority might be given to
 repair them in maintenance job scheduling even if there is another failed
 shovel waiting to be serviced. This type of information will help mine
 managers to increase the number of available shovels in operation. 
Journal: Journal of Applied Statistics 
Pages: 901-908 
Issue: 8 
Volume: 35 
Year: 2008 
Keywords: shovel, repair data, correspondence analysis, 
X-DOI: 10.1080/02664760802125627 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802125627
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:8:p:901-908




Template-Type: ReDIF-Article 1.0
Author-Name: George Halkos 
Author-X-Name-First: George 
Author-X-Name-Last: Halkos 
Author-Name: Ilias Kevork 
Author-X-Name-First: Ilias 
Author-X-Name-Last: Kevork 
Title: A sequential procedure for testing the existence of a random walk model in finite samples 
Abstract:
  Given the random walk model, we show, for the traditional unrestricted
 regression used in testing stationarity, that no matter what the initial
 value of the random walk is or its drift or its error standard deviation,
 the sampling distributions of certain statistics remain unchanged. Using
 Monte Carlo simulations, we estimate, for different finite samples, the
 sampling distributions of these statistics. After smoothing the
 percentiles of the empirical sampling distributions, we come up with a new
 set of critical values for testing the existence of a random walk, if each
 statistic is being used on an individual base. Combining the new sets of
 critical values, we finally suggest a general methodology for testing for
 a random walk model. 
Journal: Journal of Applied Statistics 
Pages: 909-925 
Issue: 8 
Volume: 35 
Year: 2008 
Keywords: random walk, critical values, uncertainty, 
X-DOI: 10.1080/02664760802185290 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802185290
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:8:p:909-925




Template-Type: ReDIF-Article 1.0
Author-Name: Donghoh Kim 
Author-X-Name-First: Donghoh 
Author-X-Name-Last: Kim 
Author-Name: Youngjo Lee 
Author-X-Name-First: Youngjo 
Author-X-Name-Last: Lee 
Author-Name: Hee-Seok Oh 
Author-X-Name-First: Hee-Seok 
Author-X-Name-Last: Oh 
Title: A fast wavelet approach for recovering damaged images 
Abstract:
  A wavelet method is proposed for recovering damaged images. The proposed
 method combines wavelet shrinkage with preprocessing based on a binning
 process and an imputation procedure that is designed to extend the scope
 of wavelet shrinkage to data with missing values and perturbed locations.
 The proposed algorithm, termed as the BTW algorithm is simple to implement
 and efficient for recovering an image. Furthermore, this algorithm can be
 easily applied to wavelet regression for one-dimensional (1-D) signal
 estimation with irregularly spaced data. Simulation studies and real
 examples show that the proposed method can produce substantially effective
 results. 
Journal: Journal of Applied Statistics 
Pages: 927-938 
Issue: 8 
Volume: 35 
Year: 2008 
Keywords: binning process, imputation, missing pixel, perturbed location, scattered data, wavelet shrinkage, 
X-DOI: 10.1080/02664760802187478 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802187478
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:8:p:927-938




Template-Type: ReDIF-Article 1.0
Author-Name: Kepher Makambi 
Author-X-Name-First: Kepher 
Author-X-Name-Last: Makambi 
Title: BOOK REVIEW 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 939-940 
Issue: 8 
Volume: 35 
Year: 2008 
X-DOI: 10.1080/02664760802066672 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802066672
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:8:p:939-940




Template-Type: ReDIF-Article 1.0
Author-Name: Hassan Bakouch 
Author-X-Name-First: Hassan 
Author-X-Name-Last: Bakouch 
Title: BOOK REVIEW 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 941-942 
Issue: 8 
Volume: 35 
Year: 2008 
X-DOI: 10.1080/02664760802066714 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802066714
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:8:p:941-942




Template-Type: ReDIF-Article 1.0
Author-Name: David Wooff 
Author-X-Name-First: David 
Author-X-Name-Last: Wooff 
Title: BOOK REVIEW 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 943-944 
Issue: 8 
Volume: 35 
Year: 2008 
X-DOI: 10.1080/02664760802187494 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802187494
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:8:p:943-944




Template-Type: ReDIF-Article 1.0
Author-Name: Robert Aykroyd 
Author-X-Name-First: Robert 
Author-X-Name-Last: Aykroyd 
Title: Editorial 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 945-946 
Issue: 9 
Volume: 35 
Year: 2008 
X-DOI: 10.1080/02664760802373342 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802373342
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:9:p:945-946




Template-Type: ReDIF-Article 1.0
Author-Name: Sugnet Gardner-Lubbe 
Author-X-Name-First: Sugnet 
Author-X-Name-Last: Gardner-Lubbe 
Author-Name: Ni&#xeb;l Le Roux 
Author-X-Name-First: Ni&#xeb;l 
Author-X-Name-Last: Le Roux 
Author-Name: John Gowers 
Author-X-Name-First: John 
Author-X-Name-Last: Gowers 
Title: Measures of fit in principal component and canonical variate analyses 
Abstract:
  Treating principal component analysis (PCA) and canonical variate
 analysis (CVA) as methods for approximating tables, we develop measures,
 collectively termed predictivity, that assess the quality of fit
 independently for each variable and for all dimensionalities. We
 illustrate their use with data from aircraft development, the African
 timber industry and copper froth measurements from the mining industry.
 Similar measures are described for assessing the predictivity associated
 with the individual samples (in the case of PCA and CVA) or group means
 (in the case of CVA). For these measures to be meaningful, certain
 essential orthogonality conditions must hold that are shown to be
 satisfied by predictivity. 
Journal: Journal of Applied Statistics 
Pages: 947-965 
Issue: 9 
Volume: 35 
Year: 2008 
Keywords: biplots, canonical variate analysis, measures of fit, prediction, principal component analysis, 
X-DOI: 10.1080/02664760802185399 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802185399
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:9:p:947-965




Template-Type: ReDIF-Article 1.0
Author-Name: Edward Boone 
Author-X-Name-First: Edward 
Author-X-Name-Last: Boone 
Author-Name: Bronson Bullock 
Author-X-Name-First: Bronson 
Author-X-Name-Last: Bullock 
Title: Spatial correlation matrix selection using Bayesian model averaging to characterize inter-tree competition in loblolly pine trees 
Abstract:
  Many applications of statistical methods for data that are spatially
 correlated require the researcher to specify the correlation structure of
 the data. This can be a difficult task as there are many candidate
 structures. Some spatial correlation structures depend on the distance
 between the observed data points while others rely on neighborhood
 structures. In this paper, Bayesian methods that systematically determine
 the 'best' correlation structure from a predefined class of structures are
 proposed. Bayes factors, Highest Probability Models, and Bayesian Model
 Averaging are employed to determine the 'best' correlation structure and
 to average across these structures to create a non-parametric alternative
 structure for a loblolly pine data-set with known tree coordinates. Tree
 diameters and heights were measured and an investigation into the spatial
 dependence between the trees was conducted. Results showed that the most
 probable model for the spatial correlation structure agreed with
 allometric trends for loblolly pine. A combined Matern, simultaneous
 autoregressive model and conditional autoregressive model best described
 the inter-tree competition among the loblolly pine tree data considered in
 this research. 
Journal: Journal of Applied Statistics 
Pages: 967-977 
Issue: 9 
Volume: 35 
Year: 2008 
Keywords: autocorrelation, Bayes factors, BMA, geostatistical models, lattice models, Pinus taeda, 
X-DOI: 10.1080/02664760802185845 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802185845
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:9:p:967-977




Template-Type: ReDIF-Article 1.0
Author-Name: David Almorza 
Author-X-Name-First: David 
Author-X-Name-Last: Almorza 
Author-Name: M. Hortensia Garcia 
Author-X-Name-First: M. 
Author-X-Name-Last: Hortensia Garcia 
Title: Results of exploratory data analysis in the broken stick model 
Abstract:
  The broken stick model is a model of the abundance of species in a
 habitat, and it has been widely extended. In this paper, we present
 results from exploratory data analysis of this model. To obtain some of
 the statistics, we formulate the broken stick model as a probability
 distribution function based on the same model, and we provide an
 expression for the cumulative distribution function, which is needed to
 obtain the results from exploratory data analysis. The inequalities we
 present are useful in ecological studies that apply broken stick models.
 These results are also useful for testing the goodness of fit of the
 broken stick model as an alternative to the chi square test, which has
 often been the main test used. Therefore, these results may be used in
 several alternative and complementary ways for testing the goodness of fit
 of the broken stick model. 
Journal: Journal of Applied Statistics 
Pages: 979-983 
Issue: 9 
Volume: 35 
Year: 2008 
Keywords: broken stick model, exploratory data analysis, 
X-DOI: 10.1080/02664760802187536 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802187536
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:9:p:979-983




Template-Type: ReDIF-Article 1.0
Author-Name: Claudia Lautensack 
Author-X-Name-First: Claudia 
Author-X-Name-Last: Lautensack 
Title: Fitting three-dimensional Laguerre tessellations to foam structures 
Abstract:
  Foam models, especially random tessellations, are powerful tools to study
 the relations between the geometric structure of foams and their physical
 properties. In this paper, we propose the use of random Laguerre
 tessellations, weighted versions of the well-known Voronoi tessellations,
 as models for the microstructure of foams. Based on geometric
 characteristics estimated from a tomographic image of a closed-cell
 polymer foam, we fit a Laguerre tessellation model to the material. It is
 shown that this model allows for a better fit of the geometric structure
 of the foam than some classical Voronoi tessellation models. 
Journal: Journal of Applied Statistics 
Pages: 985-995 
Issue: 9 
Volume: 35 
Year: 2008 
Keywords: cell characteristics, closed foam, foam model, Laguerre tessellation, random tessellation, 3D image, volume image, 
X-DOI: 10.1080/02664760802188112 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802188112
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:9:p:985-995




Template-Type: ReDIF-Article 1.0
Author-Name: Carmen Armero 
Author-X-Name-First: Carmen 
Author-X-Name-Last: Armero 
Author-Name: Antonio Lopez-Quilez 
Author-X-Name-First: Antonio 
Author-X-Name-Last: Lopez-Quilez 
Author-Name: Rut Lopez-Sanchez 
Author-X-Name-First: Rut 
Author-X-Name-Last: Lopez-Sanchez 
Title: Bayesian assessment of times to diagnosis in breast cancer screening 
Abstract:
  Breast cancer is one of the diseases with the most profound impact on
 health in developed countries and mammography is the most popular method
 for detecting breast cancer at a very early stage. This paper focuses on
 the waiting period from a positive mammogram until a confirmatory
 diagnosis is carried out in hospital. Generalized linear mixed models are
 used to perform the statistical analysis, always within the Bayesian
 reasoning. Markov chain Monte Carlo algorithms are applied for estimation
 by simulating the posterior distribution of the parameters and
 hyperparameters of the model through the free software WinBUGS. 
Journal: Journal of Applied Statistics 
Pages: 997-1009 
Issue: 9 
Volume: 35 
Year: 2008 
Keywords: Bayesian statistics, breast cancer screening program, generalized linear mixed models, Markov Chain Monte Carlo, 
X-DOI: 10.1080/02664760802191397 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802191397
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:9:p:997-1009




Template-Type: ReDIF-Article 1.0
Author-Name: Nicolas Bousquet 
Author-X-Name-First: Nicolas 
Author-X-Name-Last: Bousquet 
Title: Diagnostics of prior-data agreement in applied Bayesian analysis 
Abstract:
  This article focused on the definition and the study of a binary Bayesian
 criterion which measures a statistical agreement between a subjective
 prior and data information. The setting of this work is concrete Bayesian
 studies. It is an alternative and a complementary tool to the method
 recently proposed by Evans and Moshonov, [M. Evans and H. Moshonov,
 Checking for Prior-data conflict, Bayesian Anal. 1 (2006), pp. 893-914].
 Both methods try to help the work of the Bayesian analyst, from
 preliminary to the posterior computation. Our criterion is defined as a
 ratio of Kullback-Leibler divergences; two of its main features are to
 make easy the check of a hierarchical prior and be used as a default
 calibration tool to obtain flat but proper priors in applications.
 Discrete and continuous distributions exemplify the approach and an
 industrial case study in reliability, involving the Weibull distribution,
 is highlighted. 
Journal: Journal of Applied Statistics 
Pages: 1011-1029 
Issue: 9 
Volume: 35 
Year: 2008 
Keywords: prior-data conflict, expert opinion, subjective prior, objective prior, Kullback-Leibler diver-gence, discrete distributions, lifetime distributions, 
X-DOI: 10.1080/02664760802192981 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802192981
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:9:p:1011-1029




Template-Type: ReDIF-Article 1.0
Author-Name: A. Georgievska 
Author-X-Name-First: A. 
Author-X-Name-Last: Georgievska 
Author-Name: L. Georgievska 
Author-X-Name-First: L. 
Author-X-Name-Last: Georgievska 
Author-Name: A. Stojanovic 
Author-X-Name-First: A. 
Author-X-Name-Last: Stojanovic 
Author-Name: N. Todorovic 
Author-X-Name-First: N. 
Author-X-Name-Last: Todorovic 
Title: Sovereign rescheduling probabilities in emerging markets: a comparison with credit rating agencies' ratings 
Abstract:
  This study estimates default probabilities of 124 emerging countries from
 1981 to 2002 as a function of a set of macroeconomic and political
 variables. The estimated probabilities are then compared with the default
 rates implied by sovereign credit ratings of three major international
 credit rating agencies (CRAs) - Moody's Investor's Service, Standard &
 Poor's and Fitch Ratings. Sovereign debt default probabilities are used by
 investors in pricing sovereign bonds and loans as well as in determining
 country risk exposure. The study finds that CRAs usually underestimate the
 risk of sovereign debt as the sovereign credit ratings from rating
 agencies are usually too optimistic. 
Journal: Journal of Applied Statistics 
Pages: 1031-1051 
Issue: 9 
Volume: 35 
Year: 2008 
Keywords: sovereign debt, default probabilities, credit rating agencies, credit ratings, 
X-DOI: 10.1080/02664760802193112 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802193112
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:9:p:1031-1051




Template-Type: ReDIF-Article 1.0
Author-Name: Cristina Rueda-Sabater 
Author-X-Name-First: Cristina 
Author-X-Name-Last: Rueda-Sabater 
Author-Name: Pedro Alvarez-Esteban 
Author-X-Name-First: Pedro 
Author-X-Name-Last: Alvarez-Esteban 
Title: The analysis of age-specific fertility patterns via logistic models 
Abstract:
  In this paper, we introduce logistic models to analyse fertility curves.
 The models are formulated as linear models of the log odds of fertility
 and are defined in terms of parameters that are interpreted as measures of
 level, location and shape of the fertility schedule. This parameterization
 is useful for the evaluation, and interpretation of fertility trends and
 projections of future period fertility. For a series of years, the
 proposed models admit a state-space formulation that allows a coherent
 joint estimation of parameters and forecasting. The main features of the
 models compared with other alternatives are the functional simplicity, the
 flexibility, and the interpretability of the parameters. These and other
 features are analysed in this paper using examples and theoretical
 results. Data from different countries are analysed, and to validate the
 logistic approach, we compare the goodness of fit of the new model against
 well-known alternatives; the analysis gives superior results in most
 developed countries. 
Journal: Journal of Applied Statistics 
Pages: 1053-1070 
Issue: 9 
Volume: 35 
Year: 2008 
Keywords: logistic model, fertility schedule, state-space model, maximum-likelihood estimation, Tempo, quantum, 
X-DOI: 10.1080/02664760802192999 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802192999
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:9:p:1053-1070




Template-Type: ReDIF-Article 1.0
Author-Name: Klara Goethals 
Author-X-Name-First: Klara 
Author-X-Name-Last: Goethals 
Author-Name: Paul Janssen 
Author-X-Name-First: Paul 
Author-X-Name-Last: Janssen 
Author-Name: Luc Duchateau 
Author-X-Name-First: Luc 
Author-X-Name-Last: Duchateau 
Title: Frailty models and copulas: similarities and differences 
Abstract:
  Copulas and frailty models are important tools to model bivariate
 survival data. Equivalence between Archimedean copula models and shared
 frailty models, e.g. between the Clayton-Oakes copula model and the shared
 gamma frailty model, has often been claimed in the literature. In this
 note we show that, in both the models, there is indeed a well-known
 equivalence between the copula functions; the modeling of the marginal
 survival functions, however, is quite different. The latter fact leads to
 different joint survival functions. 
Journal: Journal of Applied Statistics 
Pages: 1071-1079 
Issue: 9 
Volume: 35 
Year: 2008 
Keywords: bivariate survival data, Clayton-Oakes copula, positive stable frailty, shared gamma frailty model, 
X-DOI: 10.1080/02664760802271389 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802271389
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:9:p:1071-1079




Template-Type: ReDIF-Article 1.0
Author-Name: Honghu Liu 
Author-X-Name-First: Honghu 
Author-X-Name-Last: Liu 
Author-Name: Yan Zheng 
Author-X-Name-First: Yan 
Author-X-Name-Last: Zheng 
Author-Name: Jie Shen 
Author-X-Name-First: Jie 
Author-X-Name-Last: Shen 
Title: Goodness-of-fit measures of R2 for repeated measures mixed effect models 
Abstract:
  Linear mixed effects model (LMEM) is efficient in modeling repeated
 measures longitudinal data. However, little research has been done in
 developing goodness-of-fit measures that can evaluate the models,
 particularly those that can be interpreted in an absolute sense without
 referencing a null model. This paper proposes three coefficient of
 determination (R2) as goodness-of-fit measures for LMEM with repeated
 measures longitudinal data. Theorems are presented describing the
 properties of R2 and relationships between the R2 statistics. A simulation
 study was conducted to evaluate and compare the R2 along with other
 criteria from literature. Finally, we applied the proposed R2 to a real
 virologic response data of an HIV-patient cohort. We conclude that our
 proposed R2 statistics have more advantages than other goodness-of-fit
 measures in the literature, in terms of robustness to sample size,
 intuitive interpretation, well-defined range, and unnecessary to determine
 a null model. 
Journal: Journal of Applied Statistics 
Pages: 1081-1092 
Issue: 10 
Volume: 35 
Year: 2008 
Keywords: repeated measures, R-square, linear mixed effects model, fixed effects, random effects, simulation, 
X-DOI: 10.1080/02664760802124422 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802124422
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:10:p:1081-1092




Template-Type: ReDIF-Article 1.0
Author-Name: Lawrence Raffalovich 
Author-X-Name-First: Lawrence 
Author-X-Name-Last: Raffalovich 
Author-Name: Glenn Deane 
Author-X-Name-First: Glenn 
Author-X-Name-Last: Deane 
Author-Name: David Armstrong 
Author-X-Name-First: David 
Author-X-Name-Last: Armstrong 
Author-Name: Hui-Shien Tsao 
Author-X-Name-First: Hui-Shien 
Author-X-Name-Last: Tsao 
Title: Model selection procedures in social research: Monte-Carlo simulation results 
Abstract:
  Model selection strategies play an important, if not explicit, role in
 quantitative research. The inferential properties of these strategies are
 largely unknown, therefore, there is little basis for recommending (or
 avoiding) any particular set of strategies. In this paper, we evaluate
 several commonly used model selection procedures [Bayesian information
 criterion (BIC), adjusted R2, Mallows' Cp, Akaike information criteria
 (AIC), AICc, and stepwise regression] using Monte-Carlo simulation of
 model selection when the true data generating processes (DGP) are known.
 We find that the ability of these selection procedures to include
 important variables and exclude irrelevant variables increases with the
 size of the sample and decreases with the amount of noise in the model.
 None of the model selection procedures do well in small samples, even when
 the true DGP is largely deterministic; thus, data mining in small samples
 should be avoided entirely. Instead, the implicit uncertainty in model
 specification should be explicitly discussed. In large samples, BIC is
 better than the other procedures at correctly identifying most of the
 generating processes we simulated, and stepwise does almost as well. In
 the absence of strong theory, both BIC and stepwise appear to be
 reasonable model selection strategies in large samples. Under the
 conditions simulated, adjusted R2, Mallows' Cp AIC, and AICc are clearly
 inferior and should be avoided. 
Journal: Journal of Applied Statistics 
Pages: 1093-1114 
Issue: 10 
Volume: 35 
Year: 2008 
Keywords: model selection, BIC, AIC, stepwise regression, 
X-DOI: 10.1080/03081070802203959 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/03081070802203959
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:10:p:1093-1114




Template-Type: ReDIF-Article 1.0
Author-Name: Donatella Vicari 
Author-X-Name-First: Donatella 
Author-X-Name-Last: Vicari 
Author-Name: Johan Rene Van Dorp 
Author-X-Name-First: Johan Rene 
Author-X-Name-Last: Van Dorp 
Author-Name: Samuel Kotz 
Author-X-Name-First: Samuel 
Author-X-Name-Last: Kotz 
Title: Two-sided generalized Topp and Leone (TS-GTL) distributions 
Abstract:
  Over 50 years ago, in a 1955 issue of JASA, a paper on a bounded
 continuous distribution by Topp and Leone [C.W. Topp and F.C. Leone, A
 family of J-shaped frequency functions, J. Am. Stat. Assoc. 50(269)
 (1955), pp. 209-219] appeared (the subject was dormant for over 40 years
 but recently the family was resurrected). Here, we shall investigate the
 so-called Two-Sided Generalized Topp and Leone (TS-GTL) distributions.
 This family of distributions is constructed by extending the Generalized
 Two-Sided Power (GTSP) family to a new two-sided framework of
 distributions, where the first (second) branch arises from the
 distribution of the largest (smallest) order statistic. The TS-GTL
 distribution is generated from this framework by sampling from a slope
 (reflected slope) distribution for the first (second) branch. The
 resulting five-parameter TS-GTL family of distributions turns out to be
 flexible, encompassing the uniform, triangular, GTSP and two-sided slope
 distributions into a single family. In addition, the probability density
 functions may have bimodal shapes or admitting shapes with a jump
 discontinuity at the 'threshold' parameter. We will discuss some
 properties of the TS-GTL family and describe a maximum likelihood
 estimation (MLE) procedure. A numerical example of the MLE procedure is
 provided by means of a bimodal Galaxy M87 data set concerning V-I color
 indices of 80 globular clusters. A comparison with a Gaussian mixture fit
 is presented. 
Journal: Journal of Applied Statistics 
Pages: 1115-1129 
Issue: 10 
Volume: 35 
Year: 2008 
Keywords: bimodal distribution, maximum likelihood estimation, order statistics, 
X-DOI: 10.1080/02664760802230583 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802230583
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:10:p:1115-1129




Template-Type: ReDIF-Article 1.0
Author-Name: Terence Mills 
Author-X-Name-First: Terence 
Author-X-Name-Last: Mills 
Title: Predicting body fat using weight-height indices 
Abstract:
  While body fat is the most accurate measure of obesity, its measurement
 requires special equipment that can be costly and time consuming to
 operate. Attention has thus typically focused on the easier to calculate
 body mass index (BMI). However, the ability of BMI to accurately identify
 obesity has been increasingly questioned. This paper focuses attention on
 whether more general body mass indices are appropriate measures of body
 fat. Using a data set of body fat, height, and weight measurements,
 general models are estimated which nest a wide variety of weight-height
 indices as special cases. In the absence of a race and gender
 categorisation, the conventional BMI was found to be the appropriate index
 with which to predict body fat. When such a categorisation was made,
 however, the BMI was never selected as the appropriate index. In general,
 predicted female body fat was some 10&#xa0;kg higher than that of a male
 of identical build and predicted % body fat was over 11 percentage points
 higher, but age effects were smaller for females. Considerable racial
 differences in predicted body fat were found for males, but such
 differences were less marked for females. The implications of this finding
 for interpreting recent research on the effect of obesity on health,
 society, and economic factors are considered. 
Journal: Journal of Applied Statistics 
Pages: 1131-1138 
Issue: 10 
Volume: 35 
Year: 2008 
Keywords: body fat, BMI, height-weight indices, obesity, 
X-DOI: 10.1080/02664760802264707 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802264707
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:10:p:1131-1138




Template-Type: ReDIF-Article 1.0
Author-Name: Shuo-Jye Wu 
Author-X-Name-First: Shuo-Jye 
Author-X-Name-Last: Wu 
Title: Estimation of the two-parameter bathtub-shaped lifetime distribution with progressive censoring 
Abstract:
  In this paper, we investigate the estimation problem concerning a
 progressively type-II censored sample from the two-parameter
 bathtub-shaped lifetime distribution. We use the maximum likelihood method
 to obtain the point estimators of the parameters. We also provide a method
 for constructing an exact confidence interval and an exact joint
 confidence region for the parameters. Two numerical examples are presented
 to illustrate the method of inference developed here. Finally, Monte Carlo
 simulation studies are used to assess the performance of our proposed
 method. 
Journal: Journal of Applied Statistics 
Pages: 1139-1150 
Issue: 10 
Volume: 35 
Year: 2008 
Keywords: confidence interval, hazard function, joint confidence region, maximum likelihood estimator, pivot, progressive type-II censoring, 
X-DOI: 10.1080/02664760802264996 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802264996
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:10:p:1139-1150




Template-Type: ReDIF-Article 1.0
Author-Name: Subburaj Ramasamy 
Author-X-Name-First: Subburaj 
Author-X-Name-Last: Ramasamy 
Author-Name: Gopal Govindasamy 
Author-X-Name-First: Gopal 
Author-X-Name-Last: Govindasamy 
Title: A software reliability growth model addressing learning 
Abstract:
  Goel proposed generalization of the Goel-Okumoto (G-O) software
 reliability growth model (SRGM), in order to model the failure intensity
 function, i.e. the rate of occurrence of failures (ROCOF) that initially
 increases and then decreases (I/D), which occurs in many projects due to
 the learning phenomenon of the testing team and a few other causes. The
 ROCOF of the generalized non-homogenous poisson process (NHPP) model can
 be expressed in the same mathematical form as that of a two-parameter
 Weibull function. However, this SRGM is susceptible to wide fluctuations
 in time between failures and sometimes it seems unable to recognize the
 I/D pattern of ROCOF present in the datasets and hence does not adequately
 describe such data. The authors therefore propose a shifted Weibull
 function ROCOF instead for the generalized NHPP model. This modification
 to the Goel-generalized NHPP model results in an SRGM that seems to
 perform better consistently, as confirmed by the goodness of fit statistic
 and predictive validity metrics, when applied to failure datasets of 11
 software projects with widely varying characteristics. A case study on
 software release time determination using the proposed SRGM is also given. 
Journal: Journal of Applied Statistics 
Pages: 1151-1168 
Issue: 10 
Volume: 35 
Year: 2008 
Keywords: failure intensity function, goodness of fit statistic, mean value function, NHPP model, predictive validity, SRGM, 
X-DOI: 10.1080/02664760802270621 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802270621
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:10:p:1151-1168




Template-Type: ReDIF-Article 1.0
Author-Name: Stavros Degiannakis 
Author-X-Name-First: Stavros 
Author-X-Name-Last: Degiannakis 
Title: ARFIMAX and ARFIMAX-TARCH realized volatility modeling 
Abstract:
  ARFIMAX models are applied in estimating the intra-day realized
 volatility of the CAC40 and DAX30 indices. Volatility clustering and
 asymmetry characterize the logarithmic realized volatility of both the
 indices. The ARFIMAX model with time-varying conditional
 heteroskedasticity is the best performing specification and, at least in
 the case of DAX30, provides statistically superior next trading day's
 realized volatility forecasts. 
Journal: Journal of Applied Statistics 
Pages: 1169-1180 
Issue: 10 
Volume: 35 
Year: 2008 
Keywords: ARFIMAX, realized volatility, TARCH, volatility forecasting, 
X-DOI: 10.1080/02664760802271017 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802271017
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:10:p:1169-1180




Template-Type: ReDIF-Article 1.0
Author-Name: Lin-An Chen 
Author-X-Name-First: Lin-An 
Author-X-Name-Last: Chen 
Author-Name: Hsien-Chueh Peter Yang 
Author-X-Name-First: Hsien-Chueh 
Author-X-Name-Last: Peter Yang 
Author-Name: Chau-Shyun Tang 
Author-X-Name-First: Chau-Shyun 
Author-X-Name-Last: Tang 
Title: Mode type quasi-range and its applications 
Abstract:
  Building from the consideration of closeness, we propose the mode
 quasi-range as an alternative scale parameter. Application of this scale
 parameter to formulate the population standard deviation is investigated
 leading to an efficient sample estimator of standard deviation from the
 point of asymptotic variance. Monte Carlo studies, in terms of finite
 sample efficiency and robustness of breakdown point, have been performed
 for the sample mode quasi-range. This study reveals that this closeness
 consideration-based mode, quasi-range, is satisfactory because these
 statistical procedures based on it are efficient and are less misleading
 for drawing conclusion from the sample results. 
Journal: Journal of Applied Statistics 
Pages: 1181-1192 
Issue: 10 
Volume: 35 
Year: 2008 
Keywords: breakdown point, range, robustness, quasi-range, scale parameter, 
X-DOI: 10.1080/02664760802271082 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802271082
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:10:p:1181-1192




Template-Type: ReDIF-Article 1.0
Author-Name: Abbas Moghimbeigi 
Author-X-Name-First: Abbas 
Author-X-Name-Last: Moghimbeigi 
Author-Name: Mohammed Reza Eshraghian 
Author-X-Name-First: Mohammed Reza 
Author-X-Name-Last: Eshraghian 
Author-Name: Kazem Mohammad 
Author-X-Name-First: Kazem 
Author-X-Name-Last: Mohammad 
Author-Name: Brian Mcardle 
Author-X-Name-First: Brian 
Author-X-Name-Last: Mcardle 
Title: Multilevel zero-inflated negative binomial regression modeling for over-dispersed count data with extra zeros 
Abstract:
  Count data with excess zeros often occurs in areas such as public health,
 epidemiology, psychology, sociology, engineering, and agriculture.
 Zero-inflated Poisson (ZIP) regression and zero-inflated negative binomial
 (ZINB) regression are useful for modeling such data, but because of
 hierarchical study design or the data collection procedure, zero-inflation
 and correlation may occur simultaneously. To overcome these challenges ZIP
 or ZINB may still be used. In this paper, multilevel ZINB regression is
 used to overcome these problems. The method of parameter estimation is an
 expectation-maximization algorithm in conjunction with the penalized
 likelihood and restricted maximum likelihood estimates for variance
 components. Alternative modeling strategies, namely the ZIP distribution
 are also considered. An application of the proposed model is shown on
 decayed, missing, and filled teeth of children aged 12 years old. 
Journal: Journal of Applied Statistics 
Pages: 1193-1202 
Issue: 10 
Volume: 35 
Year: 2008 
Keywords: count data, EM algorithm, multilevel, negative binomial regression, Poisson regression, zero-inflation, 
X-DOI: 10.1080/02664760802273203 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802273203
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:10:p:1193-1202




Template-Type: ReDIF-Article 1.0
Author-Name: Zeinab Amin 
Author-X-Name-First: Zeinab 
Author-X-Name-Last: Amin 
Title: Bayesian inference for the Pareto lifetime model under progressive censoring with binomial removals 
Abstract:
  This paper considers the estimation and prediction problems when
 lifetimes are Pareto-distributed and are collected under Type II
 progressive censoring with random removals, where the number of units
 removed at each failure time follows a Binomial distribution. The analysis
 is carried out within the Bayesian context. 
Journal: Journal of Applied Statistics 
Pages: 1203-1217 
Issue: 11 
Volume: 35 
Year: 2008 
Keywords: Bayesian estimation, Bayesian prediction, Gibbs sampling, missing data, natural conjugate prior, non-informative prior, Pareto distribution, progressive censoring, Type II censoring, total test time remaining, 
X-DOI: 10.1080/09537280802187634 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/09537280802187634
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:11:p:1203-1217




Template-Type: ReDIF-Article 1.0
Author-Name: Eun Sug Park 
Author-X-Name-First: Eun Sug 
Author-X-Name-Last: Park 
Author-Name: Roger Smith 
Author-X-Name-First: Roger 
Author-X-Name-Last: Smith 
Author-Name: Thomas Freeman 
Author-X-Name-First: Thomas 
Author-X-Name-Last: Freeman 
Author-Name: Clifford Spiegelman 
Author-X-Name-First: Clifford 
Author-X-Name-Last: Spiegelman 
Title: A Bayesian approach for improved pavement performance prediction 
Abstract:
  We present a method for predicting future pavement distresses such as
 longitudinal cracking. These predicted distress values are used to plan
 road repairs. Large inherent variability in measured cracking and an
 extremely small number of observations are the nature of the pavement
 cracking data, which calls for a parametric Bayesian approach. We model
 theoretical pavement distress with a sigmoidal equation with coefficients
 based on prior engineering knowledge. We show that a Bayesian formulation
 akin to Kalman filtering gives sensible predictions and provides
 defendable uncertainty statements for predictions. The method is
 demonstrated on data collected by the Texas Transportation Institute at
 several sites in Texas. The predictions behave in a reasonable and
 statistically valid manner. 
Journal: Journal of Applied Statistics 
Pages: 1219-1238 
Issue: 11 
Volume: 35 
Year: 2008 
Keywords: pavement management information system, Bayesian adjustment, state-space models, Kalman filtering, Markov chain Monte Carlo, 
X-DOI: 10.1080/02664760802318651 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802318651
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:11:p:1219-1238




Template-Type: ReDIF-Article 1.0
Author-Name: V. G. Cancho 
Author-X-Name-First: V. G. 
Author-X-Name-Last: Cancho 
Author-Name: Reiko Aoki 
Author-X-Name-First: Reiko 
Author-X-Name-Last: Aoki 
Author-Name: V. H. Lachos 
Author-X-Name-First: V. H. 
Author-X-Name-Last: Lachos 
Title: Bayesian analysis for a skew extension of the multivariate null intercept measurement error model 
Abstract:
  Skew-normal distribution is a class of distributions that includes the
 normal distributions as a special case. In this paper, we explore the use
 of Markov Chain Monte Carlo (MCMC) methods to develop a Bayesian analysis
 in a multivariate, null intercept, measurement error model [R. Aoki, H.
 Bolfarine, J.A. Achcar, and D. Leao Pinto Jr, Bayesian analysis of a
 multivariate null intercept error-in-variables regression model, J.
 Biopharm. Stat. 13(4) (2003b), pp. 763-771] where the unobserved value of
 the covariate (latent variable) follows a skew-normal distribution. The
 results and methods are applied to a real dental clinical trial presented
 in [A. Hadgu and G. Koch, Application of generalized estimating equations
 to a dental randomized clinical trial, J. Biopharm. Stat. 9 (1999), pp.
 161-178]. 
Journal: Journal of Applied Statistics 
Pages: 1239-1251 
Issue: 11 
Volume: 35 
Year: 2008 
Keywords: Skew-normal distribution, Gibbs algorithm, skewness, multivariate null intercepts model, measurement error, 
X-DOI: 10.1080/02664760802319667 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802319667
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:11:p:1239-1251




Template-Type: ReDIF-Article 1.0
Author-Name: M. D. Ugarte 
Author-X-Name-First: M. D. 
Author-X-Name-Last: Ugarte 
Author-Name: A. F. Militino 
Author-X-Name-First: A. F. 
Author-X-Name-Last: Militino 
Author-Name: T. Goicoa 
Author-X-Name-First: T. 
Author-X-Name-Last: Goicoa 
Title: Adjusting economic estimates in business surveys 
Abstract:
  Statistics for small areas within larger regions are recently required
 for many economic variables. However, when adding the estimates of the
 small areas within the larger regions, the results do not match up to
 those obtained with the appropriate estimator originally derived for the
 larger region. To avoid discrepancies between estimates benchmarking
 methods are commonly used in practice. In this paper, we discuss the
 suitability of using a restricted predictor versus a traditional direct
 calibrated estimator. The results are illustrated with the 2000 Business
 Survey of the Basque Country, Spain. 
Journal: Journal of Applied Statistics 
Pages: 1253-1265 
Issue: 11 
Volume: 35 
Year: 2008 
Keywords: benchmarking, restricted predictor, prorating estimator, linear mixed model, EBLUP, synthetic, 
X-DOI: 10.1080/02664760802319709 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802319709
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:11:p:1253-1265




Template-Type: ReDIF-Article 1.0
Author-Name: Zhang Wu 
Author-X-Name-First: Zhang 
Author-X-Name-Last: Wu 
Author-Name: Jianxin Jiao 
Author-X-Name-First: Jianxin 
Author-X-Name-Last: Jiao 
Author-Name: Ying Liu 
Author-X-Name-First: Ying 
Author-X-Name-Last: Liu 
Title: A binomial CUSUM chart for detecting large shifts in fraction nonconforming 
Abstract:
  This article studies a unique feature of the binomial CUSUM chart in
 which the difference (dt-d0) is replaced by (dt-d0)2 in the formulation of
 the cumulative sum Ct (where dt and d0 are the actual and in-control
 numbers of nonconforming units, respectively, in a sample). Performance
 studies are reported and the results reveal that this new feature is able
 to increase the detection effectiveness when fraction nonconforming p
 becomes three to four times as large as the in-control value p0. The
 design of the new binomial CUSUM chart is presented along with the
 calculation of the in-control and out-of-control Average Run Lengths (ARL0
 and ARL1). 
Journal: Journal of Applied Statistics 
Pages: 1267-1276 
Issue: 11 
Volume: 35 
Year: 2008 
Keywords: quality control, statistical process control, attribute control chart, binomial CUSUM control chart, fraction nonconforming, 
X-DOI: 10.1080/02664760802320533 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802320533
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:11:p:1267-1276




Template-Type: ReDIF-Article 1.0
Author-Name: Md. Mostafizur Rahman 
Author-X-Name-First: Md. Mostafizur 
Author-X-Name-Last: Rahman 
Author-Name: Jian-Ping Zhu 
Author-X-Name-First: Jian-Ping 
Author-X-Name-Last: Zhu 
Author-Name: M. Sayedur Rahman 
Author-X-Name-First: M. Sayedur 
Author-X-Name-Last: Rahman 
Title: Impact study of volatility modelling of Bangladesh stock index using non-normal density 
Abstract:
  This article examines a wide variety of popular volatility models for
 stock index return, including the random walk (RW), autoregressive,
 generalized autoregressive conditional heteroscedasticity (GARCH), and
 asymmetric GARCH models with normal and non-normal (Student's t and
 generalized error) distributional assumption. Fitting these models to the
 Chittagong stock index return data from the period 2 January 1999 to 29
 December 2005, we found that the asymmetric GARCH/GARCH model fits better
 under the assumption of non-normal distribution than under normal
 distribution. Non-parametric specification tests show that the RW-GARCH,
 RW-TGARCH, RW-EGARCH, and RW-APARCH models under the Student's
 t-distributional assumption are significant at the 5% level. Finally, the
 study suggests that these four models are suitable for the Chittagong
 Stock Exchange of Bangladesh. We believe that this study would be of great
 benefit to investors and policy makers at home and abroad. 
Journal: Journal of Applied Statistics 
Pages: 1277-1292 
Issue: 11 
Volume: 35 
Year: 2008 
Keywords: random walk, GARCH, asymmetric GARCH, non-parametric specification test, Student's t-distribution, generalized error distribution, 
X-DOI: 10.1080/02664760802320574 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802320574
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:11:p:1277-1292




Template-Type: ReDIF-Article 1.0
Author-Name: Shuo-Jye Wu 
Author-X-Name-First: Shuo-Jye 
Author-X-Name-Last: Wu 
Author-Name: Chun-Tao Chang 
Author-X-Name-First: Chun-Tao 
Author-X-Name-Last: Chang 
Author-Name: Kang-Jun Liao 
Author-X-Name-First: Kang-Jun 
Author-X-Name-Last: Liao 
Author-Name: Syuan-Rong Huang 
Author-X-Name-First: Syuan-Rong 
Author-X-Name-Last: Huang 
Title: Planning of progressive group-censoring life tests with cost considerations 
Abstract:
  This paper considers a life test under progressive type I group censoring
 with a Weibull failure time distribution. The maximum likelihood method is
 used to derive the estimators of the parameters of the failure time
 distribution. In practice, several variables, such as the number of test
 units, the number of inspections, and the length of inspection interval
 are related to the precision of estimation and the cost of experiment. An
 inappropriate setting of these decision variables not only wastes the
 resources of the experiment but also reduces the precision of estimation.
 One problem arising from designing a life test is the restricted budget of
 experiment. Therefore, under the constraint that the total cost of
 experiment does not exceed a pre-determined budget, this paper provides an
 algorithm to solve the optimal decision variables by considering three
 different criteria. An example is discussed to illustrate the proposed
 method. The sensitivity analysis is also studied. 
Journal: Journal of Applied Statistics 
Pages: 1293-1304 
Issue: 11 
Volume: 35 
Year: 2008 
Keywords: A-optimality, D-optimality, E-optimality, grouped data, maximum likelihood method, progressive censoring, Weibull distribution, 
X-DOI: 10.1080/02664760802382392 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802382392
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:11:p:1293-1304




Template-Type: ReDIF-Article 1.0
Author-Name: Dong Wan Shin 
Author-X-Name-First: Dong Wan 
Author-X-Name-Last: Shin 
Author-Name: Yoon Young Jung 
Author-X-Name-First: Yoon Young 
Author-X-Name-Last: Jung 
Author-Name: Man-Suk Oh 
Author-X-Name-First: Man-Suk 
Author-X-Name-Last: Oh 
Title: Double unit root tests for cross-sectionally dependent panel data 
Abstract:
  This paper proposes various double unit root tests for cross-sectionally
 dependent panel data. The cross-sectional correlation is handled by the
 projection method [P.C.B. Phillips and D. Sul, Dynamic panel estimation
 and homogeneity testing under cross section dependence, Econom. J. 6
 (2003), pp. 217-259; H.R. Moon and B. Perron, Testing for a unit root in
 panels with dynamic factors, J. Econom. 122 (2004), pp. 81-126] or the
 subtraction method [J. Bai and S. Ng, A PANIC attack on unit roots and
 cointegration, Econometrica 72 (2004), pp. 1127-1177]. Pooling or
 averaging is applied to combine results from different panel units. Also,
 to estimate autoregressive parameters the ordinary least squares
 estimation [D.P. Hasza and W.A. Fuller, Estimation for autoregressive
 processes with unit roots, Ann. Stat. 7 (1979), pp. 1106-1120] or the
 symmetric estimation [D.L. Sen and D.A. Dickey, Symmetric test for second
 differencing in univariate time series, J. Bus. Econ. Stat. 5 (1987), pp.
 463-473] are used, and to adjust mean functions the ordinary mean
 adjustment or the recursive mean adjustment are used. Combinations of
 different methods in defactoring to eliminate the cross-sectional
 dependency, integrating results from panel units, estimating the
 parameters, and adjusting mean functions yields various available tests
 for double unit roots in panel data. Simple asymptotic distributions of
 the proposed test statistics are derived, which can be used to find
 critical values of the test statistics. We perform a Monte Carlo
 experiment to compare the performance of these tests and to suggest
 optimal tests for a given panel data. Application of the proposed tests to
 a real data, the yearly export panel data sets of several Latin-American
 countries for the past 50 years, illustrates the usefulness of the
 proposed tests for panel data, in that they reveal stronger evidence of
 double unit roots than the componentwise double unit root tests of Hasza
 and Fuller [Estimation for autoregressive processes with unit roots, Ann.
 Stat. 7 (1979), pp. 1106-1120] or Sen and Dickey [Symmetric test for
 second differencing in univariate time series, J. Bus. Econ. Stat. 5
 (1987), pp. 463-473]. 
Journal: Journal of Applied Statistics 
Pages: 1305-1321 
Issue: 11 
Volume: 35 
Year: 2008 
Keywords: panel double unit roots, defactoring, recursive adjustment, symmetric estimation; nonstationarity, 
X-DOI: 10.1080/02664760802382400 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802382400
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:11:p:1305-1321




Template-Type: ReDIF-Article 1.0
Author-Name: Ronny Vallejos 
Author-X-Name-First: Ronny 
Author-X-Name-Last: Vallejos 
Title: Assessing the association between two spatial or temporal sequences 
Abstract:
  This paper deals with the codispersion coefficient for spatial and
 temporal series. We present some results and simulations concerning the
 codispersion coefficient in the context of spatial models. The results
 obtained are immediate consequences of the asymptotic normality of the
 sample codispersion coefficient and show certain limitations of the
 coefficient. New simulation studies provide information about the
 performance of the coefficient with respect to other coefficients of
 spatial association. The behavior of the codispersion coefficient under
 additively contaminated processes is also studied via Monte Carlo
 simulations. In the context of time series, explicit expressions for the
 asymptotic variance of the sample version of the coefficient are given for
 autoregressive and moving average processes. Resampling methods are used
 to compute the variance of the coefficient. A real data example is
 presented to explore how well the codispersion coefficient captures the
 comovement between two time series in practice. 
Journal: Journal of Applied Statistics 
Pages: 1323-1343 
Issue: 12 
Volume: 35 
Year: 2008 
Keywords: spatial association, autoregressive models, correlation coefficient, codispersion coefficient, time series, 
X-DOI: 10.1080/02664760802382418 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802382418
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:12:p:1323-1343




Template-Type: ReDIF-Article 1.0
Author-Name: M. Jahanshahi 
Author-X-Name-First: M. 
Author-X-Name-Last: Jahanshahi 
Author-Name: M. H. Sanati 
Author-X-Name-First: M. H. 
Author-X-Name-Last: Sanati 
Author-Name: Z. Babaei 
Author-X-Name-First: Z. 
Author-X-Name-Last: Babaei 
Title: Optimization of parameters for the fabrication of gelatin nanoparticles by the Taguchi robust design method 
Abstract:
  The Taguchi method is a statistical approach to overcome the limitation
 of the factorial and fractional factorial experiments by simplifying and
 standardizing the fractional factorial design. The objective of this study
 was to optimize the fabrication of gelatin nanoparticles by applying the
 Taguchi design method. Gelatin nanoparticles have been extensively studied
 in our previous works as an appropriate carrier for drug delivery, since
 they are biodegradable, non-toxic, are not usually contaminated with
 pyrogens and possess relatively low antigenicity. Taguchi method with L16
 orthogonal array robust design was implemented to optimize experimental
 conditions of the purpose. Four key process parameters - temperature,
 gelatin concentration, agitation speed and the amount of acetone - were
 considered for the optimization of gelatin nanoparticles. As a result of
 Taguchi analysis in this study, temperature and amount of acetone were the
 most influencing parameters of the particle size. For characterizing the
 nanoparticle sample, atomic force microscope and scanning electron
 microscope were employed. In this study, a minimum size of gelatin
 nanoparticles was obtained at 50 &#xb0;C temperature, 45 mg/ml gelatin
 concentration, 80 ml acetone and 700 rpm agitation speed. The nanoparticle
 size at the determined condition was less than 174 nm. 
Journal: Journal of Applied Statistics 
Pages: 1345-1353 
Issue: 12 
Volume: 35 
Year: 2008 
Keywords: gelatin, drug carrier, nanoparticles, optimization, Taguchi method, statistical experimental design, 
X-DOI: 10.1080/02664760802382426 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802382426
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:12:p:1345-1353




Template-Type: ReDIF-Article 1.0
Author-Name: Peter Austin 
Author-X-Name-First: Peter 
Author-X-Name-Last: Austin 
Title: The large-sample performance of backwards variable elimination 
Abstract:
  Prior studies have shown that automated variable selection results in
 models with substantially inflated estimates of the model R2, and that a
 large proportion of selected variables are truly noise variables. These
 earlier studies used simulated data sets whose sample sizes were at most
 100. We used Monte Carlo simulations to examine the large-sample
 performance of backwards variable elimination. We found that in large
 samples, backwards variable elimination resulted in estimates of R2 that
 were at most marginally biased. However, even in large samples, backwards
 elimination tended to identify the correct regression model in a minority
 of the simulated data sets. 
Journal: Journal of Applied Statistics 
Pages: 1355-1370 
Issue: 12 
Volume: 35 
Year: 2008 
Keywords: variable selection methods, model selection methods, regression models, Monte Carlo simulations, backwards variable elimination, 
X-DOI: 10.1080/02664760802382434 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802382434
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:12:p:1355-1370




Template-Type: ReDIF-Article 1.0
Author-Name: Shaul Bar-Lev 
Author-X-Name-First: Shaul 
Author-X-Name-Last: Bar-Lev 
Title: Point and confidence interval estimates for a global maximum via extreme value theory 
Abstract:
  The aim of this paper is to provide some practical aspects of point and
 interval estimates of the global maximum of a function using extreme value
 theory. Consider a real-valued function f:D&#x2192;&#xe045; defined on a
 bounded interval D such that f is either not known analytically or is
 known analytically but has rather a complicated analytic form. We assume
 that f possesses a global maximum attained, say, at u*&#x2208;D with
 maximal value x*=max u&#xa0;f(u)&#x2250;f(u*). The problem of seeking the
 optimum of a function which is more or less unknown to the observer has
 resulted in the development of a large variety of search techniques. In
 this paper we use the extreme-value approach as appears in Dekkers et al.
 [A moment estimator for the index of an extreme-value distribution, Ann.
 Statist. 17 (1989), pp. 1833-1855] and de Haan [Estimation of the minimum
 of a function using order statistics, J. Amer. Statist. Assoc. 76 (1981),
 pp. 467-469]. We impose some Lipschitz conditions on the functions being
 investigated and through repeated simulation-based samplings, we provide
 various practical interpretations of the parameters involved as well as
 point and interval estimates for x*. 
Journal: Journal of Applied Statistics 
Pages: 1371-1381 
Issue: 12 
Volume: 35 
Year: 2008 
Keywords: extreme value theory, global maximum, search techniques, 
X-DOI: 10.1080/02664760802382442 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802382442
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:12:p:1371-1381




Template-Type: ReDIF-Article 1.0
Author-Name: Krishna Saha 
Author-X-Name-First: Krishna 
Author-X-Name-Last: Saha 
Title: Semiparametric estimation for the dispersion parameter in the analysis of over- or underdispersed count data 
Abstract:
  This paper investigates several semiparametric estimators of the
 dispersion parameter in the analysis of over- or underdispersed count data
 when there is no likelihood available. In the context of estimating the
 dispersion parameter, we consider the double-extended quasi-likelihood
 (DEQL), the pseudo-likelihood and the optimal quadratic estimating (OQE)
 equations method and compare them with the maximum likelihood method, the
 method of moments and the extended quasi-likelihood through simulation
 study. The simulation study shows that the estimator based on the DEQL has
 superior bias and efficiency property for moderate and large sample size,
 and for small sample size the estimator based on the OQE equations
 outperforms the other estimators. Three real-life data sets arising in
 biostatistical practices are analyzed, and the findings from these
 analyses are quite similar to what are found from the simulation study. 
Journal: Journal of Applied Statistics 
Pages: 1383-1397 
Issue: 12 
Volume: 35 
Year: 2008 
Keywords: dispersion parameter, maximum likelihood, negative binomial model, semiparametric procedures, toxicological data, 
X-DOI: 10.1080/02664760802382459 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802382459
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:12:p:1383-1397




Template-Type: ReDIF-Article 1.0
Author-Name: P. Angelopoulos 
Author-X-Name-First: P. 
Author-X-Name-Last: Angelopoulos 
Author-Name: C. Koukouvinos 
Author-X-Name-First: C. 
Author-X-Name-Last: Koukouvinos 
Title: Some robust parameter designs from orthogonal arrays 
Abstract:
  Robust parameter design, originally proposed by Taguchi [System of
 Experimental Design, Vols. I and II, UNIPUB, New York, 1987], is an
 offline production technique for reducing variation and improving a
 product's quality by using product arrays. However, the use of the product
 arrays results in an exorbitant number of runs. To overcome this drawback,
 several scientists proposed the use of combined arrays, where the control
 and noise factors are combined in a single array. In this paper, we use
 non-isomorphic orthogonal arrays as combined arrays, in order to identify
 a model that contains all the main effects (control and noise), their
 control-by-noise interactions and their control-by-control interactions
 with high efficiency. Some cases where the control-by-control-noise are of
 interest are also considered. 
Journal: Journal of Applied Statistics 
Pages: 1399-1408 
Issue: 12 
Volume: 35 
Year: 2008 
Keywords: robust parameter design, combined array, control and noise factors, orthogonal arrays, identifiable models, validation, 
X-DOI: 10.1080/02664760802382467 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802382467
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:12:p:1399-1408




Template-Type: ReDIF-Article 1.0
Author-Name: Arnab Maity 
Author-X-Name-First: Arnab 
Author-X-Name-Last: Maity 
Author-Name: Michael Sherman 
Author-X-Name-First: Michael 
Author-X-Name-Last: Sherman 
Title: On adaptive linear regression 
Abstract:
  Ordinary least squares (OLS) is omnipresent in regression modeling.
 Occasionally, least absolute deviations (LAD) or other methods are used as
 an alternative when there are outliers. Although some data adaptive
 estimators have been proposed, they are typically difficult to implement.
 In this paper, we propose an easy to compute adaptive estimator which is
 simply a linear combination of OLS and LAD. We demonstrate large sample
 normality of our estimator and show that its performance is close to best
 for both light-tailed (e.g. normal and uniform) and heavy-tailed (e.g.
 double exponential and t3) error distributions. We demonstrate this
 through three simulation studies and illustrate our method on state public
 expenditures and lutenizing hormone data sets. We conclude that our method
 is general and easy to use, which gives good efficiency across a wide
 range of error distributions. 
Journal: Journal of Applied Statistics 
Pages: 1409-1422 
Issue: 12 
Volume: 35 
Year: 2008 
Keywords: adaptive regression, heavy-tailed error, least absolute deviation regression, mean squared error, ordinary least-squares regression, 
X-DOI: 10.1080/02664760802382475 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802382475
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:12:p:1409-1422




Template-Type: ReDIF-Article 1.0
Author-Name: Jacques Pienaar 
Author-X-Name-First: Jacques 
Author-X-Name-Last: Pienaar 
Title: BOOK REVIEW 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 1423-1424 
Issue: 12 
Volume: 35 
Year: 2008 
X-DOI: 10.1080/02664760802193328 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802193328
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:12:p:1423-1424




Template-Type: ReDIF-Article 1.0
Author-Name: Jacques Pienaar 
Author-X-Name-First: Jacques 
Author-X-Name-Last: Pienaar 
Title: BOOK REVIEW 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 1425-1426 
Issue: 12 
Volume: 35 
Year: 2008 
X-DOI: 10.1080/02664760802193336 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802193336
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:12:p:1425-1426




Template-Type: ReDIF-Article 1.0
Author-Name: Stuart Barber 
Author-X-Name-First: Stuart 
Author-X-Name-Last: Barber 
Title: BOOK REVIEW 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 1427-1428 
Issue: 12 
Volume: 35 
Year: 2008 
X-DOI: 10.1080/02664760802366742 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802366742
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:35:y:2008:i:12:p:1427-1428




Template-Type: ReDIF-Article 1.0
Author-Name: A. Mukhopadhyay 
Author-X-Name-First: A. 
Author-X-Name-Last: Mukhopadhyay 
Author-Name: A. Iqbal 
Author-X-Name-First: A. 
Author-X-Name-Last: Iqbal 
Title: Prediction of mechanical property of steel strips using multivariate adaptive regression splines 
Abstract:
  In recent times, the problem of prediction of properties of a steel strip
 has attracted enormous attention from different communities such as
 statistics, data mining, soft computing, and engineering. This is due to
 the prospective benefits of reduction in testing and inventory cost,
 increase in yield, and improvement in delivery compliance. The complexity
 of the problem arises due to its dependency on the chemical composition of
 the steel, and a number of processing parameters. To predict the
 mechanical properties of the strip (yield strength, ultimate tensile
 strength, and Elongation), a model based on multivariate adaptive
 regression spline has been developed. It is found that the prediction
 agrees well with the actual measured data. 
Journal: Journal of Applied Statistics 
Pages: 1-9 
Issue: 1 
Volume: 36 
Year: 2009 
Keywords: data mining, MARS, property prediction, soft computing, statistics, steel, 
X-DOI: 10.1080/02664760802193252 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802193252
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:1:p:1-9




Template-Type: ReDIF-Article 1.0
Author-Name: Min Kim 
Author-X-Name-First: Min 
Author-X-Name-Last: Kim 
Author-Name: Bong-Jin Yum 
Author-X-Name-First: Bong-Jin 
Author-X-Name-Last: Yum 
Title: Reliability acceptance sampling plans for the Weibull distribution under accelerated Type-I censoring 
Abstract:
  Type-I censored reliability acceptance sampling plans (RASPs) are
 developed for the Weibull lifetime distribution with unknown shape and
 scale parameters such that the producer and consumer risks are satisfied.
 It is assumed that the life test is conducted at an accelerated condition
 for which the acceleration factor (AF) is known, and each item is
 continuously monitored for failure. Sensitivity analyses are also
 conducted to assess the effect of the uncertainty in the assumed AF on the
 actual producer and consumer risks, and a method is developed for
 constructing RASPs that can accommodate the uncertainty in AF. 
Journal: Journal of Applied Statistics 
Pages: 11-20 
Issue: 1 
Volume: 36 
Year: 2009 
Keywords: reliability acceptance sampling plan, Type-I censoring, producer risk, consumer risk, acceleration factor, 
X-DOI: 10.1080/02664760802382483 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802382483
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:1:p:11-20




Template-Type: ReDIF-Article 1.0
Author-Name: Jose Dias Curto 
Author-X-Name-First: Jose Dias 
Author-X-Name-Last: Curto 
Author-Name: Jose Castro Pinto 
Author-X-Name-First: Jose Castro 
Author-X-Name-Last: Pinto 
Title: The coefficient of variation asymptotic distribution in the case of non-iid random variables 
Abstract:
  Due to the widespread use of the coefficient of variation in empirical
 finance, we derive its asymptotic sampling distribution in the case of
 non-iid random variables to deal with autocorrelation and/or conditional
 heteroskedasticity stylized facts of financial returns. We also propose
 statistical tests for the comparison of two coefficients of variation
 based on asymptotic normality and studentized time-series bootstrap. In an
 illustrative example, we analyze the monthly return volatility of six
 stock market indexes during the years 1990-2007. 
Journal: Journal of Applied Statistics 
Pages: 21-32 
Issue: 1 
Volume: 36 
Year: 2009 
Keywords: coefficient of variation, autocorrelation, conditional heteroskedasticity, non-iid random variables, 
X-DOI: 10.1080/02664760802382491 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802382491
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:1:p:21-32




Template-Type: ReDIF-Article 1.0
Author-Name: P. Angelopoulos 
Author-X-Name-First: P. 
Author-X-Name-Last: Angelopoulos 
Author-Name: H. Evangelaras 
Author-X-Name-First: H. 
Author-X-Name-Last: Evangelaras 
Author-Name: C. Koukouvinos 
Author-X-Name-First: C. 
Author-X-Name-Last: Koukouvinos 
Title: Model identification using 27 runs three level orthogonal arrays 
Abstract:
  In this paper we examine all the combinatorial non-isomorphic OA(27, q,
 3, t), with 3&#x2264;q&#x2264;13 three level quantitative factors, with
 respect to model identification, estimation capacity and efficiency. We
 use the popular D-efficiency criterion to evaluate the ability of each
 design considered in estimating the parameters of a second-order model
 with adequate efficiency. The prior selection of the 'middle' level of
 factors plays an important role in the results. 
Journal: Journal of Applied Statistics 
Pages: 33-38 
Issue: 1 
Volume: 36 
Year: 2009 
Keywords: orthogonal arrays, quantitative factors, geometric isomorphism, hidden projection properties, 
X-DOI: 10.1080/02664760802382509 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802382509
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:1:p:33-38




Template-Type: ReDIF-Article 1.0
Author-Name: Claire Weston 
Author-X-Name-First: Claire 
Author-X-Name-Last: Weston 
Author-Name: John Thompson 
Author-X-Name-First: John 
Author-X-Name-Last: Thompson 
Title: The definition of start time in cancer treatment studies analysed by non-mixture cure models 
Abstract:
  Non-mixture cure models are derived from a simplified representation of
 the biological process that takes place after treatment for cancer. These
 models are intended to represent the time from the end of treatment to the
 time of first recurrence of the cancer in studies when a proportion of
 those treated are completely cured. However, for many studies, other start
 times are more relevant. In a clinical trial, it may be more natural to
 model the time from randomisation rather than the time from the end of
 treatment and in an epidemiological study, the time from diagnosis might
 be more meaningful. Some simulations and two real studies of childhood
 cancer are presented to show that starting from time of diagnosis or
 randomisation can affect the estimates of the cure fraction. The
 susceptibility of different parametric kernels to errors caused by using
 start times other than the end of treatment is also assessed. Analysing
 failures on treatment and relapse after completing the treatment as two
 processes offers a simple way of overcoming many of these problems. 
Journal: Journal of Applied Statistics 
Pages: 39-52 
Issue: 1 
Volume: 36 
Year: 2009 
Keywords: non-mixture cure model, parametric survival, paediatric cancer, 
X-DOI: 10.1080/02664760802382517 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802382517
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:1:p:39-52




Template-Type: ReDIF-Article 1.0
Author-Name: P. Economou 
Author-X-Name-First: P. 
Author-X-Name-Last: Economou 
Author-Name: C. Caroni 
Author-X-Name-First: C. 
Author-X-Name-Last: Caroni 
Title: Fitting parametric frailty and mixture models under biased sampling 
Abstract:
  Biased sampling from an underlying distribution with p.d.f. f(t),
 t&#x3e;0, implies that observations follow the weighted distribution with
 p.d.f. fw(t)=w(t)f(t)/E[w(T)] for a known weight function w. In
 particular, the function w(t)=t&#x3b1; has important applications,
 including length-biased sampling (&#x3b1;=1) and area-biased sampling
 (&#x3b1;=2). We first consider here the maximum likelihood estimation of
 the parameters of a distribution f(t) under biased sampling from a
 censored population in a proportional hazards frailty model where a
 baseline distribution (e.g. Weibull) is mixed with a continuous frailty
 distribution (e.g. Gamma). A right-censored observation contributes a term
 proportional to w(t)S(t) to the likelihood; this is not the same as Sw(t),
 so the problem of fitting the model does not simply reduce to fitting the
 weighted distribution. We present results on the distribution of frailty
 in the weighted distribution and develop an EM algorithm for estimating
 the parameters of the model in the important Weibull-Gamma case. We also
 give results for the case where f(t) is a finite mixture distribution.
 Results are presented for uncensored data and for Type I right censoring.
 Simulation results are presented, and the methods are illustrated on a set
 of lifetime data. 
Journal: Journal of Applied Statistics 
Pages: 53-66 
Issue: 1 
Volume: 36 
Year: 2009 
Keywords: weighted distribution, biased sampling, frailty, finite mixture, Weibull distribution, Burr distribution, Type I right censoring, EM algorithm, 
X-DOI: 10.1080/02664760802382525 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802382525
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:1:p:53-66




Template-Type: ReDIF-Article 1.0
Author-Name: Myung-Hoe Huh 
Author-X-Name-First: Myung-Hoe 
Author-X-Name-Last: Huh 
Author-Name: Yong Lim 
Author-X-Name-First: Yong 
Author-X-Name-Last: Lim 
Title: Weighting variables in K-means clustering 
Abstract:
  The aim of this study is to assign weights w1, &#x2026;, wm to m
 clustering variables Z1, &#x2026;, Zm, so that k groups were uncovered to
 reveal more meaningful within-group coherence. We propose a new criterion
 to be minimized, which is the sum of the weighted within-cluster sums of
 squares and the penalty for the heterogeneity in variable weights w1,
 &#x2026;, wm. We will present the computing algorithm for such k-means
 clustering, a working procedure to determine a suitable value of penalty
 constant and numerical examples, among which one is simulated and the
 other two are real. 
Journal: Journal of Applied Statistics 
Pages: 67-78 
Issue: 1 
Volume: 36 
Year: 2009 
Keywords: K-means clustering, variable weighting, penalty constant, 
X-DOI: 10.1080/02664760802382533 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802382533
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:1:p:67-78




Template-Type: ReDIF-Article 1.0
Author-Name: Yih Su 
Author-X-Name-First: Yih 
Author-X-Name-Last: Su 
Author-Name: Jing-Shiang Hwang 
Author-X-Name-First: Jing-Shiang 
Author-X-Name-Last: Hwang 
Title: A two-phase approach to estimating time-varying parameters in the capital asset pricing model 
Abstract:
  Following the development of the economy and the diversification of
 investment, mutual funds are a popular investment tool nowadays. Choosing
 excellent targets from hundreds of mutual funds has become more and more
 crucial to investors. The capital asset pricing model (CAPM) has been
 widely used in the capital cost estimation and performance evaluation of
 mutual funds. In this study, we propose a new two-phase approach to
 estimating the time-varying parameters of CAPM. We implemented a
 simulation study to evaluate the efficiency of the proposed method and
 compared it with the commonly used state space and rolling regression
 methods. The results showed that the new method is more efficient in most
 scenarios. Meanwhile, the proposed approach is very practical and it is
 unnecessary to judge and adjust the estimating process for different
 situations. Finally, we applied the proposed method to equity mutual funds
 in the Taiwan stock market and reported the performances of two funds for
 demonstration. 
Journal: Journal of Applied Statistics 
Pages: 79-89 
Issue: 1 
Volume: 36 
Year: 2009 
Keywords: CAPM, two-phase estimation, time-varying parameter, 
X-DOI: 10.1080/02664760802443871 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802443871
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:1:p:79-89




Template-Type: ReDIF-Article 1.0
Author-Name: Jamel Jouini 
Author-X-Name-First: Jamel 
Author-X-Name-Last: Jouini 
Title: Analysis of structural break models based on the evolutionary spectrum: Monte Carlo study and application 
Abstract:
  We investigate the instability problem of the covariance structure of
 time series by combining the non-parametric approach based on the
 evolutionary spectral density theory of Priestley [Evolutionary spectra
 and non-stationary processes, J. R. Statist. Soc., 27 (1965), pp. 204-237;
 Wavelets and time-dependent spectral analysis, J. Time Ser. Anal., 17
 (1996), pp. 85-103] and the parametric approach based on linear regression
 models of Bai and Perron [Estimating and testing linear models with
 multiple structural changes, Econometrica 66 (1998), pp. 47-78]. A Monte
 Carlo study is presented to evaluate the performance of some parametric
 testing and estimation procedures for models characterized by breaks in
 variance. We attempt to see whether these procedures perform in the same
 way as models characterized by mean-shifts as investigated by Bai and
 Perron [Multiple structural change models: a simulation analysis, in:
 Econometric Theory and Practice: Frontiers of Analysis and Applied
 Research, D. Corbea, S. Durlauf, and B.E. Hansen, eds., Cambridge
 University Press, 2006, pp. 212-237]. We also provide an analysis of
 financial data series, of which the stability of the covariance function
 is doubtful. 
Journal: Journal of Applied Statistics 
Pages: 91-110 
Issue: 1 
Volume: 36 
Year: 2009 
Keywords: evolutionary spectrum, break dates, size and power, coverage rates, selection procedures, 
X-DOI: 10.1080/02664760802443889 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802443889
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:1:p:91-110




Template-Type: ReDIF-Article 1.0
Author-Name: Ulysses Brown 
Author-X-Name-First: Ulysses 
Author-X-Name-Last: Brown 
Author-Name: Stephen Knouse 
Author-X-Name-First: Stephen 
Author-X-Name-Last: Knouse 
Author-Name: James Stewart 
Author-X-Name-First: James 
Author-X-Name-Last: Stewart 
Author-Name: Ruby Beale 
Author-X-Name-First: Ruby 
Author-X-Name-Last: Beale 
Title: The relationship between unit diversity and perceptions of organizational performance in the military 
Abstract:
  Structural equation modeling techniques are used to examine the
 relationship between demographic diversity and perceptions of
 organizational performance in military units. Analyzing data from the
 Military Equal Opportunity Climate Survey reveals higher female and
 minority representation reduces females' and minorities' perceptions of
 organizational effectiveness, respectively. Identical factors appear to
 influence the perceptions of organizational performance across these two
 subgroups of employees. The results demonstrate the importance of
 conducting separate analyses for subgroups in examining the effects of
 demographic diversity on organizational performance. 
Journal: Journal of Applied Statistics 
Pages: 111-120 
Issue: 1 
Volume: 36 
Year: 2009 
Keywords: demographic diversity, military units, organizational performance, structural equation modeling, 
X-DOI: 10.1080/02664760802443905 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802443905
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:1:p:111-120




Template-Type: ReDIF-Article 1.0
Author-Name: M. Bebbington 
Author-X-Name-First: M. 
Author-X-Name-Last: Bebbington 
Author-Name: C. D. Lai 
Author-X-Name-First: C. D. 
Author-X-Name-Last: Lai 
Author-Name: R. Zitikis 
Author-X-Name-First: R. 
Author-X-Name-Last: Zitikis 
Title: Modeling lactation curves: classical parametric models re-examined and modified 
Abstract:
  A large number of methods for modeling lactation curves have been
 proposed - parametric and nonparametric, mathematically or biologically
 oriented. The most popular of these are methods that express the milk
 yield in terms of time via a parametric nonlinear functional equation.
 This is intuitive and allows for relatively easy mathematical and
 biological interpretations of the parameters involved. Interestingly, as
 far as we are aware, all such models generate nonzero milk yields on the
 whole positive time half-line, even though real lactation curves always
 have finite range, with spans of approximately 300 days for dairy cows.
 For this reason, we re-examine a number of existing parametric models, and
 modify them to produce finite-range lactation curves that fit remarkably
 well to data of milk yields from New Zealand cows. The use of daily or
 weekly yields rather than the monthly yields normally considered reveals
 considerable variation that is usually suppressed. Both individual and
 herd lactation curves are examined in the present paper, and median-based
 procedures explored as alternatives to the usual average-based methods.
 These suggestions offer further insights into the existing literature on
 modeling lactation curves. 
Journal: Journal of Applied Statistics 
Pages: 121-133 
Issue: 2 
Volume: 36 
Year: 2009 
Keywords: lactation curve, parametric function, wood curve, finite-range modification, 
X-DOI: 10.1080/02664760802443897 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802443897
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:2:p:121-133




Template-Type: ReDIF-Article 1.0
Author-Name: Shey-Huei Sheu 
Author-X-Name-First: Shey-Huei 
Author-X-Name-Last: Sheu 
Author-Name: Yu-Tai Hsieh 
Author-X-Name-First: Yu-Tai 
Author-X-Name-Last: Hsieh 
Title: The extended GWMA control chart 
Abstract:
  This study extends the generally weighted moving average (GWMA) control
 chart by imitating the double exponentially weighted moving average
 (DEWMA) technique. The proposed chart is called the double generally
 weighted moving average (DGWMA) control chart. Simulation is employed to
 evaluate the average run length characteristics of the GWMA, DEWMA and
 DGWMA control charts. An extensive comparison of these control charts
 reveals that the DGWMA control chart with time-varying control limits is
 more sensitive than the GWMA and the DEWMA control charts for detecting
 medium shifts in the mean of a process when the shifts are between 0.5 and
 1.5 standard deviations. Additionally, the GWMA control chart performs
 better when the mean shifts are below the 0.5 standard deviation, and the
 DEWMA control performs better when the mean shifts are above the 1.5
 standard deviation. The design of the DGWMA control chart is also
 discussed. 
Journal: Journal of Applied Statistics 
Pages: 135-147 
Issue: 2 
Volume: 36 
Year: 2009 
Keywords: GWMA control chart, DGWMA control chart, DEWMA control chart, average run length, time-varying control limits, 
X-DOI: 10.1080/02664760802443913 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802443913
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:2:p:135-147




Template-Type: ReDIF-Article 1.0
Author-Name: J. M. Vilar 
Author-X-Name-First: J. M. 
Author-X-Name-Last: Vilar 
Author-Name: R. Cao 
Author-X-Name-First: R. 
Author-X-Name-Last: Cao 
Author-Name: M. C. Ausin 
Author-X-Name-First: M. C. 
Author-X-Name-Last: Ausin 
Author-Name: C. Gonzalez-Fragueiro 
Author-X-Name-First: C. 
Author-X-Name-Last: Gonzalez-Fragueiro 
Title: Nonparametric analysis of aggregate loss models 
Abstract:
  This paper describes a nonparametric approach to make inferences for
 aggregate loss models in the insurance framework. We assume that an
 insurance company provides a historical sample of claims given by claim
 occurrence times and claim sizes. Furthermore, information may be
 incomplete as claims may be censored and/or truncated. In this context,
 the main goal of this work consists of fitting a probability model for the
 total amount that will be paid on all claims during a fixed future time
 period. In order to solve this prediction problem, we propose a new
 methodology based on nonparametric estimators for the density functions
 with censored and truncated data, the use of Monte Carlo simulation
 methods and bootstrap resampling. The developed methodology is useful to
 compare alternative pricing strategies in different insurance decision
 problems. The proposed procedure is illustrated with a real dataset
 provided by the insurance department of an international commercial
 company. 
Journal: Journal of Applied Statistics 
Pages: 149-166 
Issue: 2 
Volume: 36 
Year: 2009 
Keywords: aggregate loss models, kernel estimator, Monte Carlo method, bootstrap, censored and truncated claims, 
X-DOI: 10.1080/02664760802443921 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802443921
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:2:p:149-166




Template-Type: ReDIF-Article 1.0
Author-Name: S. S. K. Haputhantri 
Author-X-Name-First: S. S. K. 
Author-X-Name-Last: Haputhantri 
Author-Name: J. Moreau 
Author-X-Name-First: J. 
Author-X-Name-Last: Moreau 
Author-Name: S. Lek 
Author-X-Name-First: S. 
Author-X-Name-Last: Lek 
Title: Exploring gillnet catch efficiency of sardines in the coastal waters of Sri Lanka by means of three statistical techniques: a comparison of linear and nonlinear modelling techniques 
Abstract:
  The present investigation was undertaken to study the gillnet catch
 efficiency of sardines in the coastal waters of Sri Lanka using commercial
 catch and effort data. Commercial catch and effort data of small mesh
 gillnet fishery were collected in five fisheries districts during the
 period May 1999-August 2002. Gillnet catch efficiency of sardines was
 investigated by developing catch rates predictive models using data on
 commercial fisheries and environmental variables. Three statistical
 techniques [multiple linear regression, generalized additive model and
 regression tree model (RTM)] were employed to predict the catch rates of
 trenched sardine Amblygaster sirm (key target species of small mesh
 gillnet fishery) and other sardines (Sardinella longiceps, S. gibbosa, S.
 albella and S. sindensis). The data collection programme was conducted for
 another six months and the models were tested on new data. RTMs were found
 to be the strongest in terms of reliability and accuracy of the
 predictions. The two operational characteristics used here for model
 formulation (i.e. depth of fishing and number of gillnet pieces used per
 fishing operation) were more useful as predictor variables than the
 environmental variables. The study revealed a rapid tendency of increasing
 the catch rates of A. sirm with increased sea depth up to around
 32&#xa0;m. 
Journal: Journal of Applied Statistics 
Pages: 167-179 
Issue: 2 
Volume: 36 
Year: 2009 
Keywords: fisheries, modelling, multiple linear regression, generalized additive models, regression tree models, 
X-DOI: 10.1080/02664760802443939 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802443939
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:2:p:167-179




Template-Type: ReDIF-Article 1.0
Author-Name: Giovanni Celano 
Author-X-Name-First: Giovanni 
Author-X-Name-Last: Celano 
Title: Robust design of adaptive control charts for manual manufacturing/inspection workstations 
Abstract:
  Often the manufacturing and the inspection workstations in a
 manufacturing process can coincide: thus, in these workstations the
 statistical process control (SPC) procedure of collecting sample
 statistics related to a critical-to-quality parameter is a task required
 to be done by the same worker who has to complete the working operations
 on a part. The aim of this study is to design a local SPC inspection
 procedure implementing an adaptive Shewhart control chart locally managed
 by the worker within the manufacturing workstation: the economic design of
 the inspection procedure is constrained by the expected number of false
 alarms issued and is restricted to those designs feasible with respect to
 the available shared labour resource. Furthermore, a robust approach that
 models the shift of the controlled parameter mean as a random variable is
 taken into account. The numerical analysis allows the most influencing
 environmental process factors to be captured and commented upon. The
 obtained results show that a few process operating parameters drive the
 choice of performing a robust optimization and the selection of the
 optimal SPC adaptive procedure. 
Journal: Journal of Applied Statistics 
Pages: 181-203 
Issue: 2 
Volume: 36 
Year: 2009 
Keywords: statistical process control, control chart, economic design, robust optimization, labour resource, 
X-DOI: 10.1080/02664760802443947 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802443947
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:2:p:181-203




Template-Type: ReDIF-Article 1.0
Author-Name: Malin Albing 
Author-X-Name-First: Malin 
Author-X-Name-Last: Albing 
Author-Name: Kerstin Vannman 
Author-X-Name-First: Kerstin 
Author-X-Name-Last: Vannman 
Title: Skewed zero-bound distributions and process capability indices for upper specifications 
Abstract:
  A common practical situation in process capability analysis, which is not
 well developed theoretically, is when the quality characteristic of
 interest has a skewed distribution with a long tail towards relatively
 large values and an upper specification limit only exists. In such
 situations, it is not uncommon that the smallest possible value of the
 characteristic is 0 and this is also the best value to obtain. Hence a
 target value 0 is assumed to exist. We investigate a new class of process
 capability indices for this situation. Two estimators of the proposed
 index are studied and the asymptotic distributions of these estimators are
 derived. Furthermore, we suggest a decision procedure useful when drawing
 conclusions about the capability at a given significance level, based on
 the estimated indices and their asymptotic distributions. A simulation
 study is also performed, assuming that the quality characteristic is
 Weibull-distributed, to investigate the true significance level when the
 sample size is finite. 
Journal: Journal of Applied Statistics 
Pages: 205-221 
Issue: 2 
Volume: 36 
Year: 2009 
Keywords: capability index, skewed distributions, one-sided specification interval, upper specification limit, zero-bound process data, target value 0, hypothesis testing, Weibull distribution, 
X-DOI: 10.1080/02664760802443954 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802443954
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:2:p:205-221




Template-Type: ReDIF-Article 1.0
Author-Name: S. Cabras 
Author-X-Name-First: S. 
Author-X-Name-Last: Cabras 
Author-Name: M. E. Castellanos 
Author-X-Name-First: M. E. 
Author-X-Name-Last: Castellanos 
Title: Default Bayesian goodness-of-fit tests for the skew-normal model 
Abstract:
  In this paper we propose a series of goodness-of-fit tests for the family
 of skew-normal models when all parameters are unknown. As the null
 distributions of the considered test statistics depend only on asymmetry
 parameter, we used a default and proper prior on skewness parameter
 leading to the prior predictive p-value advocated by G. Box.
 Goodness-of-fit tests, here proposed, depend only on sample size and
 exhibit full agreement between nominal and actual size. They also have
 good power against local alternative models which also account for
 asymmetry in the data. 
Journal: Journal of Applied Statistics 
Pages: 223-232 
Issue: 2 
Volume: 36 
Year: 2009 
Keywords: EDF test, model checking, prior predictive distribution, power, p-values, size of test, 
X-DOI: 10.1080/02664760802443988 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802443988
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:2:p:223-232




Template-Type: ReDIF-Article 1.0
Author-Name: Weiqi Luo 
Author-X-Name-First: Weiqi 
Author-X-Name-Last: Luo 
Title: Analysing ecological data 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 233-234 
Issue: 2 
Volume: 36 
Year: 2009 
X-DOI: 10.1080/02664760802340267 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802340267
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:2:p:233-234




Template-Type: ReDIF-Article 1.0
Author-Name: Stuart Barber 
Author-X-Name-First: Stuart 
Author-X-Name-Last: Barber 
Title: Bioinformatics 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 235-236 
Issue: 2 
Volume: 36 
Year: 2009 
X-DOI: 10.1080/02664760802340275 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802340275
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:2:p:235-236




Template-Type: ReDIF-Article 1.0
Author-Name: John Kornak 
Author-X-Name-First: John 
Author-X-Name-Last: Kornak 
Author-Name: Bruce Dunham 
Author-X-Name-First: Bruce 
Author-X-Name-Last: Dunham 
Author-Name: Deborah Hall 
Author-X-Name-First: Deborah 
Author-X-Name-Last: Hall 
Author-Name: Mark Haggard 
Author-X-Name-First: Mark 
Author-X-Name-Last: Haggard 
Title: Nonlinear voxel-based modelling of the haemodynamic response in fMRI 
Abstract:
  A common assumption for data analysis in functional magnetic resonance
 imaging is that the response signal can be modelled as the convolution of
 a haemodynamic response (HDR) kernel with a stimulus reference function.
 Early approaches modelled spatially constant HDR kernels, but more
 recently spatially varying models have been proposed. However, convolution
 limits the flexibility of these models and their ability to capture
 spatial variation. Here, a range of (nonlinear) parametric curves are
 fitted by least squares minimisation directly to individual voxel HDRs
 (i.e., without using convolution). A 'constrained gamma curve' is proposed
 as an efficient form for fitting the HDR at each individual voxel. This
 curve allows for spatial variation in the delay of the HDR, but places a
 global constraint on the temporal spread. The approach of directly fitting
 individual parameters of HDR shape is demonstrated to lead to an improved
 fit of response estimates. 
Journal: Journal of Applied Statistics 
Pages: 237-253 
Issue: 3 
Volume: 36 
Year: 2009 
Keywords: constrained gamma curve, haemodynamic response function, functional magnetic resonance imaging, least squares estimation, nonlinear curve fitting, polynomial curve fitting, 
X-DOI: 10.1080/02664760802443962 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802443962
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:3:p:237-253




Template-Type: ReDIF-Article 1.0
Author-Name: Wen-Chih Chiu 
Author-X-Name-First: Wen-Chih 
Author-X-Name-Last: Chiu 
Title: Generally weighted moving average control charts with fast initial response features 
Abstract:
  The generally weighted moving average (GWMA) control chart is an
 extension model of exponentially weighted moving average (EWMA) control
 chart. Recently, some approaches have been proposed to modify EWMA charts
 with fast initial response (FIR) features. We introduce these approaches
 in GWMA-type charts. Via simulation, various control schemes are designed
 and then their average run lengths are computed and compared. Based on the
 overall performance, it is showed that the DGWMA chart is the best choice
 especially when the shift is moderate, and the GWMA charts provided with
 additional FIR feature have a good performance only in detecting large
 shifts during the initial stage. 
Journal: Journal of Applied Statistics 
Pages: 255-275 
Issue: 3 
Volume: 36 
Year: 2009 
Keywords: EWMA, GWMA, control charts, average run length, fast initial response, 
X-DOI: 10.1080/02664760802443970 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802443970
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:3:p:255-275




Template-Type: ReDIF-Article 1.0
Author-Name: Saralees Nadarajah 
Author-X-Name-First: Saralees 
Author-X-Name-Last: Nadarajah 
Title: A bivariate distribution with gamma and beta marginals with application to drought data 
Abstract:
  The first known bivariate distribution with gamma and beta marginals is
 introduced. Various representations are derived for its joint probability
 density function (pdf), joint cumulative distribution function (cdf),
 product moments, conditional pdfs, conditional cdfs, conditional moments,
 joint moment generating function, joint characteristic function and
 entropies. The method of maximum likelihood and the method of moments are
 used to derive the associated estimation procedures as well as the Fisher
 information matrix, variance-covariance matrix and the profile likelihood
 confidence intervals. An application to drought data from Nebraska is
 provided. Some other applications are also discussed. Finally, an
 extension of the bivariate distribution to the multivariate case is
 proposed. 
Journal: Journal of Applied Statistics 
Pages: 277-301 
Issue: 3 
Volume: 36 
Year: 2009 
Keywords: beta distribution, drought modeling, gamma distribution, 
X-DOI: 10.1080/02664760802443996 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802443996
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:3:p:277-301




Template-Type: ReDIF-Article 1.0
Author-Name: Li Wang 
Author-X-Name-First: Li 
Author-X-Name-Last: Wang 
Author-Name: Scott Kowalski 
Author-X-Name-First: Scott 
Author-X-Name-Last: Kowalski 
Author-Name: G. Geoffrey Vining 
Author-X-Name-First: G. Geoffrey 
Author-X-Name-Last: Vining 
Title: Orthogonal blocking of response surface split-plot designs 
Abstract:
  When all experimental runs cannot be performed under homogeneous
 conditions, blocking can be used to increase the power for testing the
 treatment effects. Orthogonal blocking provides the same estimator of the
 polynomial effects as the one that would be obtained by ignoring the
 blocks. In many real-life design scenarios, there is at least one factor
 that is hard to change, leading to a split-plot structure. This paper
 shows that for a balanced ordinary least square-generalized least square
 equivalent split-plot design, orthogonal blocking can be achieved.
 Orthogonally blocked split-plot central composite designs are constructed
 and a catalog is provided. 
Journal: Journal of Applied Statistics 
Pages: 303-321 
Issue: 3 
Volume: 36 
Year: 2009 
Keywords: central composite designs, orthogonal blocking, design of experiments, split-plot experiments, 
X-DOI: 10.1080/02664760802444002 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802444002
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:3:p:303-321




Template-Type: ReDIF-Article 1.0
Author-Name: Joao Santos 
Author-X-Name-First: Joao 
Author-X-Name-Last: Santos 
Author-Name: Solange Leite 
Author-X-Name-First: Solange 
Author-X-Name-Last: Leite 
Title: Long-term variability of the temperature time series recorded in Lisbon 
Abstract:
  As a case study for application in climate change studies, daily air
 temperature records in Lisbon are analysed by applying advanced
 statistical methodologies that take into account the dynamic nature of
 climate. A trend analysis based on two non-parametric tests (Spearman and
 Mann-Kendall) revealed the presence of statistically significant upward
 trends in the maximum temperatures, mainly during March. The minimum
 temperatures do not present significant trends, with the exception of
 March where a relatively weak positive trend is detected. A singular
 spectral analysis combined with a maximum entropy spectral analysis
 enables the detection of regularities in the annual mean time series of
 the maximum and minimum temperatures. A quasi-periodic oscillation with a
 peak period of about 50 years is superimposed in the linear trends. At the
 maximum temperature, a secondary oscillation with a peak period of nearly
 20 years is also identified. No other regularities are isolated in these
 time series. The study is enhanced by applying an extreme value analysis
 to the extreme winter and summer temperatures. The generalized extreme
 value distribution family is shown to provide high-quality adjustments to
 the distributions, and a description of the temperatures related to
 different return periods and risks is given. 
Journal: Journal of Applied Statistics 
Pages: 323-337 
Issue: 3 
Volume: 36 
Year: 2009 
Keywords: climate change, trends, extremes, oscillations, air temperature, Portugal, 
X-DOI: 10.1080/02664760802449159 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802449159
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:3:p:323-337




Template-Type: ReDIF-Article 1.0
Author-Name: Philip Hans Franses 
Author-X-Name-First: Philip Hans 
Author-X-Name-Last: Franses 
Author-Name: Bert de Groot 
Author-X-Name-First: Bert 
Author-X-Name-Last: de Groot 
Author-Name: Rianne Legerstee 
Author-X-Name-First: Rianne 
Author-X-Name-Last: Legerstee 
Title: Testing for harmonic regressors 
Abstract:
  This paper reports on the Wald test for &#x3b1;1=&#x3b1;2=0 in the
 regression model [image omitted]&#xa0; where &#x3ba; is estimated using
 nonlinear least squares. As this situation is not standard we provide
 critical values for further use. An illustration to quarterly GDP in the
 Netherlands is given. A power study shows that choosing inappropriate
 starting values for &#x3ba; leads to a quick loss of power. 
Journal: Journal of Applied Statistics 
Pages: 339-346 
Issue: 3 
Volume: 36 
Year: 2009 
Keywords: harmonic regressors, critical values, 
X-DOI: 10.1080/02664760802454837 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802454837
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:3:p:339-346




Template-Type: ReDIF-Article 1.0
Author-Name: A.H.M. Rahmatullah Imon 
Author-X-Name-First: A.H.M. 
Author-X-Name-Last: Rahmatullah Imon 
Title: Deletion residuals in the detection of heterogeneity of variances in linear regression 
Abstract:
  The heterogeneity of error variance often causes a huge interpretive
 problem in linear regression analysis. Before taking any remedial measures
 we first need to detect this problem. A large number of diagnostic plots
 are now available in the literature for detecting heteroscedasticity of
 error variances. Among them the 'residuals' and 'fits' (R-F) plot is very
 popular and commonly used. In the R-F plot residuals are plotted against
 the fitted responses, where both these components are obtained using the
 ordinary least squares (OLS) method. It is now evident that the OLS fits
 and residuals suffer a huge setback in the presence of unusual
 observations and hence the R-F plot may not exhibit the real scenario. The
 deletion residuals based on a data set free from all unusual cases should
 estimate the true errors in a better way than the OLS residuals. In this
 paper we propose 'deletion residuals' and the 'deletion fits' (DR-DF) plot
 for the detection of the heterogeneity of error variances in a linear
 regression model to get a more convincing and reliable graphical display.
 Examples show that this plot locates unusual observations more clearly
 than the R-F plot. The advantage of using deletion residuals in the
 detection of heteroscedasticity of error variance is investigated through
 Monte Carlo simulations under a variety of situations. 
Journal: Journal of Applied Statistics 
Pages: 347-358 
Issue: 3 
Volume: 36 
Year: 2009 
Keywords: R-F plot, unusual observations, deletion residuals, robust regression, DR-DF plot, 
X-DOI: 10.1080/02664760802466237 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802466237
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:3:p:347-358




Template-Type: ReDIF-Article 1.0
Author-Name: Pablo Martinez-Camblor 
Author-X-Name-First: Pablo 
Author-X-Name-Last: Martinez-Camblor 
Author-Name: Aina Yanez 
Author-X-Name-First: Aina 
Author-X-Name-Last: Yanez 
Title: Testing the equality of diagnostic effectiveness of one measure with respect to k different features 
Abstract:
  In several cases the same measurement is used as a marker for two or more
 population features, and it is useful to test whether this measurement has
 the same diagnostic effectiveness with respect to different features. In
 this paper we use the area under receiver operating characteristic curve
 as index for the discriminatory power among continuous variables and
 population features (eventuality, two or more diseases), and we propose a
 test to contrast the equality of the diagnostic effectiveness of this
 measurement. 
Journal: Journal of Applied Statistics 
Pages: 359-367 
Issue: 4 
Volume: 36 
Year: 2009 
Keywords: ROC curve, sensitivity, specificity, area under ROC curve (AUC), 
X-DOI: 10.1080/02664760802464471 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802464471
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:4:p:359-367




Template-Type: ReDIF-Article 1.0
Author-Name: E. Cankaya 
Author-X-Name-First: E. 
Author-X-Name-Last: Cankaya 
Author-Name: N. R. J. Fieller 
Author-X-Name-First: N. R. J. 
Author-X-Name-Last: Fieller 
Title: Quantal models: a review with additional methodological development 
Abstract:
  Analysis of quantal models is a particular aspect of the general problem
 of investigating multimodality. The distinction is that the spacings
 between modes are integral multiples of some unspecified fundamental unit
 and that the number of modes is not defined. Such semi-structured models
 arise in a wide variety of contexts such as biology, cosmology,
 archaeology and molecular physics. This paper presents a brief review of
 their historical development in such areas as an aid to their recognition
 in other contexts as well as giving guidance to their analysis from the
 statistical viewpoint. The available methodology for their analysis is
 collated into a coherent and self-contained account, establishing various
 optimality properties under particular parametric distributional
 assumptions. An illustrative power study shows how dependence on sample
 size and failure of assumptions such as underlying distribution, origin of
 measurements and independence affect the power of various analyses. These
 aspects are illustrated by an example from developmental biology. 
Journal: Journal of Applied Statistics 
Pages: 369-384 
Issue: 4 
Volume: 36 
Year: 2009 
Keywords: cosine quantogram, megalithic yard, quantal model, multimodality, power, 
X-DOI: 10.1080/02664760802466195 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802466195
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:4:p:369-384




Template-Type: ReDIF-Article 1.0
Author-Name: L. Bettendorf 
Author-X-Name-First: L. 
Author-X-Name-Last: Bettendorf 
Author-Name: S. A. van der Geest 
Author-X-Name-First: S. A. 
Author-X-Name-Last: van der Geest 
Author-Name: G. H. Kuper 
Author-X-Name-First: G. H. 
Author-X-Name-Last: Kuper 
Title: Do daily retail gasoline prices adjust asymmetrically? 
Abstract:
  This paper analyses adjustments in the Dutch retail gasoline prices. We
 estimate an error correction model on changes in the daily retail price
 for gasoline (taxes excluded) for the period 1996-2004, taking care of
 volatility clustering by estimating an EGARCH model. It turns out that the
 volatility process is asymmetrical: a positive shock to the retail price
 has a greater effect on the variance of the retail price than a negative
 shock. We conclude that the retail price and the spot price do not drift
 apart in the long run. However, there is a faster reaction to upward
 changes in spot prices than to downward changes in spot prices in the
 short run. This asymmetry starts 3 days after the change in the spot price
 and lasts for 4 days. 
Journal: Journal of Applied Statistics 
Pages: 385-397 
Issue: 4 
Volume: 36 
Year: 2009 
Keywords: asymmetry, retail gasoline prices, volatility, 
X-DOI: 10.1080/02664760802466468 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802466468
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:4:p:385-397




Template-Type: ReDIF-Article 1.0
Author-Name: Christian Weiss 
Author-X-Name-First: Christian 
Author-X-Name-Last: Weiss 
Title: Monitoring correlated processes with binomial marginals 
Abstract:
  Few approaches for monitoring autocorrelated attribute data have been
 proposed in the literature. If the marginal process distribution is
 binomial, then the binomial AR(1) model as a realistic and
 well-interpretable process model may be adequate. Based on known and newly
 derived statistical properties of this model, we shall develop approaches
 to monitor a binomial AR(1) process, and investigate their performance in
 a simulation study. A case study demonstrates the applicability of the
 binomial AR(1) model and of the proposed control charts to problems from
 statistical process control. 
Journal: Journal of Applied Statistics 
Pages: 399-414 
Issue: 4 
Volume: 36 
Year: 2009 
Keywords: binomial AR(1) models, statistical process control, control charts, case study, 
X-DOI: 10.1080/02664760802468803 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802468803
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:4:p:399-414




Template-Type: ReDIF-Article 1.0
Author-Name: S. H. Lin 
Author-X-Name-First: S. H. 
Author-X-Name-Last: Lin 
Author-Name: R. S. Wang 
Author-X-Name-First: R. S. 
Author-X-Name-Last: Wang 
Title: Inferences on a linear combination of K multivariate normal mean vectors 
Abstract:
  In this paper, the hypothesis testing and confidence region construction
 for a linear combination of mean vectors for K independent multivariate
 normal populations are considered. A new generalized pivotal quantity and
 a new generalized test variable are derived based on the concepts of
 generalized p-values and generalized confidence regions. When only two
 populations are considered, our results are equivalent to those proposed
 by Gamage et al. [Generalized p-values and confidence regions for the
 multivariate Behrens-Fisher problem and MANOVA, J. Multivariate Aanal. 88
 (2004), pp. 117-189] in the bivariate case, which is also known as the
 bivariate Behrens-Fisher problem. However, in some higher dimension cases,
 these two results are quite different. The generalized confidence region
 is illustrated with two numerical examples and the merits of the proposed
 method are numerically compared with those of the existing methods with
 respect to their expected areas, coverage probabilities under different
 scenarios. 
Journal: Journal of Applied Statistics 
Pages: 415-428 
Issue: 4 
Volume: 36 
Year: 2009 
Keywords: coverage probability, generalized confidence region, generalized pivotal quantity, generalized test variable, heteroscedasticity, type I error, 
X-DOI: 10.1080/02664760802474231 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802474231
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:4:p:415-428




Template-Type: ReDIF-Article 1.0
Author-Name: Rahim Mahmoudvand 
Author-X-Name-First: Rahim 
Author-X-Name-Last: Mahmoudvand 
Author-Name: Hossein Hassani 
Author-X-Name-First: Hossein 
Author-X-Name-Last: Hassani 
Title: Two new confidence intervals for the coefficient of variation in a normal distribution 
Abstract:
  In this article we introduce an approximately unbiased estimator for the
 population coefficient of variation, &#x3c4;, in a normal distribution.
 The accuracy of this estimator is examined by several criteria. Using this
 estimator and its variance, two approximate confidence intervals for
 &#x3c4; are introduced. The performance of the new confidence intervals is
 compared to those obtained by current methods. 
Journal: Journal of Applied Statistics 
Pages: 429-442 
Issue: 4 
Volume: 36 
Year: 2009 
Keywords: coefficient of variation, confidence interval, normal distribution, 
X-DOI: 10.1080/02664760802474249 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802474249
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:4:p:429-442




Template-Type: ReDIF-Article 1.0
Author-Name: Vitor Ozaki 
Author-X-Name-First: Vitor 
Author-X-Name-Last: Ozaki 
Author-Name: Ralph Silva 
Author-X-Name-First: Ralph 
Author-X-Name-Last: Silva 
Title: Bayesian ratemaking procedure of crop insurance contracts with skewed distribution 
Abstract:
  Over the years, crop insurance programs became the focus of agricultural
 policy in the USA, Spain, Mexico, and more recently in Brazil. Given the
 increasing interest in insurance, accurate calculation of the premium rate
 is of great importance. We address the crop-yield distribution issue and
 its implications in pricing an insurance contract considering the dynamic
 structure of the data and incorporating the spatial correlation in the
 Hierarchical Bayesian framework. Results show that empirical (insurers)
 rates are higher in low risk areas and lower in high risk areas. Such
 methodological improvement is primarily important in situations of limited
 data. 
Journal: Journal of Applied Statistics 
Pages: 443-452 
Issue: 4 
Volume: 36 
Year: 2009 
Keywords: crop insurance, Bayesian hierarchical model, premium rate, skew-normal distribution, spatial correlation, 
X-DOI: 10.1080/02664760802474256 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802474256
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:4:p:443-452




Template-Type: ReDIF-Article 1.0
Author-Name: Qiwei Liang 
Author-X-Name-First: Qiwei 
Author-X-Name-Last: Liang 
Author-Name: Huajiang Li 
Author-X-Name-First: Huajiang 
Author-X-Name-Last: Li 
Author-Name: Paul Mendes 
Author-X-Name-First: Paul 
Author-X-Name-Last: Mendes 
Author-Name: Hans Roethig 
Author-X-Name-First: Hans 
Author-X-Name-Last: Roethig 
Author-Name: Kim Frost-Pineda 
Author-X-Name-First: Kim 
Author-X-Name-Last: Frost-Pineda 
Title: Using bootstrap method to evaluate the estimates of nicotine equivalents from linear mixed model and generalized estimating equation 
Abstract:
  Twenty-four-hour urinary excretion of nicotine equivalents, a biomarker
 for exposure to cigarette smoke, has been widely used in biomedical
 studies in recent years. Its accurate estimate is important for examining
 human exposure to tobacco smoke. The objective of this article is to
 compare the bootstrap confidence intervals of nicotine equivalents with
 the standard confidence intervals derived from linear mixed model (LMM)
 and generalized estimation equation. We use percentile bootstrap method
 because it has practical value for real-life application and it works well
 with nicotine data. To preserve the within-subject correlation of nicotine
 equivalents between repeated measures, we bootstrap the repeated measures
 of each subject as a vector. The results indicate that the bootstrapped
 estimates in most cases give better estimates than the LMM and generalized
 estimation equation without bootstrap. 
Journal: Journal of Applied Statistics 
Pages: 453-463 
Issue: 4 
Volume: 36 
Year: 2009 
Keywords: bootstrap estimates, linear mixed models, generalized estimation equations, nicotine equivalents, 
X-DOI: 10.1080/02664760802638074 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802638074
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:4:p:453-463




Template-Type: ReDIF-Article 1.0
Author-Name: Z.Q. John Lu 
Author-X-Name-First: Z.Q. 
Author-X-Name-Last: John Lu 
Title: Bayesian biostatistics and diagnostic medicine 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 465-466 
Issue: 4 
Volume: 36 
Year: 2009 
X-DOI: 10.1080/02664760802340283 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802340283
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:4:p:465-466




Template-Type: ReDIF-Article 1.0
Author-Name: Mukesh Srivastava 
Author-X-Name-First: Mukesh 
Author-X-Name-Last: Srivastava 
Author-Name: M. Abbas 
Author-X-Name-First: M. 
Author-X-Name-Last: Abbas 
Title: Topics in biostatistics 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 467-468 
Issue: 4 
Volume: 36 
Year: 2009 
X-DOI: 10.1080/02664760802340325 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802340325
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:4:p:467-468




Template-Type: ReDIF-Article 1.0
Author-Name: Steff Lewis 
Author-X-Name-First: Steff 
Author-X-Name-Last: Lewis 
Title: Sample size calculations in clinical research 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 469-469 
Issue: 4 
Volume: 36 
Year: 2009 
X-DOI: 10.1080/02664760802366775 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802366775
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:4:p:469-469




Template-Type: ReDIF-Article 1.0
Author-Name: Elisabeth Deviere 
Author-X-Name-First: Elisabeth 
Author-X-Name-Last: Deviere 
Title: Analyzing linguistic data: a practical introduction to statistics using R 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 471-472 
Issue: 4 
Volume: 36 
Year: 2009 
X-DOI: 10.1080/02664760802366783 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802366783
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:4:p:471-472




Template-Type: ReDIF-Article 1.0
Author-Name: Seung-Hwan Lee 
Author-X-Name-First: Seung-Hwan 
Author-X-Name-Last: Lee 
Title: Some estimators and tests for accelerated hazards model using weighted cumulative hazard difference 
Abstract:
  For a censored two-sample problem, Chen and Wang [Y.Q. Chen and M.-C.
 Wang, Analysis of accelerated hazards models, J. Am. Statist. Assoc. 95
 (2000), pp. 608-618] introduced the accelerated hazards model. The
 scale-change parameter in this model characterizes the association of two
 groups. However, its estimator involves the unknown density in the
 asymptotic variance. Thus, to make an inference on the parameter,
 numerically intensive methods are needed. The goal of this article is to
 propose a simple estimation method in which estimators are asymptotically
 normal with a density-free asymptotic variance. Some lack-of-fit tests are
 also obtained from this. These tests are related to Gill-Schumacher type
 tests [R.D. Gill and M. Schumacher, A simple test of the proportional
 hazards assumption, Biometrika 74 (1987), pp. 289-300] in which the
 estimating functions are evaluated at two different weight functions
 yielding two estimators that are close to each other. Numerical studies
 show that for some weight functions, the estimators and tests perform
 well. The proposed procedures are illustrated in two applications. 
Journal: Journal of Applied Statistics 
Pages: 473-482 
Issue: 5 
Volume: 36 
Year: 2009 
Keywords: accelerated hazards model, two-sample censored data, estimation, lack-of-fit tests, 
X-DOI: 10.1080/02664760802474264 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802474264
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:5:p:473-482




Template-Type: ReDIF-Article 1.0
Author-Name: Lei Shi 
Author-X-Name-First: Lei 
Author-X-Name-Last: Shi 
Author-Name: Hongyuan Sun 
Author-X-Name-First: Hongyuan 
Author-X-Name-Last: Sun 
Author-Name: Peng Bai 
Author-X-Name-First: Peng 
Author-X-Name-Last: Bai 
Title: Bayesian confidence interval for difference of the proportions in a 2&#xd7;2 table with structural zero 
Abstract:
  This article studies the construction of a Bayesian confidence interval
 for risk difference in a 2&#xd7;2 table with structural zero. The exact
 posterior distribution of the risk difference is derived under the
 Dirichlet prior distribution, and a tail-based interval is used to
 construct the Bayesian confidence interval. The frequentist performance of
 the tail-based interval is investigated and compared with the score-based
 interval by simulation. Our results show that the tail-based interval at
 Jeffreys prior performs as well as or better than the score-based
 confidence interval. 
Journal: Journal of Applied Statistics 
Pages: 483-494 
Issue: 5 
Volume: 36 
Year: 2009 
Keywords: 2&#xd7;2 table with structural zero, risk difference, Bayesian analysis, Dirichlet prior, confidence interval, 
X-DOI: 10.1080/02664760802474272 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802474272
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:5:p:483-494




Template-Type: ReDIF-Article 1.0
Author-Name: J. B. Shah 
Author-X-Name-First: J. B. 
Author-X-Name-Last: Shah 
Author-Name: M. N. Patel 
Author-X-Name-First: M. N. 
Author-X-Name-Last: Patel 
Title: Bayesian estimation of parameters of mixed geometric failure models from Type I group censored sample 
Abstract:
  The estimation problem of the parameters of a mixed geometric lifetime
 model, using Bayesian approach and Type I group censored sample, will be
 investigated in the case of two subpopulations. The Bayes estimates are
 derived for squared error, minimum expected, general entropy and linex
 loss functions under informative and diffuse priors. A practical example
 given by Nelson (W.B. Nelson, Hazard plotting methods for analysis of the
 life data with different failure models, J. Qual. Technol. 2 (1970), pp.
 126-149) is considered. A simulation study is carried out along with risk. 
Journal: Journal of Applied Statistics 
Pages: 495-506 
Issue: 5 
Volume: 36 
Year: 2009 
Keywords: Bayes estimator, geometric model, loss functions, mixture distribution, risk function, simulation, Type I group censoring, 
X-DOI: 10.1080/02664760802553422 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802553422
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:5:p:495-506




Template-Type: ReDIF-Article 1.0
Author-Name: M. Habshah 
Author-X-Name-First: M. 
Author-X-Name-Last: Habshah 
Author-Name: M. R. Norazan 
Author-X-Name-First: M. R. 
Author-X-Name-Last: Norazan 
Author-Name: A.H.M. Rahmatullah Imon 
Author-X-Name-First: A.H.M. 
Author-X-Name-Last: Rahmatullah Imon 
Title: The performance of diagnostic-robust generalized potentials for the identification of multiple high leverage points in linear regression 
Abstract:
  Leverage values are being used in regression diagnostics as measures of
 influential observations in the $X$-space. Detection of high leverage
 values is crucial because of their responsibility for misleading
 conclusion about the fitting of a regression model, causing
 multicollinearity problems, masking and/or swamping of outliers, etc. Much
 work has been done on the identification of single high leverage points
 and it is generally believed that the problem of detection of a single
 high leverage point has been largely resolved. But there is no general
 agreement among the statisticians about the detection of multiple high
 leverage points. When a group of high leverage points is present in a data
 set, mainly because of the masking and/or swamping effects the commonly
 used diagnostic methods fail to identify them correctly. On the other
 hand, the robust alternative methods can identify the high leverage points
 correctly but they have a tendency to identify too many low leverage
 points to be points of high leverages which is not also desired. An
 attempt has been made to make a compromise between these two approaches.
 We propose an adaptive method where the suspected high leverage points are
 identified by robust methods and then the low leverage points (if any) are
 put back into the estimation data set after diagnostic checking. The
 usefulness of our newly proposed method for the detection of multiple high
 leverage points is studied by some well-known data sets and Monte Carlo
 simulations. 
Journal: Journal of Applied Statistics 
Pages: 507-520 
Issue: 5 
Volume: 36 
Year: 2009 
Keywords: diagnostic-robust generalized potentials, group deletion, high leverage points, masking, robust Mahalanobis distance, minimum volume ellipsoid, Monte Carlo simulation, 
X-DOI: 10.1080/02664760802553463 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802553463
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:5:p:507-520




Template-Type: ReDIF-Article 1.0
Author-Name: Wanbo Lu 
Author-X-Name-First: Wanbo 
Author-X-Name-Last: Lu 
Author-Name: Tzong-Ru Tsai 
Author-X-Name-First: Tzong-Ru 
Author-X-Name-Last: Tsai 
Title: Interval censored sampling plans for the log-logistic lifetime distribution 
Abstract:
  The log-logistic distribution is one of the popular distributions in
 life-testing applications. This article develops an acceptance sampling
 procedure for the log-logistic lifetime distribution based on grouped data
 when the shape parameter is given. Both producer and consumer risks are
 considered to develop the ordinary, approximate and simulated sampling
 plans. Some of the proposed sampling plans are tabulated; moreover, those
 three types of sampling plans are compared with each other under the same
 censoring rates. The use of these tables is illustrated by an example. 
Journal: Journal of Applied Statistics 
Pages: 521-536 
Issue: 5 
Volume: 36 
Year: 2009 
Keywords: consumer risk, log-logistic distribution, grouped data, maximum likelihood estimator, producer risk, 
X-DOI: 10.1080/02664760802554180 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802554180
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:5:p:521-536




Template-Type: ReDIF-Article 1.0
Author-Name: A. C. Antonakis 
Author-X-Name-First: A. C. 
Author-X-Name-Last: Antonakis 
Author-Name: M. E. Sfakianakis 
Author-X-Name-First: M. E. 
Author-X-Name-Last: Sfakianakis 
Title: Assessing naive Bayes as a method for screening credit applicants 
Abstract:
  The naive Bayes rule (NBR) is a popular and often highly effective
 technique for constructing classification rules. This study examines the
 effectiveness of NBR as a method for constructing classification rules
 (credit scorecards) in the context of screening credit applicants (credit
 scoring). For this purpose, the study uses two real-world credit scoring
 data sets to benchmark NBR against linear discriminant analysis, logistic
 regression analysis, k-nearest neighbours, classification trees and neural
 networks. Of the two aforementioned data sets, the first one is taken from
 a major Greek bank whereas the second one is the Australian Credit
 Approval data set taken from the UCI Machine Learning Repository
 (available at http://www.ics.uci.edu/~mlearn/MLRepository.html). The
 predictive ability of scorecards is measured by the total percentage of
 correctly classified cases, the Gini coefficient and the bad rate amongst
 accepts. In each of the data sets, NBR is found to have a lower predictive
 ability than some of the other five methods under all measures used.
 Reasons that may negatively affect the predictive ability of NBR relative
 to that of alternative methods in the context of credit scoring are
 examined. 
Journal: Journal of Applied Statistics 
Pages: 537-545 
Issue: 5 
Volume: 36 
Year: 2009 
Keywords: credit scorecard, credit scoring, credit risk, naive Bayes, retail banking, 
X-DOI: 10.1080/02664760802554263 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802554263
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:5:p:537-545




Template-Type: ReDIF-Article 1.0
Author-Name: Luis Martins 
Author-X-Name-First: Luis 
Author-X-Name-Last: Martins 
Title: Unit root tests and dramatic shifts with infinite variance processes 
Abstract:
  A model which explains data that is subject to sudden structural changes
 of unspecified nature is presented. The structural shifts are generated by
 a random walk component whose innovations belong to the normal domain of
 attraction of a symmetric stable law. To test the model against the
 stationarity case, several non-parametric, and regression-based statistics
 are studied. The non-parametric tests are a generalization of the variance
 ratio test to innovations with heavy-tailed distributions. The tests are
 consistent and shown to have good finite sample size and power properties
 and are applied to a set of economic variables. 
Journal: Journal of Applied Statistics 
Pages: 547-571 
Issue: 5 
Volume: 36 
Year: 2009 
Keywords: unit root, stable processes, partial sums, limit distributions, empirical size and power, 
X-DOI: 10.1080/02664760802554321 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802554321
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:5:p:547-571




Template-Type: ReDIF-Article 1.0
Author-Name: Jose Manuel Herrerias-Velasco 
Author-X-Name-First: Jose Manuel 
Author-X-Name-Last: Herrerias-Velasco 
Author-Name: Rafael Herrerias-Pleguezuelo 
Author-X-Name-First: Rafael 
Author-X-Name-Last: Herrerias-Pleguezuelo 
Author-Name: Johan Rene van Dorp 
Author-X-Name-First: Johan Rene 
Author-X-Name-Last: van Dorp 
Title: The generalized two-sided power distribution 
Abstract:
  The generalized standard two-sided power (GTSP) distribution was
 mentioned only in passing by Kotz and van Dorp Beyond Beta, Other
 Continuous Families of Distributions with Bounded Support and
 Applications, World Scientific Press, Singapore, 2004. In this paper, we
 shall further investigate this three-parameter distribution by presenting
 some novel properties and use its more general form to contrast the
 chronology of developments of various authors on the two-parameter TSP
 distribution since its initial introduction. GTSP distributions allow for
 J-shaped forms of its pdf, whereas TSP distributions are limited to
 U-shaped and unimodal forms. Hence, GTSP distributions possess the same
 three distributional shapes as the classical beta distributions. A novel
 method and algorithm for the indirect elicitation of the two-power
 parameters of the GTSP distribution is developed. We present a Project
 Evaluation Review Technique example that utilizes this algorithm and
 demonstrates the benefit of separate powers for the two branches of
 activity GTSP distributions for project completion time uncertainty
 estimation. 
Journal: Journal of Applied Statistics 
Pages: 573-587 
Issue: 5 
Volume: 36 
Year: 2009 
Keywords: Parameter elicitation, moment ratio diagram, PERT technique, 
X-DOI: 10.1080/02664760802582850 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802582850
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:5:p:573-587




Template-Type: ReDIF-Article 1.0
Author-Name: Alan Hutson 
Author-X-Name-First: Alan 
Author-X-Name-Last: Hutson 
Author-Name: Maurie Markman 
Author-X-Name-First: Maurie 
Author-X-Name-Last: Markman 
Author-Name: Mark Brady 
Author-X-Name-First: Mark 
Author-X-Name-Last: Brady 
Title: A permutation test approach to phase II historical control trials 
Abstract:
  In this note we outline 15 years of Gynecologic Oncology Group (GOG)
 experience conducting a series of phase II second-line intraperitoneal
 trials in the treatment of ovarian cancer. Using this information, the
 goal is to define a new permutation approach to historical control phase
 II trials in ovarian cancer. We utilize seven previous phase II GOG trials
 in our database to illustrate our methodology. 
Journal: Journal of Applied Statistics 
Pages: 589-599 
Issue: 6 
Volume: 36 
Year: 2009 
Keywords: clinical trial, non-randomized, 
X-DOI: 10.1080/02664760802474280 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802474280
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:6:p:589-599




Template-Type: ReDIF-Article 1.0
Author-Name: Ayman Baklizi 
Author-X-Name-First: Ayman 
Author-X-Name-Last: Baklizi 
Author-Name: B.M. Golam Kibria 
Author-X-Name-First: B.M. 
Author-X-Name-Last: Golam Kibria 
Title: One and two sample confidence intervals for estimating the mean of skewed populations: an empirical comparative study 
Abstract:
  In this paper we consider and propose some confidence intervals for
 estimating the mean or difference of means of skewed populations. We
 extend the median t interval to the two sample problem. Further, we
 suggest using the bootstrap to find the critical points for use in the
 calculation of median t intervals. A simulation study has been made to
 compare the performance of the intervals and a real life example has been
 considered to illustrate the application of the methods. 
Journal: Journal of Applied Statistics 
Pages: 601-609 
Issue: 6 
Volume: 36 
Year: 2009 
Keywords: bootstrapping, coverage probability, interval estimation, Johnson's t, mean, Student's t, skewness, 
X-DOI: 10.1080/02664760802474298 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802474298
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:6:p:601-609




Template-Type: ReDIF-Article 1.0
Author-Name: Veena Shetty 
Author-X-Name-First: Veena 
Author-X-Name-Last: Shetty 
Author-Name: Christopher Morrell 
Author-X-Name-First: Christopher 
Author-X-Name-Last: Morrell 
Author-Name: Samer Najjar 
Author-X-Name-First: Samer 
Author-X-Name-Last: Najjar 
Title: Modeling a cross-sectional response variable with longitudinal predictors: an example of pulse pressure and pulse wave velocity 
Abstract:
  We wish to model pulse wave velocity (PWV) as a function of longitudinal
 measurements of pulse pressure (PP) at the same and prior visits at which
 the PWV is measured. A number of approaches are compared. First, we use
 the PP at the same visit as the PWV in a linear regression model. In
 addition, we use the average of all available PPs as the explanatory
 variable in a linear regression model. Next, a two-stage process is
 applied. The longitudinal PP is modeled using a linear mixed-effects
 model. This modeled PP is used in the regression model to describe PWV. An
 approach for using the longitudinal PP data is to obtain a measure of the
 cumulative burden, the area under the PP curve. This area under the curve
 is used as an explanatory variable to model PWV. Finally, a joint Bayesian
 model is constructed similar to the two-stage model. 
Journal: Journal of Applied Statistics 
Pages: 611-619 
Issue: 6 
Volume: 36 
Year: 2009 
Keywords: mixed effects, linear regression, area under the curve, 
X-DOI: 10.1080/02664760802478208 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802478208
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:6:p:611-619




Template-Type: ReDIF-Article 1.0
Author-Name: S. Rao Jammalamadaka 
Author-X-Name-First: S. Rao 
Author-X-Name-Last: Jammalamadaka 
Author-Name: Md. Aleemuddin Siddiqi 
Author-X-Name-First: Md. Aleemuddin 
Author-X-Name-Last: Siddiqi 
Author-Name: Kaushik Ghosh 
Author-X-Name-First: Kaushik 
Author-X-Name-Last: Ghosh 
Title: Analysis of microtubule dynamics using growth curve models 
Abstract:
  Microtubules are part of the structural network within a cell's
 cytoplasm, providing structural support as well as taking part in many of
 the cellular processes. A large body of data provide evidence that
 dynamics of microtubules in a cell is reponsible for the performance of
 many critical cellular functions such as cell division. In this article,
 we study the effect of four different isoforms of a protein tau on
 microtubule dynamics using growth curve models. The results show that a
 linear growth curve model is sufficient to explain the data. Moreover, we
 find that a mutated version of a 3-repeat tau protein has a similar effect
 as a 4-repeat tau protein on microtubule dynamics. The latter findings
 conform with the biological understanding of the effect of the protein tau
 on microtubule dynamics. 
Journal: Journal of Applied Statistics 
Pages: 621-631 
Issue: 6 
Volume: 36 
Year: 2009 
Keywords: growth curves, microtubule dynamics, polynomial regression, splines, MANOVA, Wilks' Lamda, 
X-DOI: 10.1080/02664760802479131 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802479131
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:6:p:621-631




Template-Type: ReDIF-Article 1.0
Author-Name: Ammarin Thakkinstian 
Author-X-Name-First: Ammarin 
Author-X-Name-Last: Thakkinstian 
Author-Name: John Thompson 
Author-X-Name-First: John 
Author-X-Name-Last: Thompson 
Author-Name: Cosetta Minelli 
Author-X-Name-First: Cosetta 
Author-X-Name-Last: Minelli 
Author-Name: John Attia 
Author-X-Name-First: John 
Author-X-Name-Last: Attia 
Title: Choosing between per-genotype, per-allele, and trend approaches for initial detection of gene-disease association 
Abstract:
  There are a number of approaches to detect candidate gene-disease
 associations including: (i) 'per-genotype', which looks for any difference
 across the genotype groups without making any assumptions about the
 direction of the effect or the genetic model; (ii) 'per-allele', which
 assumes an additive genetic model, i.e. an effect for each allele copy;
 and (iii) linear trend, which looks for an incremental effect across the
 genotype groups. We simulated a number of gene-disease associations,
 varying odds ratios, allele frequency, genetic model, and deviation from
 Hardy-Weinberg equilibrium (HWE) and tested the performance of each of the
 three methods to detect the associations, where performance was judged by
 looking at critical values, power, coverage, bias, and root mean square
 error. Results indicate that the per-allele method is very susceptible to
 false positives and false negatives when deviations from HWE occur. The
 linear trend test appears to have the best power under most simulated
 scenarios, but can sometimes be biased and have poor coverage. These
 results indicate that of these strategies a linear trend test may be best
 for initially testing an association, and the per-genotype approach may be
 best for estimating the magnitude of the association. 
Journal: Journal of Applied Statistics 
Pages: 633-646 
Issue: 6 
Volume: 36 
Year: 2009 
Keywords: per-genotype, per-allele, power, bias, gene-disease association, 
X-DOI: 10.1080/02664760802484990 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802484990
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:6:p:633-646




Template-Type: ReDIF-Article 1.0
Author-Name: Gabriel Escarela 
Author-X-Name-First: Gabriel 
Author-X-Name-Last: Escarela 
Author-Name: Luis Carlos Perez-Ruiz 
Author-X-Name-First: Luis Carlos 
Author-X-Name-Last: Perez-Ruiz 
Author-Name: Russell Bowater 
Author-X-Name-First: Russell 
Author-X-Name-Last: Bowater 
Title: A copula-based Markov chain model for the analysis of binary longitudinal data 
Abstract:
  A fully parametric first-order autoregressive (AR(1)) model is proposed
 to analyse binary longitudinal data. By using a discretized version of a
 copula, the modelling approach allows one to construct separate models for
 the marginal response and for the dependence between adjacent responses.
 In particular, the transition model that is focused on discretizes the
 Gaussian copula in such a way that the marginal is a Bernoulli
 distribution. A probit link is used to take into account concomitant
 information in the behaviour of the underlying marginal distribution.
 Fixed and time-varying covariates can be included in the model. The method
 is simple and is a natural extension of the AR(1) model for Gaussian
 series. Since the approach put forward is likelihood-based, it allows
 interpretations and inferences to be made that are not possible with
 semi-parametric approaches such as those based on generalized estimating
 equations. Data from a study designed to reduce the exposure of children
 to the sun are used to illustrate the methods. 
Journal: Journal of Applied Statistics 
Pages: 647-657 
Issue: 6 
Volume: 36 
Year: 2009 
Keywords: copula, discrete time series, Markov regression models, maximum likelihood, probit regression model, serial correlation, 
X-DOI: 10.1080/02664760802499287 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802499287
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:6:p:647-657




Template-Type: ReDIF-Article 1.0
Author-Name: Xiaomo Jiang 
Author-X-Name-First: Xiaomo 
Author-X-Name-Last: Jiang 
Author-Name: Sankaran Mahadevan 
Author-X-Name-First: Sankaran 
Author-X-Name-Last: Mahadevan 
Title: Bayesian inference method for model validation and confidence extrapolation 
Abstract:
  This paper presents a Bayesian-hypothesis-testing-based methodology for
 model validation and confidence extrapolation under uncertainty, using
 limited test data. An explicit expression of the Bayes factor is derived
 for the interval hypothesis testing. The interval method is compared with
 the Bayesian point null hypothesis testing approach. The Bayesian network
 with Markov Chain Monte Carlo simulation and Gibbs sampling is explored
 for extrapolating the inference from the validated domain at the component
 level to the untested domain at the system level. The effect of the number
 of experiments on the confidence in the model validation decision is
 investigated. The probabilities of Type I and Type II errors in
 decision-making during the model validation and confidence extrapolation
 are quantified. The proposed methodologies are applied to a structural
 mechanics problem. Numerical results demonstrate that the Bayesian
 methodology provides a quantitative approach to facilitate rational
 decisions in model validation and confidence extrapolation under
 uncertainty. 
Journal: Journal of Applied Statistics 
Pages: 659-677 
Issue: 6 
Volume: 36 
Year: 2009 
Keywords: Bayesian statistics, Bayes factor, hypothesis testing, model validation, extrapolation, 
X-DOI: 10.1080/02664760802499295 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802499295
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:6:p:659-677




Template-Type: ReDIF-Article 1.0
Author-Name: F. Javier Trivez 
Author-X-Name-First: F. Javier 
Author-X-Name-Last: Trivez 
Author-Name: Beatriz Catalan 
Author-X-Name-First: Beatriz 
Author-X-Name-Last: Catalan 
Title: Detecting level shifts in ARMA-GARCH (1,1) Models 
Abstract:
  The purpose of this article is to present a new method to detect level
 shifts in the context of conditional heteroscedastic models. First, we
 define precisely what type of outlier we are referring to, a concept that
 has been scarcely touched in the field of GARCH (1,1) models, and then we
 go on to present our methodology based on the nature of the Lagrange
 multiplier tests. The validity and efficiency of the proposed procedure
 are demonstrated through different simulation experiments. To conclude, we
 present a practical application of the method to the time series of
 returns of US short-term interest rates. 
Journal: Journal of Applied Statistics 
Pages: 679-697 
Issue: 6 
Volume: 36 
Year: 2009 
Keywords: level outliers, volatility outliers, level shifts, GARCH models, LM tests, 
X-DOI: 10.1080/02664760802499303 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802499303
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:6:p:679-697




Template-Type: ReDIF-Article 1.0
Author-Name: Ana Militino 
Author-X-Name-First: Ana 
Author-X-Name-Last: Militino 
Title: Time Series Analysis 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 699-700 
Issue: 6 
Volume: 36 
Year: 2009 
X-DOI: 10.1080/02664760802366809 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802366809
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:6:p:699-700




Template-Type: ReDIF-Article 1.0
Author-Name: Andrew Robinson 
Author-X-Name-First: Andrew 
Author-X-Name-Last: Robinson 
Title: Adaptive design theory and implementation using SAS and R 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 701-702 
Issue: 6 
Volume: 36 
Year: 2009 
X-DOI: 10.1080/02664760802366817 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802366817
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:6:p:701-702




Template-Type: ReDIF-Article 1.0
Author-Name: Jacques Pienaar 
Author-X-Name-First: Jacques 
Author-X-Name-Last: Pienaar 
Title: Probability and statistics with R 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 703-704 
Issue: 6 
Volume: 36 
Year: 2009 
X-DOI: 10.1080/02664760802416539 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802416539
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:6:p:703-704




Template-Type: ReDIF-Article 1.0
Author-Name: Jacques Pienaar 
Author-X-Name-First: Jacques 
Author-X-Name-Last: Pienaar 
Title: Control Techniques for Complex Networks 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 705-705 
Issue: 6 
Volume: 36 
Year: 2009 
X-DOI: 10.1080/02664760802416554 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802416554
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:6:p:705-705




Template-Type: ReDIF-Article 1.0
Author-Name: Jacques Pienaar 
Author-X-Name-First: Jacques 
Author-X-Name-Last: Pienaar 
Title: Random networks for communication: from statistical physics to information systems 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 707-708 
Issue: 6 
Volume: 36 
Year: 2009 
X-DOI: 10.1080/02664760802416562 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802416562
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:6:p:707-708




Template-Type: ReDIF-Article 1.0
Author-Name: Antonello Maruotti 
Author-X-Name-First: Antonello 
Author-X-Name-Last: Maruotti 
Title: Fairness of the national health service in Italy: a bivariate correlated random effects model 
Abstract:
  The primary purpose of this paper is to comprehensively assess
 households' burden due to health payments. Starting from the fairness
 approach developed by the World Health Organization, we analyse the burden
 of healthcare payments on Italian households by modeling catastrophic
 payments and impoverishment due to healthcare expenditures. For this
 purpose, we propose to extend the analysis of fairness in financing
 contribution through a generalized linear mixed models by introducing a
 bivariate correlated random effects model, where association between the
 outcomes is modeled through individual- and outcome-specific latent
 effects which are assumed to be correlated. We discuss model parameter
 estimation in a finite mixture context. By using such model specification,
 the fairness of the Italian national health service is investigated. 
Journal: Journal of Applied Statistics 
Pages: 709-722 
Issue: 7 
Volume: 36 
Year: 2009 
Keywords: fairness, healthcare, random effects models, binary data, non-parametric maximum likelihood, 
X-DOI: 10.1080/02664760802499311 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802499311
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:7:p:709-722




Template-Type: ReDIF-Article 1.0
Author-Name: Montezuma Dumangane 
Author-X-Name-First: Montezuma 
Author-X-Name-Last: Dumangane 
Author-Name: Nicoletta Rosati 
Author-X-Name-First: Nicoletta 
Author-X-Name-Last: Rosati 
Author-Name: Anna Volossovitch 
Author-X-Name-First: Anna 
Author-X-Name-Last: Volossovitch 
Title: Departure from independence and stationarity in a handball match 
Abstract:
  This paper analyses direct and indirect forms of dependence in the
 probability of scoring in a handball match, taking into account the mutual
 influence of both playing teams. Non-identical distribution (i.d.) and
 non-stationarity, which are commonly observed in sport games, are studied
 through the specification of time-varying parameters. The model accounts
 for the binary character of the dependent variable, and for unobserved
 heterogeneity. The parameter dynamics is specified by a first-order
 auto-regressive process. Data from the Handball World Championships
 2001-2005 show that the dynamics of handball violate both independence and
 i.d., in some cases having a non-stationary behaviour. 
Journal: Journal of Applied Statistics 
Pages: 723-741 
Issue: 7 
Volume: 36 
Year: 2009 
Keywords: binary choice, dynamic panel data, time-varying parameters, unobserved heterogeneity, dependence, non-stationarity, 
X-DOI: 10.1080/02664760802499329 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802499329
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:7:p:723-741




Template-Type: ReDIF-Article 1.0
Author-Name: Alexander Correa 
Author-X-Name-First: Alexander 
Author-X-Name-Last: Correa 
Author-Name: Pere Grima 
Author-X-Name-First: Pere 
Author-X-Name-Last: Grima 
Author-Name: Xavier Tort-Martorell 
Author-X-Name-First: Xavier 
Author-X-Name-Last: Tort-Martorell 
Title: Experimentation order with good properties for 2k factorial designs 
Abstract:
  Randomizing the order of experimentation in a factorial design does not
 always achieve the desired effect of neutralizing the influence of unknown
 factors. In fact, with some very reasonable assumptions, an important
 proportion of random orders achieve the same degree of protection as that
 obtained by experimenting in the design matrix standard order. In
 addition, randomization can induce a large number of changes in factor
 levels and thus make experimentation expensive and difficult. De Leon et
 al. [Experimentation order in factorial designs with 8 or 16 runs, J.
 Appl. Stat. 32 (2005), pp. 297-313] proposed experimentation orders for
 designs with eight or 16 runs that combine an excellent level of
 protection against the influence of unknown factors, with the minimum
 number of changes in factor levels. This article presents a new
 methodology to obtain experimentation orders with the desired properties
 for designs with any number of runs. 
Journal: Journal of Applied Statistics 
Pages: 743-754 
Issue: 7 
Volume: 36 
Year: 2009 
Keywords: randomization, experimentation order, factorial design, bias protection, minimum number of level changes, 
X-DOI: 10.1080/02664760802499337 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802499337
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:7:p:743-754




Template-Type: ReDIF-Article 1.0
Author-Name: Rabindra Nath Das 
Author-X-Name-First: Rabindra Nath 
Author-X-Name-Last: Das 
Author-Name: Sung Park 
Author-X-Name-First: Sung 
Author-X-Name-Last: Park 
Title: A measure of robust slope-rotatability for second-order response surface experimental designs 
Abstract:
  In response surface methodology, rotatability and slope-rotatability are
 natural and highly desirable properties for second-order regression
 models. In this paper a measure of robust slope-rotatability for
 second-order response surface designs with a general correlated error
 structure is developed and illustrated with different examples for
 autocorrelated error structure. 
Journal: Journal of Applied Statistics 
Pages: 755-767 
Issue: 7 
Volume: 36 
Year: 2009 
Keywords: response surface design, rotatability, robust rotatability, robust slope-rotatability, weak slope-rotatability, weak slope-rotatability region, 
X-DOI: 10.1080/02664760802499345 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802499345
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:7:p:755-767




Template-Type: ReDIF-Article 1.0
Author-Name: Binbing Yu 
Author-X-Name-First: Binbing 
Author-X-Name-Last: Yu 
Title: Approximating the risk score for disease diagnosis using MARS 
Abstract:
  In disease screening and diagnosis, often multiple markers are measured
 and combined to improve the accuracy of diagnosis. McIntosh and Pepe
 [Combining several screening tests: optimality of the risk score,
 Biometrics 58 (2002), pp. 657-664] showed that the risk score, defined as
 the probability of disease conditional on multiple markers, is the optimal
 function for classification based on the Neyman-Pearson lemma. They
 proposed a two-step procedure to approximate the risk score. However, the
 resulting receiver operating characteristic (ROC) curve is only defined in
 a subrange (L, h) of false-positive rates in (0,1) and the determination
 of the lower limit L needs extra prior information. In practice, most
 diagnostic tests are not perfect, and it is usually rare that a single
 marker is uniformly better than the other tests. Using simulation, I show
 that multivariate adaptive regression spline is a useful tool to
 approximate the risk score when combining multiple markers, especially
 when ROC curves from multiple tests cross. The resulting ROC is defined in
 the whole range of (0,1) and is easy to implement and has intuitive
 interpretation. The sample code of the application is shown in the
 appendix. 
Journal: Journal of Applied Statistics 
Pages: 769-778 
Issue: 7 
Volume: 36 
Year: 2009 
Keywords: multivariate adaptive regression spline (MARS), Neyman-Pearson lemma, risk score, ROC, 
X-DOI: 10.1080/02664760802499352 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802499352
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:7:p:769-778




Template-Type: ReDIF-Article 1.0
Author-Name: Loredana Ureche-Rangau 
Author-X-Name-First: Loredana 
Author-X-Name-Last: Ureche-Rangau 
Author-Name: Quiterie de Rorthays 
Author-X-Name-First: Quiterie 
Author-X-Name-Last: de Rorthays 
Title: More on the volatility-trading volume relationship in emerging markets: The Chinese stock market 
Abstract:
  This paper empirically investigates the characteristics in terms of
 volatility and trading volume relationships of the Chinese stock markets,
 and specifically of the stocks comprising the SSE180 index. Our results
 show that, contrary to previous evidence, both volatility and trading
 volume appear to be multi-fractal and highly intermittent, suggesting a
 common long-run behaviour in addition to the common short-term behaviour
 underlined by former studies. Moreover, the trading volume seems to have
 no explanatory power for volatility persistence when introduced in the
 conditional variance equation. Finally, the sign of the trading volume
 coefficients is mainly negative, hence showing a negative correlation
 between the two variables. 
Journal: Journal of Applied Statistics 
Pages: 779-799 
Issue: 7 
Volume: 36 
Year: 2009 
Keywords: volatility persistence, long-memory, trading volume, 
X-DOI: 10.1080/02664760802509101 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802509101
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:7:p:779-799




Template-Type: ReDIF-Article 1.0
Author-Name: E. Ciavolino 
Author-X-Name-First: E. 
Author-X-Name-Last: Ciavolino 
Author-Name: J. J. Dahlgaard 
Author-X-Name-First: J. J. 
Author-X-Name-Last: Dahlgaard 
Title: Simultaneous Equation Model based on the generalized maximum entropy for studying the effect of management factors on enterprise performance 
Abstract:
  The aim of this paper is to study the effect of management factors on
 enterprise performance, considering a survey that the University
 Consortium in Engineering for Quality and Innovation has led. The
 relationships between management factors and enterprise performance are
 formalized by a Simultaneous Equation Model based on the generalized
 maximum entropy (GME) estimation method. The format of this paper is as
 follows. In Section 2, the data collected, the questionnaire evaluation,
 and the management model analytical formulation are introduced. In Section
 3, the GME formulation is specified, showing the main characteristics of
 the estimation method. In Section 4, the results and a comparison among
 GME, partial least squares (PLS), and maximum likelihood estimation (MLE)
 is shown. In Section 5, concluding remarks are discussed. 
Journal: Journal of Applied Statistics 
Pages: 801-815 
Issue: 7 
Volume: 36 
Year: 2009 
Keywords: generalized maximum entropy, human resources, leadership, maximum likelihood estimation, partial least squares, performance, Simultaneous Equation Model, strategic planning, 
X-DOI: 10.1080/02664760802510026 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802510026
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:7:p:801-815




Template-Type: ReDIF-Article 1.0
Author-Name: Christoph Hanck 
Author-X-Name-First: Christoph 
Author-X-Name-Last: Hanck 
Title: Cross-sectional correlation robust tests for panel cointegration 
Abstract:
  We use meta-analytic procedures to develop new tests for panel
 cointegration, combining p-values from time-series cointegration tests on
 the units of the panel. The tests are robust to heterogeneity and
 cross-sectional dependence between the panel units. To achieve the latter,
 we employ a sieve bootstrap procedure with joint resampling of the units'
 residuals. A simulation study shows that the tests can have substantially
 smaller size distortion than tests ignoring the presence of
 cross-sectional dependence while preserving high power. We apply the tests
 to a panel of post-Bretton Woods data to test for weak purchasing power
 parity. 
Journal: Journal of Applied Statistics 
Pages: 817-833 
Issue: 7 
Volume: 36 
Year: 2009 
Keywords: panel cointegration tests, cross-sectional dependence, sieve bootstrap, 
X-DOI: 10.1080/02664760802510042 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802510042
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:7:p:817-833




Template-Type: ReDIF-Article 1.0
Author-Name: Bo He 
Author-X-Name-First: Bo 
Author-X-Name-Last: He 
Author-Name: Clyde Martin 
Author-X-Name-First: Clyde 
Author-X-Name-Last: Martin 
Title: Statistical analysis of error for fourth-order ordinary differential equation solvers 
Abstract:
  We develop an autoregressive integrated moving average (ARIMA) model to
 study the statistical behavior of the numerical error generated from three
 fourth-order ordinary differential equation solvers: Milne's method,
 Adams-Bashforth method and a new method that randomly switches between the
 Milne and Adams-Bashforth methods. With the actual error data based on
 three differential equations, we desire to identify an ARIMA model for
 each data series. Results show that some of the data series can be
 described by ARIMA models but others cannot. Based on the mathematical
 form of the numerical error, other statistical models should be
 investigated in the future. Finally, we assess the multivariate normality
 of the sample mean error generated by the switching method. 
Journal: Journal of Applied Statistics 
Pages: 835-852 
Issue: 8 
Volume: 36 
Year: 2009 
Keywords: differential equations, numerical error, switching, 
X-DOI: 10.1080/02664760802510034 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802510034
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:8:p:835-852




Template-Type: ReDIF-Article 1.0
Author-Name: Agustin Hernandez Bastida 
Author-X-Name-First: Agustin Hernandez 
Author-X-Name-Last: Bastida 
Author-Name: Emilio Gomez Deniz 
Author-X-Name-First: Emilio Gomez 
Author-X-Name-Last: Deniz 
Author-Name: Jose Maria Perez Sanchez 
Author-X-Name-First: Jose Maria Perez 
Author-X-Name-Last: Sanchez 
Title: Bayesian robustness of the compound Poisson distribution under bidimensional prior: an application to the collective risk model 
Abstract:
  The distribution of the aggregate claims in one year plays an important
 role in Actuarial Statistics for computing, for example, insurance
 premiums when both the number and size of the claims must be implemented
 into the model. When the number of claims follows a Poisson distribution
 the aggregated distribution is called the compound Poisson distribution.
 In this article we assume that the claim size follows an exponential
 distribution and later we make an extensive study of this model by
 assuming a bidimensional prior distribution for the parameters of the
 Poisson and exponential distribution with marginal gamma. This study
 carries us to obtain expressions for net premiums, marginal and posterior
 distributions in terms of some well-known special functions used in
 statistics. Later, a Bayesian robustness study of this model is made.
 Bayesian robustness on bidimensional models was deeply treated in the
 1990s, producing numerous results, but few applications dealing with this
 problem can be found in the literature. 
Journal: Journal of Applied Statistics 
Pages: 853-869 
Issue: 8 
Volume: 36 
Year: 2009 
Keywords: Bayesian robustness, Bessel function, class of distributions, compound, generalized hypergeometric function, net premium, 
X-DOI: 10.1080/02664760802510059 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802510059
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:8:p:853-869




Template-Type: ReDIF-Article 1.0
Author-Name: Carla Machado 
Author-X-Name-First: Carla 
Author-X-Name-Last: Machado 
Author-Name: Carlos Daniel Paulino 
Author-X-Name-First: Carlos Daniel 
Author-X-Name-Last: Paulino 
Author-Name: Francisco Nunes 
Author-X-Name-First: Francisco 
Author-X-Name-Last: Nunes 
Title: Deprivation analysis based on Bayesian latent class models 
Abstract:
  This article seeks to measure deprivation among Portuguese households,
 taking into account four well-being dimensions - housing, durable goods,
 economic strain and social relationships - with survey data from the
 European Community Household Panel. We propose a multi-stage approach to a
 cross-sectional analysis, side-stepping the sparse nature of the
 contingency tables caused by the large number of variables considered and
 bringing together partial and overall analyses of deprivation that are
 based on Bayesian latent class models via Markov Chain Monte Carlo
 methods. The outcomes demonstrate that there was a substantial improvement
 on household overall well-being between 1995 and 2001. The dimensions that
 most contributed to the risk of household deprivation were found to be
 economic strain and social relationships. 
Journal: Journal of Applied Statistics 
Pages: 871-891 
Issue: 8 
Volume: 36 
Year: 2009 
Keywords: poverty, deprivation, Bayesian latent class model, label-switching, MCMC method, Portugal, 
X-DOI: 10.1080/02664760802520769 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802520769
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:8:p:871-891




Template-Type: ReDIF-Article 1.0
Author-Name: Ganesh Dutta 
Author-X-Name-First: Ganesh 
Author-X-Name-Last: Dutta 
Author-Name: Premadhis Das 
Author-X-Name-First: Premadhis 
Author-X-Name-Last: Das 
Author-Name: Nripes Mandal 
Author-X-Name-First: Nripes 
Author-X-Name-Last: Mandal 
Title: Optimum covariate designs in split-plot and strip-plot design set-ups 
Abstract:
  The problem considered is that of finding optimum covariate designs for
 estimation of covariate parameters in standard split-plot and strip-plot
 design set-ups with the levels of the whole-plot factor in r randomised
 blocks. Also an extended version of a mixed orthogonal array has been
 introduced, which is used to construct such optimum covariate designs.
 Hadamard matrices, as usual, play the key role for such construction. 
Journal: Journal of Applied Statistics 
Pages: 893-906 
Issue: 8 
Volume: 36 
Year: 2009 
Keywords: split-plot designs, strip-plot designs, covariates, optimal designs, extended mixed orthogonal array, Hadamard matrices, 
X-DOI: 10.1080/02664760802520777 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802520777
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:8:p:893-906




Template-Type: ReDIF-Article 1.0
Author-Name: Giovanni De Luca 
Author-X-Name-First: Giovanni 
Author-X-Name-Last: De Luca 
Author-Name: Giorgia Rivieccio 
Author-X-Name-First: Giorgia 
Author-X-Name-Last: Rivieccio 
Title: Archimedean copulae for risk measurement 
Abstract:
  In this paper some Archimedean copula functions for bivariate financial
 returns are studied. The choice of this family is due to their ability to
 capture the tail dependence, which is an association measure we can detect
 in many bivariate financial time-series. A time-varying version of these
 copulae is also investigated. Finally, the Value-at-Risk is computed and
 its performance is compared across different copula specifications. 
Journal: Journal of Applied Statistics 
Pages: 907-924 
Issue: 8 
Volume: 36 
Year: 2009 
Keywords: copula, time-varying parameters, daily equity returns, risk management, value-at-risk, 
X-DOI: 10.1080/02664760802520785 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802520785
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:8:p:907-924




Template-Type: ReDIF-Article 1.0
Author-Name: Miroslav Ristic 
Author-X-Name-First: Miroslav 
Author-X-Name-Last: Ristic 
Title: R programming for bioinformatics 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 925-925 
Issue: 8 
Volume: 36 
Year: 2009 
X-DOI: 10.1080/02664760802695884 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802695884
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:8:p:925-925




Template-Type: ReDIF-Article 1.0
Author-Name: Filia Vonta 
Author-X-Name-First: Filia 
Author-X-Name-Last: Vonta 
Title: The frailty model 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 927-928 
Issue: 8 
Volume: 36 
Year: 2009 
X-DOI: 10.1080/02664760802695892 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802695892
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:8:p:927-928




Template-Type: ReDIF-Article 1.0
Author-Name: Paul Baxter 
Author-X-Name-First: Paul 
Author-X-Name-Last: Baxter 
Title: An introduction to generalised linear models, third edition 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 929-930 
Issue: 8 
Volume: 36 
Year: 2009 
X-DOI: 10.1080/02664760802695900 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802695900
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:8:p:929-930




Template-Type: ReDIF-Article 1.0
Author-Name: Pieter Bastiaan Ober 
Author-X-Name-First: Pieter Bastiaan 
Author-X-Name-Last: Ober 
Title: Risk analysis 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 931-931 
Issue: 8 
Volume: 36 
Year: 2009 
X-DOI: 10.1080/02664760802695918 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802695918
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:8:p:931-931




Template-Type: ReDIF-Article 1.0
Author-Name: B. J. Gajewski 
Author-X-Name-First: B. J. 
Author-X-Name-Last: Gajewski 
Author-Name: R. Lee 
Author-X-Name-First: R. 
Author-X-Name-Last: Lee 
Author-Name: M. Bott 
Author-X-Name-First: M. 
Author-X-Name-Last: Bott 
Author-Name: U. Piamjariyakul 
Author-X-Name-First: U. 
Author-X-Name-Last: Piamjariyakul 
Author-Name: R. L. Taunton 
Author-X-Name-First: R. L. 
Author-X-Name-Last: Taunton 
Title: On estimating the distribution of data envelopment analysis efficiency scores: an application to nursing homes' care planning process 
Abstract:
  Data envelopment analysis (DEA) is a deterministic econometric model for
 calculating efficiency by using data from an observed set of
 decision-making units (DMUs). We propose a method for calculating the
 distribution of efficiency scores. Our framework relies on estimating data
 from an unobserved set of DMUs. The model provides posterior predictive
 data for the unobserved DMUs to augment the frontier in the DEA that
 provides a posterior predictive distribution for the efficiency scores. We
 explore the method on a multiple-input and multiple-output DEA model. The
 data for the example are from a comprehensive examination of how nursing
 homes complete a standardized mandatory assessment of residents. 
Journal: Journal of Applied Statistics 
Pages: 933-944 
Issue: 9 
Volume: 36 
Year: 2009 
Keywords: bounded DEA, MCMC, binomial distribution, posterior predictive distribution, sampling, imputation, 
X-DOI: 10.1080/02664760802552986 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802552986
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:9:p:933-944




Template-Type: ReDIF-Article 1.0
Author-Name: Mohamed Boutahar 
Author-X-Name-First: Mohamed 
Author-X-Name-Last: Boutahar 
Title: Comparison of non-parametric and semi-parametric tests in detecting long memory 
Abstract:
  The first two stages in modelling times series are hypothesis testing and
 estimation. For long memory time series, the second stage was studied in
 the paper published in [M. Boutahar et al., Estimation methods of the long
 memory parameter: monte Carlo analysis and application, J. Appl. Statist.
 34(3), pp. 261-301.] in which we have presented some estimation methods of
 the long memory parameter. The present paper is intended for the first
 stage, and hence completes the former, by exploring some tests for
 detecting long memory in time series. We consider two kinds of tests: the
 non-parametric class and the semi-parametric one. We precise the limiting
 distribution of the non-parametric tests under the null of short memory
 and we show that they are consistent against the alternative of long
 memory. We perform also some Monte Carlo simulations to analyse the size
 distortion and the power of all proposed tests. We conclude that for large
 sample size, the two classes are equivalent but for small sample size the
 non-parametric class is better than the semi-parametric one. 
Journal: Journal of Applied Statistics 
Pages: 945-972 
Issue: 9 
Volume: 36 
Year: 2009 
Keywords: hypothesis testing, long memory, power, short memory, size, 
X-DOI: 10.1080/02664760802562464 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802562464
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:9:p:945-972




Template-Type: ReDIF-Article 1.0
Author-Name: Vito Muggeo 
Author-X-Name-First: Vito 
Author-X-Name-Last: Muggeo 
Author-Name: Massimo Attanasio 
Author-X-Name-First: Massimo 
Author-X-Name-Last: Attanasio 
Author-Name: Mariano Porcu 
Author-X-Name-First: Mariano 
Author-X-Name-Last: Porcu 
Title: A segmented regression model for event history data: an application to the fertility patterns in Italy 
Abstract:
  We propose a segmented discrete-time model for the analysis of event
 history data in demographic research. Through a unified regression
 framework, the model provides estimates of the effects of explanatory
 variables and jointly accommodates flexibly non-proportional differences
 via segmented relationships. The main appeal relies on ready availability
 of parameters, changepoints, and slopes, which may provide meaningful and
 intuitive information on the topic. Furthermore, specific linear
 constraints on the slopes may also be set to investigate particular
 patterns. We investigate the intervals between cohabitation and first
 childbirth and from first to second childbirth using individual data for
 Italian women from the Second National Survey on Fertility. The model
 provides insights into dramatic decrease of fertility experienced in
 Italy, in that it detects a 'common' tendency in delaying the onset of
 childbearing for the more recent cohorts and a 'specific' postponement
 strictly depending on the educational level and age at cohabitation. 
Journal: Journal of Applied Statistics 
Pages: 973-988 
Issue: 9 
Volume: 36 
Year: 2009 
Keywords: segmented regression, discrete-time hazard models, changepoints, parity progression, event occurrence data, 
X-DOI: 10.1080/02664760802552994 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802552994
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:9:p:973-988




Template-Type: ReDIF-Article 1.0
Author-Name: P. Singh 
Author-X-Name-First: P. 
Author-X-Name-Last: Singh 
Title: Multiple comparisons of several populations with more than one control with respectto scale parameter 
Abstract:
  In this paper, one-sided and two-sided test procedures for comparing
 several treatments with more than one control with respect to scale
 parameter are proposed. The proposed test procedures are inverted to
 obtain the associated simultaneous confidence intervals. The multiple
 comparisons of test treatments with the best control are also developed.
 The computation of the critical points, required to implement the proposed
 procedures, is discussed by taking the normal probability model.
 Applications of the proposed test procedures to two-parameter exponential
 probability model are also demonstrated. 
Journal: Journal of Applied Statistics 
Pages: 989-998 
Issue: 9 
Volume: 36 
Year: 2009 
Keywords: best treatment, critical points, experiment-wise error, exponential distribution, multiple comparisons, 
X-DOI: 10.1080/02664760802663098 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802663098
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:9:p:989-998




Template-Type: ReDIF-Article 1.0
Author-Name: Manisha Pal 
Author-X-Name-First: Manisha 
Author-X-Name-Last: Pal 
Author-Name: Nripes Mandal 
Author-X-Name-First: Nripes 
Author-X-Name-Last: Mandal 
Title: Optimum designs for estimation of optimum point under cost constraint 
Abstract:
  In this paper, we consider the estimation of the optimum factor
 combination in a response surface model. Assuming that the response
 function is quadratic concave and there is a linear cost constraint on the
 factor combination, we attempt to find the optimum design using the trace
 optimality criterion. As the criterion function involves the unknown
 parameters, we adopt a pseudo-Bayesian approach to resolve the problem. 
Journal: Journal of Applied Statistics 
Pages: 999-1008 
Issue: 9 
Volume: 36 
Year: 2009 
Keywords: response surface model, second-order models, mixture experiments, weighted centroid designs, trace criterion, 
X-DOI: 10.1080/02664760802582264 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802582264
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:9:p:999-1008




Template-Type: ReDIF-Article 1.0
Author-Name: Prasun Das 
Author-X-Name-First: Prasun 
Author-X-Name-Last: Das 
Author-Name: Arup Kumar Das 
Author-X-Name-First: Arup Kumar 
Author-X-Name-Last: Das 
Author-Name: Saddam Hossain 
Author-X-Name-First: Saddam 
Author-X-Name-Last: Hossain 
Title: Forecasting models for developing control scheme to improve furnace run length 
Abstract:
  In petrochemical industries, the gaseous feedstock like ethane and
 propane are cracked in furnaces to produce ethylene and propylene as main
 products and the inputs for the other plant in the downstream. A problem
 of low furnace run length (FRL) increases furnace decoking and reduces
 productivity along with the problem of reducing life of the coil. Coil
 pressure ratio (CPR) and tube metal temperature (TMT) are the two most
 important performance measures for the FRL to decide upon the need for
 furnace decoking. This article, therefore, makes an attempt to develop the
 prediction models for CPR and TMT based on the critical process
 parameters, which would lead to take the necessary control measures along
 with a prior indication for decoking. Regression-based time series and
 double exponential smoothing techniques are used to build up the models.
 The effective operating ranges of the critical process parameters are
 found using a simulation-based approach. The models are expected to be the
 guiding principles eventually to increase the average run length of
 furnace. 
Journal: Journal of Applied Statistics 
Pages: 1009-1019 
Issue: 9 
Volume: 36 
Year: 2009 
Keywords: furnace run length, decoking, coil pressure ratio, tube metal temperature, operating ranges, simulation, 
X-DOI: 10.1080/02664760902803255 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902803255
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:9:p:1009-1019




Template-Type: ReDIF-Article 1.0
Author-Name: Muhammad Aslam 
Author-X-Name-First: Muhammad 
Author-X-Name-Last: Aslam 
Author-Name: Chi-Hyuck Jun 
Author-X-Name-First: Chi-Hyuck 
Author-X-Name-Last: Jun 
Title: A group acceptance sampling plan for truncated life test having Weibull distribution 
Abstract:
  In this paper, a group acceptance sampling plan for a truncated life test
 is proposed when a multiple number of items as a group can be tested
 simultaneously in a tester, assuming that the lifetime of a product
 follows the Weibull distribution with a known shape parameter. The design
 parameters such as the number of groups and the acceptance number will be
 determined by satisfying the producer's and the consumer's risks at the
 specified quality levels, while the termination time and the number of
 testers are specified. The results are explained with tables and examples. 
Journal: Journal of Applied Statistics 
Pages: 1021-1027 
Issue: 9 
Volume: 36 
Year: 2009 
Keywords: acceptance sampling, consumer's risk, operating characteristics, producer's risk, truncated life test, 
X-DOI: 10.1080/02664760802566788 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802566788
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:9:p:1021-1027




Template-Type: ReDIF-Article 1.0
Author-Name: J. Mazucheli 
Author-X-Name-First: J. 
Author-X-Name-Last: Mazucheli 
Author-Name: J. A. Achcar 
Author-X-Name-First: J. A. 
Author-X-Name-Last: Achcar 
Author-Name: E. A. Coelho-Barros 
Author-X-Name-First: E. A. 
Author-X-Name-Last: Coelho-Barros 
Author-Name: F. Louzada-Neto 
Author-X-Name-First: F. 
Author-X-Name-Last: Louzada-Neto 
Title: Infant mortality model for lifetime data 
Abstract:
  In this paper we introduce a parametric model for handling lifetime data
 where an early lifetime can be related to the infant-mortality failure or
 to the wear processes but we do not know which risk is responsible for the
 failure. The maximum likelihood approach and the sampling-based approach
 are used to get the inferences of interest. Some special cases of the
 proposed model are studied via Monte Carlo methods for size and power of
 hypothesis tests. To illustrate the proposed methodology, we introduce an
 example consisting of a real data set. 
Journal: Journal of Applied Statistics 
Pages: 1029-1036 
Issue: 9 
Volume: 36 
Year: 2009 
Keywords: hazard models, infant-mortality failure, mixture models, Monte Carlo study, Weibull model, bootstrap, 
X-DOI: 10.1080/02664760802526907 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802526907
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:9:p:1029-1036




Template-Type: ReDIF-Article 1.0
Author-Name: N. R. Parsons 
Author-X-Name-First: N. R. 
Author-X-Name-Last: Parsons 
Author-Name: S. G. Gilmour 
Author-X-Name-First: S. G. 
Author-X-Name-Last: Gilmour 
Author-Name: R. N. Edmondson 
Author-X-Name-First: R. N. 
Author-X-Name-Last: Edmondson 
Title: Analysis of robust design experiments with time-dependent ordinal response characteristics: a quality improvement study from the horticulture industry 
Abstract:
  An approach to the analysis of time-dependent ordinal quality score data
 from robust design experiments is developed and applied to an experiment
 from commercial horticultural research, using concepts of product
 robustness and longevity that are familiar to analysts in engineering
 research. A two-stage analysis is used to develop models describing the
 effects of a number of experimental treatments on the rate of post-sales
 product quality decline. The first stage uses a polynomial function on a
 transformed scale to approximate the quality decline for an individual
 experimental unit using derived coefficients and the second stage uses a
 joint mean and dispersion model to investigate the effects of the
 experimental treatments on these derived coefficients. The approach,
 developed specifically for an application in horticulture, is exemplified
 with data from a trial testing ornamental plants that are subjected to a
 range of treatments during production and home-life. The results of the
 analysis show how a number of control and noise factors affect the rate of
 post-production quality decline. Although the model is used to analyse
 quality data from a trial on ornamental plants, the approach developed is
 expected to be more generally applicable to a wide range of other complex
 production systems. 
Journal: Journal of Applied Statistics 
Pages: 1037-1054 
Issue: 9 
Volume: 36 
Year: 2009 
Keywords: joint mean-dispersion model, ordinal scores, proportional odds model, robust product design, two-stage analysis, 
X-DOI: 10.1080/02664760802566796 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802566796
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:9:p:1037-1054




Template-Type: ReDIF-Article 1.0
Author-Name: Holger Hurtgen 
Author-X-Name-First: Holger 
Author-X-Name-Last: Hurtgen 
Author-Name: Daniel Gervini 
Author-X-Name-First: Daniel 
Author-X-Name-Last: Gervini 
Title: Semiparametric shape-invariant models for periodic data 
Abstract:
  This article presents a novel shape-invariant modeling approach to
 quasi-periodic data. We propose a dynamic semiparametric method that
 estimates the common cycle shape in a nonparametric way and the individual
 phase and amplitude variability in a parametric way. An efficient
 algorithm to compute the estimators is proposed. The behavior of the
 estimators is studied by simulation and by a real-data example. 
Journal: Journal of Applied Statistics 
Pages: 1055-1065 
Issue: 10 
Volume: 36 
Year: 2009 
Keywords: circadian rhythms, nonparametric regression, spline smoothing, 
X-DOI: 10.1080/02664760802562472 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802562472
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:10:p:1055-1065




Template-Type: ReDIF-Article 1.0
Author-Name: E. M. Conlon 
Author-X-Name-First: E. M. 
Author-X-Name-Last: Conlon 
Author-Name: B. L. Postier 
Author-X-Name-First: B. L. 
Author-X-Name-Last: Postier 
Author-Name: B. A. Methe 
Author-X-Name-First: B. A. 
Author-X-Name-Last: Methe 
Author-Name: K. P. Nevin 
Author-X-Name-First: K. P. 
Author-X-Name-Last: Nevin 
Author-Name: D. R. Lovley 
Author-X-Name-First: D. R. 
Author-X-Name-Last: Lovley 
Title: Hierarchical Bayesian meta-analysis models for cross-platform microarray studies 
Abstract:
  The development of new technologies to measure gene expression has been
 calling for statistical methods to integrate findings across
 multiple-platform studies. A common goal of microarray analysis is to
 identify genes with differential expression between two conditions, such
 as treatment versus control. Here, we introduce a hierarchical Bayesian
 meta-analysis model to pool gene expression studies from different
 microarray platforms: spotted DNA arrays and short oligonucleotide arrays.
 The studies have different array design layouts, each with multiple
 sources of data replication, including repeated experiments, slides and
 probes. Our model produces the gene-specific posterior probability of
 differential expression, which is the basis for inference. In simulations
 combining two and five independent studies, our meta-analysis model
 outperformed separate analyses for three commonly used comparison
 measures; it also showed improved receiver operating characteristic
 curves. When combining spotted DNA and CombiMatrix short oligonucleotide
 array studies of Geobacter sulfurreducens, our meta-analysis model
 discovered more genes for fixed thresholds of posterior probability of
 differential expression and Bayesian false discovery than individual study
 analyses. We also examine an alternative model and compare models using
 the deviance information criterion. 
Journal: Journal of Applied Statistics 
Pages: 1067-1085 
Issue: 10 
Volume: 36 
Year: 2009 
Keywords: Bayesian statistics, meta-analysis, microarray data, multiple platform, Markov chain Monte Carlo, deviance information criterion, 
X-DOI: 10.1080/02664760802562480 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802562480
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:10:p:1067-1085




Template-Type: ReDIF-Article 1.0
Author-Name: G. K. Skinner 
Author-X-Name-First: G. K. 
Author-X-Name-Last: Skinner 
Author-Name: G. H. Freeman 
Author-X-Name-First: G. H. 
Author-X-Name-Last: Freeman 
Title: Soccer matches as experiments: how often does the 'best' team win? 
Abstract:
  Models in which the number of goals scored by a team in a soccer match
 follow a Poisson distribution, or a closely related one, have been widely
 discussed. We here consider a soccer match as an experiment to assess
 which of two teams is superior and examine the probability that the
 outcome of the experiment (match) truly represents the relative abilities
 of the two teams. Given a final score, it is possible by using a Bayesian
 approach to quantify the probability that it was or was not the case that
 'the best team won'. For typical scores, the probability of a misleading
 result is significant. Modifying the rules of the game to increase the
 typical number of goals scored would improve the situation, but a level of
 confidence that would normally be regarded as satisfactory could not be
 obtained unless the character of the game was radically changed. 
Journal: Journal of Applied Statistics 
Pages: 1087-1095 
Issue: 10 
Volume: 36 
Year: 2009 
Keywords: football, soccer, experiment design, Poisson statistics, Bayesian, 
X-DOI: 10.1080/02664760802715922 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802715922
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:10:p:1087-1095




Template-Type: ReDIF-Article 1.0
Author-Name: Moawia Alghalith 
Author-X-Name-First: Moawia 
Author-X-Name-Last: Alghalith 
Title: Empirical comparative statics under price and output uncertainty 
Abstract:
  This article provides empirical comparative statics under simultaneous
 price and output uncertainty. In so doing, it presents a simple (one-step)
 and general statistical methodology under price and output uncertainty. 
Journal: Journal of Applied Statistics 
Pages: 1097-1100 
Issue: 10 
Volume: 36 
Year: 2009 
Keywords: estimating equations, output uncertainty, price uncertainty, utility, 
X-DOI: 10.1080/02664760802562506 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802562506
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:10:p:1097-1100




Template-Type: ReDIF-Article 1.0
Author-Name: Corrado Crocetta 
Author-X-Name-First: Corrado 
Author-X-Name-Last: Crocetta 
Author-Name: Nicola Loperfido 
Author-X-Name-First: Nicola 
Author-X-Name-Last: Loperfido 
Title: Maximum likelihood estimation of correlation between maximal oxygen consumption and the 6-min walk test in patients with chronic heart failure 
Abstract:
  Maximal oxygen consumption (VO2max) is the standard measurement used to
 quantify cardiovascular functional capacity and aerobic fitness.
 Unfortunately, it is a costly, impractical and labour-intensive measure to
 obtain. The 6-min walk test (6MWT) also assesses cardiopulmonary function,
 but in contrast to the VO2max test, it is inexpensive and can be performed
 almost anywhere. Various medical studies have addressed the correlation
 between VO2max and 6MWT in patients with chronic heart failure. Of
 particular interest, from a medical point of view, is the conditional
 correlation between the two measures given the individual's height,
 weight, age and gender. In this paper, we have calculated the maximum
 likelihood estimate of the conditional correlation in patients with
 chronic heart failure under the assumption of skew normality. Data were
 recorded from 98 patients in the Operative Unit of Thoracic Surgery in
 Bari, Italy. The estimated conditional correlation was found to be much
 smaller than estimated marginal correlations reported in the medical
 literature. 
Journal: Journal of Applied Statistics 
Pages: 1101-1108 
Issue: 10 
Volume: 36 
Year: 2009 
Keywords: cardiopulmonary exercise testing, correlation, maximal oxygen consumption, 6-min walk test, skew-normal distribution, 
X-DOI: 10.1080/02664760802653545 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802653545
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:10:p:1101-1108




Template-Type: ReDIF-Article 1.0
Author-Name: Ofer Harel 
Author-X-Name-First: Ofer 
Author-X-Name-Last: Harel 
Title: The estimation of R2 and adjusted R2 in incomplete data sets using multiple imputation 
Abstract:
  The coefficient of determination, known also as the R2, is a common
 measure in regression analysis. Many scientists use the R2 and the
 adjusted R2 on a regular basis. In most cases, the researchers treat the
 coefficient of determination as an index of 'usefulness' or 'goodness of
 fit,' and in some cases, they even treat it as a model selection tool. In
 cases in which the data is incomplete, most researchers and common
 statistical software will use complete case analysis in order to estimate
 the R2, a procedure that might lead to biased results. In this paper, I
 introduce the use of multiple imputation for the estimation of R2 and
 adjusted R2 in incomplete data sets. I illustrate my methodology using a
 biomedical example. 
Journal: Journal of Applied Statistics 
Pages: 1109-1118 
Issue: 10 
Volume: 36 
Year: 2009 
Keywords: coefficient of determination, incomplete data, multiple imputation, linear regression, 
X-DOI: 10.1080/02664760802553000 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802553000
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:10:p:1109-1118




Template-Type: ReDIF-Article 1.0
Author-Name: Nirian Martin 
Author-X-Name-First: Nirian 
Author-X-Name-Last: Martin 
Author-Name: Leandro Pardo 
Author-X-Name-First: Leandro 
Author-X-Name-Last: Pardo 
Title: On the asymptotic distribution of Cook's distance in logistic regression models 
Abstract:
  It sometimes occurs that one or more components of the data exert a
 disproportionate influence on the model estimation. We need a reliable
 tool for identifying such troublesome cases in order to decide either
 eliminate from the sample, when the data collect was badly realized, or
 otherwise take care on the use of the model because the results could be
 affected by such components. Since a measure for detecting influential
 cases in linear regression setting was proposed by Cook [Detection of
 influential observations in linear regression, Technometrics 19 (1977),
 pp. 15-18.], apart from the same measure for other models, several new
 measures have been suggested as single-case diagnostics. For most of them
 some cutoff values have been recommended (see [D.A. Belsley, E. Kuh, and
 R.E. Welsch, Regression Diagnostics: Identifying Influential Data and
 Sources of Collinearity, 2nd ed., John Wiley & Sons, New York, Chichester,
 Brisban, (2004).], for instance), however the lack of a quantile type
 cutoff for Cook's statistics has induced the analyst to deal only with
 index plots as worthy diagnostic tools. Focussed on logistic regression,
 the aim of this paper is to provide the asymptotic distribution of Cook's
 distance in order to look for a meaningful cutoff point for detecting
 influential and leverage observations. 
Journal: Journal of Applied Statistics 
Pages: 1119-1146 
Issue: 10 
Volume: 36 
Year: 2009 
Keywords: Cook's distance, logistic regression, maximum likelihood estimation, outlier, leverage, 
X-DOI: 10.1080/02664760802562498 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802562498
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:10:p:1119-1146




Template-Type: ReDIF-Article 1.0
Author-Name: R. H. Rieger 
Author-X-Name-First: R. H. 
Author-X-Name-Last: Rieger 
Author-Name: C. R. Weinberg 
Author-X-Name-First: C. R. 
Author-X-Name-Last: Weinberg 
Title: Testing for violations of the homogeneity needed for conditional logistic regression 
Abstract:
  In epidemiologic studies where the outcome is binary, the data often
 arise as clusters, as when siblings, friends or neighbors are used as
 matched controls in a case-control study. Conditional logistic regression
 (CLR) is typically used for such studies to estimate the odds ratio for an
 exposure of interest. However, CLR assumes the exposure coefficient is the
 same in every cluster, and CLR-based inference can be badly biased when
 homogeneity is violated. Existing methods for testing goodness-of-fit for
 CLR are not designed to detect such violations. Good alternative methods
 of analysis exist if one suspects there is heterogeneity across clusters.
 However, routine use of alternative robust approaches when there is no
 appreciable heterogeneity could cause loss of precision and be
 computationally difficult, particularly if the clusters are small. We
 propose a simple non-parametric test, the test of heterogeneous
 susceptibility (THS), to assess the assumption of homogeneity of a
 coefficient across clusters. The test is easy to apply and provides
 guidance as to the appropriate method of analysis. Simulations demonstrate
 that the THS has reasonable power to reveal violations of homogeneity. We
 illustrate by applying the THS to a study of periodontal disease. 
Journal: Journal of Applied Statistics 
Pages: 1147-1157 
Issue: 10 
Volume: 36 
Year: 2009 
Keywords: clustered binary outcomes, conditional logistic regression, heterogeneity of response, 
X-DOI: 10.1080/02664760802638124 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802638124
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:10:p:1147-1157




Template-Type: ReDIF-Article 1.0
Author-Name: S. Balamurali 
Author-X-Name-First: S. 
Author-X-Name-Last: Balamurali 
Author-Name: Chi-Hyuck Jun 
Author-X-Name-First: Chi-Hyuck 
Author-X-Name-Last: Jun 
Title: Designing of a variables two-plan system by minimizing the average sample number 
Abstract:
  This article proposes a variables two-plan sampling system called
 tightened-normal-tightened (TNT) sampling inspection scheme where the
 quality characteristic follows a normal distribution or a lognormal
 distribution and has an upper or a lower specification limit. The TNT
 variables sampling inspection scheme will be useful when testing is costly
 and destructive. The advantages of the variables TNT scheme over variables
 single and double sampling plans and attributes TNT scheme are discussed.
 Tables are also constructed for the selection of parameters of known and
 unknown standard deviation variables TNT schemes for a given acceptable
 quality level (AQL) and limiting quality level (LQL). The problem is
 formulated as a nonlinear programming where the objective function to be
 minimized is the average sample number and the constraints are related to
 lot acceptance probabilities at AQL and LQL under the operating
 characteristic curve. 
Journal: Journal of Applied Statistics 
Pages: 1159-1172 
Issue: 10 
Volume: 36 
Year: 2009 
Keywords: average sample number, OC curve, sampling system, two-plan system, 
X-DOI: 10.1080/02664760802562514 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802562514
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:10:p:1159-1172




Template-Type: ReDIF-Article 1.0
Author-Name: Miroslav Ristic 
Author-X-Name-First: Miroslav 
Author-X-Name-Last: Ristic 
Title: Probability with R 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 1173-1173 
Issue: 10 
Volume: 36 
Year: 2009 
X-DOI: 10.1080/02664760902811555 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902811555
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:10:p:1173-1173




Template-Type: ReDIF-Article 1.0
Author-Name: M. Dolores Ugarte 
Author-X-Name-First: M. Dolores 
Author-X-Name-Last: Ugarte 
Title: Longitudinal data analysis 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 1175-1176 
Issue: 10 
Volume: 36 
Year: 2009 
X-DOI: 10.1080/02664760902811563 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902811563
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:10:p:1175-1176




Template-Type: ReDIF-Article 1.0
Author-Name: Silvia Salini 
Author-X-Name-First: Silvia 
Author-X-Name-Last: Salini 
Author-Name: Ron Kenett 
Author-X-Name-First: Ron 
Author-X-Name-Last: Kenett 
Title: Bayesian networks of customer satisfaction survey data 
Abstract:
  A Bayesian network (BN) is a probabilistic graphical model that
 represents a set of variables and their probabilistic dependencies.
 Formally, BNs are directed acyclic graphs whose nodes represent variables,
 and whose arcs encode the conditional dependencies among the variables.
 Nodes can represent any kind of variable, be it a measured parameter, a
 latent variable, or a hypothesis. They are not restricted to represent
 random variables, which form the &#x201c;Bayesian&#x201d; aspect of a BN.
 Efficient algorithms exist that perform inference and learning in BNs. BNs
 that model sequences of variables are called dynamic BNs. In this context,
 [A. Harel, R. Kenett, and F. Ruggeri, Modeling web usability diagnostics
 on the basis of usage statistics, in Statistical Methods in eCommerce
 Research, W. Jank and G. Shmueli, eds., Wiley, 2008] provide a comparison
 between Markov Chains and BNs in the analysis of web usability from
 e-commerce data. A comparison of regression models, structural equation
 models, and BNs is presented in Anderson et al. [R.D. Anderson, R.D.
 Mackoy, V.B. Thompson, and G. Harrell, A bayesian network estimation of
 the service-profit Chain for transport service satisfaction, Decision
 Sciences 35(4), (2004), pp. 665-689]. In this article we apply BNs to the
 analysis of customer satisfaction surveys and demonstrate the potential of
 the approach. In particular, BNs offer advantages in implementing models
 of cause and effect over other statistical techniques designed primarily
 for testing hypotheses. Other advantages include the ability to conduct
 probabilistic inference for prediction and diagnostic purposes with an
 output that can be intuitively understood by managers. 
Journal: Journal of Applied Statistics 
Pages: 1177-1189 
Issue: 11 
Volume: 36 
Year: 2009 
Keywords: Bayesian networks, customer satisfaction, Eurobarometer, service quality, 
X-DOI: 10.1080/02664760802587982 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802587982
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:11:p:1177-1189




Template-Type: ReDIF-Article 1.0
Author-Name: Angela He 
Author-X-Name-First: Angela 
Author-X-Name-Last: He 
Author-Name: Alan Wan 
Author-X-Name-First: Alan 
Author-X-Name-Last: Wan 
Title: Predicting daily highs and lows of exchange rates: a cointegration analysis 
Abstract:
  This article presents empirical evidence that links the daily highs and
 lows of exchange rates of the US dollar against two other major currencies
 over a 15 year period. We find that the log high and log low of an
 exchange rate are cointegrated, and the error correction term is
 well-approximated by the range, which is defined as the difference between
 the log high and log low. We further assess the empirical relevance of
 jointly analyzing the highs, lows and the ranges by comparing the range
 forecasts generated from the cointegration framework with those from
 random walk and autoregressive integrated moving average (ARIMA)
 specifications. The ability of range forecasts as predictors of implied
 volatility for a European style currency option is also evaluated. Our
 results show that aside from a limited set of exceptions, the
 cointegration framework generally outperforms the random walk and ARIMA
 models in an out-of-sample forecast contest. 
Journal: Journal of Applied Statistics 
Pages: 1191-1204 
Issue: 11 
Volume: 36 
Year: 2009 
Keywords: daily high, daily low, direction of change, implied volatility, VECM, 
X-DOI: 10.1080/02664760802578304 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802578304
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:11:p:1191-1204




Template-Type: ReDIF-Article 1.0
Author-Name: Chien-Wei Wu 
Author-X-Name-First: Chien-Wei 
Author-X-Name-Last: Wu 
Author-Name: Tsai-Yu Lin 
Author-X-Name-First: Tsai-Yu 
Author-X-Name-Last: Lin 
Title: A Bayesian procedure for assessing process performance based on the third-generation capability index 
Abstract:
  Capability indices that qualify process potential and process performance
 are practical tools for successful quality improvement activities and
 quality program implementation. Most existing methods to assess process
 capability were derived on the basis of the traditional frequentist point
 of view. This paper considers the problem of estimating and testing
 process capability based on the third-generation capability index Cpmk
 from the Bayesian point of view. We first derive the posterior probability
 p for the process under investigation is capable. The one-sided credible
 interval, a Bayesian analog of the classical lower confidence interval,
 can be obtained to assess process performance. To investigate the
 effectiveness of the derived results, a series of simulation was
 undertaken. The results indicate that the performance of the proposed
 Bayesian approach depends strongly on the value of
 &#x3be;=(&#x3bc;-T)/&#x3c3;. It performs very well with the accurate
 coverage rate when &#x3bc; is sufficiently far from T. In those cases,
 they have the same acceptable performance even though the sample size n is
 as small as 25. 
Journal: Journal of Applied Statistics 
Pages: 1205-1223 
Issue: 11 
Volume: 36 
Year: 2009 
Keywords: Bayesian approach, lower confidence bound, process capability, posterior probability, sampling distribution, 
X-DOI: 10.1080/02664760802582298 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802582298
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:11:p:1205-1223




Template-Type: ReDIF-Article 1.0
Author-Name: Seok-Oh Jeong 
Author-X-Name-First: Seok-Oh 
Author-X-Name-Last: Jeong 
Author-Name: Kee-Hoon Kang 
Author-X-Name-First: Kee-Hoon 
Author-X-Name-Last: Kang 
Title: Nonparametric estimation of value-at-risk 
Abstract:
  This paper develops a fully nonparametric method for estimating
 value-at-risk based on the adaptive volatility estimation and the
 nonparametric quantile estimation. The proposed method is simple, fast and
 easy to implement. We evaluated its numerical performance on the basis of
 Monte Carlo study for numerous models. We also provided an empirical
 application to KOrean Stock Price Index data, which turned out to be
 successful by backtesting. 
Journal: Journal of Applied Statistics 
Pages: 1225-1238 
Issue: 11 
Volume: 36 
Year: 2009 
Keywords: value-at-risk, volatility, local homogeneity, quantile estimation, risk management, KOSPI, 
X-DOI: 10.1080/02664760802607517 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802607517
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:11:p:1225-1238




Template-Type: ReDIF-Article 1.0
Author-Name: Kenneth Berry 
Author-X-Name-First: Kenneth 
Author-X-Name-Last: Berry 
Author-Name: Janis Johnston 
Author-X-Name-First: Janis 
Author-X-Name-Last: Johnston 
Author-Name: Paul Mielke 
Author-X-Name-First: Paul 
Author-X-Name-Last: Mielke 
Title: Exact and resampling probability values for the Piccarreta nominal-ordinal index of association 
Abstract:
  Exact, resampling, and Pearson type III permutation methods are provided
 to compute probability values for Piccarreta's nominal-ordinal index of
 association. The resampling permutation method provides good approximate
 probability values based on the proportion of resampled test statistic
 values equal to or greater than the observed test statistic value. 
Journal: Journal of Applied Statistics 
Pages: 1239-1249 
Issue: 11 
Volume: 36 
Year: 2009 
Keywords: contingency tables, nominal-ordinal association, resampling, permutation, probability, 
X-DOI: 10.1080/02664760802578312 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802578312
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:11:p:1239-1249




Template-Type: ReDIF-Article 1.0
Author-Name: Juan Sun 
Author-X-Name-First: Juan 
Author-X-Name-Last: Sun 
Author-Name: Xiaohui Ouyang 
Author-X-Name-First: Xiaohui 
Author-X-Name-Last: Ouyang 
Author-Name: Hidekatsu Yoshioka 
Author-X-Name-First: Hidekatsu 
Author-X-Name-Last: Yoshioka 
Author-Name: Wenli Wang 
Author-X-Name-First: Wenli 
Author-X-Name-Last: Wang 
Author-Name: Chun Fan 
Author-X-Name-First: Chun 
Author-X-Name-Last: Fan 
Author-Name: Hongwei Li 
Author-X-Name-First: Hongwei 
Author-X-Name-Last: Li 
Author-Name: Jianru Wang 
Author-X-Name-First: Jianru 
Author-X-Name-Last: Wang 
Author-Name: Yalin Liu 
Author-X-Name-First: Yalin 
Author-X-Name-Last: Liu 
Author-Name: Li Su 
Author-X-Name-First: Li 
Author-X-Name-Last: Su 
Author-Name: Heping Ma 
Author-X-Name-First: Heping 
Author-X-Name-Last: Ma 
Author-Name: Ying liu 
Author-X-Name-First: Ying 
Author-X-Name-Last: liu 
Author-Name: Yuwen Zhang 
Author-X-Name-First: Yuwen 
Author-X-Name-Last: Zhang 
Author-Name: Xingguang Zhang 
Author-X-Name-First: Xingguang 
Author-X-Name-Last: Zhang 
Author-Name: Xuemei Wang 
Author-X-Name-First: Xuemei 
Author-X-Name-Last: Wang 
Author-Name: Yueling Hu 
Author-X-Name-First: Yueling 
Author-X-Name-Last: Hu 
Title: A progressive rise in stomach cancer-related mortality rate during 1970-1995 in Japanese individuals over 85 years of age 
Abstract:
  A large number of studies have shown a gradual fall in stomach
 cancer-related mortality rate during the last decade. Here we analyzed the
 pattern of stomach cancer-related mortality rates in Japanese aged&#x3e;85
 years from 1970 to 1995. We used data for the entire population of Japan.
 The magnitude of change was measured by relative risk and
 cause-elimination life tables to distinguish time trends in mortality
 rates of stomach cancer for individuals over 85 years of age compared with
 other age groups (55-84 years). In the over-85 age group, stomach cancer
 mortality increased from 374 in 1970 to 662 in 1995 per 100,000 (77%) for
 males and from 232 to 296 per 100,000 (27%) for females. Using the 55-59
 years group as the reference category, the relative risk increased from
 2.3 to 9.9 and from 2.8 to 11.1 in men and women, respectively. The
 effects of mortality on life expectancy also increased 1.5 times and 1.1
 times, respectively. Our results showed a rise of stomach cancer mortality
 in Japanese aged over 85 years, which paralleled the increase in relative
 risk and negative contribution to life expectancy. While the mortality of
 younger age groups is decreasing, the change over from increase to
 decrease in the over-85 age group is only just beginning. 
Journal: Journal of Applied Statistics 
Pages: 1251-1258 
Issue: 11 
Volume: 36 
Year: 2009 
Keywords: stomach cancer, mortality, age, Japan, old, 
X-DOI: 10.1080/02664760802582272 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802582272
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:11:p:1251-1258




Template-Type: ReDIF-Article 1.0
Author-Name: Nicholas Longford 
Author-X-Name-First: Nicholas 
Author-X-Name-Last: Longford 
Title: Analysis of all-zero binomial outcomes with borderline and equilibrium priors 
Abstract:
  This article is concerned with the analysis of a random sample from a
 binomial distribution when all the outcomes are zero (or unity). We
 discuss how elicitation of the prior can be reduced to asking the expert
 whether (and which of) the so-called borderline or equilibrium priors are
 plausible. 
Journal: Journal of Applied Statistics 
Pages: 1259-1265 
Issue: 11 
Volume: 36 
Year: 2009 
Keywords: beta distribution, borderline prior, equilibrium prior, expected loss, plausible prior, 
X-DOI: 10.1080/02664760802603813 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802603813
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:11:p:1259-1265




Template-Type: ReDIF-Article 1.0
Author-Name: Getachew Dagne 
Author-X-Name-First: Getachew 
Author-X-Name-Last: Dagne 
Author-Name: James Snyder 
Author-X-Name-First: James 
Author-X-Name-Last: Snyder 
Title: Bayesian hierarchical duration model for repeated events: an application to behavioral observations 
Abstract:
  This article presents a continuous-time Bayesian model for analyzing
 durations of behavior displays in social interactions. Duration data of
 social interactions are often complex because of repeated behaviors
 (events) at individual or group (e.g. dyad) level, multiple behaviors
 (multistates), and several choices of exit from a current event (competing
 risks). A multilevel, multistate model is proposed to adequately
 characterize the behavioral processes. The model incorporates
 dyad-specific and transition-specific random effects to account for
 heterogeneity among dyads and interdependence among competing risks. The
 proposed method is applied to child-parent observational data derived from
 the School Transitions Project to assess the relation of emotional
 expression in child-parent interaction to risk for early and persisting
 child conduct problems. 
Journal: Journal of Applied Statistics 
Pages: 1267-1279 
Issue: 11 
Volume: 36 
Year: 2009 
Keywords: competing risks, event history, survival, multilevel models, multistates, Bayesian inference, semi-Markov models, 
X-DOI: 10.1080/02664760802587032 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802587032
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:11:p:1267-1279




Template-Type: ReDIF-Article 1.0
Author-Name: S. Sampath 
Author-X-Name-First: S. 
Author-X-Name-Last: Sampath 
Author-Name: V. Varalakshmi 
Author-X-Name-First: V. 
Author-X-Name-Last: Varalakshmi 
Author-Name: R. Geetha 
Author-X-Name-First: R. 
Author-X-Name-Last: Geetha 
Title: Estimation under systematic sampling schemes for parabolic populations 
Abstract:
  In this paper, three sampling-estimating strategies involving linear,
 balanced and modified systematic sampling are considered for the
 estimation of a finite population total in the presence of parabolic
 trend. Using appropriate super-population models, their performances are
 evaluated. For super-population models with constant variance, Yates
 corrected estimator under linear systematic sampling is shown to perform
 well. Choices of variance functions under which modified and balanced
 systematic sampling perform well are also identified based on extensive
 numerical studies. 
Journal: Journal of Applied Statistics 
Pages: 1281-1292 
Issue: 11 
Volume: 36 
Year: 2009 
Keywords: finite population, systematic sampling, super-population models, average variances, 
X-DOI: 10.1080/02664760802578338 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802578338
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:11:p:1281-1292




Template-Type: ReDIF-Article 1.0
Author-Name: S. Mukhopadhyay 
Author-X-Name-First: S. 
Author-X-Name-Last: Mukhopadhyay 
Author-Name: S. W. Looney 
Author-X-Name-First: S. W. 
Author-X-Name-Last: Looney 
Title: Quantile dispersion graphs to compare the efficiencies of cluster randomized designs 
Abstract:
  The purpose of this article is to compare efficiencies of several cluster
 randomized designs using the method of quantile dispersion graphs (QDGs).
 A cluster randomized design is considered whenever subjects are randomized
 at a group level but analyzed at the individual level. A prior knowledge
 of the correlation existing between subjects within the same cluster is
 necessary to design these cluster randomized trials. Using the QDG
 approach, we are able to compare several cluster randomized designs
 without requiring any information on the intracluster correlation. For a
 given design, several quantiles of the power function, which are directly
 related to the effect size, are obtained for several effect sizes. The
 quantiles depend on the intracluster correlation present in the model. The
 dispersion of these quantiles over the space of the unknown intracluster
 correlation is determined, and then depicted by the QDGs. Two applications
 of the proposed methodology are presented. 
Journal: Journal of Applied Statistics 
Pages: 1293-1305 
Issue: 11 
Volume: 36 
Year: 2009 
Keywords: quantile dispersion graphs, power function, intracluster correlation, effect size, noncentrality parameter, 
X-DOI: 10.1080/02664760902914508 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902914508
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:11:p:1293-1305




Template-Type: ReDIF-Article 1.0
Author-Name: Jonathan Bradley 
Author-X-Name-First: Jonathan 
Author-X-Name-Last: Bradley 
Author-Name: David Farnsworth 
Author-X-Name-First: David 
Author-X-Name-Last: Farnsworth 
Title: Testing for Mutual Exclusivity 
Abstract:
  A test for two events being mutually exclusive is presented for the case
 in which there are known rates of misclassification of the events. The
 test can be utilized in other situations, such as to test whether a set is
 a subset of another set. In the test, the null value of the probability of
 the intersection is replaced by the expected value of the number
 determined to be in the intersection by the imperfect diagnostic tools.
 The test statistic is the number in a sample that is judged to be in the
 intersection. Medical testing applications are emphasized. 
Journal: Journal of Applied Statistics 
Pages: 1307-1314 
Issue: 11 
Volume: 36 
Year: 2009 
Keywords: intersection test, misclassification rate, misdiagnosis rate, mutual exclusivity test, power, subset test, 
X-DOI: 10.1080/02664760802582306 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802582306
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:11:p:1307-1314




Template-Type: ReDIF-Article 1.0
Author-Name: Luigi D'Ambra 
Author-X-Name-First: Luigi 
Author-X-Name-Last: D'Ambra 
Author-Name: Onur Koksoy 
Author-X-Name-First: Onur 
Author-X-Name-Last: Koksoy 
Author-Name: Biagio Simonetti 
Author-X-Name-First: Biagio 
Author-X-Name-Last: Simonetti 
Title: Cumulative correspondence analysis of ordered categorical data from industrial experiments 
Abstract:
  Most studies of quality improvement deal with ordered categorical data
 from industrial experiments. Accounting for the ordering of such data
 plays an important role in effectively determining the optimal factor
 level of combination. This paper utilizes the correspondence analysis to
 develop a procedure to improve the ordered categorical response in a
 multifactor state system based on Taguchi's statistic. Users may find the
 proposed procedure in this paper to be attractive because we suggest a
 simple and also popular statistical tool for graphically identifying the
 really important factors and determining the levels to improve process
 quality. A case study for optimizing the polysilicon deposition process in
 a very large-scale integrated circuit is provided to demonstrate the
 effectiveness of the proposed procedure. 
Journal: Journal of Applied Statistics 
Pages: 1315-1328 
Issue: 12 
Volume: 36 
Year: 2009 
Keywords: ordered categories, correspondence analysis, quality engineering, experimental design, Taguchi's statistic, 
X-DOI: 10.1080/02664760802638090 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802638090
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:12:p:1315-1328




Template-Type: ReDIF-Article 1.0
Author-Name: Rosa Arboretti Giancristofaro 
Author-X-Name-First: Rosa 
Author-X-Name-Last: Arboretti Giancristofaro 
Author-Name: Stefano Bonnini 
Author-X-Name-First: Stefano 
Author-X-Name-Last: Bonnini 
Author-Name: Luigi Salmaso 
Author-X-Name-First: Luigi 
Author-X-Name-Last: Salmaso 
Title: Employment status and education/employment relationship of PhD graduates from the University of Ferrara 
Abstract:
  Two sample surveys of Post-Docs were planned and carried out at the
 University of Ferrara in 2004 and 2007 aimed at determining the
 professional status of Post-Docs, the relationship between their PhD
 education and employment, and their satisfaction with certain aspects of
 the education and research program. As part of these surveys, two
 methodological contributions were developed. The first concerns an
 extension of the non-parametric combination of dependent rankings to
 construct a synthesis of composite indicators measuring satisfaction with
 particular aspects of PhD programs [R. Arboretti Giancristofaro and L.
 Salmaso, Global ranking indicators with application to the evaluation of
 PhD programs, Atti del Convegno &#x201c;Valutazione e Customer
 Satisfaction per la Qualita dei Servizi&#x201d;, Roma, 8-9 Settembre 2005,
 pp. 19-22; R. Arboretti Giancristofaro, S. Bonnini, and L. Salmaso, A
 performance indicator for multivariate data, Quad. Stat. 9 (2007), pp.
 1-29; R. Arboretti Giancristofaro, F. Pesarin, and L. Salmaso,
 Nonparametric approaches for multivariate testing with mixed variables and
 for ranking on ordered categorical variables with an application to the
 evaluation of PhD programs, in Real Data Analysis, S. Sawilowsky, ed., a
 volume in Quantitative Methods in Education and the Behavioral Sciences:
 Issues, Research and Teaching, Ronald C. Serlin, series ed., Information
 Age Publishing, Charlotte, North Carolina, 2007, pp. 355-385]. The
 procedure was applied to highlight differences in the interviewed
 Post-Docs' multivariate satisfaction profiles in relation to two aspects:
 education/employment relationship; employment expectations; and
 opportunities. The second consists of an inferential procedure providing a
 solution to the problem of hypothesis testing, where the objective is to
 compare the heterogeneity of two populations on the basis of sampling data
 [G.R. Arboretti, S. Bonnini, and F. Pesarin, A permutation approach for
 testing heterogeneity in two-sample categorical variables, Stat. Comput.
 (2009) doi: 10.1007/S11222-008-9085-8.]. The procedure was applied to
 compare the degrees of heterogeneity of Post-Doc judgments in the two
 surveys with regard to the adequacy of the PhD education for the work
 carried out. 
Journal: Journal of Applied Statistics 
Pages: 1329-1344 
Issue: 12 
Volume: 36 
Year: 2009 
Keywords: employment survey, performance indicators, heterogeneity tests, 
X-DOI: 10.1080/02664760802638108 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802638108
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:12:p:1329-1344




Template-Type: ReDIF-Article 1.0
Author-Name: S. B. Caudill 
Author-X-Name-First: S. B. 
Author-X-Name-Last: Caudill 
Author-Name: F. G. Mixon 
Author-X-Name-First: F. G. 
Author-X-Name-Last: Mixon 
Title: More on testing the normality assumptionin the Tobit Model 
Abstract:
  In a recent volume of this journal, Holden [Testing the normality
 assumption in the Tobit Model, J. Appl. Stat. 31 (2004) pp. 521-532]
 presents Monte Carlo evidence comparing several tests for departures from
 normality in the Tobit Model. This study adds to the work of Holden by
 considering another test, and several information criteria, for detecting
 departures from normality in the Tobit Model. The test given here is a
 modified likelihood ratio statistic based on a partially adaptive
 estimator of the Censored Regression Model using the approach of Caudill
 [A partially adaptive estimator for the Censored Regression Model based on
 a mixture of normal distributions, Working Paper, Department of Economics,
 Auburn University, 2007]. The information criteria examined include the
 Akaike's Information Criterion (AIC), the Consistent AIC (CAIC), the
 Bayesian information criterion (BIC), and the Akaike's BIC (ABIC). In
 terms of fewest 'rejections' of a true null, the best performance is
 exhibited by the CAIC and the BIC, although, like some of the statistics
 examined by Holden, there are computational difficulties with each. 
Journal: Journal of Applied Statistics 
Pages: 1345-1352 
Issue: 12 
Volume: 36 
Year: 2009 
Keywords: Censored Regression Model, departures from normality, 
X-DOI: 10.1080/02664760802653578 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802653578
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:12:p:1345-1352




Template-Type: ReDIF-Article 1.0
Author-Name: Y. Zhao 
Author-X-Name-First: Y. 
Author-X-Name-Last: Zhao 
Author-Name: A. H. Lee 
Author-X-Name-First: A. H. 
Author-X-Name-Last: Lee 
Author-Name: V. Burke 
Author-X-Name-First: V. 
Author-X-Name-Last: Burke 
Author-Name: K. K. W. Yau 
Author-X-Name-First: K. K. W. 
Author-X-Name-Last: Yau 
Title: Testing for zero-inflation in count series: application to occupational health 
Abstract:
  Count data series with extra zeros relative to a Poisson distribution are
 common in many biomedical applications. A score test is presented to
 assess whether the zero-inflation problem is significant to warrant the
 analysis by the more complex zero-inflated Poisson autoregression model.
 The score test is implemented as a computer program in the Splus platform.
 For illustration, the test procedure is applied to a workplace injury
 series where many zero counts are observed due to the heterogeneity in
 injury risk and the dynamic population involved. 
Journal: Journal of Applied Statistics 
Pages: 1353-1359 
Issue: 12 
Volume: 36 
Year: 2009 
Keywords: occupational health, random effects, score test, workplace injuries, zero-inflation, 
X-DOI: 10.1080/02664760802653586 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802653586
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:12:p:1353-1359




Template-Type: ReDIF-Article 1.0
Author-Name: Christopher Gjestvang 
Author-X-Name-First: Christopher 
Author-X-Name-Last: Gjestvang 
Author-Name: Sarjinder Singh 
Author-X-Name-First: Sarjinder 
Author-X-Name-Last: Singh 
Title: An improved randomized response model: estimation of mean 
Abstract:
  In this paper, we suggest a new randomized response model useful for
 collecting information on quantitative sensitive variables such as drug
 use and income. The resultant estimator has been found to be better than
 the usual additive randomized response model. An interesting feature of
 the proposed model is that it is free from the known parameters of the
 scrambling variable unlike the additive model due to Himmelfarb and Edgell
 [S. Himmelfarb and S.E. Edgell, Additive constant model: a randomized
 response technique for eliminating evasiveness to quantitative response
 questions, Psychol. Bull. 87(1980), 525-530]. Relative efficiency of the
 proposed model has also been studied with the corresponding competitors.
 At the end, an application of the proposed model has been discussed. 
Journal: Journal of Applied Statistics 
Pages: 1361-1367 
Issue: 12 
Volume: 36 
Year: 2009 
Keywords: sensitive variable, estimation of mean, randomized response model, scrambling variables, 
X-DOI: 10.1080/02664760802684151 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802684151
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:12:p:1361-1367




Template-Type: ReDIF-Article 1.0
Author-Name: J. Fredrik Lindstrom 
Author-X-Name-First: J. Fredrik 
Author-X-Name-Last: Lindstrom 
Author-Name: H. E. T. Holgersson 
Author-X-Name-First: H. E. T. 
Author-X-Name-Last: Holgersson 
Title: Forecast mean squared error reductionin the VAR(1) process 
Abstract:
  When VAR models are used to predict future outcomes, the forecast error
 can be substantial. Through imposition of restrictions on the off-diagonal
 elements of the parameter matrix, however, the information in the process
 may be condensed to the marginal processes. In particular, if the
 cross-autocorrelations in the system are small and only a small sample is
 available, then such a restriction may reduce the forecast mean squared
 error considerably. In this paper, we propose three different techniques
 to decide whether to use the restricted or unrestricted model, i.e. the
 full VAR(1) model or only marginal AR(1) models. In a Monte Carlo
 simulation study, all three proposed tests have been found to behave quite
 differently depending on the parameter setting. One of the proposed tests
 stands out, however, as the preferred one and is shown to outperform other
 estimators for a wide range of parameter settings. 
Journal: Journal of Applied Statistics 
Pages: 1369-1384 
Issue: 12 
Volume: 36 
Year: 2009 
Keywords: VAR models, prediction error, pre-test, linear hypothesis, selection criteria, 
X-DOI: 10.1080/02664760802715898 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802715898
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:12:p:1369-1384




Template-Type: ReDIF-Article 1.0
Author-Name: J. L. Alfaro 
Author-X-Name-First: J. L. 
Author-X-Name-Last: Alfaro 
Author-Name: J. Fco. Ortega 
Author-X-Name-First: J. Fco. 
Author-X-Name-Last: Ortega 
Title: A comparison of robust alternatives to Hotelling's T2 control chart 
Abstract:
  Control charts are one of the widest used techniques in statistical
 process control. In Phase I, historical observations are analysed in order
 to construct a control chart. Because of the existence of multiple
 outliers that are undetected by control charts such as Hotelling's T2 due
 to the masking effect, robust alternatives to Hotelling's T2 have been
 developed based on minimum volume ellipsoid (MVE) estimators, minimum
 covariance determinant (MCD) estimators, reweighted MCD estimators or
 trimmed estimators. In this paper, we use a simulation study to analyse
 the performance of each alternative in various situations and offer
 guidance for the correct use of each estimator. 
Journal: Journal of Applied Statistics 
Pages: 1385-1396 
Issue: 12 
Volume: 36 
Year: 2009 
Keywords: statistical quality control, multivariate control chart, outliers, masking effect, robust estimators, 
X-DOI: 10.1080/02664760902810813 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902810813
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:36:y:2009:i:12:p:1385-1396




Template-Type: ReDIF-Article 1.0
Author-Name: Kahadawala Cooray 
Author-X-Name-First: Kahadawala 
Author-X-Name-Last: Cooray 
Author-Name: Malwane Ananda 
Author-X-Name-First: Malwane 
Author-X-Name-Last: Ananda 
Title: Analyzing survival data with highly negatively skewed distribution: The Gompertz-sinh family 
Abstract:
  In this article, we explore a new two-parameter family of distribution,
 which is derived by suitably replacing the exponential term in the
 Gompertz distribution with a hyperbolic sine term. The resulting new
 family of distribution is referred to as the Gompertz-sinh distribution,
 and it possesses a thicker and longer lower tail than the Gompertz family,
 which is often used to model highly negatively skewed data. Moreover, we
 introduce a useful generalization of this model by adding a second shape
 parameter to accommodate a variety of density shapes as well as
 nondecreasing hazard shapes. The flexibility and better fitness of the new
 family, as well as its generalization, is demonstrated by providing
 well-known examples that involve complete, group, and censored data. 
Journal: Journal of Applied Statistics 
Pages: 1-11 
Issue: 1 
Volume: 37 
Year: 2010 
Keywords: goodness-of-fit, gompertz distribution, maximum likelihood, 
X-DOI: 10.1080/02664760802663072 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802663072
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:1:p:1-11




Template-Type: ReDIF-Article 1.0
Author-Name: A. Karagrigoriou 
Author-X-Name-First: A. 
Author-X-Name-Last: Karagrigoriou 
Author-Name: C. Koukouvinos 
Author-X-Name-First: C. 
Author-X-Name-Last: Koukouvinos 
Author-Name: K. Mylona 
Author-X-Name-First: K. 
Author-X-Name-Last: Mylona 
Title: On the advantages of the non-concave penalized likelihood model selection method with minimum prediction errors in large-scale medical studies 
Abstract:
  Variable and model selection problems are fundamental to high-dimensional
 statistical modeling in diverse fields of sciences. Especially in health
 studies, many potential factors are usually introduced to determine an
 outcome variable. This paper deals with the problem of high-dimensional
 statistical modeling through the analysis of the trauma annual data in
 Greece for 2005. The data set is divided into the experiment and control
 sets and consists of 6334 observations and 112 factors that include
 demographic, transport and intrahospital data used to detect possible risk
 factors of death. In our study, different model selection techniques are
 applied to the experiment set and the notion of deviance is used on the
 control set to assess the fit of the overall selected model. The
 statistical methods employed in this work were the non-concave penalized
 likelihood methods, smoothly clipped absolute deviation, least absolute
 shrinkage and selection operator, and Hard, the generalized linear
 logistic regression, and the best subset variable selection.The way of
 identifying the significant variables in large medical data sets along
 with the performance and the pros and cons of the various statistical
 techniques used are discussed. The performed analysis reveals the distinct
 advantages of the non-concave penalized likelihood methods over the
 traditional model selection techniques. 
Journal: Journal of Applied Statistics 
Pages: 13-24 
Issue: 1 
Volume: 37 
Year: 2010 
Keywords: model selection, generalized linear model, non-concave penalized likelihood, high-dimensional data set, deviance, trauma, 
X-DOI: 10.1080/02664760802638116 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802638116
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:1:p:13-24




Template-Type: ReDIF-Article 1.0
Author-Name: M. Saleem 
Author-X-Name-First: M. 
Author-X-Name-Last: Saleem 
Author-Name: M. Aslam 
Author-X-Name-First: M. 
Author-X-Name-Last: Aslam 
Author-Name: P. Economou 
Author-X-Name-First: P. 
Author-X-Name-Last: Economou 
Title: On the Bayesian analysis of the mixture of power function distribution using the complete and the censored sample 
Abstract:
  The power function distribution is often used to study the electrical
 component reliability. In this paper, we model a heterogeneous population
 using the two-component mixture of the power function distribution. A
 comprehensive simulation scheme including a large number of parameter
 points is followed to highlight the properties and behavior of the
 estimates in terms of sample size, censoring rate, parameters size and the
 proportion of the components of the mixture. The parameters of the power
 function mixture are estimated and compared using the Bayes estimates. A
 simulated mixture data with censored observations is generated by
 probabilistic mixing for the computational purposes. Elegant closed form
 expressions for the Bayes estimators and their variances are derived for
 the censored sample as well as for the complete sample. Some interesting
 comparison and properties of the estimates are observed and presented. The
 system of three non-linear equations, required to be solved iteratively
 for the computations of maximum likelihood (ML) estimates, is derived. The
 complete sample expressions for the ML estimates and for their variances
 are also given. The components of the information matrix are constructed
 as well. Uninformative as well as informative priors are assumed for the
 derivation of the Bayes estimators. A real-life mixture data example has
 also been discussed. The posterior predictive distribution with the
 informative Gamma prior is derived, and the equations required to find the
 lower and upper limits of the predictive intervals are constructed. The
 Bayes estimates are evaluated under the squared error loss function. 
Journal: Journal of Applied Statistics 
Pages: 25-40 
Issue: 1 
Volume: 37 
Year: 2010 
Keywords: information matrix, censored sampling, inverse transform method, squared error loss function, predictive distribution, 
X-DOI: 10.1080/02664760902914557 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902914557
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:1:p:25-40




Template-Type: ReDIF-Article 1.0
Author-Name: Athanasios Micheas 
Author-X-Name-First: Athanasios 
Author-X-Name-Last: Micheas 
Author-Name: Yuqiang Peng 
Author-X-Name-First: Yuqiang 
Author-X-Name-Last: Peng 
Title: Bayesian Procrustes analysis with applications to hydrology 
Abstract:
  In this paper, we introduce Procrustes analysis in a Bayesian framework,
 by treating the classic Procrustes regression equation from a Bayesian
 perspective, while modeling shapes in two dimensions. The Bayesian
 approach allows us to compute point estimates and credible sets for the
 full Procrustes fit parameters. The methods are illustrated through an
 application to radar data from short-term weather forecasts (nowcasts), a
 very important problem in hydrology and meteorology. 
Journal: Journal of Applied Statistics 
Pages: 41-55 
Issue: 1 
Volume: 37 
Year: 2010 
Keywords: Bayesian computation and estimation, complex normal distribution, full Procrustes fit, Procrustes analysis, shape analysis, 
X-DOI: 10.1080/02664760802653560 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802653560
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:1:p:41-55




Template-Type: ReDIF-Article 1.0
Author-Name: Andrea Beccarini 
Author-X-Name-First: Andrea 
Author-X-Name-Last: Beccarini 
Title: Eliminating the omitted variable bias by a regime-switching approach 
Abstract:
  This work shows a procedure that aims to eliminate or reduce the bias
 caused by omitted variables by means of the so-called regime-switching
 regressions. There is a bias estimation whenever the statistical (linear)
 model is under-specified, that is, when there are some omitted variables
 and they are correlated with the regressors. This work shows how an
 appropriate specification of a regime-switching model (independent or
 Markov-switching) can eliminate or reduce this correlation, hence the
 estimation bias. A demonstration is given, together with some Monte Carlo
 simulations. An empirical verification, based on Fisher's equation, is
 also provided. 
Journal: Journal of Applied Statistics 
Pages: 57-75 
Issue: 1 
Volume: 37 
Year: 2010 
Keywords: omitted variable bias, regime-switching model, EM algorithm, Monte Carlo simulations, Fisher's equation, 
X-DOI: 10.1080/02664760902914474 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902914474
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:1:p:57-75




Template-Type: ReDIF-Article 1.0
Author-Name: A. Parchami 
Author-X-Name-First: A. 
Author-X-Name-Last: Parchami 
Author-Name: M. Mashinchi 
Author-X-Name-First: M. 
Author-X-Name-Last: Mashinchi 
Title: A new generation of process capability indices 
Abstract:
  In quality control, we may confront imprecise concepts. One case is a
 situation in which upper and lower specification limits (SLs) are
 imprecise. If we introduce vagueness into SLs, we face quite new,
 reasonable and interesting processes, and the ordinary capability indices
 are not appropriate for measuring the capability of these processes. In
 this paper, similar to the traditional process capability indices (PCIs),
 we develop a fuzzy analogue by a distance defined on a fuzzy limit space
 and introduce PCIs, where instead of precise SLs we have two membership
 functions for upper and lower SLs. These indices are necessary when SLs
 are fuzzy, and they are helpful for comparing manufacturing process with
 fuzzy SLs. Some interesting relations among these introduced indices are
 proved. Numerical examples are given to clarify the method. 
Journal: Journal of Applied Statistics 
Pages: 77-89 
Issue: 1 
Volume: 37 
Year: 2010 
Keywords: fuzzy quality, specification limits, process capability index, fuzzy statistics, 
X-DOI: 10.1080/02664760802695785 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802695785
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:1:p:77-89




Template-Type: ReDIF-Article 1.0
Author-Name: Biagio Simonetti 
Author-X-Name-First: Biagio 
Author-X-Name-Last: Simonetti 
Author-Name: Eric Beh 
Author-X-Name-First: Eric 
Author-X-Name-Last: Beh 
Author-Name: Luigi D'Ambra 
Author-X-Name-First: Luigi 
Author-X-Name-Last: D'Ambra 
Title: The analysis of dependence for three ways contingency tables with ordinal variables: A case study of patient satisfaction data 
Abstract:
  For many questionnaires and surveys in the marketing, business, and
 health disciplines, items often involve ordinal scales (such as the Likert
 scale and rating scale) that are associated in sometimes complex ways.
 Techniques such as classical correspondence analysis provide a simple
 graphical means of describing the nature of the association. However, the
 procedure does not allow the researcher to specify how one item may be
 associated with another, nor does the analysis allow for the ordinal
 structure of the scales to be reflected. This article presents a graphical
 approach that can help the researcher to study in depth the complex
 association of the items and reflect the structure of the items. We will
 demonstrate the applicability of this approach using data collected from a
 study that involves identifying major factors that influence the level of
 patient satisfaction in a Neapolitan hospital. 
Journal: Journal of Applied Statistics 
Pages: 91-103 
Issue: 1 
Volume: 37 
Year: 2010 
Keywords: correspondence analysis, orthogonal polynomials, patient satisfaction evaluation, 
X-DOI: 10.1080/02664760802653552 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802653552
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:1:p:91-103




Template-Type: ReDIF-Article 1.0
Author-Name: L.H.A. Dal Bello 
Author-X-Name-First: L.H.A. 
Author-X-Name-Last: Dal Bello 
Author-Name: A. F. C. Vieira 
Author-X-Name-First: A. F. C. 
Author-X-Name-Last: Vieira 
Title: Optimization of a product performance using mixture experiments 
Abstract:
  This article presents a case study of a chemical compound acting as a
 subsystem of a delay mechanism for starting a rocket engine. The objective
 of this study was to investigate the proportions of mix components that
 enable a previously specified burning time. Thus, a linear regression
 model with normal responses was fitted, but later considered inadequate,
 as there was evidence that the response variance was not constant. Models
 fitted by the quasi-likelihood method were tried then. Through the
 developed model, it was possible to determine the proportion of each
 component to accomplish the process optimization. For the process
 optimization, besides considering a specific burning time, it was possible
 to consider the variance minimization for this time prediction
 as&#xa0;well. 
Journal: Journal of Applied Statistics 
Pages: 105-117 
Issue: 1 
Volume: 37 
Year: 2010 
Keywords: mixture experiments, optimization, quality, quasi-likelihood, regression, 
X-DOI: 10.1080/02664760802647976 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802647976
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:1:p:105-117




Template-Type: ReDIF-Article 1.0
Author-Name: Marcus Perry 
Author-X-Name-First: Marcus 
Author-X-Name-Last: Perry 
Author-Name: Joseph Pignatiello 
Author-X-Name-First: Joseph 
Author-X-Name-Last: Pignatiello 
Title: Identifying the time of step change in the mean of autocorrelated processes 
Abstract:
  Control charts are used to detect changes in a process. Once a change is
 detected, knowledge of the change point would simplify the search for and
 identification of the special cause. Consequently, having an estimate of
 the process change point following a control chart signal would be useful
 to process analysts. Change-point methods for the uncorrelated process
 have been studied extensively in the literature; however, less attention
 has been given to change-point methods for autocorrelated processes.
 Autocorrelation is common in practice and is often modeled via the class
 of autoregressive moving average (ARMA) models. In this article, a maximum
 likelihood estimator for the time of step change in the mean of
 covariance-stationary processes that fall within the general ARMA
 framework is developed. The estimator is intended to be used as an
 &#x201c;add-on&#x201d; following a signal from a phase II control chart.
 Considering first-order pure and mixed ARMA processes, Monte Carlo
 simulation is used to evaluate the performance of the proposed
 change-point estimator across a range of step change magnitudes following
 a genuine signal from a control chart. Results indicate that the estimator
 provides process analysts with an accurate and useful estimate of the last
 sample obtained from the unchanged process. Additionally, results indicate
 that if a change-point estimator designed for the uncorrelated process is
 applied to an autocorrelated process, the performance of the estimator can
 suffer dramatically. 
Journal: Journal of Applied Statistics 
Pages: 119-136 
Issue: 1 
Volume: 37 
Year: 2010 
Keywords: ARMA(p, q) models, autocorrelated processes, change-point estimation, stationary processes, statistical process control (SPC), 
X-DOI: 10.1080/02664760802663080 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802663080
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:1:p:119-136




Template-Type: ReDIF-Article 1.0
Author-Name: R. Fuentes-Garcia 
Author-X-Name-First: R. 
Author-X-Name-Last: Fuentes-Garcia 
Author-Name: S. G. Walker 
Author-X-Name-First: S. G. 
Author-X-Name-Last: Walker 
Title: A new approach to classification 
Abstract:
  Clustering is a common and important issue, and finite mixture models
 based on the normal distribution are frequently used to address the
 problem. In this article, we consider a classification model and build a
 mixture model around it. A good assessment of the allocation of
 observations and number of clusters is easily obtained from this approach. 
Journal: Journal of Applied Statistics 
Pages: 137-146 
Issue: 1 
Volume: 37 
Year: 2010 
Keywords: clustering, classification, latent variable, normal mixture-model, random histogram, 
X-DOI: 10.1080/02664760802698987 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802698987
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:1:p:137-146




Template-Type: ReDIF-Article 1.0
Author-Name: Michele Scagliarini 
Author-X-Name-First: Michele 
Author-X-Name-Last: Scagliarini 
Title: Inference on Cpk for autocorrelated data in the presence of random measurement errors 
Abstract:
  The present paper examines the properties of the Cpk estimator when
 observations are autocorrelated and affected by measurement errors. The
 underlying reason for this choice of subject matter is that in industrial
 applications, process data are often autocorrelated, especially when
 sampling frequency is not particularly low, and even with the most
 advanced measuring instruments, gauge imprecision needs to be taken into
 consideration. In the case of a first-order stationary autoregressive
 process, we compare the statistical properties of the estimator in the
 error case with those of the estimator in the error-free case. Results
 indicate that the presence of gauge measurement errors leads the estimator
 to behave differently depending on the entity of error variability. 
Journal: Journal of Applied Statistics 
Pages: 147-158 
Issue: 1 
Volume: 37 
Year: 2010 
Keywords: process capability indices, gauge measurement errors, autocorrelation, estimator, process specifications, 
X-DOI: 10.1080/02664760902914482 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902914482
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:1:p:147-158




Template-Type: ReDIF-Article 1.0
Author-Name: K. Ben-Ahmed 
Author-X-Name-First: K. 
Author-X-Name-Last: Ben-Ahmed 
Author-Name: A. Bouratbine 
Author-X-Name-First: A. 
Author-X-Name-Last: Bouratbine 
Author-Name: M. -A. El-Aroui 
Author-X-Name-First: M. -A. 
Author-X-Name-Last: El-Aroui 
Title: Generalized linear spatial models in epidemiology: A case study of zoonotic cutaneous leishmaniasis in Tunisia 
Abstract:
  Generalized linear spatial models (GLSM) are used here to study spatial
 characters of zoonotic cutaneous leishmaniasis (ZCL) in Tunisia. The
 response variable stands for the number of affected by district during the
 period 2001-2002. The model covariates are: climates (temperature and
 rainfall), humidity and surrounding vegetation status. As the
 environmental and weather data are not available for all the studied
 districts, Kriging based on linear interpolation was used to estimate the
 missing data. To account for unexplained spatial variation in the model,
 we include a stationary Gaussian process S with a powered exponential
 spatial correlation function. Moran coefficient, DIC criterion and
 residuals variograms are used to show the high goodness-of-fit of the
 GLSM. When compared with the statistical tools used in the previous ZCL
 studies, the optimal GLSM found here yields a better assessment of the
 impact of the risk factors, a better prediction of ZCL evolution and a
 better comprehension of the disease transmission. The statistical results
 show the progressive increase in the number of affected in zones with high
 temperature, low rainfall and high surrounding vegetation index. Relative
 humidity does not seem to affect the distribution of the disease in
 Tunisia. The results of the statistical analyses stress the important risk
 of misleading epidemiological conclusions when non-spatial models are used
 to analyse spatially structured data. 
Journal: Journal of Applied Statistics 
Pages: 159-170 
Issue: 1 
Volume: 37 
Year: 2010 
Keywords: generalized linear spatial model, Leishmania major, spatial variation, Tunisia, zoonotic cutaneous leishmaniasis, 
X-DOI: 10.1080/02664760802684169 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802684169
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:1:p:159-170




Template-Type: ReDIF-Article 1.0
Author-Name: Kahadawala Cooray 
Author-X-Name-First: Kahadawala 
Author-X-Name-Last: Cooray 
Title: Generalized Gumbel distribution 
Abstract:
  A generalization of the Gumbel distribution is presented to deal with
 general situations in modeling univariate data with broad range of
 skewness in the density function. This generalization is derived by
 considering a logarithmic transformation of an odd Weibull random
 variable. As a result, the generalized Gumbel distribution is not only
 useful for testing goodness-of-fit of Gumbel and reverse-Gumbel
 distributions as submodels, but it is also convenient for modeling and
 fitting a wide variety of data sets that are not possible to be modeled by
 well-known distributions. Skewness and kurtosis shapes of the generalized
 Gumbel distribution are illustrated by constructing the Galton's skewness
 and Moor's kurtosis plane. Parameters are estimated by using maximum
 likelihood method in two different ways due to the fact that the reverse
 transformation of the proposed distribution does not change its density
 function. In order to illustrate the flexibility of this generalization,
 wave and surge height data set is analyzed, and the fitness is compared
 with Gumbel and generalized extreme value distributions. 
Journal: Journal of Applied Statistics 
Pages: 171-179 
Issue: 1 
Volume: 37 
Year: 2010 
Keywords: coverage probabilities, generalized extreme value distribution, Gumbel distribution, odd Weibull distribution, skewness and kurtosis, 
X-DOI: 10.1080/02664760802698995 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802698995
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:1:p:171-179




Template-Type: ReDIF-Article 1.0
Author-Name: Yangxin Huang 
Author-X-Name-First: Yangxin 
Author-X-Name-Last: Huang 
Title: A Bayesian approach in differential equation dynamic models incorporating clinical factors and covariates 
Abstract:
  A virologic marker, the number of HIV RNA copies or viral load, is
 currently used to evaluate antiretroviral (ARV) therapies in AIDS clinical
 trials. This marker can be used to assess the antiviral potency of
 therapies, but may be easily affected by clinical factors such as drug
 exposures and drug resistance as well as baseline characteristics during
 the long-term treatment evaluation process. HIV dynamic studies have
 significantly contributed to the understanding of HIV pathogenesis and ARV
 treatment strategies. Viral dynamic models can be formulated through
 differential equations, but there has been only limited development of
 statistical methodologies for estimating such models or assessing their
 agreement with observed data. This paper develops mechanism-based
 nonlinear differential equation models for characterizing long-term viral
 dynamics with ARV therapy. In this model we not only incorporate clinical
 factors (drug exposures, and susceptibility), but also baseline covariate
 (baseline viral load, CD4 count, weight, or age) into a function of
 treatment efficacy. A Bayesian nonlinear mixed-effects modeling approach
 is investigated with application to an AIDS clinical trial study. The
 effects of confounding interaction of clinical factors with
 covariate-based models are compared using the deviance information
 criteria (DIC), a Bayesian version of the classical deviance for model
 assessment, designed from complex hierarchical model settings.
 Relationships between baseline covariate combined with confounding
 clinical factors and drug efficacy are explored. In addition, we compared
 models incorporating each of four baseline covariates through DIC and some
 interesting findings are presented. Our results suggest that modeling HIV
 dynamics and virologic responses with consideration of time-varying
 clinical factors as well as baseline characteristics may play an important
 role in understanding HIV pathogenesis, designing new treatment strategies
 for long-term care of AIDS patients. 
Journal: Journal of Applied Statistics 
Pages: 181-199 
Issue: 2 
Volume: 37 
Year: 2010 
Keywords: AIDS, baseline characteristics, Bayesian nonlinear mixed-effects models, long-term HIV dynamics, longitudinal data, time-varying drug efficacy, 
X-DOI: 10.1080/02664760802578320 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802578320
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:2:p:181-199




Template-Type: ReDIF-Article 1.0
Author-Name: Chin Wen Cheong 
Author-X-Name-First: Chin Wen 
Author-X-Name-Last: Cheong 
Title: Estimating the Hurst parameter in financial time series via heuristic approaches 
Abstract:
  This research investigates long memory financial equity markets using
 three heuristic methodologies namely a proposed modified variance
 time-aggregated plot, modified rescaled-range plot and periodogram
 approaches. The intensity of the long memory process is quantified in
 terms of Hurst parameter (H). Five Malaysian equity market indices are
 selected in the empirical studies with the inclusion of pre- and
 post-drastic economic events. Our empirical results evidenced dissimilar
 long memory behaviours in the different regimes of significant economic
 events. It is also found that after the short-memory adjustment, all the
 equity markets exhibited substantial reductions in long memory
 estimations. 
Journal: Journal of Applied Statistics 
Pages: 201-214 
Issue: 2 
Volume: 37 
Year: 2010 
Keywords: heuristic methodology, long-range dependence, Hurst parameter, quantile regression, 
X-DOI: 10.1080/02664760802582280 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802582280
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:2:p:201-214




Template-Type: ReDIF-Article 1.0
Author-Name: Chee Kian Leong 
Author-X-Name-First: Chee Kian 
Author-X-Name-Last: Leong 
Author-Name: Weihong Huang 
Author-X-Name-First: Weihong 
Author-X-Name-Last: Huang 
Title: Testing for spurious and cointegrated regressions: A wavelet approach 
Abstract:
  This paper proposes a wavelet-based approach to analyze spurious and
 cointegrated regressions in time series. The approach is based on the
 properties of the wavelet covariance and correlation in Monte Carlo
 studies of spurious and cointegrated regression. In the case of the
 spurious regression, the null hypotheses of zero wavelet covariance and
 correlation for these series across the scales fail to be rejected.
 Conversely, these null hypotheses across the scales are rejected for the
 cointegrated bivariate time series. These nonresidual-based tests are then
 applied to analyze if any relationship exists between the extraterrestrial
 phenomenon of sunspots and the earthly economic time series of oil prices.
 Conventional residual-based tests appear sensitive to the specification in
 both the cointegrating regression and the lag order in the augmented
 Dickey-Fuller tests on the residuals. In contrast, the wavelet tests, with
 their bootstrap t-statistics and confidence intervals, detect the
 spuriousness of this relationship. 
Journal: Journal of Applied Statistics 
Pages: 215-233 
Issue: 2 
Volume: 37 
Year: 2010 
Keywords: spurious regression, cointegration, wavelet covariance and correlation, Monte Carlo simulations, bootstrap, 
X-DOI: 10.1080/02664760802638082 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802638082
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:2:p:215-233




Template-Type: ReDIF-Article 1.0
Author-Name: Nizar Bouguila 
Author-X-Name-First: Nizar 
Author-X-Name-Last: Bouguila 
Author-Name: Jian Han Wang 
Author-X-Name-First: Jian Han 
Author-X-Name-Last: Wang 
Author-Name: A. Ben Hamza 
Author-X-Name-First: A. Ben 
Author-X-Name-Last: Hamza 
Title: Software modules categorization through likelihood and bayesian analysis of finite dirichlet mixtures 
Abstract:
  In this paper, we examine deterministic and Bayesian methods for
 analyzing finite Dirichlet mixtures. The deterministic method is based on
 the likelihood approach, and the Bayesian approach is implemented using
 the Gibbs sampler. The selection of the number of clusters for both
 approaches is based on the Bayesian information criterion, which is
 equivalent to the minimum description length. Experimental results are
 presented using simulated data, and a real application for software
 modules classification is also included. 
Journal: Journal of Applied Statistics 
Pages: 235-252 
Issue: 2 
Volume: 37 
Year: 2010 
Keywords: dirichlet distribution, mixture modeling, maximum likelihood, EM, MDL, BIC, Bayesian analysis, Gibbs sampling, Metropolis-Hastings, software modules, 
X-DOI: 10.1080/02664760802684185 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802684185
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:2:p:235-252




Template-Type: ReDIF-Article 1.0
Author-Name: Gianluca Baio 
Author-X-Name-First: Gianluca 
Author-X-Name-Last: Baio 
Author-Name: Marta Blangiardo 
Author-X-Name-First: Marta 
Author-X-Name-Last: Blangiardo 
Title: Bayesian hierarchical model for the prediction of football results 
Abstract:
  The problem of modelling football data has become increasingly popular in
 the last few years and many different models have been proposed with the
 aim of estimating the characteristics that bring a team to lose or win a
 game, or to predict the score of a particular match. We propose a Bayesian
 hierarchical model to fulfil both these aims and test its predictive
 strength based on data about the Italian Serie A 1991-1992 championship.
 To overcome the issue of overshrinkage produced by the Bayesian
 hierarchical model, we specify a more complex mixture model that results
 in a better fit to the observed data. We test its performance using an
 example of the Italian Serie A 2007-2008 championship. 
Journal: Journal of Applied Statistics 
Pages: 253-264 
Issue: 2 
Volume: 37 
Year: 2010 
Keywords: Bayesian hierarchical models, overshrinkage, football data, bivariate Poisson distribution, Poisson-log normal model, 
X-DOI: 10.1080/02664760802684177 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802684177
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:2:p:253-264




Template-Type: ReDIF-Article 1.0
Author-Name: Zongrun Wang 
Author-X-Name-First: Zongrun 
Author-X-Name-Last: Wang 
Author-Name: Weitao Wu 
Author-X-Name-First: Weitao 
Author-X-Name-Last: Wu 
Author-Name: Chao Chen 
Author-X-Name-First: Chao 
Author-X-Name-Last: Chen 
Author-Name: Yanju Zhou 
Author-X-Name-First: Yanju 
Author-X-Name-Last: Zhou 
Title: The exchange rate risk of Chinese yuan: Using VaR and ES based on extreme value theory 
Abstract:
  This paper applies extreme value theory (EVT) to estimate the tails of
 return series of Chinese yuan (CNY) exchange rates. We find that the
 degree of fitting Pareto distribution to the data of the tail of return
 series is extremely high. The empirical results indicate that expected
 shortfall cannot improve the tail risk problem of value-at-risk (VaR). The
 evidence of back testing indicates that EVT-based VaR values underestimate
 the risks of exchange rates such as USD/CNY and HKD/CNY, which may be
 caused by the continuous appreciation of CNY against USD and HKD. However,
 compared with VaR values calculated by historical simulation and
 variance-covariance method, VaR values calculated by EVT can measure the
 risk more accurately for the exchange rates of JPY/CNY and EUR/CNY. 
Journal: Journal of Applied Statistics 
Pages: 265-282 
Issue: 2 
Volume: 37 
Year: 2010 
Keywords: expected shortfall, extreme value theory, historical simulation, value-at-risk, variance-covariance, 
X-DOI: 10.1080/02664760902846114 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902846114
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:2:p:265-282




Template-Type: ReDIF-Article 1.0
Author-Name: Hamdi Akcakoca 
Author-X-Name-First: Hamdi 
Author-X-Name-Last: Akcakoca 
Author-Name: Nurhak Sutcu 
Author-X-Name-First: Nurhak 
Author-X-Name-Last: Sutcu 
Title: Investigation of the diesel consumption for trucks at an overburden stripping area by SPC study 
Abstract:
  This study examined whether diesel consumption used by trucks at a
 stripping area is controlled or not. The factors affecting diesel
 consumption were also investigated and some necessary solutions were
 presented. Diesel consumption was observed with the aid of control graphs.
 Abnormal situations in the diesel consumption were explored by means of
 Shewhart control graphs. The factors which are out of control were also
 presented in a cause-effect diagram, and suggestions for improvement were
 proposed. It has been determined that the main effect of the diesel
 consumption is the daily run number of the trucks. The main factors
 affecting the daily run number were also investigated. 
Journal: Journal of Applied Statistics 
Pages: 283-298 
Issue: 2 
Volume: 37 
Year: 2010 
Keywords: statistical process control (SPC), quality control, control graphs, strip mining, diesel consumption, 
X-DOI: 10.1080/02664760902914540 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902914540
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:2:p:283-298




Template-Type: ReDIF-Article 1.0
Author-Name: A. Erhan Mergen 
Author-X-Name-First: A. Erhan 
Author-X-Name-Last: Mergen 
Author-Name: Z. Seyda Deligonul 
Author-X-Name-First: Z. Seyda 
Author-X-Name-Last: Deligonul 
Title: Assessment of acceptance sampling plans using posterior distribution for a dependent process 
Abstract:
  In this study, performance of single acceptance sampling plans by
 attribute is investigated by using the distribution of fraction
 nonconformance (i.e. lot quality distribution) for a dependent production
 process. It is the aim of this study to demonstrate that, in order to
 emphasize consumer risk (i.e. the risk of accepting a bad lot), it is
 better to evaluate a sampling plan based upon its performance as assessed
 by the posterior distribution of fractions nonconforming in accepted lots.
 Similarly, it is the desired posterior distribution that sets the basis
 for designing a sampling plan. The prior distribution used in this study
 is derived from a Markovian model of dependence. 
Journal: Journal of Applied Statistics 
Pages: 299-307 
Issue: 2 
Volume: 37 
Year: 2010 
Keywords: acceptance sampling, dependent production processes, lot quality distribution, posterior distribution, mean squared nonconformance, 
X-DOI: 10.1080/02664760902998451 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902998451
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:2:p:299-307




Template-Type: ReDIF-Article 1.0
Author-Name: Walid Gani 
Author-X-Name-First: Walid 
Author-X-Name-Last: Gani 
Author-Name: Hassen Taleb 
Author-X-Name-First: Hassen 
Author-X-Name-Last: Taleb 
Author-Name: Mohamed Limam 
Author-X-Name-First: Mohamed 
Author-X-Name-Last: Limam 
Title: Support vector regression based residual control charts 
Abstract:
  Control charts for residuals, based on the regression model, require a
 robust fitting technique for minimizing the error resulting from the
 fitted model. However, in the multivariate case, when the number of
 variables is high and data become complex, traditional fitting techniques,
 such as ordinary least squares (OLS), lose efficiency. In this paper,
 support vector regression (SVR) is used to construct robust control charts
 for residuals, called SVR-chart. This choice is based on the fact that the
 SVR is designed to minimize the structural error whereas other techniques
 minimize the empirical error. An application shows that SVR methods gives
 competitive results in comparison with the OLS and the partial least
 squares method, in terms of standard deviation of the error prediction and
 the standard error of performance. A sensitivity study is conducted to
 evaluate the SVR-chart performance based on the average run length (ARL)
 and showed that the SVR-chart has the best ARL behaviour in comparison
 with the other residuals control charts. 
Journal: Journal of Applied Statistics 
Pages: 309-324 
Issue: 2 
Volume: 37 
Year: 2010 
Keywords: SVR-chart, multivariate regression, SDEP, SEP, ARL, 
X-DOI: 10.1080/02664760903002667 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903002667
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:2:p:309-324




Template-Type: ReDIF-Article 1.0
Author-Name: Jing Wang 
Author-X-Name-First: Jing 
Author-X-Name-Last: Wang 
Title: Gibbs sampling in DP-based nonlinear mixed effects models 
Abstract:
  This article uses several approaches to deal with the difficulty involved
 in evaluating the intractable integral when using Gibbs sampling to
 estimate the nonlinear mixed effects model (NLMM) based on the Dirichlet
 process (DP). For illustration, we applied these approaches to real data
 and simulations. Comparisons are then made between these methods with
 respect to estimation accuracy and computing efficiency. 
Journal: Journal of Applied Statistics 
Pages: 325-340 
Issue: 2 
Volume: 37 
Year: 2010 
Keywords: nonlinear mixed effects model, Dirichlet process, Laplace's approximation, adaptive Gaussian quadrature approximation, No-gaps algorithm, EM algorithm, Monte Carlo approximations, Markov chain, 
X-DOI: 10.1080/02664760903117721 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903117721
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:2:p:325-340




Template-Type: ReDIF-Article 1.0
Author-Name: Jan Serroyen 
Author-X-Name-First: Jan 
Author-X-Name-Last: Serroyen 
Author-Name: Liesbeth Bruckers 
Author-X-Name-First: Liesbeth 
Author-X-Name-Last: Bruckers 
Author-Name: Geert Rogiers 
Author-X-Name-First: Geert 
Author-X-Name-Last: Rogiers 
Author-Name: Geert Molenberghs 
Author-X-Name-First: Geert 
Author-X-Name-Last: Molenberghs 
Title: Characterizing persistent disturbing behavior using longitudinal and multivariate techniques 
Abstract:
  Persistent disturbing behavior (PDB) refers to a chronic condition in
 therapy-resistant psychiatric patients. Since these patients are highly
 unstable and difficult to maintain in their natural living environment and
 even in hospital wards, it is important to properly characterize this
 group. Previous studies in the Belgian province of Limburg indicated that
 the size of this group was larger than anticipated. Here, using a score
 calculated from longitudinal psychiatric registration data in 611
 patients, we characterize the difference between PDB patients and a set of
 control patients. These differences are studied both at a given point in
 time, using discriminant analysis, as well as in terms of the evolution of
 the score over time, using longitudinal data analysis methods. Further,
 using clustering techniques, the group of PDB patients is split into two
 subgroups, characterized in terms of a number of ordinal scores. Such
 findings are useful from a scientific as well as from an organizational
 point of view. 
Journal: Journal of Applied Statistics 
Pages: 341-355 
Issue: 2 
Volume: 37 
Year: 2010 
Keywords: cluster analysis, discriminant analysis, longitudinal data, multivariate methods, psychiatry, 
X-DOI: 10.1080/02664760802688673 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802688673
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:2:p:341-355




Template-Type: ReDIF-Article 1.0
Author-Name: Ana Militino 
Author-X-Name-First: Ana 
Author-X-Name-Last: Militino 
Title: Statistics and data with R 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 357-358 
Issue: 2 
Volume: 37 
Year: 2010 
X-DOI: 10.1080/02664760902811589 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902811589
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:2:p:357-358




Template-Type: ReDIF-Article 1.0
Author-Name: Jung Hsien Chang 
Author-X-Name-First: Jung Hsien 
Author-X-Name-Last: Chang 
Author-Name: Mao Wei Hung 
Author-X-Name-First: Mao Wei 
Author-X-Name-Last: Hung 
Title: Liquidity spreads in the corporate bondmarket: Estimation using a semi-parametric model 
Abstract:
  This study utilizes the liquidity risk associated with Treasury bonds to
 directly determine the degree to which liquidity spreads account for
 corporate bond spreads. This enhances understanding of their relative
 contributions to the yield spreads of corporate bonds. To capture time
 variation on instantaneous spreads and volatility and to reduce modeling
 bias, semi-parametric techniques are applied to estimate the time-varying
 intensity process. Empirical results indicate that our semi-parametric
 model is good at capturing the time variation in default and liquidity
 intensity processes. The credit spreads are due to default risk and
 reflect the relative liquidity of the corporate bond market, indicating
 that liquidity risk plays an important role in corporate bond valuation. 
Journal: Journal of Applied Statistics 
Pages: 359-374 
Issue: 3 
Volume: 37 
Year: 2010 
Keywords: liquidity risk, on-the-run, off-the-run, semi-parameter model, reduced-form model, 
X-DOI: 10.1080/02664760802688681 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802688681
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:3:p:359-374




Template-Type: ReDIF-Article 1.0
Author-Name: D. Senthilkumar 
Author-X-Name-First: D. 
Author-X-Name-Last: Senthilkumar 
Author-Name: D. Muthuraj 
Author-X-Name-First: D. 
Author-X-Name-Last: Muthuraj 
Title: Construction and selection of tightened-normal-tightened variables sampling scheme of type TNTVSS (n1, n2; k) 
Abstract:
  This paper provides tables for the construction and selection of
 tightened-normal-tightened variables sampling scheme of type TNTVSS (n1,
 n2; k). The method of designing the scheme indexed by (AQL, &#x3b1;) and
 (LQL, &#x3b2;) is indicated. The TNTVSS (nT, nN; k) is compared with
 conventional single sampling plans for variables and with TNT (n1, n2; c)
 scheme for attributes, and it is shown that the TNTVSS is more efficient. 
Journal: Journal of Applied Statistics 
Pages: 375-390 
Issue: 3 
Volume: 37 
Year: 2010 
Keywords: variables sampling, tightened-normal-tightened scheme, AQL, LQL, switching rules, producer's risk, consumer's risk and ASN, 
X-DOI: 10.1080/02664760802695777 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802695777
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:3:p:375-390




Template-Type: ReDIF-Article 1.0
Author-Name: Fawziah Alshunnar 
Author-X-Name-First: Fawziah 
Author-X-Name-Last: Alshunnar 
Author-Name: Mohammad Raqab 
Author-X-Name-First: Mohammad 
Author-X-Name-Last: Raqab 
Author-Name: Debasis Kundu 
Author-X-Name-First: Debasis 
Author-X-Name-Last: Kundu 
Title: On the comparison of the Fisher information of the log-normal and generalized Rayleigh distributions 
Abstract:
  Surles and Padgett recently considered two-parameter Burr Type X
 distribution by introducing a scale parameter and called it the
 generalized Rayleigh distribution. It is observed that the generalized
 Rayleigh and log-normal distributions have many common properties and both
 distributions can be used quite effectively to analyze skewed data set. In
 this paper, we mainly compare the Fisher information matrices of the two
 distributions for complete and censored observations. Although, both
 distributions may provide similar data fit and are quite similar in nature
 in many aspects, the corresponding Fisher information matrices can be
 quite different. We compute the total information measures of the two
 distributions for different parameter ranges and also compare the loss of
 information due to censoring. Real data analysis has been performed for
 illustrative purposes. 
Journal: Journal of Applied Statistics 
Pages: 391-404 
Issue: 3 
Volume: 37 
Year: 2010 
Keywords: Fisher information matrix, Burr Type X distribution, generalized Rayleigh distribution, log-normal distribution, left censoring, right censoring, 
X-DOI: 10.1080/02664760802698961 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802698961
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:3:p:391-404




Template-Type: ReDIF-Article 1.0
Author-Name: Muhammad Aslam 
Author-X-Name-First: Muhammad 
Author-X-Name-Last: Aslam 
Author-Name: Chi-Hyuck Jun 
Author-X-Name-First: Chi-Hyuck 
Author-X-Name-Last: Jun 
Title: A double acceptance sampling plan for generalized log-logistic distributions with known shape parameters 
Abstract:
  A double acceptance sampling plan for the truncated life test is
 developed assuming that the lifetime of a product follows a generalized
 log-logistic distribution with known shape parameters. The zero and one
 failure scheme is mainly considered, where the lot is accepted if no
 failures are observed from the first sample and it is rejected if two or
 more failures occur. When there is one failure from the first sample, the
 second sample is drawn and tested for the same duration as the first
 sample. The minimum sample sizes of the first and second samples are
 determined to ensure that the true median life is longer than the given
 life at the specified consumer's confidence level. The operating
 characteristics are analyzed according to various ratios of the true
 median life to the specified life. The minimum such ratios are also
 obtained so as to lower the producer's risk at the specified level. The
 results are explained with examples. 
Journal: Journal of Applied Statistics 
Pages: 405-414 
Issue: 3 
Volume: 37 
Year: 2010 
Keywords: consumer's confidence, double acceptance sampling, log-logistic distribution, producer's risk, single acceptance sampling, 
X-DOI: 10.1080/02664760802698979 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802698979
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:3:p:405-414




Template-Type: ReDIF-Article 1.0
Author-Name: Christophe Demattei 
Author-X-Name-First: Christophe 
Author-X-Name-Last: Demattei 
Title: Le Cam theorem on interval division by randomly chosen points: Pedagogical explanations and application to temporal cluster detection 
Abstract:
  The aim of this paper is to propose a pedagogical explanation of the Le
 Cam theorem and to illustrate its use, through a practical application,
 for temporal cluster detection. This theorem focusses on the interval
 division by randomly chosen points. The aim of the theorem is to
 characterize the asymptotic behavior of a certain category of sums of
 functions applied to the length of successive intervals between points. It
 is not very intuitive and its understanding needs some deepening. After
 enouncing the theorem, its different aspects are explained and detailed in
 a way as pedagogical as possible. Theoretical applications are proposed
 through the proof of two propositions. Then a very concrete application of
 this theorem for temporal cluster detection is presented, tested by a
 power study, and compared with other global cluster detection tests.
 Finally, this approach is applied to the well-known Knox temporal data
 set. 
Journal: Journal of Applied Statistics 
Pages: 415-424 
Issue: 3 
Volume: 37 
Year: 2010 
Keywords: Le Cam theorem, uniform spacings, cluster detection, temporal cluster, Knox data set, 
X-DOI: 10.1080/02664760802715872 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802715872
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:3:p:415-424




Template-Type: ReDIF-Article 1.0
Author-Name: Ling-Yau Chan 
Author-X-Name-First: Ling-Yau 
Author-X-Name-Last: Chan 
Author-Name: Rahul Mukerjee 
Author-X-Name-First: Rahul 
Author-X-Name-Last: Mukerjee 
Title: Interval estimation of a small proportion via inverse sampling 
Abstract:
  On the basis of a negative binomial sampling scheme, we consider a
 uniformly most accurate upper confidence limit for a small but unknown
 proportion, such as the proportion of defectives in a manufacturing
 process. The optimal stopping rule, with reference to the twin criteria of
 the expected length of the confidence interval and the expected sample
 size, is investigated. The proposed confidence interval has also been
 compared with several others that have received attention in the recent
 literature. 
Journal: Journal of Applied Statistics 
Pages: 425-433 
Issue: 3 
Volume: 37 
Year: 2010 
Keywords: expected length, posterior quantile, negative binomial, sample size, score interval, uniformly most accurate, upper confidence limit, 
X-DOI: 10.1080/02664760802715880 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802715880
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:3:p:425-433




Template-Type: ReDIF-Article 1.0
Author-Name: C. C. Figueiredo 
Author-X-Name-First: C. C. 
Author-X-Name-Last: Figueiredo 
Author-Name: H. Bolfarine 
Author-X-Name-First: H. 
Author-X-Name-Last: Bolfarine 
Author-Name: M. C. Sandoval 
Author-X-Name-First: M. C. 
Author-X-Name-Last: Sandoval 
Author-Name: C. R. O. P. Lima 
Author-X-Name-First: C. R. O. P. 
Author-X-Name-Last: Lima 
Title: On the skew-normal calibration model 
Abstract:
  In this article, we present the EM-algorithm for performing maximum
 likelihood estimation of an asymmetric linear calibration model with the
 assumption of skew-normally distributed error. A simulation study is
 conducted for evaluating the performance of the calibration estimator with
 interpolation and extrapolation situations. As one application in a real
 data set, we fitted the model studied in a dimensional measurement method
 used for calculating the testicular volume through a caliper and its
 calibration by using ultrasonography as the standard method. By applying
 this methodology, we do not need to transform the variables to have
 symmetrical errors. Another interesting aspect of the approach is that the
 developed transformation to make the information matrix nonsingular, when
 the skewness parameter is near zero, leaves the parameter of interest
 unchanged. Model fitting is implemented and the best choice between the
 usual calibration model and the model proposed in this article was
 evaluated by developing the Akaike information criterion, Schwarz's
 Bayesian information criterion and Hannan-Quinn criterion. 
Journal: Journal of Applied Statistics 
Pages: 435-451 
Issue: 3 
Volume: 37 
Year: 2010 
Keywords: linear calibration model, EM-algorithm, skewness coefficient, skew-normal distribution, singularity of the information matrix, bias prevention, 
X-DOI: 10.1080/02664760802715906 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802715906
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:3:p:435-451




Template-Type: ReDIF-Article 1.0
Author-Name: Chi-Hyuck Jun 
Author-X-Name-First: Chi-Hyuck 
Author-X-Name-Last: Jun 
Author-Name: Hyeseon Lee 
Author-X-Name-First: Hyeseon 
Author-X-Name-Last: Lee 
Author-Name: Sang-Ho Lee 
Author-X-Name-First: Sang-Ho 
Author-X-Name-Last: Lee 
Author-Name: S. Balamurali 
Author-X-Name-First: S. 
Author-X-Name-Last: Balamurali 
Title: A variables repetitive group sampling plan under failure-censored reliability tests for Weibull distribution 
Abstract:
  We propose a variables repetitive group sampling plan under type-II or
 failure-censored life testing when the lifetime of a part follows a
 Weibull distribution with a known shape parameter. The acceptance criteria
 do not involve unknown scale parameter differently from the existing
 plans. To determine the design parameters of the proposed plan, the usual
 approach of using two points on the operating characteristic curve is
 adopted and an optimization problem is formulated so as to minimize the
 average number of failures observed. Tables for design parameters are
 constructed when the quality of parts is represented by the unreliability
 or the ratio of the mean lifetime to the specified life. It is found that
 the proposed sampling plan can reduce the sample size significantly than
 do the single sampling plan. 
Journal: Journal of Applied Statistics 
Pages: 453-460 
Issue: 3 
Volume: 37 
Year: 2010 
Keywords: acceptance sampling, consumer's risk, failure censoring, OC curve, producer's risk, progressive censoring, 
X-DOI: 10.1080/02664760802715914 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802715914
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:3:p:453-460




Template-Type: ReDIF-Article 1.0
Author-Name: Chung-Ho Chen 
Author-X-Name-First: Chung-Ho 
Author-X-Name-Last: Chen 
Title: A note on some modified Pulak and Al-Sultan's model 
Abstract:
  Pulak and Al-Sultan presented a rectifying inspection plan applying in
 the determination of optimum process mean. However, they did not point out
 whether the non-conforming items in the sample of accepted lot are
 replaced or eliminated from the lot and neglected the quality loss within
 specification limits. In this paper, we further propose the modified Pulak
 and Al-Sultan model with quadratic quality loss function. There are four
 cases considered in the modified model: (1) the non-conforming items in
 the sample of accepted lot are neither replaced nor eliminated from the
 lot; (2) the non-conforming items in the sample of accepted lot are not
 replaced but are eliminated from the lot;&#xa0;(3) the non-conforming
 items in the sample of accepted lot are replaced by conforming ones; (4)
 the non-conforming items in the sample of accepted lot are replaced by
 non-inspected items. The numerical results and sensitivity analysis of
 parameters show that their solutions are slightly different. 
Journal: Journal of Applied Statistics 
Pages: 461-472 
Issue: 3 
Volume: 37 
Year: 2010 
Keywords: rectifying inspection plan, process mean, quadratic quality loss function, specification limits, 
X-DOI: 10.1080/02664760902729658 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902729658
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:3:p:461-472




Template-Type: ReDIF-Article 1.0
Author-Name: Angela Montanari 
Author-X-Name-First: Angela 
Author-X-Name-Last: Montanari 
Author-Name: Cinzia Viroli 
Author-X-Name-First: Cinzia 
Author-X-Name-Last: Viroli 
Title: A skew-normal factor model for the analysis of student satisfaction towards university courses 
Abstract:
  Classical factor analysis relies on the assumption of normally
 distributed factors that guarantees the model to be estimated via the
 maximum likelihood method. Even when the assumption of Gaussian factors is
 not explicitly formulated and estimation is performed via the iterated
 principal factors' method, the interest is actually mainly focussed on the
 linear structure of the data, since only moments up to the second ones are
 involved. In many real situations, the factors could not be adequately
 described by the first two moments only. For example, skewness
 characterizing most latent variables in social analysis can be properly
 measured by the third moment: the factors are not normally distributed and
 covariance is no longer a sufficient statistic. In this work we propose a
 factor model characterized by skew-normally distributed factors.
 Skew-normal refers to a parametric class of probability distributions,
 that extends the normal distribution by an additional shape parameter
 regulating the skewness. The model estimation can be solved by the
 generalized EM algorithm, in which the iterative Newthon-Raphson procedure
 is needed in the M-step to estimate the factor shape parameter. The
 proposed skew-normal factor analysis is applied to the study of student
 satisfaction towards university courses, in order to identify the factors
 representing different aspects of the latent overall satisfaction. 
Journal: Journal of Applied Statistics 
Pages: 473-487 
Issue: 3 
Volume: 37 
Year: 2010 
Keywords: factor analysis, skew-normal distribution, latent variables, orthogonal rotations, EM algorithm, Gauss-Hermite quadrature points, 
X-DOI: 10.1080/02664760902736737 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902736737
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:3:p:473-487




Template-Type: ReDIF-Article 1.0
Author-Name: Hakan Demirtas 
Author-X-Name-First: Hakan 
Author-X-Name-Last: Demirtas 
Title: A distance-based rounding strategy for post-imputation ordinal data 
Abstract:
  Multiple imputation has emerged as a widely used model-based approach in
 dealing with incomplete data in many application areas. Gaussian and
 log-linear imputation models are fairly straightforward to implement for
 continuous and discrete data, respectively. However, in missing data
 settings which include a mix of continuous and discrete variables, correct
 specification of the imputation model could be a daunting task owing to
 the lack of flexible models for the joint distribution of variables of
 different nature. This complication, along with accessibility to software
 packages that are capable of carrying out multiple imputation under the
 assumption of joint multivariate normality, appears to encourage applied
 researchers for pragmatically treating the discrete variables as
 continuous for imputation purposes, and subsequently rounding the imputed
 values to the nearest observed category. In this article, I introduce a
 distance-based rounding approach for ordinal variables in the presence of
 continuous ones. The first step of the proposed rounding process is
 predicated upon creating indicator variables that correspond to the
 ordinal levels, followed by jointly imputing all variables under the
 assumption of multivariate normality. The imputed values are then
 converted to the ordinal scale based on their Euclidean distances to a set
 of indicators, with minimal distance corresponding to the closest match. I
 compare the performance of this technique to crude rounding via commonly
 accepted accuracy and precision measures with simulated data sets. 
Journal: Journal of Applied Statistics 
Pages: 489-500 
Issue: 3 
Volume: 37 
Year: 2010 
Keywords: multiple imputation, rounding, bias, precision, ordinal data, 
X-DOI: 10.1080/02664760902744954 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902744954
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:3:p:489-500




Template-Type: ReDIF-Article 1.0
Author-Name: Martin Rios 
Author-X-Name-First: Martin 
Author-X-Name-Last: Rios 
Author-Name: Toni Monleon-Getino 
Author-X-Name-First: Toni 
Author-X-Name-Last: Monleon-Getino 
Title: Application of a Markovian process to the calculation of mean time equilibrium in a genetic drift model 
Abstract:
  The most common phenomena in the evolution process are natural selection
 and genetic drift. In this article, we propose a probabilistic method to
 calculate the mean and variance time for random genetic drift equilibrium,
 measured as number of generations, based on Markov process and a complex
 probabilistic model. We studied the case of a constant, panmictic
 population of diploid organisms, which had a demonstrated lack of
 mutation, selection or migration for a determined autonomic locus, and two
 possible alleles, H and h. The calculations presented in this article were
 based on a Markov process. They explain how genetic and genotypic
 frequencies changed in different generations and how the heterozygote
 alleles became extinguished after many generations. This calculation could
 be used in more evolutionary applications. Finally, some simulations are
 presented to illustrate the theoretical calculations presented using
 different basal situations. 
Journal: Journal of Applied Statistics 
Pages: 501-513 
Issue: 3 
Volume: 37 
Year: 2010 
Keywords: Markovian process, probabilistic model, genetic drift, population genetics, mean at equilibrium, 
X-DOI: 10.1080/02664760902889981 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902889981
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:3:p:501-513




Template-Type: ReDIF-Article 1.0
Author-Name: Kanti Mardia 
Author-X-Name-First: Kanti 
Author-X-Name-Last: Mardia 
Title: Bayesian analysis for bivariate von Mises distributions 
Abstract:
  There has been renewed interest in the directional Bayesian analysis for
 the bivariate case especially in view of its fundamental new and
 challenging applications to bioinformatics. The previous work had
 concentrated on Bayesian analysis for univariate von Mises distribution.
 Here, we give the description of the general bivariate von Mises (BVM)
 distribution and its properties. There are various submodels of this
 distribution which have become important and we give a review of these
 submodels. Also, we derive the normalizing constant for the general BVM
 distribution in a compact way. Conjugate priors and posteriors for the
 general case and the submodels are obtained. The conjugate prior for a
 multivariate von Mises distribution is also examined. 
Journal: Journal of Applied Statistics 
Pages: 515-528 
Issue: 3 
Volume: 37 
Year: 2010 
Keywords: bioinformatics, bivariate angular data, conjugate priors, cosine model, directional statistics, distributions on torus, sine model, 
X-DOI: 10.1080/02664760903551267 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903551267
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:3:p:515-528




Template-Type: ReDIF-Article 1.0
Author-Name: Paul Baxter 
Author-X-Name-First: Paul 
Author-X-Name-Last: Baxter 
Author-Name: Paul Marchant 
Author-X-Name-First: Paul 
Author-X-Name-Last: Marchant 
Title: The cross-product ratio in bivariate lognormal and gamma distributions, with an application to non-randomized trials 
Abstract:
  Non-randomized trials can give a biased impression of the effectiveness
 of any intervention. We consider trials in which incidence rates are
 compared in two areas over two periods. Typically, one area receives an
 intervention, whereas the other does not. We outline and illustrate a
 method to estimate the bias in such trials under two different bivariate
 models. The illustrations use data in which no particular intervention is
 operating. The purpose is to illustrate the size of the bias that could be
 observed purely due to regression towards the mean (RTM). The
 illustrations show that the bias can be appreciably different from zero,
 and even when centred on zero, the variance of the bias can be large. We
 conclude that the results of non-randomized trials should be treated with
 caution, as interventions which show small effects could be explained as
 artefacts of RTM. 
Journal: Journal of Applied Statistics 
Pages: 529-536 
Issue: 4 
Volume: 37 
Year: 2010 
Keywords: bivariate lognormal distribution, crime reduction interventions, Kibble bivariate gamma distribution, non-randomized trials, regression towards the mean, 
X-DOI: 10.1080/02664760902744962 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902744962
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:4:p:529-536




Template-Type: ReDIF-Article 1.0
Author-Name: Georgia Kourlaba 
Author-X-Name-First: Georgia 
Author-X-Name-Last: Kourlaba 
Author-Name: Demosthenes Panagiotakos 
Author-X-Name-First: Demosthenes 
Author-X-Name-Last: Panagiotakos 
Title: The diagnostic accuracy of a composite index increases as the number of partitions of the components increases and when specific weights are assigned to each component 
Abstract:
  The aim of this work was to evaluate whether the number of partitions of
 index components and the use of specific weights for each component
 influence the diagnostic accuracy of a composite index. Simulation studies
 were conducted in order to compare the sensitivity, specificity and area
 under the ROC curve (AUC) of indices constructed using equal number of
 components but different number of partitions for all components.
 Moreover, the odds ratio obtained from the univariate logistic regression
 model for each component was proposed as potential weight. The current
 simulation results showed that the sensitivity, specificity and AUC of an
 index increase as the number of partitions of components increases.
 However, the rate that the diagnostic accuracy increases is reduced as the
 number of partitions increases. In addition, it was found that the
 diagnostic accuracy of the weighted index developed using the proposed
 weights is higher compared with that of the corresponding un-weighted
 index. The use of large-scale index components and the use of effect size
 measures (i.e. odds ratios, ORs) of index components as potential weights
 are proposed in order to obtain indices with high diagnostic accuracy for
 a particular binary outcome. 
Journal: Journal of Applied Statistics 
Pages: 537-554 
Issue: 4 
Volume: 37 
Year: 2010 
Keywords: weights, indices, specificity, AUC, simulations, application, 
X-DOI: 10.1080/02664760902751876 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902751876
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:4:p:537-554




Template-Type: ReDIF-Article 1.0
Author-Name: Muhammad Aslam 
Author-X-Name-First: Muhammad 
Author-X-Name-Last: Aslam 
Author-Name: Debasis Kundu 
Author-X-Name-First: Debasis 
Author-X-Name-Last: Kundu 
Author-Name: Munir Ahmad 
Author-X-Name-First: Munir 
Author-X-Name-Last: Ahmad 
Title: Time truncated acceptance sampling plans for generalized exponential distribution 
Abstract:
  Acceptance sampling plans for generalized exponential distribution when
 the lifetime experiment is truncated at a pre-determined time are provided
 in this article. The tables are provided for the minimum sample size
 required to ensure a certain median life of the experimental unit when the
 shape parameter is two. The operating characteristic function values of
 the sampling plans and the associated producer's risks are also presented.
 It is shown that the tables presented here can be used if instead of
 median life, other percentile life is chosen as the criterion or if the
 shape parameter is not two. Examples are provided for illustrative
 purposes. 
Journal: Journal of Applied Statistics 
Pages: 555-566 
Issue: 4 
Volume: 37 
Year: 2010 
Keywords: acceptance sampling plan, operating characteristic function value, median and percentile points, consumer and producer's risks, 
X-DOI: 10.1080/02664760902769787 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902769787
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:4:p:555-566




Template-Type: ReDIF-Article 1.0
Author-Name: M. M. Nassar 
Author-X-Name-First: M. M. 
Author-X-Name-Last: Nassar 
Author-Name: S. M. Khamis 
Author-X-Name-First: S. M. 
Author-X-Name-Last: Khamis 
Author-Name: S. S. Radwan 
Author-X-Name-First: S. S. 
Author-X-Name-Last: Radwan 
Title: Geometric sample size determination in Bayesian analysis 
Abstract:
  The problem of sample size determination in the context of Bayesian
 analysis is considered. For the familiar and practically important
 parameter of a geometric distribution with a beta prior, three different
 Bayesian approaches based on the highest posterior density intervals are
 discussed. A computer program handles all computational complexities and
 is available upon request. 
Journal: Journal of Applied Statistics 
Pages: 567-575 
Issue: 4 
Volume: 37 
Year: 2010 
Keywords: Bayesian analysis, average coverage criterion (ACC), average length criterion (ALC), worst-outcome criterion (WOC), 
X-DOI: 10.1080/02664760902803248 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902803248
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:4:p:567-575




Template-Type: ReDIF-Article 1.0
Author-Name: Jorge Cadima 
Author-X-Name-First: Jorge 
Author-X-Name-Last: Cadima 
Author-Name: Francisco Lage Calheiros 
Author-X-Name-First: Francisco Lage 
Author-X-Name-Last: Calheiros 
Author-Name: Isabel Preto 
Author-X-Name-First: Isabel 
Author-X-Name-Last: Preto 
Title: The eigenstructure of block-structured correlation matrices and its implications for principal component analysis 
Abstract:
  Block-structured correlation matrices are correlation matrices in which
 the p variables are subdivided into homogeneous groups, with equal
 correlations for variables within each group, and equal correlations
 between any given pair of variables from different groups.
 Block-structured correlation matrices arise as approximations for certain
 data sets' true correlation matrices. A block structure in a correlation
 matrix entails a certain number of properties regarding its
 eigendecomposition and, therefore, a principal component analysis of the
 underlying data. This paper explores these properties, both from an
 algebraic and a geometric perspective, and discusses their robustness.
 Suggestions are also made regarding the choice of variables to be
 subjected to a principal component analysis, when in the presence of
 (approximately) block-structured variables. 
Journal: Journal of Applied Statistics 
Pages: 577-589 
Issue: 4 
Volume: 37 
Year: 2010 
Keywords: block-structured correlation matrices, eigendecomposition, principal component analysis, within-group eigenpairs, between-group eigenpairs, 
X-DOI: 10.1080/02664760902803263 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902803263
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:4:p:577-589




Template-Type: ReDIF-Article 1.0
Author-Name: Francisco Cribari-Neto 
Author-X-Name-First: Francisco 
Author-X-Name-Last: Cribari-Neto 
Author-Name: Maria da Gloria Lima 
Author-X-Name-First: Maria da Gloria 
Author-X-Name-Last: Lima 
Title: Approximate inference in heteroskedastic regressions: A numerical evaluation 
Abstract:
  The commonly made assumption that all stochastic error terms in the
 linear regression model share the same variance (homoskedasticity) is
 oftentimes violated in practical applications, especially when they are
 based on cross-sectional data. As a precaution, a number of practitioners
 choose to base inference on the parameters that index the model on tests
 whose statistics employ asymptotically correct standard errors, i.e.
 standard errors that are asymptotically valid whether or not the errors
 are homoskedastic. In this paper, we use numerical integration methods to
 evaluate the finite-sample performance of tests based on different
 (alternative) heteroskedasticity-consistent standard errors. Emphasis is
 placed on a few recently proposed heteroskedasticity-consistent covariance
 matrix estimators. Overall, the results favor the HC4 and HC5
 heteroskedasticity-robust standard errors. We also consider the use of
 restricted residuals when constructing asymptotically valid standard
 errors. Our results show that the only test that clearly benefits from
 such a strategy is the HC0 test. 
Journal: Journal of Applied Statistics 
Pages: 591-615 
Issue: 4 
Volume: 37 
Year: 2010 
Keywords: covariance matrix estimation, heteroskedasticity, leverage point, linear regression, quasi-t test, 
X-DOI: 10.1080/02664760902803271 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902803271
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:4:p:591-615




Template-Type: ReDIF-Article 1.0
Author-Name: H. Jiang 
Author-X-Name-First: H. 
Author-X-Name-Last: Jiang 
Author-Name: M. Xie 
Author-X-Name-First: M. 
Author-X-Name-Last: Xie 
Author-Name: L. C. Tang 
Author-X-Name-First: L. C. 
Author-X-Name-Last: Tang 
Title: On MLEs of the parameters of a modified Weibull distribution for progressively type-2 censored samples 
Abstract:
  Lifetimes of modern mechanic or electronic units usually exhibit
 bathtub-shaped failure rates. An appropriate probability distribution to
 model such data is the modified Weibull distribution proposed by Lai et
 al. [15]. This distribution has both the two-parameter Weibull and type-1
 extreme value distribution as special cases. It is able to model lifetime
 data with monotonic and bathtub-shaped failure rates, and thus attracts
 some interest among researchers because of this property. In this paper,
 the procedure of obtaining the maximum likelihood estimates (MLEs) of the
 parameters for progressively type-2 censored and complete samples are
 studied. Existence and uniqueness of the MLEs are proved. 
Journal: Journal of Applied Statistics 
Pages: 617-627 
Issue: 4 
Volume: 37 
Year: 2010 
Keywords: modified Weibull distribution, bathtub-shaped failure rate, maximum likelihood estimation, Hessian matrix, uniqueness and existence, 
X-DOI: 10.1080/02664760902803289 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902803289
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:4:p:617-627




Template-Type: ReDIF-Article 1.0
Author-Name: Hani Samawi 
Author-X-Name-First: Hani 
Author-X-Name-Last: Samawi 
Author-Name: Mohammed Al-Haj Ebrahem 
Author-X-Name-First: Mohammed 
Author-X-Name-Last: Al-Haj Ebrahem 
Author-Name: Noha Al-Zubaidin 
Author-X-Name-First: Noha 
Author-X-Name-Last: Al-Zubaidin 
Title: An optimal sign test for one-sample bivariate location model using an alternative bivariate ranked-set sample 
Abstract:
  The aim of this paper is to find an optimal alternative bivariate
 ranked-set sample for one-sample location model bivariate sign test. Our
 numerical and theoretical results indicated that the optimal designs for
 the bivariate sign test are the alternative designs with quantifying order
 statistics with labels {((r+1)/2, (r+1)/2)}, when the set size r is odd
 and {(r/2+1, r/2), (r/2, r/2+1)} when the set size r is even. The
 asymptotic distribution and Pitman efficiencies of these designs are
 derived. A simulation study is conducted to investigate the power of the
 proposed optimal designs. Illustration using real data with the Bootstrap
 algorithm for P-value estimation is used. 
Journal: Journal of Applied Statistics 
Pages: 629-650 
Issue: 4 
Volume: 37 
Year: 2010 
Keywords: bivariate ranked-set sample, location model, median ranked-set sample, Pitman efficiencies, ranked-set sample, simple random sample, sign test, 
X-DOI: 10.1080/02664760902810805 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902810805
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:4:p:629-650




Template-Type: ReDIF-Article 1.0
Author-Name: Luis Nobre Pereira 
Author-X-Name-First: Luis Nobre 
Author-X-Name-Last: Pereira 
Author-Name: Pedro Simoes Coelho 
Author-X-Name-First: Pedro Simoes 
Author-X-Name-Last: Coelho 
Title: Small area estimation of mean price of habitation transaction using time-series and cross-sectional area-level models 
Abstract:
  In this paper, a new small domain estimator for area-level data is
 proposed. The proposed estimator is driven by a real problem of estimating
 the mean price of habitation transaction at a regional level in a European
 country, using data collected from a longitudinal survey conducted by a
 national statistical office. At the desired level of inference, it is not
 possible to provide accurate direct estimates because the sample sizes in
 these domains are very small. An area-level model with a heterogeneous
 covariance structure of random effects assists the proposed combined
 estimator. This model is an extension of a model due to Fay and Herriot
 [5], but it integrates information across domains and over several periods
 of time. In addition, a modified method of estimation of variance
 components for time-series and cross-sectional area-level models is
 proposed by including the design weights. A Monte Carlo simulation, based
 on real data, is conducted to investigate the performance of the proposed
 estimators in comparison with other estimators frequently used in small
 area estimation problems. In particular, we compare the performance of
 these estimators with the estimator based on the Rao-Yu model [23]. The
 simulation study also accesses the performance of the modified variance
 component estimators in comparison with the traditional ANOVA method.
 Simulation results show that the estimators proposed perform better than
 the other estimators in terms of both precision and bias. 
Journal: Journal of Applied Statistics 
Pages: 651-666 
Issue: 4 
Volume: 37 
Year: 2010 
Keywords: linear mixed models, chronological autocorrelation, estimation of variance components, empirical best linear unbiased predictor, estimation of mean price of habitation, 
X-DOI: 10.1080/02664760902810821 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902810821
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:4:p:651-666




Template-Type: ReDIF-Article 1.0
Author-Name: Karl Majeske 
Author-X-Name-First: Karl 
Author-X-Name-Last: Majeske 
Author-Name: Terri Lynch-Caris 
Author-X-Name-First: Terri 
Author-X-Name-Last: Lynch-Caris 
Author-Name: Janet Brelin-Fornari 
Author-X-Name-First: Janet 
Author-X-Name-Last: Brelin-Fornari 
Title: Quantifying R2 bias in the presence of measurement error 
Abstract:
  Measurement error (ME) is the difference between the true unknown value
 of a variable and the data assigned to that variable during the measuring
 process. The multiple correlation coefficient quantifies the strength of
 the relationship between the dependent and independent variable(s) in
 regression modeling. In this paper, we show that ME in the dependent
 variable results in a negative bias in the multiple correlation
 coefficient, making the relationship appear weaker than it should. The
 adjusted R2 provides regression modelers an unbiased estimate of the
 multiple correlation coefficient. However, due to the ME induced bias in
 the multiple correlation coefficient, the otherwise unbiased adjusted R2
 under-estimates the variance explained by a regression model. This paper
 proposes two statistics for estimating the multiple correlation
 coefficient, both of which take into account the ME in the dependent
 variable. The first statistic uses all unbiased estimators, but may
 produce values outside the [0,1] interval. The second statistic requires
 modeling a single data set, created by including descriptive variables on
 the subjects used in a gage study. Based on sums of squares, the statistic
 has the properties of an R2: it measures the proportion of variance
 explained; has values restricted to the [0,1] interval; and the endpoints
 indicate no variance explained and all variance explained respectively. We
 demonstrate the methodology using data from a study of cervical spine
 range of motion in children. 
Journal: Journal of Applied Statistics 
Pages: 667-677 
Issue: 4 
Volume: 37 
Year: 2010 
Keywords: measurement error, regression analysis, R2, bias correction, gage R&R, 
X-DOI: 10.1080/02664760902814542 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902814542
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:4:p:667-677




Template-Type: ReDIF-Article 1.0
Author-Name: Anne-Line Balduck 
Author-X-Name-First: Anne-Line 
Author-X-Name-Last: Balduck 
Author-Name: Anita Prinzie 
Author-X-Name-First: Anita 
Author-X-Name-Last: Prinzie 
Author-Name: Marc Buelens 
Author-X-Name-First: Marc 
Author-X-Name-Last: Buelens 
Title: The effectiveness of coach turnover and the effect on home team advantage, team quality and team ranking 
Abstract:
  The effectiveness of coach turnover on team performance is widely
 discussed in the literature due to the indirect impact of a team's
 performance on a club's revenues. This study examines the effect of coach
 turnover within a competition season by focusing on the change in team
 quality and the change in home team advantage under the new coach. The
 change in team quality or home team advantage can vary according to the
 team (team specific) or might be an independent quantity (non-team
 specific). We estimated nine possible regression models, given no change,
 team-specific change and non-team-specific change in quality or home team
 advantage. The data are the match results of Belgian male soccer teams
 playing in the highest national division during seven seasons. Results
 point to a team-specific effect of a new coach on a team's quality. This
 article further contributes by evaluating the new coach's success with
 regard to whether his ability to improve team quality also results in a
 better position of the team in the final ranking. A new coach will be able
 to improve the ranking of the team if the improved team quality under the
 new coach renders a positive team quality. 
Journal: Journal of Applied Statistics 
Pages: 679-689 
Issue: 4 
Volume: 37 
Year: 2010 
Keywords: managerial change, home team advantage, team performance, team quality, regression model, individual match data, team ranking, 
X-DOI: 10.1080/02664760902824731 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902824731
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:4:p:679-689




Template-Type: ReDIF-Article 1.0
Author-Name: Shola Adeyemi 
Author-X-Name-First: Shola 
Author-X-Name-Last: Adeyemi 
Author-Name: Thierry Chaussalet 
Author-X-Name-First: Thierry 
Author-X-Name-Last: Chaussalet 
Author-Name: Haifeng Xie 
Author-X-Name-First: Haifeng 
Author-X-Name-Last: Xie 
Author-Name: Md Asaduzaman 
Author-X-Name-First: Md 
Author-X-Name-Last: Asaduzaman 
Title: Random effects models for operational patient pathways 
Abstract:
  Patient flow modeling is a growing field of interest in health services
 research. Several techniques have been applied to model movement of
 patients within and between health-care facilities. However, individual
 patient experience during the delivery of care has always been overlooked.
 In this work, a random effects model is introduced to patient flow
 modeling and applied to a London Hospital Neonatal unit data. In
 particular, a random effects multinomial logit model is used to capture
 individual patient trajectories in the process of care with patient
 frailties modeled as random effects. Intuitively, both operational and
 clinical patient flow are modeled, the former being physical and the
 latter latent. Two variants of the model are proposed, one based on mere
 patient pathways and the other based on patient characteristics. Our
 technique could identify interesting pathways such as those that result in
 high probability of death (survival), pathways incurring the least
 (highest) cost of care or pathways with the least (highest) length of
 stay. Patient-specific discharge probabilities from the health care system
 could also be predicted. These are of interest to health-care managers in
 planning the scarce resources needed to run health-care institutions. 
Journal: Journal of Applied Statistics 
Pages: 691-701 
Issue: 4 
Volume: 37 
Year: 2010 
Keywords: patient flow, frailty, pathways, transition, 
X-DOI: 10.1080/02664760902873951 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902873951
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:4:p:691-701




Template-Type: ReDIF-Article 1.0
Author-Name: S&#xf8;ren Feodor Nielsen 
Author-X-Name-First: S&#xf8;ren Feodor 
Author-X-Name-Last: Nielsen 
Title: Generalized linear models for insurance data 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 703-703 
Issue: 4 
Volume: 37 
Year: 2010 
X-DOI: 10.1080/02664760902811571 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902811571
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:4:p:703-703




Template-Type: ReDIF-Article 1.0
Author-Name: Z.Q. John Lu 
Author-X-Name-First: Z.Q. 
Author-X-Name-Last: John Lu 
Title: Bayesian methods for data analysis, third edition 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 705-706 
Issue: 4 
Volume: 37 
Year: 2010 
X-DOI: 10.1080/02664760902811621 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902811621
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:4:p:705-706




Template-Type: ReDIF-Article 1.0
Author-Name: Y. H. Michlin 
Author-X-Name-First: Y. H. 
Author-X-Name-Last: Michlin 
Author-Name: G. Grabarnik 
Author-X-Name-First: G. 
Author-X-Name-Last: Grabarnik 
Title: Search boundaries of the truncated discrete sequential test 
Abstract:
  The theme of this paper is improved planning of binomial sequential
 probability ratio tests in the context of comparison of two objects as to
 their time between failures or to failure, assumed to be exponentially
 distributed. The authors' earlier works established that the probabilities
 of I- and II- type errors (&#x3b1; and &#x3b2;) are discrete in character
 and do not lend themselves to analytical expression. Accordingly, the
 choice of the optimal parameters for the decision boundaries necessitates
 a search for extrema in discrete sets. The present work outlines a
 procedure that involves application of the continued-fractions theory, and
 permits finding the set of boundary positions in which the test
 characteristics undergo changes. It was established, that in the domains
 described in the earlier papers, the relationships of &#x3b1; and &#x3b2;
 versus these positions are close to planar and - within narrow limits -
 stepwise. The step sizes are highly variable, so that the standard minimum
 search procedures are either cumbersome or actually useless. On the basis
 of these relationships~- and others - a search algorithm is proposed for
 the optimal test boundaries. An example is presented - planning and
 implementation of this test in the integrated-circuit industry. 
Journal: Journal of Applied Statistics 
Pages: 707-724 
Issue: 5 
Volume: 37 
Year: 2010 
Keywords: reliability, test planning, time between failures, time to failure, exponential distribution, binomial distribution, 
X-DOI: 10.1080/02664760903254078 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903254078
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:5:p:707-724




Template-Type: ReDIF-Article 1.0
Author-Name: M. I. Rolfe 
Author-X-Name-First: M. I. 
Author-X-Name-Last: Rolfe 
Author-Name: K. Mengersen 
Author-X-Name-First: K. 
Author-X-Name-Last: Mengersen 
Author-Name: G. Beadle 
Author-X-Name-First: G. 
Author-X-Name-Last: Beadle 
Author-Name: K. Vearncombe 
Author-X-Name-First: K. 
Author-X-Name-Last: Vearncombe 
Author-Name: B. Andrew 
Author-X-Name-First: B. 
Author-X-Name-Last: Andrew 
Author-Name: H. L. Johnson 
Author-X-Name-First: H. L. 
Author-X-Name-Last: Johnson 
Author-Name: C. Walsh 
Author-X-Name-First: C. 
Author-X-Name-Last: Walsh 
Title: Latent class piecewise linear trajectory modelling for short-term cognition responses after chemotherapy for breast cancer patients 
Abstract:
  This paper investigates the impact of chemotherapy on cognitive function
 of breast cancer patients and whether this response is homogeneous for all
 patients. Latent class piecewise linear trajectory (growth) models were
 employed to describe changes and identify subgroups in three Auditory
 Verbal Learning Test measures (learning, immediate retention and delayed
 recall) in 130 breast cancer patients taken at three time periods: before
 chemotherapy and 1 and 6 months post-chemotherapy. Two distinct subgroups
 of women exhibiting different patterns of response were identified for
 learning and delayed recall and three for immediate retention. The groups
 differed in level (intercept) at 1 month post-chemotherapy and patterns of
 decline and recovery. Binomial and multinomial logistic regressions on the
 latent classes found that age, initial National Adult Reading Test
 (NART)-predicted IQ, stage of cancer and the initial Functional Assessment
 of Cancer Therapy-Breast subscale (or subsets thereof) to be significant
 predictors of classes. 
Journal: Journal of Applied Statistics 
Pages: 725-738 
Issue: 5 
Volume: 37 
Year: 2010 
Keywords: latent class, piecewise linear, trajectory, cognition, breast cancer, chemotherapy, growth models, mixtures, 
X-DOI: 10.1080/02664760902729641 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902729641
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:5:p:725-738




Template-Type: ReDIF-Article 1.0
Author-Name: Marco Morales 
Author-X-Name-First: Marco 
Author-X-Name-Last: Morales 
Title: Lag order selection for an optimal autoregressive covariance matrix estimator 
Abstract:
  A good parametric spectral estimator requires an accurate estimate of the
 sum of AR coefficients, however a criterion which minimizes the innovation
 variance not necessarily yields the best spectral estimate. This paper
 develops an alternative information criterion considering the bias in the
 sum of the parameters for the autoregressive estimator of the spectral
 density at frequency zero. 
Journal: Journal of Applied Statistics 
Pages: 739-748 
Issue: 5 
Volume: 37 
Year: 2010 
Keywords: Spectral density, covariance matrix, autoregressive, lag-order selection, statistical inference, 
X-DOI: 10.1080/02664760902873969 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902873969
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:5:p:739-748




Template-Type: ReDIF-Article 1.0
Author-Name: J. Jacques 
Author-X-Name-First: J. 
Author-X-Name-Last: Jacques 
Author-Name: C. Biernacki 
Author-X-Name-First: C. 
Author-X-Name-Last: Biernacki 
Title: Extension of model-based classification for binary data when training and test populations differ 
Abstract:
  Standard discriminant analysis supposes that both the training sample and
 the test sample are derived from the same population. When these samples
 arise from populations differing in their descriptive parameters, a
 generalization of discriminant analysis consists of adapting the
 classification rule related to the training population to another rule
 related to the test population, by estimating a link map between both
 populations. This paper extends an existing work in the multinormal
 context to the case of binary data. In order to solve the problem of
 defining a link map between the two binary populations, it is assumed that
 the binary data result from the discretization of latent Gaussian data. An
 estimation method and a robustness study are presented, and two
 applications in a biological context illustrate this work. 
Journal: Journal of Applied Statistics 
Pages: 749-766 
Issue: 5 
Volume: 37 
Year: 2010 
Keywords: Biological application, discriminant analysis, EM algorithm, latent class model, Stochastic link, 
X-DOI: 10.1080/02664760902889957 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902889957
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:5:p:749-766




Template-Type: ReDIF-Article 1.0
Author-Name: Hyo-Il Park 
Author-X-Name-First: Hyo-Il 
Author-X-Name-Last: Park 
Author-Name: Seung-Man Hong 
Author-X-Name-First: Seung-Man 
Author-X-Name-Last: Hong 
Title: A permutation test for multivariate data with grouped components 
Abstract:
  In this paper, we consider a nonparametric test procedure for
 multivariate data with grouped components under the two sample problem
 setting. For the construction of the test statistic, we use linear rank
 statistics which were derived by applying the likelihood ratio principle
 for each component. For the null distribution of the test statistic, we
 apply the permutation principle for small or moderate sample sizes and
 derive the limiting distribution for the large sample case. Also we
 illustrate our test procedure with an example and compare with other
 procedures through simulation study. Finally, we discuss some additional
 interesting features as concluding remarks. 
Journal: Journal of Applied Statistics 
Pages: 767-778 
Issue: 5 
Volume: 37 
Year: 2010 
Keywords: grouped data, liner rank statistic, multivariate data, nonparametric test, permutation principle, 
X-DOI: 10.1080/02664760902889973 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902889973
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:5:p:767-778




Template-Type: ReDIF-Article 1.0
Author-Name: Hyonggin An 
Author-X-Name-First: Hyonggin 
Author-X-Name-Last: An 
Author-Name: Roderick Little 
Author-X-Name-First: Roderick 
Author-X-Name-Last: Little 
Author-Name: Andrea Bozoki 
Author-X-Name-First: Andrea 
Author-X-Name-Last: Bozoki 
Title: A statistical algorithm for detecting cognitive plateaus in Alzheimer's disease 
Abstract:
  Repeated neuropsychological measurements, such as mini-mental state
 examination (MMSE) scores, are frequently used in Alzheimer's disease (AD)
 research to study change in cognitive function of AD patients. A question
 of interest among dementia researchers is whether some AD patients exhibit
 transient &#x201c;plateaus&#x201d; of cognitive function in the course of
 the disease. We consider a statistical approach to this question, based on
 irregularly spaced repeated MMSE scores. We propose an algorithm that
 formalizes the measurement of an apparent cognitive plateau, and a
 procedure to evaluate the evidence of plateaus in AD using this algorithm
 based on applying the algorithm to the observed data and to data sets
 simulated from a linear mixed model. We apply these methods to repeated
 MMSE data from the Michigan Alzheimer's Disease Research Center, finding a
 high rate of apparent plateaus and also a high rate of false discovery.
 Simulation studies are also conducted to assess the performance of the
 algorithm. In general, the false discovery rate of the algorithm is high
 unless the rate of decline is high compared with the measurement error of
 the cognitive test. It is argued that the results are not a problem of the
 specific algorithm chosen, but reflect a lack of information concerning
 the presence of plateaus in the data. 
Journal: Journal of Applied Statistics 
Pages: 779-789 
Issue: 5 
Volume: 37 
Year: 2010 
Keywords: Alzheimer's disease, longitudinal data, linear mixed model, nonlinear model, false discovery rate, cognitive plateau, 
X-DOI: 10.1080/02664760902889999 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902889999
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:5:p:779-789




Template-Type: ReDIF-Article 1.0
Author-Name: Zhiguo Xiao 
Author-X-Name-First: Zhiguo 
Author-X-Name-Last: Xiao 
Author-Name: Jun Shao 
Author-X-Name-First: Jun 
Author-X-Name-Last: Shao 
Author-Name: Mari Palta 
Author-X-Name-First: Mari 
Author-X-Name-Last: Palta 
Title: GMM in linear regression for longitudinal data with multiple covariates measured with error 
Abstract:
  Griliches and Hausman 5 and Wansbeek 11 proposed using the generalized
 method of moments (GMM) to obtain consistent estimators in linear
 regression models for longitudinal data with measurement error in one
 covariate, without requiring additional validation or replicate data. For
 usefulness of this methodology, we must extend it to the more realistic
 situation where more than one covariate are measured with error. Such an
 extension is not straightforward, since measurement errors across
 different covariates may be correlated. By a careful construction of the
 measurement error correlation structure, we are able to extend Wansbeek's
 GMM and show that the extended Griliches and Hausman's GMM is equivalent
 to the extended Wansbeek's GMM. For illustration, we apply the extended
 GMM to data from two medical studies, and compare it with the naive method
 and the method assuming only one covariate having measurement error. 
Journal: Journal of Applied Statistics 
Pages: 791-805 
Issue: 5 
Volume: 37 
Year: 2010 
Keywords: longitudinal data, multiple covariates, measurement error, generalized method of moments, 
X-DOI: 10.1080/02664760902890005 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902890005
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:5:p:791-805




Template-Type: ReDIF-Article 1.0
Author-Name: Fu-Kwun Wang 
Author-X-Name-First: Fu-Kwun 
Author-X-Name-Last: Wang 
Author-Name: Yung-Fu Cheng 
Author-X-Name-First: Yung-Fu 
Author-X-Name-Last: Cheng 
Title: Robust regression for estimating the Burr XII parameters with outliers 
Abstract:
  The Burr XII distribution offers a more flexible alternative to the
 lognormal, log-logistic and Weibull distributions. Outliers can occur
 during reliability life testing. Thus, we need an efficient method to
 estimate the parameters of the Burr XII distribution for censored data
 with outliers. The objective of this paper is to present a robust
 regression (RR) method called M-estimator to estimate the parameters of a
 two-parameter Burr XII distribution based on the probability plotting
 procedure for both the complete and multiply-censored data with outliers.
 The simulation results show that the RR method outperforms the unweighted
 least squares and maximum likelihood methods in most cases in terms of
 bias and errors in the root mean square. 
Journal: Journal of Applied Statistics 
Pages: 807-819 
Issue: 5 
Volume: 37 
Year: 2010 
Keywords: Burr XII distribution, robust regression, M-estimator, least squares, maximum likelihood, 
X-DOI: 10.1080/02664760902906231 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902906231
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:5:p:807-819




Template-Type: ReDIF-Article 1.0
Author-Name: J. Anzures-Cabrera 
Author-X-Name-First: J. 
Author-X-Name-Last: Anzures-Cabrera 
Author-Name: J. L. Hutton 
Author-X-Name-First: J. L. 
Author-X-Name-Last: Hutton 
Title: Competing risks, left truncation and late entry effect in A-bomb survivors cohort 
Abstract:
  The cohort under study comprises A-bomb survivors residing in Hiroshima
 Prefecture since 1968. After this year, thousands of survivors were newly
 recognized every year. The aim of this study is to determine whether the
 survival experience of the late entrants to the cohort is significantly
 different from the registered population in 1968. Parametric models that
 account for left truncation and competing risks were developed by using
 sub-hazard functions. A Weibull distribution was used to determine the
 possible existence of a late entry effect in Hiroshima A-bomb survivors.
 The competing risks framework shows that there might be a late entry
 effect in the male and female groups. Our findings are congruent with
 previous studies analysing similar populations. 
Journal: Journal of Applied Statistics 
Pages: 821-831 
Issue: 5 
Volume: 37 
Year: 2010 
Keywords: competing risks, late entry effect, left truncation, sub-hazard function, Weibull distribution, 
X-DOI: 10.1080/02664760902914417 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902914417
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:5:p:821-831




Template-Type: ReDIF-Article 1.0
Author-Name: Jan Serroyen 
Author-X-Name-First: Jan 
Author-X-Name-Last: Serroyen 
Author-Name: Geert Molenberghs 
Author-X-Name-First: Geert 
Author-X-Name-Last: Molenberghs 
Author-Name: Marc Aerts 
Author-X-Name-First: Marc 
Author-X-Name-Last: Aerts 
Author-Name: Ellen Vloeberghs 
Author-X-Name-First: Ellen 
Author-X-Name-Last: Vloeberghs 
Author-Name: Peter Paul De Deyn 
Author-X-Name-First: Peter Paul 
Author-X-Name-Last: De Deyn 
Author-Name: Geert Verbeke 
Author-X-Name-First: Geert 
Author-X-Name-Last: Verbeke 
Title: Flexible estimation of serial correlation in nonlinear mixed models 
Abstract:
  In the conventional linear mixed-effects model, four structures can be
 distinguished: fixed effects, random effects, measurement error and serial
 correlation. The latter captures the phenomenon that the correlation
 structure within a subject depends on the time lag between two
 measurements. While the general linear mixed model is rather flexible, the
 need has arisen to further increase flexibility. In addition to work done
 in the area, we propose the use of spline-based modeling of the serial
 correlation function, so as to allow for additional flexibility. This
 approach is applied to data from a pre-clinical experiment in dementia
 which studied the eating and drinking behavior in mice. 
Journal: Journal of Applied Statistics 
Pages: 833-846 
Issue: 5 
Volume: 37 
Year: 2010 
Keywords: Alzheimer's disease, dementia, ordinary least squares, random effect, 
X-DOI: 10.1080/02664760902914425 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902914425
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:5:p:833-846




Template-Type: ReDIF-Article 1.0
Author-Name: Adriana Bortoluzzo 
Author-X-Name-First: Adriana 
Author-X-Name-Last: Bortoluzzo 
Author-Name: Pedro Morettin 
Author-X-Name-First: Pedro 
Author-X-Name-Last: Morettin 
Author-Name: Clelia Toloi 
Author-X-Name-First: Clelia 
Author-X-Name-Last: Toloi 
Title: Time-varying autoregressive conditional duration model 
Abstract:
  The main goal of this work is to generalize the autoregressive
 conditional duration (ACD) model applied to times between trades to the
 case of time-varying parameters. The use of wavelets allows that
 parameters vary through time and makes possible the modeling of
 non-stationary processes without preliminary data transformations. The
 time-varying ACD model estimation was done by maximum-likelihood with
 standard exponential distributed errors. The properties of the estimators
 were assessed via bootstrap. We present a simulation exercise for a
 non-stationary process and an empirical application to a real series,
 namely the TELEMAR stock. Diagnostic and goodness of fit analysis suggest
 that the time-varying ACD model simultaneously modeled the dependence
 between durations, intra-day seasonality and volatility. 
Journal: Journal of Applied Statistics 
Pages: 847-864 
Issue: 5 
Volume: 37 
Year: 2010 
Keywords: ACD model, bootstrap, durations, non-stationarity, time-varying parameters, wavelet, 
X-DOI: 10.1080/02664760902914458 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902914458
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:5:p:847-864




Template-Type: ReDIF-Article 1.0
Author-Name: Emilio Augusto Coelho-Barros 
Author-X-Name-First: Emilio Augusto 
Author-X-Name-Last: Coelho-Barros 
Author-Name: Jorge Alberto Achcar 
Author-X-Name-First: Jorge Alberto 
Author-X-Name-Last: Achcar 
Author-Name: Josmar Mazucheli 
Author-X-Name-First: Josmar 
Author-X-Name-Last: Mazucheli 
Title: Longitudinal Poisson modeling: an application for CD4 counting in HIV-infected patients 
Abstract:
  In this paper, we present different &#x201c;frailty&#x201d; models to
 analyze longitudinal data in the presence of covariates. These models
 incorporate the extra-Poisson variability and the possible correlation
 among the repeated counting data for each individual. Assuming a CD4
 counting data set in HIV-infected patients, we develop a hierarchical
 Bayesian analysis considering the different proposed models and using
 Markov Chain Monte Carlo methods. We also discuss some Bayesian
 discrimination aspects for the choice of the best model. 
Journal: Journal of Applied Statistics 
Pages: 865-880 
Issue: 5 
Volume: 37 
Year: 2010 
Keywords: longitudinal Poisson data, &#x201c;frailty&#x201d; models, hierarchical Bayesian analysis, Winbugs software, clinical data, 
X-DOI: 10.1080/02664760902914466 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902914466
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:5:p:865-880




Template-Type: ReDIF-Article 1.0
Author-Name: B. D. McCullough 
Author-X-Name-First: B. D. 
Author-X-Name-Last: McCullough 
Author-Name: Thomas McWilliams 
Author-X-Name-First: Thomas 
Author-X-Name-Last: McWilliams 
Title: Baseball players with the initial &#x201c;K&#x201d; do not strike out more often 
Abstract:
  It has been claimed that baseball players whose first or last name begins
 with the letter K have a tendency to strike out more than players whose
 initials do not contain the letter K. This &#x201c;result&#x201d; was
 achieved by a naive application of statistical methods. We show that this
 result is a spurious statistical artifact that can be reversed by the use
 of only slightly less naive statistical methods. We also show that other
 letters have larger and/or more significant effects than the letter K.
 Finally, we show that the original study applied the&#xa0;wrong
 statistical test and tested the hypothesis incorrectly. When these errors
 are corrected, most of the letters of the alphabet have a statistically
 significant strikeout effect. 
Journal: Journal of Applied Statistics 
Pages: 881-891 
Issue: 6 
Volume: 37 
Year: 2010 
Keywords: name-letter effect, spurious correlation, 
X-DOI: 10.1080/02664760902889965 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902889965
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:6:p:881-891




Template-Type: ReDIF-Article 1.0
Author-Name: Jurate Saltyte Benth 
Author-X-Name-First: Jurate Saltyte 
Author-X-Name-Last: Benth 
Author-Name: Fred Espen Benth 
Author-X-Name-First: Fred Espen 
Author-X-Name-Last: Benth 
Title: Analysis and modelling of wind speed in New York 
Abstract:
  In this paper we propose an ARMA time-series model for the wind speed at
 a single spatial location, and estimate it on in-sample data recorded in
 three different wind farm regions in New York state. The data have a
 three-hour granularity, but based on applications to financial wind
 derivatives contracts, we also consider daily average wind speeds. We
 demonstrate that there are large discrepancies in the behaviour of daily
 average and three-hourly wind speed records. The validation procedure
 based on out-of-sample observations reflects that the proposed model is
 reliable and can be used for various practical applications, like, for
 instance, weather prediction, pricing of financial wind contracts, wind
 generated power, etc. Furthermore, we discuss some striking resemblances
 with temperature dynamics. 
Journal: Journal of Applied Statistics 
Pages: 893-909 
Issue: 6 
Volume: 37 
Year: 2010 
Keywords: wind speed, time series, ARMA, seasonality, seasonal variance, 
X-DOI: 10.1080/02664760902914490 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902914490
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:6:p:893-909




Template-Type: ReDIF-Article 1.0
Author-Name: Andrew Copas 
Author-X-Name-First: Andrew 
Author-X-Name-Last: Copas 
Author-Name: Shaun Seaman 
Author-X-Name-First: Shaun 
Author-X-Name-Last: Seaman 
Title: Bias from the use of generalized estimating equations to analyze incomplete longitudinal binary data 
Abstract:
  Patient dropout is a common problem in studies that collect repeated
 binary measurements. Generalized estimating equations (GEE) are often used
 to analyze such data. The dropout mechanism may be plausibly missing at
 random (MAR), i.e. unrelated to future measurements given covariates and
 past measurements. In this case, various authors have recommended weighted
 GEE with weights based on an assumed dropout model, or an imputation
 approach, or a doubly robust approach based on weighting and imputation.
 These approaches provide asymptotically unbiased inference, provided the
 dropout or imputation model (as appropriate) is correctly specified. Other
 authors have suggested that, provided the working correlation structure is
 correctly specified, GEE using an improved estimator of the correlation
 parameters ('modified GEE') show minimal bias. These modified GEE have not
 been thoroughly examined. In this paper, we study the asymptotic bias
 under MAR dropout of these modified GEE, the standard GEE, and also GEE
 using the true correlation. We demonstrate that all three methods are
 biased in general. The modified GEE may be preferred to the standard GEE
 and are subject to only minimal bias in many MAR scenarios but in others
 are substantially biased. Hence, we recommend the modified GEE be used
 with caution. 
Journal: Journal of Applied Statistics 
Pages: 911-922 
Issue: 6 
Volume: 37 
Year: 2010 
Keywords: binary data, generalized estimating equations, missing data, missing at random, repeated measures, 
X-DOI: 10.1080/02664760902939604 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902939604
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:6:p:911-922




Template-Type: ReDIF-Article 1.0
Author-Name: M. Qamarul Islam 
Author-X-Name-First: M. Qamarul 
Author-X-Name-Last: Islam 
Author-Name: Moti Tiku 
Author-X-Name-First: Moti 
Author-X-Name-Last: Tiku 
Title: Multiple linear regression model with stochastic design variables 
Abstract:
  In a simple multiple linear regression model, the design variables have
 traditionally been assumed to be non-stochastic. In numerous real-life
 situations, however, they are stochastic and non-normal. Estimators of
 parameters applicable to such situations are developed. It is shown that
 these estimators are efficient and robust. A real-life example is given. 
Journal: Journal of Applied Statistics 
Pages: 923-943 
Issue: 6 
Volume: 37 
Year: 2010 
Keywords: correlation coefficient, least squares, linear regression, modified maximum likelihood, multivariate distributions, non-normality, random design, 
X-DOI: 10.1080/02664760902939612 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902939612
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:6:p:923-943




Template-Type: ReDIF-Article 1.0
Author-Name: K. M. Matawie 
Author-X-Name-First: K. M. 
Author-X-Name-Last: Matawie 
Author-Name: A. Assaf 
Author-X-Name-First: A. 
Author-X-Name-Last: Assaf 
Title: Bayesian and DEA efficiency modelling: an application to hospital foodservice operations 
Abstract:
  The significant impact of health foodservice operations on the total
 operational cost of the hospital sector has increased the need to improve
 the efficiency of these operations. Although important studies on the
 performance of foodservice operations have been published in various
 academic journals and industrial reports, the findings and implications
 remain simple and limited in scope and methodology. This paper
 investigates two popular methodologies in the efficiency literature:
 Bayesian &#x201c;stochastic frontier analysis&#x201d; (SFA) and
 &#x201c;data envelopment analysis&#x201d; (DEA). The paper discusses the
 statistical advantages of the Bayesian SFA and compares it with an
 extended DEA model. The results from a sample of 101 hospital foodservice
 operations show the existence of inefficiency in the sample, and indicate
 significant differences between the average efficiency generated by the
 Bayesian SFA and DEA models. The ranking of efficiency is, however,
 statistically independent of the methodologies. 
Journal: Journal of Applied Statistics 
Pages: 945-953 
Issue: 6 
Volume: 37 
Year: 2010 
Keywords: Bayesian SFA, DEA, efficiency, hospitals, 
X-DOI: 10.1080/02664760902949058 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902949058
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:6:p:945-953




Template-Type: ReDIF-Article 1.0
Author-Name: Chau-Chen Torng 
Author-X-Name-First: Chau-Chen 
Author-X-Name-Last: Torng 
Author-Name: Chun-Chieh Tseng 
Author-X-Name-First: Chun-Chieh 
Author-X-Name-Last: Tseng 
Author-Name: Pei-Hsi Lee 
Author-X-Name-First: Pei-Hsi 
Author-X-Name-Last: Lee 
Title: Non-normality and combined double sampling and variable sampling interval [image omitted] control charts 
Abstract:
  A combination of double sampling the [image omitted]&#xa0; chart and the
 variable sampling interval of the [image omitted]&#xa0; chart (DSVSI
 [image omitted]&#xa0; chart) increases the sensitivity in the small shift
 detection. A usual assumption of control charts is that the process
 observations are normally distributed. However, this assumption may not be
 true for some processes in the real industry. This paper presented the
 performance of DSVSI [image omitted]&#xa0; chart under non-normality and
 compared it with the Shewhart [image omitted]&#xa0; chart and the variable
 parameters [image omitted]&#xa0; chart. The compared results show that the
 DSVSI [image omitted]&#xa0; chart has the best performance in detecting
 small process mean shifts. 
Journal: Journal of Applied Statistics 
Pages: 955-967 
Issue: 6 
Volume: 37 
Year: 2010 
Keywords: double sampling X chart, variable sampling intervals X chart, variable parameters X chart, non-normality, 
X-DOI: 10.1080/02664760902984634 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902984634
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:6:p:955-967




Template-Type: ReDIF-Article 1.0
Author-Name: Felix Famoye 
Author-X-Name-First: Felix 
Author-X-Name-Last: Famoye 
Title: On the bivariate negative binomial regression model 
Abstract:
  In this paper, a new bivariate negative binomial regression (BNBR) model
 allowing any type of correlation is defined and studied. The marginal
 means of the bivariate model are functions of the explanatory variables.
 The parameters of the bivariate regression model are estimated by using
 the maximum likelihood method. Some test statistics including
 goodness-of-fit are discussed. Two numerical data sets are used to
 illustrate the techniques. The BNBR model tends to perform better than the
 bivariate Poisson regression model, but compares well with the bivariate
 Poisson log-normal regression model. 
Journal: Journal of Applied Statistics 
Pages: 969-981 
Issue: 6 
Volume: 37 
Year: 2010 
Keywords: correlated count data, over-dispersion, goodness-of-fit, estimation, 
X-DOI: 10.1080/02664760902984618 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902984618
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:6:p:969-981




Template-Type: ReDIF-Article 1.0
Author-Name: Ilmari Juutilainen 
Author-X-Name-First: Ilmari 
Author-X-Name-Last: Juutilainen 
Author-Name: Juha Roning 
Author-X-Name-First: Juha 
Author-X-Name-Last: Roning 
Title: How to compare interpretatively different models for the conditional variance function 
Abstract:
  This study considers regression-type models with heteroscedastic Gaussian
 errors. The conditional variance is assumed to depend on the explanatory
 variables via a parametric or non-parametric variance function. The
 variance function has usually been selected on the basis of the
 log-likelihoods of fitted models. However, log-likelihood is a difficult
 quantity to interpret - the practical importance of differences in
 log-likelihoods has been difficult to assess. This study overcomes these
 difficulties by transforming the difference in log-likelihood to easily
 interpretative difference in the error of predicted deviation. In
 addition, methods for testing the statistical significance of the observed
 difference in test data log-likelihood are proposed. 
Journal: Journal of Applied Statistics 
Pages: 983-997 
Issue: 6 
Volume: 37 
Year: 2010 
Keywords: conditional variance, variance function, predictive likelihood, log-scoring rule, predictive density, out-of-sample testing, model performance measure, 
X-DOI: 10.1080/02664760902984642 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902984642
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:6:p:983-997




Template-Type: ReDIF-Article 1.0
Author-Name: Nursel Koyuncu 
Author-X-Name-First: Nursel 
Author-X-Name-Last: Koyuncu 
Author-Name: Cem Kadilar 
Author-X-Name-First: Cem 
Author-X-Name-Last: Kadilar 
Title: On improvement in estimating population mean in stratified random sampling 
Abstract:
  Gupta and Shabbir 2 have suggested an alternative form of ratio-type
 estimators for estimating the population mean. In this paper, we obtained
 a corrected version for the mean square error (MSE) of the Gupta-Shabbir
 estimator, up to first order of approximation, and the optimum case is
 discussed. We expand this estimator to the stratified random sampling and
 propose general classes for combined and separate estimators. Also an
 empirical study is carried out to show the properties of the proposed
 estimators. 
Journal: Journal of Applied Statistics 
Pages: 999-1013 
Issue: 6 
Volume: 37 
Year: 2010 
Keywords: ratio estimator, auxiliary information, mean square error, efficiency, stratified random sampling, 
X-DOI: 10.1080/02664760903002675 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903002675
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:6:p:999-1013




Template-Type: ReDIF-Article 1.0
Author-Name: Young-Ju Kim 
Author-X-Name-First: Young-Ju 
Author-X-Name-Last: Kim 
Title: Semiparametric analysis for case-control studies: a partial smoothing spline approach 
Abstract:
  Case-control data are often used in medical-related applications, and
 most studies have applied parametric logistic regression to analyze such
 data. In this study, we investigated a semiparametric model for the
 analysis of case-control data by relaxing the linearity assumption of risk
 factors by using a partial smoothing spline model. A faster computation
 method for the model by extending the lower-dimensional approximation
 approach of Gu and Kim 4 developed in penalized likelihood regression is
 considered to apply to case-control studies. Simulations were conducted to
 evaluate the performance of the method with selected smoothing parameters
 and to compare the method with existing methods. The method was applied to
 Korean gastric cancer case-control data to estimate the nonparametric
 probability function of age and regression parameters for other
 categorical risk factors simultaneously. The method could be used in
 preliminary studies to identify whether there is a flexible function form
 of risk factors in the semiparametric logistic regression analysis
 involving a large data set. 
Journal: Journal of Applied Statistics 
Pages: 1015-1025 
Issue: 6 
Volume: 37 
Year: 2010 
Keywords: case-control data, partial smoothing spline, penalized likelihood, smoothing parameter, semiparametric, 
X-DOI: 10.1080/02664760903008979 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903008979
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:6:p:1015-1025




Template-Type: ReDIF-Article 1.0
Author-Name: Sandra De Iaco 
Author-X-Name-First: Sandra 
Author-X-Name-Last: De Iaco 
Title: Space-time correlation analysis: a comparative study 
Abstract:
  Space-time correlation modelling is one of the crucial steps of
 traditional structural analysis, since space-time models are used for
 prediction purposes. A comparative study among some classes of space-time
 covariance functions is proposed. The relevance of choosing a suitable
 model by taking into account the characteristic behaviour of the models is
 proved by using a space-time data set of ozone daily averages and the
 flexibility of the product-sum model is also highlighted through simulated
 data sets. 
Journal: Journal of Applied Statistics 
Pages: 1027-1041 
Issue: 6 
Volume: 37 
Year: 2010 
Keywords: space-time random field, space-time covariance, characteristic behaviour, product-sum model, structural analysis, 
X-DOI: 10.1080/02664760903019422 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903019422
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:6:p:1027-1041




Template-Type: ReDIF-Article 1.0
Author-Name: Miao-Yu Tsai 
Author-X-Name-First: Miao-Yu 
Author-X-Name-Last: Tsai 
Title: Extended Bayesian model averaging for heritability in twin studies 
Abstract:
  Family studies are often conducted to examine the existence of familial
 aggregation. Particularly, twin studies can model separately the genetic
 and environmental contribution. Here we estimate the heritability of
 quantitative traits via variance components of random-effects in linear
 mixed models (LMMs). The motivating example was a myopia twin study
 containing complex nesting data structures: twins and siblings in the same
 family and observations on both eyes for each individual. Three models are
 considered for this nesting structure. Our proposal takes into account the
 model uncertainty in both covariates and model structures via an extended
 Bayesian model averaging (EBMA) procedure. We estimate the heritability
 using EBMA under three suggested model structures. When compared with the
 results under the model with the highest posterior model probability, the
 EBMA estimate has smaller variation and is slightly conservative.
 Simulation studies are conducted to evaluate the performance of
 variance-components estimates, as well as the selections of risk factors,
 under the correct or incorrect structure. The results indicate that EBMA,
 with consideration of uncertainties in both covariates and model
 structures, is robust in model misspecification than the usual Bayesian
 model averaging (BMA) that considers only uncertainty in covariates
 selection. 
Journal: Journal of Applied Statistics 
Pages: 1043-1058 
Issue: 6 
Volume: 37 
Year: 2010 
Keywords: Bayesian model averaging, boundary Laplace approximation, heritability, linear mixed models, model uncertainty, 
X-DOI: 10.1080/02664760903093625 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903093625
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:6:p:1043-1058




Template-Type: ReDIF-Article 1.0
Author-Name: Mukesh Srivastava 
Author-X-Name-First: Mukesh 
Author-X-Name-Last: Srivastava 
Title: Analysis of variance and covariance: how to choose and construct models for the life sciences 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 1059-1060 
Issue: 6 
Volume: 37 
Year: 2010 
X-DOI: 10.1080/02664760902885203 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902885203
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:6:p:1059-1060




Template-Type: ReDIF-Article 1.0
Author-Name: Juana Sanchez 
Author-X-Name-First: Juana 
Author-X-Name-Last: Sanchez 
Title: International migration in Europe 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 1061-1061 
Issue: 6 
Volume: 37 
Year: 2010 
X-DOI: 10.1080/02664760902899733 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902899733
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:6:p:1061-1061




Template-Type: ReDIF-Article 1.0
Author-Name: Juana Sanchez 
Author-X-Name-First: Juana 
Author-X-Name-Last: Sanchez 
Title: Introduction to modern time series analysis 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 1063-1063 
Issue: 6 
Volume: 37 
Year: 2010 
X-DOI: 10.1080/02664760902899766 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902899766
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:6:p:1063-1063




Template-Type: ReDIF-Article 1.0
Author-Name: Shih-Chou Kao 
Author-X-Name-First: Shih-Chou 
Author-X-Name-Last: Kao 
Title: Normalization of the origin-shifted exponential distribution for control chart construction 
Abstract:
  This study demonstrates that a location parameter of an exponential
 distribution significantly influences normalization of the exponential.
 The Kullback-Leibler information number is shown to be an appropriate
 index for measuring data normality using a location parameter. Control
 charts based on probability limits and transformation are compared for
 known and estimated location parameters. The probabilities of type II
 error (&#x3b2;-risks) and average run length (ARL) without a location
 parameter indicate an ability to detect an out-of-control signal of an
 individual chart using a power transformation similar to using probability
 limits. The &#x3b2;-risks and ARL of control charts with an estimated
 location parameter deviate significantly from their theoretical values
 when a small sample size of n&#x2264;50 is used. Therefore, without taking
 into account of the existence of a location parameter, the control charts
 result in inaccurate detection of an out-of-control signal regardless of
 whether a power or natural logarithmic transformation is used. The effects
 of a location parameter should be eliminated before transformation. Two
 examples are presented to illustrate these findings. 
Journal: Journal of Applied Statistics 
Pages: 1067-1087 
Issue: 7 
Volume: 37 
Year: 2010 
Keywords: location parameter, exponential distribution, power transformation, natural logarithmic transformation, Kullback-Leibler information number, 
X-DOI: 10.1080/02664760802571333 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760802571333
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:7:p:1067-1087




Template-Type: ReDIF-Article 1.0
Author-Name: Dong Han 
Author-X-Name-First: Dong 
Author-X-Name-Last: Han 
Author-Name: Fugee Tsung 
Author-X-Name-First: Fugee 
Author-X-Name-Last: Tsung 
Author-Name: Yanting Li 
Author-X-Name-First: Yanting 
Author-X-Name-Last: Li 
Author-Name: Jinguo Xian 
Author-X-Name-First: Jinguo 
Author-X-Name-Last: Xian 
Title: Detection of changes in a random financial sequence with a stable distribution 
Abstract:
  Quick detection of unanticipated changes in a financial sequence is a
 critical problem for practitioners in the finance industry. Based on
 refined logarithmic moment estimators for the four parameters of a stable
 distribution, this article presents a stable-distribution-based
 multi-CUSUM chart that consists of several CUSUM charts and detects
 changes in the four parameters in an independent and identically
 distributed random sequence with the stable distribution. Numerical
 results of the average run lengths show that the multi-CUSUM chart is
 superior (robust and quick) on the whole to a single CUSUM chart in
 detecting the shift change of the four parameters. A real example that
 monitors changes in IBM's stock returns is used to demonstrate the
 performance of the proposed method. 
Journal: Journal of Applied Statistics 
Pages: 1089-1111 
Issue: 7 
Volume: 37 
Year: 2010 
Keywords: logarithmic moment estimators, multi-CUSUM charts, detection of changes, random sequence with stable distribution, 
X-DOI: 10.1080/02664760902914433 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902914433
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:7:p:1089-1111




Template-Type: ReDIF-Article 1.0
Author-Name: David Scollnik 
Author-X-Name-First: David 
Author-X-Name-Last: Scollnik 
Title: Bayesian statistical inference for start-up demonstration tests with rejection of units upon observing d failures 
Abstract:
  This paper is concerned with Bayesian estimation and prediction in the
 context of start-up demonstration tests in which rejection of a unit is
 possible when a pre-specified number of failures is observed prior to
 obtaining the number of consecutive successes required for acceptance of
 the unit. A method for implementing Bayesian inference on the probability
 of success is developed for use when the test result of each start-up is
 not reported or even recorded, and only the number of trials until
 termination of the testing is available. Some errors in the related
 literature on the Bayesian analysis of start-up demonstration tests are
 corrected. The method developed in this paper is a Markov chain Monte
 Carlo (MCMC) method incorporating data augmentation, and it additionally
 enables Bayesian posterior inference on the number of failures given the
 number of start-up trials until termination to be made, along with
 Bayesian predictive inferences on the number of start-up trials and the
 number of failures until termination for any future run of the start-up
 demonstration test. An illustrative example is also included. 
Journal: Journal of Applied Statistics 
Pages: 1113-1121 
Issue: 7 
Volume: 37 
Year: 2010 
Keywords: start-up demonstration test, Bayesian estimation, MCMC, data augmentation, 
X-DOI: 10.1080/02664760902914516 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902914516
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:7:p:1113-1121




Template-Type: ReDIF-Article 1.0
Author-Name: Kosei Fukuda 
Author-X-Name-First: Kosei 
Author-X-Name-Last: Fukuda 
Title: Parameter changes in GARCH model 
Abstract:
  A new method for detecting the parameter changes in generalized
 autoregressive heteroskedasticity GARCH (1,1) model is proposed. In the
 proposed method, time series observations are divided into several
 segments and a GARCH (1,1) model is fitted to each segment. The
 goodness-of-fit of the global model composed of these local GARCH (1,1)
 models is evaluated using the corresponding information criterion (IC).
 The division that minimizes IC defines the best model. Furthermore, since
 the simultaneous estimation of all possible models requires huge
 computational time, a new time-saving algorithm is proposed. Simulation
 results and empirical results both indicate that the proposed method is
 useful in analysing financial data. 
Journal: Journal of Applied Statistics 
Pages: 1123-1135 
Issue: 7 
Volume: 37 
Year: 2010 
Keywords: GARCH(1,1), information criterion, model selection, parameter change, 
X-DOI: 10.1080/02664760902914524 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902914524
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:7:p:1123-1135




Template-Type: ReDIF-Article 1.0
Author-Name: Eva Fiserova 
Author-X-Name-First: Eva 
Author-X-Name-Last: Fiserova 
Author-Name: Karel Hron 
Author-X-Name-First: Karel 
Author-X-Name-Last: Hron 
Title: Total least squares solution for compositional data using linear models 
Abstract:
  The restrictive properties of compositional data, that is multivariate
 data with positive parts that carry only relative information in their
 components, call for special care to be taken while performing standard
 statistical methods, for example, regression analysis. Among the special
 methods suitable for handling this problem is the total least squares
 procedure (TLS, orthogonal regression, regression with errors in
 variables, calibration problem), performed after an appropriate log-ratio
 transformation. The difficulty or even impossibility of deeper statistical
 analysis (confidence regions, hypotheses testing) using the standard TLS
 techniques can be overcome by calibration solution based on linear
 regression. This approach can be combined with standard statistical
 inference, for example, confidence and prediction regions and bounds,
 hypotheses testing, etc., suitable for interpretation of results. Here, we
 deal with the simplest TLS problem where we assume a linear relationship
 between two errorless measurements of the same object (substance,
 quantity). We propose an iterative algorithm for estimating the
 calibration line and also give confidence ellipses for the location of
 unknown errorless results of measurement. Moreover, illustrative examples
 from the fields of geology, geochemistry and medicine are included. It is
 shown that the iterative algorithm converges to the same values as those
 obtained using the standard TLS techniques. Fitted lines and confidence
 regions are presented for both original and transformed compositional
 data. The paper contains basic principles of linear models and addresses
 many related problems. 
Journal: Journal of Applied Statistics 
Pages: 1137-1152 
Issue: 7 
Volume: 37 
Year: 2010 
Keywords: isometric log-ratio transformation, total least squares, linear regression model, calibration line, estimation, confidence ellipse, multivariate outliers, 
X-DOI: 10.1080/02664760902914532 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902914532
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:7:p:1137-1152




Template-Type: ReDIF-Article 1.0
Author-Name: Elizabeth Heron 
Author-X-Name-First: Elizabeth 
Author-X-Name-Last: Heron 
Author-Name: Cathal Walsh 
Author-X-Name-First: Cathal 
Author-X-Name-Last: Walsh 
Title: Bayesian discrete latent spatial modeling of crack initiation in orthopaedic hip replacement bone cement 
Abstract:
  In this paper, we propose a spatial model for the initiation of cracks in
 the bone cement of hip replacement specimens. The failure of hip
 replacements can be attributed mainly to damage accumulation, consisting
 of crack initiation and growth, occurring in the cement mantle that
 interlocks the hip prosthesis and the femur bone. Since crack initiation
 is an important factor in determining the lifetime of a replacement, the
 understanding of the reasons for crack initiation is vital in attempting
 to prolong the life of the hip replacement. The data consist of crack
 location coordinates from five laboratory experimental models, together
 with stress measurements. It is known that stress plays a major role in
 the initiation of cracks, and it is also known that other unmeasurable
 factors such as air bubbles (pores) in the cement mantle are also
 influential. We propose an identity-link spatial Poisson regression model
 for the counts of cracks in discrete regions of the cement, incorporating
 both the measured (stress), and through a latent process, any unmeasured
 factors (possibly pores) that may be influential. All analysis is carried
 out in a Bayesian framework, allowing for the inclusion of prior
 information obtained from engineers, and parameter estimation for the
 model is done via Markov chain Monte Carlo techniques. 
Journal: Journal of Applied Statistics 
Pages: 1153-1171 
Issue: 7 
Volume: 37 
Year: 2010 
Keywords: orthopaedic hip replacement, crack initiation, identity-link Poisson regression, latent spatial process, Bayesian analysis, Markov chain Monte Carlo, zero-inflated Poisson, 
X-DOI: 10.1080/02664760902939620 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902939620
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:7:p:1153-1171




Template-Type: ReDIF-Article 1.0
Author-Name: Ming-Yuan Leon Li 
Author-X-Name-First: Ming-Yuan Leon 
Author-X-Name-Last: Li 
Author-Name: Chun-Nan Chen 
Author-X-Name-First: Chun-Nan 
Author-X-Name-Last: Chen 
Title: Examining the interrelation dynamics between option and stock markets using the Markov-switching vector error correction model 
Abstract:
  This study examines the dynamics of the interrelation between option and
 stock markets using the Markov-switching vector error correction model.
 Specifically, we calculate the implied stock prices from the Black-Scholes
 6 model and establish a statistic framework in which the parameter of the
 price discrepancy between the observed and implied prices switches
 according to the phase of the volatility regime. The model is tested in
 the US S&P 500 stock market. The empirical findings of this work are
 consistent with the following notions. First, while option markets react
 more quickly to the newest stock-option disequilibrium shocks than spot
 markets, as found by earlier studies, we further indicate that the price
 adjustment process occurring in option markets is pronounced when the high
 variance condition is concerned, but less so during the stable period.
 Second, the degree of the co-movement between the observed and implied
 prices is significantly reduced during the high variance state. Last, the
 lagged price deviation between the observed and implied prices functions
 as an indicator of the variance-turning process. 
Journal: Journal of Applied Statistics 
Pages: 1173-1191 
Issue: 7 
Volume: 37 
Year: 2010 
Keywords: option market, Markov-switching, error correction model, volatility, 
X-DOI: 10.1080/02664760902939638 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902939638
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:7:p:1173-1191




Template-Type: ReDIF-Article 1.0
Author-Name: Claudia Castro-Kuriss 
Author-X-Name-First: Claudia 
Author-X-Name-Last: Castro-Kuriss 
Author-Name: Diana Kelmansky 
Author-X-Name-First: Diana 
Author-X-Name-Last: Kelmansky 
Author-Name: Victor Leiva 
Author-X-Name-First: Victor 
Author-X-Name-Last: Leiva 
Author-Name: Elena Martinez 
Author-X-Name-First: Elena 
Author-X-Name-Last: Martinez 
Title: On a goodness-of-fit test for normality with unknown parameters and type-II censored data 
Abstract:
  We propose a new goodness-of-fit test for normal and lognormal
 distributions with unknown parameters and type-II censored data. This test
 is a generalization of Michael's test for censored samples, which is based
 on the empirical distribution and a variance stabilizing transformation.
 We estimate the parameters of the model by using maximum likelihood and
 Gupta's methods. The quantiles of the distribution of the test statistic
 under the null hypothesis are obtained through Monte Carlo simulations.
 The power of the proposed test is estimated and compared to that of the
 Kolmogorov-Smirnov test also using simulations. The new test is more
 powerful than the Kolmogorov-Smirnov test in most of the studied cases.
 Acceptance regions for the PP, QQ and Michael's stabilized probability
 plots are derived, making it possible to visualize which data contribute
 to the decision of rejecting the null hypothesis. Finally, an illustrative
 example is presented. 
Journal: Journal of Applied Statistics 
Pages: 1193-1211 
Issue: 7 
Volume: 37 
Year: 2010 
Keywords: Kolmogorov-Smirnov test, maximum likelihood and Gupta's estimators, Monte Carlo simulation, PP, QQ and stabilized probability plots, 
X-DOI: 10.1080/02664760902984626 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902984626
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:7:p:1193-1211




Template-Type: ReDIF-Article 1.0
Author-Name: Qin Yu 
Author-X-Name-First: Qin 
Author-X-Name-Last: Yu 
Author-Name: Wan Tang 
Author-X-Name-First: Wan 
Author-X-Name-Last: Tang 
Author-Name: Sue Marcus 
Author-X-Name-First: Sue 
Author-X-Name-Last: Marcus 
Author-Name: Yan Ma 
Author-X-Name-First: Yan 
Author-X-Name-Last: Ma 
Author-Name: Hui Zhang 
Author-X-Name-First: Hui 
Author-X-Name-Last: Zhang 
Author-Name: Xin Tu 
Author-X-Name-First: Xin 
Author-X-Name-Last: Tu 
Title: Modeling sensitivity and specificity with a time-varying reference standard within a longitudinal setting 
Abstract:
  Diagnostic tests are used in a wide range of behavioral, medical,
 psychosocial, and healthcare-related research. Test sensitivity and
 specificity are the most popular measures of accuracy for diagnostic
 tests. Available methods for analyzing longitudinal study designs assume
 fixed gold or reference standards and as such do not apply to studies with
 dynamically changing reference standards, which are especially popular in
 psychosocial research. In this article, we develop a novel approach to
 address missing data and other related issues for modeling sensitivity and
 specificity within such a time-varying reference standard setting. The
 approach is illustrated with real as well as simulated data. 
Journal: Journal of Applied Statistics 
Pages: 1213-1230 
Issue: 7 
Volume: 37 
Year: 2010 
Keywords: augmented inverse probability weighted (AIPW) estimate, bivariate monotone missing data pattern (BMMDP), diagnostic test, double robust estimate, inverse probability weighted (IPW) estimate, missing data, 
X-DOI: 10.1080/02664760902998444 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902998444
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:7:p:1213-1230




Template-Type: ReDIF-Article 1.0
Author-Name: Demetrios Antzoulakos 
Author-X-Name-First: Demetrios 
Author-X-Name-Last: Antzoulakos 
Author-Name: Athanasios Rakitzis 
Author-X-Name-First: Athanasios 
Author-X-Name-Last: Rakitzis 
Title: Runs rules schemes for monitoring process variability 
Abstract:
  To increase the sensitivity of Shewhart control charts in detecting small
 process shifts sensitizing rules based on runs and scans are often used in
 practice. Shewhart control charts supplemented with runs rules for
 detecting shifts in process variance have not received as much attention
 as their counterparts for detecting shifts in process mean. In this
 article, we examine the performance of simple runs rules schemes for
 monitoring increases and/or decreases in process variance based on the
 sample standard deviation. We introduce one-sided S charts that overcome
 the weakness of high false-alarm rates when runs rules are added to a
 Shewhart control chart. The average run length performance and design
 aspects of the charts are studied thoroughly. The performance of
 associated two-sided control schemes is investigated as well. 
Journal: Journal of Applied Statistics 
Pages: 1231-1247 
Issue: 7 
Volume: 37 
Year: 2010 
Keywords: average run length, markov chain embedding technique, optimization, runs rules, Shewhart control charts, standard deviation, 
X-DOI: 10.1080/02664760903002683 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903002683
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:7:p:1231-1247




Template-Type: ReDIF-Article 1.0
Author-Name: Mahmoud Mahmoud 
Author-X-Name-First: Mahmoud 
Author-X-Name-Last: Mahmoud 
Author-Name: J. P. Morgan 
Author-X-Name-First: J. P. 
Author-X-Name-Last: Morgan 
Author-Name: William Woodall 
Author-X-Name-First: William 
Author-X-Name-Last: Woodall 
Title: The monitoring of simple linear regression profiles with two observations per sample 
Abstract:
  We evaluate and compare the performance of Phase II simple linear
 regression profile approaches when only two observations are used to
 establish each profile. We propose an EWMA control chart based on average
 squared deviations from the in-control line, to be used in conjunction
 with two EWMA control charts based on the slope and Y-intercept
 estimators, to monitor changes in the three regression model parameters,
 i.e., the slope, intercept and variance. Simulations establish that the
 performance of the proposed technique is generally better than that of
 other approaches in detecting parameter shifts. 
Journal: Journal of Applied Statistics 
Pages: 1249-1263 
Issue: 8 
Volume: 37 
Year: 2010 
Keywords: change point, exponentially weighted moving average chart, likelihood ratio, phase II analysis, profile monitoring, statistical process control, 
X-DOI: 10.1080/02664760903008995 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903008995
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:8:p:1249-1263




Template-Type: ReDIF-Article 1.0
Author-Name: Alireza Akbarzadeh Bagheban 
Author-X-Name-First: Alireza Akbarzadeh 
Author-X-Name-Last: Bagheban 
Author-Name: Farid Zayeri 
Author-X-Name-First: Farid 
Author-X-Name-Last: Zayeri 
Title: A generalization of the uniform association model for assessing rater agreement in ordinal scales 
Abstract:
  Recently, the data analysts pay more attention to the assessment of rater
 agreement, especially in areas of medical sciences. In this context, the
 statistical indices such as kappa and weighted kappa are the most common
 choices. These indices are simple to calculate and interpret, although,
 they fail to describe the structure of agreement, particularly when the
 available outcome has an ordinal nature. In the previous decades,
 statisticians suggested more efficient statistical tools such as diagonal
 parameter, linear by linear association and agreement plus linear by
 linear association models for describing the structure of rater agreement.
 In these models, the equal interval scores are the common choice for the
 levels of the ordinal scales. In this manuscript, we show that choosing
 the common equal interval scores does not necessarily lead to the best fit
 and propose a modification using a power transformation for the ordinal
 scores. We also use two different data sets (IOTN and ovarian masses data)
 to illustrate our suggestion more clearly. In addition, we utilize the
 category distinguishability concept for interpreting the model parameter
 estimates. 
Journal: Journal of Applied Statistics 
Pages: 1265-1273 
Issue: 8 
Volume: 37 
Year: 2010 
Keywords: rater agreement, association model, log-linear model, ordinal scales, 
X-DOI: 10.1080/02664760903012666 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903012666
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:8:p:1265-1273




Template-Type: ReDIF-Article 1.0
Author-Name: A. Bhattacharya 
Author-X-Name-First: A. 
Author-X-Name-Last: Bhattacharya 
Title: Estimating crop yield via Gaussian quadrature 
Abstract:
  The present study proposes a method to estimate the yield of a crop. The
 proposed Gaussian quadrature (GQ) method makes it possible to estimate the
 crop yield from a smaller subsample. Identification of plots and
 corresponding weights to be assigned to the yield of plots comprising a
 subsample is done with the help of information about the full sample on
 certain auxiliary variables relating to biometrical characteristics of the
 plant. Computational experience reveals that the proposed method leads to
 about 78% reduction in sample size with absolute percentage error of 2.7%.
 Performance of the proposed method has been compared with that of random
 sampling on the basis of the values of average absolute percentage error
 and standard deviation of yield estimates obtained from 40 samples of
 comparable size. Interestingly, average absolute percentage error as well
 as standard deviation is considerably smaller for the GQ estimates than
 for the random sample estimates. The proposed method is quite general and
 can be applied for other crops as well-provided information on auxiliary
 variables relating to yield contributing biometrical characteristics is
 available. 
Journal: Journal of Applied Statistics 
Pages: 1275-1281 
Issue: 8 
Volume: 37 
Year: 2010 
Keywords: estimation of crop yield, crop cutting experiments, Gaussian quadrature, polynomial approximation, moments, 
X-DOI: 10.1080/02664760903012674 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903012674
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:8:p:1275-1281




Template-Type: ReDIF-Article 1.0
Author-Name: Ming-Hung Shu 
Author-X-Name-First: Ming-Hung 
Author-X-Name-Last: Shu 
Author-Name: Hsien-Chung Wu 
Author-X-Name-First: Hsien-Chung 
Author-X-Name-Last: Wu 
Title: Monitoring imprecise fraction of nonconforming items using p control charts 
Abstract:
  The quality characteristics, which are known as attributes, cannot be
 conveniently and numerically represented. Generally, the attribute data
 can be regarded as the fuzzy data, which are ubiquitous in the
 manufacturing process and cannot be measured precisely and often be
 collected by visual inspection. In this paper, we construct a p control
 chart for monitoring the fraction of nonconforming items in the process in
 which fuzzy sample data are collected from the manufacturing process. The
 resolution identity - a well-known theorem in the fuzzy set theory - is
 invoked to construct the control limits of fuzzy-p control charts using
 fuzzy data. In order to determine whether the plotted imprecise fraction
 of nonconforming items is within the fuzzy lower and upper control limits,
 we also propose a ranking method for a set of fuzzy numbers. Using the
 fuzzy-p control charts and the proposed acceptability function to classify
 the manufacturing process allows the decision-maker to make linguistic
 decisions such as rather in control or rather out of control. A practical
 example is provided to describe the applicability of the fuzzy set theory
 to a conventional p control chart. 
Journal: Journal of Applied Statistics 
Pages: 1283-1297 
Issue: 8 
Volume: 37 
Year: 2010 
Keywords: acceptability function, fuzzy-p control chart, fuzzy number, LR-fuzzy number, resolution identity, 
X-DOI: 10.1080/02664760903030205 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903030205
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:8:p:1283-1297




Template-Type: ReDIF-Article 1.0
Author-Name: Marko Sarstedt 
Author-X-Name-First: Marko 
Author-X-Name-Last: Sarstedt 
Author-Name: Christian Ringle 
Author-X-Name-First: Christian 
Author-X-Name-Last: Ringle 
Title: Treating unobserved heterogeneity in PLS path modeling: a comparison of FIMIX-PLS with different data analysis strategies 
Abstract:
  In the social science disciplines, the assumption that the data stem from
 a single homogeneous population is often unrealistic in respect of
 empirical research. When applying a causal modeling approach, such as
 partial least squares path modeling, segmentation is a key issue in coping
 with the problem of heterogeneity in the estimated cause-effect
 relationships. This article uses the novel finite-mixture partial least
 squares (FIMIX-PLS) method to uncover unobserved heterogeneity in a
 complex path modeling example in the field of marketing. An evaluation of
 the results includes a comparison with the outcomes of several data
 analysis strategies based on a priori information or k-means cluster
 analysis. The results of this article underpin the effectiveness and the
 advantageous capabilities of FIMIX-PLS in general PLS path model set-ups
 by means of empirical data and formative as well as reflective measurement
 models. Consequently, this research substantiates the general
 applicability of FIMIX-PLS to path modeling as a standard means of
 evaluating PLS results by addressing the problem of unobserved
 heterogeneity. 
Journal: Journal of Applied Statistics 
Pages: 1299-1318 
Issue: 8 
Volume: 37 
Year: 2010 
Keywords: partial least square (PLS), path modeling, heterogeneity, latent class, finite mixture, market segmentation, corporate reputation, 
X-DOI: 10.1080/02664760903030213 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903030213
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:8:p:1299-1318




Template-Type: ReDIF-Article 1.0
Author-Name: JrJung Lyu 
Author-X-Name-First: JrJung 
Author-X-Name-Last: Lyu 
Author-Name: MingNan Chen 
Author-X-Name-First: MingNan 
Author-X-Name-Last: Chen 
Title: Measurement of bivariate attributes using a novel statistical model 
Abstract:
  Reducing process variability is essential to many organisations.
 According to the pertinent literature, a quality system that utilizes
 quality techniques to reduce process variability is necessary. Quality
 programs that respond to measurement precision are central to quality
 systems, and the most common method of assessing the precision of a
 measurement system is repeatability and reproducibility (R&R). Few studies
 have investigated R&R using attribute data. In modern manufacturing
 environments, automated manufacturing is becoming increasingly common;
 however, a measurement resolution problem exists in automatic inspection
 equipment, resulting in clusters and product defects. It is vital to
 monitor effectively these bivariate quality characteristics. This study
 presents a novel model for calculating R&R for bivariate attribute data.
 An alloy manufacturing case is utilized to illustrate the process and
 potential of the proposed model. Findings can be employed to evaluate and
 improve measurement systems with bivariate attribute data. 
Journal: Journal of Applied Statistics 
Pages: 1319-1334 
Issue: 8 
Volume: 37 
Year: 2010 
Keywords: measurement system analysis, attribute data, repeatability, reproducibility, 
X-DOI: 10.1080/02664760903030221 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903030221
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:8:p:1319-1334




Template-Type: ReDIF-Article 1.0
Author-Name: Fabio Principato 
Author-X-Name-First: Fabio 
Author-X-Name-Last: Principato 
Author-Name: Angela Vullo 
Author-X-Name-First: Angela 
Author-X-Name-Last: Vullo 
Author-Name: Domenica Matranga 
Author-X-Name-First: Domenica 
Author-X-Name-Last: Matranga 
Title: On implementation of the Gibbs sampler for estimating the accuracy of multiple diagnostic tests 
Abstract:
  Implementation of the Gibbs sampler for estimating the accuracy of
 multiple binary diagnostic tests in one population has been investigated.
 This method, proposed by Joseph, Gyorkos and Coupal, makes use of a
 Bayesian approach and is used in the absence of a gold standard to
 estimate the prevalence, the sensitivity and specificity of medical
 diagnostic tests. The expressions that allow this method to be implemented
 for an arbitrary number of tests are given. By using the convergence
 diagnostics procedure of Raftery and Lewis, the relation between the
 number of iterations of Gibbs sampling and the precision of the estimated
 quantiles of the posterior distributions is derived. An example concerning
 a data set of gastro-esophageal reflux disease patients collected to
 evaluate the accuracy of the water siphon test compared with 24&#xa0;h
 pH-monitoring, endoscopy and histology tests is presented. The main
 message that emerges from our analysis is that implementation of the Gibbs
 sampler to estimate the parameters of multiple binary diagnostic tests can
 be critical and convergence diagnostic is advised for this method. The
 factors which affect the convergence of the chains to the posterior
 distributions and those that influence the precision of their quantiles
 are analyzed. 
Journal: Journal of Applied Statistics 
Pages: 1335-1354 
Issue: 8 
Volume: 37 
Year: 2010 
Keywords: Gibbs sampler, Bayesian analysis, convergence diagnostics, diagnostic tests, gastro-esophageal reflux disease, 
X-DOI: 10.1080/02664760903030239 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903030239
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:8:p:1335-1354




Template-Type: ReDIF-Article 1.0
Author-Name: Aparecida Souza 
Author-X-Name-First: Aparecida 
Author-X-Name-Last: Souza 
Author-Name: Helio Migon 
Author-X-Name-First: Helio 
Author-X-Name-Last: Migon 
Title: Bayesian outlier analysis in binary regression 
Abstract:
  We propose alternative approaches to analyze residuals in binary
 regression models based on random effect components. Our preferred model
 does not depend upon any tuning parameter, being completely automatic.
 Although the focus is mainly on accommodation of outliers, the proposed
 methodology is also able to detect them. Our approach consists of
 evaluating the posterior distribution of random effects included in the
 linear predictor. The evaluation of the posterior distributions of
 interest involves cumbersome integration, which is easily dealt with
 through stochastic simulation methods. We also discuss different
 specifications of prior distributions for the random effects. The
 potential of these strategies is compared in a real data set. The main
 finding is that the inclusion of extra variability accommodates the
 outliers, improving the adjustment of the model substantially, besides
 correctly indicating the possible outliers. 
Journal: Journal of Applied Statistics 
Pages: 1355-1368 
Issue: 8 
Volume: 37 
Year: 2010 
Keywords: binary regression models, Bayesian residual, random effect, mixture of normals, Markov chain Monte Carlo, 
X-DOI: 10.1080/02664760903031153 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903031153
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:8:p:1355-1368




Template-Type: ReDIF-Article 1.0
Author-Name: M. Aslam 
Author-X-Name-First: M. 
Author-X-Name-Last: Aslam 
Author-Name: C. -H. Jun 
Author-X-Name-First: C. -H. 
Author-X-Name-Last: Jun 
Author-Name: M. Ahmad 
Author-X-Name-First: M. 
Author-X-Name-Last: Ahmad 
Title: Design of a time-truncated double sampling plan for a general life distribution 
Abstract:
  A double sampling plan based on truncated life tests is proposed and
 designed under a general life distribution. The design parameters such as
 sample sizes and acceptance numbers for the first and the second samples
 are determined so as to minimize the average sample number subject to
 satisfying the consumer's and producer's risks at the respectively
 specified quality levels. The resultant tables can be used regardless of
 the underlying distribution as long as the reliability requirements are
 specified at two risks. In addition, Gamma and Weibull distributions are
 particularly considered to report the design parameters according to the
 quality levels in terms of the mean ratios. 
Journal: Journal of Applied Statistics 
Pages: 1369-1379 
Issue: 8 
Volume: 37 
Year: 2010 
Keywords: acceptance probability, average sample number, consumer's risk, life distribution, life test, producer's risk, 
X-DOI: 10.1080/02664760903030247 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903030247
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:8:p:1369-1379




Template-Type: ReDIF-Article 1.0
Author-Name: Matei Demetrescu 
Author-X-Name-First: Matei 
Author-X-Name-Last: Demetrescu 
Author-Name: Uwe Hassler 
Author-X-Name-First: Uwe 
Author-X-Name-Last: Hassler 
Author-Name: Adina Tarcolea 
Author-X-Name-First: Adina 
Author-X-Name-Last: Tarcolea 
Title: Testing for stationarity in large panels with cross-dependence, and US evidence on unit labor cost 
Abstract:
  A new stationarity test for heterogeneous panel data with large
 cross-sectional dimension is developed and used to examine a panel with
 growth rates of unit labor cost in the USA. The test allows for strong
 cross-unit dependence in the form of unbounded long-run correlation
 matrices, for which a simple parameterization is proposed. A KPSS-type
 distribution results asymptotically if letting T&#x2192;&#x221e; be
 followed by N&#x2192;&#x221e;. Some evidence against stationarity (short
 memory) is found for the examined series. 
Journal: Journal of Applied Statistics 
Pages: 1381-1397 
Issue: 8 
Volume: 37 
Year: 2010 
Keywords: panel KPSS-type test, cross-correlation, inflation dynamics, 
X-DOI: 10.1080/02664760903038398 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903038398
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:8:p:1381-1397




Template-Type: ReDIF-Article 1.0
Author-Name: Emmanouil Androulakis 
Author-X-Name-First: Emmanouil 
Author-X-Name-Last: Androulakis 
Author-Name: Christos Koukouvinos 
Author-X-Name-First: Christos 
Author-X-Name-Last: Koukouvinos 
Author-Name: Kalliopi Mylona 
Author-X-Name-First: Kalliopi 
Author-X-Name-Last: Mylona 
Author-Name: Filia Vonta 
Author-X-Name-First: Filia 
Author-X-Name-Last: Vonta 
Title: A real survival analysis application via variable selection methods for Cox's proportional hazards model 
Abstract:
  Variable selection is fundamental to high-dimensional statistical
 modeling in diverse fields of sciences. In our health study, different
 statistical methods are applied to analyze trauma annual data, collected
 by 30 General Hospitals in Greece. The dataset consists of 6334
 observations and 111 factors that include demographic, transport, and
 clinical data. The statistical methods employed in this work are the
 nonconcave penalized likelihood methods, Smoothly Clipped Absolute
 Deviation, Least Absolute Shrinkage and Selection Operator, and Hard, the
 maximum partial likelihood estimation method, and the best subset variable
 selection, adjusted to Cox's proportional hazards model and used to detect
 possible risk factors, which affect the length of stay in a hospital. A
 variety of different statistical models are considered, with respect to
 the combinations of factors while censored observations are present. A
 comparative survey reveals several differences between results and
 execution times of each method. Finally, we provide useful biological
 justification of our results. 
Journal: Journal of Applied Statistics 
Pages: 1399-1406 
Issue: 8 
Volume: 37 
Year: 2010 
Keywords: variable selection, survival analysis, Cox's proportional hazards model, nonconcave penalized likelihood, high-dimensional dataset, trauma, 
X-DOI: 10.1080/02664760903038406 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903038406
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:8:p:1399-1406




Template-Type: ReDIF-Article 1.0
Author-Name: Terence Tai-Leung Chong 
Author-X-Name-First: Terence Tai-Leung 
Author-X-Name-Last: Chong 
Author-Name: Zimu Li 
Author-X-Name-First: Zimu 
Author-X-Name-Last: Li 
Author-Name: Haiqiang Chen 
Author-X-Name-First: Haiqiang 
Author-X-Name-Last: Chen 
Author-Name: Melvin Hinich 
Author-X-Name-First: Melvin 
Author-X-Name-Last: Hinich 
Title: An investigation of duration dependence in the American stock market cycle 
Abstract:
  This paper investigates the duration dependence of the US stock market
 cycles. A new classification method for bull and bear market regimes based
 on the crossing of the market index and its moving average is proposed. We
 show evidence of duration dependence in whole cycles. The half cycles,
 however, are found to be duration independent. More importantly, we find
 that the degree of duration dependence of the US stock market cycles has
 dropped after the launch of the NASDAQ index. 
Journal: Journal of Applied Statistics 
Pages: 1407-1416 
Issue: 8 
Volume: 37 
Year: 2010 
Keywords: duration dependence, stock market cycles, moving average, 
X-DOI: 10.1080/02664760903039875 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903039875
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:8:p:1407-1416




Template-Type: ReDIF-Article 1.0
Author-Name: Dougal Hutchison 
Author-X-Name-First: Dougal 
Author-X-Name-Last: Hutchison 
Title: Handbook of multilevel analysis 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 1417-1418 
Issue: 8 
Volume: 37 
Year: 2010 
X-DOI: 10.1080/02664760902899741 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902899741
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:8:p:1417-1418




Template-Type: ReDIF-Article 1.0
Author-Name: Ilia Vonta 
Author-X-Name-First: Ilia 
Author-X-Name-Last: Vonta 
Title: Model selection and model averaging 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 1419-1420 
Issue: 8 
Volume: 37 
Year: 2010 
X-DOI: 10.1080/02664760902899774 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902899774
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:8:p:1419-1420




Template-Type: ReDIF-Article 1.0
Author-Name: John Shade 
Author-X-Name-First: John 
Author-X-Name-Last: Shade 
Title: Software for data analysis 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 1421-1422 
Issue: 8 
Volume: 37 
Year: 2010 
X-DOI: 10.1080/02664760902899790 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902899790
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:8:p:1421-1422




Template-Type: ReDIF-Article 1.0
Author-Name: Paresh Kumar Narayan 
Author-X-Name-First: Paresh Kumar 
Author-X-Name-Last: Narayan 
Author-Name: Stephan Popp 
Author-X-Name-First: Stephan 
Author-X-Name-Last: Popp 
Title: A new unit root test with two structural breaks in level and slope at unknown time 
Abstract:
  In this paper, we propose a new augmented Dickey-Fuller-type test for
 unit roots which accounts for two structural breaks. We consider two
 different specifications: (a) two breaks in the level of a trending data
 series and (b) two breaks in the level and slope of a trending data
 series. The breaks whose time of occurrence is assumed to be unknown are
 modeled as innovational outliers and thus take effect gradually. Using
 Monte Carlo simulations, we show that our proposed test has correct size,
 stable power, and identifies the structural breaks accurately. 
Journal: Journal of Applied Statistics 
Pages: 1425-1438 
Issue: 9 
Volume: 37 
Year: 2010 
Keywords: unit root test, multiple structural breaks, break date estimation, Monte Carlo simulations, US macroeconomic variables, 
X-DOI: 10.1080/02664760903039883 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903039883
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:9:p:1425-1438




Template-Type: ReDIF-Article 1.0
Author-Name: Yousung Park 
Author-X-Name-First: Yousung 
Author-X-Name-Last: Park 
Author-Name: Bo-Seung Choi 
Author-X-Name-First: Bo-Seung 
Author-X-Name-Last: Choi 
Title: Bayesian analysis for incomplete multi-way contingency tables with nonignorable nonresponse 
Abstract:
  We propose Bayesian methods with five types of priors to estimate cell
 probabilities in an incomplete multi-way contingency table under
 nonignorable nonresponse. In this situation, the maximum likelihood (ML)
 estimates often fall in the boundary solution, causing the ML estimates to
 become unstable. To deal with such a multi-way table, we present an EM
 algorithm which generalizes the previous algorithm used for incomplete
 one-way tables. Three of the five types of priors were previously
 introduced while the other two are newly proposed to reflect different
 response patterns between respondents and nonrespondents. Data analysis
 and simulation studies show that Bayesian estimates based on the old three
 priors can be worse than the ML regardless of occurrence of boundary
 solution, contrary to previous studies. The Bayesian estimates from the
 two new priors are most preferable when a boundary solution occurs. We
 provide an illustrating example using data for a study of the relationship
 between a mother's smoking and her newborn's weight. 
Journal: Journal of Applied Statistics 
Pages: 1439-1453 
Issue: 9 
Volume: 37 
Year: 2010 
Keywords: Bayesian analysis, nonignorable nonresponse, priors, boundary solution, EM algorithm, 
X-DOI: 10.1080/02664760903046078 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903046078
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:9:p:1439-1453




Template-Type: ReDIF-Article 1.0
Author-Name: Marc Aerts 
Author-X-Name-First: Marc 
Author-X-Name-Last: Aerts 
Author-Name: Niel Hens 
Author-X-Name-First: Niel 
Author-X-Name-Last: Hens 
Author-Name: Jeffrey Simonoff 
Author-X-Name-First: Jeffrey 
Author-X-Name-Last: Simonoff 
Title: Model selection in regression based on pre-smoothing 
Abstract:
  In this paper, we investigate the effect of pre-smoothing on model
 selection. Christobal et al 6 showed the beneficial effect of
 pre-smoothing on estimating the parameters in a linear regression model.
 Here, in a regression setting, we show that smoothing the response data
 prior to model selection by Akaike's information criterion can lead to an
 improved selection procedure. The bootstrap is used to control the
 magnitude of the random error structure in the smoothed data. The effect
 of pre-smoothing on model selection is shown in simulations. The method is
 illustrated in a variety of settings, including the selection of the best
 fractional polynomial in a generalized linear model. 
Journal: Journal of Applied Statistics 
Pages: 1455-1472 
Issue: 9 
Volume: 37 
Year: 2010 
Keywords: Akaike information criterion, fractional polynomial, latent variable model, model selection, pre-smoothing, 
X-DOI: 10.1080/02664760903046086 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903046086
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:9:p:1455-1472




Template-Type: ReDIF-Article 1.0
Author-Name: Panagiotis Mantalos 
Author-X-Name-First: Panagiotis 
Author-X-Name-Last: Mantalos 
Author-Name: Ghazi Shukur 
Author-X-Name-First: Ghazi 
Author-X-Name-Last: Shukur 
Title: The effect of spillover on the Granger causality test 
Abstract:
  In this paper, we investigate the properties of the Granger causality
 test in stationary and stable vector autoregressive models under the
 presence of spillover effects, that is, causality in variance. The Wald
 test and the WW test (the Wald test with White's proposed
 heteroskedasticity-consistent covariance matrix estimator imposed) are
 analyzed. The investigation is undertaken by using Monte Carlo simulation
 in which two different sample sizes and six different kinds of
 data-generating processes are used. The results show that the Wald test
 over-rejects the null hypothesis both with and without the spillover
 effect, and that the over-rejection in the latter case is more severe in
 larger samples. The size properties of the WW test are satisfactory when
 there is spillover between the variables. Only when there is feedback in
 the variance is the size of the WW test slightly affected. The Wald test
 is shown to have higher power than the WW test when the errors follow a
 GARCH(1,1) process without a spillover effect. When there is a spillover,
 the power of both tests deteriorates, which implies that the spillover has
 a negative effect on the causality tests. 
Journal: Journal of Applied Statistics 
Pages: 1473-1486 
Issue: 9 
Volume: 37 
Year: 2010 
Keywords: causality in variance, GARCH, Granger causality, volatility spillover, 
X-DOI: 10.1080/02664760903046094 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903046094
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:9:p:1473-1486




Template-Type: ReDIF-Article 1.0
Author-Name: Shiquan Ren 
Author-X-Name-First: Shiquan 
Author-X-Name-Last: Ren 
Author-Name: Hong Lai 
Author-X-Name-First: Hong 
Author-X-Name-Last: Lai 
Author-Name: Wenjing Tong 
Author-X-Name-First: Wenjing 
Author-X-Name-Last: Tong 
Author-Name: Mostafa Aminzadeh 
Author-X-Name-First: Mostafa 
Author-X-Name-Last: Aminzadeh 
Author-Name: Xuezhang Hou 
Author-X-Name-First: Xuezhang 
Author-X-Name-Last: Hou 
Author-Name: Shenghan Lai 
Author-X-Name-First: Shenghan 
Author-X-Name-Last: Lai 
Title: Nonparametric bootstrapping for hierarchical data 
Abstract:
  Nonparametric bootstrapping for hierarchical data is relatively
 underdeveloped and not straightforward: certainly it does not make sense
 to use simple nonparametric resampling, which treats all observations as
 independent. We have provided some resampling strategies of hierarchical
 data, proved that the strategy of nonparametric bootstrapping on the
 highest level (randomly sampling all other levels without replacement
 within the highest level selected by randomly sampling the highest levels
 with replacement) is better than that on lower levels, analyzed real data
 and performed simulation studies. 
Journal: Journal of Applied Statistics 
Pages: 1487-1498 
Issue: 9 
Volume: 37 
Year: 2010 
Keywords: random effects model, hierarchical data, nonparametric bootstrapping, resampling schemes, unbalanced data, 
X-DOI: 10.1080/02664760903046102 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903046102
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:9:p:1487-1498




Template-Type: ReDIF-Article 1.0
Author-Name: Firoozeh Haghighi 
Author-X-Name-First: Firoozeh 
Author-X-Name-Last: Haghighi 
Author-Name: Mikhail Nikulin 
Author-X-Name-First: Mikhail 
Author-X-Name-Last: Nikulin 
Title: On the linear degradation model with multiple failure modes 
Abstract:
  The purpose of this work is to develop statistical methods for using
 degradation measure to estimate a survival function for a linear
 degradation model. In this paper, we review existing methods and then
 describe a parametric approach. We focus on estimating the survival
 function. A simulation study is conducted to evaluate the performance of
 the estimating method and the method is illustrated using real data. 
Journal: Journal of Applied Statistics 
Pages: 1499-1507 
Issue: 9 
Volume: 37 
Year: 2010 
Keywords: degradation model, failure time, intensity function, survival function, parametric method, 
X-DOI: 10.1080/02664760903055434 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903055434
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:9:p:1499-1507




Template-Type: ReDIF-Article 1.0
Author-Name: Feng-Chang Xie 
Author-X-Name-First: Feng-Chang 
Author-X-Name-Last: Xie 
Author-Name: Jin-Guan Lin 
Author-X-Name-First: Jin-Guan 
Author-X-Name-Last: Lin 
Author-Name: Bo-Cheng Wei 
Author-X-Name-First: Bo-Cheng 
Author-X-Name-Last: Wei 
Title: Testing for varying zero-inflation and dispersion in generalized Poisson regression models 
Abstract:
  Homogeneity of dispersion parameters and zero-inflation parameters is a
 standard assumption in zero-inflated generalized Poisson regression
 (ZIGPR) models. However, this assumption may be not appropriate in some
 situations. This work develops a score test for varying dispersion and/or
 zero-inflation parameter in the ZIGPR models, and corresponding test
 statistics are obtained. Two numerical examples are given to illustrate
 our methodology, and the properties of score test statistics are
 investigated through Monte Carlo simulations. 
Journal: Journal of Applied Statistics 
Pages: 1509-1522 
Issue: 9 
Volume: 37 
Year: 2010 
Keywords: generalized Poisson regression models, zero-inflation, dispersion, score test, test of homogeneity, simulation study, 
X-DOI: 10.1080/02664760903055442 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903055442
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:9:p:1509-1522




Template-Type: ReDIF-Article 1.0
Author-Name: Claire Weston 
Author-X-Name-First: Claire 
Author-X-Name-Last: Weston 
Author-Name: John Thompson 
Author-X-Name-First: John 
Author-X-Name-Last: Thompson 
Title: Modeling survival in childhood cancer studies using two-stage non-mixture cure models 
Abstract:
  Non-mixture cure models (NMCMs) are derived from a simplified
 representation of the biological process that takes place after treatment
 for cancer. These models are intended to represent the time from the end
 of treatment to the time of first recurrence of cancer in studies when a
 proportion of those treated are completely cured. However, for many
 studies overall survival is also of interest. A two-stage NMCM that
 estimates the overall survival from a combination of two cure models, one
 from end of treatment to first recurrence and one from first recurrence to
 death, is proposed. The model is applied to two studies of Ewing's tumor
 in young patients. Caution needs to be exercised when extrapolating from
 cure models fitted to short follow-up times, but these data and associated
 simulations show how, when follow-up is limited, a two-stage model can
 give more stable estimates of the cure fraction than a one-stage model
 applied directly to overall survival. 
Journal: Journal of Applied Statistics 
Pages: 1523-1535 
Issue: 9 
Volume: 37 
Year: 2010 
Keywords: non-mixture cure model, parametric survival, paediatric cancer, 
X-DOI: 10.1080/02664760903055459 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903055459
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:9:p:1523-1535




Template-Type: ReDIF-Article 1.0
Author-Name: Emilio Gomez-Deniz 
Author-X-Name-First: Emilio 
Author-X-Name-Last: Gomez-Deniz 
Author-Name: Enrique Calderin-Ojeda 
Author-X-Name-First: Enrique 
Author-X-Name-Last: Calderin-Ojeda 
Title: A study of Bayesian local robustness with applications in actuarial statistics 
Abstract:
  Local or infinitesimal Bayesian robustness is a powerful tool to study
 the sensitivity of posterior magnitudes, which cannot be expressed in a
 simple manner. For these expressions, the global Bayesian robustness
 methodology does not seem adequate since the practitioner cannot avoid
 using inappropriate classes of prior distributions in order to make the
 model mathematically tractable. This situation occurs, for example, when
 we compute some types of premiums in actuarial statistics in order to fix
 the premium to be charged to an insurance policy. In this paper,
 analytical and simple expressions that allow us to study the sensitivity
 of premiums, which are usually used in automobile insurance are provided
 by using the local Bayesian robustness methodology. Some examples are
 examined by using real automobile claim insurance data. 
Journal: Journal of Applied Statistics 
Pages: 1537-1546 
Issue: 9 
Volume: 37 
Year: 2010 
Keywords: posterior, local robustness, norm, premium, 
X-DOI: 10.1080/02664760903082156 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903082156
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:9:p:1537-1546




Template-Type: ReDIF-Article 1.0
Author-Name: D. J. Best 
Author-X-Name-First: D. J. 
Author-X-Name-Last: Best 
Author-Name: J. C. W. Rayner 
Author-X-Name-First: J. C. W. 
Author-X-Name-Last: Rayner 
Author-Name: O. Thas 
Author-X-Name-First: O. 
Author-X-Name-Last: Thas 
Title: Four tests of fit for the beta-binomial distribution 
Abstract:
  Tests based on the Anderson-Darling statistic, a third moment statistic
 and the classical Pearson-Fisher X2 statistic, along with its third-order
 component, are considered. A small critical value and power study are
 given. Some examples illustrate important applications. 
Journal: Journal of Applied Statistics 
Pages: 1547-1554 
Issue: 9 
Volume: 37 
Year: 2010 
Keywords: Anderson-Darling statistic, multinomial distribution, Pearson's X2, third moment statistic, third-order component, 
X-DOI: 10.1080/02664760903089664 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903089664
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:9:p:1547-1554




Template-Type: ReDIF-Article 1.0
Author-Name: Aristidis Nikoloulopoulos 
Author-X-Name-First: Aristidis 
Author-X-Name-Last: Nikoloulopoulos 
Author-Name: Dimitris Karlis 
Author-X-Name-First: Dimitris 
Author-X-Name-Last: Karlis 
Title: Regression in a copula model for bivariate count data 
Abstract:
  In many cases of modeling bivariate count data, the interest lies on
 studying the association rather than the marginal properties. We form a
 flexible regression copula-based model where covariates are used not only
 for the marginal but also for the copula parameters. Since copula measures
 the association, the use of covariates in its parameters allow for direct
 modeling of association. A real-data application related to transaction
 market basket data is used. Our goal is to refine and understand whether
 the association between the number of purchases of certain product
 categories depends on particular demographic customers' characteristics.
 Such information is important for decision making for marketing purposes. 
Journal: Journal of Applied Statistics 
Pages: 1555-1568 
Issue: 9 
Volume: 37 
Year: 2010 
Keywords: dependence modeling, Kendall's tau, covariate function, negative binomial distribution, 
X-DOI: 10.1080/02664760903093591 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903093591
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:9:p:1555-1568




Template-Type: ReDIF-Article 1.0
Author-Name: Claudio Agostinelli 
Author-X-Name-First: Claudio 
Author-X-Name-Last: Agostinelli 
Author-Name: Luisa Bisaglia 
Author-X-Name-First: Luisa 
Author-X-Name-Last: Bisaglia 
Title: ARFIMA processes and outliers: a weighted likelihood approach 
Abstract:
  In this paper, we consider the problem of robust estimation of the
 fractional parameter, d, in long memory autoregressive fractionally
 integrated moving average processes, when two types of outliers, i.e.
 additive and innovation, are taken into account without knowing their
 number, position or intensity. The proposed method is a weighted
 likelihood estimation (WLE) approach for which needed definitions and
 algorithm are given. By an extensive Monte Carlo simulation study, we
 compare the performance of the WLE method with the performance of both the
 approximated maximum likelihood estimation (MLE) and the robust
 M-estimator proposed by Beran (Statistics for Long-Memory Processes,
 Chapman & Hall, London, 1994). We find that robustness against the two
 types of considered outliers can be achieved without loss of efficiency.
 Moreover, as a byproduct of the procedure, we can classify the suspicious
 observations in different kinds of outliers. Finally, we apply the
 proposed methodology to the Nile River annual minima time series. 
Journal: Journal of Applied Statistics 
Pages: 1569-1584 
Issue: 9 
Volume: 37 
Year: 2010 
Keywords: ARFIMA processes, outliers, robust estimation, weighted likelihood, 
X-DOI: 10.1080/02664760903093609 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903093609
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:9:p:1569-1584




Template-Type: ReDIF-Article 1.0
Author-Name: Xavier Puig 
Author-X-Name-First: Xavier 
Author-X-Name-Last: Puig 
Author-Name: Josep Ginebra 
Author-X-Name-First: Josep 
Author-X-Name-Last: Ginebra 
Author-Name: Marti Font 
Author-X-Name-First: Marti 
Author-X-Name-Last: Font 
Title: The Sichel model and the mixing and truncation order 
Abstract:
  The analysis of word frequency count data can be very useful in
 authorship attribution problems. Zero-truncated generalized inverse
 Gaussian-Poisson mixture models are very helpful in the analysis of these
 kinds of data because their model-mixing density estimates can be used as
 estimates of the density of the word frequencies of the vocabulary. It is
 found that this model provides excellent fits for the word frequency
 counts of very long texts, where the truncated inverse Gaussian-Poisson
 special case fails because it does not allow for the large degree of
 over-dispersion in the data. The role played by the three parameters of
 this truncated GIG-Poisson model is also explored. Our second goal is to
 compare the fit of the truncated GIG-Poisson mixture model with the fit of
 the model that results from switching the order of the mixing and
 truncation stages. A heuristic interpretation of the mixing distribution
 estimates obtained under this alternative GIG-truncated Poisson mixture
 model is also provided. 
Journal: Journal of Applied Statistics 
Pages: 1585-1603 
Issue: 9 
Volume: 37 
Year: 2010 
Keywords: categorical data, generalized inverse Gaussian, mixture model, Poisson mixture, stylometry, truncated model, truncated mixture, word frequency, 
X-DOI: 10.1080/02664760903093617 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903093617
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:9:p:1585-1603




Template-Type: ReDIF-Article 1.0
Author-Name: A. A. M. Nurunnabi 
Author-X-Name-First: A. A. M. 
Author-X-Name-Last: Nurunnabi 
Author-Name: A.H.M. Rahmatullah Imon 
Author-X-Name-First: A.H.M. 
Author-X-Name-Last: Rahmatullah Imon 
Author-Name: M. Nasser 
Author-X-Name-First: M. 
Author-X-Name-Last: Nasser 
Title: Identification of multiple influential observations in logistic regression 
Abstract:
  The identification of influential observations in logistic regression has
 drawn a great deal of attention in recent years. Most of the available
 techniques like Cook's distance and difference of fits (DFFITS) are based
 on single-case deletion. But there is evidence that these techniques
 suffer from masking and swamping problems and consequently fail to detect
 multiple influential observations. In this paper, we have developed a new
 measure for the identification of multiple influential observations in
 logistic regression based on a generalized version of DFFITS. The
 advantage of the proposed method is then investigated through several
 well-referred data sets and a simulation study. 
Journal: Journal of Applied Statistics 
Pages: 1605-1624 
Issue: 10 
Volume: 37 
Year: 2010 
Keywords: generalized DFFITS, generalized Studentized Pearson residual, generalized weight, high leverage point, influential observation, masking, outlier, swamping, 
X-DOI: 10.1080/02664760903104307 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903104307
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:10:p:1605-1624




Template-Type: ReDIF-Article 1.0
Author-Name: Nandini Kannan 
Author-X-Name-First: Nandini 
Author-X-Name-Last: Kannan 
Author-Name: Debasis Kundu 
Author-X-Name-First: Debasis 
Author-X-Name-Last: Kundu 
Author-Name: P. Nair 
Author-X-Name-First: P. 
Author-X-Name-Last: Nair 
Author-Name: R. C. Tripathi 
Author-X-Name-First: R. C. 
Author-X-Name-Last: Tripathi 
Title: The generalized exponential cure rate model with covariates 
Abstract:
  In this article, we consider a parametric survival model that is
 appropriate when the population of interest contains long-term survivors
 or immunes. The model referred to as the cure rate model was introduced by
 Boag 1 in terms of a mixture model that included a component representing
 the proportion of immunes and a distribution representing the life times
 of the susceptible population. We propose a cure rate model based on the
 generalized exponential distribution that incorporates the effects of risk
 factors or covariates on the probability of an individual being a
 long-time survivor. Maximum likelihood estimators of the model parameters
 are obtained using the the expectation-maximisation (EM) algorithm. A
 graphical method is also provided for assessing the goodness-of-fit of the
 model. We present an example to illustrate the fit of this model to data
 that examines the effects of different risk factors on relapse time for
 drug addicts. 
Journal: Journal of Applied Statistics 
Pages: 1625-1636 
Issue: 10 
Volume: 37 
Year: 2010 
Keywords: cure rate, long-term survivor, generalized exponential distribution, EM algorithm, goodness-of-fit, 
X-DOI: 10.1080/02664760903117739 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903117739
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:10:p:1625-1636




Template-Type: ReDIF-Article 1.0
Author-Name: Ralf Ostermark 
Author-X-Name-First: Ralf 
Author-X-Name-Last: Ostermark 
Title: Concurrent processing of heteroskedastic vector-valued mixture density models 
Abstract:
  We introduce a combined two-stage least-squares (2SLS)-expectation
 maximization (EM) algorithm for estimating vector-valued autoregressive
 conditional heteroskedasticity models with standardized errors generated
 by Gaussian mixtures. The procedure incorporates the identification of the
 parametric settings as well as the estimation of the model parameters. Our
 approach does not require a priori knowledge of the Gaussian densities.
 The parametric settings of the 2SLS_EM algorithm are determined by the
 genetic hybrid algorithm (GHA). We test the GHA-driven 2SLS_EM algorithm
 on some simulated cases and on international asset pricing data. The
 statistical properties of the estimated models and the derived mixture
 densities indicate good performance of the algorithm. We conduct tests on
 a massively parallel processor supercomputer to cope with situations
 involving numerous mixtures. We show that the algorithm is scalable. 
Journal: Journal of Applied Statistics 
Pages: 1637-1659 
Issue: 10 
Volume: 37 
Year: 2010 
Keywords: vector-valued ARCH processes, mixture densities, geno-mathematical monitoring, parallel programming, high-performance computing, 
X-DOI: 10.1080/02664760903121236 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903121236
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:10:p:1637-1659




Template-Type: ReDIF-Article 1.0
Author-Name: D. Todem 
Author-X-Name-First: D. 
Author-X-Name-Last: Todem 
Author-Name: Y. Zhang 
Author-X-Name-First: Y. 
Author-X-Name-Last: Zhang 
Author-Name: A. Ismail 
Author-X-Name-First: A. 
Author-X-Name-Last: Ismail 
Author-Name: W. Sohn 
Author-X-Name-First: W. 
Author-X-Name-Last: Sohn 
Title: Random effects regression models for count data with excess zeros in caries research 
Abstract:
  We extend the family of Poisson and negative binomial models to derive
 the joint distribution of clustered count outcomes with extra zeros. Two
 random effects models are formulated. The first model assumes a shared
 random effects term between the conditional probability of perfect zeros
 and the conditional mean of the imperfect state. The second formulation
 relaxes the shared random effects assumption by relating the conditional
 probability of perfect zeros and the conditional mean of the imperfect
 state to two different but correlated random effects variables. Under the
 conditional independence and the missing data at random assumption, a
 direct optimization of the marginal likelihood and an EM algorithm are
 proposed to fit the proposed models. Our proposed models are fitted to
 dental caries counts of children under the age of six in the city of
 Detroit. 
Journal: Journal of Applied Statistics 
Pages: 1661-1679 
Issue: 10 
Volume: 37 
Year: 2010 
Keywords: adaptive Gaussian quadrature, dental caries scores, clustered data, factor loadings, inflated zeros, negative binomial/Poisson distribution, random effects, 
X-DOI: 10.1080/02664760903127605 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903127605
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:10:p:1661-1679




Template-Type: ReDIF-Article 1.0
Author-Name: Yi-Ting Hwang 
Author-X-Name-First: Yi-Ting 
Author-X-Name-Last: Hwang 
Author-Name: Jia-Jung Lai 
Author-X-Name-First: Jia-Jung 
Author-X-Name-Last: Lai 
Author-Name: Shyh-Tyan Ou 
Author-X-Name-First: Shyh-Tyan 
Author-X-Name-Last: Ou 
Title: Evaluations of FWER-controlling methods in multiple hypothesis testing 
Abstract:
  Simultaneously testing a family of n null hypotheses can arise in many
 applications. A common problem in multiple hypothesis testing is to
 control Type-I error. The probability of at least one false rejection
 referred to as the familywise error rate (FWER) is one of the earliest
 error rate measures. Many FWER-controlling procedures have been proposed.
 The ability to control the FWER and achieve higher power is often used to
 evaluate the performance of a controlling procedure. However, when testing
 multiple hypotheses, FWER and power are not sufficient for evaluating
 controlling procedure's performance. Furthermore, the performance of a
 controlling procedure is also governed by experimental parameters such as
 the number of hypotheses, sample size, the number of true null hypotheses
 and data structure. This paper evaluates, under various experimental
 settings, the performance of some FWER-controlling procedures in terms of
 five indices, the FWER, the false discovery rate, the false non-discovery
 rate, the sensitivity and the specificity. The results can provide
 guidance on how to select an appropriate FWER-controlling procedure to
 meet a study's objective. 
Journal: Journal of Applied Statistics 
Pages: 1681-1694 
Issue: 10 
Volume: 37 
Year: 2010 
Keywords: Bonferroni's method, false discovery rate, false non-discovery rate, familywise error rate, multiple hypothesis testing, sensitivity, specificity, 
X-DOI: 10.1080/02664760903136960 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903136960
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:10:p:1681-1694




Template-Type: ReDIF-Article 1.0
Author-Name: Jesper Ryden 
Author-X-Name-First: Jesper 
Author-X-Name-Last: Ryden 
Author-Name: Sven Erick Alm 
Author-X-Name-First: Sven Erick 
Author-X-Name-Last: Alm 
Title: The effect of interaction and rounding error in two-way ANOVA: example of impact on testing for normality 
Abstract:
  A key issue in various applications of analysis of variance (ANOVA) is
 testing for the interaction and the interpretation of resulting ANOVA
 tables. In this note it is demonstrated that for a two-way ANOVA, whether
 interactions are incorporated or not may have a dramatic influence when
 considering the usual statistical tests for normality of residuals. The
 effect of numerical rounding is also discussed. 
Journal: Journal of Applied Statistics 
Pages: 1695-1701 
Issue: 10 
Volume: 37 
Year: 2010 
Keywords: analysis of variance, rounding error, Shapiro-Wilk test, factorial experiment, interaction, 
X-DOI: 10.1080/02664760903143925 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903143925
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:10:p:1695-1701




Template-Type: ReDIF-Article 1.0
Author-Name: Douglas Jones 
Author-X-Name-First: Douglas 
Author-X-Name-Last: Jones 
Author-Name: Francis Mendez Mediavilla 
Author-X-Name-First: Francis 
Author-X-Name-Last: Mendez Mediavilla 
Title: A Bayesian method for query approximation 
Abstract:
  This study presents statistical techniques to obtain local approximate
 query answers for aggregate multivariate materialized views thus
 eliminating the need for repetitive scanning of the source data. In widely
 distributed management information systems, detailed data do not
 necessarily reside in the same physical location as the decision-maker;
 thus, requiring scanning of the source data as needed by the query demand.
 Decision-making, business intelligence and data analysis could involve
 multiple data sources, data diversity, aggregates and large amounts of
 data. Management often confronts delays in information acquisition from
 remote sites. Management decisions usually involve analyses that require
 the most precise summary data available. These summaries are readily
 available from data warehouses and can be used to estimate or approximate
 data in exchange for a quicker response. An approach to supporting
 aggregate materialized view management is proposed that reconstructs data
 sets locally using posterior parameter estimates based on sufficient
 statistics in a log-linear model with a multinomial likelihood. 
Journal: Journal of Applied Statistics 
Pages: 1703-1715 
Issue: 10 
Volume: 37 
Year: 2010 
Keywords: query approximation, data reduction, materialized view management, BIPF, 
X-DOI: 10.1080/02664760903148791 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903148791
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:10:p:1703-1715




Template-Type: ReDIF-Article 1.0
Author-Name: Jing-Er Chiu 
Author-X-Name-First: Jing-Er 
Author-X-Name-Last: Chiu 
Author-Name: Tsen-I Kuo 
Author-X-Name-First: Tsen-I 
Author-X-Name-Last: Kuo 
Title: Control charts for fraction nonconforming in a bivariate binomial process 
Abstract:
  Many multivariate quality control techniques are used for multivariate
 variable processes, but few work for multivariate attribute processes. To
 monitor multivariate attributes, controlling the false alarms (type I
 errors) and considering the correlation between attributes are two
 important issues. By taking into account these two issues, a new control
 chart is presented to monitor a bivariate binomial process. An example is
 illustrated for the proposed method. To evaluate the performance of the
 proposed method, a simulation study is conducted to compare the results
 with those using both the multivariate np chart and skewness reduction
 approaches. The results show that the correlation is taken into account in
 the designed chart and the overall false alarm is controlled at the
 nominal value. Moreover, the process shift can be quickly detected and the
 variable that is responsible for a signal can be determined. 
Journal: Journal of Applied Statistics 
Pages: 1717-1728 
Issue: 10 
Volume: 37 
Year: 2010 
Keywords: multi-attribute control chart, control of bivariate binomial processes, average run length, multivariate process monitoring, 
X-DOI: 10.1080/02664760903150698 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903150698
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:10:p:1717-1728




Template-Type: ReDIF-Article 1.0
Author-Name: Assam Pryseley 
Author-X-Name-First: Assam 
Author-X-Name-Last: Pryseley 
Author-Name: Koen Mintiens 
Author-X-Name-First: Koen 
Author-X-Name-Last: Mintiens 
Author-Name: Katia Knapen 
Author-X-Name-First: Katia 
Author-X-Name-Last: Knapen 
Author-Name: Yves Van der Stede 
Author-X-Name-First: Yves 
Author-X-Name-Last: Van der Stede 
Author-Name: Geert Molenberghs 
Author-X-Name-First: Geert 
Author-X-Name-Last: Molenberghs 
Title: Estimating precision, repeatability, and reproducibility from Gaussian and non- Gaussian data: a mixed models approach 
Abstract:
  Quality control relies heavily on the use of formal assessment metrics.
 In this paper, for the context of veterinary epidemiology, we review the
 main proposals, precision, repeatability, reproducibility, and
 intermediate precision, in agreement with ISO (international Organization
 for Standardization) practice, generalize these by placing them within the
 linear mixed model framework, which we then extend to the generalized
 linear mixed model setting, so that both Gaussian as well as non-Gaussian
 data can be employed. Similarities and differences are discussed between
 the classical ANOVA (analysis of variance) approach and the proposed mixed
 model settings, on the one hand, and between the Gaussian and non-Gaussian
 cases, on the other hand. The new proposals are applied to five studies in
 three diseases: Aujeszky's disease, enzootic bovine leucosis (EBL) and
 bovine brucellosis. The mixed-models proposals are also discussed in the
 light of their computational requirements. 
Journal: Journal of Applied Statistics 
Pages: 1729-1747 
Issue: 10 
Volume: 37 
Year: 2010 
Keywords: accuracy, analysis of variance, Aujeszky's disease, bias, bovine brucellosis, enzootic bovine leucosis, generalized linear mixed models, linear mixed models, quality control, 
X-DOI: 10.1080/02664760903150706 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903150706
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:10:p:1729-1747




Template-Type: ReDIF-Article 1.0
Author-Name: Jie Wang 
Author-X-Name-First: Jie 
Author-X-Name-Last: Wang 
Author-Name: James Stamey 
Author-X-Name-First: James 
Author-X-Name-Last: Stamey 
Title: A Bayesian algorithm for sample size determination for equivalence and non-inferiority test 
Abstract:
  Bayesian sample size estimation for equivalence and non-inferiority tests
 for diagnostic methods is considered. The goal of the study is to test
 whether a new screening test of interest is equivalent to, or not inferior
 to the reference test, which may or may not be a gold standard. Sample
 sizes are chosen by the model performance criteria of average posterior
 variance, length and coverage probability. In the absence of a gold
 standard, sample sizes are evaluated by the ratio of marginal
 probabilities of the two screening tests; whereas in the presence of gold
 standard, sample sizes are evaluated by the measures of sensitivity and
 specificity. 
Journal: Journal of Applied Statistics 
Pages: 1749-1759 
Issue: 10 
Volume: 37 
Year: 2010 
Keywords: average length criterion, average posterior variance criteria, average coverage criterion, equivalence test, non-inferiority test, sample size determination, 
X-DOI: 10.1080/02664760903150714 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903150714
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:10:p:1749-1759




Template-Type: ReDIF-Article 1.0
Author-Name: Y. Samimi 
Author-X-Name-First: Y. 
Author-X-Name-Last: Samimi 
Author-Name: A. Aghaie 
Author-X-Name-First: A. 
Author-X-Name-Last: Aghaie 
Title: Monitoring heterogeneous serially correlated usage behavior in subscription-based services 
Abstract:
  Effective monitoring of usage behavior necessitates applying accurate
 stochastic models to represent customer heterogeneous time-dependent
 behavior. In this research, it is assumed that the sequence of customer
 visits over a subscription period occurs based on the Poisson process,
 while usage at each purchase occasion follows an autoregressive Bernoulli
 model of first order. The autocorrelated observations are derived from a
 two-state Markov chain model. Generalized linear models are employed to
 describe heterogeneous behavior across customers. In order to monitor the
 number of visits as well as the fraction of visits eventuated in a
 purchase, control statistics are defined on the basis of generalized
 likelihood ratio (GLR) test. Furthermore, in the case of the marginal
 logistic model for dependent observations, a chi-square test statistic
 based on the asymptotic multivariate normal distribution of
 quasi-likelihood estimates is employed. Performances of the monitoring
 schemes are compared with an illustrative case provided by simulation.
 Results indicate that the adjusted Shewhart c chart resembles the deviance
 residual control chart for monitoring the frequency of customer visit. On
 the other hand, the GLR statistic based on the conditional logistic
 regression is more powerful in detecting unnatural usage behavior when
 compared with the chi-square control statistic based on the marginal
 logistic model. 
Journal: Journal of Applied Statistics 
Pages: 1761-1777 
Issue: 10 
Volume: 37 
Year: 2010 
Keywords: generalized linear models, autocorrelated Bernoulli process, quasi-likelihood, longitudinal data, customer usage behavior, 
X-DOI: 10.1080/02664760903159103 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903159103
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:10:p:1761-1777




Template-Type: ReDIF-Article 1.0
Author-Name: Pieter Bastiaan Ober 
Author-X-Name-First: Pieter Bastiaan 
Author-X-Name-Last: Ober 
Title: Modeling with Data 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 1779-1779 
Issue: 10 
Volume: 37 
Year: 2010 
X-DOI: 10.1080/02664760902919721 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902919721
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:10:p:1779-1779




Template-Type: ReDIF-Article 1.0
Author-Name: Mukesh Srivastava 
Author-X-Name-First: Mukesh 
Author-X-Name-Last: Srivastava 
Title: Analysis of Variance and Covariance 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 1781-1782 
Issue: 10 
Volume: 37 
Year: 2010 
X-DOI: 10.1080/02664760902919747 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902919747
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:10:p:1781-1782




Template-Type: ReDIF-Article 1.0
Author-Name: Jennifer Klapper 
Author-X-Name-First: Jennifer 
Author-X-Name-Last: Klapper 
Title: Discrete Fourier Analysis and Wavelets 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 1783-1784 
Issue: 10 
Volume: 37 
Year: 2010 
X-DOI: 10.1080/02664760902919762 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902919762
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:10:p:1783-1784




Template-Type: ReDIF-Article 1.0
Author-Name: Stan Lipovetsky 
Author-X-Name-First: Stan 
Author-X-Name-Last: Lipovetsky 
Title: Double logistic curve in regression modeling 
Abstract:
  The logistic sigmoid curve is widely used in nonlinear regression and in
 binary response modeling. There are problems corresponding to a double
 sigmoid behavior which consists of the first increase to an early
 saturation at an intermediate level, and the second sigmoid with the
 eventual plateau of saturation. A double sigmoid behavior is usually
 achieved using additive or multiplicative combinations of logit and more
 complicated functions with numerous parameters. In this work, double
 sigmoid functions are constructed as logistic ones with a sign defining
 the point of inflection and with an additional powering parameter. The
 elaborated models describe rather complicated double saturation behavior
 via only four or five parameters which can be efficiently estimated by
 nonlinear optimization techniques. Theoretical features and practical
 applications of the models are discussed. 
Journal: Journal of Applied Statistics 
Pages: 1785-1793 
Issue: 11 
Volume: 37 
Year: 2010 
Keywords: logistic function, double sigmoid function, two levels of saturation, 
X-DOI: 10.1080/02664760903093633 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903093633
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:11:p:1785-1793




Template-Type: ReDIF-Article 1.0
Author-Name: M. A. Graham 
Author-X-Name-First: M. A. 
Author-X-Name-Last: Graham 
Author-Name: S. W. Human 
Author-X-Name-First: S. W. 
Author-X-Name-Last: Human 
Author-Name: S. Chakraborti 
Author-X-Name-First: S. 
Author-X-Name-Last: Chakraborti 
Title: A Phase I nonparametric Shewhart-type control chart based on the median 
Abstract:
  A nonparametric Shewhart-type control chart is proposed for monitoring
 the location of a continuous variable in a Phase I process control
 setting. The chart is based on the pooled median of the available Phase I
 samples and the charting statistics are the counts (number of
 observations) in each sample that are less than the pooled median. An
 exact expression for the false alarm probability (FAP) is given in terms
 of the multivariate hypergeometric distribution and this is used to
 provide tables for the control limits for a specified nominal FAP value
 (of 0.01, 0.05 and 0.10, respectively) and for some values of the sample
 size (n) and the number of Phase I samples (m). Some approximations are
 discussed in terms of the univariate hypergeometric and the normal
 distributions. A simulation study shows that the proposed chart performs
 as well as, and in some cases better than, an existing Shewhart-type chart
 based on the normal distribution. Numerical examples are given to
 demonstrate the implementation of the new chart. 
Journal: Journal of Applied Statistics 
Pages: 1795-1813 
Issue: 11 
Volume: 37 
Year: 2010 
Keywords: false alarm rate, false alarm probability, retrospective, prospective, distribution-free, multivariate hypergeometric, 
X-DOI: 10.1080/02664760903164913 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903164913
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:11:p:1795-1813




Template-Type: ReDIF-Article 1.0
Author-Name: Silvia Figini 
Author-X-Name-First: Silvia 
Author-X-Name-Last: Figini 
Author-Name: Paolo Giudici 
Author-X-Name-First: Paolo 
Author-X-Name-Last: Giudici 
Author-Name: Pierpaolo Uberti 
Author-X-Name-First: Pierpaolo 
Author-X-Name-Last: Uberti 
Title: A threshold based approach to merge data in financial risk management 
Abstract:
  According to the last proposals by the Basel Committee, banks are allowed
 to use statistical approaches for the computation of their capital charge
 covering financial risks such as credit risk, market risk and operational
 risk. It is widely recognized that internal loss data alone do not suffice
 to provide accurate capital charge in financial risk management,
 especially for high-severity and low-frequency events. Financial
 institutions typically use external loss data to augment the available
 evidence and, therefore, provide more accurate risk estimates. Rigorous
 statistical treatments are required to make internal and external data
 comparable and to ensure that merging the two databases leads to unbiased
 estimates. The goal of this paper is to propose a correct statistical
 treatment to make the external and internal data comparable and,
 therefore, mergeable. Such methodology augments internal losses with
 relevant, rather than redundant, external loss data. 
Journal: Journal of Applied Statistics 
Pages: 1815-1824 
Issue: 11 
Volume: 37 
Year: 2010 
Keywords: data merging, threshold, financial risk management, 
X-DOI: 10.1080/02664760903164921 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903164921
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:11:p:1815-1824




Template-Type: ReDIF-Article 1.0
Author-Name: Claudio Conversano 
Author-X-Name-First: Claudio 
Author-X-Name-Last: Conversano 
Author-Name: Domenico Vistocco 
Author-X-Name-First: Domenico 
Author-X-Name-Last: Vistocco 
Title: Analysis of mutual funds' management styles: a modeling, ranking and visualizing approach 
Abstract:
  A method to rank mutual funds according to their investment style
 measured with respect to the returns of a reference portfolio (benchmark)
 is introduced. It is based on a style analysis model estimating a mutual
 fund portfolio composition as well as the benchmark one. Starting from
 such compositions, it computes a proximity measure based on the L1 or L2
 norm to assess the similarity between each mutual fund portfolio returns
 and the benchmark returns as well as between the returns of each benchmark
 constituent and that of the corresponding mutual fund constituent. To this
 purpose the mean integrated absolute error and the mean integrated squared
 error are computed to derive both a global ranking of mutual fund
 management styles and partial rankings expressing the over- (under-)
 weighting of each portfolio constituent. A visual inspection of the
 results emphasizing main differences in management styles is provided,
 using a parallel coordinates plot. Since a modeling, a ranking and a
 visualizing approach are integrated, the method is named MoRaViA. From the
 practitioners' point of view, it allows the identification of a specific
 management style for each mutual fund, discriminating active management
 funds from passive management ones. To evaluate the effectiveness of
 MoRaViA, many sets of artificial portfolios are generated and an
 application on a set of equity funds operating in the European market is
 presented. 
Journal: Journal of Applied Statistics 
Pages: 1825-1845 
Issue: 11 
Volume: 37 
Year: 2010 
Keywords: constrained linear regression, mean integrated squared error, mean integrated absolute error, parallel coordinates, subsampling, active vs. passive management, benchmarking, 
X-DOI: 10.1080/02664760903166272 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903166272
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:11:p:1825-1845




Template-Type: ReDIF-Article 1.0
Author-Name: Steffen Unkel 
Author-X-Name-First: Steffen 
Author-X-Name-Last: Unkel 
Author-Name: Nickolay Trendafilov 
Author-X-Name-First: Nickolay 
Author-X-Name-Last: Trendafilov 
Author-Name: Abdel Hannachi 
Author-X-Name-First: Abdel 
Author-X-Name-Last: Hannachi 
Author-Name: Ian Jolliffe 
Author-X-Name-First: Ian 
Author-X-Name-Last: Jolliffe 
Title: Independent exploratory factor analysis with application to atmospheric science data 
Abstract:
  The independent exploratory factor analysis method is introduced for
 recovering independent latent sources from their observed mixtures. The
 new model is viewed as a method of factor rotation in exploratory factor
 analysis (EFA). First, estimates for all EFA model parameters are obtained
 simultaneously. Then, an orthogonal rotation matrix is sought that
 minimizes the dependence between the common factors. The rotation of the
 scores is compensated by a rotation of the initial loading matrix. The
 proposed approach is applied to study winter monthly sea-level pressure
 anomalies over the Northern Hemisphere. The North Atlantic Oscillation,
 the North Pacific Oscillation, and the Scandinavian pattern are identified
 among the rotated spatial patterns with a physically interpretable
 structure. 
Journal: Journal of Applied Statistics 
Pages: 1847-1862 
Issue: 11 
Volume: 37 
Year: 2010 
Keywords: noisy independent component analysis, exploratory factor analysis, factor rotation, more variables than observations, rotated spatial patterns, gridded climate data, sea-level pressure anomalies, 
X-DOI: 10.1080/02664760903166280 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903166280
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:11:p:1847-1862




Template-Type: ReDIF-Article 1.0
Author-Name: Man-Suk Oh 
Author-X-Name-First: Man-Suk 
Author-X-Name-Last: Oh 
Author-Name: Dong Wan Shin 
Author-X-Name-First: Dong Wan 
Author-X-Name-Last: Shin 
Title: Bayesian tests for unit root and multiple breaks 
Abstract:
  A Bayesian approach is considered for identifying sources of
 nonstationarity for models with a unit root and breaks. Different types of
 multiple breaks are allowed through crash models, changing growth models,
 and mixed models. All possible nonstationary models are represented by
 combinations of zero or nonzero parameters associated with time trends,
 dummy for breaks, or previous levels, for which Bayesian posterior
 probabilities are computed. Multiple tests based on Markov chain Monte
 Carlo procedures are implemented. The proposed method is applied to a real
 data set, the Korean GDP data set, showing a strong evidence for two
 breaks rather than the usual unit root or one break. 
Journal: Journal of Applied Statistics 
Pages: 1863-1874 
Issue: 11 
Volume: 37 
Year: 2010 
Keywords: multiple breaks, unit root test, Markov chain Monte Carlo, 
X-DOI: 10.1080/02664760903173450 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903173450
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:11:p:1863-1874




Template-Type: ReDIF-Article 1.0
Author-Name: Giancarlo Diana 
Author-X-Name-First: Giancarlo 
Author-X-Name-Last: Diana 
Author-Name: Pier Francesco Perri 
Author-X-Name-First: Pier Francesco 
Author-X-Name-Last: Perri 
Title: New scrambled response models for estimating the mean of a sensitive quantitative character 
Abstract:
  Moving from the scrambling mechanism recently suggested by Saha [25],
 three scrambled randomized response (SRR) models are introduced with the
 intent to realize a right trade-off between efficiency and privacy
 protection. The models perturb the true response on the sensitive variable
 by resorting to the multiplicative and additive approaches in different
 ways. Some analytical and numerical comparisons of efficiency are
 performed to set up the conditions under which improvements upon Saha's
 model can be obtained and to quantify the efficiency gain. The use of
 auxiliary information is also discussed in a class of estimators for the
 sensitive mean under a generic randomization scheme. The class includes
 also the three proposed SRR models. Finally, some graphical comparisons
 are carried out from the double perspective of the accuracy in the
 estimates and respondents' privacy protection. 
Journal: Journal of Applied Statistics 
Pages: 1875-1890 
Issue: 11 
Volume: 37 
Year: 2010 
Keywords: auxiliary variable, class of estimators, privacy protection, sensitive variable, 
X-DOI: 10.1080/02664760903186031 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903186031
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:11:p:1875-1890




Template-Type: ReDIF-Article 1.0
Author-Name: Tai Vo Van 
Author-X-Name-First: Tai 
Author-X-Name-Last: Vo Van 
Author-Name: T. Pham-Gia 
Author-X-Name-First: T. 
Author-X-Name-Last: Pham-Gia 
Title: Clustering probability distributions 
Abstract:
  This article presents some theoretical results on the maximum of several
 functions, and its use to define the joint distance of k probability
 densities, which, in turn, serves to derive new algorithms for clustering
 densities. Numerical examples are presented to illustrate the theory. 
Journal: Journal of Applied Statistics 
Pages: 1891-1910 
Issue: 11 
Volume: 37 
Year: 2010 
Keywords: maximum function, cluster, L1-distance, Bayes error, hierarchical approach, 
X-DOI: 10.1080/02664760903186049 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903186049
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:11:p:1891-1910




Template-Type: ReDIF-Article 1.0
Author-Name: Ross Sparks 
Author-X-Name-First: Ross 
Author-X-Name-Last: Sparks 
Author-Name: Tim Keighley 
Author-X-Name-First: Tim 
Author-X-Name-Last: Keighley 
Author-Name: David Muscatello 
Author-X-Name-First: David 
Author-X-Name-Last: Muscatello 
Title: Early warning CUSUM plans for surveillance of negative binomial daily disease counts 
Abstract:
  Automated public health surveillance of disease counts for rapid
 outbreak, epidemic or bioterrorism detection using conventional control
 chart methods can be hampered by over-dispersion and background
 ('in-control') mean counts that vary over time. An adaptive cumulative sum
 (CUSUM) plan is developed for signalling unusually high incidence in
 prospectively monitored time series of over-dispersed daily disease counts
 with a non-homogeneous mean. Negative binomial transitional regression is
 used to prospectively model background counts and provide 'one-step-ahead'
 forecasts of the next day's count. A CUSUM plan then accumulates
 departures of observed counts from an offset (reference value) that is
 dynamically updated using the modelled forecasts. The CUSUM signals
 whenever the accumulated departures exceed a threshold. The amount of
 memory of past observations retained by the CUSUM plan is determined by
 the offset value; a smaller offset retains more memory and is efficient at
 detecting smaller shifts. Our approach optimises early outbreak detection
 by dynamically adjusting the offset value. We demonstrate the practical
 application of the 'optimal' CUSUM plans to daily counts of
 laboratory-notified influenza and Ross River virus diagnoses, with
 particular emphasis on the steady-state situation (i.e. changes that occur
 after the CUSUM statistic has run through several in-control counts). 
Journal: Journal of Applied Statistics 
Pages: 1911-1929 
Issue: 11 
Volume: 37 
Year: 2010 
Keywords: average run length, cumulative sum, monitoring, outbreak detection, surveillance, 
X-DOI: 10.1080/02664760903186056 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903186056
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:11:p:1911-1929




Template-Type: ReDIF-Article 1.0
Author-Name: Govert Bijwaard 
Author-X-Name-First: Govert 
Author-X-Name-Last: Bijwaard 
Title: Regularity in individual shopping trips: implications for duration models in marketing 
Abstract:
  Most models for purchase-timing behavior of households do not take into
 account that many households have regular and non-shopping days. We
 propose a statistical model for purchase timing that exploits information
 on the shopping days of households. The model is formulated in a counting
 process framework that counts the recurrent purchases for each household
 over (calendar) time. In our empirical application of yogurt and detergent
 purchases from the ERIM1 database, we show that calendar time effects and
 regular and non-shopping days are important features to include in models
 for purchase-timing behavior. We find, for instance, that for these
 product categories the probability of purchasing is 50-60% higher on
 Saturdays and 70% higher on regular shopping days. We highlight the
 managerial implications of these model features by simulating some
 promotional actions. 
Journal: Journal of Applied Statistics 
Pages: 1931-1945 
Issue: 11 
Volume: 37 
Year: 2010 
Keywords: purchase timing, regular shopping days, non-shopping days, counting process, mixed proportional hazard, 
X-DOI: 10.1080/02664760903186064 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903186064
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:11:p:1931-1945




Template-Type: ReDIF-Article 1.0
Author-Name: Rosa Bernardini Papalia 
Author-X-Name-First: Rosa Bernardini 
Author-X-Name-Last: Papalia 
Title: Data disaggregation procedures within a maximum entropy framework 
Abstract:
  The aim of this paper is to formulate an
 analytical-informational-theoretical approach which, given the incomplete
 nature of the available micro-level data, can be used to provide
 disaggregated values of a given variable. A functional relationship
 between the variable to be disaggregated and the available
 variables/indicators at the area level is specified through a combination
 of different macro- and micro-data sources. Data disaggregation is
 accomplished by considering two different cases. In the first case,
 sub-area level information on the variable of interest is available, and a
 generalized maximum entropy approach is employed to estimate the optimal
 disaggregate model. In the second case, we assume that the sub-area level
 information is partial and/or incomplete, and we estimate the model on a
 smaller scale by developing a generalized cross-entropy-based formulation.
 The proposed spatial-disaggregation approach is used in relation to an
 Italian data set in order to compute the value-added per manufacturing
 sector of local labour systems within the Umbria region, by combining the
 available micro/macro-level data and by formulating a suitable set of
 constraints for the optimization problem in the presence of errors in
 micro-aggregates. 
Journal: Journal of Applied Statistics 
Pages: 1947-1959 
Issue: 11 
Volume: 37 
Year: 2010 
Keywords: data disaggregation, maximum entropy, cross-entropy, 
X-DOI: 10.1080/02664760903199489 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903199489
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:11:p:1947-1959




Template-Type: ReDIF-Article 1.0
Author-Name: Martin Tanco 
Author-X-Name-First: Martin 
Author-X-Name-Last: Tanco 
Author-Name: Elisabeth Viles 
Author-X-Name-First: Elisabeth 
Author-X-Name-Last: Viles 
Author-Name: Maria Jesus Alvarez 
Author-X-Name-First: Maria 
Author-X-Name-Last: Jesus Alvarez 
Author-Name: Laura Ilzarbe 
Author-X-Name-First: Laura 
Author-X-Name-Last: Ilzarbe 
Title: Why is not design of experiments widely used by engineers in Europe? 
Abstract:
  An extensive literature review was carried out to detect why design of
 experiments (DoE) is not widely used among engineers in Europe. Once 16
 main barriers were identified, a survey was carried out to obtain
 first-hand information about the significance of each. We obtained 101
 responses from academics, consultants and practitioners interested in DoE.
 A statistical analysis of the survey is introduced, including: (a) a
 ranking of the barriers, (b) grouping of barriers using factorial
 analysis, (c) differences between characteristics of respondents. This
 exploratory analysis showed that the main barriers that hinder the
 widespread use of DoE are low managerial commitment and engineers' general
 weakness in statistics. Once the barriers were classified, the most
 important resultant group was that related to business barriers. 
Journal: Journal of Applied Statistics 
Pages: 1961-1977 
Issue: 12 
Volume: 37 
Year: 2010 
Keywords: barriers, design of experiments, engineers, industry, survey, 
X-DOI: 10.1080/02664760903207308 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903207308
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:12:p:1961-1977




Template-Type: ReDIF-Article 1.0
Author-Name: Manuel Galea 
Author-X-Name-First: Manuel 
Author-X-Name-Last: Galea 
Author-Name: David Cademartori 
Author-X-Name-First: David 
Author-X-Name-Last: Cademartori 
Author-Name: Filidor Vilca 
Author-X-Name-First: Filidor 
Author-X-Name-Last: Vilca 
Title: The structural Sharpe model under t-distributions 
Abstract:
  In this paper we consider Sharpe's single-index model or Sharpe's model,
 by assuming that the returns obtained follow a multivariate t elliptical
 distribution. Also, given that the returns of the market are not
 observable, the statistical analysis was made in the context of an
 errors-in-variables model. In order to analyze the sensibility to possible
 outliers and/or atypical returns of the maximum likelihood estimators the
 local influence method [10] was implemented. The results are illustrated
 by using a set of shares of companies belonging to the Chilean Stock
 Market. The main conclusion is that the t model with small degrees of
 freedom is able to incorporate possible outliers and influential returns
 in the data. 
Journal: Journal of Applied Statistics 
Pages: 1979-1990 
Issue: 12 
Volume: 37 
Year: 2010 
Keywords: diagnostics, t-distribution, errors-in-variables models, portfolios, Sharpe model, 
X-DOI: 10.1080/02664760903207316 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903207316
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:12:p:1979-1990




Template-Type: ReDIF-Article 1.0
Author-Name: Yafen Liu 
Author-X-Name-First: Yafen 
Author-X-Name-Last: Liu 
Author-Name: Zhen He 
Author-X-Name-First: Zhen 
Author-X-Name-Last: He 
Author-Name: M. Shamsuzzaman 
Author-X-Name-First: M. 
Author-X-Name-Last: Shamsuzzaman 
Author-Name: Zhang Wu 
Author-X-Name-First: Zhang 
Author-X-Name-Last: Wu 
Title: A combined control scheme for monitoring the frequency and size of an attribute event 
Abstract:
  A traffic accident can be considered as an example of the attribute
 events, and the number of the injured in each accident is called the event
 size. Some control charts have been developed for monitoring either the
 time interval (T) between the occurrences of an event or the event size
 (C) in each occurrence. This article studies the statistical monitoring of
 the attribute events in which T and C are monitored simultaneously and C
 is an integer. Essentially, it integrates a T chart and a C chart, and is
 therefore referred to as a T&C scheme. Our studies show that the new chart
 is more effective than an individual T chart or C chart for detecting the
 out-of-control status of the event, in particular for detecting downward
 shifts (sparse occurrence and/or small size). Another desirable feature of
 the T&C scheme is that its detection effectiveness is more invariable
 against different types of shifts (i.e. T shift, C shift and joint shift
 in T&C) compared with an individual T or C chart. The improvement in
 performance is achieved due to the simultaneous monitoring of T and C. The
 T&C scheme can be applied in manufacturing systems and especially in
 non-manufacturing sectors (e.g. supply chain management, health care
 industry, disaster management and security control). 
Journal: Journal of Applied Statistics 
Pages: 1991-2013 
Issue: 12 
Volume: 37 
Year: 2010 
Keywords: quality control, statistical process control, control chart, time between events, attribute event, 
X-DOI: 10.1080/02664760903207324 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903207324
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:12:p:1991-2013




Template-Type: ReDIF-Article 1.0
Author-Name: Gavin Ross 
Author-X-Name-First: Gavin 
Author-X-Name-Last: Ross 
Author-Name: C. Sarada 
Author-X-Name-First: C. 
Author-X-Name-Last: Sarada 
Title: Reparameterization of nonlinear statistical models: a case study 
Abstract:
  The importance of finding appropriate parameterizations for nonlinear
 statistical models is highlighted. The purpose of this paper is to explore
 the principles of reparameterization, using an example from real data. It
 is shown that stable parameterizations allow likelihood-based confidence
 intervals to be computed. Further, it is noted that the choice of error
 distribution may seriously affect the estimates and confidence intervals
 of quantities of interest. The influence of each observation on the
 estimation of each parameter is displayed for each error model.
 Multidimensional likelihood contours may be displayed pairwise using
 profile likelihood computations. 
Journal: Journal of Applied Statistics 
Pages: 2015-2026 
Issue: 12 
Volume: 37 
Year: 2010 
Keywords: aphid population growth model, normal distributed errors, Poisson distributed errors, profile likelihoods, reparameterization, stable parameters, 
X-DOI: 10.1080/02664760903207332 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903207332
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:12:p:2015-2026




Template-Type: ReDIF-Article 1.0
Author-Name: Jaromir Antoch 
Author-X-Name-First: Jaromir 
Author-X-Name-Last: Antoch 
Author-Name: Lubos Prchal 
Author-X-Name-First: Lubos 
Author-X-Name-Last: Prchal 
Author-Name: Maria Rosaria De Rosa 
Author-X-Name-First: Maria 
Author-X-Name-Last: Rosaria De Rosa 
Author-Name: Pascal Sarda 
Author-X-Name-First: Pascal 
Author-X-Name-Last: Sarda 
Title: Electricity consumption prediction with functional linear regression using spline estimators 
Abstract:
  A functional linear regression model linking observations of a functional
 response variable with measurements of an explanatory functional variable
 is considered. This model serves to analyse a real data set describing
 electricity consumption in Sardinia. The interest lies in predicting
 either oncoming weekends' or oncoming weekdays' consumption, provided
 actual weekdays' consumption is known. A B-spline estimator of the
 functional parameter is used. Selected computational issues are addressed
 as well. 
Journal: Journal of Applied Statistics 
Pages: 2027-2041 
Issue: 12 
Volume: 37 
Year: 2010 
Keywords: functional linear regression, functional response, ARH(1), penalized least squares, B-spline, electricity consumption in Sardinia, 
X-DOI: 10.1080/02664760903214395 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903214395
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:12:p:2027-2041




Template-Type: ReDIF-Article 1.0
Author-Name: Chin Wen Cheong 
Author-X-Name-First: Chin Wen 
Author-X-Name-Last: Cheong 
Title: Optimal choice of sample fraction in univariate financial tail index estimation 
Abstract:
  This study introduces a technique to estimate the Pareto distribution of
 the stock exchange index by using the maximum-likelihood Hill estimator.
 Recursive procedures based on the goodness-of-fit statistics are used to
 determine the optimal threshold fraction of extreme values to be included
 in tail estimation. These procedures are applied to three indices in the
 Malaysian stock market which included the consideration of a drastic
 economic event such as the Asian financial crisis. The empirical results
 evidenced alternating varying behavior of heavy-tailed distributions in
 the regimes for both upper and lower tails. 
Journal: Journal of Applied Statistics 
Pages: 2043-2056 
Issue: 12 
Volume: 37 
Year: 2010 
Keywords: heavy-tailed distribution, Hill estimator, goodness-of-fit test, structural change, 
X-DOI: 10.1080/02664760903214403 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903214403
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:12:p:2043-2056




Template-Type: ReDIF-Article 1.0
Author-Name: Kung-Jong Lui 
Author-X-Name-First: Kung-Jong 
Author-X-Name-Last: Lui 
Author-Name: Kuang-Chao Chang 
Author-X-Name-First: Kuang-Chao 
Author-X-Name-Last: Chang 
Title: Notes on odds ratio estimation for a randomized clinical trial with noncompliance and missing outcomes 
Abstract:
  The odds ratio (OR) has been recommended elsewhere to measure the
 relative treatment efficacy in a randomized clinical trial (RCT), because
 it possesses a few desirable statistical properties. In practice, it is
 not uncommon to come across an RCT in which there are patients who do not
 comply with their assigned treatments and patients whose outcomes are
 missing. Under the compound exclusion restriction, latent ignorable and
 monotonicity assumptions, we derive the maximum likelihood estimator (MLE)
 of the OR and apply Monte Carlo simulation to compare its performance with
 those of the other two commonly used estimators for missing completely at
 random (MCAR) and for the intention-to-treat (ITT) analysis based on
 patients with known outcomes, respectively. We note that both estimators
 for MCAR and the ITT analysis may produce a misleading inference of the OR
 even when the relative treatment effect is equal. We further derive three
 asymptotic interval estimators for the OR, including the interval
 estimator using Wald's statistic, the interval estimator using the
 logarithmic transformation, and the interval estimator using an ad hoc
 procedure of combining the above two interval estimators. On the basis of
 a Monte Carlo simulation, we evaluate the finite-sample performance of
 these interval estimators in a variety of situations. Finally, we use the
 data taken from a randomized encouragement design studying the effect of
 flu shots on the flu-related hospitalization rate to illustrate the use of
 the MLE and the asymptotic interval estimators for the OR
 developed&#xa0;here. 
Journal: Journal of Applied Statistics 
Pages: 2057-2071 
Issue: 12 
Volume: 37 
Year: 2010 
Keywords: odds ratio, noncompliance, missing outcomes, interval estimators, ITT analysis, 
X-DOI: 10.1080/02664760903214411 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903214411
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:12:p:2057-2071




Template-Type: ReDIF-Article 1.0
Author-Name: M. P. Gadre 
Author-X-Name-First: M. P. 
Author-X-Name-Last: Gadre 
Author-Name: K. A. Joshi 
Author-X-Name-First: K. A. 
Author-X-Name-Last: Joshi 
Author-Name: R. N. Rattihalli 
Author-X-Name-First: R. N. 
Author-X-Name-Last: Rattihalli 
Title: A side sensitive modified group runs control chart to detect shifts in the process mean 
Abstract:
  Gadre and Rattihalli [5] have introduced the Modified Group Runs (MGR)
 control chart to identify the increases in fraction non-conforming and to
 detect shifts in the process mean. The MGR chart reduces the
 out-of-control average time-to-signal (ATS), as compared with most of the
 well-known control charts. In this article, we develop the Side Sensitive
 Modified Group Runs (SSMGR) chart to detect shifts in the process mean.
 With the help of numerical examples, it is illustrated that the SSMGR
 chart performs better than the Shewhart's X chart, the synthetic chart
 [12], the Group Runs chart [4], the Side Sensitive Group Runs chart [6],
 as well as the MGR chart [5]. In some situations it is also superior to
 the Cumulative Sum chart p9] and the exponentially weighed moving average
 chart [10]. In the steady state also, its performance is better than the
 above charts. 
Journal: Journal of Applied Statistics 
Pages: 2073-2087 
Issue: 12 
Volume: 37 
Year: 2010 
Keywords: average time-to-signal, CRL chart, EWMA chart, GR chart, MGR chart, SSGR chart, steady-state ATS, synthetic chart, 
X-DOI: 10.1080/02664760903222190 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903222190
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:12:p:2073-2087




Template-Type: ReDIF-Article 1.0
Author-Name: Marianne Frisen 
Author-X-Name-First: Marianne 
Author-X-Name-Last: Frisen 
Author-Name: Eva Andersson 
Author-X-Name-First: Eva 
Author-X-Name-Last: Andersson 
Author-Name: Linus Schioler 
Author-X-Name-First: Linus 
Author-X-Name-Last: Schioler 
Title: Evaluation of multivariate surveillance 
Abstract:
  Multivariate surveillance is of interest in many areas such as industrial
 production, bioterrorism detection, spatial surveillance, and financial
 transaction strategies. Some of the suggested approaches to multivariate
 surveillance have been multivariate counterparts to the univariate
 Shewhart, EWMA, and CUSUM methods. Our emphasis is on the special
 challenges of evaluating multivariate surveillance methods. Some new
 measures are suggested and the properties of several measures are
 demonstrated by applications to various situations. It is demonstrated
 that zero-state and steady-state ARL, which are widely used in univariate
 surveillance, should be used with care in multivariate surveillance. 
Journal: Journal of Applied Statistics 
Pages: 2089-2100 
Issue: 12 
Volume: 37 
Year: 2010 
Keywords: average run length, EWMA, false alarms, FDR, performance metrics, predictive value, steady state, zero state, 
X-DOI: 10.1080/02664760903222208 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903222208
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:12:p:2089-2100




Template-Type: ReDIF-Article 1.0
Author-Name: Rosaria Lombardo 
Author-X-Name-First: Rosaria 
Author-X-Name-Last: Lombardo 
Author-Name: Eric Beh 
Author-X-Name-First: Eric 
Author-X-Name-Last: Beh 
Title: Simple and multiple correspondence analysis for ordinal-scale variables using orthogonal polynomials 
Abstract:
  Correspondence analysis (CA) has gained a reputation for being a very
 useful statistical technique for determining the nature of association
 between two or more categorical variables. For simple and multiple CA, the
 singular value decomposition (SVD) is the primary tool used and allows the
 user to construct a low-dimensional space to visualize this association.
 As an alternative to SVD, one may consider the bivariate moment
 decomposition (BMD), a method of decomposition that involves using
 orthogonal polynomials to reflect the structure of ordered categorical
 responses. When the features of BMD are combined with SVD, a hybrid
 decomposition (HD) is formed. The aim of this paper is to show the
 applicability of HD when performing simple and multiple CA. 
Journal: Journal of Applied Statistics 
Pages: 2101-2116 
Issue: 12 
Volume: 37 
Year: 2010 
Keywords: multiple correspondence analysis, ordinal-scale variables, singular value decomposition, bivariate moment decomposition, orthogonal polynomials, hybrid decomposition, 
X-DOI: 10.1080/02664760903247692 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903247692
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:12:p:2101-2116




Template-Type: ReDIF-Article 1.0
Author-Name: Tobias Verbeke 
Author-X-Name-First: Tobias 
Author-X-Name-Last: Verbeke 
Title: BOOK REVIEW 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 2117-2118 
Issue: 12 
Volume: 37 
Year: 2010 
X-DOI: 10.1080/02664760902931346 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760902931346
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:12:p:2117-2118




Template-Type: ReDIF-Article 1.0
Author-Name: Faisel Yunus 
Author-X-Name-First: Faisel 
Author-X-Name-Last: Yunus 
Title: Statistics Using SPSS: An Integrative Approach, second edition 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 2119-2120 
Issue: 12 
Volume: 37 
Year: 2010 
X-DOI: 10.1080/02664760903075515 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903075515
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:12:p:2119-2120




Template-Type: ReDIF-Article 1.0
Author-Name: A. C. Brooms 
Author-X-Name-First: A. C. 
Author-X-Name-Last: Brooms 
Title: Data Manipulation with R 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 2121-2121 
Issue: 12 
Volume: 37 
Year: 2010 
X-DOI: 10.1080/02664760903075531 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903075531
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:37:y:2010:i:12:p:2121-2121




Template-Type: ReDIF-Article 1.0
Author-Name: Robert Aykroyd 
Author-X-Name-First: Robert 
Author-X-Name-Last: Aykroyd 
Title: Editorial 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 1-1 
Issue: 1 
Volume: 38 
Year: 2011 
X-DOI: 10.1080/02664763.2011.537062 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664763.2011.537062
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:1:p:1-1




Template-Type: ReDIF-Article 1.0
Author-Name: Laura Gosoniu 
Author-X-Name-First: Laura 
Author-X-Name-Last: Gosoniu 
Author-Name: Penelope Vounatsou 
Author-X-Name-First: Penelope 
Author-X-Name-Last: Vounatsou 
Title: Non-stationary partition modeling of geostatistical data for malaria risk mapping 
Abstract:
  The most common assumption in geostatistical modeling of malaria is
 stationarity, that is spatial correlation is a function of the separation
 vector between locations. However, local factors (environmental or
 human-related activities) may influence geographical dependence in malaria
 transmission differently at different locations, introducing
 non-stationarity. Ignoring this characteristic in malaria spatial modeling
 may lead to inaccurate estimates of the standard errors for both the
 covariate effects and the predictions. In this paper, a model based on
 random Voronoi tessellation that takes into account non-stationarity was
 developed. In particular, the spatial domain was partitioned into
 sub-regions (tiles), a stationary spatial process was assumed within each
 tile and between-tile correlation was taken into account. The number and
 configuration of the sub-regions are treated as random parameters in the
 model and inference is made using reversible jump Markov chain Monte Carlo
 simulation. This methodology was applied to analyze malaria survey data
 from Mali and to produce a country-level smooth map of malaria risk. 
Journal: Journal of Applied Statistics 
Pages: 3-13 
Issue: 1 
Volume: 38 
Year: 2011 
Keywords: Bayesian inference, geostatistics, kriging, malaria risk, prevalence data, non-stationarity, reversible jump Markov chain Monte Carlo, Voronoi tessellation, 
X-DOI: 10.1080/02664760903008961 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903008961
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:1:p:3-13




Template-Type: ReDIF-Article 1.0
Author-Name: Chengjie Xiong 
Author-X-Name-First: Chengjie 
Author-X-Name-Last: Xiong 
Author-Name: Gerald van Belle 
Author-X-Name-First: Gerald 
Author-X-Name-Last: van Belle 
Author-Name: Kejun Zhu 
Author-X-Name-First: Kejun 
Author-X-Name-Last: Zhu 
Author-Name: J. Philip Miller 
Author-X-Name-First: J. Philip 
Author-X-Name-Last: Miller 
Author-Name: John Morris 
Author-X-Name-First: John 
Author-X-Name-Last: Morris 
Title: A unified approach of meta-analysis: application to an antecedent biomarker study in Alzheimer's disease 
Abstract:
  This article provides a unified methodology of meta-analysis that
 synthesizes medical evidence by using both available individual patient
 data (IPD) and published summary statistics within the framework of
 likelihood principle. Most up-to-date scientific evidence on medicine is
 crucial information not only to consumers but also to decision makers, and
 can only be obtained when existing evidence from the literature and the
 most recent IPD are optimally synthesized. We propose a general linear
 mixed effects model to conduct meta-analyses when IPD are only available
 for some of the studies and summary statistics have to be used for the
 rest of the studies. Our approach includes both the traditional
 meta-analyses in which only summary statistics are available for all
 studies and the other extreme case in which IPD are available for all
 studies as special examples. We implement the proposed model with
 statistical procedures from standard computing packages. We provide
 measures of heterogeneity based on the proposed model. Finally, we
 demonstrate the proposed methodology through a real-life example by
 studying the cerebrospinal fluid biomarkers to identify individuals with a
 high risk of developing Alzheimer's disease when they are still
 cognitively normal. 
Journal: Journal of Applied Statistics 
Pages: 15-27 
Issue: 1 
Volume: 38 
Year: 2011 
Keywords: confidence interval, general linear mixed effects model, heterogeneity index, individual patient data, maximum likelihood estimate (MLE), meta-analyses, 
X-DOI: 10.1080/02664760903008987 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903008987
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:1:p:15-27




Template-Type: ReDIF-Article 1.0
Author-Name: Fahimah Al-Awadhi 
Author-X-Name-First: Fahimah 
Author-X-Name-Last: Al-Awadhi 
Author-Name: Merrilee Hurn 
Author-X-Name-First: Merrilee 
Author-X-Name-Last: Hurn 
Author-Name: Christopher Jennison 
Author-X-Name-First: Christopher 
Author-X-Name-Last: Jennison 
Title: Three-dimensional Bayesian image analysis and confocal microscopy 
Abstract:
  We report methods for tackling a challenging three-dimensional (3D)
 deconvolution problem arising in confocal microscopy. We fit a marked
 point process model for the set of cells in the sample using Bayesian
 methods; this produces automatic or semi-automatic segmentations showing
 the shape, size, orientation and spatial arrangement of objects in a
 sample. Importantly, the methods also provide measures of uncertainty
 about size and shape attributes. The 3D problem is considerably more
 demanding computationally than the two-dimensional analogue considered in
 Al-Awadhi et al. [2] due to the much larger data set and
 higher-dimensional descriptors for objects in the image. In using Markov
 chain Monte Carlo simulation to draw samples from the posterior
 distribution, substantial computing effort can be consumed simply in
 reaching the main area of support of the posterior distribution. For more
 effective use of computation time, we use morphological techniques to help
 construct an initial typical image under the posterior distribution. 
Journal: Journal of Applied Statistics 
Pages: 29-46 
Issue: 1 
Volume: 38 
Year: 2011 
Keywords: Bayesian statistics, confocal microscopy, image analysis, Markov chain Monte Carlo methods, mathematical morphology, object recognition, stochastic simulation, three-dimensional deconvolution, 
X-DOI: 10.1080/02664760903117747 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903117747
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:1:p:29-46




Template-Type: ReDIF-Article 1.0
Author-Name: Hakan Demirtas 
Author-X-Name-First: Hakan 
Author-X-Name-Last: Demirtas 
Author-Name: Donald Hedeker 
Author-X-Name-First: Donald 
Author-X-Name-Last: Hedeker 
Title: Generating multivariate continuous data via the notion of nearest neighbors 
Abstract:
  Taylor and Thompson [15] introduced a clever algorithm for simulating
 multivariate continuous data sets that resemble the original data. Their
 approach is predicated upon determining a few nearest neighbors of a given
 row of data through a statistical distance measure, and subsequently
 combining the observations by stochastic multipliers that are drawn from a
 uniform distribution to generate simulated data that essentially maintain
 the original data trends. The newly drawn values are assumed to come from
 the same underlying hypothetical process that governs the mechanism of how
 the data are formed. This technique is appealing in that no density
 estimation is required. We believe that this data-based simulation method
 has substantial potential in multivariate data generation due to the local
 nature of the generation scheme, which does not have strict specification
 requirements as in most other algorithms. In this work, we provide two R
 routines: one has a built-in simulator for finding the optimal number of
 nearest neighbors for any given data set, and the other generates
 pseudo-random data using this optimal number. 
Journal: Journal of Applied Statistics 
Pages: 47-55 
Issue: 1 
Volume: 38 
Year: 2011 
Keywords: simulation, random number generation, density estimation, bootstrap, nearest neighbors, 
X-DOI: 10.1080/02664760903229260 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903229260
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:1:p:47-55




Template-Type: ReDIF-Article 1.0
Author-Name: Vicente Cancho 
Author-X-Name-First: Vicente 
Author-X-Name-Last: Cancho 
Author-Name: Josemar Rodrigues 
Author-X-Name-First: Josemar 
Author-X-Name-Last: Rodrigues 
Author-Name: Mario de Castro 
Author-X-Name-First: Mario 
Author-X-Name-Last: de Castro 
Title: A flexible model for survival data with a cure rate: a Bayesian approach 
Abstract:
  In this paper we deal with a Bayesian analysis for right-censored
 survival data suitable for populations with a cure rate. We consider a
 cure rate model based on the negative binomial distribution, encompassing
 as a special case the promotion time cure model. Bayesian analysis is
 based on Markov chain Monte Carlo (MCMC) methods. We also present some
 discussion on model selection and an illustration with a real
 data&#xa0;set. 
Journal: Journal of Applied Statistics 
Pages: 57-70 
Issue: 1 
Volume: 38 
Year: 2011 
Keywords: survival analysis, cure rate models, long-term survival models, negative binomial distribution, Bayesian analysis, piecewise exponential distribution, Weibull distribution, 
X-DOI: 10.1080/02664760903254052 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903254052
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:1:p:57-70




Template-Type: ReDIF-Article 1.0
Author-Name: Guglielmo Maria Caporale 
Author-X-Name-First: Guglielmo Maria 
Author-X-Name-Last: Caporale 
Author-Name: Luis Gil-Alana 
Author-X-Name-First: Luis 
Author-X-Name-Last: Gil-Alana 
Title: Fractional integration and impulse responses: a bivariate application to real output in the USA and four Scandinavian countries 
Abstract:
  This article analyzes impulse response functions in the context of vector
 fractionally integrated time series. We derive analytically the
 restrictions required to identify the structural-form system. As an
 illustration of the recommended procedure, we carry out an empirical
 application based on a bivariate system including real output in the USA
 and, in turn, in one of the four Scandinavian countries (Denmark, Finland,
 Norway, and Sweden). The empirical results appear to be sensitive, to some
 extent, to the specification of the stochastic process driving the
 disturbances, but generally a positive shock to US output has a positive
 effect on the Scandinavian countries, which tend to disappear in the long
 run. 
Journal: Journal of Applied Statistics 
Pages: 71-85 
Issue: 1 
Volume: 38 
Year: 2011 
Keywords: long memory, multivariate time series, impulse response functions, 
X-DOI: 10.1080/02664760903254060 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903254060
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:1:p:71-85




Template-Type: ReDIF-Article 1.0
Author-Name: Aurelia De Araujo Rodrigues 
Author-X-Name-First: Aurelia 
Author-X-Name-Last: De Araujo Rodrigues 
Author-Name: Eugenio Kahn Epprecht 
Author-X-Name-First: Eugenio Kahn 
Author-X-Name-Last: Epprecht 
Author-Name: Maysa Sacramento De Magalhaes 
Author-X-Name-First: Maysa Sacramento 
Author-X-Name-Last: De Magalhaes 
Title: Double-sampling control charts for attributes 
Abstract:
  In this article, we propose a double-sampling (DS) np control chart. We
 assume that the time interval between samples is fixed. The choice of the
 design parameters of the proposed chart and also comparisons between
 charts are based on statistical properties, such as the average number of
 samples until a signal. The optimal design parameters of the proposed
 control chart are obtained. During the optimization procedure, constraints
 are imposed on the in-control average sample size and on the in-control
 average run length. In this way, required statistical properties can be
 assured. Varying some input parameters, the proposed DS np chart is
 compared with the single-sampling np chart, variable sample size np chart,
 CUSUM np and EWMA np charts. The comparisons are carried out considering
 the optimal design for each chart. For the ranges of parameters
 considered, the DS scheme is the fastest one for the detection of
 increases of 100% or more in the fraction non-conforming and, moreover,
 the DS np chart is easy to operate. 
Journal: Journal of Applied Statistics 
Pages: 87-112 
Issue: 1 
Volume: 38 
Year: 2011 
Keywords: double sampling, np charts, statistical design, EWMA, CUSUM, variable sample size, 
X-DOI: 10.1080/02664760903266007 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903266007
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:1:p:87-112




Template-Type: ReDIF-Article 1.0
Author-Name: Li-Chu Chien 
Author-X-Name-First: Li-Chu 
Author-X-Name-Last: Chien 
Title: A robust diagnostic plot for explanatory variables under model mis-specification 
Abstract:
  A typical added variable plot is a commonly used plot in assessing the
 accuracy of a normal linear model. This plot is often used to evaluate the
 effect of adding an explanatory variable into the model and to detect
 possibly high leverage points or influential observations on the added
 variable. However, this type of plot is generally in doubt, once the
 normal distributional assumptions are violated. In this article, we extend
 the robust likelihood technique introduced by Royall and Tsou [11] to
 propose a robust added variable plot. The validity of this diagnostic plot
 requires no knowledge of the true underlying distributions so long as
 their second moments exist. The usefulness of the robust graphical
 approach is demonstrated through a few illustrations and simulations. 
Journal: Journal of Applied Statistics 
Pages: 113-126 
Issue: 1 
Volume: 38 
Year: 2011 
Keywords: added variable plot, high leverage points, influential data, adjusted normal regression, 
X-DOI: 10.1080/02664760903271940 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903271940
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:1:p:113-126




Template-Type: ReDIF-Article 1.0
Author-Name: Jeremy Balka 
Author-X-Name-First: Jeremy 
Author-X-Name-Last: Balka 
Author-Name: Anthony Desmond 
Author-X-Name-First: Anthony 
Author-X-Name-Last: Desmond 
Author-Name: Paul McNicholas 
Author-X-Name-First: Paul 
Author-X-Name-Last: McNicholas 
Title: Bayesian and likelihood inference for cure rates based on defective inverse Gaussian regression models 
Abstract:
  Failure time models are considered when there is a subpopulation of
 individuals that is immune, or not susceptible, to an event of interest.
 Such models are of considerable interest in biostatistics. The most common
 approach is to postulate a proportion p of immunes or long-term survivors
 and to use a mixture model [5]. This paper introduces the defective
 inverse Gaussian model as a cure model and examines the use of the Gibbs
 sampler together with a data augmentation algorithm to study Bayesian
 inferences both for the cured fraction and the regression parameters. The
 results of the Bayesian and likelihood approaches are illustrated on two
 real data sets. 
Journal: Journal of Applied Statistics 
Pages: 127-144 
Issue: 1 
Volume: 38 
Year: 2011 
Keywords: cure rates, defective inverse Gaussian, Gibbs sampler, survival analysis, 
X-DOI: 10.1080/02664760903301127 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903301127
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:1:p:127-144




Template-Type: ReDIF-Article 1.0
Author-Name: E. J. Allen 
Author-X-Name-First: E. J. 
Author-X-Name-Last: Allen 
Author-Name: V. T. Farewell 
Author-X-Name-First: V. T. 
Author-X-Name-Last: Farewell 
Title: The use of variance components for the assessment of outcome measures in rheumatology 
Abstract:
  There is current interest in the development of new or improved outcome
 measures for rheumatological diseases. In the early stages of development,
 attention is usually directed to how well the measure distinguishes
 between patients and whether different observers attach similar values of
 the measure to the same patient. An approach, based on variance
 components, to the assessment of outcome measures is presented. The need
 to assess different aspects of variation associated with a measure is
 stressed. The terms 'observer reliability' and 'agreement' are frequently
 used in the evaluation of measurement instruments, and are often used
 interchangeably. In this paper, we use the terms to refer to different
 concepts assessing different aspects of variation. They are likely to
 correspond well in heterogeneous populations, but not in homogeneous
 populations where reliability will generally be low but agreement may well
 be high. Results from a real patient exercise, designed to study a set of
 tools for assessing myositis outcomes, are used to illustrate the approach
 that examines both reliability and agreement, and the need to evaluate
 both is demonstrated. A new measure of agreement, based on the ratio of
 standard deviations, is presented and inference procedures are discussed.
 To facilitate the interpretation of the combination of measures of
 reliability and agreement, a classification system is proposed that
 provides a summary of the performance of the tools. The approach is
 demonstrated for discrete ordinal and continuous outcomes. 
Journal: Journal of Applied Statistics 
Pages: 145-159 
Issue: 1 
Volume: 38 
Year: 2011 
Keywords: agreement, reliability, variance components, myositis, measurement, 
X-DOI: 10.1080/02664760903301135 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903301135
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:1:p:145-159




Template-Type: ReDIF-Article 1.0
Author-Name: Guillermo de Leon 
Author-X-Name-First: Guillermo 
Author-X-Name-Last: de Leon 
Author-Name: Pere Grima 
Author-X-Name-First: Pere 
Author-X-Name-Last: Grima 
Author-Name: Xavier Tort-Martorell 
Author-X-Name-First: Xavier 
Author-X-Name-Last: Tort-Martorell 
Title: Comparison of normal probability plots and dot plots in judging the significance of effects in two level factorial designs 
Abstract:
  In this article, we present a study carried out to compare the
 effectiveness of the normal probability plot (NPP) and a simple dot plot
 in assessing the significance of the effects in experimental designs with
 factors at two levels (2k-p designs). Several groups of students who had
 just completed a course that covered factorial designs were asked to
 identify the significant effects in a total of 32 situations, 16 of which
 were represented using NPPs and the other 16 using dot plots. Although the
 32 scenarios were said to be different, there were really only 16
 different situations, each of which was represented using the two methods
 to be compared. A simple graphical analysis shows no evidence that there
 is a difference between the two procedures. However, in designs with 16
 runs there are some cases where NPP seems to give slightly
 better&#xa0;results. 
Journal: Journal of Applied Statistics 
Pages: 161-174 
Issue: 1 
Volume: 38 
Year: 2011 
Keywords: normal probability plot, significance of effects, factorial design, teaching statistics, experimental design, 
X-DOI: 10.1080/02664760903301143 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903301143
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:1:p:161-174




Template-Type: ReDIF-Article 1.0
Author-Name: Bin Li 
Author-X-Name-First: Bin 
Author-X-Name-Last: Li 
Author-Name: R. S. Sanderlin 
Author-X-Name-First: R. S. 
Author-X-Name-Last: Sanderlin 
Author-Name: Rebecca Melanson 
Author-X-Name-First: Rebecca 
Author-X-Name-Last: Melanson 
Author-Name: Qingzhao Yu 
Author-X-Name-First: Qingzhao 
Author-X-Name-Last: Yu 
Title: Spatio-temporal analysis of a plant disease in a non-uniform crop: a Monte Carlo approach 
Abstract:
  Identification of the type of disease pattern and spread in a field is
 critical in epidemiological investigations of plant diseases. For example,
 an aggregation pattern of infected plants suggests that, at the time of
 observation, the pathogen is spreading from a proximal source. Conversely,
 a random pattern suggests a lack of spread from a proximal source. Most of
 the existing methods of spatial pattern analysis work with only one
 variety of plant at each location and with uniform genetic disease
 susceptibility across the field. Pecan orchards, used in this study, and
 other orchard crops are usually composed of different varieties with
 different levels of susceptibility to disease. A new measure is suggested
 to characterize the spatio-temporal transmission patterns of disease; a
 Monte Carlo test procedure is proposed to test whether the transmission of
 disease is random or aggregated. In addition, we propose a
 mixed-transmission model, which allows us to quantify the degree of
 aggregation&#xa0;effect. 
Journal: Journal of Applied Statistics 
Pages: 175-182 
Issue: 1 
Volume: 38 
Year: 2011 
Keywords: hypothesis testing, lattice system, Monte Carlo, spatial, spatio-temporal analysis, 
X-DOI: 10.1080/02664760903301150 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903301150
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:1:p:175-182




Template-Type: ReDIF-Article 1.0
Author-Name: J. A. A. Andrade 
Author-X-Name-First: J. A. A. 
Author-X-Name-Last: Andrade 
Author-Name: J. P. Gosling 
Author-X-Name-First: J. P. 
Author-X-Name-Last: Gosling 
Title: Predicting rainy seasons: quantifying the beliefs of prophets 
Abstract:
  In general, meteorologists find it difficult to make seasonal predictions
 in the north-east region of Brazil due to the contrasting atmospheric
 phenomena that take place there. The rain prophets claim to be able to
 predict the seasonal weather by observing the behavior of nature. Their
 predictions have a strong degree of subjectivity; this makes science
 (especially meteorology) disregard these predictions, which could be a
 relevant source of information for prediction models. In this article, we
 regard the prophets' knowledge from a subjectivist point of view: we apply
 elicitation of expert knowledge techniques to extract their opinions and
 convert them into probability densities that represent their predictions
 of forthcoming rainy seasons. 
Journal: Journal of Applied Statistics 
Pages: 183-193 
Issue: 1 
Volume: 38 
Year: 2011 
Keywords: Brazilian climate, elicitation, Kumaraswamy distribution, rain prophets, seasonal weather, 
X-DOI: 10.1080/02664760903301168 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903301168
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:1:p:183-193




Template-Type: ReDIF-Article 1.0
Author-Name: Ahmad Zubaidi Baharumshah 
Author-X-Name-First: Ahmad Zubaidi 
Author-X-Name-Last: Baharumshah 
Author-Name: Nor Aishah Hamzah 
Author-X-Name-First: Nor Aishah 
Author-X-Name-Last: Hamzah 
Author-Name: Shamsul Rijal Muhammad Sabri 
Author-X-Name-First: Shamsul Rijal Muhammad 
Author-X-Name-Last: Sabri 
Title: Inflation uncertainty and economic growth: evidence from the LAD ARCH model 
Abstract:
  In this paper, we combined the panel data and least absolute deviation
 autoregressive conditional heteroscedastic (ARCH) (L1-ARCH) model to infer
 on the relationship between inflation uncertainty and economic growth in
 five emerging market economies. Two interesting findings emerged from the
 analysis; first, we confirmed that the inflation uncertainty has a
 significant and negative effect on economic growth. Second, inflation is
 also an important variable and it is detrimental to economic prospects in
 the fast-growing Association of Southeast Asian Nations (ASEAN) economies.
 All in all, the empirical findings suggest that greater stability in the
 economy may be desirable in order to stimulate economic growth in the
 region. 
Journal: Journal of Applied Statistics 
Pages: 195-206 
Issue: 1 
Volume: 38 
Year: 2011 
Keywords: inflation uncertainty, economic growth, LAD ARCH, 
X-DOI: 10.1080/02664760903406397 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903406397
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:1:p:195-206




Template-Type: ReDIF-Article 1.0
Author-Name: Paul Mielke 
Author-X-Name-First: Paul 
Author-X-Name-Last: Mielke 
Author-Name: Kenneth Berry 
Author-X-Name-First: Kenneth 
Author-X-Name-Last: Berry 
Author-Name: Janis Johnston 
Author-X-Name-First: Janis 
Author-X-Name-Last: Johnston 
Title: Robustness without rank order statistics 
Abstract:
  An alternative to conventional rank tests based on a Euclidean distance
 analysis space is described. Comparisons based on exact probability values
 among classical two-sample t-tests and the Wilcoxon-Mann-Whitney test
 illustrate the advantages of the Euclidean distance analysis space
 alternative. 
Journal: Journal of Applied Statistics 
Pages: 207-214 
Issue: 1 
Volume: 38 
Year: 2011 
Keywords: analysis space, Euclidean distance, rank-order statistics, robustness, 
X-DOI: 10.1080/02664760903406439 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664760903406439
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:1:p:207-214




Template-Type: ReDIF-Article 1.0
Author-Name: S. W. Human 
Author-X-Name-First: S. W. 
Author-X-Name-Last: Human 
Author-Name: P. Kritzinger 
Author-X-Name-First: P. 
Author-X-Name-Last: Kritzinger 
Author-Name: S. Chakraborti 
Author-X-Name-First: S. 
Author-X-Name-Last: Chakraborti 
Title: Robustness of the EWMA control chart for individual observations 
Abstract:
  The traditional exponentially weighted moving average (EWMA) chart is one
 of the most popular control charts used in practice today. The in-control
 robustness is the key to the proper design and implementation of any
 control chart, lack of which can render its out-of-control shift detection
 capability almost meaningless. To this end, Borror et al. [5] studied the
 performance of the traditional EWMA chart for the mean for i.i.d. data. We
 use a more extensive simulation study to further investigate the
 in-control robustness (to non-normality) of the three different EWMA
 designs studied by Borror et al. [5]. Our study includes a much wider
 collection of non-normal distributions including light- and heavy-tailed
 and symmetric and asymmetric bi-modal as well as the contaminated normal,
 which is particularly useful to study the effects of outliers. Also, we
 consider two separate cases: (i) when the process mean and standard
 deviation are both known and (ii) when they are both unknown and estimated
 from an in-control Phase I sample. In addition, unlike in the study done
 by Borror et al. [5], the average run-length (ARL) is not used as the sole
 performance measure in our study, we consider the standard deviation of
 the run-length (SDRL), the median run-length (MDRL), and the first and the
 third quartiles as well as the first and the 99th percentiles of the
 in-control run-length distribution for a better overall assessment of the
 traditional EWMA chart's in-control performance. Our findings sound a
 cautionary note to the (over) use of the EWMA chart in practice, at least
 with some types of non-normal data. A summary and recommendations are
 provided. 
Journal: Journal of Applied Statistics 
Pages: 2071-2087 
Issue: 10 
Volume: 38 
Year: 2011 
Keywords: average run-length, boxplot, distribution-free, median run-length, non-parametric, percentile, run-length, simulation, 
X-DOI: 10.1080/02664763.2010.545114 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664763.2010.545114
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:10:p:2071-2087




Template-Type: ReDIF-Article 1.0
Author-Name: Yun Zhao 
Author-X-Name-First: Yun 
Author-X-Name-Last: Zhao 
Author-Name: Andy Lee 
Author-X-Name-First: Andy 
Author-X-Name-Last: Lee 
Author-Name: Kelvin Yau 
Author-X-Name-First: Kelvin 
Author-X-Name-Last: Yau 
Author-Name: Geoffrey McLachlan 
Author-X-Name-First: Geoffrey 
Author-X-Name-Last: McLachlan 
Title: Assessing the adequacy of Weibull survival models: a simulated envelope approach 
Abstract:
  The Weibull proportional hazards model is commonly used for analysing
 survival data. However, formal tests of model adequacy are still lacking.
 It is well known that residual-based goodness-of-fit measures are
 inappropriate for censored data. In this paper, a graphical diagnostic
 plot of Cox-Snell residuals with a simulated envelope added is proposed to
 assess the adequacy of Weibull survival models. Both single component and
 two-component mixture models with random effects are considered for
 recurrent failure time data. The effectiveness of the diagnostic method is
 illustrated using simulated data sets and data on recurrent urinary tract
 infections of elderly women. 
Journal: Journal of Applied Statistics 
Pages: 2089-2097 
Issue: 10 
Volume: 38 
Year: 2011 
Keywords: goodness-of-fit, mixture models, model adequacy, simulated envelope, survival analysis, 
X-DOI: 10.1080/02664763.2010.545115 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664763.2010.545115
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:10:p:2089-2097




Template-Type: ReDIF-Article 1.0
Author-Name: Luis Gutierrez 
Author-X-Name-First: Luis 
Author-X-Name-Last: Gutierrez 
Author-Name: Fernando Quintana 
Author-X-Name-First: Fernando 
Author-X-Name-Last: Quintana 
Author-Name: Dietrich von Baer 
Author-X-Name-First: Dietrich 
Author-X-Name-Last: von Baer 
Author-Name: Claudia Mardones 
Author-X-Name-First: Claudia 
Author-X-Name-Last: Mardones 
Title: Multivariate Bayesian discrimination for varietal authentication of Chilean red wine 
Abstract:
  The process through which food or beverages is verified as complying with
 its label description is called food authentication. We propose to treat
 the authentication process as a classification problem. We consider
 multivariate observations and propose a multivariate Bayesian classifier
 that extends results from the univariate linear mixed model to the
 multivariate case. The model allows for correlation between wine samples
 from the same valley. We apply the proposed model to concentration
 measurements of nine chemical compounds named anthocyanins in 399 samples
 of Chilean red wines of the varieties Merlot, Carmenere and Cabernet
 Sauvignon, vintages 2001-2004. We find satisfactory results, with a
 misclassification error rate based on a leave-one-out cross-validation
 approach of about 4%. The multivariate extension can be generally applied
 to authentication of food and beverages, where it is common to have
 several dependent measurements per sample unit, and it would not be
 appropriate to treat these as independent univariate versions of a common
 model. 
Journal: Journal of Applied Statistics 
Pages: 2099-2109 
Issue: 10 
Volume: 38 
Year: 2011 
Keywords: Bayesian classifier, Gibbs sampling, hierarchical linear models, food authentication, 
X-DOI: 10.1080/02664763.2010.545116 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664763.2010.545116
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:10:p:2099-2109




Template-Type: ReDIF-Article 1.0
Author-Name: Thomas Smith 
Author-X-Name-First: Thomas 
Author-X-Name-Last: Smith 
Author-Name: Cornelius McKenna 
Author-X-Name-First: Cornelius 
Author-X-Name-Last: McKenna 
Title: A weighted test of internal symmetry 
Abstract:
  This study examines extensions of McNemar's Test with multinomial
 responses, and proposes a linear weighting scheme, based on the distance
 of the response change, that is applied to one of these extensions
 (Bowker's test). This weighted version of Bowker's test is then
 appropriate for ordinal response variables. A Monte Carlo simulation was
 conducted to examine the Type I error rate of the weighted Bowker's test
 for a cross-classification table based on a five-category ordinal response
 scale. The weighted Bowker's test was also applied to a data set involving
 change in student attitudes towards mathematics. The results of the
 weighted Bowker's test were compared with the results of Bowker's test
 applied to the same set of data. 
Journal: Journal of Applied Statistics 
Pages: 2111-2118 
Issue: 10 
Volume: 38 
Year: 2011 
Keywords: Bowker's test for internal symmetry, McNemar's test, homogeneity, contingency table, simultaneous inference, 
X-DOI: 10.1080/02664763.2010.545117 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664763.2010.545117
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:10:p:2111-2118




Template-Type: ReDIF-Article 1.0
Author-Name: Rosaria Lombardo 
Author-X-Name-First: Rosaria 
Author-X-Name-Last: Lombardo 
Author-Name: Eric Beh 
Author-X-Name-First: Eric 
Author-X-Name-Last: Beh 
Author-Name: Antonello D'Ambra 
Author-X-Name-First: Antonello 
Author-X-Name-Last: D'Ambra 
Title: Studying the dependence between ordinal-nominal categorical variables via orthogonal polynomials 
Abstract:
  In situations where the structure of one of the variables of a
 contingency table is ordered recent theory involving the augmentation of
 singular vectors and orthogonal polynomials has shown to be applicable for
 performing symmetric and non-symmetric correspondence analysis. Such an
 approach has the advantage of allowing the user to identify the source of
 variation between the categories in terms of components that reflect
 linear, quadratic and higher-order trends. The purpose of this paper is to
 focus on the study of two asymmetrically related variables
 cross-classified to form a two-way contingency table where only one of the
 variables has an ordinal structure. 
Journal: Journal of Applied Statistics 
Pages: 2119-2132 
Issue: 10 
Volume: 38 
Year: 2011 
Keywords: ordered categorical variables, non-symmetric correspondence analysis, bivariate moment decomposition, singular value decomposition, orthogonal polynomials, 
X-DOI: 10.1080/02664763.2010.545118 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664763.2010.545118
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:10:p:2119-2132




Template-Type: ReDIF-Article 1.0
Author-Name: G. Barbato 
Author-X-Name-First: G. 
Author-X-Name-Last: Barbato 
Author-Name: E. M. Barini 
Author-X-Name-First: E. M. 
Author-X-Name-Last: Barini 
Author-Name: G. Genta 
Author-X-Name-First: G. 
Author-X-Name-Last: Genta 
Author-Name: R. Levi 
Author-X-Name-First: R. 
Author-X-Name-Last: Levi 
Title: Features and performance of some outlier detection methods 
Abstract:
  A review of several statistical methods that are currently in use for
 outlier identification is presented, and their performances are compared
 theoretically for typical statistical distributions of experimental data,
 considering values derived from the distribution of extreme order
 statistics as reference terms. A simple modification of a popular, broadly
 used method based upon box-plot is introduced, in order to overcome a
 major limitation concerning sample size. Examples are presented concerning
 exploitation of methods considered on two data sets: a historical one
 concerning evaluation of an astronomical constant performed by a number of
 leading observatories and a substantial database pertaining to an ongoing
 investigation on absolute measurement of gravity acceleration, exhibiting
 peculiar aspects concerning outliers. Some problems related to outlier
 treatment are examined, and the requirement of both statistical analysis
 and expert opinion for proper outlier management is underlined. 
Journal: Journal of Applied Statistics 
Pages: 2133-2149 
Issue: 10 
Volume: 38 
Year: 2011 
Keywords: exclusion rules, order statistics, outliers, robust statistics, statistical test, 
X-DOI: 10.1080/02664763.2010.545119 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664763.2010.545119
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:10:p:2133-2149




Template-Type: ReDIF-Article 1.0
Author-Name: I. Albarran 
Author-X-Name-First: I. 
Author-X-Name-Last: Albarran 
Author-Name: P. J. Alonso 
Author-X-Name-First: P. J. 
Author-X-Name-Last: Alonso 
Author-Name: J. M. Marin 
Author-X-Name-First: J. M. 
Author-X-Name-Last: Marin 
Title: Nonlinear models of disability and age applied to census data 
Abstract:
  It is usually considered that the proportion of handicapped people grows
 with age. Namely, the older the man/woman, the more the level of
 disability he/she suffers. However, empirical evidence shows that this
 assessment is not always true, or at least, it is not true in the Spanish
 population. The study tries to assess the impact of age on disability in
 Spain. Each gender has been treated separately because it can be shown
 that men and women have their own pattern of behaviour. Three different
 methods of estimation have been used to check the link between those
 variables. The results seem to support the idea that the relationship
 among age and the intensity of disability is not always direct. One of the
 concluding remarks in this analysis is that the method of estimation has a
 great incidence in the final results, especially in central ages between
 20 and 80 years old. 
Journal: Journal of Applied Statistics 
Pages: 2151-2163 
Issue: 10 
Volume: 38 
Year: 2011 
Keywords: disability, local estimation, splines, neural networks, BARS, 
X-DOI: 10.1080/02664763.2010.545120 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664763.2010.545120
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:10:p:2151-2163




Template-Type: ReDIF-Article 1.0
Author-Name: R. S. Sparks 
Author-X-Name-First: R. S. 
Author-X-Name-Last: Sparks 
Author-Name: T. Keighley 
Author-X-Name-First: T. 
Author-X-Name-Last: Keighley 
Author-Name: D. Muscatello 
Author-X-Name-First: D. 
Author-X-Name-Last: Muscatello 
Title: Optimal exponentially weighted moving average (EWMA) plans for detecting seasonal epidemics when faced with non-homogeneous negative binomial counts 
Abstract:
  Exponentially weighted moving average (EWMA) plans for non-homogeneous
 negative binomial counts are developed for detecting the onset of seasonal
 disease outbreaks in public health surveillance. These plans are robust to
 changes in the in-control mean and over-dispersion parameter of the
 negative binomial distribution, and therefore are referred to as adaptive
 plans. They differ from the traditional approach of using standardized
 forecast errors based on the normality assumption. Plans are investigated
 in terms of early signal properties for seasonal epidemics. The paper
 demonstrates that the proposed EWMA plan has efficient early detection
 properties that can be useful to epidemiologists for communicable and
 other disease control and is compared with the CUSUM plan. 
Journal: Journal of Applied Statistics 
Pages: 2165-2181 
Issue: 10 
Volume: 38 
Year: 2011 
Keywords: control charts, EWMA, monitoring, negative binomial counts, statistical process control, 
X-DOI: 10.1080/02664763.2010.545184 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664763.2010.545184
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:10:p:2165-2181




Template-Type: ReDIF-Article 1.0
Author-Name: Kerry Patterson 
Author-X-Name-First: Kerry 
Author-X-Name-Last: Patterson 
Author-Name: Hossein Hassani 
Author-X-Name-First: Hossein 
Author-X-Name-Last: Hassani 
Author-Name: Saeed Heravi 
Author-X-Name-First: Saeed 
Author-X-Name-Last: Heravi 
Author-Name: Anatoly Zhigljavsky 
Author-X-Name-First: Anatoly 
Author-X-Name-Last: Zhigljavsky 
Title: Multivariate singular spectrum analysis for forecasting revisions to real-time data 
Abstract:
  Real-time data on national accounts statistics typically undergo an
 extensive revision process, leading to multiple vintages on the same
 generic variable. The time between the publication of the initial and
 final data is a lengthy one and raises the question of how to model and
 forecast the final vintage of data - an issue that dates from seminal
 articles by Mankiw et al. [51], Mankiw and Shapiro [52] and Nordhaus [57].
 To solve this problem, we develop the non-parametric method of
 multivariate singular spectrum analysis (MSSA) for multi-vintage data.
 MSSA is much more flexible than the standard methods of modelling that
 involve at least one of the restrictive assumptions of linearity,
 normality and stationarity. The benefits are illustrated with data on the
 UK index of industrial production: neither the preliminary vintages nor
 the competing models are as accurate as the forecasts using MSSA. 
Journal: Journal of Applied Statistics 
Pages: 2183-2211 
Issue: 10 
Volume: 38 
Year: 2011 
Keywords: non-parametric methods, data revisions, trajectory matrix, reconstruction, Hankelisation, recurrence formula, forecasting, 
X-DOI: 10.1080/02664763.2010.545371 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664763.2010.545371
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:10:p:2183-2211




Template-Type: ReDIF-Article 1.0
Author-Name: Carlos dos Santos 
Author-X-Name-First: Carlos 
Author-X-Name-Last: dos Santos 
Author-Name: Jorge Alberto Achcar 
Author-X-Name-First: Jorge Alberto 
Author-X-Name-Last: Achcar 
Title: A Bayesian analysis for the Block and Basu bivariate exponential distribution in the presence of covariates and censored data 
Abstract:
  In this paper, we introduce a Bayesian Analysis for the Block and Basu
 bivariate exponential distribution using Markov Chain Monte Carlo (MCMC)
 methods and considering lifetimes in presence of covariates and censored
 data. Posterior summaries of interest are obtained using the popular
 WinBUGS software. Numerical illustrations are introduced considering a
 medical data set related to the recurrence times of infection for kidney
 patients and a medical data set related to bone marrow transplantation for
 leukemia. 
Journal: Journal of Applied Statistics 
Pages: 2213-2223 
Issue: 10 
Volume: 38 
Year: 2011 
Keywords: Block and Basu exponential distribution, Bayesian analysis, MCMC methods, covariates, censored lifetimes, 
X-DOI: 10.1080/02664763.2010.545372 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664763.2010.545372
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:10:p:2213-2223




Template-Type: ReDIF-Article 1.0
Author-Name: F. Belloc 
Author-X-Name-First: F. 
Author-X-Name-Last: Belloc 
Author-Name: A. Maruotti 
Author-X-Name-First: A. 
Author-X-Name-Last: Maruotti 
Author-Name: L. Petrella 
Author-X-Name-First: L. 
Author-X-Name-Last: Petrella 
Title: How individual characteristics affect university students drop-out: a semiparametric mixed-effects model for an Italian case study 
Abstract:
  University drop-out is a topic of increasing concern in Italy as well as
 in other countries. In empirical analysis, university drop-out is
 generally measured by means of a binary variable indicating the drop-out
 versus retention. In this paper, we argue that the withdrawal decision is
 one of the possible outcomes of a set of four alternatives: retention in
 the same faculty, drop out, change of faculty within the same university,
 and change of institution. We examine individual-level data collected by
 the administrative offices of &#x201c;Sapienza&#x201d; University of Rome,
 which cover 117&#xa0;072 students enrolling full-time for a 3-year degree
 in the academic years from 2001/2002 to 2006/2007. Relying on a
 non-parametric maximum likelihood approach in a finite mixture context, we
 introduce a multinomial latent effects model with endogeneity that
 accounts for both heterogeneity and omitted covariates. Our estimation
 results show that the decisions to change faculty or university have their
 own peculiarities, thus we suggest that caution should be used in
 interpreting results obtained without modeling all the relevant
 alternatives that students face. 
Journal: Journal of Applied Statistics 
Pages: 2225-2239 
Issue: 10 
Volume: 38 
Year: 2011 
Keywords: University drop-out, mixed effects models, multinomial regression, Italian university system, 
X-DOI: 10.1080/02664763.2010.545373 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664763.2010.545373
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:10:p:2225-2239




Template-Type: ReDIF-Article 1.0
Author-Name: Beibei Guo 
Author-X-Name-First: Beibei 
Author-X-Name-Last: Guo 
Author-Name: Yuehua Wu 
Author-X-Name-First: Yuehua 
Author-X-Name-Last: Wu 
Author-Name: Hong Xie 
Author-X-Name-First: Hong 
Author-X-Name-Last: Xie 
Author-Name: Baiqi Miao 
Author-X-Name-First: Baiqi 
Author-X-Name-Last: Miao 
Title: A segmented regime-switching model with its application to stock market indices 
Abstract:
  This paper evaluates the ability of a Markov regime-switching log-normal
 (RSLN) model to capture the time-varying features of stock return and
 volatility. The model displays a better ability to depict a fat tail
 distribution as compared with using a log-normal model, which means that
 the RSLN model can describe observed market behavior better. Our major
 objective is to explore the capability of the model to capture stock
 market behavior over time. By analyzing the behavior of calibrated
 regime-switching parameters over different lengths of time intervals, the
 change-point concept is introduced and an algorithm is proposed for
 identifying the change-points in the series corresponding to the times
 when there are changes in parameter estimates. This algorithm for
 identifying change-points is tested on the Standard and Poor's 500 monthly
 index data from 1971 to 2008, and the Nikkei 225 monthly index data from
 1984 to 2008. It is evident that the change-points we identify match the
 big events observed in the US stock market and the Japan stock market
 (e.g., the October 1987 stock market crash), and that the segmentations of
 stock index series, which are defined as the periods between
 change-points, match the observed bear-bull market phases. 
Journal: Journal of Applied Statistics 
Pages: 2241-2252 
Issue: 10 
Volume: 38 
Year: 2011 
Keywords: algorithm, change-point, log-normal, log-returns, Markov process, maximum likelihood estimation, segmented regime-switching model, stock market index, time series, 
X-DOI: 10.1080/02664763.2010.545374 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664763.2010.545374
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:10:p:2241-2252




Template-Type: ReDIF-Article 1.0
Author-Name: Stephen Walters 
Author-X-Name-First: Stephen 
Author-X-Name-Last: Walters 
Author-Name: C. Jane Morrell 
Author-X-Name-First: C. 
Author-X-Name-Last: Jane Morrell 
Author-Name: Pauline Slade 
Author-X-Name-First: Pauline 
Author-X-Name-Last: Slade 
Title: Analysing data from a cluster randomized trial (cRCT) in primary care: a case study 
Abstract:
  Health technology assessment often requires the evaluation of
 interventions which are implemented at the level of the health service
 organization unit (e.g. GP practice) for clusters of individuals. In a
 cluster randomized controlled trial (cRCT), clusters of patients are
 randomized; not each patient individually. The majority of statistical
 analyses, in individually RCT, assume that the outcomes on different
 patients are independent. In cRCTs there is doubt about the validity of
 this assumption as the outcomes of patients, in the same cluster, may be
 correlated. Hence, the analysis of data from cRCTs presents a number of
 difficulties. The aim of this paper is to describe the statistical methods
 of adjusting for clustering, in the context of cRCTs. There are
 essentially four approaches to analysing cRCTs: Cluster-level analysis
 using aggregate summary data. Regression analysis with robust standard
 errors. Random-effects/cluster-specific approach.
 Marginal/population-averaged approach. This paper will compare and
 contrast the four approaches, using example data, with binary and
 continuous outcomes, from a cRCT designed to evaluate the effectiveness of
 training Health Visitors in psychological approaches to identify
 post-natal depressive symptoms and support post-natal women compared with
 usual care. The PoNDER Trial randomized 101 clusters (GP practices) and
 collected data on 2659 new mothers with an 18-month follow-up. 
Journal: Journal of Applied Statistics 
Pages: 2253-2269 
Issue: 10 
Volume: 38 
Year: 2011 
Keywords: cluster randomized trial, GLM, marginal model, random-effects model, GEEs, 
X-DOI: 10.1080/02664763.2010.545375 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664763.2010.545375
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:10:p:2253-2269




Template-Type: ReDIF-Article 1.0
Author-Name: Peng Shi 
Author-X-Name-First: Peng 
Author-X-Name-Last: Shi 
Author-Name: Wei Zhang 
Author-X-Name-First: Wei 
Author-X-Name-Last: Zhang 
Title: A copula regression model for estimating firm efficiency in the insurance industry 
Abstract:
  This article considers the estimation of insurers' cost-efficiency in a
 longitudinal context. The current practice ignores the tails of the cost
 distribution, where the most and least efficient insurers belong to. To
 address this issue, we propose a copula regression model to estimate
 insurers' cost frontier. Both time-invariant and time-varying efficiency
 are adapted to this framework and various temporal patterns are
 considered. In our method, flexible distributions are allowed for the
 marginals, and the subject heterogeneity is accommodated through an
 association matrix. Specifically, when fitting to the insurance data, we
 perform a GB2 regression on insurers total cost and employ a t-copula to
 capture their intertemporal dependencies. In doing so, we provide a
 nonlinear formulation of the stochastic panel frontier and the parameters
 are easily estimated by likelihood-based method. Based on a translog cost
 function, the X-efficiency is estimated for US property-casualty insurers.
 An economic analysis provides evidences of economies of scale and the
 consistency between the cost-efficiency and other performance measures. 
Journal: Journal of Applied Statistics 
Pages: 2271-2287 
Issue: 10 
Volume: 38 
Year: 2011 
Keywords: copula, long-tail regression, longitudinal data, GB2, cost-efficiency, 
X-DOI: 10.1080/02664763.2010.545376 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664763.2010.545376
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:10:p:2271-2287




Template-Type: ReDIF-Article 1.0
Author-Name: Yinglei Lai 
Author-X-Name-First: Yinglei 
Author-X-Name-Last: Lai 
Author-Name: Baolin Wu 
Author-X-Name-First: Baolin 
Author-X-Name-Last: Wu 
Author-Name: Hongyu Zhao 
Author-X-Name-First: Hongyu 
Author-X-Name-Last: Zhao 
Title: A permutation test approach to the choice of size k for the nearest neighbors classifier 
Abstract:
  The k nearest neighbors (k-NN) classifier is one of the most popular
 methods for statistical pattern recognition and machine learning. In
 practice, the size k, the number of neighbors used for classification, is
 usually arbitrarily set to one or some other small numbers, or based on
 the cross-validation procedure. In this study, we propose a novel
 alternative approach to decide the size k. Based on a k-NN-based
 multivariate multi-sample test, we assign each k a permutation test based
 Z-score. The number of NN is set to the k with the highest Z-score. This
 approach is computationally efficient since we have derived the formulas
 for the mean and variance of the test statistic under permutation
 distribution for multiple sample groups. Several simulation and real-world
 data sets are analyzed to investigate the performance of our approach. The
 usefulness of our approach is demonstrated through the evaluation of
 prediction accuracies using Z-score as a criterion to select the size k.
 We also compare our approach to the widely used cross-validation
 approaches. The results show that the size k selected by our approach
 yields high prediction accuracies when informative features are used for
 classification, whereas the cross-validation approach may fail in some
 cases. 
Journal: Journal of Applied Statistics 
Pages: 2289-2302 
Issue: 10 
Volume: 38 
Year: 2011 
Keywords: nearest neighbors classifier, number of neighbors, permutation test, prediction accuracy, cross-validation, 
X-DOI: 10.1080/02664763.2010.547565 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664763.2010.547565
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:10:p:2289-2302




Template-Type: ReDIF-Article 1.0
Author-Name: A. Snoussi 
Author-X-Name-First: A. 
Author-X-Name-Last: Snoussi 
Title: SPC for short-run multivariate autocorrelated processes 
Abstract:
  This paper discusses the development of a multivariate control charting
 technique for short-run autocorrelated data manufacturing environment. The
 proposed approach is a combination of the multivariate residual charts for
 autocorrelated data and the multivariate transformation technique for
 i.i.d. process observations of short lengths. The proposed approach
 consists in fitting adequate multivariate time-series model of various
 process outputs and computes the residuals, transforming them into
 standard normal N(0, 1) data and then using standardized data as inputs to
 plot conventional univariate i.i.d. control charts. The objective for
 applying multivariate finite horizon techniques for autocorrelated
 processes is to allow continuous process monitoring, since all process
 outputs are controlled trough the use of a single control chart with
 constant control limits. Throughout simulated examples, it is shown that
 the proposed short-run process monitoring technique provides approximately
 similar shifts detection properties as VAR residual charts. 
Journal: Journal of Applied Statistics 
Pages: 2303-2312 
Issue: 10 
Volume: 38 
Year: 2011 
Keywords: time-series model, univariate statistical process control, multivariate statistical process control, SCC control charts, VAR Residual control charts, V statistics, T2 statistics, average run length, 
X-DOI: 10.1080/02664763.2010.547566 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664763.2010.547566
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:10:p:2303-2312




Template-Type: ReDIF-Article 1.0
Author-Name: R. A. Hubbard 
Author-X-Name-First: R. A. 
Author-X-Name-Last: Hubbard 
Author-Name: X. H. Zhou 
Author-X-Name-First: X. H. 
Author-X-Name-Last: Zhou 
Title: A comparison of non-homogeneous Markov regression models with application to Alzheimer's disease progression 
Abstract:
  Markov regression models are useful tools for estimating risk factor
 effects on transition rates between multiple disease states. Alzheimer's
 disease (AD) is an example of a multi-state disease process where great
 interest lies in identifying risk factors for transition. In this context,
 non-homogeneous models are required because transition rates change as
 subjects age. In this report we propose a non-homogeneous Markov
 regression model that allows for reversible and recurrent states,
 transitions among multiple states between observations, and unequally
 spaced observation times. We conducted simulation studies to compare
 performance of estimators for covariate effects from this model and
 alternative models when the underlying non-homogeneous process was
 correctly specified and under model misspecification. In simulation
 studies, we found that covariate effects were biased if non-homogeneity of
 the disease process was not accounted for. However, estimates from
 non-homogeneous models were robust to misspecification of the form of the
 non-homogeneity. We used our model to estimate risk factors for transition
 to mild cognitive impairment (MCI) and AD in a longitudinal study of
 subjects included in the National Alzheimer's Coordinating Center's
 Uniform Data Set. We found that subjects with MCI affecting multiple
 cognitive domains were significantly less likely to revert to normal
 cognition. 
Journal: Journal of Applied Statistics 
Pages: 2313-2326 
Issue: 10 
Volume: 38 
Year: 2011 
Keywords: Alzheimer's disease, interval censoring, Markov process, mild cognitive impairment, non-homogeneous, panel data, 
X-DOI: 10.1080/02664763.2010.547567 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664763.2010.547567
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:10:p:2313-2326




Template-Type: ReDIF-Article 1.0
Author-Name: Dipankor Coondoo 
Author-X-Name-First: Dipankor 
Author-X-Name-Last: Coondoo 
Author-Name: Amita Majumder 
Author-X-Name-First: Amita 
Author-X-Name-Last: Majumder 
Author-Name: Somnath Chattopadhyay 
Author-X-Name-First: Somnath 
Author-X-Name-Last: Chattopadhyay 
Title: District-level poverty estimation: a proposed method 
Abstract:
  This paper develops a method of estimating micro-level poverty in cases
 where data are scarce. The method is applied to estimate district-level
 poverty using the household level Indian national sample survey data for
 two states, viz., West Bengal and Madhya Pradesh. The method involves
 estimation of state-level poverty indices from the data formed by pooling
 data of all the districts (each time excluding one district) and
 multiplying this poverty vector with a known weight matrix to obtain the
 unknown district-level poverty vector. The proposed method is expected to
 yield reliable estimates at the district level, because the district-level
 estimate is now based on a much larger sample size obtained by pooling
 data of several districts. This method can be an alternative to the
 &#x201c;small area estimation technique&#x201d; for estimating poverty at
 sub-state levels in developing countries. 
Journal: Journal of Applied Statistics 
Pages: 2327-2343 
Issue: 10 
Volume: 38 
Year: 2011 
Keywords: district-level poverty, scarce data, bootstrap, extraneous information, sub-sample estimate, 
X-DOI: 10.1080/02664763.2010.547568 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664763.2010.547568
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:10:p:2327-2343




Template-Type: ReDIF-Article 1.0
Author-Name: Pao-Sheng Shen 
Author-X-Name-First: Pao-Sheng 
Author-X-Name-Last: Shen 
Title: Empirical likelihood ratio with doubly truncated data 
Abstract:
  Doubly truncated data appear in a number of applications, including
 astronomy and survival analysis. For doubly-truncated data, the lifetime T
 is observable only when U&#x2264;T&#x2264;V, where U and V are the
 left-truncated and right-truncated time, respectively. Based on the
 empirical likelihood approach of Zhou [21], we propose a modified EM
 algorithm of Turnbull [19] to construct the interval estimator of the
 distribution function of T. Simulation results indicate that the empirical
 likelihood method can be more efficient than the bootstrap method. 
Journal: Journal of Applied Statistics 
Pages: 2345-2353 
Issue: 10 
Volume: 38 
Year: 2011 
Keywords: likelihood ratio, double truncation, maximization, 
X-DOI: 10.1080/02664763.2010.549216 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664763.2010.549216
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:10:p:2345-2353




Template-Type: ReDIF-Article 1.0
Author-Name: Gregory Wilding 
Author-X-Name-First: Gregory 
Author-X-Name-Last: Wilding 
Author-Name: Xueya Cai 
Author-X-Name-First: Xueya 
Author-X-Name-Last: Cai 
Author-Name: Alan Hutson 
Author-X-Name-First: Alan 
Author-X-Name-Last: Hutson 
Author-Name: Zhangsheng Yu 
Author-X-Name-First: Zhangsheng 
Author-X-Name-Last: Yu 
Title: A linear model-based test for the heterogeneity of conditional correlations 
Abstract:
  Current methods of testing the equality of conditional correlations of
 bivariate data on a third variable of interest (covariate) are limited due
 to discretizing of the covariate when it is continuous. In this study, we
 propose a linear model approach for estimation and hypothesis testing of
 the Pearson correlation coefficient, where the correlation itself can be
 modeled as a function of continuous covariates. The restricted maximum
 likelihood method is applied for parameter estimation, and the corrected
 likelihood ratio test is performed for hypothesis testing. This approach
 allows for flexible and robust inference and prediction of the conditional
 correlations based on the linear model. Simulation studies show that the
 proposed method is statistically more powerful and more flexible in
 accommodating complex covariate patterns than the existing methods. In
 addition, we illustrate the approach by analyzing the correlation between
 the physical component summary and the mental component summary of the MOS
 SF-36 form across a fair number of covariates in the national survey data. 
Journal: Journal of Applied Statistics 
Pages: 2355-2366 
Issue: 10 
Volume: 38 
Year: 2011 
Keywords: correlation coefficient, heterogeneity, linear model, multivariate normal distribution, MOS SF-36, 
X-DOI: 10.1080/02664763.2011.559201 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664763.2011.559201
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:10:p:2355-2366




Template-Type: ReDIF-Article 1.0
Author-Name: Pieter Bastiaan Ober 
Author-X-Name-First: Pieter Bastiaan 
Author-X-Name-Last: Ober 
Title: Basic statistics 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 2367-2367 
Issue: 10 
Volume: 38 
Year: 2011 
X-DOI: 10.1080/02664763.2010.484892 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664763.2010.484892
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:10:p:2367-2367




Template-Type: ReDIF-Article 1.0
Author-Name: Kassim Mwitondi 
Author-X-Name-First: Kassim 
Author-X-Name-Last: Mwitondi 
Title: Bayesian computation with R 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 2367-2368 
Issue: 10 
Volume: 38 
Year: 2011 
X-DOI: 10.1080/02664763.2010.484893 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664763.2010.484893
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:10:p:2367-2368




Template-Type: ReDIF-Article 1.0
Author-Name: Andrey Kostenko 
Author-X-Name-First: Andrey 
Author-X-Name-Last: Kostenko 
Title: Picturing the uncertain world 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 2368-2369 
Issue: 10 
Volume: 38 
Year: 2011 
X-DOI: 10.1080/02664763.2010.517932 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664763.2010.517932
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:10:p:2368-2369




Template-Type: ReDIF-Article 1.0
Author-Name: Han Lin Shang 
Author-X-Name-First: Han 
Author-X-Name-Last: Lin Shang 
Title: Dynamic linear models with R 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 2369-2370 
Issue: 10 
Volume: 38 
Year: 2011 
X-DOI: 10.1080/02664763.2010.517938 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664763.2010.517938
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:10:p:2369-2370




Template-Type: ReDIF-Article 1.0
Author-Name: S&#xf8;ren Feodor Nielsen 
Author-X-Name-First: S&#xf8;ren Feodor 
Author-X-Name-Last: Nielsen 
Title: Introductory time series with R 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 2370-2371 
Issue: 10 
Volume: 38 
Year: 2011 
X-DOI: 10.1080/02664763.2010.517940 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664763.2010.517940
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:10:p:2370-2371




Template-Type: ReDIF-Article 1.0
Author-Name: Long Kang 
Author-X-Name-First: Long 
Author-X-Name-Last: Kang 
Title: Volatility and time series econometrics: essays in honor of Robert Engle 
Abstract:
Journal: Journal of Applied Statistics 
Pages: 2371-2372 
Issue: 10 
Volume: 38 
Year: 2011 
X-DOI: 10.1080/02664763.2010.530388 
File-URL: http://www.tandfonline.com/doi/abs/10.1080/02664763.2010.530388
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:10:p:2371-2372




Template-Type: ReDIF-Article 1.0
Author-Name: H. Zhang 
Author-X-Name-First: H. 
Author-X-Name-Last: Zhang 
Author-Name: Y. Xia 
Author-X-Name-First: Y. 
Author-X-Name-Last: Xia 
Author-Name: R. Chen 
Author-X-Name-First: R. 
Author-X-Name-Last: Chen 
Author-Name: D. Gunzler 
Author-X-Name-First: D. 
Author-X-Name-Last: Gunzler 
Author-Name: W. Tang 
Author-X-Name-First: W. 
Author-X-Name-Last: Tang 
Author-Name: Xin Tu 
Author-X-Name-First: Xin 
Author-X-Name-Last: Tu 
Title: Modeling longitudinal binomial responses: implications from two dueling paradigms 
Abstract:
  The generalized estimating equations (GEEs) and generalized linear
 mixed-effects model (GLMM) are the two most popular paradigms to extend
 models for cross-sectional data to a longitudinal setting. Although the
 two approaches yield well-interpreted models for continuous outcomes, it
 is quite a different story when applied to binomial responses. We discuss
 major modeling differences between the GEE- and GLMM-derived models by
 presenting new results regarding the model-driven differences. Our results
 show that GLMM induces some artifacts in the marginal models at assessment
 times, making it inappropriate when applied to such responses from real
 study data. The different interpretations of parameters resulting from the
 conceptual difference between the two modeling approaches also carry quite
 significant implications and ramifications with respect to data and power
 analyses. Although a special case involving a scale difference in
 parameters between GEE and GLMM has been noted in the literature, its
 implications in real data analysis has not been thoroughly addressed.
 Further, this special case has a very limited covariate structure and does
 not apply to most real studies, especially multi-center clinical trials.
 The new results presented fill a substantial gap in the literature
 regarding the model-driven differences between the two dueling paradigms. 
Journal: Journal of Applied Statistics 
Pages: 2373-2390 
Issue: 11 
Volume: 38 
Year: 2011 
Month: 12 
X-DOI: 10.1080/02664763.2010.550038 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.550038 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:11:p:2373-2390




Template-Type: ReDIF-Article 1.0
Author-Name: I. Gijbels 
Author-X-Name-First: I. 
Author-X-Name-Last: Gijbels 
Author-Name: I. Prosdocimi 
Author-X-Name-First: I. 
Author-X-Name-Last: Prosdocimi 
Title: Smooth estimation of mean and dispersion function in extended generalized additive models with application to Italian induced abortion data 
Abstract:
  We analyse data on abortion rate (AR) in Italy with a particular focus on
 different behaviours in different regions in Italy. The aim is to try to
 reveal the relationship between the AR and several covariates that
 describe in some way the modernity of the region and the condition of the
 women there. The data are mostly underdispersed and the degree of
 underdispersion also varies with the covariates. To analyse these data,
 recent techniques for flexible modelling of a mean and dispersion function
 in a double exponential family framework are further developed now in a
 generalized additive model context for dealing with the multivariate
 set-up. The appealing unified framework and approach even allow to
 semi-parametric modelling of the covariates without any additional
 efforts. The methodology is illustrated on ozone-level data and leads to
 interesting findings in the Italian abortion data. 
Journal: Journal of Applied Statistics 
Pages: 2391-2411 
Issue: 11 
Volume: 38 
Year: 2011 
Month: 12 
X-DOI: 10.1080/02664763.2010.550039 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.550039 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:11:p:2391-2411




Template-Type: ReDIF-Article 1.0
Author-Name: Hukum Chandra 
Author-X-Name-First: Hukum 
Author-X-Name-Last: Chandra 
Author-Name: Nicola Salvati 
Author-X-Name-First: Nicola 
Author-X-Name-Last: Salvati 
Author-Name: U.   C. Sud 
Author-X-Name-First: U.   C. 
Author-X-Name-Last: Sud 
Title: Disaggregate-level estimates of indebtedness in the state of Uttar Pradesh in India: an application of small-area estimation technique 
Abstract:
  The National Sample Survey Organisation (NSSO) surveys are the main
 source of official statistics in India, and generate a range of invaluable
 data at the macro level (e.g. state and national levels). However, the
 NSSO data cannot be used directly to produce reliable estimates at the
 micro level (e.g. district or further disaggregate level) due to small
 sample sizes. There is a rapidly growing demand of such micro-level
 statistics in India, as the country is moving from centralized to more
 decentralized planning system. In this article, we employ small-area
 estimation (SAE) techniques to derive model-based estimates of the
 proportion of indebted households at district or at other small-area
 levels in the state of Uttar Pradesh in India by linking data from the
 Debt--Investment Survey 2002--2003 of NSSO and the Population Census 2001
 and the Agriculture Census 2003. Our results show that the model-based
 estimates are precise and representative. For many small areas, it is even
 not possible to produce estimates using sample data alone. The model-based
 estimates generated using SAE are still reliable for such areas. The
 estimates are expected to provide invaluable information to policy
 analysts and decision-makers. 
Journal: Journal of Applied Statistics 
Pages: 2413-2432 
Issue: 11 
Volume: 38 
Year: 2011 
Month: 1 
X-DOI: 10.1080/02664763.2011.559202 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.559202 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:11:p:2413-2432




Template-Type: ReDIF-Article 1.0
Author-Name: Ricardo   S. Ehlers 
Author-X-Name-First: Ricardo   S. 
Author-X-Name-Last: Ehlers 
Title: Comparison of Bayesian models for production efficiency 
Abstract:
  In this paper, we use Markov Chain Monte Carlo (MCMC) methods in order to
 estimate and compare stochastic production frontier models from a Bayesian
 perspective. We consider a number of competing models in terms of
 different production functions and the distribution of the asymmetric
 error term. All MCMC simulations are done using the package
 <monospace>JAGS</monospace> (Just Another Gibbs Sampler), a clone of the
 classic <monospace>BUGS</monospace> package which works closely with the
 <monospace>R</monospace> package where all the statistical computations
 and graphics are done. 
Journal: Journal of Applied Statistics 
Pages: 2433-2443 
Issue: 11 
Volume: 38 
Year: 2011 
Month: 1 
X-DOI: 10.1080/02664763.2011.559203 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.559203 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:11:p:2433-2443




Template-Type: ReDIF-Article 1.0
Author-Name: Nicola Lama 
Author-X-Name-First: Nicola 
Author-X-Name-Last: Lama 
Author-Name: Patrizia Boracchi 
Author-X-Name-First: Patrizia 
Author-X-Name-Last: Boracchi 
Author-Name: Elia Biganzoli 
Author-X-Name-First: Elia 
Author-X-Name-Last: Biganzoli 
Title: Partial logistic relevance vector machines in survival analysis 
Abstract:
  The use of relevance vector machines to flexibly model hazard rate
 functions is explored. This technique is adapted to survival analysis
 problems through the partial logistic approach. The method exploits the
 Bayesian automatic relevance determination procedure to obtain sparse
 solutions and it incorporates the flexibility of kernel-based models.
 Example results are presented on literature data from a head-and-neck
 cancer survival study using Gaussian and spline kernels. Sensitivity
 analysis is conducted to assess the influence of hyperprior distribution
 parameters. The proposed method is then contrasted with other flexible
 hazard regression methods, in particular the HARE model proposed by
 Kooperberg <italic>et al.</italic> [16]. A simulation study is conducted
 to carry out the comparison. The model developed in this paper exhibited
 good performance in the prediction of hazard rate. The application of this
 sparse Bayesian technique to a real cancer data set demonstrated that the
 proposed method can potentially reveal characteristics of the hazards,
 associated with the dynamics of the studied diseases, which may be missed
 by existing modeling approaches based on different perspectives on the
 bias <italic>vs.</italic> variance balance. 
Journal: Journal of Applied Statistics 
Pages: 2445-2458 
Issue: 11 
Volume: 38 
Year: 2011 
Month: 1 
X-DOI: 10.1080/02664763.2011.559204 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.559204 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:11:p:2445-2458




Template-Type: ReDIF-Article 1.0
Author-Name: Asghar Seif 
Author-X-Name-First: Asghar 
Author-X-Name-Last: Seif 
Author-Name: Alireza Faraz 
Author-X-Name-First: Alireza 
Author-X-Name-Last: Faraz 
Author-Name: C�dric Heuchenne 
Author-X-Name-First: C�dric 
Author-X-Name-Last: Heuchenne 
Author-Name: Erwin Saniga 
Author-X-Name-First: Erwin 
Author-X-Name-Last: Saniga 
Author-Name: M.   B. Moghadam 
Author-X-Name-First: M.   B. 
Author-X-Name-Last: Moghadam 
Title: A modified economic-statistical design of the T-super-2 control chart with variable sample sizes and control limits 
Abstract:
  Recent studies have shown that using variable sampling size and control
 limits (VSSC) schemes result in charts with more statistical power than
 variable sampling size (VSS) when detecting small to moderate shifts in
 the process mean vector. This paper presents an economic-statistical
 design (ESD) of the VSSC T-super-2 control chart using the general model
 of Lorenzen and Vance [22]. The genetic algorithm approach is then
 employed to search for the optimal values of the six test parameters of
 the chart. We then compare the expected cost per unit of time of the
 optimally designed VSSC chart with optimally designed VSS and FRS (fixed
 ratio sampling) T-super-2 charts as well as MEWMA charts. 
Journal: Journal of Applied Statistics 
Pages: 2459-2469 
Issue: 11 
Volume: 38 
Year: 2011 
Month: 1 
X-DOI: 10.1080/02664763.2011.559205 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.559205 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:11:p:2459-2469




Template-Type: ReDIF-Article 1.0
Author-Name: Sandra De Iaco 
Author-X-Name-First: Sandra 
Author-X-Name-Last: De Iaco 
Title: A new space--time multivariate approach for environmental data analysis 
Abstract:
  Air quality control usually requires a monitoring system of multiple
 indicators measured at various points in space and time. Hence, the use of
 space--time multivariate techniques are of fundamental importance in this
 context, where decisions and actions regarding environmental protection
 should be supported by studies based on either inter-variables relations
 and spatial--temporal correlations. This paper describes how canonical
 correlation analysis can be combined with space--time geostatistical
 methods for analysing two spatial--temporal correlated aspects, such as
 air pollution concentrations and meteorological conditions. Hourly
 averages of three pollutants (nitric oxide, nitrogen dioxide and ozone)
 and three atmospheric indicators (temperature, humidity and wind speed)
 taken for two critical months (February and August) at several monitoring
 stations are considered and space--time variograms for the variables are
 estimated. Simultaneous relationships between such sample space--time
 variograms are determined through canonical correlation analysis. The most
 correlated canonical variates are used for describing synthetically the
 underlying space--time behaviour of the components of the two sets. 
Journal: Journal of Applied Statistics 
Pages: 2471-2483 
Issue: 11 
Volume: 38 
Year: 2011 
Month: 1 
X-DOI: 10.1080/02664763.2011.559206 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.559206 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:11:p:2471-2483




Template-Type: ReDIF-Article 1.0
Author-Name: Suzan Gazio&#x11F;lu 
Author-X-Name-First: Suzan 
Author-X-Name-Last: Gazio&#x11F;lu 
Author-Name: E.   Marian Scott 
Author-X-Name-First: E.   Marian 
Author-X-Name-Last: Scott 
Title: Sensitivity analysis of linear time-invariant compartmental models with steady-state constraint 
Abstract:
  Compartmental models have been widely used in modelling systems in
 pharmaco-kinetics, engineering, biomedicine and ecology since 1943 and
 turn out to be very good approximations for many different real-life
 systems. Sensitivity analysis (SA) is commonly employed at a preliminary
 stage of model development process to increase the confidence in the model
 and its predictions by providing an understanding of how the model
 response variables respond to changes in the inputs, data used to
 calibrate it and model structures. This paper concerns the application of
 some SA techniques to a linear, deterministic, time-invariant
 compartmental model of global carbon cycle (GCC). The same approach is
 also illustrated with a more complex GCC model which has some nonlinear
 components. By focusing on these two structurally different models for
 estimating the atmospheric CO<sub>2</sub> content in the year 2100,
 sensitivity of model predictions to uncertainty attached to the model
 input factors is studied. The application/modification of SA techniques to
 compartmental models with steady-state constraint is explored using the
 8-compartment model, and computational methods developed to maintain the
 initial steady-state condition are presented. In order to adjust the
 values of model input factors to achieve an acceptable match between
 observed and predicted model conditions, windowing analysis is used. 
Journal: Journal of Applied Statistics 
Pages: 2485-2509 
Issue: 11 
Volume: 38 
Year: 2011 
Month: 1 
X-DOI: 10.1080/02664763.2011.559207 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.559207 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:11:p:2485-2509




Template-Type: ReDIF-Article 1.0
Author-Name: N.   T. Longford 
Author-X-Name-First: N.   T. 
Author-X-Name-Last: Longford 
Author-Name: Pierpaolo D'Urso 
Author-X-Name-First: Pierpaolo 
Author-X-Name-Last: D'Urso 
Title: Mixture models with an improper component 
Abstract:
  A class of mixture models in which a component is associated with an
 improper distribution is introduced. This component is intended mainly for
 outliers. The models are motivated by the EM algorithm, and are fitted by
 its simple adaptation. They are illustrated on several examples with large
 samples, one of them about transactions of residential properties in
 Wellington, New Zealand, in 2006. 
Journal: Journal of Applied Statistics 
Pages: 2511-2521 
Issue: 11 
Volume: 38 
Year: 2011 
Month: 1 
X-DOI: 10.1080/02664763.2011.559208 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.559208 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:11:p:2511-2521




Template-Type: ReDIF-Article 1.0
Author-Name: Melody   S. Goodman 
Author-X-Name-First: Melody   S. 
Author-X-Name-Last: Goodman 
Author-Name: Yi Li 
Author-X-Name-First: Yi 
Author-X-Name-Last: Li 
Author-Name: Ram   C. Tiwari 
Author-X-Name-First: Ram   C. 
Author-X-Name-Last: Tiwari 
Title: Detecting multiple change points in piecewise constant hazard functions 
Abstract:
  The National Cancer Institute (NCI) suggests a sudden reduction in
 prostate cancer mortality rates, likely due to highly successful
 treatments and screening methods for early diagnosis. We are interested in
 understanding the impact of medical breakthroughs, treatments, or
 interventions, on the survival experience for a population. For this
 purpose, estimating the underlying hazard function, with possible time
 change points, would be of substantial interest, as it will provide a
 general picture of the survival trend and when this trend is disrupted.
 Increasing attention has been given to testing the assumption of a
 constant failure rate against a failure rate that changes at a single
 point in time. We expand the set of alternatives to allow for the
 consideration of multiple change-points, and propose a model selection
 algorithm using sequential testing for the piecewise constant hazard
 model. These methods are data driven and allow us to estimate not only the
 number of change points in the hazard function but where those changes
 occur. Such an analysis allows for better understanding of how changing
 medical practice affects the survival experience for a patient population.
 We test for change points in prostate cancer mortality rates using the NCI
 Surveillance, Epidemiology, and End Results dataset. 
Journal: Journal of Applied Statistics 
Pages: 2523-2532 
Issue: 11 
Volume: 38 
Year: 2011 
Month: 1 
X-DOI: 10.1080/02664763.2011.559209 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.559209 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:11:p:2523-2532




Template-Type: ReDIF-Article 1.0
Author-Name: Himadri Ghosh 
Author-X-Name-First: Himadri 
Author-X-Name-Last: Ghosh 
Author-Name:  Prajneshu 
Author-X-Name-First:  
Author-X-Name-Last: Prajneshu 
Title: Statistical learning theory for fitting multimodal distribution to rainfall data: an application 
Abstract:
  The promising methodology of the &#x201C;Statistical Learning
 Theory&#x201D; for the estimation of multimodal distribution is thoroughly
 studied. The &#x201C;tail&#x201D; is estimated through Hill's, UH and
 moment methods. The threshold value is determined by nonparametric
 bootstrap and the minimum mean square error criterion. Further, the
 &#x201C;body&#x201D; is estimated by the nonparametric structural risk
 minimization method of the empirical distribution function under the
 regression set-up. As an illustration, rainfall data for the
 meteorological subdivision of Orissa, India during the period 1871--2006
 are used. It is shown that Hill's method has performed the best for tail
 density. Finally, the combined estimated &#x201C;body&#x201D; and
 &#x201C;tail&#x201D; of the multimodal distribution is shown to capture
 the multimodality present in the data. 
Journal: Journal of Applied Statistics 
Pages: 2533-2545 
Issue: 11 
Volume: 38 
Year: 2011 
Month: 1 
X-DOI: 10.1080/02664763.2011.559210 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.559210 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:11:p:2533-2545




Template-Type: ReDIF-Article 1.0
Author-Name: Rakhee   Dinubhai Patel 
Author-X-Name-First: Rakhee   Dinubhai 
Author-X-Name-Last: Patel 
Author-Name: Frederic   Paik Schoenberg 
Author-X-Name-First: Frederic   Paik 
Author-X-Name-Last: Schoenberg 
Title: A graphical test for local self-similarity in univariate data 
Abstract:
  The Pareto distribution, or power-law distribution, has long been used to
 model phenomena in many fields, including wildfire sizes, earthquake
 seismic moments and stock price changes. Recent observations have brought
 the fit of the Pareto into question, however, particularly in the upper
 tail where it often overestimates the frequency of the largest events.
 This paper proposes a graphical self-similarity test specifically designed
 to assess whether a Pareto distribution fits better than a tapered Pareto
 or another alternative. Unlike some model selection methods, this
 graphical test provides the advantage of highlighting where the model fits
 well and where it breaks down. Specifically, for data that seem to be
 better modeled by the tapered Pareto or other alternatives, the test
 assesses the degree of local self-similarity at each value where the test
 is computed. The basic properties of the graphical test and its
 implementation are discussed, and applications of the test to
 seismological, wildfire, and financial data are considered. 
Journal: Journal of Applied Statistics 
Pages: 2547-2562 
Issue: 11 
Volume: 38 
Year: 2011 
Month: 1 
X-DOI: 10.1080/02664763.2011.559211 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.559211 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:11:p:2547-2562




Template-Type: ReDIF-Article 1.0
Author-Name: Chia-Lin Chang 
Author-X-Name-First: Chia-Lin 
Author-X-Name-Last: Chang 
Author-Name: Michael McAleer 
Author-X-Name-First: Michael 
Author-X-Name-Last: McAleer 
Author-Name: Les Oxley 
Author-X-Name-First: Les 
Author-X-Name-Last: Oxley 
Title: How are journal impact, prestige and article influence related? An application to neuroscience 
Abstract:
  The paper analyzes the leading journals in neurosciences using
 quantifiable research assessment measures (RAM), highlights the
 similarities and differences in alternative RAM, shows that several RAM
 capture similar performance characteristics of highly cited journals, and
 shows that some other RAM have low correlations with each other, and hence
 add significant informational value. Alternative RAM are discussed for the
 Thomson Reuters ISI Web of Science database (hereafter ISI). The RAM that
 are calculated annually or updated daily include the classic 2-year impact
 factor (2YIF), 5-year impact factor, immediacy (or zero-year impact
 factor), Eigenfactor score, article influence score, C3PO (citation
 performance per paper online), h-index, Zinfluence, PI-BETA (papers
 ignored by even the authors), 2-year and historical self-citation
 threshold approval ratings, impact factor inflation, and cited article
 influence (CAI). The RAM are analyzed for 26 highly cited journals in the
 ISI category of neurosciences. The paper finds that the Eigenfactor score
 and PI-BETA are not highly correlated with the other RAM scores, so that
 they convey additional information regarding journal rankings, that
 article influence is highly correlated with some existing RAM, so that it
 has little informative incremental value, and that CAI has additional
 informational value to that of article influence. Harmonic mean rankings
 of the 13 RAM criteria for the 26 highly cited journals are also
 presented. Emphasizing the 2YIF of a journal to the exclusion of other
 informative RAM criteria is shown to lead to a distorted evaluation of
 journal performance and influence, especially given the informative value
 of several other RAM. 
Journal: Journal of Applied Statistics 
Pages: 2563-2573 
Issue: 11 
Volume: 38 
Year: 2011 
Month: 1 
X-DOI: 10.1080/02664763.2011.559212 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.559212 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:11:p:2563-2573




Template-Type: ReDIF-Article 1.0
Author-Name: Chi-Shuan Liu 
Author-X-Name-First: Chi-Shuan 
Author-X-Name-Last: Liu 
Author-Name: Fang-Chih Tien 
Author-X-Name-First: Fang-Chih 
Author-X-Name-Last: Tien 
Title: A single-featured EWMA-<italic>X</italic> control chart for detecting shifts in process mean and standard deviation 
Abstract:
  The combined EWMA-<italic>X</italic> chart is a commonly used tool for
 monitoring both large and small process shifts. However, this chart
 requires calculating and monitoring two statistics along with two sets of
 control limits. Thus, this study develops a single-featured
 EWMA-<italic>X</italic> (called SFEWMA-<italic>X</italic>) control chart
 which has the ability to simultaneously monitor both large and small
 process shifts using only one set of statistic and control limits. The
 proposed SFEWMA-<italic>X</italic> chart is further extended to monitoring
 the shifts in process standard deviation. A set of simulated data are used
 to demonstrate the proposed chart's superior performance in terms of
 average run length compared with that of the traditional charts. The
 experimental examples also show that the SFEWMA-<italic>X</italic> chart
 is neater and easier to visually interpret than the original
 EWMA-<italic>X</italic> chart. 
Journal: Journal of Applied Statistics 
Pages: 2575-2596 
Issue: 11 
Volume: 38 
Year: 2011 
Month: 1 
X-DOI: 10.1080/02664763.2011.559213 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.559213 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:11:p:2575-2596




Template-Type: ReDIF-Article 1.0
Author-Name: Kadri   Ulas Akay 
Author-X-Name-First: Kadri   Ulas 
Author-X-Name-Last: Akay 
Author-Name: M&#xFC;jgan Tez 
Author-X-Name-First: M&#xFC;jgan 
Author-X-Name-Last: Tez 
Title: Alternative modeling techniques for the quantal response data in mixture experiments 
Abstract:
  Mixture experiments are commonly encountered in many fields including
 chemical, pharmaceutical and consumer product industries. Due to their
 wide applications, mixture experiments, a special study of response
 surface methodology, have been given greater attention in both model
 building and determination of designs compared with other experimental
 studies. In this paper, some new approaches are suggested on model
 building and selection for the analysis of the data in mixture experiments
 by using a special generalized linear models, logistic regression model,
 proposed by Chen <italic>et al.</italic> [7]. Generally, the special
 mixture models, which do not have a constant term, are highly affected by
 collinearity in modeling the mixture experiments. For this reason, in
 order to alleviate the undesired effects of collinearity in the analysis
 of mixture experiments with logistic regression, a new mixture model is
 defined with an alternative ratio variable. The deviance analysis table is
 given for standard mixture polynomial models defined by transformations
 and special mixture models used as linear predictors. The effects of
 components on the response in the restricted experimental region are given
 by using an alternative representation of Cox's direction approach. In
 addition, odds ratio and the confidence intervals of odds ratio are
 identified according to the chosen reference and control groups. To
 compare the suggested models, some model selection criteria, graphical
 odds ratio and the confidence intervals of the odds ratio are used. The
 advantage of the suggested approaches is illustrated on tumor incidence
 data set. 
Journal: Journal of Applied Statistics 
Pages: 2597-2616 
Issue: 11 
Volume: 38 
Year: 2011 
Month: 1 
X-DOI: 10.1080/02664763.2011.559214 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.559214 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:11:p:2597-2616




Template-Type: ReDIF-Article 1.0
Author-Name: Raymond Hubbard 
Author-X-Name-First: Raymond 
Author-X-Name-Last: Hubbard 
Title: The widespread misinterpretation of <italic>p</italic>-values as error probabilities 
Abstract:
  The anonymous mixing of Fisherian (<italic>p</italic>-values) and
 Neyman--Pearsonian (&#x3B1; levels) ideas about testing, distilled in the
 customary but misleading <italic>p</italic> > &#x3B1; criterion of
 statistical significance, has led researchers in the social and management
 sciences (and elsewhere) to commonly misinterpret the
 <italic>p</italic>-value as a &#x2018;data-adjusted&#x2019; Type I error
 rate. Evidence substantiating this claim is provided from a number of
 fronts, including comments by statisticians, articles judging the value of
 significance testing, textbooks, surveys of scholars, and the statistical
 reporting behaviours of applied researchers. That many investigators do
 not know the difference between <italic>p</italic>&#x2019;s and
 &#x3B1;&#x2019;s indicates much bewilderment over what those most ardently
 sought research outcomes&#x2014;statistically significant
 results&#x2014;means. Statisticians can play a leading role in clearing
 this confusion. A good starting point would be to abolish the
 <italic>p</italic> > &#x3B1; criterion of statistical significance. 
Journal: Journal of Applied Statistics 
Pages: 2617-2626 
Issue: 11 
Volume: 38 
Year: 2011 
Month: 1 
X-DOI: 10.1080/02664763.2011.567245 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.567245 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:11:p:2617-2626




Template-Type: ReDIF-Article 1.0
Author-Name: Alessio Farcomeni 
Author-X-Name-First: Alessio 
Author-X-Name-Last: Farcomeni 
Author-Name: Alessandra Nardi 
Author-X-Name-First: Alessandra 
Author-X-Name-Last: Nardi 
Author-Name: Elena Fabrizi 
Author-X-Name-First: Elena 
Author-X-Name-Last: Fabrizi 
Title: Joint analysis of occurrence and time to stability after entrance into the Italian labour market: an approach based on a Bayesian cure model with structured stochastic search variable selection 
Abstract:
  Precarious employment is a serious social problem, especially in those
 countries, such as Italy, where there are limited benefits from social
 security. We investigate this phenomenon by analysing the initial part of
 the career of employees starting with unstable contracts for a panel of
 Italian workers. Our aim is to estimate the probability of getting a
 stable job and to detect factors influencing both this probability and the
 duration of precariousness. To answer these questions, we use an
 <italic>ad hoc</italic> mixture cure rate model in a Bayesian framework. 
Journal: Journal of Applied Statistics 
Pages: 2627-2646 
Issue: 11 
Volume: 38 
Year: 2011 
Month: 2 
X-DOI: 10.1080/02664763.2011.567246 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.567246 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:11:p:2627-2646




Template-Type: ReDIF-Article 1.0
Author-Name: Shuen-Lin Jeng 
Author-X-Name-First: Shuen-Lin 
Author-X-Name-Last: Jeng 
Author-Name: Yu-Te Liu 
Author-X-Name-First: Yu-Te 
Author-X-Name-Last: Liu 
Title: Adaptive tangent distance classifier on recognition of handwritten digits 
Abstract:
  Simard <italic>et al.</italic> [1617] proposed a transformation distance
 called &#x201C;tangent distance&#x201D; (TD) which can make pattern
 recognition be efficient. The key idea is to construct a distance measure
 which is invariant with respect to some chosen transformations. In this
 research, we provide a method using adaptive TD based on an idea inspired
 by &#x201C;discriminant adaptive nearest neighbor&#x201D; [7]. This method
 is relatively easy compared with many other complicated ones. A real
 handwritten recognition data set is used to illustrate our new method. Our
 results demonstrate that the proposed method gives lower classification
 error rates than those by standard implementation of neural networks and
 support vector machines and is as good as several other complicated
 approaches. 
Journal: Journal of Applied Statistics 
Pages: 2647-2659 
Issue: 11 
Volume: 38 
Year: 2011 
Month: 2 
X-DOI: 10.1080/02664763.2011.567247 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.567247 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:11:p:2647-2659




Template-Type: ReDIF-Article 1.0
Author-Name: K. Fern&#xE1;ndez-Aguirre 
Author-X-Name-First: K. 
Author-X-Name-Last: Fern&#xE1;ndez-Aguirre 
Author-Name: M.   I. Landaluce-Calvo 
Author-X-Name-First: M.   I. 
Author-X-Name-Last: Landaluce-Calvo 
Author-Name: A. Mart�n-Arroyuelos 
Author-X-Name-First: A. 
Author-X-Name-Last: Mart�n-Arroyuelos 
Author-Name: J.   I. Modro&#xF1;o-Herr&#xE1;n 
Author-X-Name-First: J.   I. 
Author-X-Name-Last: Modro&#xF1;o-Herr&#xE1;n 
Title: Knowledge extraction from a large on-line survey: a case study for a higher education corporate marketing 
Abstract:
  For a higher education public institution, young in relative terms,
 featuring local competition with another private and both long-established
 and reputed one, it is of great importance to become a reference
 university institution to be better known and felt with identification in
 the society it belongs to and ultimately to reach a good position within
 the European Higher Education Area. These considerations have made the
 university governors setting up the objective of achieving an adequate
 management of the university institutional brand focused on its logo and
 on image promotion, leading to the establishment of a university shop as
 it is considered a highly adequate instrument for such promotion. In this
 context, an on-line survey is launched on three different kinds of members
 of the institution, resulting in a large data sample. Different kinds of
 variables are analysed through appropriate exploratory multivariate
 techniques (symmetrical methods) and regression-related techniques
 (non-symmetrical methods). An advocacy for such combination is given as a
 conclusion. The application of statistical techniques of data and text
 mining provides us with empirical insights about the institution
 members&#x2019; perceptions and helps us to extract some facts valuable to
 establish policies that would improve the corporate identity and the
 success of the corporate shop. 
Journal: Journal of Applied Statistics 
Pages: 2661-2679 
Issue: 11 
Volume: 38 
Year: 2011 
Month: 2 
X-DOI: 10.1080/02664763.2011.567248 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.567248 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:11:p:2661-2679




Template-Type: ReDIF-Article 1.0
Author-Name: Ron   S. Kenett 
Author-X-Name-First: Ron   S. 
Author-X-Name-Last: Kenett 
Title: On the planning and design of sample surveys 
Journal: Journal of Applied Statistics 
Pages: 2681-2681 
Issue: 11 
Volume: 38 
Year: 2011 
Month: 11 
X-DOI: 10.1080/02664763.2011.616688 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.616688 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:11:p:2681-2681




Template-Type: ReDIF-Article 1.0
Author-Name: M.   J. Faddy 
Author-X-Name-First: M.   J. 
Author-X-Name-Last: Faddy 
Author-Name: D.   M. Smith 
Author-X-Name-First: D.   M. 
Author-X-Name-Last: Smith 
Title: Analysis of count data with covariate dependence in both mean and variance 
Abstract:
  Extended Poisson process modelling is generalised to allow for
 covariate-dependent dispersion as well as a covariate-dependent mean
 response. This is done by a re-parameterisation that uses approximate
 expressions for the mean and variance. Such modelling allows under- and
 over-dispersion, or a combination of both, in the same data set to be
 accommodated within the same modelling framework. All the necessary
 calculations can be done numerically, enabling maximum likelihood
 estimation of all model parameters to be carried out. The modelling is
 applied to re-analyse two published data sets, where there is evidence of
 covariate-dependent dispersion, with the modelling leading to more
 informative analyses of these data and more appropriate measures of the
 precision of any estimates. 
Journal: Journal of Applied Statistics 
Pages: 2683-2694 
Issue: 12 
Volume: 38 
Year: 2011 
Month: 2 
X-DOI: 10.1080/02664763.2011.567250 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.567250 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:12:p:2683-2694




Template-Type: ReDIF-Article 1.0
Author-Name: Ramesh   C. Gupta 
Author-X-Name-First: Ramesh   C. 
Author-X-Name-Last: Gupta 
Author-Name: Debasis Kundu 
Author-X-Name-First: Debasis 
Author-X-Name-Last: Kundu 
Title: Weighted inverse Gaussian -- a versatile lifetime model 
Abstract:
  Jorgensen <italic>et al</italic>. [14] introduced a three-parameter
 generalized inverse Gaussian distribution, which is a mixture of the
 inverse Gaussian distribution and length biased inverse Gaussian
 distribution. Also Birnbaum--Saunders distribution is a special case for
 <inline-formula> <inline-graphic
 xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_567251_o_ilm0001.gif"/> </inline-formula>, where
 <italic>p</italic> is the mixing parameter. It is observed that the
 estimators of the unknown parameters can be obtained by solving a
 three-dimensional optimization process, which may not be a trivial issue.
 Most of the iterative algorithms are quite sensitive to the initial
 guesses. In this paper, we propose to use the EM algorithm to estimate the
 unknown parameters for complete and censored samples. In the proposed EM
 algorithm, at the M-step the optimization problem can be solved
 analytically, and the observed Fisher information matrix can be obtained.
 These can be used to construct asymptotic confidence intervals of the
 unknown parameters. Some simulation experiments are conducted to examine
 the performance of the proposed EM algorithm, and it is observed that the
 performances are quite satisfactory. The methodology proposed here is
 illustrated by three data sets. 
Journal: Journal of Applied Statistics 
Pages: 2695-2708 
Issue: 12 
Volume: 38 
Year: 2011 
Month: 2 
X-DOI: 10.1080/02664763.2011.567251 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.567251 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:12:p:2695-2708




Template-Type: ReDIF-Article 1.0
Author-Name: Wen-Liang Hung 
Author-X-Name-First: Wen-Liang 
Author-X-Name-Last: Hung 
Author-Name: Yen-Chang Chang 
Author-X-Name-First: Yen-Chang 
Author-X-Name-Last: Chang 
Title: Comparison between method of moments and entropy regularization algorithm applied to parameter estimation for mixed-Weibull distribution 
Abstract:
  Mixed-Weibull distribution has been used to model a wide range of failure
 data sets, and in many practical situations the number of components in a
 mixture model is unknown. Thus, the parameter estimation of a
 mixed-Weibull distribution is considered and the important issue of how to
 determine the number of components is discussed. Two approaches are
 proposed to solve this problem. One is the method of moments and the other
 is a regularization type of fuzzy clustering algorithm. Finally, numerical
 examples and two real data sets are given to illustrate the features of
 the proposed approaches. 
Journal: Journal of Applied Statistics 
Pages: 2709-2722 
Issue: 12 
Volume: 38 
Year: 2011 
Month: 2 
X-DOI: 10.1080/02664763.2011.567252 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.567252 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:12:p:2709-2722




Template-Type: ReDIF-Article 1.0
Author-Name: G.   B. Cybis 
Author-X-Name-First: G.   B. 
Author-X-Name-Last: Cybis 
Author-Name: S. R.C. Lopes 
Author-X-Name-First: S. R.C. 
Author-X-Name-Last: Lopes 
Author-Name: H.   P. Pinheiro 
Author-X-Name-First: H.   P. 
Author-X-Name-Last: Pinheiro 
Title: Power of the likelihood ratio test for models of DNA base substitution 
Abstract:
  The goal of this work is to study the properties of the likelihood ratio
 (LR) tests comparing base substitution models. These are the most widely
 used hypothesis tests. With mild regularity conditions, we show that the
 asymptotic distribution of the LR statistic test, under the alternative
 hypothesis, is a non-central chi-square <inline-formula> <inline-graphic
 xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_567253_o_ilm0001.gif"/> </inline-formula> distribution.
 The asymptotic normal distribution of the LR test is proved when the
 sequence length <italic>S</italic> goes to infinity. We also propose a
 consistent estimator for the non-centrality parameter <italic>D</italic>.
 Through asymptotic theory and based on this consistent estimator for
 <italic>D</italic>, we propose a low computational cost estimator for the
 power of the LR test. The methodology is applied to 17 different gene
 sequences of the ECP--EDN family in primates. 
Journal: Journal of Applied Statistics 
Pages: 2723-2737 
Issue: 12 
Volume: 38 
Year: 2011 
Month: 1 
X-DOI: 10.1080/02664763.2011.567253 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.567253 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:12:p:2723-2737




Template-Type: ReDIF-Article 1.0
Author-Name: Chang-Xing Ma 
Author-X-Name-First: Chang-Xing 
Author-X-Name-Last: Ma 
Author-Name: Albert Vexler 
Author-X-Name-First: Albert 
Author-X-Name-Last: Vexler 
Author-Name: Enrique   F. Schisterman 
Author-X-Name-First: Enrique   F. 
Author-X-Name-Last: Schisterman 
Author-Name: Lili Tian 
Author-X-Name-First: Lili 
Author-X-Name-Last: Tian 
Title: Cost-efficient designs based on linearly associated biomarkers 
Abstract:
  A major limiting factor in much of the epidemiological and environmental
 researches is the cost of measuring the biomarkers or analytes of
 interest. Often, the number of specimens available for analysis is greater
 than the number of assays that is budgeted for. These assays are then
 performed on a random sample of specimens. Regression calibration is then
 utilized to infer biomarker levels of expensive assays from other
 correlated biomarkers that are relatively inexpensive to obtain and
 analyze. In other contexts, use of pooled specimens has been shown to
 increase efficiency in estimation. In this article, we examine two types
 of pooling <italic>in lieu</italic> of a random sample. The first is
 random (or traditional) pooling, and we characterize the second as
 &#x201C;optimal&#x201D; pooling. The second, which we propose for
 regression analysis, is pooling based on specimens ranked on the less
 expensive biomarker. The more expensive assay is then performed on the
 pool of relatively similar measurements. The optimal nature of this
 technique is also exemplified via Monte Carlo evaluations and real
 biomarker data. By displaying the considerable robustness of our method
 via a Monte Carlo study, it is shown that the proposed pooling design is a
 viable option whenever expensive assays are considered. 
Journal: Journal of Applied Statistics 
Pages: 2739-2750 
Issue: 12 
Volume: 38 
Year: 2011 
Month: 1 
X-DOI: 10.1080/02664763.2011.567254 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.567254 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:12:p:2739-2750




Template-Type: ReDIF-Article 1.0
Author-Name: Tina &#x17D;agar 
Author-X-Name-First: Tina 
Author-X-Name-Last: &#x17D;agar 
Author-Name: Vesna Zadnik 
Author-X-Name-First: Vesna 
Author-X-Name-Last: Zadnik 
Author-Name: Maja   Primic &#x17D;akelj 
Author-X-Name-First: Maja   Primic 
Author-X-Name-Last: &#x17D;akelj 
Title: Local standardized incidence ratio estimates and comparison with other mapping methods for small geographical areas using Slovenian breast cancer data 
Abstract:
  Cancer maps are important tools in the descriptive presentation of the
 cancer burden. The objective is to explore the advantages and
 disadvantages of mapping methods based on point data in comparison with
 maps based on aggregated data. Four types of maps were prepared based on
 the same underlying data set on breast cancer incidence in Slovenian
 females, 2002--2004. First, the standardized incidence ratios (SIR) by
 municipalities are mapped in a traditional way. Second, two maps applying
 widely used smoothing methods are presented, both based on aggregated
 municipalities&#x2019; data: floating weighted averages and the Bayesian
 hierarchical modelling. Finally, the new alternative method based on exact
 cancer cases and population coordinates is applied -- called the local SIR
 estimates. The decreasing west to east trend is visible on all map types.
 Smoothing produced more stable and less noisy SIR estimates. The map of
 the local SIR estimates emphasizes extremes, but unlike the map, based on
 the observed SIR, these estimates are statistically stable, enabling more
 accurate evaluation. The main advantages of local SIR estimates over the
 other three methods are the abilities of revealing more localized patterns
 and ignoring the arbitrary administrative borders. The disadvantage is
 that the geocoded data are not always available. 
Journal: Journal of Applied Statistics 
Pages: 2751-2761 
Issue: 12 
Volume: 38 
Year: 2011 
Month: 2 
X-DOI: 10.1080/02664763.2011.570314 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.570314 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:12:p:2751-2761




Template-Type: ReDIF-Article 1.0
Author-Name: Solaiman Afroughi 
Author-X-Name-First: Solaiman 
Author-X-Name-Last: Afroughi 
Author-Name: Soghrat Faghihzadeh 
Author-X-Name-First: Soghrat 
Author-X-Name-Last: Faghihzadeh 
Author-Name: Majid   Jafari Khaledi 
Author-X-Name-First: Majid   Jafari 
Author-X-Name-Last: Khaledi 
Author-Name: Mehdi   Ghandehari Motlagh 
Author-X-Name-First: Mehdi   Ghandehari 
Author-X-Name-Last: Motlagh 
Author-Name: Ebrahim Hajizadeh 
Author-X-Name-First: Ebrahim 
Author-X-Name-Last: Hajizadeh 
Title: Analysis of clustered spatially correlated binary data using autologistic model and Bayesian method with an application to dental caries of 3--5-year-old children 
Abstract:
  The autologistic model, first introduced by Besag, is a popular tool for
 analyzing binary data in spatial lattices. However, no investigation was
 found to consider modeling of binary data clustered in uncorrelated
 lattices. Owing to spatial dependency of responses, the exact likelihood
 estimation of parameters is not possible. For circumventing this
 difficulty, many studies have been designed to approximate the likelihood
 and the related partition function of the model. So, the traditional and
 Bayesian estimation methods based on the likelihood function are often
 time-consuming and require heavy computations and recursive techniques.
 Some investigators have introduced and implemented data augmentation and
 latent variable model to reduce computational complications in parameter
 estimation. In this work, the spatially correlated binary data distributed
 in uncorrelated lattices were modeled using autologistic regression, a
 Bayesian inference was developed with contribution of data augmentation
 and the proposed models were applied to caries experiences of deciduous
 dents. 
Journal: Journal of Applied Statistics 
Pages: 2763-2774 
Issue: 12 
Volume: 38 
Year: 2011 
Month: 2 
X-DOI: 10.1080/02664763.2011.570315 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.570315 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:12:p:2763-2774




Template-Type: ReDIF-Article 1.0
Author-Name: Silvia Bacci 
Author-X-Name-First: Silvia 
Author-X-Name-Last: Bacci 
Author-Name: Valeria Caviezel 
Author-X-Name-First: Valeria 
Author-X-Name-Last: Caviezel 
Title: Multilevel IRT models for the university teaching evaluation 
Abstract:
  In this paper, a generalization of the two-parameter partial credit model
 (2PL-PCM) and of two special cases, the partial credit model (PCM) and the
 rating scale model (RSM), with a hierarchical data structure will be
 presented. Having shown how 2PL-PCM, as with other item response theory
 (IRT) models, may be read in terms of a generalized linear mixed model
 (GLMM) with two aggregation levels, a presentation will be given of an
 extension to the case of measuring the latent trait of individuals
 aggregated in groups. The use of this Multilevel IRT model will be
 illustrated via reference to the evaluation of university teaching by
 students following the courses. The aim is to generate a ranking of
 teaching on the basis of student satisfaction, so as to give teachers, and
 those responsible for organizing study courses, a background of
 information that takes the opinions of the direct target group for
 university teaching (that is, the students) into account, in the context
 of improving the teaching courses available. 
Journal: Journal of Applied Statistics 
Pages: 2775-2791 
Issue: 12 
Volume: 38 
Year: 2011 
Month: 2 
X-DOI: 10.1080/02664763.2011.570316 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.570316 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:12:p:2775-2791




Template-Type: ReDIF-Article 1.0
Author-Name: Manuel   G. Scotto 
Author-X-Name-First: Manuel   G. 
Author-X-Name-Last: Scotto 
Author-Name: Susana   M. Barbosa 
Author-X-Name-First: Susana   M. 
Author-X-Name-Last: Barbosa 
Author-Name: Andr�s   M. Alonso 
Author-X-Name-First: Andr�s   M. 
Author-X-Name-Last: Alonso 
Title: Extreme value and cluster analysis of European daily temperature series 
Abstract:
  Time series of daily mean temperature obtained from the European Climate
 Assessment data set is analyzed with respect to their extremal properties.
 A time-series clustering approach which combines Bayesian methodology,
 extreme value theory and classification techniques is adopted for the
 analysis of the regional variability of temperature extremes. The daily
 mean temperature records are clustered on the basis of their corresponding
 predictive distributions for 25-, 50- and 100-year return values. The
 results of the cluster analysis show a clear distinction between the
 highest altitude stations, for which the return values are lowest, and the
 remaining stations. Furthermore, a clear distinction is also found between
 the northernmost stations in Scandinavia and the stations in central and
 southern Europe. This spatial structure of the return period distributions
 for 25-, 50- and 100-years seems to be consistent with projected changes
 in the variability of temperature extremes over Europe pointing to a
 different behavior in central Europe than in northern Europe and the
 Mediterranean area, possibly related to the effect of soil moisture and
 land-atmosphere coupling. 
Journal: Journal of Applied Statistics 
Pages: 2793-2804 
Issue: 12 
Volume: 38 
Year: 2011 
Month: 3 
X-DOI: 10.1080/02664763.2011.570317 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.570317 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:12:p:2793-2804




Template-Type: ReDIF-Article 1.0
Author-Name: Peng Bai 
Author-X-Name-First: Peng 
Author-X-Name-Last: Bai 
Author-Name: Wen Gan 
Author-X-Name-First: Wen 
Author-X-Name-Last: Gan 
Author-Name: Lei Shi 
Author-X-Name-First: Lei 
Author-X-Name-Last: Shi 
Title: Bayesian confidence interval for the risk ratio in a correlated 2 &#xD7; 2 table with structural zero 
Abstract:
  This paper studies the construction of a Bayesian confidence interval for
 the risk ratio (RR) in a 2 &#xD7; 2 table with structural zero. Under a
 Dirichlet prior distribution, the exact posterior distribution of the RR
 is derived, and tail-based interval is suggested for constructing Bayesian
 confidence interval. The frequentist performance of this confidence
 interval is investigated by simulation and compared with the score-based
 interval in terms of the mean coverage probability and mean expected width
 of the interval. An advantage of the Bayesian confidence interval is that
 it is well defined for all data structure and has shorter expected width.
 Our simulation shows that the Bayesian tail-based interval under
 Jeffreys&#x2019; prior performs as well as or better than the score-based
 confidence interval. 
Journal: Journal of Applied Statistics 
Pages: 2805-2817 
Issue: 12 
Volume: 38 
Year: 2011 
Month: 2 
X-DOI: 10.1080/02664763.2011.570318 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.570318 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:12:p:2805-2817




Template-Type: ReDIF-Article 1.0
Author-Name: V�ctor Leiva 
Author-X-Name-First: V�ctor 
Author-X-Name-Last: Leiva 
Author-Name: Emilia Athayde 
Author-X-Name-First: Emilia 
Author-X-Name-Last: Athayde 
Author-Name: Cecilia Azevedo 
Author-X-Name-First: Cecilia 
Author-X-Name-Last: Azevedo 
Author-Name: Carolina Marchant 
Author-X-Name-First: Carolina 
Author-X-Name-Last: Marchant 
Title: Modeling wind energy flux by a Birnbaum--Saunders distribution with an unknown shift parameter 
Abstract:
  In this paper, we discuss a Birnbaum--Saunders distribution with an
 unknown shift parameter and apply it to wind energy modeling. We describe
 structural aspects of this distribution including properties, moments,
 mode and hazard and shape analyses. We also discuss estimation, goodness
 of fit and diagnostic methods for this distribution. A computational
 implementation in <monospace>R</monospace> language of the obtained
 results is provided. Finally, we apply such results to two unpublished
 real wind speed data from Chile, which allows us to show the
 characteristics of this statistical distribution and to model wind energy
 flux. 
Journal: Journal of Applied Statistics 
Pages: 2819-2838 
Issue: 12 
Volume: 38 
Year: 2011 
Month: 2 
X-DOI: 10.1080/02664763.2011.570319 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.570319 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:12:p:2819-2838




Template-Type: ReDIF-Article 1.0
Author-Name: Athanasios   C. Rakitzis 
Author-X-Name-First: Athanasios   C. 
Author-X-Name-Last: Rakitzis 
Author-Name: Demetrios   L. Antzoulakos 
Author-X-Name-First: Demetrios   L. 
Author-X-Name-Last: Antzoulakos 
Title: On the improvement of one-sided <italic>S</italic> control charts 
Abstract:
  The most common charting procedure used for monitoring the variance of
 the distribution of a quality characteristic is the <italic>S</italic>
 control chart. As a Shewhart-type control chart, it is relatively
 insensitive in the quick detection of small and moderate shifts in process
 variance. The performance of the <italic>S</italic> chart can be improved
 by supplementing it with runs rules or by varying the sample size and the
 sampling interval. In this work, we introduce and study one-sided adaptive
 <italic>S</italic> control charts, supplemented or not with one powerful
 runs rule, for detecting increases or decreases in process variation. The
 properties of the proposed control schemes are obtained by using a Markov
 chain approach. Furthermore, a practical guidance for the choice of the
 most suitable control scheme is also provided. 
Journal: Journal of Applied Statistics 
Pages: 2839-2858 
Issue: 12 
Volume: 38 
Year: 2011 
Month: 2 
X-DOI: 10.1080/02664763.2011.570320 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.570320 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:12:p:2839-2858




Template-Type: ReDIF-Article 1.0
Author-Name: P.   G. Sankaran 
Author-X-Name-First: P.   G. 
Author-X-Name-Last: Sankaran 
Author-Name: P. Anisha 
Author-X-Name-First: P. 
Author-X-Name-Last: Anisha 
Title: Shared frailty model for recurrent event data with multiple causes 
Abstract:
  The topic of heterogeneity in the analysis of recurrent event data has
 received considerable attention recent times. Frailty models are widely
 employed in such situations as they allow us to model the heterogeneity
 through common random effect. In this paper, we introduce a shared frailty
 model for gap time distributions of recurrent events with multiple causes.
 The parameters of the model are estimated using EM algorithm. An extensive
 simulation study is used to assess the performance of the method. Finally,
 we apply the proposed model to a real-life data. 
Journal: Journal of Applied Statistics 
Pages: 2859-2868 
Issue: 12 
Volume: 38 
Year: 2011 
Month: 2 
X-DOI: 10.1080/02664763.2011.570321 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.570321 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:12:p:2859-2868




Template-Type: ReDIF-Article 1.0
Author-Name: Jin Zhang 
Author-X-Name-First: Jin 
Author-X-Name-Last: Zhang 
Title: Adaptive normal reference bandwidth based on quantile for kernel density estimation 
Abstract:
  Bandwidth selection is an important problem of kernel density estimation.
 Traditional simple and quick bandwidth selectors usually oversmooth the
 density estimate. Existing sophisticated selectors usually have
 computational difficulties and occasionally do not exist. Besides, they
 may not be robust against outliers in the sample data, and some are highly
 variable, tending to undersmooth the density. In this paper, a highly
 robust simple and quick bandwidth selector is proposed, which adapts to
 different types of densities. 
Journal: Journal of Applied Statistics 
Pages: 2869-2880 
Issue: 12 
Volume: 38 
Year: 2011 
Month: 3 
X-DOI: 10.1080/02664763.2011.570322 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.570322 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:12:p:2869-2880




Template-Type: ReDIF-Article 1.0
Author-Name: Martin Huber 
Author-X-Name-First: Martin 
Author-X-Name-Last: Huber 
Title: Testing for covariate balance using quantile regression and resampling methods 
Abstract:
  Consistency of propensity score matching estimators hinges on the
 propensity score's ability to balance the distributions of covariates in
 the pools of treated and non-treated units. Conventional balance tests
 merely check for differences in covariates&#x2019; means, but cannot
 account for differences in higher moments. For this reason, this paper
 proposes balance tests which test for differences in the entire
 distributions of continuous covariates based on quantile regression (to
 derive Kolmogorov--Smirnov and Cramer--von-Mises--Smirnov-type test
 statistics) and resampling methods (for inference). Simulations suggest
 that these methods are very powerful and capture imbalances related to
 higher moments when conventional balance tests fail to do so. 
Journal: Journal of Applied Statistics 
Pages: 2881-2899 
Issue: 12 
Volume: 38 
Year: 2011 
Month: 2 
X-DOI: 10.1080/02664763.2011.570323 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.570323 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:12:p:2881-2899




Template-Type: ReDIF-Article 1.0
Author-Name: Chien-Hung Chen 
Author-X-Name-First: Chien-Hung 
Author-X-Name-Last: Chen 
Author-Name: Tsung-Shan Tsou 
Author-X-Name-First: Tsung-Shan 
Author-X-Name-Last: Tsou 
Title: Robust likelihood inferences for multivariate correlated data 
Abstract:
  Multivariate normal, due to its well-established theories, is commonly
 utilized to analyze correlated data of various types. However, the
 validity of the resultant inference is, more often than not, erroneous if
 the model assumption fails. We present a modification for making the
 multivariate normal likelihood acclimatize itself to general correlated
 data. The modified likelihood is asymptotically legitimate for any true
 underlying joint distributions so long as they have finite second moments.
 One can, hence, acquire full likelihood inference without knowing the true
 random mechanisms underlying the data. Simulations and real data analysis
 are provided to demonstrate the merit of our proposed parametric robust
 method. 
Journal: Journal of Applied Statistics 
Pages: 2901-2910 
Issue: 12 
Volume: 38 
Year: 2011 
Month: 3 
X-DOI: 10.1080/02664763.2011.573539 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.573539 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:12:p:2901-2910




Template-Type: ReDIF-Article 1.0
Author-Name: Nan Jia 
Author-X-Name-First: Nan 
Author-X-Name-Last: Jia 
Author-Name: Thomas   M. Braun 
Author-X-Name-First: Thomas   M. 
Author-X-Name-Last: Braun 
Title: The adaptive accelerated biased coin design for phase I clinical trials 
Abstract:
  Phase I clinical trials are designed to study several doses of the same
 drug in a small group of patients to determine the maximum tolerated dose
 (MTD), which is defined as the dose that is associated with dose-limiting
 toxicity (DLT) in a desired fraction &#x393; of patients. Durham and
 Flournoy [5] proposed the biased coin design (BCD), which is an
 up-and-down design that assigns a new patient to a dose depending upon
 whether or not the current patient experienced a DLT. However, the BCD in
 its standard form requires the complete follow-up of the current patient
 before the new patient can be assigned a dose. In situations where
 patients&#x2019; follow-up times are relatively long compared to patient
 inter-arrival times, the BCD will result in an impractically long trial
 and cause patients to either have delayed entry into the trial or refusal
 of entry altogether. We propose an adaptive accelerated BCD (aaBCD) that
 generalizes the traditional BCD design algorithm by incorporating an
 adaptive weight function based upon the amount of follow-up of each
 enrolled patient. By doing so, the dose assignment for each eligible
 patient can be determined immediately with no delay, leading to a shorter
 trial overall. We show, via simulation, that the frequency of correctly
 identifying the MTD at the end of the study with the aaBCD, as well as the
 number of patients assigned to the MTD, are comparable to that of the
 traditional BCD design. We also compare the performance of the aaBCD with
 the accelerated BCD (ABCD) of Stylianou and Follman [19], as well as the
 time-to-event continual reassessment method (TITE-CRM) of Cheung and
 Chappell [4]. 
Journal: Journal of Applied Statistics 
Pages: 2911-2924 
Issue: 12 
Volume: 38 
Year: 2011 
Month: 3 
X-DOI: 10.1080/02664763.2011.573540 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.573540 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:12:p:2911-2924




Template-Type: ReDIF-Article 1.0
Author-Name: Xinyu Tang 
Author-X-Name-First: Xinyu 
Author-X-Name-Last: Tang 
Author-Name: Abdus   S. Wahed 
Author-X-Name-First: Abdus   S. 
Author-X-Name-Last: Wahed 
Title: Comparison of treatment regimes with adjustment for auxiliary variables 
Abstract:
  Treatment regimes are algorithms for assigning treatments to patients
 with complex diseases, where treatment consists of more than one episode
 of therapy, potentially with different dosages of the same agent or
 different agents. Sequentially randomized clinical trials are usually
 designed to evaluate and compare the effect of different treatment
 regimes. In such designs, eligible patients are first randomly assigned to
 receive one of the initial treatments. Patients meeting some criteria
 (e.g. no progressive disease) are then randomized to receive one of the
 maintenance treatments. Usually, the procedure continues until all
 treatment options are exhausted. Such multistage treatment assignment
 results in treatment regimes consisting of initial treatments,
 intermediate responses and second-stage treatments. However, methods for
 efficient analysis of sequentially randomized trials have only been
 developed very recently. As a result, earlier clinical trials reported
 results based only on the comparison of stage-specific treatments. In this
 article, we propose a model that applies to comparisons of any combination
 of any number of treatment regimes regardless of the number of stages of
 treatment adjusted for auxiliary variables. Contrasts of treatment regimes
 are tested using the Wald chi-square method. Both the model and Wald
 chi-square tests of contrasts are illustrated through a simulation study
 and an application to a high-risk neuroblastoma study to complement the
 earlier results reported on this study. 
Journal: Journal of Applied Statistics 
Pages: 2925-2938 
Issue: 12 
Volume: 38 
Year: 2011 
Month: 3 
X-DOI: 10.1080/02664763.2011.573541 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.573541 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:12:p:2925-2938




Template-Type: ReDIF-Article 1.0
Author-Name: Harry Haupt 
Author-X-Name-First: Harry 
Author-X-Name-Last: Haupt 
Author-Name: Kathrin Kagerer 
Author-X-Name-First: Kathrin 
Author-X-Name-Last: Kagerer 
Author-Name: Joachim Schnurbus 
Author-X-Name-First: Joachim 
Author-X-Name-Last: Schnurbus 
Title: Cross-validating fit and predictive accuracy of nonlinear quantile regressions 
Abstract:
  The paper proposes a cross-validation method to address the question of
 specification search in a multiple nonlinear quantile regression
 framework. Linear parametric, spline-based partially linear and
 kernel-based fully nonparametric specifications are contrasted as
 competitors using cross-validated weighted <italic>L</italic>
 <sub>1</sub>-norm based goodness-of-fit and prediction error criteria. The
 aim is to provide a fair comparison with respect to estimation accuracy
 and/or predictive ability for different semi- and nonparametric
 specification paradigms. This is challenging as the model dimension cannot
 be estimated for all competitors and the meta-parameters such as kernel
 bandwidths, spline knot numbers and polynomial degrees are difficult to
 compare. General issues of specification comparability and automated
 data-driven meta-parameter selection are discussed. The proposed method
 further allows us to assess the balance between fit and model complexity.
 An extensive Monte Carlo study and an application to a well-known data set
 provide empirical illustration of the method. 
Journal: Journal of Applied Statistics 
Pages: 2939-2954 
Issue: 12 
Volume: 38 
Year: 2011 
Month: 3 
X-DOI: 10.1080/02664763.2011.573542 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.573542 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:12:p:2939-2954




Template-Type: ReDIF-Article 1.0
Author-Name: Roland Langrock 
Author-X-Name-First: Roland 
Author-X-Name-Last: Langrock 
Title: Some applications of nonlinear and non-Gaussian state--space modelling by means of hidden Markov models 
Abstract:
  Nonlinear and non-Gaussian state--space models (SSMs) are fitted to
 different types of time series. The applications include homogeneous and
 seasonal time series, in particular earthquake counts, polio counts,
 rainfall occurrence data, glacial varve data and daily returns on a share.
 The considered SSMs comprise Poisson, Bernoulli, gamma and
 Student-<italic>t</italic> distributions at the observation level.
 Parameter estimations for the SSMs are carried out using a likelihood
 approximation that is obtained after discretization of the state space.
 The approximation can be made arbitrarily accurate, and the approximated
 likelihood is precisely that of a finite-state hidden Markov model (HMM).
 The proposed method enables us to apply standard HMM techniques. It is
 easy to implement and can be extended to all kinds of SSMs in a
 straightforward manner. 
Journal: Journal of Applied Statistics 
Pages: 2955-2970 
Issue: 12 
Volume: 38 
Year: 2011 
Month: 3 
X-DOI: 10.1080/02664763.2011.573543 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.573543 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:12:p:2955-2970




Template-Type: ReDIF-Article 1.0
Author-Name: Filippo Domma 
Author-X-Name-First: Filippo 
Author-X-Name-Last: Domma 
Author-Name: Sabrina Giordano 
Author-X-Name-First: Sabrina 
Author-X-Name-Last: Giordano 
Author-Name: Mariangela Zenga 
Author-X-Name-First: Mariangela 
Author-X-Name-Last: Zenga 
Title: Maximum likelihood estimation in Dagum distribution with censored samples 
Abstract:
  In this work, we show that the Dagum distribution [3] may be a
 competitive model for describing data which include censored observations
 in lifetime and reliability problems. Maximum likelihood estimates of the
 three parameters of the Dagum distribution are determined from samples
 with type I right and type II doubly censored data. We perform an
 empirical analysis using published censored data sets: in certain cases,
 the Dagum distribution fits the data better than other parametric
 distributions that are more commonly used in survival and reliability
 analysis. Graphical comparisons confirm that the Dagum model behaves
 better than a number of competitive distributions in describing the
 empirical hazard rate of the analyzed data. A probability plot to provide
 graphical check of the appropriateness of the Dagum model for right
 censored data is constructed, and the details are given in the appendix.
 Finally, a simulation study that shows the good performance of the maximum
 likelihood estimators of the Dagum shape parameters for finite type II
 doubly censored samples is carried out. 
Journal: Journal of Applied Statistics 
Pages: 2971-2985 
Issue: 12 
Volume: 38 
Year: 2011 
Month: 3 
X-DOI: 10.1080/02664763.2011.578613 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.578613 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:12:p:2971-2985




Template-Type: ReDIF-Article 1.0
Author-Name: Isaac Dialsingh 
Author-X-Name-First: Isaac 
Author-X-Name-Last: Dialsingh 
Title: Multiple testing problems in pharmaceutical statistics 
Journal: Journal of Applied Statistics 
Pages: 2987-2987 
Issue: 12 
Volume: 38 
Year: 2011 
Month: 12 
X-DOI: 10.1080/02664763.2010.536309 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.536309 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:12:p:2987-2987




Template-Type: ReDIF-Article 1.0
Author-Name: Alex Karagrigoriou 
Author-X-Name-First: Alex 
Author-X-Name-Last: Karagrigoriou 
Title: Frailty Models in Survival Analysis 
Journal: Journal of Applied Statistics 
Pages: 2988-2989 
Issue: 12 
Volume: 38 
Year: 2011 
Month: 12 
X-DOI: 10.1080/02664763.2011.559371 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.559371 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:12:p:2988-2989




Template-Type: ReDIF-Article 1.0
Author-Name: Pieter   Bastiaan Ober 
Author-X-Name-First: Pieter   Bastiaan 
Author-X-Name-Last: Ober 
Title: Random Phenomena: Fundamentals of Probability and Statistics for Engineers 
Journal: Journal of Applied Statistics 
Pages: 2989-2990 
Issue: 12 
Volume: 38 
Year: 2011 
Month: 12 
X-DOI: 10.1080/02664763.2011.559372 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.559372 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:12:p:2989-2990




Template-Type: ReDIF-Article 1.0
Author-Name: Han   Lin Shang 
Author-X-Name-First: Han   Lin 
Author-X-Name-Last: Shang 
Title: Bayesian Nonparametrics 
Journal: Journal of Applied Statistics 
Pages: 2990-2990 
Issue: 12 
Volume: 38 
Year: 2011 
Month: 12 
X-DOI: 10.1080/02664763.2011.559374 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.559374 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:12:p:2990-2990




Template-Type: ReDIF-Article 1.0
Author-Name: Yves Laberge 
Author-X-Name-First: Yves 
Author-X-Name-Last: Laberge 
Title: Advising on Research Methods: A Consultant's Companion 
Journal: Journal of Applied Statistics 
Pages: 2991-2991 
Issue: 12 
Volume: 38 
Year: 2011 
Month: 12 
X-DOI: 10.1080/02664763.2011.559375 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.559375 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:12:p:2991-2991




Template-Type: ReDIF-Article 1.0
Author-Name: Han   Lin Shang 
Author-X-Name-First: Han   Lin 
Author-X-Name-Last: Shang 
Title: Non-Parametric Econometrics 
Journal: Journal of Applied Statistics 
Pages: 2992-2992 
Issue: 12 
Volume: 38 
Year: 2011 
Month: 12 
X-DOI: 10.1080/02664763.2011.575999 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.575999 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:12:p:2992-2992




Template-Type: ReDIF-Article 1.0
Author-Name: Kassim   S. Mwitondi 
Author-X-Name-First: Kassim   S. 
Author-X-Name-Last: Mwitondi 
Title: Data Analysis Using SAS ENTERPRISE GUIDE 
Journal: Journal of Applied Statistics 
Pages: 2993-2993 
Issue: 12 
Volume: 38 
Year: 2011 
Month: 12 
X-DOI: 10.1080/02664763.2011.576807 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.576807 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:12:p:2993-2993




Template-Type: ReDIF-Article 1.0
Author-Name: Philippe Castagliola 
Author-X-Name-First: Philippe 
Author-X-Name-Last: Castagliola 
Title: Introduction to Time-Series Modelling 
Journal: Journal of Applied Statistics 
Pages: 2993-2994 
Issue: 12 
Volume: 38 
Year: 2011 
Month: 12 
X-DOI: 10.1080/02664763.2011.583725 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.583725 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:12:p:2993-2994




Template-Type: ReDIF-Article 1.0
Author-Name: Kassim   S. Mwitondi 
Author-X-Name-First: Kassim   S. 
Author-X-Name-Last: Mwitondi 
Title: Interpreting Economic and Social Data 
Journal: Journal of Applied Statistics 
Pages: 2994-2995 
Issue: 12 
Volume: 38 
Year: 2011 
Month: 12 
X-DOI: 10.1080/02664763.2011.583726 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.583726 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:12:p:2994-2995




Template-Type: ReDIF-Article 1.0
Author-Name: Tony Vangeneugden 
Author-X-Name-First: Tony 
Author-X-Name-Last: Vangeneugden 
Author-Name: Geert Molenberghs 
Author-X-Name-First: Geert 
Author-X-Name-Last: Molenberghs 
Author-Name: Geert Verbeke 
Author-X-Name-First: Geert 
Author-X-Name-Last: Verbeke 
Author-Name: Clarice G.B. Dem�trio 
Author-X-Name-First: Clarice G.B. 
Author-X-Name-Last: Dem�trio 
Title: Marginal correlation from an extended random-effects model for repeated and overdispersed counts 
Abstract:
  Vangeneugden <italic>et al.</italic> [15] derived approximate correlation
 functions for longitudinal sequences of general data type, Gaussian and
 non-Gaussian, based on generalized linear mixed-effects models (GLMM).
 Their focus was on binary sequences, as well as on a combination of binary
 and Gaussian sequences. Here, we focus on the specific case of repeated
 count data, important in two respects. First, we employ the model proposed
 by Molenberghs <italic>et al.</italic> [13], which generalizes at the same
 time the Poisson-normal GLMM and the conventional overdispersion models,
 in particular the negative-binomial model. The model flexibly accommodates
 data hierarchies, intra-sequence correlation, and overdispersion. Second,
 means, variances, and joint probabilities can be expressed in closed form,
 allowing for exact intra-sequence correlation expressions. Next to the
 general situation, some important special cases such as exchangeable
 clustered outcomes are considered, producing insightful expressions. The
 closed-form expressions are contrasted with the generic approximate
 expressions of Vangeneugden <italic>et al.</italic> [15]. Data from an
 epileptic-seizures trial are analyzed and correlation functions derived.
 It is shown that the proposed extension strongly outperforms the classical
 GLMM. 
Journal: Journal of Applied Statistics 
Pages: 215-232 
Issue: 2 
Volume: 38 
Year: 2011 
Month: 9 
X-DOI: 10.1080/02664760903406405 
File-URL: http://hdl.handle.net/10.1080/02664760903406405 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:2:p:215-232




Template-Type: ReDIF-Article 1.0
Author-Name: A. F.B. Costa 
Author-X-Name-First: A. F.B. 
Author-X-Name-Last: Costa 
Author-Name: M. A.G. Machado 
Author-X-Name-First: M. A.G. 
Author-X-Name-Last: Machado 
Title: A control chart based on sample ranges for monitoring the covariance matrix of the multivariate processes 
Abstract:
  For the univariate case, the <italic>R</italic> chart and the
 <italic>S</italic> -super-2 chart are the most common charts used for
 monitoring the process dispersion. With the usual sample size of 4 and 5,
 the <italic>R</italic> chart is slightly inferior to the
 <italic>S</italic> -super-2 chart in terms of efficiency in detecting
 process shifts. In this article, we show that for the multivariate case,
 the chart based on the standardized sample ranges, we call the RMAX chart,
 is substantially inferior in terms of efficiency in detecting shifts in
 the covariance matrix than the VMAX chart, which is based on the
 standardized sample variances. The user's familiarity with sample ranges
 is a point in favor of the RMAX chart. An example is presented to
 illustrate the application of the proposed chart. 
Journal: Journal of Applied Statistics 
Pages: 233-245 
Issue: 2 
Volume: 38 
Year: 2011 
Month: 9 
X-DOI: 10.1080/02664760903406413 
File-URL: http://hdl.handle.net/10.1080/02664760903406413 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:2:p:233-245




Template-Type: ReDIF-Article 1.0
Author-Name: Kuo-Chin Lin 
Author-X-Name-First: Kuo-Chin 
Author-X-Name-Last: Lin 
Title: Assessing cumulative logit models via a score test in random effect models 
Abstract:
  The purpose of this article is to develop a goodness-of-fit test based on
 score test statistics for cumulative logit models with extra variation of
 random effects. Two main theorems for the proposed score test statistics
 are derived. In simulation studies, the powers of the proposed tests are
 discussed and the power curve against a variety of dispersion parameters
 and bandwidths is depicted. The proposed method is illustrated by an
 ordinal data set from Mosteller and Tukey [23]. 
Journal: Journal of Applied Statistics 
Pages: 247-259 
Issue: 2 
Volume: 38 
Year: 2011 
Month: 9 
X-DOI: 10.1080/02664760903406421 
File-URL: http://hdl.handle.net/10.1080/02664760903406421 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:2:p:247-259




Template-Type: ReDIF-Article 1.0
Author-Name: N. Crato 
Author-X-Name-First: N. 
Author-X-Name-Last: Crato 
Author-Name: R.   R. Linhares 
Author-X-Name-First: R.   R. 
Author-X-Name-Last: Linhares 
Author-Name: S. R.C. Lopes 
Author-X-Name-First: S. R.C. 
Author-X-Name-Last: Lopes 
Title: &#x3B1;-stable laws for noncoding regions in DNA sequences 
Abstract:
  In this work, we analyze the <italic>long-range dependence</italic>
 parameter for a nucleotide sequence in several different transformations.
 The long-range dependence parameter is estimated by the approximated
 maximum likelihood method, by a novel estimator based on the spectral
 envelope theory, by a regression method based on the periodogram function,
 and also by the <italic>detrended fluctuation analysis</italic> method. We
 study the length distribution of coding and noncoding regions for all
 <italic>Homo sapiens</italic> chromosomes available from the European
 Bioinformatics Institute. The parameter of the tail rate decay is
 estimated by the Hill estimator &#x2C6;&#x3B1;. We show that the tail rate
 decay is greater than 2 for coding regions, while for almost all noncoding
 regions it is less than 2. 
Journal: Journal of Applied Statistics 
Pages: 261-271 
Issue: 2 
Volume: 38 
Year: 2011 
Month: 9 
X-DOI: 10.1080/02664760903406447 
File-URL: http://hdl.handle.net/10.1080/02664760903406447 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:2:p:261-271




Template-Type: ReDIF-Article 1.0
Author-Name: T. Banerjee 
Author-X-Name-First: T. 
Author-X-Name-Last: Banerjee 
Author-Name: G. Grover 
Author-X-Name-First: G. 
Author-X-Name-Last: Grover 
Author-Name: T. Pensi 
Author-X-Name-First: T. 
Author-X-Name-Last: Pensi 
Author-Name: D. Banerjee 
Author-X-Name-First: D. 
Author-X-Name-Last: Banerjee 
Title: Estimation of hazard of death in vertically transmitted HIV-1-infected children for doubly censored failure times and fixed covariates 
Abstract:
  This work estimates the effect of covariates on survival data when times
 of both originating and failure events are interval-censored. Proportional
 hazards model [16] along with log-linear models was applied on a data of
 130 vertically infected HIV-1 children visiting the paediatrics clinic.
 The covariates considered for the analysis were antiretroviral (ARV)
 therapy, age at diagnosis, and change in CD4+T cell count. Change in CD4+T
 cell count was the difference in the last and first count in non-ARV
 therapy group, while in the ARV therapy group the same was considered
 after the start of the treatment. Our findings suggest that children on
 ARV therapy had significantly lower risk of death
 (<italic>p</italic>>0.001). We further investigated the effect of age and
 change in CD4+T cell count on risk of death. These covariates exhibited a
 possible association with risk of death by both the procedures
 (<italic>p</italic>>0.0001). The effect of number of years under ARV
 therapy with diagnosis year as a confounding factor was directly related
 to longevity. The results obtained by the two procedures gave reasonable
 estimates. We conclude that when the lengths of intervals are narrow, we
 can opt for parametric modeling which is less computationally intensive. 
Journal: Journal of Applied Statistics 
Pages: 273-285 
Issue: 2 
Volume: 38 
Year: 2011
Month: 9 
X-DOI: 10.1080/02664760903406454 
File-URL: http://hdl.handle.net/10.1080/02664760903406454 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:2:p:273-285




Template-Type: ReDIF-Article 1.0
Author-Name: Aquiles E.G. Kalatzis 
Author-X-Name-First: Aquiles E.G. 
Author-X-Name-Last: Kalatzis 
Author-Name: Camila   F. Bassetto 
Author-X-Name-First: Camila   F. 
Author-X-Name-Last: Bassetto 
Author-Name: Carlos   R. Azzoni 
Author-X-Name-First: Carlos   R. 
Author-X-Name-Last: Azzoni 
Title: Multicollinearity and financial constraint in investment decisions: a Bayesian generalized ridge regression 
Abstract:
  This paper addresses the investment decisions considering the presence of
 financial constraints of 373 large Brazilian firms from 1997 to 2004,
 using panel data. A Bayesian econometric model was used considering ridge
 regression for multicollinearity problems among the variables in the
 model. Prior distributions are assumed for the parameters, classifying the
 model into random or fixed effects. We used a Bayesian approach to
 estimate the parameters, considering normal and Student <italic>t</italic>
 distributions for the error and assumed that the initial values for the
 lagged dependent variable are not fixed, but generated by a random
 process. The recursive predictive density criterion was used for model
 comparisons. Twenty models were tested and the results indicated that
 multicollinearity does influence the value of the estimated parameters.
 Controlling for capital intensity, financial constraints are found to be
 more important for capital-intensive firms, probably due to their lower
 profitability indexes, higher fixed costs and higher degree of property
 diversification. 
Journal: Journal of Applied Statistics 
Pages: 287-299 
Issue: 2 
Volume: 38 
Year: 2011 
Month: 9 
X-DOI: 10.1080/02664760903406462 
File-URL: http://hdl.handle.net/10.1080/02664760903406462 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:2:p:287-299




Template-Type: ReDIF-Article 1.0
Author-Name: Xu Xiaosi 
Author-X-Name-First: Xu 
Author-X-Name-Last: Xiaosi 
Author-Name: Chen Ying 
Author-X-Name-First: Chen 
Author-X-Name-Last: Ying 
Author-Name: Zheng Haitao 
Author-X-Name-First: Zheng 
Author-X-Name-Last: Haitao 
Title: The comparison of enterprise bankruptcy forecasting method 
Abstract:
  The enterprise bankruptcy forecasting is vital to manage credit risk,
 which can be solved through classifying method. There are three typical
 classifying methods to forecast enterprise bankruptcy: the statistics
 method, the Artificial Neural Network method and the kernel-based learning
 method. The paper introduces the first two methods briefly, and then
 introduces Support Vector Machine (SVM) of the kernel-based learning
 method, and lastly compares the bankruptcy forecasting accuracies of the
 three methods by building the corresponding models with the data of
 China's stock exchange data. From the positive analysis, we can draw a
 conclusion that the SVM method has a higher adaptability and precision to
 forecast enterprise bankruptcy. 
Journal: Journal of Applied Statistics 
Pages: 301-308 
Issue: 2 
Volume: 38 
Year: 2011 
Month: 9 
X-DOI: 10.1080/02664760903406470 
File-URL: http://hdl.handle.net/10.1080/02664760903406470 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:2:p:301-308




Template-Type: ReDIF-Article 1.0
Author-Name: Heonsang Lim 
Author-X-Name-First: Heonsang 
Author-X-Name-Last: Lim 
Author-Name: Bong-Jin Yum 
Author-X-Name-First: Bong-Jin 
Author-X-Name-Last: Yum 
Title: Optimal design of accelerated degradation tests based on Wiener process models 
Abstract:
  Optimal accelerated degradation test (ADT) plans are developed assuming
 that the constant-stress loading method is employed and the degradation
 characteristic follows a Wiener process. Unlike the previous works on
 planning ADTs based on stochastic process models, this article determines
 the test stress levels and the proportion of test units allocated to each
 stress level such that the asymptotic variance of the maximum-likelihood
 estimator of the <italic>q</italic>th quantile of the lifetime
 distribution at the use condition is minimized. In addition, compromise
 plans are also developed for checking the validity of the relationship
 between the model parameters and the stress variable. Finally, using an
 example, sensitivity analysis procedures are presented for evaluating the
 robustness of optimal and compromise plans against the uncertainty in the
 pre-estimated parameter value, and the importance of optimally determining
 test stress levels and the proportion of units allocated to each stress
 level are illustrated. 
Journal: Journal of Applied Statistics 
Pages: 309-325 
Issue: 2 
Volume: 38 
Year: 2011 
Month: 9 
X-DOI: 10.1080/02664760903406488 
File-URL: http://hdl.handle.net/10.1080/02664760903406488 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:2:p:309-325




Template-Type: ReDIF-Article 1.0
Author-Name: P.   P. Balestrassi 
Author-X-Name-First: P.   P. 
Author-X-Name-Last: Balestrassi 
Author-Name: A.   P. Paiva 
Author-X-Name-First: A.   P. 
Author-X-Name-Last: Paiva 
Author-Name: A.   C. Zambroni de Souza 
Author-X-Name-First: A.   C. Zambroni 
Author-X-Name-Last: de Souza 
Author-Name: J.   B. Turrioni 
Author-X-Name-First: J.   B. 
Author-X-Name-Last: Turrioni 
Author-Name: Elmira Popova 
Author-X-Name-First: Elmira 
Author-X-Name-Last: Popova 
Title: A multivariate descriptor method for change-point detection in nonlinear time series 
Abstract:
  The purpose of this paper is to present a novel method that is applied to
 detect dynamic changes in nonlinear time series. The method combines a
 multivariate control chart that monitors the variation of three normalized
 descriptors -- Hjorth's descriptors of activity, mobility and complexity
 -- and is applied to the change-point detection problem of nonlinear time
 series. The approach is estimated using six simulated nonlinear time
 series. In addition, a case study of six time series of short-term
 electricity load consumption was used to illustrate the power of the
 method. 
Journal: Journal of Applied Statistics 
Pages: 327-342 
Issue: 2 
Volume: 38 
Year: 2011 
Month: 9 
X-DOI: 10.1080/02664760903406496 
File-URL: http://hdl.handle.net/10.1080/02664760903406496 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:2:p:327-342




Template-Type: ReDIF-Article 1.0
Author-Name: C.   B. Zeller 
Author-X-Name-First: C.   B. 
Author-X-Name-Last: Zeller 
Author-Name: V.   H. Lachos 
Author-X-Name-First: V.   H. 
Author-X-Name-Last: Lachos 
Author-Name: F.   E. Vilca-Labra 
Author-X-Name-First: F.   E. 
Author-X-Name-Last: Vilca-Labra 
Title: Local influence analysis for regression models with scale mixtures of skew-normal distributions 
Abstract:
  The robust estimation and the local influence analysis for linear
 regression models with scale mixtures of multivariate skew-normal
 distributions have been developed in this article. The main virtue of
 considering the linear regression model under the class of scale mixtures
 of skew-normal distributions is that they have a nice hierarchical
 representation which allows an easy implementation of inference. Inspired
 by the expectation maximization algorithm, we have developed a local
 influence analysis based on the conditional expectation of the
 complete-data log-likelihood function, which is a measurement invariant
 under reparametrizations. This is because the observed data log-likelihood
 function associated with the proposed model is somewhat complex and with
 Cook's well-known approach it can be very difficult to obtain measures of
 the local influence. Some useful perturbation schemes are discussed. In
 order to examine the robust aspect of this flexible class against outlying
 and influential observations, some simulation studies have also been
 presented. Finally, a real data set has been analyzed, illustrating the
 usefulness of the proposed methodology. 
Journal: Journal of Applied Statistics 
Pages: 343-368 
Issue: 2 
Volume: 38 
Year: 2011 
Month: 10 
X-DOI: 10.1080/02664760903406504 
File-URL: http://hdl.handle.net/10.1080/02664760903406504 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:2:p:343-368




Template-Type: ReDIF-Article 1.0
Author-Name: K. Triantafyllopoulos 
Author-X-Name-First: K. 
Author-X-Name-Last: Triantafyllopoulos 
Title: Time-varying vector autoregressive models with stochastic volatility 
Abstract:
  The purpose of this paper is to propose a time-varying vector
 autoregressive model (TV-VAR) for forecasting multivariate time series.
 The model is casted into a state-space form that allows flexible
 description and analysis. The volatility covariance matrix of the time
 series is modelled via inverted Wishart and singular multivariate beta
 distributions allowing a fully conjugate Bayesian inference. Model
 assessment and model comparison are performed via the log-posterior
 function, sequential Bayes factors, the mean of squared standardized
 forecast errors, the mean of absolute forecast errors (known also as mean
 absolute deviation), and the mean forecast error. Bayes factors are also
 used in order to choose the autoregressive (AR) order of the model.
 Multi-step forecasting is discussed in detail and a flexible formula is
 proposed to approximate the forecast function. Two examples, consisting of
 bivariate data of IBM and Microsoft shares and of a 30-dimensional asset
 selection problem, illustrate the methods. For the IBM and Microsoft data
 we discuss model performance and multi-step forecasting in some detail.
 For the basket of 30 assets we discuss sequential portfolio allocation;
 for both data sets our empirical findings suggest that the TV-VAR models
 outperform the widely used vector AR models. 
Journal: Journal of Applied Statistics 
Pages: 369-382 
Issue: 2 
Volume: 38 
Year: 2011 
Month: 9 
X-DOI: 10.1080/02664760903406512 
File-URL: http://hdl.handle.net/10.1080/02664760903406512 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:2:p:369-382




Template-Type: ReDIF-Article 1.0
Author-Name: Christian   H. Wei&#xDF; 
Author-X-Name-First: Christian   H. 
Author-X-Name-Last: Wei&#xDF; 
Title: Detecting mean increases in Poisson INAR(1) processes with EWMA control charts 
Abstract:
  Processes of serially dependent Poisson counts are commonly observed in
 real-world applications and can often be modeled by the first-order
 integer-valued autoregressive (INAR) model. For detecting positive shifts
 in the mean of a Poisson INAR(1) process, we propose the one-sided s
 exponentially weighted moving average (EWMA) control chart, which is based
 on a new type of rounding operation. The s-EWMA chart allows computing
 average run length (ARLs) exactly and efficiently with a Markov chain
 approach. Using an implementation of this procedure for ARL computation,
 the s-EWMA chart is easily designed, which is demonstrated with a
 real-data example. Based on an extensive study of ARLs, the out-of-control
 performance of the chart is analyzed and compared with that of a
 <italic>c</italic> chart and a one-sided cumulative sum (CUSUM) chart. We
 also investigate the robustness of the chart against departures from the
 assumed Poisson marginal distribution. 
Journal: Journal of Applied Statistics 
Pages: 383-398 
Issue: 2 
Volume: 38 
Year: 2011 
Month: 9 
X-DOI: 10.1080/02664760903406520 
File-URL: http://hdl.handle.net/10.1080/02664760903406520 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:2:p:383-398




Template-Type: ReDIF-Article 1.0
Author-Name: Amitava Mukherjee 
Author-X-Name-First: Amitava 
Author-X-Name-Last: Mukherjee 
Author-Name: Barendra Purkait 
Author-X-Name-First: Barendra 
Author-X-Name-Last: Purkait 
Title: Simultaneous semi-sequential testing of dual alternatives for pattern recognition 
Abstract:
  In this paper, we propose a new nonparametric simultaneous test for dual
 alternatives. Simultaneous tests for dual alternatives are used for
 pattern detection of <italic>arsenic contamination</italic> level in
 ground water. We consider two possible patterns, namely, monotone shift
 and an umbrella-type location alternative, as the dual alternatives.
 Pattern recognition problems of this nature are addressed in Bandyopadhyay
 <italic>et al.</italic> [5], stretching the idea of multiple hypotheses
 tests as in Benjamini and Hochberg [6]. In the present context, we develop
 an alternative approach based on contrasts that helps us to detect three
 underlying pattern much more efficiently. We illustrate the new
 methodology through a motivating example related to highly sensitive issue
 of <italic>arsenic contamination</italic> in ground water. We provide some
 Monte-Carlo studies related to the proposed technique and give a
 comparative study between different detection procedures. We also obtain
 some related asymptotic results. 
Journal: Journal of Applied Statistics 
Pages: 399-419 
Issue: 2 
Volume: 38 
Year: 2011 
Month: 10 
X-DOI: 10.1080/02664760903456392 
File-URL: http://hdl.handle.net/10.1080/02664760903456392 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:2:p:399-419




Template-Type: ReDIF-Article 1.0
Author-Name: Jun Yang 
Author-X-Name-First: Jun 
Author-X-Name-Last: Yang 
Author-Name: Min Xie 
Author-X-Name-First: Min 
Author-X-Name-Last: Xie 
Author-Name: Thong   Ngee Goh 
Author-X-Name-First: Thong   Ngee 
Author-X-Name-Last: Goh 
Title: Outlier identification and robust parameter estimation in a zero-inflated Poisson model 
Abstract:
  The Zero-inflated Poisson distribution has been used in the modeling of
 count data in different contexts. This model tends to be influenced by
 outliers because of the excessive occurrence of zeroes, thus outlier
 identification and robust parameter estimation are important for such
 distribution. Some outlier identification methods are studied in this
 paper, and their applications and results are also presented with an
 example. To eliminate the effect of outliers, two robust parameter
 estimates are proposed based on the trimmed mean and the Winsorized mean.
 Simulation results show the robustness of our proposed parameter
 estimates. 
Journal: Journal of Applied Statistics 
Pages: 421-430 
Issue: 2 
Volume: 38 
Year: 2011 
Month: 10 
X-DOI: 10.1080/02664760903456426 
File-URL: http://hdl.handle.net/10.1080/02664760903456426 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:2:p:421-430




Template-Type: ReDIF-Article 1.0
Author-Name: Andrew Robinson 
Author-X-Name-First: Andrew 
Author-X-Name-Last: Robinson 
Title: BOOK REVIEW 
Journal: Journal of Applied Statistics 
Pages: 431-431 
Issue: 2 
Volume: 38 
Year: 2011 
Month: 2 
X-DOI: 10.1080/02664760903075556 
File-URL: http://hdl.handle.net/10.1080/02664760903075556 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:2:p:431-431




Template-Type: ReDIF-Article 1.0
Author-Name: A.   C. Brooms 
Author-X-Name-First: A.   C. 
Author-X-Name-Last: Brooms 
Title: BOOK REVIEW 
Journal: Journal of Applied Statistics 
Pages: 433-434 
Issue: 2 
Volume: 38 
Year: 2011 
Month: 2 
X-DOI: 10.1080/02664760903075572 
File-URL: http://hdl.handle.net/10.1080/02664760903075572 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:2:p:433-434




Template-Type: ReDIF-Article 1.0
Author-Name: Kassim   S. Mwitondi 
Author-X-Name-First: Kassim   S. 
Author-X-Name-Last: Mwitondi 
Title: BOOK REVIEW 
Journal: Journal of Applied Statistics 
Pages: 435-435 
Issue: 2 
Volume: 38 
Year: 2011 
Month: 2 
X-DOI: 10.1080/02664760903075580 
File-URL: http://hdl.handle.net/10.1080/02664760903075580 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:2:p:435-435




Template-Type: ReDIF-Article 1.0
Author-Name: Wojtek   J. Krzanowski 
Author-X-Name-First: Wojtek   J. 
Author-X-Name-Last: Krzanowski 
Author-Name: David   J. Hand 
Author-X-Name-First: David   J. 
Author-X-Name-Last: Hand 
Title: Testing the difference between two Kolmogorov--Smirnov values in the context of receiver operating characteristic curves 
Abstract:
  The maximum vertical distance between a receiver operating characteristic
 (ROC) curve and its chance diagonal is a common measure of effectiveness
 of the classifier that gives rise to this curve. This measure is known to
 be equivalent to a two-sample Kolmogorov--Smirnov statistic; so the
 absolute difference <italic>D</italic> between two such statistics is
 often used informally as a measure of difference between the corresponding
 classifiers. A significance test of <italic>D</italic> is of great
 practical interest, but the available Kolmogorov--Smirnov distribution
 theory precludes easy analytical construction of such a significance test.
 We, therefore, propose a Monte Carlo procedure for conducting the test,
 using the binormal model for the underlying ROC curves. We provide Splus/R
 routines for the computation, tabulate the results for a number of
 illustrative cases, apply the methods to some practical examples and
 discuss some implications. 
Journal: Journal of Applied Statistics 
Pages: 437-450 
Issue: 3 
Volume: 38 
Year: 2011 
Month: 10 
X-DOI: 10.1080/02664760903456400 
File-URL: http://hdl.handle.net/10.1080/02664760903456400 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:3:p:437-450




Template-Type: ReDIF-Article 1.0
Author-Name: Qingzhao Yu 
Author-X-Name-First: Qingzhao 
Author-X-Name-Last: Yu 
Title: Weighted bagging: a modification of AdaBoost from the perspective of importance sampling 
Abstract:
  We motivate the success of AdaBoost (ADA) in classification problems by
 appealing to an importance sampling perspective. Based on this insight, we
 propose the Weighted Bagging (WB) algorithm, a regularization method that
 naturally extends ADA to solve both classification and regression
 problems. WB uses a part of the available data to build models, and a
 separate part to modify the weights of observations. The method is used
 with categorical and regression tress and is compared with ADA, Boosting,
 Bagging, Random Forest and Support Vector Machine. We apply these methods
 to some real data sets and report some results of simulations. These
 applications and simulations show the effectiveness of WB. 
Journal: Journal of Applied Statistics 
Pages: 451-463 
Issue: 3 
Volume: 38 
Year: 2011 
Month: 10 
X-DOI: 10.1080/02664760903456418 
File-URL: http://hdl.handle.net/10.1080/02664760903456418 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:3:p:451-463




Template-Type: ReDIF-Article 1.0
Author-Name: Vasyl Golosnoy 
Author-X-Name-First: Vasyl 
Author-X-Name-Last: Golosnoy 
Author-Name: Roman Liesenfeld 
Author-X-Name-First: Roman 
Author-X-Name-Last: Liesenfeld 
Title: Interval shrinkage estimators 
Abstract:
  This paper considers estimation of an unknown distribution parameter in
 situations where we believe that the parameter belongs to a finite
 interval. We propose for such situations an interval shrinkage approach
 which combines in a coherent way an unbiased conventional estimator and
 non-sample information about the range of plausible parameter values. The
 approach is based on an infeasible interval shrinkage estimator which
 uniformly dominates the underlying conventional estimator with respect to
 the mean square error criterion. This infeasible estimator allows us to
 obtain useful feasible counterparts. The properties of these feasible
 interval shrinkage estimators are illustrated both in a simulation study
 and in empirical examples. 
Journal: Journal of Applied Statistics 
Pages: 465-477 
Issue: 3 
Volume: 38 
Year: 2011 
Month: 10 
X-DOI: 10.1080/02664760903456434 
File-URL: http://hdl.handle.net/10.1080/02664760903456434 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:3:p:465-477




Template-Type: ReDIF-Article 1.0
Author-Name: Shande Chen 
Author-X-Name-First: Shande 
Author-X-Name-Last: Chen 
Title: A class of confidence intervals for sequential phase II clinical trials with binary outcome 
Abstract:
  The phase II clinical trials often use the binary outcome. Thus,
 accessing the success rate of the treatment is a primary objective for the
 phase II clinical trials. Reporting confidence intervals is a common
 practice for clinical trials. Due to the group sequence design and
 relatively small sample size, many existing confidence intervals for phase
 II trials are much conservative. In this paper, we propose a class of
 confidence intervals for binary outcomes. We also provide a general theory
 to assess the coverage of confidence intervals for discrete distributions,
 and hence make recommendations for choosing the parameter in calculating
 the confidence interval. The proposed method is applied to Simon's [14]
 optimal two-stage design with numerical studies. The proposed method can
 be viewed as an alternative approach for the confidence interval for
 discrete distributions in general. 
Journal: Journal of Applied Statistics 
Pages: 479-489 
Issue: 3 
Volume: 38 
Year: 2011 
Month: 11 
X-DOI: 10.1080/02664760903456442 
File-URL: http://hdl.handle.net/10.1080/02664760903456442 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:3:p:479-489




Template-Type: ReDIF-Article 1.0
Author-Name: Himadri Ghosh 
Author-X-Name-First: Himadri 
Author-X-Name-Last: Ghosh 
Author-Name: M.   A. Iquebal 
Author-X-Name-First: M.   A. 
Author-X-Name-Last: Iquebal 
Author-Name:  Prajneshu 
Author-X-Name-First:  
Author-X-Name-Last: Prajneshu 
Title: Bootstrap study of parameter estimates for nonlinear Richards growth model through genetic algorithm 
Abstract:
  Richards nonlinear growth model, which is a generalization of the
 well-known logistic and Gompertz models, generally provides a realistic
 description of many phenomena. However, this model is very rarely used as
 it is extremely difficult to fit it by employing nonlinear estimation
 procedures. To this end, utility of using a very powerful optimization
 technique of genetic algorithm is advocated. Parametric bootstrap
 methodology is then used to obtain standard errors of the estimates.
 Subsequently, bootstrap confidence-intervals are constructed by two
 methods, viz. the Percentile method, and Bias-corrected and accelerated
 method. The methodology is illustrated by applying it to India's total
 annual foodgrain production time-series data. 
Journal: Journal of Applied Statistics 
Pages: 491-500 
Issue: 3 
Volume: 38 
Year: 2011 
Month: 11 
X-DOI: 10.1080/02664760903521401 
File-URL: http://hdl.handle.net/10.1080/02664760903521401 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:3:p:491-500




Template-Type: ReDIF-Article 1.0
Author-Name: J.   L&#xF3;pez Fidalgo 
Author-X-Name-First: J.   L&#xF3;pez 
Author-X-Name-Last: Fidalgo 
Author-Name: I.   M. Ortiz Rodr�guez 
Author-X-Name-First: I.   M. 
Author-X-Name-Last: Ortiz Rodr�guez 
Author-Name: Weng   Kee Wong 
Author-X-Name-First: Weng   Kee 
Author-X-Name-Last: Wong 
Title: Design issues for population growth models 
Abstract:
  We briefly review and discuss design issues for population growth and
 decline models. We then use a flexible growth and decline model as an
 illustrative example and apply optimal design theory to find optimal
 sampling times for estimating model parameters, specific parameters and
 interesting functions of the model parameters for the model with two real
 applications. Robustness properties of the optimal designs are
 investigated when nominal values or the model is mis-specified, and also
 under a different optimality criterion. To facilitate use of optimal
 design ideas in practice, we also introduce a website for generating a
 variety of optimal designs for popular models from different disciplines. 
Journal: Journal of Applied Statistics 
Pages: 501-512 
Issue: 3 
Volume: 38 
Year: 2011 
Month: 11 
X-DOI: 10.1080/02664760903521419 
File-URL: http://hdl.handle.net/10.1080/02664760903521419 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:3:p:501-512




Template-Type: ReDIF-Article 1.0
Author-Name: Marco Riquelme 
Author-X-Name-First: Marco 
Author-X-Name-Last: Riquelme 
Author-Name: V�ctor Leiva 
Author-X-Name-First: V�ctor 
Author-X-Name-Last: Leiva 
Author-Name: Manuel Galea 
Author-X-Name-First: Manuel 
Author-X-Name-Last: Galea 
Author-Name: Antonio Sanhueza 
Author-X-Name-First: Antonio 
Author-X-Name-Last: Sanhueza 
Title: Influence diagnostics on the coefficient of variation of elliptically contoured distributions 
Abstract:
  In this article, we study the behavior of the coefficient of variation
 (CV) of a random variable that follows a symmetric distribution in the
 real line. Specifically, we estimate this coefficient using the
 maximum-likelihood (ML) method. In addition, we provide asymptotic
 inference for this parameter, which allows us to contrast hypothesis and
 construct confidence intervals. Furthermore, we produce influence
 diagnostics to evaluate the sensitivity of the ML estimate of this
 coefficient when atypical data are present. Moreover, we illustrate the
 obtained results by using financial real data. Finally, we carry out a
 simulation study to detect the potential influence of atypical
 observations on the ML estimator of the CV of a symmetric distribution.
 The illustration and simulation demonstrate the robustness of the ML
 estimation of this coefficient. 
Journal: Journal of Applied Statistics 
Pages: 513-532 
Issue: 3 
Volume: 38 
Year: 2011 
Month: 11 
X-DOI: 10.1080/02664760903521427 
File-URL: http://hdl.handle.net/10.1080/02664760903521427 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:3:p:513-532




Template-Type: ReDIF-Article 1.0
Author-Name: Altaf Hossain 
Author-X-Name-First: Altaf 
Author-X-Name-Last: Hossain 
Author-Name: Mohammed Nasser 
Author-X-Name-First: Mohammed 
Author-X-Name-Last: Nasser 
Title: Comparison of the finite mixture of ARMA-GARCH, back propagation neural networks and support-vector machines in forecasting financial returns 
Abstract:
  The use of GARCH type models and computational-intelligence-based
 techniques for forecasting financial time series has been proved extremely
 successful in recent times. In this article, we apply the finite mixture
 of ARMA-GARCH model instead of AR or ARMA models to compare with the
 standard BP and SVM in forecasting financial time series (daily stock
 market index returns and exchange rate returns). We do not apply the pure
 GARCH model as the finite mixture of the ARMA-GARCH model outperforms the
 pure GARCH model. These models are evaluated on five performance metrics
 or criteria. Our experiment shows that the SVM model outperforms both the
 finite mixture of ARMA-GARCH and BP models in deviation performance
 criteria. In direction performance criteria, the finite mixture of
 ARMA-GARCH model performs better. The memory property of these forecasting
 techniques is also examined using the behavior of forecasted values
 vis-&#xE0;-vis the original values. Only the SVM model shows long memory
 property in forecasting financial returns. 
Journal: Journal of Applied Statistics 
Pages: 533-551 
Issue: 3 
Volume: 38 
Year: 2011 
Month: 11 
X-DOI: 10.1080/02664760903521435 
File-URL: http://hdl.handle.net/10.1080/02664760903521435 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:3:p:533-551




Template-Type: ReDIF-Article 1.0
Author-Name: Yu-Chang Lin 
Author-X-Name-First: Yu-Chang 
Author-X-Name-Last: Lin 
Author-Name: Chao-Yu Chou 
Author-X-Name-First: Chao-Yu 
Author-X-Name-Last: Chou 
Title: Robustness of the EWMA and the combined <italic>X&#xAF;</italic>--EWMA control charts with variable sampling intervals to non-normality 
Abstract:
  The exponentially weighted moving average (EWMA) control charts with
 variable sampling intervals (VSIs) have been shown to be substantially
 quicker than the fixed sampling intervals (FSI) EWMA control charts in
 detecting process mean shifts. The usual assumption for designing a
 control chart is that the data or measurements are normally distributed.
 However, this assumption may not be true for some processes. In the
 present paper, the performances of the EWMA and combined
 <italic>X&#xAF;</italic>--EWMA control charts with VSIs are evaluated
 under non-normality. It is shown that adding the VSI feature to the EWMA
 control charts results in very substantial decreases in the expected time
 to detect shifts in process mean under both normality and non-normality.
 However, the combined <italic>X&#xAF;</italic>--EWMA chart has its false
 alarm rate and its detection ability is affected if the process data are
 not normally distributed. 
Journal: Journal of Applied Statistics 
Pages: 553-570 
Issue: 3 
Volume: 38 
Year: 2011 
Month: 11 
X-DOI: 10.1080/02664760903521443 
File-URL: http://hdl.handle.net/10.1080/02664760903521443 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:3:p:553-570




Template-Type: ReDIF-Article 1.0
Author-Name: Nasir Abbas 
Author-X-Name-First: Nasir 
Author-X-Name-Last: Abbas 
Author-Name: Muhammad Aslam 
Author-X-Name-First: Muhammad 
Author-X-Name-Last: Aslam 
Title: Extending the Bradley--Terry model for paired comparisons to accommodate weights 
Abstract:
  In the method of paired comparisons (PCs), treatments are compared on the
 basis of qualitative characteristics they possess, in the light of their
 sensory evaluations made by judges. However, there may emerge the
 situations where in addition to qualitative merits/worths, judges may
 assign quantitative weights to reflect/specify the relative importance of
 the treatments. In this study, an attempt is made to reconcile the
 qualitative and the quantitative PCs through assigning quantitative
 weights to treatments having qualitative merits using/extending the
 Bradley--Terry (BT) model. Behaviors of the existing BT model and the
 proposed weighted BT model are studied through the test of
 goodness-of-fit. Experimental and simulated data sets are used for
 illustration. 
Journal: Journal of Applied Statistics 
Pages: 571-580 
Issue: 3 
Volume: 38 
Year: 2011 
Month: 11 
X-DOI: 10.1080/02664760903521450 
File-URL: http://hdl.handle.net/10.1080/02664760903521450 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:3:p:571-580




Template-Type: ReDIF-Article 1.0
Author-Name: Reza Pakyari 
Author-X-Name-First: Reza 
Author-X-Name-Last: Pakyari 
Title: Nonparametric mixture analysis of rock crab of the genus <italic>Leptograpsus</italic>  
Abstract:
  A nonparametric mixture analysis has been applied to study morphological
 characteristics of <italic>Leptograpsus</italic> crab. Two Gaussian models
 were also considered, one with the assumption of independent components
 and one with arbitrary relationship between the components. The three
 models then fitted to several combination of variables based on selecting
 different morphological characteristics. It has been observed that the
 nonparametric method gave the best result overall. 
Journal: Journal of Applied Statistics 
Pages: 581-589 
Issue: 3 
Volume: 38 
Year: 2011 
Month: 11 
X-DOI: 10.1080/02664760903521468 
File-URL: http://hdl.handle.net/10.1080/02664760903521468 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:3:p:581-589




Template-Type: ReDIF-Article 1.0
Author-Name: Jiajia Zhang 
Author-X-Name-First: Jiajia 
Author-X-Name-Last: Zhang 
Author-Name: Andrew   B. Lawson 
Author-X-Name-First: Andrew   B. 
Author-X-Name-Last: Lawson 
Title: Bayesian parametric accelerated failure time spatial model and its application to prostate cancer 
Abstract:
  Prostate cancer (PrCA) is the most common cancer diagnosed in American
 men and the second leading cause of death from malignancies. There are
 large geographical variation and racial disparities existing in the
 survival rate of PrCA. Much work on the spatial survival model is based on
 the proportional hazards (PH) model, but few focused on the accelerated
 failure time (AFT) model. In this paper, we investigate the PrCA data of
 Louisiana from the Surveillance, Epidemiology, and End Results program and
 the violation of the PH assumption suggests that the spatial survival
 model based on the AFT model is more appropriate for this data set. To
 account for the possible extra-variation, we consider spatially referenced
 independent or dependent spatial structures. The deviance information
 criterion is used to select a best-fitting model within the Bayesian frame
 work. The results from our study indicate that age, race, stage, and
 geographical distribution are significant in evaluating PrCA survival. 
Journal: Journal of Applied Statistics 
Pages: 591-603 
Issue: 3 
Volume: 38 
Year: 2011 
Month: 11 
X-DOI: 10.1080/02664760903521476 
File-URL: http://hdl.handle.net/10.1080/02664760903521476 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:3:p:591-603




Template-Type: ReDIF-Article 1.0
Author-Name: Jianwen Xu 
Author-X-Name-First: Jianwen 
Author-X-Name-Last: Xu 
Author-Name: Hu Yang 
Author-X-Name-First: Hu 
Author-X-Name-Last: Yang 
Title: On the restricted almost unbiased estimators in linear regression 
Abstract:
  In this paper, the restricted almost unbiased ridge regression estimator
 and restricted almost unbiased Liu estimator are introduced for the vector
 of parameters in a multiple linear regression model with linear
 restrictions. The bias, variance matrices and mean square error (MSE) of
 the proposed estimators are derived and compared. It is shown that the
 proposed estimators will have smaller quadratic bias but larger variance
 than the corresponding competitors in literatures. However, they will
 respectively outperform the latter according to the MSE criterion under
 certain conditions. Finally, a simulation study and a numerical example
 are given to illustrate some of the theoretical results. 
Journal: Journal of Applied Statistics 
Pages: 605-617 
Issue: 3 
Volume: 38 
Year: 2011 
Month: 11 
X-DOI: 10.1080/02664760903521484 
File-URL: http://hdl.handle.net/10.1080/02664760903521484 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:3:p:605-617




Template-Type: ReDIF-Article 1.0
Author-Name: Suely   Ruiz Giolo 
Author-X-Name-First: Suely   Ruiz 
Author-X-Name-Last: Giolo 
Author-Name: Clarice   Garcia Borges Dem�trio 
Author-X-Name-First: Clarice   Garcia Borges 
Author-X-Name-Last: Dem�trio 
Title: A frailty modeling approach for parental effects in animal breeding 
Abstract:
  Survival models involving frailties are commonly applied in studies where
 correlated event time data arise due to natural or artificial clustering.
 In this paper we present an application of such models in the animal
 breeding field. Specifically, a mixed survival model with a multivariate
 correlated frailty term is proposed for the analysis of data from over
 3611 Brazilian Nellore cattle. The primary aim is to evaluate parental
 genetic effects on the trait length in days that their progeny need to
 gain a commercially specified standard weight gain. This trait is not
 measured directly but can be estimated from growth data. Results point to
 the importance of genetic effects and suggest that these models constitute
 a valuable data analysis tool for beef cattle breeding. 
Journal: Journal of Applied Statistics 
Pages: 619-629 
Issue: 3 
Volume: 38 
Year: 2011 
Month: 11 
X-DOI: 10.1080/02664760903521492 
File-URL: http://hdl.handle.net/10.1080/02664760903521492 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:3:p:619-629




Template-Type: ReDIF-Article 1.0
Author-Name: R.   K. Mishra 
Author-X-Name-First: R.   K. 
Author-X-Name-Last: Mishra 
Author-Name: Zillur Rahman 
Author-X-Name-First: Zillur 
Author-X-Name-Last: Rahman 
Title: Nonparametric approach to rank global petroleum business opportunities 
Abstract:
  Crude oil continues to be one of the significant energy sources. Several
 countries do not have enough indigenous oil and gas resources. These
 countries resort to overseas business of Exploration and Production (E&P)
 of oil to secure a stable supply. Profitability, risk and growth guide
 overseas investment decisions. Selection of overseas investment
 opportunities are critical for a firm because of uncertainty in
 identifying and quantifying the attendant geological, commercial, social
 and political risks as well as return on investment. To secure overseas
 oil acreage, business entities intend to invest in overseas E&P
 destination having reasonable petroleum reserve, favorable contract terms
 (fiscal terms), well-developed infrastructure, sound legal system, minimum
 country risk (CR) (economic, social and political) and facilitate relative
 ease to do business in that country. The countries have varied mix of
 these parameters, and it leads to growing concern to screen and rank
 overseas investment opportunities. Methodologies to rank global
 opportunities should take into consideration the risk factors such as
 petroleum potential, infrastructure, geo-political scenario, contract
 terms, etc. We coin the term for the numerical rank as Globalization Index
 (GI), which is a function of the factors considered to affect the decision
 of a business entity in screening the global destinations for venturing in
 to E&P business of crude oil. This paper is an attempt to model these
 factors by invoking Alternating Conditional Expectation methodology to
 find GI. 
Journal: Journal of Applied Statistics 
Pages: 631-646 
Issue: 3 
Volume: 38 
Year: 2011 
Month: 11 
X-DOI: 10.1080/02664760903563601 
File-URL: http://hdl.handle.net/10.1080/02664760903563601 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:3:p:631-646




Template-Type: ReDIF-Article 1.0
Author-Name: George Saridakis 
Author-X-Name-First: George 
Author-X-Name-Last: Saridakis 
Title: Violent crime and incentives in the long-run: evidence from England and Wales 
Abstract:
  This study uses recent advances in time-series econometrics to
 investigate the non-stationarity and co-integration properties of violent
 crime series in England and Wales. In particular, we estimate the long-run
 impact of economic conditions, beer consumption and various deterrents on
 different categories of recorded violent crime. The results suggest that a
 long-run causal model exists for only minor crimes of violence, with beer
 consumption being a predominant factor. 
Journal: Journal of Applied Statistics 
Pages: 647-660 
Issue: 4 
Volume: 38 
Year: 2011 
Month: 11 
X-DOI: 10.1080/02664760903563619 
File-URL: http://hdl.handle.net/10.1080/02664760903563619 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:4:p:647-660




Template-Type: ReDIF-Article 1.0
Author-Name: Antonio F. B. Costa 
Author-X-Name-First: Antonio F. B. 
Author-X-Name-Last: Costa 
Author-Name: Philippe Castagliola 
Author-X-Name-First: Philippe 
Author-X-Name-Last: Castagliola 
Title: Effect of measurement error and autocorrelation on the <italic>X&#xAF;</italic> chart 
Abstract:
  Measurement error and autocorrelation often exist in quality control
 applications. Both have an adverse effect on the <italic>X&#xAF;</italic>
 chart's performance. To counteract the undesired effect of
 autocorrelation, we build-up the samples with non-neighbouring items,
 according to the time they were produced. To counteract the undesired
 effect of measurement error, we measure the quality characteristic of each
 item of the sample several times. The chart's performance is assessed when
 multiple measurements are applied and the samples are built by taking one
 item from the production line and skipping one, two or more before
 selecting the next. 
Journal: Journal of Applied Statistics 
Pages: 661-673 
Issue: 4 
Volume: 38 
Year: 2011 
Month: 12 
X-DOI: 10.1080/02664760903563627 
File-URL: http://hdl.handle.net/10.1080/02664760903563627 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:4:p:661-673




Template-Type: ReDIF-Article 1.0
Author-Name: Pao-Sheng Shen 
Author-X-Name-First: Pao-Sheng 
Author-X-Name-Last: Shen 
Title: Semiparametric analysis of transformation models with doubly censored data 
Abstract:
  Double censoring arises when <italic>T</italic> represents an outcome
 variable that can only be accurately measured within a certain range,
 [<italic>L, U</italic>], where <italic>L</italic> and <italic>U</italic>
 are the left- and right-censoring variables, respectively. In this note,
 using Martingale arguments of Chen <italic>et al.</italic> [3], we propose
 an estimator (denoted by &#x2DC;&#x3B2;) for estimating regression
 coefficients of transformation model when <italic>L</italic> is always
 observed. Under Cox proportional hazards model, the proposed estimator is
 equivalent to the partial likelihood estimator for left-truncated and
 right-censored data if the left-censoring variables <italic>L</italic>
 were regarded as left-truncated variables. In this case, the estimator
 &#x2DC;&#x3B2; can be obtained by the standard software. A simulation
 study is conducted to investigate the performance of &#x2DC;&#x3B2;. For
 the purpose of comparison, the simulation study also includes the
 estimator proposed by Cai and Cheng [2] for the case when
 <italic>L</italic> and <italic>U</italic> are always observed. 
Journal: Journal of Applied Statistics 
Pages: 675-682 
Issue: 4 
Volume: 38 
Year: 2011 
Month: 11 
X-DOI: 10.1080/02664760903563635 
File-URL: http://hdl.handle.net/10.1080/02664760903563635 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:4:p:675-682




Template-Type: ReDIF-Article 1.0
Author-Name: Ehab   F. Abd-Elfattah 
Author-X-Name-First: Ehab   F. 
Author-X-Name-Last: Abd-Elfattah 
Author-Name: Ronald   W. Butler 
Author-X-Name-First: Ronald   W. 
Author-X-Name-Last: Butler 
Title: Tests for symmetry with right censoring 
Abstract:
  Permutation tests for symmetry are suggested using data that are subject
 to right censoring. Such tests are directly relevant to the assumptions
 that underlie the generalized Wilcoxon test since the symmetric logistic
 distribution for log-errors has been used to motivate Wilcoxon scores in
 the censored accelerated failure time model. Its principal competitor is
 the log-rank (LGR) test motivated by an extreme value error distribution
 that is positively skewed. The proposed one-sided tests for symmetry
 against the alternative of positive skewness are directly relevant to the
 choice between usage of these two tests. The permutation tests use
 statistics from the weighted LGR class normally used for making two-sample
 comparisons. From this class, the test using LGR weights (all weights
 equal) showed the greatest discriminatory power in simulations that
 compared the possibility of logistic errors versus extreme value errors.
 In the test construction, a median estimate, determined by inverting the
 Kaplan--Meier estimator, is used to divide the data into a
 &#x201C;control&#x201D; group to its left that is compared with a
 &#x201C;treatment&#x201D; group to its right. As an unavoidable
 consequence of testing symmetry, data in the control group that have been
 censored become uninformative in performing this two-sample test. Thus,
 early heavy censoring of data can reduce the effective sample size of the
 control group and result in diminished power for discriminating symmetry
 in the population distribution. 
Journal: Journal of Applied Statistics 
Pages: 683-693 
Issue: 4 
Volume: 38 
Year: 2011 
Month: 12 
X-DOI: 10.1080/02664760903563643 
File-URL: http://hdl.handle.net/10.1080/02664760903563643 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:4:p:683-693




Template-Type: ReDIF-Article 1.0
Author-Name: Liliane Bel 
Author-X-Name-First: Liliane 
Author-X-Name-Last: Bel 
Author-Name: Avner Bar-Hen 
Author-X-Name-First: Avner 
Author-X-Name-Last: Bar-Hen 
Author-Name: R�my Petit 
Author-X-Name-First: R�my 
Author-X-Name-Last: Petit 
Author-Name: Rachid Cheddadi 
Author-X-Name-First: Rachid 
Author-X-Name-Last: Cheddadi 
Title: Spatio-temporal functional regression on paleoecological data 
Abstract:
  There is much interest in predicting the impact of global warming on the
 genetic diversity of natural populations and the influence of climate on
 biodiversity is an important ecological question. Since Holocene, we face
 many climate perturbations and the geographical ranges of plant taxa have
 changed substantially. Actual genetic diversity of plant is a result of
 these processes and a first step to study the impact of future climate
 change is to understand the important features of reconstructed climate
 variables such as temperature or precipitation for the last 15,000 years
 on actual genetic diversity of forest. We model the relationship between
 genetic diversity in the European beech (Fagus sylvatica) forests and
 curves of temperature and precipitation reconstructed from pollen
 databases. Our model links the genetic measure to the climate curves. We
 adapt classical functional linear model to take into account interactions
 between climate variables as a bilinear form. Since the data are
 georeferenced, our extensions also account for the spatial dependence
 among the observations. The practical issues of these methodological
 extensions are discussed. 
Journal: Journal of Applied Statistics 
Pages: 695-704 
Issue: 4 
Volume: 38 
Year: 2011 
Month: 11 
X-DOI: 10.1080/02664760903563650 
File-URL: http://hdl.handle.net/10.1080/02664760903563650 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:4:p:695-704




Template-Type: ReDIF-Article 1.0
Author-Name: G. Muniz Terrera 
Author-X-Name-First: G. 
Author-X-Name-Last: Muniz Terrera 
Author-Name: A. van den Hout 
Author-X-Name-First: A. 
Author-X-Name-Last: van den Hout 
Author-Name: F.   E. Matthews 
Author-X-Name-First: F.   E. 
Author-X-Name-Last: Matthews 
Title: Random change point models: investigating cognitive decline in the presence of missing data 
Abstract:
  With the aim of identifying the age of onset of change in the rate of
 cognitive decline while accounting for the missing observations, we
 considered a selection modelling framework. A random change point model
 was fitted to data from a population-based longitudinal study of ageing
 (the Cambridge City over 75 Cohort Study) to model the longitudinal
 process. A missing at random mechanism was modelled using logistic
 regression. Random effects such as initial cognitive status, rate of
 decline before and after the change point, and the age of onset of change
 in rate of decline were estimated after adjustment for risk factors for
 cognitive decline. Among other possible predictors, the last observed
 cognitive score was used to adjust the probability of death and dropout.
 Individuals who experienced less variability in their cognitive scores
 experienced a change in their rate of decline at older ages than
 individuals whose cognitive scores varied more. 
Journal: Journal of Applied Statistics 
Pages: 705-716 
Issue: 4 
Volume: 38 
Year: 2011 
Month: 11 
X-DOI: 10.1080/02664760903563668 
File-URL: http://hdl.handle.net/10.1080/02664760903563668 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:4:p:705-716




Template-Type: ReDIF-Article 1.0
Author-Name: Bi-Min Hsu 
Author-X-Name-First: Bi-Min 
Author-X-Name-Last: Hsu 
Author-Name: Ming-Hung Shu 
Author-X-Name-First: Ming-Hung 
Author-X-Name-Last: Shu 
Title: A two-phase method for controlling Erlang-failure processes with high reliability 
Abstract:
  Monitoring a failure process and measuring its performance are important
 issues for complex nonrepairable and repairable systems. For a highly
 reliable process, traditional methods for reliability monitoring and
 performance measuring become inapplicable. This paper proposes a new
 two-phase controlling method for monitoring and measuring an
 Erlang-failure process (EFP). In the first-phase controlling method, a
 control chart is used to monitor the EFP condition. When special causes of
 variation have been removed from the EFP and all of the failure times
 plotted on the control chart lie within the control limits, the EFP is
 considered to be in control. However, the in-control EFP still likely
 carries out bad or out-of-lifetime-specification conditions. Thus, its
 lifetime-specification limit is taken into consideration as the
 second-phase controlling method for measuring the in-control EFP
 performance. We propose a lifetime-capability index. Its value has a
 one-to-one corresponding relationship with the lifetime-conforming rate,
 which indicates the lifetime performance of this EFP. Without collecting
 additional data efforts, in-control data gathered from the control chart
 in the first phase is employed to estimate the lifetime-capability index.
 To realize main lifetime-capability of the EFP impacting on downstream
 customers, the lower confidence bound of the estimate of the
 lifetime-capability index, capturing its minimum lifetime capability, is
 considered. The advantage of this two-phase method for controlling the
 failure processes can motivate the manufacturer to develop a
 reliability-monitoring technique, establish an adequate reliability
 improvement program and implement an appropriate analysis to ensure its
 lifetime performance meeting the customers requirement. 
Journal: Journal of Applied Statistics 
Pages: 717-734 
Issue: 4 
Volume: 38 
Year: 2011 
Month: 12 
X-DOI: 10.1080/02664760903563676 
File-URL: http://hdl.handle.net/10.1080/02664760903563676 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:4:p:717-734




Template-Type: ReDIF-Article 1.0
Author-Name: Darryl Holden 
Author-X-Name-First: Darryl 
Author-X-Name-Last: Holden 
Title: Testing for heteroskedasticity in the tobit and probit models 
Abstract:
  Non-constant variance across observations (heteroskedasticity) results in
 the maximum likelihood estimators of tobit and probit model parameters
 being inconsistent. Some of the available tests for constant variance
 across observations (homoskedasticity) are discussed and examined in a
 small Monte Carlo experiment. 
Journal: Journal of Applied Statistics 
Pages: 735-744 
Issue: 4 
Volume: 38 
Year: 2011 
Month: 11 
X-DOI: 10.1080/02664760903563684 
File-URL: http://hdl.handle.net/10.1080/02664760903563684 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:4:p:735-744




Template-Type: ReDIF-Article 1.0
Author-Name: Peter Congdon 
Author-X-Name-First: Peter 
Author-X-Name-Last: Congdon 
Title: Structural equation models for area health outcomes with model selection 
Abstract:
  Recent analyses seeking to explain variation in area health outcomes
 often consider the impact on them of latent measures (i.e. unobserved
 constructs) of population health risk. The latter are typically obtained
 by forms of multivariate analysis, with a small set of latent constructs
 derived from a collection of observed indicators, and a few recent area
 studies take such constructs to be spatially structured rather than
 independent over areas. A confirmatory approach is often applicable to the
 model linking indicators to constructs, based on substantive knowledge of
 relevant risks for particular diseases or outcomes. In this paper,
 population constructs relevant to a particular set of health outcomes are
 derived using an integrated model containing all the manifest variables,
 namely health outcome variables, as well as indicator variables underlying
 the latent constructs. A further feature of the approach is the use of
 variable selection techniques to select significant loadings and factors
 (especially in terms of effects of constructs on health outcomes), so
 ensuring parsimonious models are selected. A case study considers suicide
 mortality and self-harm contrasts in the East of England in relation to
 three latent constructs: deprivation, fragmentation and urbanicity. 
Journal: Journal of Applied Statistics 
Pages: 745-767 
Issue: 4 
Volume: 38 
Year: 2011 
Month: 11 
X-DOI: 10.1080/02664760903563692 
File-URL: http://hdl.handle.net/10.1080/02664760903563692 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:4:p:745-767




Template-Type: ReDIF-Article 1.0
Author-Name: Albert Vexler 
Author-X-Name-First: Albert 
Author-X-Name-Last: Vexler 
Author-Name: Shuling Liu 
Author-X-Name-First: Shuling 
Author-X-Name-Last: Liu 
Author-Name: Enrique   F. Schisterman 
Author-X-Name-First: Enrique   F. 
Author-X-Name-Last: Schisterman 
Title: Nonparametric-likelihood inference based on cost-effectively-sampled-data 
Abstract:
  Costs associated with the evaluation of biomarkers can restrict the
 number of relevant biological samples to be measured. This common problem
 has been dealt with extensively in the epidemiologic and biostatistical
 literature that proposes to apply different cost-efficient procedures,
 including pooling and random sampling strategies. The pooling design has
 been widely addressed as a very efficient sampling method under certain
 parametric assumptions regarding data distribution. When cost is not a
 main factor in the evaluation of biomarkers but measurement is subject to
 a limit of detection, a common instrument limitation on the measurement
 process, the pooling design can partially overcome this instrumental
 limitation. In certain situations, the pooling design can provide data
 that is less informative than a simple random sample; however this is not
 always the case. Pooled-data-based nonparametric inferences have not been
 well addressed in the literature. In this article, a distribution-free
 method based on the empirical likelihood technique is proposed to
 substitute the traditional parametric-likelihood approach, providing the
 true coverage, confidence interval estimation and powerful tests based on
 data obtained after the cost-efficient designs. We also consider several
 nonparametric tests to compare with the proposed procedure. We examine the
 proposed methodology via a broad Monte Carlo study and a real data
 example. 
Journal: Journal of Applied Statistics 
Pages: 769-783 
Issue: 4 
Volume: 38 
Year: 2011 
Month: 2 
X-DOI: 10.1080/02664761003692290 
File-URL: http://hdl.handle.net/10.1080/02664761003692290 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:4:p:769-783




Template-Type: ReDIF-Article 1.0
Author-Name: Jimin Lee 
Author-X-Name-First: Jimin 
Author-X-Name-Last: Lee 
Author-Name: Seunggeun Hyun 
Author-X-Name-First: Seunggeun 
Author-X-Name-Last: Hyun 
Title: Confidence bands for the difference of two survival functions under the additive risk model 
Abstract:
  In many clinical studies, a commonly encountered problem is to compare
 the survival probabilities of two treatments for a given patient with a
 certain set of covariates, and there is often a need to make adjustments
 for other covariates that may affect outcomes. One approach is to plot the
 difference between the two subject-specific predicted survival estimates
 with a simultaneous confidence band. Such a band will provide useful
 information about when these two treatments differ and which treatment has
 a better survival probability. In this paper, we show how to construct
 such a band based on the additive risk model and we use the martingale
 central limit theorem to derive its asymptotic distribution. The proposed
 method is evaluated from a simulation study and is illustrated with two
 real examples. 
Journal: Journal of Applied Statistics 
Pages: 785-797 
Issue: 4 
Volume: 38 
Year: 2011 
Month: 2 
X-DOI: 10.1080/02664761003692308 
File-URL: http://hdl.handle.net/10.1080/02664761003692308 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:4:p:785-797




Template-Type: ReDIF-Article 1.0
Author-Name: E. Silva 
Author-X-Name-First: E. 
Author-X-Name-Last: Silva 
Author-Name: V.   M. Guerrero 
Author-X-Name-First: V.   M. 
Author-X-Name-Last: Guerrero 
Author-Name: D. Pe&#xF1;a 
Author-X-Name-First: D. 
Author-X-Name-Last: Pe&#xF1;a 
Title: Temporal disaggregation and restricted forecasting of multiple population time series 
Abstract:
  This article presents some applications of time-series procedures to
 solve two typical problems that arise when analyzing demographic
 information in developing countries: (1) unavailability of annual time
 series of population growth rates (PGRs) and their corresponding
 population time series and (2) inappropriately defined population growth
 goals in official population programs. These problems are considered as
 situations that require combining information of population time series.
 Firstly, we suggest the use of temporal disaggregation techniques to
 combine census data with vital statistics information in order to estimate
 annual PGRs. Secondly, we apply multiple restricted forecasting to combine
 the official targets on future PGRs with the disaggregated series. Then,
 we propose a mechanism to evaluate the compatibility of the demographic
 goals with the annual data. We apply the aforementioned procedures to data
 of the Mexico City Metropolitan Zone divided by concentric rings and
 conclude that the targets established in the official program are not
 feasible. Hence, we derive future PGRs that are both in line with the
 official targets and with the historical demographic behavior. We conclude
 that growth population programs should be based on this kind of analysis
 to be supported empirically. So, through specialized multivariate
 time-series techniques, we propose to obtain first an optimal estimate of
 a disaggregate vector of population time series and then, produce
 restricted forecasts in agreement with some data-based population policies
 here derived. 
Journal: Journal of Applied Statistics 
Pages: 799-815 
Issue: 4 
Volume: 38 
Year: 2011 
Month: 1 
X-DOI: 10.1080/02664761003692316 
File-URL: http://hdl.handle.net/10.1080/02664761003692316 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:4:p:799-815




Template-Type: ReDIF-Article 1.0
Author-Name: Z.   Rezaei Ghahroodi 
Author-X-Name-First: Z.   Rezaei 
Author-X-Name-Last: Ghahroodi 
Author-Name: M. Ganjali 
Author-X-Name-First: M. 
Author-X-Name-Last: Ganjali 
Author-Name: F. Harandi 
Author-X-Name-First: F. 
Author-X-Name-Last: Harandi 
Author-Name: D. Berridge 
Author-X-Name-First: D. 
Author-X-Name-Last: Berridge 
Title: Bivariate transition model for analysing ordinal and nominal categorical responses: an application to the Labour Force Survey data 
Abstract:
  In many panel studies, bivariate ordinal--nominal responses are measured
 and the aim is to investigate the effects of explanatory variables on
 these responses. A regression analysis for these types of data must allow
 for the correlation among responses of the same individual. To analyse
 such ordinal--nominal responses using a proper weighting approach, an
 ordinal--nominal bivariate transition model is proposed and maximum
 likelihood is used to find the parameter estimates. We propose a method in
 which the likelihood function can be partitioned to make possible the use
 of existing software. The approach is applied to the Labour Force Survey
 data in Iran, where the ordinal response, at the first period, is the
 duration of unemployment for unemployed people and the nominal response,
 in the second period, is economic activity status of these individuals.
 The interest is to find the reasons for staying unemployed or moving to
 another status of economic activity. 
Journal: Journal of Applied Statistics 
Pages: 817-832 
Issue: 4 
Volume: 38 
Year: 2011 
Month: 2 
X-DOI: 10.1080/02664761003692324 
File-URL: http://hdl.handle.net/10.1080/02664761003692324 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:4:p:817-832




Template-Type: ReDIF-Article 1.0
Author-Name: A. Sch&#xF6;rgendorfer 
Author-X-Name-First: A. 
Author-X-Name-Last: Sch&#xF6;rgendorfer 
Author-Name: L.   V. Madden 
Author-X-Name-First: L.   V. 
Author-X-Name-Last: Madden 
Author-Name: A.   C. Bathke 
Author-X-Name-First: A.   C. 
Author-X-Name-Last: Bathke 
Title: Choosing appropriate covariance matrices in a nonparametric analysis of factorials in block designs 
Abstract:
  The standard nonparametric, rank-based approach to the analysis of
 dependent data from factorial designs is based on an estimated
 unstructured (UN) variance--covariance matrix, but the large number of
 variance--covariance terms in many designs can seriously affect test
 performance. In a simulation study for a factorial arranged in blocks, we
 compared estimates of type-I error probability and power based on the UN
 structure with the estimates obtained with a more parsimonious
 heterogeneous-compound-symmetry structure (CSH). Although tests based on
 the UN structure were anti-conservative with small number of factor
 levels, especially with four or six blocks, they became conservative at
 higher number of factor levels. Tests based on the CSH structure were
 anti-conservative, and results did not depend on the number of factor
 levels. When both tests were anti-conservative, tests based on the CSH
 structure were less so. Although use of the CSH structure is concluded to
 be more suitable than use of the UN structure for the small number of
 blocks typical in agricultural experiments, results suggest that further
 improvement of test statistics is needed for such situations. 
Journal: Journal of Applied Statistics 
Pages: 833-850 
Issue: 4 
Volume: 38 
Year: 2011 
Month: 1 
X-DOI: 10.1080/02664761003692332 
File-URL: http://hdl.handle.net/10.1080/02664761003692332 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:4:p:833-850




Template-Type: ReDIF-Article 1.0
Author-Name: Diego   F. de Bernardini 
Author-X-Name-First: Diego   F. 
Author-X-Name-Last: de Bernardini 
Author-Name: Laura L.R. Rifo 
Author-X-Name-First: Laura L.R. 
Author-X-Name-Last: Rifo 
Title: Full Bayesian significance test for extremal distributions 
Abstract:
  A new Bayesian measure of evidence is used for model choice within the
 generalized extreme value family of distributions, given an absolutely
 continuous posterior distribution on the related parametric space. This
 criterion allows quantitative measurement of evidence of any sharp
 hypothesis, with no need of a prior distribution assignment to it. We
 apply this methodology to the testing of the precise hypothesis given by
 the Gumbel model using real data. Performance is compared with usual
 evidence measures, such as Bayes factor, Bayesian information criterion,
 deviance information criterion and descriptive level for deviance
 statistic. 
Journal: Journal of Applied Statistics 
Pages: 851-863 
Issue: 4 
Volume: 38 
Year: 2011 
Month: 1 
X-DOI: 10.1080/02664761003692340 
File-URL: http://hdl.handle.net/10.1080/02664761003692340 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:4:p:851-863




Template-Type: ReDIF-Article 1.0
Author-Name: Miroslav   M. Risti&#x107; 
Author-X-Name-First: Miroslav   M. 
Author-X-Name-Last: Risti&#x107; 
Title: Statistics: A Very Short Introduction 
Journal: Journal of Applied Statistics 
Pages: 865-865 
Issue: 4 
Volume: 38 
Year: 2011 
Month: 4 
X-DOI: 10.1080/02664760903075598 
File-URL: http://hdl.handle.net/10.1080/02664760903075598 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:4:p:865-865




Template-Type: ReDIF-Article 1.0
Author-Name: Andreas Rosenblad 
Author-X-Name-First: Andreas 
Author-X-Name-Last: Rosenblad 
Title: The Concise Encyclopedia of Statistics 
Journal: Journal of Applied Statistics 
Pages: 867-868 
Issue: 4 
Volume: 38 
Year: 2011 
Month: 4 
X-DOI: 10.1080/02664760903075614 
File-URL: http://hdl.handle.net/10.1080/02664760903075614 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:4:p:867-868




Template-Type: ReDIF-Article 1.0
Author-Name: Pieter   Bastiaan Ober 
Author-X-Name-First: Pieter   Bastiaan 
Author-X-Name-Last: Ober 
Title: Asymptotic Theory of Statistics and Probability 
Journal: Journal of Applied Statistics 
Pages: 869-869 
Issue: 4 
Volume: 38 
Year: 2011 
Month: 4 
X-DOI: 10.1080/02664760903075630 
File-URL: http://hdl.handle.net/10.1080/02664760903075630 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:4:p:869-869




Template-Type: ReDIF-Article 1.0
Author-Name: Guoqing Wu 
Author-X-Name-First: Guoqing 
Author-X-Name-Last: Wu 
Author-Name: Chao Chen 
Author-X-Name-First: Chao 
Author-X-Name-Last: Chen 
Author-Name: Xuefeng Yan 
Author-X-Name-First: Xuefeng 
Author-X-Name-Last: Yan 
Title: Modified minimum covariance determinant estimator and its application to outlier detection of chemical process data 
Abstract:
  To overcome the main flaw of minimum covariance determinant (MCD)
 estimator, i.e. difficulty to determine its main parameter
 <italic>h</italic>, a modified-MCD (M-MCD) algorithm is proposed. In
 M-MCD, the self-adaptive iteration is proposed to minimize the deflection
 between the standard deviation of robust mahalanobis distance square,
 which is calculated by MCD with the parameter <italic>h</italic> based on
 the sample, and the standard deviation of theoretical mahalanobis distance
 square by adjusting the parameter <italic>h</italic> of MCD. Thus, the
 optimal parameter <italic>h</italic> of M-MCD is determined when the
 minimum deflection is obtained. The results of convergence analysis
 demonstrate that M-MCD has good convergence property. Further, M-MCD and
 MCD were applied to detect outliers for two typical data and chemical
 process data, respectively. The results show that M-MCD can get the
 optimal parameter <italic>h</italic> by using the self-adaptive iteration
 and thus its performances of outlier detection are better than MCD. 
Journal: Journal of Applied Statistics 
Pages: 1007-1020 
Issue: 5 
Volume: 38 
Year: 2011 
Month: 1 
X-DOI: 10.1080/02664761003692456 
File-URL: http://hdl.handle.net/10.1080/02664761003692456 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:5:p:1007-1020




Template-Type: ReDIF-Article 1.0
Author-Name: A.   R. de Leon 
Author-X-Name-First: A.   R. 
Author-X-Name-Last: de Leon 
Author-Name: A. Soo 
Author-X-Name-First: A. 
Author-X-Name-Last: Soo 
Author-Name: T. Williamson 
Author-X-Name-First: T. 
Author-X-Name-Last: Williamson 
Title: Classification with discrete and continuous variables via general mixed-data models 
Abstract:
  We study the problem of classifying an individual into one of several
 populations based on mixed nominal, continuous, and ordinal data.
 Specifically, we obtain a classification procedure as an extension to the
 so-called location linear discriminant function, by specifying a general
 mixed-data model for the joint distribution of the mixed discrete and
 continuous variables. We outline methods for estimating misclassification
 error rates. Results of simulations of the performance of proposed
 classification rules in various settings vis-&#xE0;-vis a robust
 mixed-data discrimination method are reported as well. We give an example
 utilizing data on croup in children. 
Journal: Journal of Applied Statistics 
Pages: 1021-1032 
Issue: 5 
Volume: 38 
Year: 2011 
Month: 2 
X-DOI: 10.1080/02664761003758976 
File-URL: http://hdl.handle.net/10.1080/02664761003758976 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:5:p:1021-1032




Template-Type: ReDIF-Article 1.0
Author-Name: Camillo Cammarota 
Author-X-Name-First: Camillo 
Author-X-Name-Last: Cammarota 
Title: The difference-sign runs length distribution in testing for serial independence 
Abstract:
  We investigate the sequence of difference-sign runs length of a time
 series in the context of non-parametric tests for serial independence.
 This sequence is, under suitable conditioning, a stationary sequence and
 we prove that the normalized correlation of two consecutive runs length is
 small (&#x2248;0.0427). We use this result in a test based on the relative
 entropy of the empirical distribution of the runs length. We investigate
 the performance of the test in simulated series and test serial
 independence of cardiac data series in atrial fibrillation. 
Journal: Journal of Applied Statistics 
Pages: 1033-1043 
Issue: 5 
Volume: 38 
Year: 2011 
Month: 2 
X-DOI: 10.1080/02664761003758984 
File-URL: http://hdl.handle.net/10.1080/02664761003758984 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:5:p:1033-1043




Template-Type: ReDIF-Article 1.0
Author-Name: M.   M. Nassar 
Author-X-Name-First: M.   M. 
Author-X-Name-Last: Nassar 
Author-Name: S.   M. Khamis 
Author-X-Name-First: S.   M. 
Author-X-Name-Last: Khamis 
Author-Name: S.   S. Radwan 
Author-X-Name-First: S.   S. 
Author-X-Name-Last: Radwan 
Title: On Bayesian sample size determination 
Abstract:
  Three Bayesian methods are considered for the determination of sample
 sizes for sampling from the Laplace distribution -- the distribution of
 time between rare events -- with a normal prior. These methods are applied
 to the sizing of aircraft mid-air collisions in a navigation system or
 large flight path deviations of aircraft in air traffic management
 scenarios. A computer program handles all computations and gives a good
 insight about the best suggested method. 
Journal: Journal of Applied Statistics 
Pages: 1045-1054 
Issue: 5 
Volume: 38 
Year: 2011 
Month: 2 
X-DOI: 10.1080/02664761003758992 
File-URL: http://hdl.handle.net/10.1080/02664761003758992 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:5:p:1045-1054




Template-Type: ReDIF-Article 1.0
Author-Name: Bidemi Yusuf 
Author-X-Name-First: Bidemi 
Author-X-Name-Last: Yusuf 
Author-Name: Olayinka Omigbodun 
Author-X-Name-First: Olayinka 
Author-X-Name-Last: Omigbodun 
Author-Name: Babatunde Adedokun 
Author-X-Name-First: Babatunde 
Author-X-Name-Last: Adedokun 
Author-Name: Odunayo Akinyemi 
Author-X-Name-First: Odunayo 
Author-X-Name-Last: Akinyemi 
Title: Identifying predictors of violent behaviour among students using the conventional logistic and multilevel logistic models 
Abstract:
  Analysing individual-, school- and class-level observations is a good and
 efficient approach in epidemiologic research. Using data on violent
 behaviour among secondary school students we compared results from the
 conventional logistic modelling with multilevel logistic modelling
 approach using the gllamm command in Stata. We illustrated the advantage
 of multilevel modelling over the conventional logistic modelling through
 an example of data from violence experience among secondary school
 students. We constructed a logistic model with a random intercept on the
 school and class levels to account for unexplained heterogeneity between
 schools and classes. In the multilevel model, we estimated that, in an
 average school, the odds of experiencing violence are 3 (OR=2.99, 95% CI:
 1.86, 4.81, <italic>p</italic>>0.0001) times higher for students who use
 drugs as opposed to the odds of experiencing violence for students who do
 not use drugs. However, the estimates in the conventional logistic model
 are slightly lower. &#x2003; We estimated that a normally distributed
 random intercept for schools and classes that accounts for any unexplained
 heterogeneity between schools and classes has variances 0.017 and 0.035,
 respectively. We therefore recommend the multilevel logistic modelling
 when data are clustered. 
Journal: Journal of Applied Statistics 
Pages: 1055-1061 
Issue: 5 
Volume: 38 
Year: 2011 
Month: 2 
X-DOI: 10.1080/02664761003759008 
File-URL: http://hdl.handle.net/10.1080/02664761003759008 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:5:p:1055-1061




Template-Type: ReDIF-Article 1.0
Author-Name: Juv&#xEA;ncio   S. Nobre 
Author-X-Name-First: Juv&#xEA;ncio   S. 
Author-X-Name-Last: Nobre 
Author-Name: Julio   M. Singer 
Author-X-Name-First: Julio   M. 
Author-X-Name-Last: Singer 
Title: Leverage analysis for linear mixed models 
Abstract:
  We consider a generalized leverage matrix useful for the identification
 of influential units and observations in linear mixed models and show how
 a decomposition of this matrix may be employed to identify high leverage
 points for both the marginal fitted values and the random effect component
 of the conditional fitted values. We illustrate the different uses of the
 two components of the decomposition with a simulated example as well as
 with a real data set. 
Journal: Journal of Applied Statistics 
Pages: 1063-1072 
Issue: 5 
Volume: 38 
Year: 2011 
Month: 2 
X-DOI: 10.1080/02664761003759016 
File-URL: http://hdl.handle.net/10.1080/02664761003759016 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:5:p:1063-1072




Template-Type: ReDIF-Article 1.0
Author-Name: Arthur Pewsey 
Author-X-Name-First: Arthur 
Author-X-Name-Last: Pewsey 
Author-Name: Kunio Shimizu 
Author-X-Name-First: Kunio 
Author-X-Name-Last: Shimizu 
Author-Name: Rolando de la Cruz 
Author-X-Name-First: Rolando 
Author-X-Name-Last: de la Cruz 
Title: On an extension of the von Mises distribution due to Batschelet 
Abstract:
  This paper considers the three-parameter family of symmetric unimodal
 circular distributions proposed by Batschelet in [1], an extension of the
 von Mises distribution containing distributional forms ranging from the
 highly leptokurtic to the very platykurtic. The family's fundamental
 properties are given, and likelihood-based techniques described which can
 be used to perform estimation and hypothesis testing. Analyses are
 presented of two data sets which illustrate how the family and three of
 its most direct competitors can be applied in the search for parsimonious
 models for circular data. 
Journal: Journal of Applied Statistics 
Pages: 1073-1085 
Issue: 5 
Volume: 38 
Year: 2011 
Month: 2 
X-DOI: 10.1080/02664761003759024 
File-URL: http://hdl.handle.net/10.1080/02664761003759024 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:5:p:1073-1085




Template-Type: ReDIF-Article 1.0
Author-Name: Leakemariam Berhe 
Author-X-Name-First: Leakemariam 
Author-X-Name-Last: Berhe 
Author-Name: G&#xF6;ran Arnoldsson 
Author-X-Name-First: G&#xF6;ran 
Author-X-Name-Last: Arnoldsson 
Title:  <italic>D</italic> <sub> <italic>s</italic> </sub>-optimal designs for Kozak's tree taper model 
Abstract:
  In this work, we study <italic>D</italic> <sub> <italic>s</italic>
 </sub>-optimal design for Kozak's tree taper model. The approximate
 <italic>D</italic> <sub> <italic>s</italic> </sub>-optimal designs are
 found invariant to tree size and hence create a ground to construct a
 general replication-free <italic>D</italic> <sub> <italic>s</italic>
 </sub>-optimal design. Even though the designs are found not to be
 dependent on the parameter value <italic>p</italic> of the Kozak's model,
 they are sensitive to the <italic>s</italic>&#xD7;1 subset parameter
 vector values of the model. The 12 points replication-free design (with
 91% efficiency) suggested in this study is believed to reduce cost and
 time for data collection and more importantly to precisely estimate the
 subset parameters of interest. 
Journal: Journal of Applied Statistics 
Pages: 1087-1102 
Issue: 5 
Volume: 38 
Year: 2011 
Month: 2 
X-DOI: 10.1080/02664761003759925 
File-URL: http://hdl.handle.net/10.1080/02664761003759925 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:5:p:1087-1102




Template-Type: ReDIF-Article 1.0
Author-Name: Artur   J. Lemonte 
Author-X-Name-First: Artur   J. 
Author-X-Name-Last: Lemonte 
Author-Name: Alexandre   G. Patriota 
Author-X-Name-First: Alexandre   G. 
Author-X-Name-Last: Patriota 
Title: Influence diagnostics in Birnbaum--Saunders nonlinear regression models 
Abstract:
  We consider the issue of assessing influence of observations in the class
 of Birnbaum--Saunders nonlinear regression models, which is useful in
 lifetime data analysis. Our results generalize those in Galea <italic>et
 al.</italic> [8] which are confined to Birnbaum--Saunders linear
 regression models. Some influence methods, such as the local influence,
 total local influence of an individual and generalized leverage are
 discussed. Additionally, the normal curvatures for studying local
 influence are derived under some perturbation schemes. We also give an
 application to a real fatigue data set. 
Journal: Journal of Applied Statistics 
Pages: 871-884 
Issue: 5 
Volume: 38 
Year: 2011 
Month: 2 
X-DOI: 10.1080/02664761003692357 
File-URL: http://hdl.handle.net/10.1080/02664761003692357 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:5:p:871-884




Template-Type: ReDIF-Article 1.0
Author-Name: Hani   M. Samawi 
Author-X-Name-First: Hani   M. 
Author-X-Name-Last: Samawi 
Author-Name: Amal Helu 
Author-X-Name-First: Amal 
Author-X-Name-Last: Helu 
Author-Name: Robert Vogel 
Author-X-Name-First: Robert 
Author-X-Name-Last: Vogel 
Title: A nonparametric test of symmetry based on the overlapping coefficient 
Abstract:
  In this paper, we introduce a new nonparametric test of symmetry based on
 the empirical overlap coefficient using kernel density estimation. Our
 investigation reveals that the new test is more powerful than the runs
 test of symmetry proposed by McWilliams [31]. Intensive simulation is
 conducted to examine the power of the proposed test. Data from a level I
 Trauma center are used to illustrate the procedures developed in this
 paper. 
Journal: Journal of Applied Statistics 
Pages: 885-898 
Issue: 5 
Volume: 38 
Year: 2011 
Month: 2 
X-DOI: 10.1080/02664761003692365 
File-URL: http://hdl.handle.net/10.1080/02664761003692365 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:5:p:885-898




Template-Type: ReDIF-Article 1.0
Author-Name: Luis   Mariano Esteban 
Author-X-Name-First: Luis   Mariano 
Author-X-Name-Last: Esteban 
Author-Name: Gerardo Sanz 
Author-X-Name-First: Gerardo 
Author-X-Name-Last: Sanz 
Author-Name: Angel Borque 
Author-X-Name-First: Angel 
Author-X-Name-Last: Borque 
Title: A step-by-step algorithm for combining diagnostic tests 
Abstract:
  Combining data of several tests or markers for the classification of
 patients according to their health status for assigning better treatments
 is a major issue in the study of diseases such as cancer. In order to
 tackle this problem, several approaches have been proposed in the
 literature. In this paper, a step-by-step algorithm for estimating the
 parameters of a linear classifier that combines several measures is
 considered. The optimization criterion is to maximize the area under the
 receiver operating characteristic curve. The algorithm is applied to
 different simulated data sets and its performance is evaluated. Finally,
 the method is illustrated with a prostate cancer staging database. 
Journal: Journal of Applied Statistics 
Pages: 899-911 
Issue: 5 
Volume: 38 
Year: 2011 
Month: 2 
X-DOI: 10.1080/02664761003692373 
File-URL: http://hdl.handle.net/10.1080/02664761003692373 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:5:p:899-911




Template-Type: ReDIF-Article 1.0
Author-Name: Andr�s Farall 
Author-X-Name-First: Andr�s 
Author-X-Name-Last: Farall 
Author-Name: Ricardo Maronna 
Author-X-Name-First: Ricardo 
Author-X-Name-Last: Maronna 
Author-Name: Tom&#xE1;s Tetzlaff 
Author-X-Name-First: Tom&#xE1;s 
Author-X-Name-Last: Tetzlaff 
Title: A mixture model for the detection of <italic>Neosporosis</italic> without a gold standard 
Abstract:
   <italic>Neosporosis</italic> is a bovine disease caused by the parasite
 <italic>Neospora caninum</italic>. It is not yet sufficiently studied, and
 it is supposed to cause an important number of abortions. Its clinical
 symptoms do not yet allow the reliable identification of infected animals.
 Its study and treatment would improve if a test based on antibody counts
 were available. Knowing the distribution functions of observed counts of
 uninfected and infected cows would allow the determination of a cutoff
 value. These distributions cannot be estimated directly. This paper deals
 with the indirect estimation of these distributions based on a data set
 consisting of the antibody counts for some 200 pairs of cows and their
 calves. The desired distributions are estimated through a mixture model
 based on simple assumptions that describe the relationship between each
 cow and its calf. The model then allows the estimation of the cutoff value
 and of the error probabilities. 
Journal: Journal of Applied Statistics 
Pages: 913-926 
Issue: 5 
Volume: 38 
Year: 2011 
Month: 2 
X-DOI: 10.1080/02664761003692381 
File-URL: http://hdl.handle.net/10.1080/02664761003692381 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:5:p:913-926




Template-Type: ReDIF-Article 1.0
Author-Name: Baisuo Jin 
Author-X-Name-First: Baisuo 
Author-X-Name-Last: Jin 
Author-Name: Mong-Na   Lo Huang 
Author-X-Name-First: Mong-Na   Lo 
Author-X-Name-Last: Huang 
Author-Name: Baiqi Miao 
Author-X-Name-First: Baiqi 
Author-X-Name-Last: Miao 
Title: Testing for variance changes in autoregressive models with unknown order 
Abstract:
  The problem of change point in autoregressive process is studied in this
 article. We propose a Bayesian information criterion-iterated cumulative
 sums of squares algorithm to detect the variance changes in an
 autoregressive series with unknown order. Simulation results and two
 examples are presented, where it is shown to have good performances when
 the sample size is relatively small. 
Journal: Journal of Applied Statistics 
Pages: 927-936 
Issue: 5 
Volume: 38 
Year: 2011 
Month: 1 
X-DOI: 10.1080/02664761003692399 
File-URL: http://hdl.handle.net/10.1080/02664761003692399 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:5:p:927-936




Template-Type: ReDIF-Article 1.0
Author-Name: Suzy Van Sanden 
Author-X-Name-First: Suzy 
Author-X-Name-Last: Van Sanden 
Author-Name: Tomasz Burzykowski 
Author-X-Name-First: Tomasz 
Author-X-Name-Last: Burzykowski 
Title: Evaluation of Laplace distribution-based ANOVA models applied to microarray data 
Abstract:
  In a microarray experiment, intensity measurements tend to vary due to
 various systematic and random effects, which enter at the different stages
 of the measurement process. Common test statistics do not take these
 effects into account. An alternative is to use, for example, ANOVA models.
 In many cases, we can, however, not make the assumption of normally
 distributed error terms. Purdom and Holmes [6] have concluded that the
 distribution of microarray intensity measurements can often be better
 approximated by a Laplace distribution. In this paper, we consider the
 analysis of microarray data by using ANOVA models under the assumption of
 Laplace-distributed error terms. We explain the methodology and discuss
 problems related to fitting of this type of models. In addition to
 evaluating the models using several real-life microarray experiments, we
 conduct a simulation study to investigate different aspects of the models
 in detail. We find that, while the normal model is less sensitive to model
 misspecifications, the Laplace model has more power when the data are
 truly Laplace distributed. However, in the latter situation, neither of
 the models is able to control the false discovery rate at the
 pre-specified significance level. This problem is most likely related to
 sample size issues. 
Journal: Journal of Applied Statistics 
Pages: 937-950 
Issue: 5 
Volume: 38 
Year: 2011 
Month: 1 
X-DOI: 10.1080/02664761003692407 
File-URL: http://hdl.handle.net/10.1080/02664761003692407 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:5:p:937-950




Template-Type: ReDIF-Article 1.0
Author-Name: Eren Demir 
Author-X-Name-First: Eren 
Author-X-Name-Last: Demir 
Author-Name: Thierry Chaussalet 
Author-X-Name-First: Thierry 
Author-X-Name-Last: Chaussalet 
Title: Capturing the re-admission process: focus on time window 
Abstract:
  In the majority of studies on patient re-admissions, a re-admission is
 deemed to have occurred if a patient is admitted within a time window of
 the previous discharge date. However, these time windows have rarely been
 objectively justified. We capture the re-admission process from the
 community using a special case of a Coxian phase-type distribution,
 expressed as a mixture of two generalized Erlang distributions. Using the
 Bayes theorem, we compute the optimal time windows in defining
 re-admission. From the national data set in England, we defined
 re-admission for chronic obstructive pulmonary disease (COPD), stroke,
 congestive heart failure, and hip- and thigh-fractured patients as 41, 9,
 37, and 8 days, respectively. These time windows could be used to classify
 patients into two groups (binary response), namely those patients who are
 at high risk (e.g. within 41 days for COPD) and low risk of re-admission
 group (respectively, greater than 41 days). The generality of the
 modelling framework and the capability of supporting a broad class of
 distributions enables the applicability into other domains, to capture the
 process within the field of interest and to determine an appropriate time
 window (a cut-off value) based on evidence objectively derived from
 operational data. 
Journal: Journal of Applied Statistics 
Pages: 951-960 
Issue: 5 
Volume: 38 
Year: 2011 
Month: 12 
X-DOI: 10.1080/02664761003692415 
File-URL: http://hdl.handle.net/10.1080/02664761003692415 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:5:p:951-960




Template-Type: ReDIF-Article 1.0
Author-Name: S. McKay Curtis 
Author-X-Name-First: S. 
Author-X-Name-Last: McKay Curtis 
Author-Name: Sujit   K. Ghosh 
Author-X-Name-First: Sujit   K. 
Author-X-Name-Last: Ghosh 
Title: A variable selection approach to monotonic regression with Bernstein polynomials 
Abstract:
  One of the standard problems in statistics consists of determining the
 relationship between a response variable and a single predictor variable
 through a regression function. Background scientific knowledge is often
 available that suggests that the regression function should have a certain
 shape (e.g. monotonically increasing or concave) but not necessarily a
 specific parametric form. Bernstein polynomials have been used to impose
 certain shape restrictions on regression functions. The Bernstein
 polynomials are known to provide a smooth estimate over equidistant knots.
 Bernstein polynomials are used in this paper due to their ease of
 implementation, continuous differentiability, and theoretical properties.
 In this work, we demonstrate a connection between the monotonic regression
 problem and the variable selection problem in the linear model. We develop
 a Bayesian procedure for fitting the monotonic regression model by
 adapting currently available variable selection procedures. We demonstrate
 the effectiveness of our method through simulations and the analysis of
 real data. 
Journal: Journal of Applied Statistics 
Pages: 961-976 
Issue: 5 
Volume: 38 
Year: 2011 
Month: 2 
X-DOI: 10.1080/02664761003692423 
File-URL: http://hdl.handle.net/10.1080/02664761003692423 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:5:p:961-976




Template-Type: ReDIF-Article 1.0
Author-Name: Gunnar Taraldsen 
Author-X-Name-First: Gunnar 
Author-X-Name-Last: Taraldsen 
Title: Analysis of rounded exponential data 
Abstract:
  The problem of inference based on a rounded random sample from the
 exponential distribution is treated. The main results are given by an
 explicit expression for the maximum-likelihood estimator, a confidence
 interval with a guaranteed level of confidence, and a conjugate class of
 distributions for Bayesian analysis. These results are exemplified on two
 concrete examples. The large and increasing body of results on the topic
 of grouped data has been mostly focused on the effect on the estimators.
 The methods and results for the derivation of confidence intervals here
 are hence of some general theoretical value as a model approach for other
 parametric models. The Bayesian credibility interval recommended in cases
 with a lack of other prior information follows by letting the prior equal
 the inverted exponential with a scale equal to one divided by the
 resolution. It is shown that this corresponds to the standard
 non-informative prior for the scale in the case of non-rounded data. For
 cases with the absence of explicit prior information it is argued that the
 inverted exponential prior with a scale given by the resolution is a
 reasonable choice for more general digitized scale families also. 
Journal: Journal of Applied Statistics 
Pages: 977-986 
Issue: 5 
Volume: 38 
Year: 2011 
Month: 2 
X-DOI: 10.1080/02664761003692431 
File-URL: http://hdl.handle.net/10.1080/02664761003692431 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:5:p:977-986




Template-Type: ReDIF-Article 1.0
Author-Name: Ao Yuan 
Author-X-Name-First: Ao 
Author-X-Name-Last: Yuan 
Author-Name: Guanjie Chen 
Author-X-Name-First: Guanjie 
Author-X-Name-Last: Chen 
Author-Name: Juan Xiong 
Author-X-Name-First: Juan 
Author-X-Name-Last: Xiong 
Author-Name: Wenqing He 
Author-X-Name-First: Wenqing 
Author-X-Name-Last: He 
Author-Name: Wen Jin 
Author-X-Name-First: Wen 
Author-X-Name-Last: Jin 
Author-Name: Charles Rotimi 
Author-X-Name-First: Charles 
Author-X-Name-Last: Rotimi 
Title: Bayesian--frequentist hybrid model with application to the analysis of gene copy number changes 
Abstract:
  Gene copy number (GCN) changes are common characteristics of many genetic
 diseases. Comparative genomic hybridization (CGH) is a new technology
 widely used today to screen the GCN changes in mutant cells with high
 resolution genome-wide. Statistical methods for analyzing such CGH data
 have been evolving. Existing methods are either frequentist's or full
 Bayesian. The former often has computational advantage, while the latter
 can incorporate prior information into the model, but could be misleading
 when one does not have sound prior information. In an attempt to take full
 advantages of both approaches, we develop a Bayesian-frequentist hybrid
 approach, in which a subset of the model parameters is inferred by the
 Bayesian method, while the rest parameters by the frequentist's. This new
 hybrid approach provides advantages over those of the Bayesian or
 frequentist's method used alone. This is especially the case when sound
 prior information is available on part of the parameters, and the sample
 size is relatively small. Spatial dependence and false discovery rate are
 also discussed, and the parameter estimation is efficient. As an
 illustration, we used the proposed hybrid approach to analyze a real CGH
 data. 
Journal: Journal of Applied Statistics 
Pages: 987-1005 
Issue: 5 
Volume: 38 
Year: 2011 
Month: 2 
X-DOI: 10.1080/02664761003692449 
File-URL: http://hdl.handle.net/10.1080/02664761003692449 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:5:p:987-1005




Template-Type: ReDIF-Article 1.0
Author-Name: E. Bahrami Samani 
Author-X-Name-First: E. 
Author-X-Name-Last: Bahrami Samani 
Author-Name: M. Ganjali 
Author-X-Name-First: M. 
Author-X-Name-Last: Ganjali 
Title: Bayesian latent variable model for mixed continuous and ordinal responses with possibility of missing responses 
Abstract:
  A general framework is proposed for joint modelling of mixed correlated
 ordinal and continuous responses with missing values for responses, where
 the missing mechanism for both kinds of responses is also considered.
 Considering the posterior distribution of unknowns given all available
 information, a Markov Chain Monte Carlo sampling algorithm via winBUGS is
 used for estimating the posterior distribution of the parameters. For
 sensitivity analysis to investigate the perturbation from missing at
 random to not missing at random, it is shown how one can use some elements
 of covariance structure. These elements associate responses and their
 missing mechanisms. Influence of small perturbation of these elements on
 posterior displacement and posterior estimates is also studied. The model
 is illustrated using data from a foreign language achievement study. 
Journal: Journal of Applied Statistics 
Pages: 1103-1116 
Issue: 6 
Volume: 38 
Year: 2011 
Month: 3 
X-DOI: 10.1080/02664763.2010.484485 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.484485 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:6:p:1103-1116




Template-Type: ReDIF-Article 1.0
Author-Name: Pablo Mart�nez-Camblor 
Author-X-Name-First: Pablo 
Author-X-Name-Last: Mart�nez-Camblor 
Title: Testing the equality among distribution functions from independent and right censored samples via Cram�r--von Mises criterion 
Abstract:
  The traditional Cram�r--von Mises criterion is used in order to develop a
 test to compare the equality of the underlying lifetime distributions in
 the presence of independent censoring times. Its asymptotic distribution
 is proved and a resampling plan, which is valid for unbalanced data
 situations, is proposed. Its statistical power is studied and compared
 with commonly used linear rank tests by Monte Carlo simulations and a real
 data analysis is also considered. It is observed that the new test is
 clearly more powerful than the traditional ones when there exists
 <italic>no uniform dominance</italic> among involved distributions and in
 the presence of late differences. Its statistical power is also good in
 the other considered scenarios. 
Journal: Journal of Applied Statistics 
Pages: 1117-1131 
Issue: 6 
Volume: 38 
Year: 2011 
Month: 3 
X-DOI: 10.1080/02664763.2010.484486 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.484486 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:6:p:1117-1131




Template-Type: ReDIF-Article 1.0
Author-Name: Chi   Tim Ng 
Author-X-Name-First: Chi   Tim 
Author-X-Name-Last: Ng 
Author-Name: Johan Lim 
Author-X-Name-First: Johan 
Author-X-Name-Last: Lim 
Author-Name: Kyu   S. Hahn 
Author-X-Name-First: Kyu   S. 
Author-X-Name-Last: Hahn 
Title: Testing stochastic orders in tails of contingency tables 
Abstract:
  Testing for the difference in the strength of bivariate association in
 two independent contingency tables is an important issue that finds
 applications in various disciplines. Currently, many of the commonly used
 tests are based on single-index measures of association. More
 specifically, one obtains single-index measurements of association from
 two tables and compares them based on asymptotic theory. Although they are
 usually easy to understand and use, often much of the information
 contained in the data is lost with single-index measures. Accordingly,
 they fail to fully capture the association in the data. To remedy this
 shortcoming, we introduce a new summary statistic measuring various types
 of association in a contingency table. Based on this new summary
 statistic, we propose a likelihood ratio test comparing the strength of
 association in two independent contingency tables. The proposed test
 examines the stochastic order between summary statistics. We derive its
 asymptotic null distribution and demonstrate that the least favorable
 distributions are chi-bar distributions. We numerically compare the power
 of the proposed test to that of the tests based on single-index measures.
 Finally, we provide two examples illustrating the new summary statistics
 and the related tests. 
Journal: Journal of Applied Statistics 
Pages: 1133-1149 
Issue: 6 
Volume: 38 
Year: 2011 
Month: 3 
X-DOI: 10.1080/02664763.2010.484487 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.484487 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:6:p:1133-1149




Template-Type: ReDIF-Article 1.0
Author-Name: J&#x16B;rat&#x117; &#x160;altyt&#x117; Benth 
Author-X-Name-First: J&#x16B;rat&#x117; 
Author-X-Name-Last: &#x160;altyt&#x117; Benth 
Author-Name: Laura &#x160;altyt&#x117; 
Author-X-Name-First: Laura 
Author-X-Name-Last: &#x160;altyt&#x117; 
Title: Spatial--temporal model for wind speed in Lithuania 
Abstract:
  In this paper, we propose a spatial--temporal model for the wind speed
 (WS). We first estimate the model at the single spatial meteorological
 station independently on spatial correlations. The temporal model contains
 seasonality, a higher-order autoregressive component and a variance
 describing the remaining heteroskedesticity in residuals. We then model
 spatial dependencies by a Gaussian random field. The model is estimated on
 daily WS records from 18 meteorological stations in Lithuania. The
 validation procedure based on out-of-sample observations shows that the
 proposed model is reliable and can be used for various practical
 applications. 
Journal: Journal of Applied Statistics 
Pages: 1151-1168 
Issue: 6 
Volume: 38 
Year: 2011 
Month: 4 
X-DOI: 10.1080/02664763.2010.491857 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.491857 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:6:p:1151-1168




Template-Type: ReDIF-Article 1.0
Author-Name: Malin Albing 
Author-X-Name-First: Malin 
Author-X-Name-Last: Albing 
Author-Name: Kerstin V&#xE4;nnman 
Author-X-Name-First: Kerstin 
Author-X-Name-Last: V&#xE4;nnman 
Title: Elliptical safety region plots for <italic>C</italic> <sub> <italic>pk</italic> </sub>  
Abstract:
  The process capability index <italic>C</italic> <sub> <italic>pk</italic>
 </sub> is widely used when measuring the capability of a manufacturing
 process. A process is defined to be capable if the capability index
 exceeds a stated threshold value, e.g. <italic>C</italic> <sub>
 <italic>pk</italic> </sub>>4/3. This inequality can be expressed
 graphically using a process capability plot, which is a plot in the plane
 defined by the process mean and the process standard deviation, showing
 the region for a capable process. In the process capability plot, a safety
 region can be plotted to obtain a simple graphical decision rule to assess
 process capability at a given significance level. We consider safety
 regions to be used for the index <italic>C</italic> <sub>
 <italic>pk</italic> </sub>. Under the assumption of normality, we derive
 elliptical safety regions so that, using a random sample, conclusions
 about the process capability can be drawn at a given significance level.
 This simple graphical tool is helpful when trying to understand whether it
 is the variability, the deviation from target, or both that need to be
 reduced to improve the capability. Furthermore, using safety regions,
 several characteristics with different specification limits and different
 sample sizes can be monitored in the same plot. The proposed graphical
 decision rule is also investigated with respect to power. 
Journal: Journal of Applied Statistics 
Pages: 1169-1187 
Issue: 6 
Volume: 38 
Year: 2011 
Month: 4 
X-DOI: 10.1080/02664763.2010.491858 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.491858 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:6:p:1169-1187




Template-Type: ReDIF-Article 1.0
Author-Name: Guillermo Villa 
Author-X-Name-First: Guillermo 
Author-X-Name-Last: Villa 
Author-Name: Isabel Molina 
Author-X-Name-First: Isabel 
Author-X-Name-Last: Molina 
Author-Name: Roland Fried 
Author-X-Name-First: Roland 
Author-X-Name-Last: Fried 
Title: Modeling attendance at Spanish professional football league 
Abstract:
  Prediction of demand for professional sports is increasingly drawing the
 attention of economists. We apply linear mixed models for modeling
 attendance figures at Spanish professional football. We investigate
 economic variables, such as the price of the tickets or the size of the
 market, and sporting variables, such as the quality of a team or the level
 of competition within the league, as potential predictors of attendance.
 It turns out that a model with temporally correlated random team effects
 provides good forecasts of attendance at a time horizon of two seasons.
 Results from this model agree with economic theory. 
Journal: Journal of Applied Statistics 
Pages: 1189-1206 
Issue: 6 
Volume: 38 
Year: 2011 
Month: 4 
X-DOI: 10.1080/02664763.2010.491859 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.491859 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:6:p:1189-1206




Template-Type: ReDIF-Article 1.0
Author-Name: Frederico   Z. Poleto 
Author-X-Name-First: Frederico   Z. 
Author-X-Name-Last: Poleto 
Author-Name: Julio   M. Singer 
Author-X-Name-First: Julio   M. 
Author-X-Name-Last: Singer 
Author-Name: Carlos   Daniel Paulino 
Author-X-Name-First: Carlos   Daniel 
Author-X-Name-Last: Paulino 
Title: Comparing diagnostic tests with missing data 
Abstract:
  When missing data occur in studies designed to compare the accuracy of
 diagnostic tests, a common, though naive, practice is to base the
 comparison of sensitivity, specificity, as well as of positive and
 negative predictive values on some subset of the data that fits into
 methods implemented in standard statistical packages. Such methods are
 usually valid only under the strong missing completely at random (MCAR)
 assumption and may generate biased and less precise estimates. We review
 some models that use the dependence structure of the completely observed
 cases to incorporate the information of the partially categorized
 observations into the analysis and show how they may be fitted via a
 two-stage hybrid process involving maximum likelihood in the first stage
 and weighted least squares in the second. We indicate how computational
 subroutines written in <monospace>R</monospace> may be used to fit the
 proposed models and illustrate the different analysis strategies with
 observational data collected to compare the accuracy of three distinct
 non-invasive diagnostic methods for endometriosis. The results indicate
 that even when the MCAR assumption is plausible, the naive partial
 analyses should be avoided. 
Journal: Journal of Applied Statistics 
Pages: 1207-1222 
Issue: 6 
Volume: 38 
Year: 2011 
Month: 4 
X-DOI: 10.1080/02664763.2010.491860 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.491860 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:6:p:1207-1222




Template-Type: ReDIF-Article 1.0
Author-Name: Steen Magnussen 
Author-X-Name-First: Steen 
Author-X-Name-Last: Magnussen 
Author-Name: Ron McRoberts 
Author-X-Name-First: Ron 
Author-X-Name-Last: McRoberts 
Title: A modified bootstrap procedure for cluster sampling variance estimation of species richness 
Abstract:
  Variance estimators for probability sample-based predictions of species
 richness (<italic>S</italic>) are typically conditional on the sample
 (expected variance). In practical applications, sample sizes are typically
 small, and the variance of input parameters to a richness estimator should
 not be ignored. We propose a modified bootstrap variance estimator that
 attempts to capture the sampling variance by generating <italic>B</italic>
 replications of the richness prediction from stochastically resampled data
 of species incidence. The variance estimator is demonstrated for the
 observed richness (SO), five richness estimators, and with simulated
 cluster sampling (without replacement) in 11 finite populations of forest
 tree species. A key feature of the bootstrap procedure is a probabilistic
 augmentation of a species incidence matrix by the number of species
 expected to be &#x2018;lost&#x2019; in a conventional bootstrap resampling
 scheme. In Monte-Carlo (MC) simulations, the modified bootstrap procedure
 performed well in terms of tracking the average MC estimates of richness
 and standard errors. Bootstrap-based estimates of standard errors were as
 a rule conservative. Extensions to other sampling designs, estimators of
 species richness and diversity, and estimates of change are possible. 
Journal: Journal of Applied Statistics 
Pages: 1223-1238 
Issue: 6 
Volume: 38 
Year: 2011 
Month: 4 
X-DOI: 10.1080/02664763.2010.491861 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.491861 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:6:p:1223-1238




Template-Type: ReDIF-Article 1.0
Author-Name: Francisco Louzada-Neto 
Author-X-Name-First: Francisco 
Author-X-Name-Last: Louzada-Neto 
Author-Name: Vicente   G. Cancho 
Author-X-Name-First: Vicente   G. 
Author-X-Name-Last: Cancho 
Author-Name: Gladys   D.C. Barriga 
Author-X-Name-First: Gladys   D.C. 
Author-X-Name-Last: Barriga 
Title: The Poisson--exponential distribution: a Bayesian approach 
Abstract:
  In this paper, we proposed a new two-parameter lifetime distribution with
 increasing failure rate. The new distribution arises on a latent
 complementary risk scenario. The properties of the proposed distribution
 are discussed, including a formal proof of its density function and an
 explicit algebraic formulae for its quantiles and survival and hazard
 functions. Also, we have discussed inference aspects of the model proposed
 via Bayesian inference by using Markov chain Monte Carlo simulation. A
 simulation study investigates the frequentist properties of the proposed
 estimators obtained under the assumptions of non-informative priors.
 Further, some discussions on models selection criteria are given. The
 developed methodology is illustrated on a real data set. 
Journal: Journal of Applied Statistics 
Pages: 1239-1248 
Issue: 6 
Volume: 38 
Year: 2011 
Month: 4 
X-DOI: 10.1080/02664763.2010.491862 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.491862 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:6:p:1239-1248




Template-Type: ReDIF-Article 1.0
Author-Name: Sara   B. Crawford 
Author-X-Name-First: Sara   B. 
Author-X-Name-Last: Crawford 
Author-Name: John   J. Hanfelt 
Author-X-Name-First: John   J. 
Author-X-Name-Last: Hanfelt 
Title: Testing for qualitative interaction of multiple sources of informative dropout in longitudinal data 
Abstract:
  Longitudinal studies suffer from patient dropout. The dropout process may
 be informative if there exists an association between dropout patterns and
 the rate of change in the response over time. Multiple patterns are
 plausible in that different causes of dropout might contribute to
 different patterns. These multiple patterns can be dichotomized into two
 groups: quantitative and qualitative interaction. Quantitative interaction
 indicates that each of the multiple sources is biasing the estimate of the
 rate of change in the same direction, although with differing magnitudes.
 Alternatively, qualitative interaction results in the multiple sources
 biasing the estimate of the rate of change in opposing directions.
 Qualitative interaction is of special concern, since it is less likely to
 be detected by conventional methods and can lead to highly misleading
 slope estimates. We explore a test for qualitative interaction based on
 simultaneous confidence intervals. The test accommodates the realistic
 situation where reasons for dropout are not fully understood, or even
 entirely unknown. It allows for an additional level of clustering among
 participating subjects. We apply these methods to a study exploring tumor
 growth rates in mice as well as a longitudinal study exploring rates of
 change in cognitive functioning for Alzheimer's patients. 
Journal: Journal of Applied Statistics 
Pages: 1249-1264 
Issue: 6 
Volume: 38 
Year: 2011 
Month: 4 
X-DOI: 10.1080/02664763.2010.491969 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.491969 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:6:p:1249-1264




Template-Type: ReDIF-Article 1.0
Author-Name: Zhensheng Huang 
Author-X-Name-First: Zhensheng 
Author-X-Name-Last: Huang 
Title: Empirical likelihood for generalized partially linear varying-coefficient models 
Abstract:
  Generalized partially linear varying-coefficient models (GPLVCM) are
 frequently used in statistical modeling. However, the statistical
 inference of the GPLVCM, such as confidence region/interval construction,
 has not been very well developed. In this article, empirical
 likelihood-based inference for the parametric components in the GPLVCM is
 investigated. Based on the local linear estimators of the GPLVCM, an
 estimated empirical likelihood-based statistic is proposed. We show that
 the resulting statistic is asymptotically non-standard chi-squared. By the
 proposed empirical likelihood method, the confidence regions for the
 parametric components are constructed. In addition, when some components
 of the parameter are of particular interest, the construction of their
 confidence intervals is also considered. A simulation study is undertaken
 to compare the empirical likelihood and the other existing methods in
 terms of coverage accuracies and average lengths. The proposed method is
 applied to a real example. 
Journal: Journal of Applied Statistics 
Pages: 1265-1275 
Issue: 6 
Volume: 38 
Year: 2011 
Month: 5 
X-DOI: 10.1080/02664763.2010.498500 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.498500 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:6:p:1265-1275




Template-Type: ReDIF-Article 1.0
Author-Name: Arzu   Altin Yavuz 
Author-X-Name-First: Arzu   Altin 
Author-X-Name-Last: Yavuz 
Author-Name: Birdal Senoglu 
Author-X-Name-First: Birdal 
Author-X-Name-Last: Senoglu 
Title: Comparison of estimation methods for the finite population mean in simple random sampling: symmetric super-populations 
Abstract:
  In this paper, a new estimator combined estimator (CE) is proposed for
 estimating the finite population mean &#xAF; <italic>Y</italic> <sub>
 <italic>N</italic> </sub> in simple random sampling assuming a long-tailed
 symmetric super-population model. The efficiency and robustness properties
 of the CE is compared with the widely used and well-known estimators of
 the finite population mean &#xAF; <italic>Y</italic> <sub>
 <italic>N</italic> </sub> by Monte Carlo simulation. The parameter
 estimators considered in this study are the classical least squares
 estimator, trimmed mean, winsorized mean, trimmed L-mean, modified
 maximum-likelihood estimator, Huber estimator (W24) and the non-parametric
 Hodges--Lehmann estimator. The mean square error criteria are used to
 compare the performance of the estimators. We show that the CE is overall
 more efficient than the other estimators. The CE is also shown to be more
 robust for estimating the finite population mean &#xAF; <italic>Y</italic>
 <sub> <italic>N</italic> </sub>, since it is insensitive to outliers and
 to misspecification of the distribution. We give a real life example. 
Journal: Journal of Applied Statistics 
Pages: 1277-1288 
Issue: 6 
Volume: 38 
Year: 2011 
Month: 5 
X-DOI: 10.1080/02664763.2010.498501 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.498501 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:6:p:1277-1288




Template-Type: ReDIF-Article 1.0
Author-Name: Hamid Shahriari 
Author-X-Name-First: Hamid 
Author-X-Name-Last: Shahriari 
Author-Name: Orod Ahmadi 
Author-X-Name-First: Orod 
Author-X-Name-Last: Ahmadi 
Author-Name: Amir   H. Shokouhi 
Author-X-Name-First: Amir   H. 
Author-X-Name-Last: Shokouhi 
Title: A two-step robust estimation of the process mean using M-estimator 
Abstract:
  Parameter estimation is the first step in constructing control charts.
 One of these parameters is the process mean. The classical estimators of
 the process mean are sensitive to the presence of outlying data and
 subgroups which contaminate the whole data. In existing robust estimators
 for the process mean, the effects of the presence of the individual
 outliers are being considered, while, in this paper, a robust estimator is
 being proposed to reduce the effect of outlying subgroups as well as the
 individual outliers within a subgroup. The proposed estimator was compared
 with some classical and robust estimators of the process mean. Although,
 its relative efficiency is fourth among the estimators tested, its
 robustness and efficiency are large when the outlying subgroups are
 present. Evaluation of the results indicated that the proposed estimator
 is less sensitive to the presence of outliers and the process mean
 performs well when there are no individual outliers or outlying subgroups. 
Journal: Journal of Applied Statistics 
Pages: 1289-1301 
Issue: 6 
Volume: 38 
Year: 2011 
Month: 5 
X-DOI: 10.1080/02664763.2010.498502 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.498502 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:6:p:1289-1301




Template-Type: ReDIF-Article 1.0
Author-Name: Edson   Zangiacomi Martinez 
Author-X-Name-First: Edson   Zangiacomi 
Author-X-Name-Last: Martinez 
Author-Name: Davi   Casale Aragon 
Author-X-Name-First: Davi   Casale 
Author-X-Name-Last: Aragon 
Author-Name: Jorge   Alberto Achcar 
Author-X-Name-First: Jorge   Alberto 
Author-X-Name-Last: Achcar 
Title: A Bayesian model for estimating the malaria transition probabilities considering individuals lost to follow-up 
Abstract:
  It is known that patients may cease participating in a longitudinal study
 and become lost to follow-up. The objective of this article is to present
 a Bayesian model to estimate the malaria transition probabilities
 considering individuals lost to follow-up. We consider a homogeneous
 population, and it is assumed that the considered period of time is small
 enough to avoid two or more transitions from one state of health to
 another. The proposed model is based on a Gibbs sampling algorithm that
 uses information of lost to follow-up at the end of the longitudinal
 study. To simulate the unknown number of individuals with positive and
 negative states of malaria at the end of the study and lost to follow-up,
 two latent variables were introduced in the model. We used a real data set
 and a simulated data to illustrate the application of the methodology. The
 proposed model showed a good fit to these data sets, and the algorithm did
 not show problems of convergence or lack of identifiability. We conclude
 that the proposed model is a good alternative to estimate probabilities of
 transitions from one state of health to the other in studies with low
 adherence to follow-up. 
Journal: Journal of Applied Statistics 
Pages: 1303-1309 
Issue: 6 
Volume: 38 
Year: 2011 
Month: 5 
X-DOI: 10.1080/02664763.2010.498503 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.498503 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:6:p:1303-1309




Template-Type: ReDIF-Article 1.0
Author-Name: John Pemberton 
Author-X-Name-First: John 
Author-X-Name-Last: Pemberton 
Title: Time Series Analysis with Applications in R, Second edition 
Journal: Journal of Applied Statistics 
Pages: 1311-1312 
Issue: 6 
Volume: 38 
Year: 2011 
Month: 6 
X-DOI: 10.1080/02664760903075663 
File-URL: http://hdl.handle.net/10.1080/02664760903075663 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:6:p:1311-1312




Template-Type: ReDIF-Article 1.0
Author-Name: Jennifer   H. Klapper 
Author-X-Name-First: Jennifer   H. 
Author-X-Name-Last: Klapper 
Title: Introductory Statistics with R, second edition 
Journal: Journal of Applied Statistics 
Pages: 1312-1313 
Issue: 6 
Volume: 38 
Year: 2011 
Month: 6 
X-DOI: 10.1080/02664760903230516 
File-URL: http://hdl.handle.net/10.1080/02664760903230516 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:6:p:1312-1313




Template-Type: ReDIF-Article 1.0
Author-Name: Miroslav   M. Risti&#x107; 
Author-X-Name-First: Miroslav   M. 
Author-X-Name-Last: Risti&#x107; 
Title: Guide to Teaching Statistics 
Journal: Journal of Applied Statistics 
Pages: 1313-1314 
Issue: 6 
Volume: 38 
Year: 2011 
Month: 6 
X-DOI: 10.1080/02664760903230524 
File-URL: http://hdl.handle.net/10.1080/02664760903230524 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:6:p:1313-1314




Template-Type: ReDIF-Article 1.0
Author-Name: Boran Gazi 
Author-X-Name-First: Boran 
Author-X-Name-Last: Gazi 
Title: Credit Risk Management 
Journal: Journal of Applied Statistics 
Pages: 1314-1314 
Issue: 6 
Volume: 38 
Year: 2011 
Month: 6 
X-DOI: 10.1080/02664760903335083 
File-URL: http://hdl.handle.net/10.1080/02664760903335083 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:6:p:1314-1314




Template-Type: ReDIF-Article 1.0
Author-Name: Pieter Bastiaan Ober 
Author-X-Name-First: Pieter 
Author-X-Name-Last: Bastiaan Ober 
Title: Modern Regression Methods 
Journal: Journal of Applied Statistics 
Pages: 1315-1315 
Issue: 6 
Volume: 38 
Year: 2011 
Month: 6 
X-DOI: 10.1080/02664760903370791 
File-URL: http://hdl.handle.net/10.1080/02664760903370791 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:6:p:1315-1315




Template-Type: ReDIF-Article 1.0
Author-Name: Pablo Mart�nez-Camblor 
Author-X-Name-First: Pablo 
Author-X-Name-Last: Mart�nez-Camblor 
Author-Name: Carlos Carleos 
Author-X-Name-First: Carlos 
Author-X-Name-Last: Carleos 
Author-Name: Norberto Corral 
Author-X-Name-First: Norberto 
Author-X-Name-Last: Corral 
Title: Powerful nonparametric statistics to compare <italic>k</italic> independent ROC curves 
Abstract:
  The authors deal with the problem of comparing receiver operating
 characteristic (ROC) curves from independent samples. From a nonparametric
 approach, they propose and study three different statistics. Their
 asymptotic distributions are obtained and a resample plan is considered.
 In order to study the statistical power of the introduced statistics, a
 simulation study is carried out. The (observed) results suggest that, for
 the considered models, the new statistics are more powerful than the
 usually employed ones (the Venkatraman test and the usual area under the
 ROC curve criterion) in non-uniform dominance situations and quite good
 otherwise. 
Journal: Journal of Applied Statistics 
Pages: 1317-1332 
Issue: 7 
Volume: 38 
Year: 2011 
Month: 5 
X-DOI: 10.1080/02664763.2010.498504 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.498504 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:7:p:1317-1332




Template-Type: ReDIF-Article 1.0
Author-Name: Albert Vexler 
Author-X-Name-First: Albert 
Author-X-Name-Last: Vexler 
Author-Name: Jihnhee Yu 
Author-X-Name-First: Jihnhee 
Author-X-Name-Last: Yu 
Author-Name: Alan   D. Hutson 
Author-X-Name-First: Alan   D. 
Author-X-Name-Last: Hutson 
Title: Likelihood testing populations modeled by autoregressive process subject to the limit of detection in applications to longitudinal biomedical data 
Abstract:
  Dependent and often incomplete outcomes are commonly found in
 longitudinal biomedical studies. We develop a likelihood function, which
 implements the autoregressive process of outcomes, incorporating the limit
 of detection problem and the probability of drop-out. The proposed
 approach incorporates the characteristics of the longitudinal data in
 biomedical research allowing us to carry out powerful tests to detect a
 difference between study populations in terms of the growth rate and
 drop-out rate. The formal notation of the likelihood function is
 developed, making it possible to adapt the proposed method easily for
 various different scenarios in terms of the number of groups to compare
 and a variety of growth trend patterns. Useful inferential properties for
 the proposed method are established, which take advantage of many
 well-developed theorems regarding the likelihood approach. A broad
 Monte-Carlo study confirms both the asymptotic results and illustrates
 good power properties of the proposed method. We apply the proposed method
 to three data sets obtained from mouse tumor experiments. 
Journal: Journal of Applied Statistics 
Pages: 1333-1346 
Issue: 7 
Volume: 38 
Year: 2011 
Month: 5 
X-DOI: 10.1080/02664763.2010.498505 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.498505 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:7:p:1333-1346




Template-Type: ReDIF-Article 1.0
Author-Name: Patrick   D. Gerard 
Author-X-Name-First: Patrick   D. 
Author-X-Name-Last: Gerard 
Author-Name: Julia   L. Sharp 
Author-X-Name-First: Julia   L. 
Author-X-Name-Last: Sharp 
Title: Testing for co-directional interactions using union--intersection and intersection--union methods 
Abstract:
  When interaction terms exist in a two-factor, factorial experiment, the
 consideration and analysis of main effects are often restricted to those
 situations where the interaction between factors is not significant.
 Hinkelman and Kempthorne [4] softened that stance somewhat and advocate
 testing main effects when the interaction is deemed co-directional but not
 anti-directional. A test for the main effects in that situation may be
 pragmatic to the practitioner and appealing to researchers in other
 disciplines. Intersection--union and union--intersection methods are
 examined for assessing the directional nature of significant interactions
 so that the main effects in a two-factor factorial may be evaluated. The
 tests suggested are conceptually straightforward and practical and
 maintain the nominal Type-I error rate. Examples are provided to
 illustrate the methods. 
Journal: Journal of Applied Statistics 
Pages: 1347-1358 
Issue: 7 
Volume: 38 
Year: 2011 
Month: 5 
X-DOI: 10.1080/02664763.2010.498506 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.498506 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:7:p:1347-1358




Template-Type: ReDIF-Article 1.0
Author-Name: Rand   R. Wilcox 
Author-X-Name-First: Rand   R. 
Author-X-Name-Last: Wilcox 
Author-Name: Tian   S. Tian 
Author-X-Name-First: Tian   S. 
Author-X-Name-Last: Tian 
Title: Measuring effect size: a robust heteroscedastic approach for two or more groups 
Abstract:
  Motivated by involvement in an intervention study, the paper proposes a
 robust, heteroscedastic generalization of what is popularly known as
 Cohen's d. The approach has the additional advantage of being readily
 extended to situations where the goal is to compare more than two groups.
 The method arises quite naturally from a regression perspective in
 conjunction with a robust version of explanatory power. Moreover, it
 provides a single numeric summary of how the groups compare in contrast to
 other strategies aimed at dealing with heteroscedasticity. Kulinskaya and
 Staudte [16] studied a heteroscedastic measure of effect size similar to
 the one proposed here, but their measure of effect size depends on the
 sample sizes making it difficult for applied researchers to interpret the
 results. The approach used here is based on a generalization of Cohen's d
 that obviates the issue of unequal sample sizes. Simulations and
 illustrations demonstrate that the new measure of effect size can make a
 practical difference regarding the conclusions reached. 
Journal: Journal of Applied Statistics 
Pages: 1359-1368 
Issue: 7 
Volume: 38 
Year: 2011 
Month: 5 
X-DOI: 10.1080/02664763.2010.498507 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.498507 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:7:p:1359-1368




Template-Type: ReDIF-Article 1.0
Author-Name: Ardian Harri 
Author-X-Name-First: Ardian 
Author-X-Name-Last: Harri 
Author-Name: Keith   H. Coble 
Author-X-Name-First: Keith   H. 
Author-X-Name-Last: Coble 
Title: Normality testing: two new tests using L-moments 
Abstract:
  Establishing that there is no compelling evidence that some population is
 not normally distributed is fundamental to many statistical inferences,
 and numerous approaches to testing the null hypothesis of normality have
 been proposed. Fundamentally, the power of a test depends on which
 specific deviation from normality may be presented in a distribution.
 Knowledge of the potential nature of deviation from normality should
 reasonably guide the researcher's selection of testing for non-normality.
 In most settings, little is known aside from the data available for
 analysis, so that selection of a test based on general applicability is
 typically necessary. This research proposes and reports the power of two
 new tests of normality. One of the new tests is a version of the
 <italic>R</italic>-test that uses the L-moments, respectively, L-skewness
 and L-kurtosis and the other test is based on normalizing transformations
 of L-skewness and L-kurtosis. Both tests have high power relative to
 alternatives. The test based on normalized transformations, in particular,
 shows consistently high power and outperforms other normality tests
 against a variety of distributions. 
Journal: Journal of Applied Statistics 
Pages: 1369-1379 
Issue: 7 
Volume: 38 
Year: 2011 
Month: 5 
X-DOI: 10.1080/02664763.2010.498508 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.498508 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:7:p:1369-1379




Template-Type: ReDIF-Article 1.0
Author-Name: Katsuyuki Takahashi 
Author-X-Name-First: Katsuyuki 
Author-X-Name-Last: Takahashi 
Author-Name: Isao Shoji 
Author-X-Name-First: Isao 
Author-X-Name-Last: Shoji 
Title: An empirical analysis of the volatility of the Japanese stock price index: a non-parametric approach 
Abstract:
  This paper presents an empirical analysis of stochastic features of
 volatility in the Japanese stock price index, or TOPIX, using
 high-frequency data sampled every 5&#xA0;min. The process of TOPIX is
 modeled by a stochastic differential equation with the time-homogeneous
 drift and diffusion coefficients. To avoid the risk of misspecification
 for the volatility function, which is defined by the squared diffusion
 coefficient, the local polynomial model is applied to the data, and then
 produced the estimates of the volatility function together with their
 confidence intervals. The result of the estimation suggests that the
 volatility function shows similar patterns for one period, but drastically
 changes for another. 
Journal: Journal of Applied Statistics 
Pages: 1381-1394 
Issue: 7 
Volume: 38 
Year: 2011 
Month: 6 
X-DOI: 10.1080/02664763.2010.505947 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.505947 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:7:p:1381-1394




Template-Type: ReDIF-Article 1.0
Author-Name: Gleici   Castro Perdon&#xE1; 
Author-X-Name-First: Gleici   Castro 
Author-X-Name-Last: Perdon&#xE1; 
Author-Name: Francisco Louzada-Neto 
Author-X-Name-First: Francisco 
Author-X-Name-Last: Louzada-Neto 
Title: A general hazard model for lifetime data in the presence of cure rate 
Abstract:
  Historically, the cure rate model has been used for modeling
 time-to-event data within which a significant proportion of patients are
 assumed to be cured of illnesses, including breast cancer, non-Hodgkin
 lymphoma, leukemia, prostate cancer, melanoma, and head and neck cancer.
 Perhaps the most popular type of cure rate model is the mixture model
 introduced by Berkson and Gage [1]. In this model, it is assumed that a
 certain proportion of the patients are cured, in the sense that they do
 not present the event of interest during a long period of time and can
 found to be immune to the cause of failure under study. In this paper, we
 propose a general hazard model which accommodates comprehensive families
 of cure rate models as particular cases, including the model proposed by
 Berkson and Gage. The maximum-likelihood-estimation procedure is
 discussed. A simulation study analyzes the coverage probabilities of the
 asymptotic confidence intervals for the parameters. A real data set on
 children exposed to HIV by vertical transmission illustrates the
 methodology. 
Journal: Journal of Applied Statistics 
Pages: 1395-1405 
Issue: 7 
Volume: 38 
Year: 2011 
Month: 6 
X-DOI: 10.1080/02664763.2010.505948 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.505948 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:7:p:1395-1405




Template-Type: ReDIF-Article 1.0
Author-Name: Cheolwoo Park 
Author-X-Name-First: Cheolwoo 
Author-X-Name-Last: Park 
Author-Name: F�lix Hern&#xE1;ndez-Campos 
Author-X-Name-First: F�lix 
Author-X-Name-Last: Hern&#xE1;ndez-Campos 
Author-Name: Long Le 
Author-X-Name-First: Long 
Author-X-Name-Last: Le 
Author-Name: J.   S. Marron 
Author-X-Name-First: J.   S. 
Author-X-Name-Last: Marron 
Author-Name: Juhyun Park 
Author-X-Name-First: Juhyun 
Author-X-Name-Last: Park 
Author-Name: Vladas Pipiras 
Author-X-Name-First: Vladas 
Author-X-Name-Last: Pipiras 
Author-Name: F.   D. Smith 
Author-X-Name-First: F.   D. 
Author-X-Name-Last: Smith 
Author-Name: Richard   L. Smith 
Author-X-Name-First: Richard   L. 
Author-X-Name-Last: Smith 
Author-Name: Michele Trovero 
Author-X-Name-First: Michele 
Author-X-Name-Last: Trovero 
Author-Name: Zhengyuan Zhu 
Author-X-Name-First: Zhengyuan 
Author-X-Name-Last: Zhu 
Title: Long-range dependence analysis of Internet traffic 
Abstract:
  Long-range-dependent time series are endemic in the statistical analysis
 of Internet traffic. The Hurst parameter provides a good summary of
 important self-similar scaling properties. We compare a number of
 different Hurst parameter estimation methods and some important
 variations. This is done in the context of a wide range of simulated,
 laboratory-generated, and real data sets. Important differences between
 the methods are highlighted. Deep insights are revealed on how well the
 laboratory data mimic the real data. Non-stationarities, which are local
 in time, are seen to be central issues and lead to both conceptual and
 practical recommendations. 
Journal: Journal of Applied Statistics 
Pages: 1407-1433 
Issue: 7 
Volume: 38 
Year: 2011 
Month: 6 
X-DOI: 10.1080/02664763.2010.505949 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.505949 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:7:p:1407-1433




Template-Type: ReDIF-Article 1.0
Author-Name: Giovana   O. Silva 
Author-X-Name-First: Giovana   O. 
Author-X-Name-Last: Silva 
Author-Name: Edwin M.M. Ortega 
Author-X-Name-First: Edwin M.M. 
Author-X-Name-Last: Ortega 
Author-Name: Gilberto   A. Paula 
Author-X-Name-First: Gilberto   A. 
Author-X-Name-Last: Paula 
Title: Residuals for log-Burr XII regression models in survival analysis 
Abstract:
  In this paper, we compare three residuals to assess departures from the
 error assumptions as well as to detect outlying observations in log-Burr
 XII regression models with censored observations. These residuals can also
 be used for the log-logistic regression model, which is a special case of
 the log-Burr XII regression model. For different parameter settings,
 sample sizes and censoring percentages, various simulation studies are
 performed and the empirical distribution of each residual is displayed and
 compared with the standard normal distribution. These studies suggest that
 the residual analysis usually performed in normal linear regression models
 can be straightforwardly extended to the modified martingale-type residual
 in log-Burr XII regression models with censored data. 
Journal: Journal of Applied Statistics 
Pages: 1435-1445 
Issue: 7 
Volume: 38 
Year: 2011 
Month: 6 
X-DOI: 10.1080/02664763.2010.505950 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.505950 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:7:p:1435-1445




Template-Type: ReDIF-Article 1.0
Author-Name: Yalian Li 
Author-X-Name-First: Yalian 
Author-X-Name-Last: Li 
Author-Name: Hu Yang 
Author-X-Name-First: Hu 
Author-X-Name-Last: Yang 
Title: Two kinds of restricted modified estimators in linear regression model 
Abstract:
  In this paper, we introduce two kinds of new restricted estimators called
 restricted modified Liu estimator and restricted modified ridge estimator
 based on prior information for the vector of parameters in a linear
 regression model with linear restrictions. Furthermore, the performance of
 the proposed estimators in mean squares error matrix sense is derived and
 compared. Finally, a numerical example and a Monte Carlo simulation are
 given to illustrate some of the theoretical results. 
Journal: Journal of Applied Statistics 
Pages: 1447-1454 
Issue: 7 
Volume: 38 
Year: 2011 
Month: 6 
X-DOI: 10.1080/02664763.2010.505951 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.505951 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:7:p:1447-1454




Template-Type: ReDIF-Article 1.0
Author-Name: Tommaso Proietti 
Author-X-Name-First: Tommaso 
Author-X-Name-Last: Proietti 
Title: Multivariate temporal disaggregation with cross-sectional constraints 
Abstract:
  Multivariate temporal disaggregation deals with the historical
 reconstruction and nowcasting of economic variables subject to temporal
 and contemporaneous aggregation constraints. The problem involves a system
 of time series that are related not only by a dynamic model but also by
 accounting constraints. The paper introduces two fundamental (and
 realistic) models that implement the multivariate best linear unbiased
 estimation approach that has potential application to the temporal
 disaggregation of the national accounts series. The multivariate
 regression model with random walk disturbances is most suitable to deal
 with the chained linked volumes (as the nature of the national accounts
 time series suggests); however, in this case the accounting constraints
 are not binding and the discrepancy has to be modeled by either a
 trend-stationary or an integrated process. The tiny, compared with other
 driving disturbances, size of the discrepancy prevents maximum-likelihood
 estimation to be carried out, and the parameters have to be estimated
 separately. The multivariate disaggregation with integrated random walk
 disturbances is suitable for the national accounts aggregates expressed at
 current prices, in which case the accounting constraints are binding. 
Journal: Journal of Applied Statistics 
Pages: 1455-1466 
Issue: 7 
Volume: 38 
Year: 2011 
Month: 6 
X-DOI: 10.1080/02664763.2010.505952 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.505952 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:7:p:1455-1466




Template-Type: ReDIF-Article 1.0
Author-Name: Shih-Chia Liu 
Author-X-Name-First: Shih-Chia 
Author-X-Name-Last: Liu 
Author-Name: Kuo-Szu Chiang 
Author-X-Name-First: Kuo-Szu 
Author-X-Name-Last: Chiang 
Author-Name: Cheng-Hsiang Lin 
Author-X-Name-First: Cheng-Hsiang 
Author-X-Name-Last: Lin 
Author-Name: Ting-Chin Deng 
Author-X-Name-First: Ting-Chin 
Author-X-Name-Last: Deng 
Title: Confidence interval procedures for proportions estimated by group testing with groups of unequal size adjusted for overdispersion 
Abstract:
  Group testing is a method of pooling a number of units together and
 performing a single test on the resulting group. Group testing is an
 appealing option when few individual units are thought to be infected and
 the cost of the testing is non-negligible. Overdispersion is the
 phenomenon of having greater variability than predicted by the random
 component of the model; this is common in the modeling of binomial
 distribution for group testing. The purpose of this paper is to provide a
 comparison of several established methods of constructing confidence
 intervals after adjusting for overdispersion. We evaluate and investigate
 each method in six different cases of group testing. A method based on the
 score statistic with correction for skewness is recommended. We illustrate
 the methods using two data sets, one from the detection of seed
 transmission and the other from serological testing for malaria. 
Journal: Journal of Applied Statistics 
Pages: 1467-1482 
Issue: 7 
Volume: 38 
Year: 2011 
Month: 6 
X-DOI: 10.1080/02664763.2010.505953 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.505953 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:7:p:1467-1482




Template-Type: ReDIF-Article 1.0
Author-Name: D. Stogiannis 
Author-X-Name-First: D. 
Author-X-Name-Last: Stogiannis 
Author-Name: C. Caroni 
Author-X-Name-First: C. 
Author-X-Name-Last: Caroni 
Author-Name: C.   E. Anagnostopoulos 
Author-X-Name-First: C.   E. 
Author-X-Name-Last: Anagnostopoulos 
Author-Name: I.   K. Toumpoulis 
Author-X-Name-First: I.   K. 
Author-X-Name-Last: Toumpoulis 
Title: Comparing first hitting time and proportional hazards regression models 
Abstract:
  Cox's widely used semi-parametric proportional hazards (PH) regression
 model places restrictions on the possible shapes of the hazard function.
 Models based on the first hitting time (FHT) of a stochastic process are
 among the alternatives and have the attractive feature of being based on a
 model of the underlying process. We review and compare the PH model and an
 FHT model based on a Wiener process which leads to an inverse Gaussian
 (IG) regression model. This particular model can also represent a
 &#x201C;cured fraction&#x201D; or long-term survivors. A case study of
 survival after coronary artery bypass grafting is used to examine the
 interpretation of the IG model, especially in relation to covariates that
 affect both of its parameters. 
Journal: Journal of Applied Statistics 
Pages: 1483-1492 
Issue: 7 
Volume: 38 
Year: 2011 
Month: 6 
X-DOI: 10.1080/02664763.2010.505954 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.505954 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:7:p:1483-1492




Template-Type: ReDIF-Article 1.0
Author-Name: Zheng Su 
Author-X-Name-First: Zheng 
Author-X-Name-Last: Su 
Title: A class of designs for Phase I cancer clinical trials combining Bayesian and likelihood approaches 
Abstract:
  The Bayesian continual reassessment method (CRM) and its likelihood
 version (CRML) provide important tools for the design of Phase I cancer
 clinical trials. However, a poorly chosen prior distribution in CRM may
 lead to inferior performance of the method in the early stage of a trial,
 whereas the maximum-likelihood estimate used in CRML may result in initial
 high variability. These features of CRM and CRML served as the motivations
 for the development of this new class of designs, which combines the
 Bayesian and the likelihood approaches and has CRM and CRML as special
 cases. Simulation studies on a leukaemia trial show that the proposed
 class of designs significantly outperforms the traditional up-and-down
 design. 
Journal: Journal of Applied Statistics 
Pages: 1493-1498 
Issue: 7 
Volume: 38 
Year: 2011 
Month: 6 
X-DOI: 10.1080/02664763.2010.505955 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.505955 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:7:p:1493-1498




Template-Type: ReDIF-Article 1.0
Author-Name: Jos�   Dias Curto 
Author-X-Name-First: Jos�   Dias 
Author-X-Name-Last: Curto 
Author-Name: Jos�   Castro Pinto 
Author-X-Name-First: Jos�   Castro 
Author-X-Name-Last: Pinto 
Title: The corrected VIF (CVIF) 
Abstract:
  In this paper, we propose a new corrected variance inflation factor (VIF)
 measure to evaluate the impact of the correlation among the explanatory
 variables in the variance of the ordinary least squares estimators. We
 show that the real impact on variance can be overestimated by the
 traditional VIF when the explanatory variables contain no redundant
 information about the dependent variable and a corrected version of this
 multicollinearity indicator becomes necessary. 
Journal: Journal of Applied Statistics 
Pages: 1499-1507 
Issue: 7 
Volume: 38 
Year: 2011 
Month: 6 
X-DOI: 10.1080/02664763.2010.505956 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.505956 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:7:p:1499-1507




Template-Type: ReDIF-Article 1.0
Author-Name: Jin-Guan Lin 
Author-X-Name-First: Jin-Guan 
Author-X-Name-Last: Lin 
Author-Name: Li-Xing Zhu 
Author-X-Name-First: Li-Xing 
Author-X-Name-Last: Zhu 
Author-Name: Chun-Zheng Cao 
Author-X-Name-First: Chun-Zheng 
Author-X-Name-Last: Cao 
Author-Name: Yong Li 
Author-X-Name-First: Yong 
Author-X-Name-Last: Li 
Title: Tests of heteroscedasticity and correlation in multivariate <italic>t</italic> regression models with AR and ARMA errors 
Abstract:
  Heteroscedasticity checking in regression analysis plays an important
 role in modelling. It is of great interest when random errors are
 correlated, including autocorrelated and partial autocorrelated errors. In
 this paper, we consider multivariate <italic>t</italic> linear regression
 models, and construct the score test for the case of AR(1) errors, and
 ARMA(<italic>s,d</italic>) errors. The asymptotic properties, including
 asymptotic chi-square and approximate powers under local alternatives of
 the score tests, are studied. Based on modified profile likelihood, the
 adjusted score test is also developed. The finite sample performance of
 the tests is investigated through Monte Carlo simulations, and also the
 tests are illustrated with two real data sets. 
Journal: Journal of Applied Statistics 
Pages: 1509-1531 
Issue: 7 
Volume: 38 
Year: 2011 
Month: 8 
X-DOI: 10.1080/02664763.2010.515301 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.515301 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:7:p:1509-1531




Template-Type: ReDIF-Article 1.0
Author-Name: An Creemers 
Author-X-Name-First: An 
Author-X-Name-Last: Creemers 
Author-Name: Marc Aerts 
Author-X-Name-First: Marc 
Author-X-Name-Last: Aerts 
Author-Name: Niel Hens 
Author-X-Name-First: Niel 
Author-X-Name-Last: Hens 
Author-Name: Ziv Shkedy 
Author-X-Name-First: Ziv 
Author-X-Name-Last: Shkedy 
Author-Name: Frank De Smet 
Author-X-Name-First: Frank 
Author-X-Name-Last: De Smet 
Author-Name: Philippe Beutels 
Author-X-Name-First: Philippe 
Author-X-Name-Last: Beutels 
Title: Revealing age-specific past and future unrelated costs of pneumococcal infections by flexible generalized estimating equations 
Abstract:
  We aimed to study the excess health-care expenditures for persons with a
 known positive isolate of <italic>Streptococcus pneumoniae</italic>. The
 data set was compiled by linking the database of the largest Belgian
 Sickness Fund with data obtained from laboratories reporting pneumococcal
 isolates. We analyzed the age-specific per-patient cumulative costs over
 time, using generalized estimating equations (GEEs). The mean structure
 was described by fractional polynomials. The quasi-likelihood under the
 independence model criterion was used to compare different correlation
 structures. We show for all age groups that the health-care costs incurred
 by diagnosed pneumococcal patients are significantly larger than those
 incurred by undiagnosed matched persons. This is not only the case at the
 time of diagnosis but also long before and after the time of diagnosis.
 These findings can be informative for the current debate on unrelated
 costs in health economic evaluation, and GEEs could be used to estimate
 these costs for other diseases. Finally, these results can be used to
 inform policy on the expected budget impact of preventing pneumococcal
 infections. 
Journal: Journal of Applied Statistics 
Pages: 1533-1547 
Issue: 8 
Volume: 38 
Year: 2011 
Month: 8 
X-DOI: 10.1080/02664763.2010.515302 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.515302 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:8:p:1533-1547




Template-Type: ReDIF-Article 1.0
Author-Name: Vijay Verma 
Author-X-Name-First: Vijay 
Author-X-Name-Last: Verma 
Author-Name: Gianni Betti 
Author-X-Name-First: Gianni 
Author-X-Name-Last: Betti 
Title: Taylor linearization sampling errors and design effects for poverty measures and other complex statistics 
Abstract:
  A systematic procedure for the derivation of linearized variables for the
 estimation of sampling errors of complex nonlinear statistics involved in
 the analysis of poverty and income inequality is developed. The linearized
 variable extends the use of standard variance estimation formulae,
 developed for linear statistics such as sample aggregates, to nonlinear
 statistics. The context is that of cross-sectional samples of complex
 design and reasonably large size, as typically used in population-based
 surveys. Results of application of the procedure to a wide range of
 poverty and inequality measures are presented. A standardized software for
 the purpose has been developed and can be provided to interested users on
 request. Procedures are provided for the estimation of the design effect
 and its decomposition into the contribution of unequal sample weights and
 of other design complexities such as clustering and stratification. The
 consequence of treating a complex statistic as a simple ratio in
 estimating its sampling error is also quantified. The second theme of the
 paper is to compare the linearization approach with an alternative
 approach based on the concept of replication, namely the Jackknife
 repeated replication (JRR) method. The basis and application of the JRR
 method is described, the exposition paralleling that of the linearization
 method but in somewhat less detail. Based on data from an actual national
 survey, estimates of standard errors and design effects from the two
 methods are analysed and compared. The numerical results confirm that the
 two alternative approaches generally give very similar results, though
 notable differences can exist for certain statistics. Relative advantages
 and limitations of the approaches are identified. 
Journal: Journal of Applied Statistics 
Pages: 1549-1576 
Issue: 8 
Volume: 38 
Year: 2011 
Month: 8 
X-DOI: 10.1080/02664763.2010.515674 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.515674 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:8:p:1549-1576




Template-Type: ReDIF-Article 1.0
Author-Name: Keunbaik Lee 
Author-X-Name-First: Keunbaik 
Author-X-Name-Last: Lee 
Author-Name: Sanggil Kang 
Author-X-Name-First: Sanggil 
Author-X-Name-Last: Kang 
Author-Name: Xuefeng Liu 
Author-X-Name-First: Xuefeng 
Author-X-Name-Last: Liu 
Author-Name: Daekwan Seo 
Author-X-Name-First: Daekwan 
Author-X-Name-Last: Seo 
Title: Likelihood-based approach for analysis of longitudinal nominal data using marginalized random effects models 
Abstract:
  Likelihood-based marginalized models using random effects have become
 popular for analyzing longitudinal categorical data. These models permit
 direct interpretation of marginal mean parameters and characterize the
 serial dependence of longitudinal outcomes using random effects [12,22].
 In this paper, we propose model that expands the use of previous models to
 accommodate longitudinal nominal data. Random effects using a new
 covariance matrix with a Kronecker product composition are used to explain
 serial and categorical dependence. The Quasi-Newton algorithm is developed
 for estimation. These proposed methods are illustrated with a real data
 set and compared with other standard methods. 
Journal: Journal of Applied Statistics 
Pages: 1577-1590 
Issue: 8 
Volume: 38 
Year: 2011 
Month: 7 
X-DOI: 10.1080/02664763.2010.515675 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.515675 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:8:p:1577-1590




Template-Type: ReDIF-Article 1.0
Author-Name: Ming-Yuan   Leon Li 
Author-X-Name-First: Ming-Yuan   Leon 
Author-X-Name-Last: Li 
Author-Name: Shang-En   Shine Yu 
Author-X-Name-First: Shang-En   Shine 
Author-X-Name-Last: Yu 
Title: Do large firms overly use stock-based incentive compensation? 
Abstract:
  This study employs the panel threshold model to reexamine the
 non-monotonic relationship between CEO stock-based compensation and firm
 earnings across various firm-size conditions. The feasibility of the model
 is tested using data for US non-financial firms from 1993 to 2005. Our
 empirical results indicate that while a positive relationship between the
 CEO stock-based pay and earnings is presented for small-size firms, a
 negative impact of CEO stock-based compensation on earnings is shown when
 large-size firms are concerned. Further, the longstanding puzzle of
 whether the CEO stock-based pay could enhance earnings among earlier
 studies could be satisfactorily explained by our empirical results. 
Journal: Journal of Applied Statistics 
Pages: 1591-1606 
Issue: 8 
Volume: 38 
Year: 2011 
Month: 7 
X-DOI: 10.1080/02664763.2010.515676 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.515676 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:8:p:1591-1606




Template-Type: ReDIF-Article 1.0
Author-Name: Li-Chu Chien 
Author-X-Name-First: Li-Chu 
Author-X-Name-Last: Chien 
Title: Diagnostic plots in beta-regression models 
Abstract:
  Two diagnostic plots for selecting explanatory variables are introduced
 to assess the accuracy of a generalized beta-linear model. The added
 variable plot is developed to examine the need for adding a new
 explanatory variable to the model. The constructed variable plot is
 developed to identify the nonlinearity of the explanatory variable in the
 model. The two diagnostic procedures are also useful for detecting unusual
 observations that may affect the regression much. Simulation studies and
 analysis of two practical examples are conducted to illustrate the
 performances of the proposed plots. 
Journal: Journal of Applied Statistics 
Pages: 1607-1622 
Issue: 8 
Volume: 38 
Year: 2011 
Month: 7 
X-DOI: 10.1080/02664763.2010.515677 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.515677 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:8:p:1607-1622




Template-Type: ReDIF-Article 1.0
Author-Name: Liansheng Tang 
Author-X-Name-First: Liansheng 
Author-X-Name-Last: Tang 
Author-Name: Ming Tan 
Author-X-Name-First: Ming 
Author-X-Name-Last: Tan 
Author-Name: Xiao-Hua Zhou 
Author-X-Name-First: Xiao-Hua 
Author-X-Name-Last: Zhou 
Title: A sequential conditional probability ratio test procedure for comparing diagnostic tests 
Abstract:
  In this paper, we derive sequential conditional probability ratio tests
 to compare diagnostic tests without distributional assumptions on test
 results. The test statistics in our method are nonparametric weighted
 areas under the receiver-operating characteristic curves. By using the new
 method, the decision of stopping the diagnostic trial early is unlikely to
 be reversed should the trials continue to the planned end. The
 conservatism reflected in this approach to have more conservative stopping
 boundaries during the course of the trial is especially appealing for
 diagnostic trials since the end point is not death. In addition, the
 maximum sample size of our method is not greater than a fixed sample test
 with similar power functions. Simulation studies are performed to evaluate
 the properties of the proposed sequential procedure. We illustrate the
 method using data from a thoracic aorta imaging study. 
Journal: Journal of Applied Statistics 
Pages: 1623-1632 
Issue: 8 
Volume: 38 
Year: 2011 
Month: 7 
X-DOI: 10.1080/02664763.2010.515678 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.515678 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:8:p:1623-1632




Template-Type: ReDIF-Article 1.0
Author-Name: Lucia Santana 
Author-X-Name-First: Lucia 
Author-X-Name-Last: Santana 
Author-Name: Filidor Vilca 
Author-X-Name-First: Filidor 
Author-X-Name-Last: Vilca 
Author-Name: V�ctor Leiva 
Author-X-Name-First: V�ctor 
Author-X-Name-Last: Leiva 
Title: Influence analysis in skew-Birnbaum--Saunders regression models and applications 
Abstract:
  In this paper, we propose a method to assess influence in
 skew-Birnbaum--Saunders regression models, which are an extension based on
 the skew-normal distribution of the usual Birnbaum--Saunders (BS)
 regression model. An interesting characteristic that the new regression
 model has is the capacity of predicting extreme percentiles, which is not
 possible with the BS model. In addition, since the observed likelihood
 function associated with the new regression model is more complex than
 that from the usual model, we facilitate the parameter estimation using a
 type-EM algorithm. Moreover, we employ influence diagnostic tools that
 considers this algorithm. Finally, a numerical illustration includes a
 brief simulation study and an analysis of real data in order to show the
 proposed methodology. 
Journal: Journal of Applied Statistics 
Pages: 1633-1649 
Issue: 8 
Volume: 38 
Year: 2011 
Month: 7 
X-DOI: 10.1080/02664763.2010.515679 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.515679 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:8:p:1633-1649




Template-Type: ReDIF-Article 1.0
Author-Name: Mitra Rahimzadeh 
Author-X-Name-First: Mitra 
Author-X-Name-Last: Rahimzadeh 
Author-Name: Ebrahim Hajizadeh 
Author-X-Name-First: Ebrahim 
Author-X-Name-Last: Hajizadeh 
Author-Name: Farzad Eskandari 
Author-X-Name-First: Farzad 
Author-X-Name-Last: Eskandari 
Title: Non-mixture cure correlated frailty models in Bayesian approach 
Abstract:
  In this article, we develop a Bayesian approach for the estimation of two
 cure correlated frailty models that have been extended to the cure frailty
 models introduced by Yin [34]. We used the two different type of frailty
 with bivariate log-normal distribution instead of gamma distribution. A
 likelihood function was constructed based on a piecewise exponential
 distribution function. The model parameters were estimated by the Markov
 chain Monte Carlo method. The comparison of models is based on the Cox
 correlated frailty model with log-normal distribution. A real data set of
 bilateral corneal graft rejection was used to compare these models. The
 results of this data, based on deviance information criteria, showed the
 advantage of the proposed models. 
Journal: Journal of Applied Statistics 
Pages: 1651-1663 
Issue: 8 
Volume: 38 
Year: 2011 
Month: 8 
X-DOI: 10.1080/02664763.2010.515966 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.515966 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:8:p:1651-1663




Template-Type: ReDIF-Article 1.0
Author-Name: William   J. Reed 
Author-X-Name-First: William   J. 
Author-X-Name-Last: Reed 
Title: A flexible parametric survival model which allows a bathtub-shaped hazard rate function 
Abstract:
  A new parametric (three-parameter) survival distribution, the
 <italic>lognormal--power function</italic> distribution, with flexible
 behaviour is introduced. Its hazard rate function can be either unimodal,
 monotonically decreasing or can exhibit a bathtub shape. Special cases
 include the lognormal distribution and the power function distribution,
 with finite support. Regions of parameter space where the various forms of
 the hazard-rate function prevail are established analytically. The
 distribution lends itself readily to accelerated life regression
 modelling. Applications to five data sets taken from the literature are
 given. Also it is shown how the distribution can behave like a Weibull
 distribution (with negative aging) for certain parameter values. 
Journal: Journal of Applied Statistics 
Pages: 1665-1680 
Issue: 8 
Volume: 38 
Year: 2011 
Month: 8 
X-DOI: 10.1080/02664763.2010.516388 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.516388 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:8:p:1665-1680




Template-Type: ReDIF-Article 1.0
Author-Name: Yeliz   Mert Kantar 
Author-X-Name-First: Yeliz   Mert 
Author-X-Name-Last: Kantar 
Author-Name: Ilhan Usta 
Author-X-Name-First: Ilhan 
Author-X-Name-Last: Usta 
Author-Name: &#x15E;&#xFC;kr&#xFC; Ac&#x131;ta&#x15F; 
Author-X-Name-First: &#x15E;&#xFC;kr&#xFC; 
Author-X-Name-Last: Ac&#x131;ta&#x15F; 
Title: A Monte Carlo simulation study on partially adaptive estimators of linear regression models 
Abstract:
  This paper presents a comprehensive comparison of well-known partially
 adaptive estimators (PAEs) in terms of efficiency in estimating regression
 parameters. The aim is to identify the best estimators of regression
 parameters when error terms follow from normal, Laplace, Student's
 <italic>t</italic>, normal mixture, lognormal and gamma distribution via
 the Monte Carlo simulation. In the results of the simulation, efficient
 PAEs are determined in the case of symmetric leptokurtic and skewed
 leptokurtic regression error data. Additionally, these estimators are also
 compared in terms of regression applications. Regarding these
 applications, using certain standard error estimators, it is shown that
 PAEs can reduce the standard error of the slope parameter estimate
 relative to ordinary least squares. 
Journal: Journal of Applied Statistics 
Pages: 1681-1699 
Issue: 8 
Volume: 38 
Year: 2011 
Month: 8 
X-DOI: 10.1080/02664763.2010.516389 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.516389 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:8:p:1681-1699




Template-Type: ReDIF-Article 1.0
Author-Name: L.H.A.   Dal Bello 
Author-X-Name-First: L.H.A.   Dal 
Author-X-Name-Last: Bello 
Author-Name: A.   F.C. Vieira 
Author-X-Name-First: A.   F.C. 
Author-X-Name-Last: Vieira 
Title: Optimization of a product performance using mixture experiments including process variables 
Abstract:
  This article presents a case study of a chemical compound used in the
 delay mechanism to start a rocket engine. The compound consists in a
 three-component mixture. Besides the components proportions, two process
 variables are considered. The aim of the study is to investigate the mix
 components proportions and the levels of process variables that set the
 expected delay time as close as possible to the target value and, at the
 same time, minimize the width of prediction interval for the response. A
 linear regression model with normal responses was fitted. Through the
 model developed, the optimal components proportions and the levels of the
 process variables were determined. For the model selection, the use of the
 backward method with an information criterion proved to be efficient in
 the case under study. 
Journal: Journal of Applied Statistics 
Pages: 1701-1715 
Issue: 8 
Volume: 38 
Year: 2011 
Month: 8 
X-DOI: 10.1080/02664763.2010.518370 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.518370 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:8:p:1701-1715




Template-Type: ReDIF-Article 1.0
Author-Name: Afr&#xE2;nio   M.C. Vieira 
Author-X-Name-First: Afr&#xE2;nio   M.C. 
Author-X-Name-Last: Vieira 
Author-Name: Roseli   A. Leandro 
Author-X-Name-First: Roseli   A. 
Author-X-Name-Last: Leandro 
Author-Name: Clarice   G.B. Dem�trio 
Author-X-Name-First: Clarice   G.B. 
Author-X-Name-Last: Dem�trio 
Author-Name: Geert Molenberghs 
Author-X-Name-First: Geert 
Author-X-Name-Last: Molenberghs 
Title: Double generalized linear model for tissue culture proportion data: a Bayesian perspective 
Abstract:
  Joint generalized linear models and double generalized linear models
 (DGLMs) were designed to model outcomes for which the variability can be
 explained using factors and/or covariates. When such factors operate, the
 usual normal regression models, which inherently exhibit constant
 variance, will under-represent variation in the data and hence may lead to
 erroneous inferences. For count and proportion data, such noise factors
 can generate a so-called overdispersion effect, and the use of binomial
 and Poisson models underestimates the variability and, consequently,
 incorrectly indicate significant effects. In this manuscript, we propose a
 DGLM from a Bayesian perspective, focusing on the case of proportion data,
 where the overdispersion can be modeled using a random effect that depends
 on some noise factors. The posterior joint density function was sampled
 using Monte Carlo Markov Chain algorithms, allowing inferences over the
 model parameters. An application to a data set on apple tissue culture is
 presented, for which it is shown that the Bayesian approach is quite
 feasible, even when limited prior information is available, thereby
 generating valuable insight for the researcher about its experimental
 results. 
Journal: Journal of Applied Statistics 
Pages: 1717-1731 
Issue: 8 
Volume: 38 
Year: 2011 
Month: 9 
X-DOI: 10.1080/02664763.2010.529875 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.529875 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:8:p:1717-1731




Template-Type: ReDIF-Article 1.0
Author-Name: Chun-Shu Chen 
Author-X-Name-First: Chun-Shu 
Author-X-Name-Last: Chen 
Author-Name: Hong-Ding Yang 
Author-X-Name-First: Hong-Ding 
Author-X-Name-Last: Yang 
Title: A joint modeling approach for spatial earthquake risk variations 
Abstract:
  Modeling spatial patterns and processes to assess the spatial variations
 of data over a study region is an important issue in many fields. In this
 paper, we focus on investigating the spatial variations of earthquake
 risks after a main shock. Although earthquake risks have been extensively
 studied in the literatures, to our knowledge, there does not exist a
 suitable spatial model for assessing the problem. Therefore, we propose a
 joint modeling approach based on spatial hierarchical Bayesian models and
 spatial conditional autoregressive models to describe the spatial
 variations in earthquake risks over the study region during two periods. A
 family of stochastic algorithms based on a Markov chain Monte Carlo
 technique is then performed for posterior computations. The probabilistic
 issue for the changes of earthquake risks after a main shock is also
 discussed. Finally, the proposed method is applied to the earthquake
 records for Taiwan before and after the Chi-Chi earthquake. 
Journal: Journal of Applied Statistics 
Pages: 1733-1741 
Issue: 8 
Volume: 38 
Year: 2011 
Month: 9 
X-DOI: 10.1080/02664763.2010.529883 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.529883 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:8:p:1733-1741




Template-Type: ReDIF-Article 1.0
Author-Name: S&#xF8;ren   Feodor Nielsen 
Author-X-Name-First: S&#xF8;ren   Feodor 
Author-X-Name-Last: Nielsen 
Title: SAS for data analysis 
Journal: Journal of Applied Statistics 
Pages: 1743-1744 
Issue: 8 
Volume: 38 
Year: 2011 
Month: 8 
X-DOI: 10.1080/02664760903466805 
File-URL: http://hdl.handle.net/10.1080/02664760903466805 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:8:p:1743-1744




Template-Type: ReDIF-Article 1.0
Author-Name: Long Kang 
Author-X-Name-First: Long 
Author-X-Name-Last: Kang 
Title: Time-series data analysis using EViews 
Journal: Journal of Applied Statistics 
Pages: 1744-1745 
Issue: 8 
Volume: 38 
Year: 2011 
Month: 8 
X-DOI: 10.1080/02664760903466813 
File-URL: http://hdl.handle.net/10.1080/02664760903466813 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:8:p:1744-1745




Template-Type: ReDIF-Article 1.0
Author-Name: Kam Hamidieh 
Author-X-Name-First: Kam 
Author-X-Name-Last: Hamidieh 
Title: Synthetic CDOs modelling, valuation and risk management 
Journal: Journal of Applied Statistics 
Pages: 1745-1746 
Issue: 8 
Volume: 38 
Year: 2011 
Month: 8 
X-DOI: 10.1080/02664760903520148 
File-URL: http://hdl.handle.net/10.1080/02664760903520148 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:8:p:1745-1746




Template-Type: ReDIF-Article 1.0
Author-Name: Hassan   S. Bakouch 
Author-X-Name-First: Hassan   S. 
Author-X-Name-Last: Bakouch 
Title: Probability, Markov chains, queues, and simulation 
Journal: Journal of Applied Statistics 
Pages: 1746-1746 
Issue: 8 
Volume: 38 
Year: 2011 
Month: 8 
X-DOI: 10.1080/02664763.2010.484891 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.484891 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:8:p:1746-1746




Template-Type: ReDIF-Article 1.0
Author-Name: Yinghui Wei 
Author-X-Name-First: Yinghui 
Author-X-Name-Last: Wei 
Author-Name: Peter Neal 
Author-X-Name-First: Peter 
Author-X-Name-Last: Neal 
Title: Statement of Withdrawal: Statistical analysis of an endemic disease from a capture--recapture experiment 
Abstract:
  There are a number of statistical techniques for analysing epidemic
 outbreaks. However, many diseases are endemic within populations and the
 analysis of such diseases are complicated by changing population
 demography. Motivated by the spread of cowpox among rodent populations, a
 combined mathematical model for population and disease dynamics is
 introduced. An MCMC algorithm is then constructed to make statistical
 inference for the model based on data being obtained from a
 capture--recapture experiment. The statistical analysis is used to
 identify the key elements in the spread of the cowpox virus. 
Journal: Journal of Applied Statistics 
Pages: 1747-1747 
Issue: 8 
Volume: 38 
Year: 2011 
Month: 1 
X-DOI: 10.1080/02664763.2011.590298 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.590298 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:8:p:1747-1747




Template-Type: ReDIF-Article 1.0
Author-Name: Ashis SenGupta 
Author-X-Name-First: Ashis 
Author-X-Name-Last: SenGupta 
Author-Name: Hon   Keung Tony Ng 
Author-X-Name-First: Hon   Keung Tony 
Author-X-Name-Last: Ng 
Title: Nonparametric test for the homogeneity of the overall variability 
Abstract:
  In this paper, we propose a nonparametric test for homogeneity of overall
 variabilities for two multi-dimensional populations. Comparisons between
 the proposed nonparametric procedure and the asymptotic parametric
 procedure and a permutation test based on standardized generalized
 variances are made when the underlying populations are multivariate
 normal. We also study the performance of these test procedures when the
 underlying populations are non-normal. We observe that the nonparametric
 procedure and the permutation test based on standardized generalized
 variances are not as powerful as the asymptotic parametric test under
 normality. However, they are reliable and powerful tests for comparing
 overall variability under other multivariate distributions such as the
 multivariate Cauchy, the multivariate Pareto and the multivariate
 exponential distributions, even with small sample sizes. A Monte Carlo
 simulation study is used to evaluate the performance of the proposed
 procedures. An example from an educational study is used to illustrate the
 proposed nonparametric test. 
Journal: Journal of Applied Statistics 
Pages: 1751-1768 
Issue: 9 
Volume: 38 
Year: 2011 
Month: 9 
X-DOI: 10.1080/02664763.2010.529876 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.529876 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:9:p:1751-1768




Template-Type: ReDIF-Article 1.0
Author-Name: Mahmoud Torabi 
Author-X-Name-First: Mahmoud 
Author-X-Name-Last: Torabi 
Author-Name: Rhonda   J. Rosychuk 
Author-X-Name-First: Rhonda   J. 
Author-X-Name-Last: Rosychuk 
Title: Spatio-temporal modelling using B-spline for disease mapping: analysis of childhood cancer trends 
Abstract:
  To examine childhood cancer diagnoses in the province of Alberta, Canada
 during 1983--2004, we construct a generalized additive mixed model for the
 analysis of geographic and temporal variability of cancer ratios. In this
 model, spatially correlated random effects and temporal components are
 adopted. The interaction between space and time is also accommodated.
 Spatio-temporal models that use conditional autoregressive smoothing
 across the spatial dimension and B-spline over the temporal dimension are
 considered. We study the patterns of incidence ratios over time and
 identify areas with consistently high ratio estimates as areas for
 potential further investigation. We apply the method of penalized
 quasi-likelihood to estimate the model parameters. We illustrate this
 approach using a yearly data set of childhood cancer diagnoses in the
 province of Alberta, Canada during 1983--2004. 
Journal: Journal of Applied Statistics 
Pages: 1769-1781 
Issue: 9 
Volume: 38 
Year: 2011 
Month: 10 
X-DOI: 10.1080/02664763.2010.529877 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.529877 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:9:p:1769-1781




Template-Type: ReDIF-Article 1.0
Author-Name: O. Collignon 
Author-X-Name-First: O. 
Author-X-Name-Last: Collignon 
Author-Name: J.-M. Monnez 
Author-X-Name-First: J.-M. 
Author-X-Name-Last: Monnez 
Author-Name: P. Vallois 
Author-X-Name-First: P. 
Author-X-Name-Last: Vallois 
Author-Name: F. Codreanu 
Author-X-Name-First: F. 
Author-X-Name-Last: Codreanu 
Author-Name: J.-M. Renaudin 
Author-X-Name-First: J.-M. 
Author-X-Name-Last: Renaudin 
Author-Name: G. Kanny 
Author-X-Name-First: G. 
Author-X-Name-Last: Kanny 
Author-Name: M. Brulliard 
Author-X-Name-First: M. 
Author-X-Name-Last: Brulliard 
Author-Name: B.   E. Bihain 
Author-X-Name-First: B.   E. 
Author-X-Name-Last: Bihain 
Author-Name: S. Jacquenet 
Author-X-Name-First: S. 
Author-X-Name-Last: Jacquenet 
Author-Name: D. Moneret-Vautrin 
Author-X-Name-First: D. 
Author-X-Name-Last: Moneret-Vautrin 
Title: Discriminant analyses of peanut allergy severity scores 
Abstract:
  Peanut allergy is one of the most prevalent food allergies. The
 possibility of a lethal accidental exposure and the persistence of the
 disease make it a public health problem. Evaluating the intensity of
 symptoms is accomplished with a double blind placebo-controlled food
 challenge (DBPCFC), which scores the severity of reactions and measures
 the dose of peanut that elicits the first reaction. Since DBPCFC can
 result in life-threatening responses, we propose an alternate procedure
 with the long-term goal of replacing invasive allergy tests. Discriminant
 analyses of DBPCFC score, the eliciting dose and the first accidental
 exposure score were performed in 76 allergic patients using 6 immunoassays
 and 28 skin prick tests. A multiple factorial analysis was performed to
 assign equal weights to both groups of variables, and predictive models
 were built by cross-validation with linear discriminant analysis,
 <italic>k</italic>-nearest neighbours, classification and regression
 trees, penalized support vector machine, stepwise logistic regression and
 AdaBoost methods. We developed an algorithm for simultaneously clustering
 eliciting dose values and selecting discriminant variables. Our main
 conclusion is that antibody measurements offer information on the allergy
 severity, especially those directed against <italic>rAra-h1</italic> and
 <italic>rAra-h3</italic>. Further independent validation of these results
 and the use of new predictors will help extend this study to clinical
 practices. 
Journal: Journal of Applied Statistics 
Pages: 1783-1799 
Issue: 9 
Volume: 38 
Year: 2011 
Month: 8 
X-DOI: 10.1080/02664763.2010.529878 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.529878 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:9:p:1783-1799




Template-Type: ReDIF-Article 1.0
Author-Name: Hannes Kazianka 
Author-X-Name-First: Hannes 
Author-X-Name-Last: Kazianka 
Author-Name: Michael Mulyk 
Author-X-Name-First: Michael 
Author-X-Name-Last: Mulyk 
Author-Name: J&#xFC;rgen Pilz 
Author-X-Name-First: J&#xFC;rgen 
Author-X-Name-Last: Pilz 
Title: A Bayesian approach to estimating linear mixtures with unknown covariance structure 
Abstract:
  In this paper, we study a new Bayesian approach for the analysis of
 linearly mixed structures. In particular, we consider the case of
 hyperspectral images, which have to be decomposed into a collection of
 distinct spectra, called endmembers, and a set of associated proportions
 for every pixel in the scene. This problem, often referred to as spectral
 unmixing, is usually considered on the basis of the linear mixing model
 (LMM). In unsupervised approaches, the endmember signatures have to be
 calculated by an endmember extraction algorithm, which generally relies on
 the supposition that there are pure (unmixed) pixels contained in the
 image. In practice, this assumption may not hold for highly mixed data and
 consequently the extracted endmember spectra differ from the true ones. A
 way out of this dilemma is to consider the problem under the normal
 compositional model (NCM). Contrary to the LMM, the NCM treats the
 endmembers as random Gaussian vectors and not as deterministic quantities.
 Existing Bayesian approaches for estimating the proportions under the NCM
 are restricted to the case that the covariance matrix of the Gaussian
 endmembers is a multiple of the identity matrix. The self-evident
 conclusion is that this model is not suitable when the variance differs
 from one spectral channel to the other, which is a common phenomenon in
 practice. In this paper, we first propose a Bayesian strategy for the
 estimation of the mixing proportions under the assumption of varying
 variances in the spectral bands. Then we generalize this model to handle
 the case of a completely unknown covariance structure. For both
 algorithms, we present Gibbs sampling strategies and compare their
 performance with other, state of the art, unmixing routines on synthetic
 as well as on real hyperspectral fluorescence spectroscopy data. 
Journal: Journal of Applied Statistics 
Pages: 1801-1817 
Issue: 9 
Volume: 38 
Year: 2011 
Month: 9 
X-DOI: 10.1080/02664763.2010.529879 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.529879 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:9:p:1801-1817




Template-Type: ReDIF-Article 1.0
Author-Name: Folefac   D. Atem 
Author-X-Name-First: Folefac   D. 
Author-X-Name-Last: Atem 
Author-Name: Ravi   K. Sharma 
Author-X-Name-First: Ravi   K. 
Author-X-Name-Last: Sharma 
Author-Name: Stewart   J. Anderson 
Author-X-Name-First: Stewart   J. 
Author-X-Name-Last: Anderson 
Title: Fitting bivariate multilevel models to assess long-term changes in body mass index and cigarette smoking 
Abstract:
  Using data from the National Health interview Survey from 1997 to 2006,
 we present a multilevel analysis of change in body mass index (BMI) and
 number of cigarettes smoked per day in the USA. Smoking and obesity are
 the leading causes of preventable mortality and morbidity in the USA and
 most parts of the developed world. A two-stage bivariate model of changes
 in obesity and number of cigarette smoked per day is proposed. At the
 within subject stage, an individual's BMI status and the number of
 cigarette smoked per day are jointly modeled as a function of an
 individual growth trajectory plus a random error. At the between-subject
 stage, the parameters of the individual growth trajectories are allowed to
 vary as a function of differences between subjects with respect to
 demographic and behavioral characteristics and with respect to the four
 regions of the USA (Northeast, West, South and North central). Our
 two-stage modeling techniques are more informative than standard
 regression because they characterize both group-level (nomothetic) and
 individual-level (idiographic) effects, yielding a more complete
 understanding of the phenomena under study. 
Journal: Journal of Applied Statistics 
Pages: 1819-1831 
Issue: 9 
Volume: 38 
Year: 2011 
Month: 9 
X-DOI: 10.1080/02664763.2010.529880 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.529880 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:9:p:1819-1831




Template-Type: ReDIF-Article 1.0
Author-Name: Q. Li 
Author-X-Name-First: Q. 
Author-X-Name-Last: Li 
Author-Name: G. Zheng 
Author-X-Name-First: G. 
Author-X-Name-Last: Zheng 
Author-Name: R. Tiwari 
Author-X-Name-First: R. 
Author-X-Name-Last: Tiwari 
Title: Analysis of ordered categorical data with score averaging: with applications to case-control genetic associations 
Abstract:
  The trend test is often used for the analysis of
 2&#xD7;<italic>K</italic> ordered categorical data, in which
 <italic>K</italic> pre-specified increasing scores are used. There have
 been discussions on how to assign these scores and the impact of the
 outcomes on different scores. The scores are often assigned based on the
 data-generating model. When this model is unknown, using the trend test is
 not robust. We discuss the weighted average of a trend test over all
 scientifically plausible choices of scores or models. This approach is
 more computationally efficient than a commonly used robust test MAX when
 <italic>K</italic> is large. Our discussion is for any ordered
 2&#xD7;<italic>K</italic> table, but simulation and applications to real
 data are focused on case-control genetic association studies. Although
 there is no single test optimal for all choices of scores, our numerical
 results show that some score averaging tests can achieve the performance
 of MAX. 
Journal: Journal of Applied Statistics 
Pages: 1833-1843 
Issue: 9 
Volume: 38 
Year: 2011 
Month: 9 
X-DOI: 10.1080/02664763.2010.529881 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.529881 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:9:p:1833-1843




Template-Type: ReDIF-Article 1.0
Author-Name: Anne   C. Black 
Author-X-Name-First: Anne   C. 
Author-X-Name-Last: Black 
Author-Name: Ofer Harel 
Author-X-Name-First: Ofer 
Author-X-Name-Last: Harel 
Author-Name: D. Betsy McCoach 
Author-X-Name-First: D. 
Author-X-Name-Last: Betsy McCoach 
Title: Missing data techniques for multilevel data: implications of model misspecification 
Abstract:
  When modeling multilevel data, it is important to accurately represent
 the interdependence of observations within clusters. Ignoring data
 clustering may result in parameter misestimation. However, it is not well
 established to what degree parameter estimates are affected by model
 misspecification when applying missing data techniques (MDTs) to
 incomplete multilevel data. We compare the performance of three MDTs with
 incomplete hierarchical data. We consider the impact of imputation model
 misspecification on the quality of parameter estimates by employing
 multiple imputation under assumptions of a normal model (MI/NM) with
 two-level cross-sectional data when values are missing at random on the
 dependent variable at rates of 10%, 30%, and 50%. Five criteria are used
 to compare estimates from MI/NM to estimates from MI assuming a linear
 mixed model (MI/LMM) and maximum likelihood estimation to the same
 incomplete data sets. With 10% missing data (MD), techniques performed
 similarly for fixed-effects estimates, but variance components were biased
 with MI/NM. Effects of model misspecification worsened at higher rates of
 MD, with the hierarchical structure of the data markedly underrepresented
 by biased variance component estimates. MI/LMM and maximum likelihood
 provided generally accurate and unbiased parameter estimates but
 performance was negatively affected by increased rates of MD. 
Journal: Journal of Applied Statistics 
Pages: 1845-1865 
Issue: 9 
Volume: 38 
Year: 2011 
Month: 9 
X-DOI: 10.1080/02664763.2010.529882 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.529882 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:9:p:1845-1865




Template-Type: ReDIF-Article 1.0
Author-Name: John   J. Chen 
Author-X-Name-First: John   J. 
Author-X-Name-Last: Chen 
Author-Name: Guangxiang Zhang 
Author-X-Name-First: Guangxiang 
Author-X-Name-Last: Zhang 
Author-Name: Chen Ji 
Author-X-Name-First: Chen 
Author-X-Name-Last: Ji 
Author-Name: George   F. Steinhardt 
Author-X-Name-First: George   F. 
Author-X-Name-Last: Steinhardt 
Title: Simple moment-based inferences of generalized concordance correlation 
Abstract:
  We proposed two simple moment-based procedures, one with (GCCC1) and one
 without (GCCC2) normality assumptions, to generalize the inference of
 concordance correlation coefficient for the evaluation of agreement among
 multiple observers for measurements on a continuous scale. A modified
 Fisher's <italic>Z</italic>-transformation was adapted to further improve
 the inference. We compared the proposed methods with
 <italic>U</italic>-statistic-based inference approach. Simulation analysis
 showed desirable statistical properties of the simplified approach GCCC1,
 in terms of coverage probabilities and coverage balance, especially for
 small samples. GCCC2, which is distribution-free, behaved comparably with
 the <italic>U</italic>-statistic-based procedure, but had a more intuitive
 and explicit variance estimator. The utility of these approaches were
 illustrated using two clinical data examples. 
Journal: Journal of Applied Statistics 
Pages: 1867-1882 
Issue: 9 
Volume: 38 
Year: 2011 
Month: 10 
X-DOI: 10.1080/02664763.2010.529884 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.529884 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:9:p:1867-1882




Template-Type: ReDIF-Article 1.0
Author-Name: Kathryn Bartimote-Aufflick 
Author-X-Name-First: Kathryn 
Author-X-Name-Last: Bartimote-Aufflick 
Author-Name: Peter   C. Thomson 
Author-X-Name-First: Peter   C. 
Author-X-Name-Last: Thomson 
Title: The analysis of ordinal time-series data via a transition (Markov) model 
Abstract:
  While standard techniques are available for the analysis of time-series
 (longitudinal) data, and for ordinal (rating) data, not much is available
 for the combination of the two, at least in a readily-usable form.
 However, this data type is common place in the natural and health sciences
 where repeated ratings are recorded on the same subject. To analyse these
 data, this paper considers a transition (Markov) model where the rating of
 a subject at one time depends explicitly on the observed rating at the
 previous point of time by incorporating the previous rating as a predictor
 variable. Complications arise with adequate handling of data at the first
 observation (<italic>t</italic>=1), as there is no prior observation to
 use as a predictor. To overcome this, it is postulated the existence of a
 rating at time <italic>t</italic>=0; however it is treated as
 &#x2018;missing data&#x2019; and the expectation--maximisation algorithm
 used to accommodate this. The particular benefits of this method are shown
 for shorter time series. 
Journal: Journal of Applied Statistics 
Pages: 1883-1897 
Issue: 9 
Volume: 38 
Year: 2011 
Month: 9 
X-DOI: 10.1080/02664763.2010.529885 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.529885 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:9:p:1883-1897




Template-Type: ReDIF-Article 1.0
Author-Name: Jie Chen 
Author-X-Name-First: Jie 
Author-X-Name-Last: Chen 
Author-Name: Ayten Yi&#x11F;iter 
Author-X-Name-First: Ayten 
Author-X-Name-Last: Yi&#x11F;iter 
Author-Name: Kuang-Chao Chang 
Author-X-Name-First: Kuang-Chao 
Author-X-Name-Last: Chang 
Title: A Bayesian approach to inference about a change point model with application to DNA copy number experimental data 
Abstract:
  In this paper, we study the change-point inference problem motivated by
 the genomic data that were collected for the purpose of monitoring DNA
 copy number changes. DNA copy number changes or copy number variations
 (CNVs) correspond to chromosomal aberrations and signify abnormality of a
 cell. Cancer development or other related diseases are usually relevant to
 DNA copy number changes on the genome. There are inherited random noises
 in such data, therefore, there is a need to employ an appropriate
 statistical model for identifying statistically significant DNA copy
 number changes. This type of statistical inference is evidently crucial in
 cancer researches, clinical diagnostic applications, and other related
 genomic researches. For the high-throughput genomic data resulting from
 DNA copy number experiments, a mean and variance change point model (MVCM)
 for detecting the CNVs is appropriate. We propose to use a Bayesian
 approach to study the MVCM for the cases of one change and propose to use
 a sliding window to search for all CNVs on a given chromosome. We carry
 out simulation studies to evaluate the estimate of the locus of the DNA
 copy number change using the derived posterior probability. These
 simulation results show that the approach is suitable for identifying copy
 number changes. The approach is also illustrated on several chromosomes
 from nine fibroblast cancer cell line data (array-based comparative
 genomic hybridization data). All DNA copy number aberrations that have
 been identified and verified by karyotyping are detected by our approach
 on these cell lines. 
Journal: Journal of Applied Statistics 
Pages: 1899-1913 
Issue: 9 
Volume: 38 
Year: 2011 
Month: 9 
X-DOI: 10.1080/02664763.2010.529886 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.529886 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:9:p:1899-1913




Template-Type: ReDIF-Article 1.0
Author-Name: Paul   H. Garthwaite 
Author-X-Name-First: Paul   H. 
Author-X-Name-Last: Garthwaite 
Author-Name: John   R. Crawford 
Author-X-Name-First: John   R. 
Author-X-Name-Last: Crawford 
Title: Inference for a binomial proportion in the presence of ties 
Abstract:
  We suppose a case is to be compared with controls on the basis of a test
 that gives a single discrete score. The score of the case may tie with the
 scores of one or more controls. However, scores relate to an underlying
 quantity of interest that is continuous and so an observed score can be
 treated as the rounded value of an underlying continuous score. This makes
 it reasonable to break ties. This paper addresses the problem of forming a
 confidence interval for the proportion of controls that have a lower
 underlying score than the case. In the absence of ties, this is the
 standard task of making inferences about a binomial proportion and many
 methods for forming confidence intervals have been proposed. We give a
 general procedure to extend these methods to handle ties, under the
 assumption that ties may be broken at random. Properties of the procedure
 are given and an example examines its performance when it is used to
 extend several methods. A real example shows that an estimated confidence
 interval can be much too small if the uncertainty associated with ties is
 not taken into account. Software implementing the procedure is freely
 available. 
Journal: Journal of Applied Statistics 
Pages: 1915-1934 
Issue: 9 
Volume: 38 
Year: 2011 
Month: 10 
X-DOI: 10.1080/02664763.2010.537649 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.537649 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:9:p:1915-1934




Template-Type: ReDIF-Article 1.0
Author-Name: Anandamayee Majumdar 
Author-X-Name-First: Anandamayee 
Author-X-Name-Last: Majumdar 
Author-Name: Corinna Gries 
Author-X-Name-First: Corinna 
Author-X-Name-Last: Gries 
Author-Name: Jason Walker 
Author-X-Name-First: Jason 
Author-X-Name-Last: Walker 
Title: A non-stationary spatial generalized linear mixed model approach for studying plant diversity 
Abstract:
  We analyze the multivariate spatial distribution of plant species
 diversity, distributed across three ecologically distinct land uses, the
 urban residential, urban non-residential, and desert. We model these data
 using a spatial generalized linear mixed model. Here plant species counts
 are assumed to be correlated within and among the spatial locations. We
 implement this model across the Phoenix metropolis and surrounding desert.
 Using a Bayesian approach, we utilized the Langevin--Hastings hybrid
 algorithm. Under a generalization of a spatial log-Gaussian Cox model, the
 log-intensities of the species count processes follow Gaussian
 distributions. The purely spatial component corresponding to these
 log-intensities are jointly modeled using a cross-convolution approach, in
 order to depict a valid cross-correlation structure. We observe that this
 approach yields non-stationarity of the model ensuing from different land
 use types. We obtain predictions of various measures of plant diversity
 including plant richness and the Shannon--Weiner diversity at observed
 locations. We also obtain a prediction framework for plant preferences in
 urban and desert plots. 
Journal: Journal of Applied Statistics 
Pages: 1935-1950 
Issue: 9 
Volume: 38 
Year: 2011 
Month: 10 
X-DOI: 10.1080/02664763.2010.537650 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.537650 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:9:p:1935-1950




Template-Type: ReDIF-Article 1.0
Author-Name: Tatiana   B. Bordin 
Author-X-Name-First: Tatiana   B. 
Author-X-Name-Last: Bordin 
Author-Name: Hildete   P. Pinheiro 
Author-X-Name-First: Hildete   P. 
Author-X-Name-Last: Pinheiro 
Author-Name: Alu�sio Pinheiro 
Author-X-Name-First: Alu�sio 
Author-X-Name-Last: Pinheiro 
Title: Homogeneity tests among groups for microsatellite data 
Abstract:
  We propose a homogeneity test among groups on a quadratic distance
 measure. The underlying mutation process in the microsatellite loci is
 studied using the stepwise mutation model. Asymptotic normality of the
 test statistic is proved under very mild regularity conditions. Resampling
 methods, such as jackknife, are used in the application to build
 confidence intervals for the difference in allelic variation between and
 within groups. The method is applied in a real data to test whether there
 are differences in the distribution of the repeated sequence among groups
 defined by ethnicity and alcoholism index (ALDX1). 
Journal: Journal of Applied Statistics 
Pages: 1951-1962 
Issue: 9 
Volume: 38 
Year: 2011 
Month: 10 
X-DOI: 10.1080/02664763.2010.537651 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.537651 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:9:p:1951-1962




Template-Type: ReDIF-Article 1.0
Author-Name: Younan Chen 
Author-X-Name-First: Younan 
Author-X-Name-Last: Chen 
Author-Name: Keying Ye 
Author-X-Name-First: Keying 
Author-X-Name-Last: Ye 
Title: A Bayesian hierarchical approach to dual response surface modelling 
Abstract:
  In modern quality engineering, dual response surface methodology is a
 powerful tool to model an industrial process by using both the mean and
 the standard deviation of the measurements as the responses. The least
 squares method in regression is often used to estimate the coefficients in
 the mean and standard deviation models, and various decision criteria are
 proposed by researchers to find the optimal conditions. Based on the
 inherent hierarchical structure of the dual response problems, we propose
 a Bayesian hierarchical approach to model dual response surfaces. Such an
 approach is compared with two frequentist least squares methods by using
 two real data sets and simulated data. 
Journal: Journal of Applied Statistics 
Pages: 1963-1975 
Issue: 9 
Volume: 38 
Year: 2011
Month: 11 
X-DOI: 10.1080/02664763.2010.545106 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.545106 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:9:p:1963-1975




Template-Type: ReDIF-Article 1.0
Author-Name: Amjad   D. Al-Nasser 
Author-X-Name-First: Amjad   D. 
Author-X-Name-Last: Al-Nasser 
Author-Name: Mohammad   Y. Al-Rawwash 
Author-X-Name-First: Mohammad   Y. 
Author-X-Name-Last: Al-Rawwash 
Author-Name: Anas   S. Alakhras 
Author-X-Name-First: Anas   S. 
Author-X-Name-Last: Alakhras 
Title: An approach to setting up a national customer satisfaction index: the Jordan case study 
Abstract:
  The aim of this paper was to develop a national customer satisfaction
 index (CSI) in Jordan and to derive its theory using generalized maximum
 entropy. During the course of this research, we conducted two different
 surveys to complete the framework of this CSI. The first one is a pilot
 study conducted based on a CSI basket in order to select the main factors
 that comprise the Jordanian customer satisfaction index (JCSI). Based on
 two different analyses, namely nonlinear principal component analysis and
 factor analysis, the explained variances in the first and second
 dimensions were 50.32 and 16.99% respectively. Also, Cronbach coefficients
 &#x3B1; in the first and second dimensions were 0.923 and 0.521,
 respectively. The results of this survey suggests the inclusion of
 loyalty, complaint, expectation, image and service quality as the main CS
 factors of our proposed model. The second study is a practical
 implementation conducted on the Vocational Training Corporation in order
 to evaluate the proposed JCSI. The results indicated that the suggested
 components of the proposed model are significant and form a good fitted
 model. We used the comparative fit index and the normed fit index as
 goodness-of-fit measures to evaluate the effectiveness of our proposed
 model. Both measures indicated that the proposed model is a promising one. 
Journal: Journal of Applied Statistics 
Pages: 1977-1993 
Issue: 9 
Volume: 38 
Year: 2011
Month: 12 
X-DOI: 10.1080/02664763.2010.545107 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.545107 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:9:p:1977-1993




Template-Type: ReDIF-Article 1.0
Author-Name: Firoozeh Rivaz 
Author-X-Name-First: Firoozeh 
Author-X-Name-Last: Rivaz 
Author-Name: Mohsen Mohammadzadeh 
Author-X-Name-First: Mohsen 
Author-X-Name-Last: Mohammadzadeh 
Author-Name: Majid   Jafari Khaledi 
Author-X-Name-First: Majid   Jafari 
Author-X-Name-Last: Khaledi 
Title: Spatio-temporal modeling and prediction of CO concentrations in Tehran city 
Abstract:
  One of the most important agents responsible for high pollution in Tehran
 is carbon monoxide. Prediction of carbon monoxide is of immense help for
 sustaining the inhabitants&#x2019; health level. In this paper, motivated
 by the statistical analysis of carbon monoxide using the empirical Bayes
 approach, we deal with the issue of prior specification for the model
 parameters. In fact, the hyperparameters (the parameters of the prior law)
 are estimated based on a sampling-based method which depends only on the
 specification of the marginal spatial and temporal correlation structures.
 We compare the predictive performance of this approach with the type II
 maximum likelihood method. Results indicate that the proposed procedure
 performs better for this data set. 
Journal: Journal of Applied Statistics 
Pages: 1995-2007 
Issue: 9 
Volume: 38 
Year: 2011 
Month: 11 
X-DOI: 10.1080/02664763.2010.545108 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.545108 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:9:p:1995-2007




Template-Type: ReDIF-Article 1.0
Author-Name: Karimollah Hajian-Tilaki 
Author-X-Name-First: Karimollah 
Author-X-Name-Last: Hajian-Tilaki 
Author-Name: James   A. Hanley 
Author-X-Name-First: James   A. 
Author-X-Name-Last: Hanley 
Author-Name: Vahid Nassiri 
Author-X-Name-First: Vahid 
Author-X-Name-Last: Nassiri 
Title: An extension of parametric ROC analysis for calculating diagnostic accuracy when underlying distributions are mixture of Gaussian 
Abstract:
  The semiparametric LABROC approach of fitting binormal model for
 estimating AUC as a global index of accuracy has been justified (except
 for bimodal forms), while for estimating a local index of accuracy such as
 TPF, it may lead to a bias in severe departure of data from binormality.
 We extended parametric ROC analysis for quantitative data when one or both
 pair members are mixture of Gaussian (MG) in particular for bimodal forms.
 We analytically showed that AUC and TPF are a mixture of weighting
 parameters of different components of AUCs and TPFs of a mixture of
 underlying distributions. In a simulation study of six configurations of
 MG distributions:{bimodal, normal} and {bimodal, bimodal} pairs, the
 parameters of MG distributions were estimated using the EM algorithm. The
 results showed that the estimated AUC from our proposed model was
 essentially unbiased, and that the bias in the estimated TPF at a
 clinically relevant range of FPF was roughly 0.01 for a sample size of
 <italic>n</italic>=100/100. In practice, with severe departures from
 binormality, we recommend an extension of the LABROC and software
 development for future research to allow for each member of the pair of
 distributions to be a mixture of Gaussian that is a more flexible
 parametric form. 
Journal: Journal of Applied Statistics 
Pages: 2009-2022 
Issue: 9 
Volume: 38 
Year: 2011 
Month: 11 
X-DOI: 10.1080/02664763.2010.545109 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.545109 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:9:p:2009-2022




Template-Type: ReDIF-Article 1.0
Author-Name: P&#xE4;r Stockhammar 
Author-X-Name-First: P&#xE4;r 
Author-X-Name-Last: Stockhammar 
Author-Name: Lars-Erik &#xD6;ller 
Author-X-Name-First: Lars-Erik 
Author-X-Name-Last: &#xD6;ller 
Title: On the probability distribution of economic growth 
Abstract:
  Three important and significantly heteroscedastic gross domestic product
 series are studied. Omnipresent heteroscedasticity is removed and the
 distributions of the series are then compared to normal, normal mixture
 and normal--asymmetric Laplace (NAL) distributions. NAL represents a
 skewed and leptokurtic distribution, which is in line with the Aghion and
 Howitt [1] model for economic growth, based on Schumpeter's idea of
 creative destruction. Statistical properties of the NAL distributions are
 provided and it is shown that NAL fits the data better than the
 alternatives. 
Journal: Journal of Applied Statistics 
Pages: 2023-2041 
Issue: 9 
Volume: 38 
Year: 2011 
Month: 11 
X-DOI: 10.1080/02664763.2010.545110 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.545110 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:9:p:2023-2041




Template-Type: ReDIF-Article 1.0
Author-Name: Eylem Deniz 
Author-X-Name-First: Eylem 
Author-X-Name-Last: Deniz 
Author-Name: Oguz Akbilgic 
Author-X-Name-First: Oguz 
Author-X-Name-Last: Akbilgic 
Author-Name: J.   Andrew Howe 
Author-X-Name-First: J.   Andrew 
Author-X-Name-Last: Howe 
Title: Model selection using information criteria under a new estimation method: least squares ratio 
Abstract:
  In this study, we evaluate several forms of both Akaike-type and
 Information Complexity (ICOMP)-type information criteria, in the context
 of selecting an optimal subset least squares ratio (LSR) regression model.
 Our simulation studies are designed to mimic many characteristics present
 in real data -- heavy tails, multicollinearity, redundant variables, and
 completely unnecessary variables. Our findings are that LSR in conjunction
 with one of the ICOMP criteria is very good at selecting the true model.
 Finally, we apply these methods to the familiar body fat data set. 
Journal: Journal of Applied Statistics 
Pages: 2043-2050 
Issue: 9 
Volume: 38 
Year: 2011 
Month: 11 
X-DOI: 10.1080/02664763.2010.545111 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.545111 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:9:p:2043-2050




Template-Type: ReDIF-Article 1.0
Author-Name: Nader Fallah 
Author-X-Name-First: Nader 
Author-X-Name-Last: Fallah 
Author-Name: Arnold Mitnitski 
Author-X-Name-First: Arnold 
Author-X-Name-Last: Mitnitski 
Author-Name: Kenneth Rockwood 
Author-X-Name-First: Kenneth 
Author-X-Name-Last: Rockwood 
Title: Applying neural network Poisson regression to predict cognitive score changes 
Abstract:
  In this study, we combined a Poisson regression model with neural
 networks (neural network Poisson regression) to relax the traditional
 Poisson regression assumption of linearity of the Poisson mean as a
 function of covariates, while including it as a special case. In four
 simulated examples, we found that the neural network Poisson regression
 improved the performance of simple Poisson regression if the Poisson mean
 was nonlinearly related to covariates. We also illustrated the performance
 of the model in predicting five-year changes in cognitive scores, in
 association with age and education level; we found that the proposed
 approach had superior accuracy to conventional linear Poisson regression.
 As the interpretability of the neural networks is often difficult, its
 combination with conventional and more readily interpretable approaches
 under the generalized linear model can benefit applications in
 biomedicine. 
Journal: Journal of Applied Statistics 
Pages: 2051-2062 
Issue: 9 
Volume: 38 
Year: 2011 
Month: 11 
X-DOI: 10.1080/02664763.2010.545112 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.545112 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:9:p:2051-2062




Template-Type: ReDIF-Article 1.0
Author-Name: Bhamidipati   Narasimha Murthy 
Author-X-Name-First: Bhamidipati   Narasimha 
Author-X-Name-Last: Murthy 
Author-Name: Ngianga-Bakwin Kandala 
Author-X-Name-First: Ngianga-Bakwin 
Author-X-Name-Last: Kandala 
Author-Name: Radhakrishnan Ezhil 
Author-X-Name-First: Radhakrishnan 
Author-X-Name-Last: Ezhil 
Author-Name: Prabhdeep Kaur 
Author-X-Name-First: Prabhdeep 
Author-X-Name-Last: Kaur 
Author-Name: Ramachandra Sudha 
Author-X-Name-First: Ramachandra 
Author-X-Name-Last: Sudha 
Title: Statistical issues in studying the relative importance of body mass index, waist circumference, waist hip ratio and waist stature ratio to predict type 2 diabetes 
Abstract:
  Systematic and appropriate statistical analysis is needed to examine the
 relative performance of anthropometrical indices, viz. body mass index
 (BMI), waist circumference (WC), waist hip ratio (WHR) and waist stature
 ratio (WSR) for predicting type 2 diabetes. Using information on
 socio-demographic, anthropometric and biochemical variables from 2148
 males, we examined collinearity and non-linearity among the predictors
 before studying the association between anthropometric indices and type 2
 diabetes. The variable involving in collinearity was removed from further
 analysis, and the relative importance of BMI, WC and WHR was examined by
 logistic regression analysis. To avoid non-interpretable odds ratios
 (ORs), cut point theory is used. Optimal cut points are derived and tested
 for significance. Multivariable fractional polynomial (MFP) algorithm is
 applied to reconcile non-linearity. As expected, WSR and WC were collinear
 with WHR and BMI. Since WSR was jointly as well as independently
 collinear, it was dropped from further analysis. The OR for WHR could not
 be interpreted meaningfully. Cut point theory was adopted. Deciles emerged
 as the optimal cut point. MFP recognized non-linearity effects on the
 outcome. Multicollinearity among the anthropometric indices was examined.
 Optimal cut points were identified and used to study the relative ORs. On
 the basis of the results of analysis, MFP is recommended to accommodate
 non-linearity among the predictors. WHR is relatively more important and
 significant than WC and BMI. 
Journal: Journal of Applied Statistics 
Pages: 2063-2070 
Issue: 9 
Volume: 38 
Year: 2011 
Month: 11 
X-DOI: 10.1080/02664763.2010.545113 
File-URL: http://hdl.handle.net/10.1080/02664763.2010.545113 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:38:y:2011:i:9:p:2063-2070




Template-Type: ReDIF-Article 1.0
Author-Name: Robert   G. Aykroyd 
Author-X-Name-First: Robert   G. 
Author-X-Name-Last: Aykroyd 
Title: Editorial 
Journal: Journal of Applied Statistics 
Pages: 1-1 
Issue: 1 
Volume: 39 
Year: 2012 
Month: 1 
X-DOI: 10.1080/02664763.2012.643025 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.643025 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:1:p:1-1




Template-Type: ReDIF-Article 1.0
Author-Name: Charles   G. Minard 
Author-X-Name-First: Charles   G. 
Author-X-Name-Last: Minard 
Author-Name: Wenyaw Chan 
Author-X-Name-First: Wenyaw 
Author-X-Name-Last: Chan 
Author-Name: David   W. Wetter 
Author-X-Name-First: David   W. 
Author-X-Name-Last: Wetter 
Author-Name: Carol   J. Etzel 
Author-X-Name-First: Carol   J. 
Author-X-Name-Last: Etzel 
Title: Trends in smoking cessation: a Markov model approach 
Abstract:
  Intervention trials such as studies on smoking cessation may observe
 multiple, discrete outcomes over time. When the outcome is binary,
 participant observations may alternate between two states over the course
 of the study. The generalized estimating equation (GEE) approach is
 commonly used to analyze binary, longitudinal data in the context of
 independent variables. However, the sequence of observations may be
 assumed to follow a Markov chain with stationary transition probabilities
 when observations are made at fixed time points. Participants favoring the
 transition to one particular state over the other would be evidence of a
 trend in the observations. Using a log-transformed trend parameter, the
 determinants of a trend in a binary, longitudinal study may be evaluated
 by maximizing the likelihood function. A new methodology is presented here
 to test for the presence and determinants of a trend in binary,
 longitudinal observations. Empirical studies are evaluated and comparisons
 are made with the GEE approach. Practical application of the proposed
 method is made to the data available from an intervention study on smoking
 cessation. 
Journal: Journal of Applied Statistics 
Pages: 113-127 
Issue: 1 
Volume: 39 
Year: 2012 
Month: 3 
X-DOI: 10.1080/02664763.2011.578619 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.578619 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:1:p:113-127




Template-Type: ReDIF-Article 1.0
Author-Name: Claudia Angelini 
Author-X-Name-First: Claudia 
Author-X-Name-Last: Angelini 
Author-Name: Daniela De Canditiis 
Author-X-Name-First: Daniela 
Author-X-Name-Last: De Canditiis 
Author-Name: Marianna Pensky 
Author-X-Name-First: Marianna 
Author-X-Name-Last: Pensky 
Title: Clustering time-course microarray data using functional Bayesian infinite mixture model 
Abstract:
  This paper presents a new Bayesian, infinite mixture model based,
 clustering approach, specifically designed for time-course microarray
 data. The problem is to group together genes which have
 &#x201C;similar&#x201D; expression profiles, given the set of noisy
 measurements of their expression levels over a specific time interval. In
 order to capture temporal variations of each curve, a non-parametric
 regression approach is used. Each expression profile is expanded over a
 set of basis functions and the sets of coefficients of each curve are
 subsequently modeled through a Bayesian infinite mixture of Gaussian
 distributions. Therefore, the task of finding clusters of genes with
 similar expression profiles is then reduced to the problem of grouping
 together genes whose coefficients are sampled from the same distribution
 in the mixture. Dirichlet processes prior is naturally employed in such
 kinds of models, since it allows one to deal automatically with the
 uncertainty about the number of clusters. The posterior inference is
 carried out by a <italic>split and merge</italic> MCMC sampling scheme
 which integrates out parameters of the component distributions and updates
 only the latent vector of the cluster membership. The final configuration
 is obtained via the maximum <italic>a posteriori</italic> estimator. The
 performance of the method is studied using synthetic and real microarray
 data and is compared with the performances of competitive techniques. 
Journal: Journal of Applied Statistics 
Pages: 129-149 
Issue: 1 
Volume: 39 
Year: 2012 
Month: 3 
X-DOI: 10.1080/02664763.2011.578620 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.578620 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:1:p:129-149




Template-Type: ReDIF-Article 1.0
Author-Name: Guoyi Zhang 
Author-X-Name-First: Guoyi 
Author-X-Name-Last: Zhang 
Author-Name: Yan Lu 
Author-X-Name-First: Yan 
Author-X-Name-Last: Lu 
Title: Bias-corrected random forests in regression 
Abstract:
  It is well known that random forests reduce the variance of the
 regression predictors compared to a single tree, while leaving the bias
 unchanged. In many situations, the dominating component in the risk turns
 out to be the squared bias, which leads to the necessity of bias
 correction. In this paper, random forests are used to estimate the
 regression function. Five different methods for estimating bias are
 proposed and discussed. Simulated and real data are used to study the
 performance of these methods. Our proposed methods are significantly
 effective in reducing bias in regression context. 
Journal: Journal of Applied Statistics 
Pages: 151-160 
Issue: 1 
Volume: 39 
Year: 2012 
Month: 3 
X-DOI: 10.1080/02664763.2011.578621 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.578621 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:1:p:151-160




Template-Type: ReDIF-Article 1.0
Author-Name: Faisal   M. Zahid 
Author-X-Name-First: Faisal   M. 
Author-X-Name-Last: Zahid 
Author-Name: Shahla Ramzan 
Author-X-Name-First: Shahla 
Author-X-Name-Last: Ramzan 
Title: Ordinal ridge regression with categorical predictors 
Abstract:
  In multi-category response models, categories are often ordered. In the
 case of ordinal response models, the usual likelihood approach becomes
 unstable with ill-conditioned predictor space or when the number of
 parameters to be estimated is large relative to the sample size. The
 likelihood estimates do not exist when the number of observations is less
 than the number of parameters. The same problem arises if constraint on
 the order of intercept values is not met during the iterative procedure.
 Proportional odds models (POMs) are most commonly used for ordinal
 responses. In this paper, penalized likelihood with quadratic penalty is
 used to address these issues with a special focus on POMs. To avoid large
 differences between two parameter values corresponding to the consecutive
 categories of an ordinal predictor, the differences between the parameters
 of two adjacent categories should be penalized. The considered
 penalized-likelihood function penalizes the parameter estimates or
 differences between the parameter estimates according to the type of
 predictors. Mean-squared error for parameter estimates, deviance of fitted
 probabilities and prediction error for ridge regression are compared with
 usual likelihood estimates in a simulation study and an application. 
Journal: Journal of Applied Statistics 
Pages: 161-171 
Issue: 1 
Volume: 39 
Year: 2012 
Month: 3 
X-DOI: 10.1080/02664763.2011.578622 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.578622 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:1:p:161-171




Template-Type: ReDIF-Article 1.0
Author-Name: Miao-Yu Tsai 
Author-X-Name-First: Miao-Yu 
Author-X-Name-Last: Tsai 
Title: Assessing inter- and intra-agreement for dependent binary data: a Bayesian hierarchical correlation approach 
Abstract:
  Agreement measures are designed to assess consistency between different
 instruments rating measurements of interest. When the individual responses
 are correlated with multilevel structure of nestings and clusters,
 traditional approaches are not readily available to estimate the inter-
 and intra-agreement for such complex multilevel settings. Our research
 stems from conformity evaluation between optometric devices with
 measurements on both eyes, equality tests of agreement in high myopic
 status between monozygous twins and dizygous twins, and assessment of
 reliability for different pathologists in dysplasia. In this paper, we
 focus on applying a Bayesian hierarchical correlation model incorporating
 adjustment for explanatory variables and nesting correlation structures to
 assess the inter- and intra-agreement through correlations of random
 effects for various sources. This Bayesian generalized linear
 mixed-effects model (GLMM) is further compared with the approximate
 intra-class correlation coefficients and kappa measures by the traditional
 Cohen&#x2019;s kappa statistic and the generalized estimating equations
 (GEE) approach. The results of comparison studies reveal that the Bayesian
 GLMM provides a reliable and stable procedure in estimating inter- and
 intra-agreement simultaneously after adjusting for covariates and
 correlation structures, in marked contrast to Cohen&#x2019;s kappa and the
 GEE approach. 
Journal: Journal of Applied Statistics 
Pages: 173-187 
Issue: 1 
Volume: 39 
Year: 2012 
Month: 3 
X-DOI: 10.1080/02664763.2011.578623 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.578623 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:1:p:173-187




Template-Type: ReDIF-Article 1.0
Author-Name: R.   M. Jacques 
Author-X-Name-First: R.   M. 
Author-X-Name-Last: Jacques 
Author-Name: N.   R.J. Fieller 
Author-X-Name-First: N.   R.J. 
Author-X-Name-Last: Fieller 
Author-Name: E.   K. Ainscow 
Author-X-Name-First: E.   K. 
Author-X-Name-Last: Ainscow 
Title: A classification updating procedure motivated by high-content screening data 
Abstract:
  The current paradigm for the identification of candidate drugs within the
 pharmaceutical industry typically involves the use of high-throughput
 screens. High-content screening (HCS) is the term given to the process of
 using an imaging platform to screen large numbers of compounds for some
 desirable biological activity. Classification methods have important
 applications in HCS experiments, where they are used to predict which
 compounds have the potential to be developed into new drugs. In this
 paper, a new classification method is proposed for batches of compounds
 where the rule is updated sequentially using information from the
 classification of previous batches. This methodology accounts for the
 possibility that the training data are not a representative sample of the
 test data and that the underlying group distributions may change as new
 compounds are analysed. This technique is illustrated on an example data
 set using linear discriminant analysis, <italic>k</italic>-nearest
 neighbour and random forest classifiers. Random forests are shown to be
 superior to the other classifiers and are further improved by the
 additional updating algorithm in terms of an increase in the number of
 true positives as well as a decrease in the number of false positives. 
Journal: Journal of Applied Statistics 
Pages: 189-198 
Issue: 1 
Volume: 39 
Year: 2012 
Month: 4 
X-DOI: 10.1080/02664763.2011.580335 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.580335 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:1:p:189-198




Template-Type: ReDIF-Article 1.0
Author-Name: P. Congdon 
Author-X-Name-First: P. 
Author-X-Name-Last: Congdon 
Author-Name: C.   D. Lloyd 
Author-X-Name-First: C.   D. 
Author-X-Name-Last: Lloyd 
Title: A spatial random-effects model for interzone flows: commuting in Northern Ireland 
Abstract:
  Government policy on employment, transport, and housing often depends on
 reliable information about spatial variation in commuting flows across a
 region. Simple commuting rates summarising inter-area flows may not
 provide a full perspective on the underlying levels of commuting
 attractivity of different areas (as destinations), or the varying
 dependence of different areas (as origins) on outside employment. Areas
 also vary in the degree of commuting self-containment, as expressed in
 intra-area flows. This paper uses a spatial random-effects model to
 develop indices of attractivity, extra-dependence, and self-containment
 using a latent factor method. The methodology allows consideration of the
 degree to which different explanatory influences (e.g. socioeconomic
 structure, characteristics of road networks, employment density) affect
 these aspects of commuting. The particular application is to commuting
 flows in Northern Ireland, using 139 zones that aggregate smaller areas
 (wards), so avoiding undue sparsity in the flow matrix. The analysis
 involves Bayesian estimation, with the outputs comprising full densities
 for extra-dependence, and attractivity scores and scores for intra-area
 containment of zones. Spatial patterning in these aspects of commuting is
 allowed for in the model used. One key pattern is the difference in latent
 effect estimates for urban (in particular, Belfast) and rural areas
 reflecting variable job opportunities in these areas. 
Journal: Journal of Applied Statistics 
Pages: 199-213 
Issue: 1 
Volume: 39 
Year: 2012 
Month: 4 
X-DOI: 10.1080/02664763.2011.580336 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.580336 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:1:p:199-213




Template-Type: ReDIF-Article 1.0
Author-Name: Wanbo Lu 
Author-X-Name-First: Wanbo 
Author-X-Name-Last: Lu 
Author-Name: Daimin Shi 
Author-X-Name-First: Daimin 
Author-X-Name-Last: Shi 
Title: A new compounding life distribution: the Weibull--Poisson distribution 
Abstract:
  In this paper, a new compounding distribution, named the Weibull--Poisson
 distribution is introduced. The shape of failure rate function of the new
 compounding distribution is flexible, it can be decreasing, increasing,
 upside-down bathtub-shaped or unimodal. A comprehensive mathematical
 treatment of the proposed distribution and expressions of its density,
 cumulative distribution function, survival function, failure rate
 function, the <italic>k</italic>th raw moment and quantiles are provided.
 Maximum likelihood method using EM algorithm is developed for parameter
 estimation. Asymptotic properties of the maximum likelihood estimates are
 discussed, and intensive simulation studies are conducted for evaluating
 the performance of parameter estimation. The use of the proposed
 distribution is illustrated with examples. 
Journal: Journal of Applied Statistics 
Pages: 21-38 
Issue: 1 
Volume: 39 
Year: 2012 
Month: 3 
X-DOI: 10.1080/02664763.2011.575126 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.575126 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:1:p:21-38




Template-Type: ReDIF-Article 1.0
Author-Name: Jan   C. Schuller 
Author-X-Name-First: Jan   C. 
Author-X-Name-Last: Schuller 
Title: The malicious host: a minimax solution of the Monty Hall problem 
Abstract:
  The classic solution of the Monty Hall problem tacitly assumes that,
 after the candidate made his/her first choice, the host always allows the
 candidate to switch doors after he/she showed to the candidate a losing
 door, not initially chosen by the candidate. In view of actual TV shows,
 it seems a more credible assumption that the host will or will not allow
 switching. Under this assumption, possible strategies for the candidate
 are discussed, with respect to a minimax solution of the problem. In
 conclusion, the classic solution does not necessarily provide a good
 guidance for a candidate on a game show. It is discussed that the
 popularity of the problem is due to its incompleteness. 
Journal: Journal of Applied Statistics 
Pages: 215-221 
Issue: 1 
Volume: 39 
Year: 2012 
Month: 4 
X-DOI: 10.1080/02664763.2011.580337 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.580337 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:1:p:215-221




Template-Type: ReDIF-Article 1.0
Author-Name: Paula Brito 
Author-X-Name-First: Paula 
Author-X-Name-Last: Brito 
Author-Name: A.   Pedro Duarte Silva 
Author-X-Name-First: A.   Pedro 
Author-X-Name-Last: Duarte Silva 
Title: Modelling interval data with Normal and Skew-Normal distributions 
Abstract:
  A parametric modelling for interval data is proposed, assuming a
 multivariate Normal or Skew-Normal distribution for the midpoints and
 log-ranges of the interval variables. The intrinsic nature of the interval
 variables leads to special structures of the variance--covariance matrix,
 which is represented by five different possible configurations. Maximum
 likelihood estimation for both models under all considered configurations
 is studied. The proposed modelling is then considered in the context of
 analysis of variance and multivariate analysis of variance testing. To
 access the behaviour of the proposed methodology, a simulation study is
 performed. The results show that, for medium or large sample sizes, tests
 have good power and their true significance level approaches nominal
 levels when the constraints assumed for the model are respected; however,
 for small samples, sizes close to nominal levels cannot be guaranteed.
 Applications to Chinese meteorological data in three different regions and
 to credit card usage variables for different card designations, illustrate
 the proposed methodology. 
Journal: Journal of Applied Statistics 
Pages: 3-20 
Issue: 1 
Volume: 39 
Year: 2012 
Month: 3 
X-DOI: 10.1080/02664763.2011.575125 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.575125 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:1:p:3-20




Template-Type: ReDIF-Article 1.0
Author-Name: Sammy Zahran 
Author-X-Name-First: Sammy 
Author-X-Name-Last: Zahran 
Author-Name: Michael   A. Long 
Author-X-Name-First: Michael   A. 
Author-X-Name-Last: Long 
Author-Name: Kenneth   J. Berry 
Author-X-Name-First: Kenneth   J. 
Author-X-Name-Last: Berry 
Title: Measures of predictor sensitivity for order-insensitive partitioning of multiple correlation 
Abstract:
  Lindeman <italic>et al.</italic> [12] provide a unique solution to the
 relative importance of correlated predictors in multiple regression by
 averaging squared semi-partial correlations obtained for each predictor
 across all <italic>p</italic>! orderings. In this paper, we propose a
 series of predictor sensitivity statistics that complement the variance
 decomposition procedure advanced by Lindeman <italic>et al</italic>. [12].
 First, we detail the logic of averaging over orderings as a technique of
 variance partitioning. Second, we assess predictors by conditional
 dominance analysis, a qualitative procedure designed to overcome defects
 in the Lindeman <italic>et al.</italic> [12] variance decomposition
 solution. Third, we introduce a suite of indices to assess the sensitivity
 of a predictor to model specification, advancing a series of
 sensitivity-adjusted contribution statistics that allow for more definite
 quantification of predictor relevance. Fourth, we describe the analytic
 efficiency of our proposed technique against the Budescu conditional
 dominance solution to the uneven contribution of predictors across all
 <italic>p</italic>! orderings. 
Journal: Journal of Applied Statistics 
Pages: 39-51 
Issue: 1 
Volume: 39 
Year: 2012 
Month: 3 
X-DOI: 10.1080/02664763.2011.578614 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.578614 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:1:p:39-51




Template-Type: ReDIF-Article 1.0
Author-Name: Giancarlo Diana 
Author-X-Name-First: Giancarlo 
Author-X-Name-Last: Diana 
Author-Name: Pier   Francesco Perri 
Author-X-Name-First: Pier   Francesco 
Author-X-Name-Last: Perri 
Title: A calibration-based approach to sensitive data: a simulation study 
Abstract:
  In this paper, we discuss the use of auxiliary information to estimate
 the population mean of a sensitive variable when data are perturbed by
 means of three scrambled response devices, namely the additive, the
 multiplicative and the mixed model. Emphasis is given to the calibration
 approach, and the behavior of different estimators is investigated through
 simulated and real data. It is shown that the use of auxiliary information
 can considerably improve the efficiency of the estimates without
 jeopardizing respondent privacy. 
Journal: Journal of Applied Statistics 
Pages: 53-65 
Issue: 1 
Volume: 39 
Year: 2012 
Month: 3 
X-DOI: 10.1080/02664763.2011.578615 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.578615 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:1:p:53-65




Template-Type: ReDIF-Article 1.0
Author-Name: Eugene Demidenko 
Author-X-Name-First: Eugene 
Author-X-Name-Last: Demidenko 
Title: Confidence intervals and bands for the binormal ROC curve revisited 
Abstract:
  Two types of confidence intervals (CIs) and confidence bands (CBs) for
 the receiver operating characteristic (ROC) curve are studied: pointwise
 CIs and simultaneous CBs. An optimized version of the pointwise CI with
 the shortest width is developed. A new ellipse-envelope simultaneous CB
 for the ROC curve is suggested as an adaptation of the
 Working--Hotelling-type CB implemented in a paper by Ma and Hall (1993).
 Statistical simulations show that our ellipse-envelope CB covers the true
 ROC curve with a probability close to nominal while the coverage
 probability of the Ma and Hall CB is significantly smaller. Simulations
 also show that our CI for the area under the ROC curve is close to nominal
 while the coverage probability of the CI suggested by Hanley and McNail
 (1982) uniformly overestimates the nominal value. Two examples illustrate
 our simultaneous ROC bands: radiation dose estimation from time to
 vomiting and discrimination of breast cancer from benign abnormalities
 using electrical impedance measurements. 
Journal: Journal of Applied Statistics 
Pages: 67-79 
Issue: 1 
Volume: 39 
Year: 2012 
Month: 3 
X-DOI: 10.1080/02664763.2011.578616 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.578616 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:1:p:67-79




Template-Type: ReDIF-Article 1.0
Author-Name: Giovanni Masala 
Author-X-Name-First: Giovanni 
Author-X-Name-Last: Masala 
Title: Earthquakes occurrences estimation through a parametric semi-Markov approach 
Abstract:
  The estimation of earthquakes&#x2019; occurrences prediction in seismic
 areas is a challenging problem in seismology and earthquake engineering.
 Indeed, the prevention and the quantification of possible damage provoked
 by destructive earthquakes are directly linked to this kind of prevision.
 In our paper, we adopt a parametric semi-Markov approach. This model
 assumes that a sequence of earthquakes is seen as a Markov process and
 besides it permits to take into consideration the more realistic
 assumption of events&#x2019; dependence in space and time. The elapsed
 time between two consecutive events is modeled as a general Weibull
 distribution. We determine then the transition probabilities and the
 so-called crossing states probabilities. We conclude then with a Monte
 Carlo simulation and the model is validated through a large database
 containing real data. 
Journal: Journal of Applied Statistics 
Pages: 81-96 
Issue: 1 
Volume: 39 
Year: 2012 
Month: 3 
X-DOI: 10.1080/02664763.2011.578617 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.578617 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:1:p:81-96




Template-Type: ReDIF-Article 1.0
Author-Name: Rabindra   Nath Das 
Author-X-Name-First: Rabindra   Nath 
Author-X-Name-Last: Das 
Author-Name: Jeong-Soo Park 
Author-X-Name-First: Jeong-Soo 
Author-X-Name-Last: Park 
Title: Discrepancy in regression estimates between log-normal and gamma: some case studies 
Abstract:
  In regression models with multiplicative error, estimation is often based
 on either the log-normal or the gamma model. It is well known that the
 gamma model with constant coefficient of variation and the log-normal
 model with constant variance give almost the same analysis. This article
 focuses on the discrepancies of the regression estimates between the two
 models based on real examples. It identifies that even though the variance
 or the coefficient of variation remains constant, but regression estimates
 may be different between the two models. It also identifies that for the
 <italic>same</italic> positive data set, the variance is constant under
 the log-normal model but non-constant under the gamma model. For this data
 set, the regression estimates are completely <italic>different</italic>
 between the two models. In the process, it explains the causes of
 discrepancies between the two models. 
Journal: Journal of Applied Statistics 
Pages: 97-111 
Issue: 1 
Volume: 39 
Year: 2012 
Month: 3 
X-DOI: 10.1080/02664763.2011.578618 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.578618 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:1:p:97-111




Template-Type: ReDIF-Article 1.0
Author-Name: Linus Schi&#xF6;ler 
Author-X-Name-First: Linus 
Author-X-Name-Last: Schi&#xF6;ler 
Author-Name: Marianne Fris�n 
Author-X-Name-First: Marianne 
Author-X-Name-Last: Fris�n 
Title: Multivariate outbreak detection 
Abstract:
  Online monitoring is needed to detect outbreaks of diseases such as
 influenza. Surveillance is also needed for other kinds of outbreaks, in
 the sense of an increasing expected value after a constant period.
 Information on spatial location or other variables might be available and
 may be utilized. We adapted a robust method for outbreak detection to a
 multivariate case. The relation between the times of the onsets of the
 outbreaks at different locations (or some other variable) was used to
 determine the sufficient statistic for surveillance. The derived
 maximum-likelihood estimator of the outbreak regression was
 semi-parametric in the sense that the baseline and the slope were
 non-parametric while the distribution belonged to the one-parameter
 exponential family. The estimator was used in a generalized-likelihood
 ratio surveillance method. The method was evaluated with respect to
 robustness and efficiency in a simulation study and applied to spatial
 data for detection of influenza outbreaks in Sweden. 
Journal: Journal of Applied Statistics 
Pages: 223-242 
Issue: 2 
Volume: 39 
Year: 2012 
Month: 4 
X-DOI: 10.1080/02664763.2011.584522 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.584522 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:2:p:223-242




Template-Type: ReDIF-Article 1.0
Author-Name: J&#xF6;rg Drechsler 
Author-X-Name-First: J&#xF6;rg 
Author-X-Name-Last: Drechsler 
Title: New data dissemination approaches in old Europe -- synthetic datasets for a German establishment survey 
Abstract:
  Disseminating microdata to the public that provide a high level of data
 utility, while at the same time guaranteeing the confidentiality of the
 survey respondent is a difficult task. Generating multiply imputed
 synthetic datasets is an innovative statistical disclosure limitation
 technique with the potential of enabling the data disseminating agency to
 achieve this twofold goal. So far, the approach was successfully
 implemented only for a limited number of datasets in the U.S. In this
 paper, we present the first successful implementation outside the U.S.:
 the generation of partially synthetic datasets for an establishment panel
 survey at the German Institute for Employment Research. We describe the
 whole evolution of the project: from the early discussions concerning
 variables at risk to the final synthesis. We also present our disclosure
 risk evaluations and provide some first results on the data utility of the
 generated datasets. A variance-inflated imputation model is introduced
 that incorporates additional variability in the model for records that are
 not sufficiently protected by the standard synthesis. 
Journal: Journal of Applied Statistics 
Pages: 243-265 
Issue: 2 
Volume: 39 
Year: 2012 
Month: 4 
X-DOI: 10.1080/02664763.2011.584523 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.584523 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:2:p:243-265




Template-Type: ReDIF-Article 1.0
Author-Name: Yu-Mei Chang 
Author-X-Name-First: Yu-Mei 
Author-X-Name-Last: Chang 
Author-Name: Chun-Shu Chen 
Author-X-Name-First: Chun-Shu 
Author-X-Name-Last: Chen 
Author-Name: Pao-Sheng Shen 
Author-X-Name-First: Pao-Sheng 
Author-X-Name-Last: Shen 
Title: A jackknife-based versatile test for two-sample problems with right-censored data 
Abstract:
  For testing the equality of two survival functions, the weighted logrank
 test and the weighted Kaplan--Meier test are the two most widely used
 methods. Actually, each of these tests has advantages and defects against
 various alternatives, while we cannot specify in advance the possible
 types of the survival differences. Hence, how to choose a single test or
 combine a number of competitive tests for indicating the diversities of
 two survival functions without suffering a substantial loss in power is an
 important issue. Instead of directly using a particular test which
 generally performs well in some situations and poorly in others, we
 further consider a class of tests indexed by a weighted parameter for
 testing the equality of two survival functions in this paper. A delete-1
 jackknife method is implemented for selecting weights such that the
 variance of the test is minimized. Some numerical experiments are
 performed under various alternatives for illustrating the superiority of
 the proposed method. Finally, the proposed testing procedure is applied to
 two real-data examples as well. 
Journal: Journal of Applied Statistics 
Pages: 267-277 
Issue: 2 
Volume: 39 
Year: 2012 
Month: 4 
X-DOI: 10.1080/02664763.2011.584524 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.584524 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:2:p:267-277




Template-Type: ReDIF-Article 1.0
Author-Name: Kiyotaka Iki 
Author-X-Name-First: Kiyotaka 
Author-X-Name-Last: Iki 
Author-Name: Kouji Tahata 
Author-X-Name-First: Kouji 
Author-X-Name-Last: Tahata 
Author-Name: Sadao Tomizawa 
Author-X-Name-First: Sadao 
Author-X-Name-Last: Tomizawa 
Title: Measure of departure from marginal homogeneity using marginal odds for multi-way tables with ordered categories 
Abstract:
  For square contingency tables with ordered categories, this paper
 proposes a measure to represent the degree of departure from the marginal
 homogeneity model. It is expressed as the weighted sum of the
 power-divergence or Patil--Taillie diversity index, and is a function of
 marginal log odds ratios. The measure represents the degree of departure
 from the equality of the log odds that the row variable is
 <italic>i</italic> or below instead of <italic>i</italic>+1 or above and
 the log odds that the column variable is <italic>i</italic> or below
 instead of <italic>i</italic>+1 or above for every <italic>i</italic>. The
 measure is also extended to multi-way tables. Examples are given. 
Journal: Journal of Applied Statistics 
Pages: 279-295 
Issue: 2 
Volume: 39 
Year: 2012 
Month: 4 
X-DOI: 10.1080/02664763.2011.586682 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.586682 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:2:p:279-295




Template-Type: ReDIF-Article 1.0
Author-Name: Julie McIntyre 
Author-X-Name-First: Julie 
Author-X-Name-Last: McIntyre 
Author-Name: Ronald   P. Barry 
Author-X-Name-First: Ronald   P. 
Author-X-Name-Last: Barry 
Title: Bivariate deconvolution with SIMEX: an application to mapping Alaska earthquake density 
Abstract:
  Constructing spatial density maps of seismic events, such as earthquake
 hypocentres, is complicated by the fact that events are not located
 precisely. In this paper, we present a method for estimating density maps
 from event locations that are measured with error. The estimator is based
 on the simulation--extrapolation method of estimation and is appropriate
 for location errors that are either homoscedastic or heteroscedastic. A
 simulation study shows that the estimator outperforms the standard
 estimator of density that ignores location errors in the data, even when
 location errors are spatially dependent. We apply our method to construct
 an estimated density map of earthquake hypocenters using data from the
 Alaska earthquake catalogue. 
Journal: Journal of Applied Statistics 
Pages: 297-308 
Issue: 2 
Volume: 39 
Year: 2012 
Month: 4 
X-DOI: 10.1080/02664763.2011.586683 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.586683 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:2:p:297-308




Template-Type: ReDIF-Article 1.0
Author-Name: Y.   L. Lio 
Author-X-Name-First: Y.   L. 
Author-X-Name-Last: Lio 
Author-Name: Tzong-Ru Tsai 
Author-X-Name-First: Tzong-Ru 
Author-X-Name-Last: Tsai 
Title: Estimation of &#x3B4;=<italic>P</italic>(<italic>X</italic>><italic>Y</italic>) for Burr XII distribution based on the progressively first failure-censored samples 
Abstract:
  Let <italic>X</italic> and <italic>Y</italic> have two-parameter Burr XII
 distributions. The maximum-likelihood estimator of
 &#x3B4;=<italic>P</italic>(<italic>X</italic>><italic>Y</italic>) is
 studied under the progressively first failure-censored samples. Three
 confidence intervals of &#x3B4; are constructed by using an asymptotic
 distribution of the maximum-likelihood estimator of &#x3B4; and two
 bootstrapping procedures, respectively. Some computational results from
 intensive simulations are presented. An illustrative example is provided
 to demonstrate the application of the proposed method. 
Journal: Journal of Applied Statistics 
Pages: 309-322 
Issue: 2 
Volume: 39 
Year: 2012 
Month: 4 
X-DOI: 10.1080/02664763.2011.586684 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.586684 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:2:p:309-322




Template-Type: ReDIF-Article 1.0
Author-Name: Petros   E. Maravelakis 
Author-X-Name-First: Petros   E. 
Author-X-Name-Last: Maravelakis 
Title: Measurement error effect on the CUSUM control chart 
Abstract:
  The performance of the cumulative sum (CUSUM) control chart for the mean
 when measurement error exists is investigated. It is shown that the CUSUM
 chart is greatly affected by the measurement error. A similar result holds
 for the case of the CUSUM chart for the mean with linearly increasing
 variance. In this paper, we consider multiple measurements to reduce the
 effect of measurement error on the charts performance. Finally, a
 comparison of the CUSUM and EWMA charts is presented and certain
 recommendations are given. 
Journal: Journal of Applied Statistics 
Pages: 323-336 
Issue: 2 
Volume: 39 
Year: 2012 
Month: 5 
X-DOI: 10.1080/02664763.2011.590188 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.590188 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:2:p:323-336




Template-Type: ReDIF-Article 1.0
Author-Name: Helle S&#xF8;rensen 
Author-X-Name-First: Helle 
Author-X-Name-Last: S&#xF8;rensen 
Author-Name: Anders Tolver 
Author-X-Name-First: Anders 
Author-X-Name-Last: Tolver 
Author-Name: Maj   Halling Thomsen 
Author-X-Name-First: Maj   Halling 
Author-X-Name-Last: Thomsen 
Author-Name: Pia   Haubro Andersen 
Author-X-Name-First: Pia   Haubro 
Author-X-Name-Last: Andersen 
Title: Quantification of symmetry for functional data with application to equine lameness classification 
Abstract:
  This paper presents a study on symmetry of repeated bi-phased data
 signals, in particular, on quantification of the deviation between the two
 parts of the signal. Three symmetry scores are defined using functional
 data techniques such as smoothing and registration. One score is related
 to the <italic>L</italic> <sub>2</sub>-distance between the two parts of
 the signal, whereas the other two are constructed to specifically measure
 differences in amplitude and phase. Moreover, symmetry scores based on
 functional principal component analysis (PCA) are examined. The scores are
 applied to acceleration signals from a study on equine gait. The scores
 turn out to be highly associated with lameness, and their applicability
 for lameness quantification and detection is investigated. Four
 classification approaches turn out to give similar results. The scores
 describing amplitude and phase variation turn out to outperform the PCA
 scores when it comes to the classification of lameness. 
Journal: Journal of Applied Statistics 
Pages: 337-360 
Issue: 2 
Volume: 39 
Year: 2012 
Month: 5 
X-DOI: 10.1080/02664763.2011.590189 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.590189 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:2:p:337-360




Template-Type: ReDIF-Article 1.0
Author-Name: Terence   C. Mills 
Author-X-Name-First: Terence   C. 
Author-X-Name-Last: Mills 
Title: Semi-parametric modelling of temperature records 
Abstract:
  A range of instrumental and proxy temperature records are examined
 semi-parametrically, using empirical densities and quantile
 autoregressions containing a unit root, to assess the extent of
 non-stationarity and the presence of global warming trends. Only the
 instrumental records covering the last century and a half show any
 evidence of non-stationarity, but the trend behaviour of these series
 remains elusive. 
Journal: Journal of Applied Statistics 
Pages: 361-383 
Issue: 2 
Volume: 39 
Year: 2012 
Month: 5 
X-DOI: 10.1080/02664763.2011.590190 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.590190 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:2:p:361-383




Template-Type: ReDIF-Article 1.0
Author-Name: Caiya Zhang 
Author-X-Name-First: Caiya 
Author-X-Name-Last: Zhang 
Author-Name: Yanbiao Xiang 
Author-X-Name-First: Yanbiao 
Author-X-Name-Last: Xiang 
Author-Name: Xinmei Shen 
Author-X-Name-First: Xinmei 
Author-X-Name-Last: Shen 
Title: Some multivariate goodness-of-fit tests based on data depth 
Abstract:
  Based on data depth, three types of nonparametric goodness-of-fit tests
 for multivariate distribution are proposed in this paper. They are
 Pearson&#x2019;s chi-square test, tests based on EDF and tests based on
 spacings, respectively. The Anderson--Darling (AD) test and the Greenwood
 test for bivariate normal distribution and uniform distribution are
 simulated. The results of simulation show that these two tests have low
 type I error rates and become more efficient with the increase in sample
 size. The AD-type test performs more powerfully than the Greenwood type
 test. 
Journal: Journal of Applied Statistics 
Pages: 385-397 
Issue: 2 
Volume: 39 
Year: 2012 
Month: 5 
X-DOI: 10.1080/02664763.2011.594033 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.594033 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:2:p:385-397




Template-Type: ReDIF-Article 1.0
Author-Name: Michael   R. Crager 
Author-X-Name-First: Michael   R. 
Author-X-Name-Last: Crager 
Title: Generalizing the standardized hazard ratio to multivariate proportional hazards regression, with an application to clinical~genomic studies 
Abstract:
  The standardized hazard ratio for univariate proportional hazards
 regression is generalized as a scalar to multivariate proportional hazards
 regression. Estimators of the standardized log hazard ratio are developed,
 with corrections for bias and for regression to the mean in
 high-dimensional analyses. Tests of point and interval null hypotheses and
 confidence intervals are constructed. Cohort sampling study designs,
 commonly used in prospective--retrospective clinical genomic studies, are
 accommodated. 
Journal: Journal of Applied Statistics 
Pages: 399-417 
Issue: 2 
Volume: 39 
Year: 2012 
Month: 5 
X-DOI: 10.1080/02664763.2011.594034 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.594034 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:2:p:399-417




Template-Type: ReDIF-Article 1.0
Author-Name: Marcus   B. Perry 
Author-X-Name-First: Marcus   B. 
Author-X-Name-Last: Perry 
Author-Name: Joseph   J. Pignatiello 
Author-X-Name-First: Joseph   J. 
Author-X-Name-Last: Pignatiello 
Title: Identifying the time of change in the mean of a two-stage nested process 
Abstract:
  Statistical process control charts are used to distinguish between common
 cause and special cause sources of variability. Once a control chart
 signals, a search to find the special cause should be initiated. If
 process analysts had knowledge of the change point, the search to find the
 special cause could be easily facilitated. Relevant literature contains an
 array of solutions to the change-point problem; however, these solutions
 are most appropriate when the samples are assumed to be independent.
 Unfortunately, the assumption of independence is often violated in
 practice. This work considers one such case of non-independence that
 frequently occurs in practice as a result of multi-stage sampling. Due to
 its commonality in practice, we assume a two-stage nested random model as
 the underlying process model and derive and evaluate a maximum-likelihood
 estimator for the change point in the fixed-effects component of this
 model. The estimator is applied to electron microscopy data obtained
 following a genuine control chart signal and from a real machining process
 where the important quality characteristic is the size of the surface
 grains produced by the machining operation. We conduct a simulation study
 to compare relative performances between the proposed change-point
 estimator and a commonly used alternative developed under the assumption
 of independent observations. The results suggest that both estimators are
 approximately unbiased; however, the proposed estimator yields smaller
 variance. The implication is that the proposed estimator is more precise,
 and thus, the quality of the estimator is improved relative to the
 alternative. 
Journal: Journal of Applied Statistics 
Pages: 419-433 
Issue: 2 
Volume: 39 
Year: 2012 
Month: 5 
X-DOI: 10.1080/02664763.2011.594035 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.594035 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:2:p:419-433




Template-Type: ReDIF-Article 1.0
Author-Name: Stephanie   N. Dixon 
Author-X-Name-First: Stephanie   N. 
Author-X-Name-Last: Dixon 
Author-Name: Gerarda   A. Darlington 
Author-X-Name-First: Gerarda   A. 
Author-X-Name-Last: Darlington 
Author-Name: Victoria Edge 
Author-X-Name-First: Victoria 
Author-X-Name-Last: Edge 
Title: Applying a marginalized frailty model to competing risks 
Abstract:
  The marginalized frailty model is often used for the analysis of
 correlated times in survival data. When only two correlated times are
 analyzed, this model is often referred to as the Clayton--Oakes model
 [7,22]. With time-to-event data, there may exist multiple end points
 (competing risks) suggesting that an analysis focusing on all available
 outcomes is of interest. The purpose of this work is to extend the single
 risk marginalized frailty model to the multiple risk setting via
 cause-specific hazards (CSH). The methods herein make use of the
 marginalized frailty model described by Pipper and Martinussen [24]. As
 such, this work uses the martingale theory to develop a likelihood based
 on estimating equations and observed histories. The proposed multivariate
 CSH model yields marginal regression parameter estimates while
 accommodating the clustering of outcomes. The multivariate CSH model can
 be fitted using a data augmentation algorithm described by Lunn and McNeil
 [21] or by fitting a series of single risk models for each of the
 competing risks. An example of the application of the multivariate CSH
 model is provided through the analysis of a family-based follow-up study
 of breast cancer with death in absence of breast cancer as a competing
 risk. 
Journal: Journal of Applied Statistics 
Pages: 435-443 
Issue: 2 
Volume: 39 
Year: 2012 
Month: 6 
X-DOI: 10.1080/02664763.2011.595399 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.595399 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:2:p:435-443




Template-Type: ReDIF-Article 1.0
Author-Name: Inna Chervoneva 
Author-X-Name-First: Inna 
Author-X-Name-Last: Chervoneva 
Author-Name: Tingting Zhan 
Author-X-Name-First: Tingting 
Author-X-Name-Last: Zhan 
Author-Name: Boris Iglewicz 
Author-X-Name-First: Boris 
Author-X-Name-Last: Iglewicz 
Author-Name: Walter   W. Hauck 
Author-X-Name-First: Walter   W. 
Author-X-Name-Last: Hauck 
Author-Name: David   E. Birk 
Author-X-Name-First: David   E. 
Author-X-Name-Last: Birk 
Title: Two-stage hierarchical modeling for analysis of subpopulations in conditional distributions 
Abstract:
  In this work, we develop the modeling and estimation approach for the
 analysis of cross-sectional clustered data with multimodal conditional
 distributions, where the main interest is in analysis of subpopulations.
 It is proposed to model such data in a hierarchical model with conditional
 distributions viewed as finite mixtures of normal components. With a large
 number of observations in the lowest level clusters, a two-stage
 estimation approach is used. In the first stage, the normal mixture
 parameters in each lowest level cluster are estimated using robust
 methods. Robust alternatives to the maximum-likelihood (ML) estimation are
 used to provide stable results even for data with conditional
 distributions such that their components may not quite meet normality
 assumptions. Then the lowest level cluster-specific means and standard
 deviations are modeled in a mixed effects model in the second stage. A
 small simulation study was conducted to compare performance of finite
 normal mixture population parameter estimates based on robust and ML
 estimation in stage 1. The proposed modeling approach is illustrated
 through the analysis of mice tendon fibril diameters data. Analyses
 results address genotype differences between corresponding components in
 the mixtures and demonstrate advantages of robust estimation in stage 1. 
Journal: Journal of Applied Statistics 
Pages: 445-460 
Issue: 2 
Volume: 39 
Year: 2012 
Month: 6 
X-DOI: 10.1080/02664763.2011.596193 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.596193 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:2:p:445-460




Template-Type: ReDIF-Article 1.0
Author-Name: M.   Tariqul Hasan 
Author-X-Name-First: M.   Tariqul 
Author-X-Name-Last: Hasan 
Author-Name: Gary Sneddon 
Author-X-Name-First: Gary 
Author-X-Name-Last: Sneddon 
Author-Name: Renjun Ma 
Author-X-Name-First: Renjun 
Author-X-Name-Last: Ma 
Title: Regression analysis of zero-inflated time-series counts: application to air pollution related emergency room visit data 
Abstract:
  Time-series count data with excessive zeros frequently occur in
 environmental, medical and biological studies. These data have been
 traditionally handled by conditional and marginal modeling approaches
 separately in the literature. The conditional modeling approaches are
 computationally much simpler, whereas marginal modeling approaches can
 link the overall mean with covariates directly. In this paper, we propose
 new models that can have conditional and marginal modeling interpretations
 for zero-inflated time-series counts using compound Poisson distributed
 random effects. We also develop a computationally efficient estimation
 method for our models using a quasi-likelihood approach. The proposed
 method is illustrated with an application to air pollution-related
 emergency room visits. We also evaluate the performance of our method
 through simulation studies. 
Journal: Journal of Applied Statistics 
Pages: 467-476 
Issue: 3 
Volume: 39 
Year: 2012 
Month: 6 
X-DOI: 10.1080/02664763.2011.595778 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.595778 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:3:p:467-476




Template-Type: ReDIF-Article 1.0
Author-Name: Steven   B. Caudill 
Author-X-Name-First: Steven   B. 
Author-X-Name-Last: Caudill 
Author-Name: James   E. Long 
Author-X-Name-First: James   E. 
Author-X-Name-Last: Long 
Author-Name: Franklin   G. Mixon 
Author-X-Name-First: Franklin   G. 
Author-X-Name-Last: Mixon 
Title: Female athletic participation and income: evidence from a latent class model 
Abstract:
  This paper introduces and applies an EM algorithm for the
 maximum-likelihood estimation of a latent class version of the
 grouped-data regression model. This new model is applied to examine the
 effects of college athletic participation of females on incomes. No
 evidence for an &#x201C;athlete&#x201D; effect in the case of females has
 been found in the previous work by Long and Caudill [12], Henderson
 <italic>et al.</italic> [10], and Caudill and Long [5]. Our study is the
 first to find evidence of a lower wage for female athletes. This effect is
 present in a regime characterizing 42% of the sample. Further analysis
 indicates that female athletes in many otherwise low-paying jobs actually
 get paid less than non-athletes. 
Journal: Journal of Applied Statistics 
Pages: 477-488 
Issue: 3 
Volume: 39 
Year: 2012 
Month: 6 
X-DOI: 10.1080/02664763.2011.596194 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.596194 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:3:p:477-488




Template-Type: ReDIF-Article 1.0
Author-Name: Jie Song 
Author-X-Name-First: Jie 
Author-X-Name-Last: Song 
Author-Name: Herman   W. Raadsma 
Author-X-Name-First: Herman   W. 
Author-X-Name-Last: Raadsma 
Author-Name: Peter   C. Thomson 
Author-X-Name-First: Peter   C. 
Author-X-Name-Last: Thomson 
Title: Evaluation of false discovery rate and power via sample size in microarray studies 
Abstract:
  Microarray studies are now common for human, agricultural plant and
 animal studies. False discovery rate (FDR) is widely used in the analysis
 of large-scale microarray data to account for problems associated with
 multiple testing. A well-designed microarray study should have adequate
 statistical power to detect the differentially expressed (DE) genes, while
 keeping the FDR acceptably low. In this paper, we used a mixture model of
 expression responses involving DE genes and non-DE genes to analyse
 theoretical FDR and power for simple scenarios where it is assumed that
 each gene has equal error variance and the gene effects are independent. A
 simulation study was used to evaluate the empirical FDR and power for more
 complex scenarios with unequal error variance and gene dependence. Based
 on this approach, we present a general guide for sample size requirement
 at the experimental design stage for prospective microarray studies. This
 paper presented an approach to explicitly connect the sample size with FDR
 and power. While the methods have been developed in the context of
 one-sample microarray studies, they are readily applicable to two-sample,
 and could be adapted to multiple-sample studies. 
Journal: Journal of Applied Statistics 
Pages: 489-500 
Issue: 3 
Volume: 39 
Year: 2012 
Month: 6 
X-DOI: 10.1080/02664763.2011.602054 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.602054 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:3:p:489-500




Template-Type: ReDIF-Article 1.0
Author-Name: Alessandro Barbiero 
Author-X-Name-First: Alessandro 
Author-X-Name-Last: Barbiero 
Title: Interval estimators for reliability: the bivariate normal case 
Abstract:
  This paper proposes procedures to provide confidence intervals (CIs) for
 reliability in stress--strength models, considering the particular case of
 a bivariate normal set-up. The suggested CIs are obtained by employing
 either asymptotic variances of maximum-likelihood estimators or a
 bootstrap procedure. The coverage and the accuracy of these intervals are
 empirically checked through a simulation study and compared with those of
 another proposal in the literature. An application to real data is
 provided. 
Journal: Journal of Applied Statistics 
Pages: 501-512 
Issue: 3 
Volume: 39 
Year: 2012 
Month: 6 
X-DOI: 10.1080/02664763.2011.602055 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.602055 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:3:p:501-512




Template-Type: ReDIF-Article 1.0
Author-Name: Joseph Kang 
Author-X-Name-First: Joseph 
Author-X-Name-Last: Kang 
Author-Name: Xiaogang Su 
Author-X-Name-First: Xiaogang 
Author-X-Name-Last: Su 
Author-Name: Brian Hitsman 
Author-X-Name-First: Brian 
Author-X-Name-Last: Hitsman 
Author-Name: Kiang Liu 
Author-X-Name-First: Kiang 
Author-X-Name-Last: Liu 
Author-Name: Donald Lloyd-Jones 
Author-X-Name-First: Donald 
Author-X-Name-Last: Lloyd-Jones 
Title: Tree-structured analysis of treatment effects with large observational data 
Abstract:
  Treatment effect in an observational study of relatively large scale can
 be described as a mixture of effects among subgroups. In particular,
 analysis for estimating the treatment effect at the level of an entire
 sample potentially involves not only differential effects across subgroups
 of the entire study cohort, but also differential propensities --
 probabilities of receiving treatment given study subjects&#x2019;
 pretreatment history. Such complex heterogeneity is of great research
 interest because the analysis of treatment effects can substantially
 depend on the hidden data structure for effect sizes and propensities. To
 uncover the unseen data structure, we propose a likelihood-based
 regression tree method which we call marginal tree (MT). The MT method is
 aimed at a simultaneous assessment of differential effects and propensity
 scores so that both become homogeneous within each terminal node of the
 resultant tree structure. We assess simulation performances of the MT
 method by comparing it with other existing tree methods and illustrate its
 use with a simulated data set, where the objective is to assess the
 effects of dieting behavior on its subsequent emotional distress among
 adolescent girls. 
Journal: Journal of Applied Statistics 
Pages: 513-529 
Issue: 3 
Volume: 39 
Year: 2012 
Month: 6 
X-DOI: 10.1080/02664763.2011.602056 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.602056 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:3:p:513-529




Template-Type: ReDIF-Article 1.0
Author-Name: Victor   H. Lachos 
Author-X-Name-First: Victor   H. 
Author-X-Name-Last: Lachos 
Author-Name: Celso   R.B. Cabral 
Author-X-Name-First: Celso   R.B. 
Author-X-Name-Last: Cabral 
Author-Name: Carlos   A. Abanto-Valle 
Author-X-Name-First: Carlos   A. 
Author-X-Name-Last: Abanto-Valle 
Title: A non-iterative sampling Bayesian method for linear mixed models with normal independent distributions 
Abstract:
  In this paper, we utilize normal/independent (NI) distributions as a tool
 for robust modeling of linear mixed models (LMM) under a Bayesian
 paradigm. The purpose is to develop a non-iterative sampling method to
 obtain i.i.d. samples approximately from the observed posterior
 distribution by combining the inverse Bayes formulae, sampling/importance
 resampling and posterior mode estimates from the expectation maximization
 algorithm to LMMs with NI distributions, as suggested by Tan <italic>et
 al</italic>. [33]. The proposed algorithm provides a novel alternative to
 perfect sampling and eliminates the convergence problems of Markov chain
 Monte Carlo methods. In order to examine the robust aspects of the NI
 class, against outlying and influential observations, we present a
 Bayesian case deletion influence diagnostics based on the
 Kullback--Leibler divergence. Further, some discussions on model selection
 criteria are given. The new methodologies are exemplified through a real
 data set, illustrating the usefulness of the proposed methodology. 
Journal: Journal of Applied Statistics 
Pages: 531-549 
Issue: 3 
Volume: 39 
Year: 2012 
Month: 7 
X-DOI: 10.1080/02664763.2011.603292 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.603292 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:3:p:531-549




Template-Type: ReDIF-Article 1.0
Author-Name: C�lia Nunes 
Author-X-Name-First: C�lia 
Author-X-Name-Last: Nunes 
Author-Name: D&#xE1;rio Ferreira 
Author-X-Name-First: D&#xE1;rio 
Author-X-Name-Last: Ferreira 
Author-Name: Sandra   S. Ferreira 
Author-X-Name-First: Sandra   S. 
Author-X-Name-Last: Ferreira 
Author-Name: Jo&#xE3;o   T. Mexia 
Author-X-Name-First: Jo&#xE3;o   T. 
Author-X-Name-Last: Mexia 
Title:  <italic>F</italic>-tests with a rare pathology 
Abstract:
  ANOVA is routinely used to compare pathologies. Nevertheless, in many
 situations, the sample dimensions may not be known when planning the
 study. This is specially relevant when one of the pathologies is rare.
 Thus, the sample size for that pathology or for all pathologies must be
 considered as random. Sample selection for the non-rare pathologies may be
 carried out to increase the balance of the model. This leads to
 <italic>F</italic>-tests with random non-centrality parameters and random
 degrees of freedom for the errors. The distribution of such tests
 statistics is obtained. 
Journal: Journal of Applied Statistics 
Pages: 551-561 
Issue: 3 
Volume: 39 
Year: 2012 
Month: 7 
X-DOI: 10.1080/02664763.2011.603293 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.603293 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:3:p:551-561




Template-Type: ReDIF-Article 1.0
Author-Name: Reza Drikvandi 
Author-X-Name-First: Reza 
Author-X-Name-Last: Drikvandi 
Author-Name: Ahmad Khodadadi 
Author-X-Name-First: Ahmad 
Author-X-Name-Last: Khodadadi 
Author-Name: Geert Verbeke 
Author-X-Name-First: Geert 
Author-X-Name-Last: Verbeke 
Title: Testing variance components in balanced linear growth curve models 
Abstract:
  It is well known that the testing of zero variance components is a
 non-standard problem since the null hypothesis is on the boundary of the
 parameter space. The usual asymptotic chi-square distribution of the
 likelihood ratio and score statistics under the null does not necessarily
 hold because of this null hypothesis. To circumvent this difficulty in
 balanced linear growth curve models, we introduce an appropriate test
 statistic and suggest a permutation procedure to approximate its
 finite-sample distribution. The proposed test alleviates the necessity of
 any distributional assumptions for the random effects and errors and can
 easily be applied for testing multiple variance components. Our simulation
 studies show that the proposed test has Type&#xA0;I error rate close to
 the nominal level. The power of the proposed test is also compared with
 the likelihood ratio test in the simulations. An application on data from
 an orthodontic study is presented and discussed. 
Journal: Journal of Applied Statistics 
Pages: 563-572 
Issue: 3 
Volume: 39 
Year: 2012 
Month: 7 
X-DOI: 10.1080/02664763.2011.603294 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.603294 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:3:p:563-572




Template-Type: ReDIF-Article 1.0
Author-Name: Richard   W. Miller 
Author-X-Name-First: Richard   W. 
Author-X-Name-Last: Miller 
Title: Quad folding: a simple idea for the subjective property characterization of large sample sets 
Abstract:
  Quad analysis has proven useful for characterizing subjective properties,
 primarily properties such as carpet hand and body in the textile industry.
 In essence, it provides an efficient method for conducting paired
 comparisons, the preferred method for quantifying such properties. An
 extension to quad analysis, quad folding of one quad design into another,
 is likewise an efficient method to rank order the subjective properties of
 larger data sets. A rank ordering of 62 carpets by their body is provided
 as an example of folding six groups of eight carpets (two replicated) into
 one another. 
Journal: Journal of Applied Statistics 
Pages: 573-580 
Issue: 3 
Volume: 39 
Year: 2012 
Month: 7 
X-DOI: 10.1080/02664763.2011.604307 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.604307 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:3:p:573-580




Template-Type: ReDIF-Article 1.0
Author-Name: Christian   H. Wei&#xDF; 
Author-X-Name-First: Christian   H. 
Author-X-Name-Last: Wei&#xDF; 
Title: Fully observed INAR(1) processes 
Abstract:
  The innovations of an INAR(1) process (<italic>in</italic>teger-valued
 <italic>a</italic>uto<italic>r</italic>egressive) are usually assumed to
 be unobservable. There are, however, situations in practice, where also
 the innovations can be uncovered, i.e. where we are concerned with a
 <italic>fully observed INAR<roman>(<italic>1</italic>)</roman>
 process</italic>. We analyze stochastic properties of such a fully
 observed INAR(1) process and explore the relation between the INAR(1)
 model and certain metapopulation models. We show how the additional
 knowledge about the innovations can be used for parameter estimation, for
 model diagnostics, and for forecasting. Our findings are illustrated with
 two real-data examples. 
Journal: Journal of Applied Statistics 
Pages: 581-598 
Issue: 3 
Volume: 39 
Year: 2012 
Month: 7 
X-DOI: 10.1080/02664763.2011.604308 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.604308 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:3:p:581-598




Template-Type: ReDIF-Article 1.0
Author-Name: Hsiu-Wen Chen 
Author-X-Name-First: Hsiu-Wen 
Author-X-Name-Last: Chen 
Author-Name: Weng   Kee Wong 
Author-X-Name-First: Weng   Kee 
Author-X-Name-Last: Wong 
Author-Name: Hongquan Xu 
Author-X-Name-First: Hongquan 
Author-X-Name-Last: Xu 
Title: An augmented approach to the desirability function 
Abstract:
  The desirability function is widely used in the engineering field to
 tackle the problem of optimizing multiple responses simultaneously. This
 approach does not account for the variability in the predicted responses
 and minimizing this variability to have narrower prediction intervals is
 desirable. We propose to add this capability in the desirability function
 and also incorporate the relative importance of optimizing the multiple
 responses and minimizing the variances of the predicted responses at the
 same time. We show that the benefits of our augmented approach using two
 real data sets by comparing our solutions with those obtained from the
 desirability approach. In particular, it is shown that our approach offers
 greater flexibility and the solutions can reduce the variances of all the
 predicted responses resulting in narrower prediction intervals. 
Journal: Journal of Applied Statistics 
Pages: 599-613 
Issue: 3 
Volume: 39 
Year: 2012 
Month: 7 
X-DOI: 10.1080/02664763.2011.605437 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.605437 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:3:p:599-613




Template-Type: ReDIF-Article 1.0
Author-Name: Miguel   Angel Uribe-Opazo 
Author-X-Name-First: Miguel   Angel 
Author-X-Name-Last: Uribe-Opazo 
Author-Name: Joelmir   Andr� Borssoi 
Author-X-Name-First: Joelmir   Andr� 
Author-X-Name-Last: Borssoi 
Author-Name: Manuel Galea 
Author-X-Name-First: Manuel 
Author-X-Name-Last: Galea 
Title: Influence diagnostics in Gaussian spatial linear models 
Abstract:
  Spatial linear models have been applied in numerous fields such as
 agriculture, geoscience and environmental sciences, among many others.
 Spatial dependence structure modelling, using a geostatistical approach,
 is an indispensable tool to estimate the parameters that define this
 structure. However, this estimation may be greatly affected by the
 presence of atypical observations in the sampled data. The purpose of this
 paper is to use diagnostic techniques to assess the sensitivity of the
 maximum-likelihood estimators, covariance functions and linear predictor
 to small perturbations in the data and/or the spatial linear model
 assumptions. The methodology is illustrated with two real data sets. The
 results allowed us to conclude that the presence of atypical values in the
 sample data have a strong influence on thematic maps, changing the spatial
 dependence structure. 
Journal: Journal of Applied Statistics 
Pages: 615-630 
Issue: 3 
Volume: 39 
Year: 2012 
Month: 7 
X-DOI: 10.1080/02664763.2011.607802 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.607802 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:3:p:615-630




Template-Type: ReDIF-Article 1.0
Author-Name: Miran   A. Jaffa 
Author-X-Name-First: Miran   A. 
Author-X-Name-Last: Jaffa 
Author-Name: Ayad   A. Jaffa 
Author-X-Name-First: Ayad   A. 
Author-X-Name-Last: Jaffa 
Author-Name: Stuart   R. Lipsitz 
Author-X-Name-First: Stuart   R. 
Author-X-Name-Last: Lipsitz 
Title: Slope estimation of covariates that influence renal outcome following renal transplant adjusting for informative right censoring 
Abstract:
  A new statistical model is proposed to estimate population and individual
 slopes that are adjusted for covariates and informative right censoring.
 Individual slopes are assumed to have a mean that depends on the
 population slope for the covariates. The number of observations for each
 individual is modeled as a truncated discrete distribution with mean
 dependent on the individual subjects&#x2019; slopes. Our simulation study
 results indicated that the associated bias and mean squared errors for the
 proposed model were comparable to those associated with the model that
 only adjusts for informative right censoring. The proposed model was
 illustrated using renal transplant dataset to estimate population slopes
 for covariates that could impact the outcome of renal function following
 renal transplantation. 
Journal: Journal of Applied Statistics 
Pages: 631-642 
Issue: 3 
Volume: 39 
Year: 2012 
Month: 8 
X-DOI: 10.1080/02664763.2011.610441 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.610441 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:3:p:631-642




Template-Type: ReDIF-Article 1.0
Author-Name: Hsiuying Wang 
Author-X-Name-First: Hsiuying 
Author-X-Name-Last: Wang 
Author-Name: Shan-Lin Hung 
Author-X-Name-First: Shan-Lin 
Author-X-Name-Last: Hung 
Title: Phylogenetic tree selection by the adjusted <italic>k</italic>-means approach 
Abstract:
  The reconstruction of phylogenetic trees is one of the most important and
 interesting problems of the evolutionary study. There are many methods
 proposed in the literature for constructing phylogenetic trees. Each
 approach is based on different criteria and evolutionary models. However,
 the topologies of trees constructed from different methods may be quite
 different. The topological errors may be due to unsuitable criterions or
 evolutionary models. Since there are many tree construction approaches, we
 are interested in selecting a better tree to fit the true model. In this
 study, we propose an adjusted <italic>k</italic>-means approach and a
 misclassification error score criterion to solve the problem. The
 simulation study shows this method can select better trees among the
 potential candidates, which can provide a useful way in phylogenetic tree
 selection. 
Journal: Journal of Applied Statistics 
Pages: 643-655 
Issue: 3 
Volume: 39 
Year: 2012 
Month: 8 
X-DOI: 10.1080/02664763.2011.610442 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.610442 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:3:p:643-655




Template-Type: ReDIF-Article 1.0
Author-Name: H. E.T. Holgersson 
Author-X-Name-First: H. E.T. 
Author-X-Name-Last: Holgersson 
Author-Name: Peter   S. Karlsson 
Author-X-Name-First: Peter   S. 
Author-X-Name-Last: Karlsson 
Author-Name: Rashid Mansoor 
Author-X-Name-First: Rashid 
Author-X-Name-Last: Mansoor 
Title: Estimating mean-standard deviation ratios of financial data 
Abstract:
  This article treats the problem of linking the relation between excess
 return and risk of financial assets when the returns follow a factor
 structure. The authors propose three different estimators and their
 consistencies are established in cases when the number of assets in the
 cross-section (<italic>n</italic>) and the number of observations over
 time (<italic>T</italic>) are of comparable size. An empirical
 investigation is conducted on the Stockholm stock exchange market where
 the mean-standard deviation ratio is calculated for small- mid- and large
 cap segments, respectively. 
Journal: Journal of Applied Statistics 
Pages: 657-671 
Issue: 3 
Volume: 39 
Year: 2012 
Month: 8 
X-DOI: 10.1080/02664763.2011.610443 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.610443 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:3:p:657-671




Template-Type: ReDIF-Article 1.0
Author-Name: Manabu Kuroki 
Author-X-Name-First: Manabu 
Author-X-Name-Last: Kuroki 
Title: Optimizing a control plan using a structural equation model with an application to statistical process analysis 
Abstract:
  In the case where non-experimental data are available from an industrial
 process and a directed graph for how various factors affect a response
 variable is known based on a substantive understanding of the process, we
 consider a problem in which a control plan involving multiple treatment
 variables is conducted in order to bring a response variable close to a
 target value with variation reduction. Using statistical causal analysis
 with linear (recursive and non-recursive) structural equation models, we
 configure an optimal control plan involving multiple treatment variables
 through causal parameters. Based on the formulation, we clarify the causal
 mechanism for how the variance of a response variable changes when the
 control plan is conducted. The results enable us to evaluate the effect of
 a control plan on the variance of a response variable from
 non-experimental data and provide a new application of linear structural
 equation models to engineering science. 
Journal: Journal of Applied Statistics 
Pages: 673-694 
Issue: 3 
Volume: 39 
Year: 2012 
Month: 8 
X-DOI: 10.1080/02664763.2011.610444 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.610444 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:3:p:673-694




Template-Type: ReDIF-Article 1.0
Author-Name: J. C.F. de Winter 
Author-X-Name-First: J. C.F. 
Author-X-Name-Last: de Winter 
Author-Name: D. Dodou 
Author-X-Name-First: D. 
Author-X-Name-Last: Dodou 
Title: Factor recovery by principal axis factoring and maximum likelihood factor analysis as a function of factor pattern and sample size 
Abstract:
  Principal axis factoring (PAF) and maximum likelihood factor analysis
 (MLFA) are two of the most popular estimation methods in exploratory
 factor analysis. It is known that PAF is better able to recover weak
 factors and that the maximum likelihood estimator is asymptotically
 efficient. However, there is almost no evidence regarding which method
 should be preferred for different types of factor patterns and sample
 sizes. Simulations were conducted to investigate factor recovery by PAF
 and MLFA for distortions of ideal simple structure and sample sizes
 between 25 and 5000. Results showed that PAF is preferred for population
 solutions with few indicators per factor and for overextraction. MLFA
 outperformed PAF in cases of unequal loadings within factors and for
 underextraction. It was further shown that PAF and MLFA do not always
 converge with increasing sample size. The simulation findings were
 confirmed by an empirical study as well as by a classic plasmode,
 Thurstone's box problem. The present results are of practical value for
 factor analysts. 
Journal: Journal of Applied Statistics 
Pages: 695-710 
Issue: 4 
Volume: 39 
Year: 2012 
Month: 8 
X-DOI: 10.1080/02664763.2011.610445 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.610445 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:4:p:695-710




Template-Type: ReDIF-Article 1.0
Author-Name: Irene Rocchetti 
Author-X-Name-First: Irene 
Author-X-Name-Last: Rocchetti 
Author-Name: Domenica Taruscio 
Author-X-Name-First: Domenica 
Author-X-Name-Last: Taruscio 
Author-Name: Marco Alf&#xF2; 
Author-X-Name-First: Marco 
Author-X-Name-Last: Alf&#xF2; 
Title: Modeling delay in diagnosis of NF: under reportincg, incidence and prevalence estimates 
Abstract:
  In this paper, we analyze data from the Italian National Register of Rare
 Diseases (NRRD) focusing, in particular, on the geo-temporal distribution
 of patients affected by neurofibromatosis type 1 (NF1, ICD9CM code
 237.71). The aim is at deriving a corrected measure of incidence for the
 period 2007--2009 using a single source, and to provide NF1 prevalence
 estimates for the period 2001--2006 through the use of capture--recapture
 methods over two sources. In the first case, a reverse hazard estimator
 for the delay in diagnosis of NF1 is used to estimate the probability that
 a generic unit belonging to the population of interest has been registered
 by the archive of reference. For the second purpose, two-source
 capture--recapture methods have been used to estimate the number of NF1
 prevalent units in Italy for the period 2001--2006, matching information
 provided by the NRRD and the national register of hospital discharge,
 Scheda di Dimissione Ospedaliera (in the following SDO), archives. 
Journal: Journal of Applied Statistics 
Pages: 711-721 
Issue: 4 
Volume: 39 
Year: 2012 
Month: 8 
X-DOI: 10.1080/02664763.2011.610446 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.610446 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:4:p:711-721




Template-Type: ReDIF-Article 1.0
Author-Name: Kouji Tahata 
Author-X-Name-First: Kouji 
Author-X-Name-Last: Tahata 
Title: Quasi-asymmetry model for square tables with nominal categories 
Abstract:
  For an <italic>R</italic>&#xD7;<italic>R</italic> square contingency
 table with nominal categories, the present paper proposes a model which
 indicates that the absolute values of log odds of the odds ratio for rows
 <italic>i</italic> and <italic>j</italic> and columns <italic>j</italic>
 and <italic>R</italic> to the corresponding symmetric odds ratio for rows
 <italic>j</italic> and <italic>R</italic> and columns <italic>i</italic>
 and <italic>j</italic> are constant for every
 <italic>i</italic>><italic>j</italic>><italic>R</italic>. The model is an
 extension of the quasi-symmetry model and states a structure of asymmetry
 of odds ratios. An example is given. 
Journal: Journal of Applied Statistics 
Pages: 723-729 
Issue: 4 
Volume: 39 
Year: 2012 
Month: 8 
X-DOI: 10.1080/02664763.2011.610447 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.610447 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:4:p:723-729




Template-Type: ReDIF-Article 1.0
Author-Name: Rosaria Lombardo 
Author-X-Name-First: Rosaria 
Author-X-Name-Last: Lombardo 
Author-Name: Pietro Amenta 
Author-X-Name-First: Pietro 
Author-X-Name-Last: Amenta 
Author-Name: Myrtille Vivien 
Author-X-Name-First: Myrtille 
Author-X-Name-Last: Vivien 
Author-Name: Robert Sabatier 
Author-X-Name-First: Robert 
Author-X-Name-Last: Sabatier 
Title: Sensory analysis via multi-block multivariate additive PLS splines 
Abstract:
  In the last decade, much effort has been spent on modelling dependence
 between sensory variables and chemical--physical ones, especially when
 observed at different occasions/spaces/times or if collected from several
 groups (blocks) of variables. In this paper, we propose a nonlinear
 generalization of multi-block partial least squares with the inclusion of
 variable interactions. We show the performance of the method on a known
 data set. 
Journal: Journal of Applied Statistics 
Pages: 731-743 
Issue: 4 
Volume: 39 
Year: 2012 
Month: 8 
X-DOI: 10.1080/02664763.2011.611239 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.611239 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:4:p:731-743




Template-Type: ReDIF-Article 1.0
Author-Name: Amer   Ibrahim Al-Omari 
Author-X-Name-First: Amer   Ibrahim 
Author-X-Name-Last: Al-Omari 
Author-Name: Abdul Haq 
Author-X-Name-First: Abdul 
Author-X-Name-Last: Haq 
Title: Improved quality control charts for monitoring the process mean, using double-ranked set sampling methods 
Abstract:
  Statistical control charts are widely used in the manufacturing industry.
 The Shewhart-type control charts are developed to improve the monitoring
 process mean by using the double quartile-ranked set sampling, quartile
 double-ranked set sampling, and double extreme-ranked set sampling
 methods. In terms of the average run length, the performance of the
 proposed control charts are compared with the existing control charts
 based on simple random sampling, ranked set sampling and extreme-ranked
 set sampling methods. An application of real data is also considered to
 investigate the performance of the suggested process mean control charts.
 The findings of the study revealed that the newly suggested control charts
 are superior to the existing counterparts. 
Journal: Journal of Applied Statistics 
Pages: 745-763 
Issue: 4 
Volume: 39 
Year: 2012 
Month: 8 
X-DOI: 10.1080/02664763.2011.611488 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.611488 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:4:p:745-763




Template-Type: ReDIF-Article 1.0
Author-Name: Krishna   K. Saha 
Author-X-Name-First: Krishna   K. 
Author-X-Name-Last: Saha 
Author-Name: Debaraj Sen 
Author-X-Name-First: Debaraj 
Author-X-Name-Last: Sen 
Author-Name: Chun Jin 
Author-X-Name-First: Chun 
Author-X-Name-Last: Jin 
Title: Profile likelihood-based confidence interval for the dispersion parameter in count data 
Abstract:
  The importance of the dispersion parameter in counts occurring in
 toxicology, biology, clinical medicine, epidemiology, and other similar
 studies is well known. A&#xA0;couple of procedures for the construction of
 confidence intervals (CIs) of the dispersion parameter have been
 investigated, but little attention has been paid to the accuracy of its
 CIs. In this paper, we introduce the profile likelihood (PL) approach and
 the hybrid profile variance (HPV) approach for constructing the CIs of the
 dispersion parameter for counts based on the negative binomial model. The
 non-parametric bootstrap (NPB) approach based on the maximum likelihood
 (ML) estimates of the dispersion parameter is also considered. We then
 compare our proposed approaches with an asymptotic approach based on the
 ML and the restricted ML (REML) estimates of the dispersion parameter as
 well as the parametric bootstrap (PB) approach based on the ML estimates
 of the dispersion parameter. As assessed by Monte Carlo simulations, the
 PL approach has the best small-sample performance, followed by the REML,
 HPV, NPB, and PB approaches. Three examples to biological count data are
 presented. 
Journal: Journal of Applied Statistics 
Pages: 765-783 
Issue: 4 
Volume: 39 
Year: 2012 
Month: 8 
X-DOI: 10.1080/02664763.2011.616581 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.616581 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:4:p:765-783




Template-Type: ReDIF-Article 1.0
Author-Name: Yao Yu 
Author-X-Name-First: Yao 
Author-X-Name-Last: Yu 
Author-Name: Jun Wang 
Author-X-Name-First: Jun 
Author-X-Name-Last: Wang 
Title: Lattice-oriented percolation system applied to volatility behavior of stock market 
Abstract:
  In this paper, a discrete time series of stock price process is modeled
 by the two-dimensional lattice-oriented bond percolation system.
 Percolation theory, as one of statistical physics systems, has brought new
 understanding and techniques to a broad range of topics in nature and
 society. According to this financial model, we studied the statistical
 behaviors of the stock price from the model and the real stock prices by
 comparison. We also investigated the probability distributions, the long
 memory and the long-range correlations of price returns for the actual
 data and the simulative data. The empirical research exhibits that for
 proper parameters, the simulative data of the financial model can fit the
 real markets to a certain extent. 
Journal: Journal of Applied Statistics 
Pages: 785-797 
Issue: 4 
Volume: 39 
Year: 2012 
Month: 8 
X-DOI: 10.1080/02664763.2011.620081 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.620081 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:4:p:785-797




Template-Type: ReDIF-Article 1.0
Author-Name: Rahim Alhamzawi 
Author-X-Name-First: Rahim 
Author-X-Name-Last: Alhamzawi 
Author-Name: Keming Yu 
Author-X-Name-First: Keming 
Author-X-Name-Last: Yu 
Title: Variable selection in quantile regression via Gibbs sampling 
Abstract:
  Due to computational challenges and non-availability of conjugate prior
 distributions, Bayesian variable selection in quantile regression models
 is often a difficult task. In this paper, we address these two issues for
 quantile regression models. In particular, we develop an informative
 stochastic search variable selection (ISSVS) for quantile regression
 models that introduces an informative prior distribution. We adopt prior
 structures which incorporate historical data into the current data by
 quantifying them with a suitable prior distribution on the model
 parameters. This allows ISSVS to search more efficiently in the model
 space and choose the more likely models. In addition, a Gibbs sampler is
 derived to facilitate the computation of the posterior probabilities. A
 major advantage of ISSVS is that it avoids instability in the posterior
 estimates for the Gibbs sampler as well as convergence problems that may
 arise from choosing vague priors. Finally, the proposed methods are
 illustrated with both simulation and real data. 
Journal: Journal of Applied Statistics 
Pages: 799-813 
Issue: 4 
Volume: 39 
Year: 2012 
Month: 8 
X-DOI: 10.1080/02664763.2011.620082 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.620082 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:4:p:799-813




Template-Type: ReDIF-Article 1.0
Author-Name: Darold   T. Barnum 
Author-X-Name-First: Darold   T. 
Author-X-Name-Last: Barnum 
Author-Name: John   M. Gleason 
Author-X-Name-First: John   M. 
Author-X-Name-Last: Gleason 
Author-Name: Matthew   G. Karlaftis 
Author-X-Name-First: Matthew   G. 
Author-X-Name-Last: Karlaftis 
Author-Name: Glen   T. Schumock 
Author-X-Name-First: Glen   T. 
Author-X-Name-Last: Schumock 
Author-Name: Karen   L. Shields 
Author-X-Name-First: Karen   L. 
Author-X-Name-Last: Shields 
Author-Name: Sonali Tandon 
Author-X-Name-First: Sonali 
Author-X-Name-Last: Tandon 
Author-Name: Surrey   M. Walton 
Author-X-Name-First: Surrey   M. 
Author-X-Name-Last: Walton 
Title: Estimating DEA confidence intervals with statistical panel data analysis 
Abstract:
  This paper describes a statistical method for estimating data envelopment
 analysis (DEA) score confidence intervals for individual organizations or
 other entities. This method applies statistical panel data analysis, which
 provides proven and powerful methodologies for diagnostic testing and for
 estimation of confidence intervals. DEA scores are tested for violations
 of the standard statistical assumptions including contemporaneous
 correlation, serial correlation, heteroskedasticity and the absence of a
 normal distribution. Generalized least squares statistical models are used
 to adjust for violations that are present and to estimate valid confidence
 intervals within which the true efficiency of each individual
 decision-making unit occurs. This method is illustrated with two sets of
 panel data, one from large US urban transit systems and the other from a
 group of US hospital pharmacies. 
Journal: Journal of Applied Statistics 
Pages: 815-828 
Issue: 4 
Volume: 39 
Year: 2012 
Month: 9 
X-DOI: 10.1080/02664763.2011.620948 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.620948 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:4:p:815-828




Template-Type: ReDIF-Article 1.0
Author-Name: Chun-Xia Zhang 
Author-X-Name-First: Chun-Xia 
Author-X-Name-Last: Zhang 
Author-Name: Guan-Wei Wang 
Author-X-Name-First: Guan-Wei 
Author-X-Name-Last: Wang 
Author-Name: Jiang-She Zhang 
Author-X-Name-First: Jiang-She 
Author-X-Name-Last: Zhang 
Title: An empirical bias--variance analysis of DECORATE ensemble method at different training sample sizes 
Abstract:
  DECORATE (Diverse Ensemble Creation by Oppositional Relabeling of
 Artificial Training Examples) is a classifier combination technique to
 construct a set of diverse base classifiers using additional artificially
 generated training instances. The predictions from the base classifiers
 are then integrated into one by the mean combination rule. In order to
 gain more insight about its effectiveness and advantages, this paper
 utilizes a large experiment to study the bias--variance analysis of
 DECORATE as well as some other widely used ensemble methods (such as
 bagging, AdaBoost, random forest) at different training sample sizes. The
 experimental results yield the following conclusions. For small training
 sets, DECORATE has a dominant advantage over its rivals and its success is
 attributed to the larger bias reduction achieved by it than the other
 algorithms. With increase in training data, AdaBoost benefits most and the
 bias reduced by it gradually turns to be significant while its variance
 reduction is also medium. Thus, AdaBoost performs best with large training
 samples. Moreover, random forest behaves always second best regardless of
 small or large training sets and it is seen to mainly decrease variance
 while maintaining low bias. Bagging seems to be an intermediate one since
 it reduces variance primarily. 
Journal: Journal of Applied Statistics 
Pages: 829-850 
Issue: 4 
Volume: 39 
Year: 2012 
Month: 9 
X-DOI: 10.1080/02664763.2011.620949 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.620949 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:4:p:829-850




Template-Type: ReDIF-Article 1.0
Author-Name: F. Lombard 
Author-X-Name-First: F. 
Author-X-Name-Last: Lombard 
Author-Name: C.   J. Potgieter 
Author-X-Name-First: C.   J. 
Author-X-Name-Last: Potgieter 
Title: A multivariate rank test for comparing mass size distributions 
Abstract:
  Particle size analyses of a raw material are commonplace in the mineral
 processing industry. Knowledge of particle size distributions is crucial
 in planning milling operations to enable an optimum degree of liberation
 of valuable mineral phases, to minimize plant losses due to an excess of
 oversize or undersize material or to attain a size distribution that fits
 a contractual specification. The problem addressed in the present paper is
 how to test the equality of two or more underlying size distributions. A
 distinguishing feature of these size distributions is that they are not
 based on counts of individual particles. Rather, they are mass size
 distributions giving the fractions of the total mass of a sampled material
 lying in each of a number of size intervals. As such, the data are
 compositional in nature, using the terminology of Aitchison [1] that is,
 multivariate vectors the components of which add to 100%. In the
 literature, various versions of Hotelling's <italic>T</italic> -super-2
 have been used to compare matched pairs of such compositional data. In
 this paper, we propose a robust test procedure based on ranks as a
 competitor to Hotelling's <italic>T</italic> -super-2. In contrast to the
 latter statistic, the power of the rank test is not unduly affected by the
 presence of outliers or of zeros among the data. 
Journal: Journal of Applied Statistics 
Pages: 851-865 
Issue: 4 
Volume: 39 
Year: 2012 
Month: 9 
X-DOI: 10.1080/02664763.2011.623155 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.623155 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:4:p:851-865




Template-Type: ReDIF-Article 1.0
Author-Name: P. Berchialla 
Author-X-Name-First: P. 
Author-X-Name-Last: Berchialla 
Author-Name: S. Snidero 
Author-X-Name-First: S. 
Author-X-Name-Last: Snidero 
Author-Name: A. Stancu 
Author-X-Name-First: A. 
Author-X-Name-Last: Stancu 
Author-Name: C. Scarinzi 
Author-X-Name-First: C. 
Author-X-Name-Last: Scarinzi 
Author-Name: R. Corradetti 
Author-X-Name-First: R. 
Author-X-Name-Last: Corradetti 
Author-Name: D. Gregori 
Author-X-Name-First: D. 
Author-X-Name-Last: Gregori 
Title: Understanding the epidemiology of foreign body injuries in children using a data-driven Bayesian network 
Abstract:
  Bayesian networks (BNs) are probabilistic expert systems which have
 emerged over the last few decades as a powerful data mining technique.
 Also, BNs have become especially popular in biomedical applications where
 they have been used for diagnosing diseases and studying complex cellular
 networks, among many other applications. In this study, we built a BN in a
 fully automated way in order to analyse data regarding injuries due to the
 inhalation, ingestion and aspiration of foreign bodies (FBs) in children.
 Then, a sensitivity analysis was carried out to characterize the
 uncertainty associated with the model. While other studies focused on
 characteristics such as shape, consistency and dimensions of the FBs which
 caused injuries, we propose an integrated environment which makes the
 relationships among the factors underlying the problem clear. The
 advantage of this approach is that it gives a picture of the influence of
 critical factors on the injury severity and allows for the comparison of
 the effect of different FB characteristics (volume, FB type, shape and
 consistency) and children's features (age and gender) on the risk of
 experiencing a hospitalization. The rates it consents to calculate provide
 a more rational basis for promoting care-givers&#x2019; education of the
 most influential risk factors regarding the adverse outcomes. 
Journal: Journal of Applied Statistics 
Pages: 867-874 
Issue: 4 
Volume: 39 
Year: 2012 
Month: 9 
X-DOI: 10.1080/02664763.2011.623156 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.623156 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:4:p:867-874




Template-Type: ReDIF-Article 1.0
Author-Name: Sifat Sharmin 
Author-X-Name-First: Sifat 
Author-X-Name-Last: Sharmin 
Author-Name: Md.   Israt Rayhan 
Author-X-Name-First: Md.   Israt 
Author-X-Name-Last: Rayhan 
Title: Spatio-temporal modeling of infectious disease dynamics 
Abstract:
  A stochastic model, which is well suited to capture space--time
 dependence of an infectious disease, was employed in this study to
 describe the underlying spatial and temporal pattern of measles in Barisal
 Division, Bangladesh. The model has two components: an endemic component
 and an epidemic component; weights are used in the epidemic component for
 better accounting of the disease spread into different geographical
 regions. We illustrate our findings using a data set of monthly measles
 counts in the six districts of Barisal, from January 2000 to August 2009,
 collected from the Expanded Program on Immunization, Bangladesh. The
 negative binomial model with both the seasonal and autoregressive
 components was found to be suitable for capturing space--time dependence
 of measles in Barisal. Analyses were done using general optimization
 routines, which provided the maximum likelihood estimates with the
 corresponding standard errors. 
Journal: Journal of Applied Statistics 
Pages: 875-882 
Issue: 4 
Volume: 39 
Year: 2012 
Month: 9 
X-DOI: 10.1080/02664763.2011.624593 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.624593 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:4:p:875-882




Template-Type: ReDIF-Article 1.0
Author-Name: Benjamin Neustifter 
Author-X-Name-First: Benjamin 
Author-X-Name-Last: Neustifter 
Author-Name: Stephen   L. Rathbun 
Author-X-Name-First: Stephen   L. 
Author-X-Name-Last: Rathbun 
Author-Name: Saul Shiffman 
Author-X-Name-First: Saul 
Author-X-Name-Last: Shiffman 
Title: Mixed-Poisson point process with partially observed covariates: ecological momentary assessment of smoking 
Abstract:
  Ecological momentary assessment is an emerging method of data collection
 in behavioral research that may be used to capture the times of repeated
 behavioral events on electronic devices and information on
 subjects&#x2019; psychological states through the electronic
 administration of questionnaires at times selected from a
 probability-based design as well as the event times. A method for fitting
 a mixed-Poisson point-process model is proposed for the impact of
 partially observed, time-varying covariates on the timing of repeated
 behavioral events. A random frailty is included in the point-process
 intensity to describe the variation in baseline rates of event occurrence
 among subjects. Covariate coefficients are estimated using estimating
 equations constructed by replacing the integrated intensity in the Poisson
 score equations with a design-unbiased estimator. An estimator is also
 proposed for the variance of the random frailties. Our estimators are
 robust in the sense that no model assumptions are made regarding the
 distribution of the time-varying covariates or the distribution of the
 random effects. However, subject effects are estimated under gamma
 frailties using an approximate hierarchical likelihood. The proposed
 approach is illustrated using smoking data. 
Journal: Journal of Applied Statistics 
Pages: 883-899 
Issue: 4 
Volume: 39 
Year: 2012 
Month: 9 
X-DOI: 10.1080/02664763.2011.626848 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.626848 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:4:p:883-899




Template-Type: ReDIF-Article 1.0
Author-Name: Megan Othus 
Author-X-Name-First: Megan 
Author-X-Name-Last: Othus 
Author-Name: Yi Li 
Author-X-Name-First: Yi 
Author-X-Name-Last: Li 
Author-Name: Ram Tiwari 
Author-X-Name-First: Ram 
Author-X-Name-Last: Tiwari 
Title: Change-point cure models with application to estimating the change-point effect of age of diagnosis among prostate cancer patients 
Abstract:
  Previous research on prostate cancer survival trends in the United States
 National Cancer Institute's Surveillance Epidemiology and End Results
 database has indicated a potential change-point in the age of diagnosis of
 prostate cancer around age 50. Identifying a change-point value in
 prostate cancer survival and cure could have important policy and health
 care management implications. Statistical analysis of this data has to
 address two complicating features: (1) change-point models are not smooth
 functions and so present computational and theoretical difficulties; and
 (2) models for prostate cancer survival need to account for the fact that
 many men diagnosed with prostate cancer can be effectively cured of their
 disease with early treatment. We develop a cure survival model that allows
 for change-point effects in covariates to investigate a potential
 change-point in the age of diagnosis of prostate cancer. Our results do
 not indicate that age under 50 is associated with increased hazard of
 death from prostate cancer. 
Journal: Journal of Applied Statistics 
Pages: 901-911 
Issue: 4 
Volume: 39 
Year: 2012 
Month: 9 
X-DOI: 10.1080/02664763.2011.626849 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.626849 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:4:p:901-911




Template-Type: ReDIF-Article 1.0
Author-Name: Yun-Huan Lee 
Author-X-Name-First: Yun-Huan 
Author-X-Name-Last: Lee 
Author-Name: Chun-Shu Chen 
Author-X-Name-First: Chun-Shu 
Author-X-Name-Last: Chen 
Title: Autoregressive model selection based on a prediction perspective 
Abstract:
  The autoregressive (AR) model is a popular method for fitting and
 prediction in analyzing time-dependent data, where selecting an accurate
 model among considered orders is a crucial issue. Two commonly used
 selection criteria are the Akaike information criterion and the Bayesian
 information criterion. However, the two criteria are known to suffer
 potential problems regarding overfit and underfit, respectively.
 Therefore, using them would perform well in some situations, but poorly in
 others. In this paper, we propose a new criterion in terms of the
 prediction perspective based on the concept of generalized degrees of
 freedom for AR model selection. We derive an approximately unbiased
 estimator of mean-squared prediction errors based on a data perturbation
 technique for selecting the order parameter, where the estimation
 uncertainty involved in a modeling procedure is considered. Some numerical
 experiments are performed to illustrate the superiority of the proposed
 method over some commonly used order selection criteria. Finally, the
 methodology is applied to a real data example to predict the weekly rate
 of return on the stock price of Taiwan Semiconductor Manufacturing Company
 and the results indicate that the proposed method is satisfactory. 
Journal: Journal of Applied Statistics 
Pages: 913-922 
Issue: 4 
Volume: 39 
Year: 2012 
Month: 10 
X-DOI: 10.1080/02664763.2011.636418 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.636418 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:4:p:913-922




Template-Type: ReDIF-Article 1.0
Author-Name: Hassan   S. Bakouch 
Author-X-Name-First: Hassan   S. 
Author-X-Name-Last: Bakouch 
Title: Time series: Modeling, Computation, and Inference 
Journal: Journal of Applied Statistics 
Pages: 923-923 
Issue: 4 
Volume: 39 
Year: 2012 
Month: 4 
X-DOI: 10.1080/02664763.2012.657378 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.657378 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:4:p:923-923




Template-Type: ReDIF-Article 1.0
Author-Name: S&#xF8;ren   Feodor Nielsen 
Author-X-Name-First: S&#xF8;ren   Feodor 
Author-X-Name-Last: Nielsen 
Title: Introduction to general and generalized linear models 
Journal: Journal of Applied Statistics 
Pages: 923-924 
Issue: 4 
Volume: 39 
Year: 2012 
Month: 4 
X-DOI: 10.1080/02664763.2012.657403 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.657403 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:4:p:923-924




Template-Type: ReDIF-Article 1.0
Author-Name: Long Kang 
Author-X-Name-First: Long 
Author-X-Name-Last: Kang 
Title: The oxford handbook of credit derivatives 
Journal: Journal of Applied Statistics 
Pages: 924-925 
Issue: 4 
Volume: 39 
Year: 2012 
Month: 4 
X-DOI: 10.1080/02664763.2012.657406 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.657406 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:4:p:924-925




Template-Type: ReDIF-Article 1.0
Author-Name: Mariano   Ruiz Espejo 
Author-X-Name-First: Mariano   Ruiz 
Author-X-Name-Last: Espejo 
Title: Exercises and solutions in biostatistical theory 
Journal: Journal of Applied Statistics 
Pages: 925-926 
Issue: 4 
Volume: 39 
Year: 2012 
Month: 4 
X-DOI: 10.1080/02664763.2012.657407 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.657407 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:4:p:925-926




Template-Type: ReDIF-Article 1.0
Author-Name: Adri&#xE1;n Quintero-Sarmiento 
Author-X-Name-First: Adri&#xE1;n 
Author-X-Name-Last: Quintero-Sarmiento 
Author-Name: Edilberto Cepeda-Cuervo 
Author-X-Name-First: Edilberto 
Author-X-Name-Last: Cepeda-Cuervo 
Author-Name: Vicente N&#xFA;&#xF1;ez-Ant&#xF3;n 
Author-X-Name-First: Vicente 
Author-X-Name-Last: N&#xFA;&#xF1;ez-Ant&#xF3;n 
Title: Estimating infant mortality in Colombia: some overdispersion modelling approaches 
Abstract:
  It is common to fit generalized linear models with binomial and Poisson
 responses, where the data show a variability that is greater than the
 theoretical variability assumed by the model. This phenomenon, known as
 overdispersion, may spoil inferences about the model by considering
 significant parameters associated with variables that have no significant
 effect on the dependent variable. This paper explains some methods to
 detect overdispersion and presents and evaluates three well-known
 methodologies that have shown their usefulness in correcting this problem,
 using random mean models, quasi-likelihood methods and a double
 exponential family. In addition, it proposes some new Bayesian model
 extensions that have proved their usefulness in correcting the
 overdispersion problem. Finally, using the information provided by the
 National Demographic and Health Survey 2005, the departmental factors that
 have an influence on the mortality of children under 5 years and female
 postnatal period screening are determined. Based on the results,
 extensions that generalize some of the aforementioned models are also
 proposed, and their use is motivated by the data set under study. The
 results conclude that the proposed overdispersion models provide a better
 statistical fit of the data. 
Journal: Journal of Applied Statistics 
Pages: 1011-1036 
Issue: 5 
Volume: 39 
Year: 2012 
Month: 10 
X-DOI: 10.1080/02664763.2011.634395 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.634395 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:5:p:1011-1036




Template-Type: ReDIF-Article 1.0
Author-Name: Ali   Reza Soltanian 
Author-X-Name-First: Ali   Reza 
Author-X-Name-Last: Soltanian 
Author-Name: Soghrat Faghihzadeh 
Author-X-Name-First: Soghrat 
Author-X-Name-Last: Faghihzadeh 
Title: A generalization of the Grizzle model to the estimation of treatment effects in crossover trials with non-compliance 
Abstract:
  Compliance with one specified dosing strategy of assigned treatments is a
 common problem in randomized drug clinical trials. Recently, there has
 been much interest in methods used for analysing treatment effects in
 randomized clinical trials that are subject to non-compliance. In this
 paper, we estimate and compare treatment effects based on the Grizzle
 model (GM) (ignorable non-compliance) as the custom model and the
 generalized Grizzle model (GGM) (non-ignorable non-compliance) as the new
 model. A real data set based on the treatment of knee osteoarthritis is
 used to compare these models. The results based on the likelihood ratio
 statistics and simulation study show the advantage of the proposed model
 (GGM) over the custom model (GGM). 
Journal: Journal of Applied Statistics 
Pages: 1037-1048 
Issue: 5 
Volume: 39 
Year: 2012 
Month: 10 
X-DOI: 10.1080/02664763.2011.634396 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.634396 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:5:p:1037-1048




Template-Type: ReDIF-Article 1.0
Author-Name: Cibele   M. Russo 
Author-X-Name-First: Cibele   M. 
Author-X-Name-Last: Russo 
Author-Name: Gilberto   A. Paula 
Author-X-Name-First: Gilberto   A. 
Author-X-Name-Last: Paula 
Author-Name: Francisco   Jos� A. Cysneiros 
Author-X-Name-First: Francisco   Jos� A. 
Author-X-Name-Last: Cysneiros 
Author-Name: Reiko Aoki 
Author-X-Name-First: Reiko 
Author-X-Name-Last: Aoki 
Title: Influence diagnostics in heteroscedastic and/or autoregressive nonlinear elliptical models for correlated data 
Abstract:
  In this paper, we propose nonlinear elliptical models for correlated data
 with heteroscedastic and/or autoregressive structures. Our aim is to
 extend the models proposed by Russo <italic>et al.</italic> 22 by
 considering a more sophisticated scale structure to deal with variations
 in data dispersion and/or a possible autocorrelation among measurements
 taken throughout the same experimental unit. Moreover, to avoid the
 possible influence of outlying observations or to take into account the
 non-normal symmetric tails of the data, we assume elliptical contours for
 the joint distribution of random effects and errors, which allows us to
 attribute different weights to the observations. We propose an iterative
 algorithm to obtain the maximum-likelihood estimates for the parameters
 and derive the local influence curvatures for some specific perturbation
 schemes. The motivation for this work comes from a pharmacokinetic
 indomethacin data set, which was analysed previously by Bocheng and Xuping
 1 under normality. 
Journal: Journal of Applied Statistics 
Pages: 1049-1067 
Issue: 5 
Volume: 39 
Year: 2012 
Month: 10 
X-DOI: 10.1080/02664763.2011.636030 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.636030 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:5:p:1049-1067




Template-Type: ReDIF-Article 1.0
Author-Name: Vinicius   P. Israel 
Author-X-Name-First: Vinicius   P. 
Author-X-Name-Last: Israel 
Author-Name: H�lio   S. Migon 
Author-X-Name-First: H�lio   S. 
Author-X-Name-Last: Migon 
Title: Stochastic models for greenhouse gas emission rate estimation from hydroelectric reservoirs: a Bayesian hierarchical approach 
Abstract:
  Herein, we propose a fully Bayesian approach to the greenhouse gas
 emission problem. The goal of this work is to estimate the emission rate
 of polluting gases from the area flooded by hydroelectric reservoirs. We
 present models for gas concentration evolution in two ways: first, by
 proposing them from ordinary differential equation solutions and, second,
 by using stochastic differential equations with a discretization scheme.
 Finally, we present techniques to estimate the emission rate for the
 entire reservoir. In order to carry out the inference, we use the Bayesian
 framework with Monte Carlo via Markov Chain methods. Discretization
 schemes over continuous differential equations are used when necessary.
 These models applied to greenhouse gas emission and Bayesian inference for
 this purpose are completely new in statistical literature, as far as we
 know, and contribute to estimate the amount of polluting gases released
 from hydroelectric reservoirs in Brazil. The proposed models are applied
 in a real data set and results are presented. 
Journal: Journal of Applied Statistics 
Pages: 1069-1086 
Issue: 5 
Volume: 39 
Year: 2012 
Month: 10 
X-DOI: 10.1080/02664763.2011.636417 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.636417 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:5:p:1069-1086




Template-Type: ReDIF-Article 1.0
Author-Name: Marjan Mansourian 
Author-X-Name-First: Marjan 
Author-X-Name-Last: Mansourian 
Author-Name: Anoshirvan Kazemnejad 
Author-X-Name-First: Anoshirvan 
Author-X-Name-Last: Kazemnejad 
Author-Name: Iraj Kazemi 
Author-X-Name-First: Iraj 
Author-X-Name-Last: Kazemi 
Author-Name: Farid Zayeri 
Author-X-Name-First: Farid 
Author-X-Name-Last: Zayeri 
Author-Name: Masoud Soheilian 
Author-X-Name-First: Masoud 
Author-X-Name-Last: Soheilian 
Title: Bayesian analysis of longitudinal ordered data with flexible random effects using McMC: application to diabetic macular Edema data 
Abstract:
  In the analysis of correlated ordered data, mixed-effect models are
 frequently used to control the subject heterogeneity effects. A common
 assumption in fitting these models is the normality of random effects. In
 many cases, this is unrealistic, making the estimation results unreliable.
 This paper considers several flexible models for random effects and
 investigates their properties in the model fitting. We adopt a
 proportional odds logistic regression model and incorporate the skewed
 version of the normal, Student's <italic>t</italic> and slash
 distributions for the effects. Stochastic representations for various
 flexible distributions are proposed afterwards based on the mixing
 strategy approach. This reduces the computational burden being performed
 by the McMC technique. Furthermore, this paper addresses the
 identifiability restrictions and suggests a procedure to handle this
 issue. We analyze a real data set taken from an ophthalmic clinical trial.
 Model selection is performed by suitable Bayesian model selection
 criteria. 
Journal: Journal of Applied Statistics 
Pages: 1087-1100 
Issue: 5 
Volume: 39 
Year: 2012 
Month: 11 
X-DOI: 10.1080/02664763.2011.638367 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.638367 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:5:p:1087-1100




Template-Type: ReDIF-Article 1.0
Author-Name: Sa&#xEF;d Hanchane 
Author-X-Name-First: Sa&#xEF;d 
Author-X-Name-Last: Hanchane 
Author-Name: Tarek Mostafa 
Author-X-Name-First: Tarek 
Author-X-Name-Last: Mostafa 
Title: Solving endogeneity problems in multilevel estimation: an example using education production functions 
Abstract:
  This paper explores endogeneity problems in multilevel estimation of
 education production functions. The focus is on level 2 endogeneity which
 arises from correlations between student characteristics and omitted
 school variables. Theses correlations are mainly the result of student
 stratification between schools. From an econometric point of view, the
 correlations between student and school characteristics imply that the
 omission of some variables may generate endogeneity bias. Therefore, an
 estimation approach based on the Mundlak [20] technique is developed in
 order to tackle bias and to generate consistent estimates. Note that our
 analysis can be extended to any multilevel-structured data (students
 nested within schools, employees within firms, firms within regions, etc).
 The entire analysis is undertaken in a comparative context between three
 countries: Germany, Finland and the UK. Each one of them represents a
 particular system. For instance, Finland is known for its extreme
 comprehensiveness, Germany for early selection and the UK for its
 liberalism. These countries are used to illustrate the theory and to prove
 that the level of bias arising from omitted variables varies according to
 the characteristics of education systems. 
Journal: Journal of Applied Statistics 
Pages: 1101-1114 
Issue: 5 
Volume: 39 
Year: 2012 
Month: 11 
X-DOI: 10.1080/02664763.2011.638705 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.638705 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:5:p:1101-1114




Template-Type: ReDIF-Article 1.0
Author-Name: K. Hron 
Author-X-Name-First: K. 
Author-X-Name-Last: Hron 
Author-Name: P. Filzmoser 
Author-X-Name-First: P. 
Author-X-Name-Last: Filzmoser 
Author-Name: K. Thompson 
Author-X-Name-First: K. 
Author-X-Name-Last: Thompson 
Title: Linear regression with compositional explanatory variables 
Abstract:
  Compositional explanatory variables should not be directly used in a
 linear regression model because any inference statistic can become
 misleading. While various approaches for this problem were proposed, here
 an approach based on the isometric logratio (ilr) transformation is used.
 It turns out that the resulting model is easy to handle, and that
 parameter estimation can be done in like in usual linear regression.
 Moreover, it is possible to use the ilr variables for inference statistics
 in order to obtain an appropriate interpretation of the model. 
Journal: Journal of Applied Statistics 
Pages: 1115-1128 
Issue: 5 
Volume: 39 
Year: 2012 
Month: 11 
X-DOI: 10.1080/02664763.2011.644268 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.644268 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:5:p:1115-1128




Template-Type: ReDIF-Article 1.0
Author-Name: C. Kiruthika 
Author-X-Name-First: C. 
Author-X-Name-Last: Kiruthika 
Author-Name: R. Chandrasekaran 
Author-X-Name-First: R. 
Author-X-Name-Last: Chandrasekaran 
Title: Classification of textile fabrics using statistical multivariate techniques 
Abstract:
  In this study, an attempt has been made to classify the textile fabrics
 based on the physical properties using statistical multivariate techniques
 like discriminant analysis and cluster analysis. Initially, the
 discriminant functions have been constructed for the classification of the
 three known categories of fabrics made up of polyster, lyocell/viscose and
 treated-polyster. The classification yielded hundred per cent accuracy.
 Each of the three different categories of fabrics has been further
 subjected to the K-means clustering algorithm that yielded three clusters.
 These clusters are subjected to discriminant analysis which again yielded
 a 100% correct classification, indicating that the clusters are well
 separated. The properties of clusters are also investigated with respect
 to the measurements. 
Journal: Journal of Applied Statistics 
Pages: 1129-1138 
Issue: 5 
Volume: 39 
Year: 2012 
Month: 11 
X-DOI: 10.1080/02664763.2011.644521 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.644521 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:5:p:1129-1138




Template-Type: ReDIF-Article 1.0
Author-Name: Rob Deardon 
Author-X-Name-First: Rob 
Author-X-Name-Last: Deardon 
Author-Name: Babak Habibzadeh 
Author-X-Name-First: Babak 
Author-X-Name-Last: Habibzadeh 
Author-Name: Hau   Yi Chung 
Author-X-Name-First: Hau   Yi 
Author-X-Name-Last: Chung 
Title: Spatial measurement error in infectious disease models 
Abstract:
  Individual-level models (ILMs) for infectious disease can be used to
 model disease spread between individuals while taking into account
 important covariates. One important covariate in determining the risk of
 infection transfer can be spatial location. At the same time, measurement
 error is a concern in many areas of statistical analysis, and infectious
 disease modelling is no exception. In this paper, we are concerned with
 the issue of measurement error in the recorded location of individuals
 when using a simple spatial ILM to model the spread of disease within a
 population. An ILM that incorporates spatial location random effects is
 introduced within a hierarchical Bayesian framework. This model is tested
 upon both simulated data and data from the UK 2001 foot-and-mouth disease
 epidemic. The ability of the model to successfully identify both the
 spatial infection kernel and the basic reproduction number
 (<italic>R</italic> <sub>0</sub>) of the disease is tested. 
Journal: Journal of Applied Statistics 
Pages: 1139-1150 
Issue: 5 
Volume: 39 
Year: 2012 
Month: 11 
X-DOI: 10.1080/02664763.2011.644522 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.644522 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:5:p:1139-1150




Template-Type: ReDIF-Article 1.0
Author-Name: Francesco Lagona 
Author-X-Name-First: Francesco 
Author-X-Name-Last: Lagona 
Author-Name: Marco Picone 
Author-X-Name-First: Marco 
Author-X-Name-Last: Picone 
Title: Model-based clustering of multivariate skew data with circular components and missing values 
Abstract:
  Motivated by classification issues that arise in marine studies, we
 propose a latent-class mixture model for the unsupervised classification
 of incomplete quadrivariate data with two linear and two circular
 components. The model integrates bivariate circular densities and
 bivariate skew normal densities to capture the association between
 toroidal clusters of bivariate circular observations and planar clusters
 of bivariate linear observations. Maximum-likelihood estimation of the
 model is facilitated by an expectation maximization (EM) algorithm that
 treats unknown class membership and missing values as different sources of
 incomplete information. The model is exploited on hourly observations of
 wind speed and direction and wave height and direction to identify a
 number of sea regimes, which represent specific distributional shapes that
 the data take under environmental latent conditions. 
Journal: Journal of Applied Statistics 
Pages: 927-945 
Issue: 5 
Volume: 39 
Year: 2012 
Month: 9 
X-DOI: 10.1080/02664763.2011.626850 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.626850 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:5:p:927-945




Template-Type: ReDIF-Article 1.0
Author-Name: Wei Ning 
Author-X-Name-First: Wei 
Author-X-Name-Last: Ning 
Title: Empirical likelihood ratio test for a mean change point model with a linear trend followed by an abrupt change 
Abstract:
  In this paper, a change point model with the mean being constant up to
 some unknown point, and increasing linearly to another unknown point, then
 dropping back to the original level is studied. A nonparametric method
 based on the empirical likelihood test is proposed to detect and estimate
 the locations of change points. Under some mild conditions, the asymptotic
 null distribution of an empirical likelihood ratio test statistic is shown
 to have the extreme distribution. The consistency of the test is also
 proved. Simulations of the powers of the test indicate that it performs
 well under different assumptions of the data distribution. The test is
 applied to the aircraft arrival time data set and the Stanford heart
 transplant data set. 
Journal: Journal of Applied Statistics 
Pages: 947-961 
Issue: 5 
Volume: 39 
Year: 2012 
Month: 9 
X-DOI: 10.1080/02664763.2011.628647 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.628647 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:5:p:947-961




Template-Type: ReDIF-Article 1.0
Author-Name: Wei Liu 
Author-X-Name-First: Wei 
Author-X-Name-Last: Liu 
Author-Name: Lang Wu 
Author-X-Name-First: Lang 
Author-X-Name-Last: Wu 
Title: Two-step and likelihood methods for HIV viral dynamic models with covariate measurement errors and missing data 
Abstract:
  HIV viral dynamic models have received much attention in the literature.
 Long-term viral dynamics may be modelled by semiparametric nonlinear
 mixed-effect models, which incorporate large variation between subjects
 and autocorrelation within subjects and are flexible in modelling complex
 viral load trajectories. Time-dependent covariates may be introduced in
 the dynamic models to partially explain the between-individual variations.
 In the presence of measurement errors and missing data in time-dependent
 covariates, we show that the commonly used two-step method may give
 approximately unbiased estimates but may under-estimate standard errors.
 We propose a two-stage bootstrap method to adjust the standard errors in
 the two-step method and a likelihood method. 
Journal: Journal of Applied Statistics 
Pages: 963-978 
Issue: 5 
Volume: 39 
Year: 2012 
Month: 10 
X-DOI: 10.1080/02664763.2011.632404 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.632404 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:5:p:963-978




Template-Type: ReDIF-Article 1.0
Author-Name: Susana Mendes 
Author-X-Name-First: Susana 
Author-X-Name-Last: Mendes 
Author-Name: M.   Jos� Fern&#xE1;ndez-G&#xF3;mez 
Author-X-Name-First: M.   Jos� 
Author-X-Name-Last: Fern&#xE1;ndez-G&#xF3;mez 
Author-Name: M&#xE1;rio   Jorge Pereira 
Author-X-Name-First: M&#xE1;rio   Jorge 
Author-X-Name-Last: Pereira 
Author-Name: Ulisses   Miranda Azeiteiro 
Author-X-Name-First: Ulisses   Miranda 
Author-X-Name-Last: Azeiteiro 
Author-Name: M.   Purificaci&#xF3;n Galindo-Villard&#xF3;n 
Author-X-Name-First: M.   Purificaci&#xF3;n 
Author-X-Name-Last: Galindo-Villard&#xF3;n 
Title: An empirical comparison of Canonical Correspondence Analysis and STATICO in the identification of spatio-temporal ecological relationships 
Abstract:
  The wide-ranging and rapidly evolving nature of ecological studies mean
 that it is not possible to cover all existing and emerging techniques for
 analyzing multivariate data. However, two important methods enticed many
 followers: the Canonical Correspondence Analysis (CCA) and the STATICO
 analysis. Despite the particular characteristics of each, they have
 similarities and differences, which when analyzed properly, can, together,
 provide important complementary results to those that are usually
 exploited by researchers. If on one hand, the use of CCA is completely
 generalized and implemented, solving many problems formulated by
 ecologists, on the other hand, this method has some weaknesses mainly
 caused by the imposition of the number of variables that is required to be
 applied (much higher in comparison with samples). Also, the STATICO method
 has no such restrictions, but requires that the number of variables
 (species or environment) is the same in each time or space. Yet, the
 STATICO method presents information that can be more detailed since it
 allows visualizing the variability within groups (either in time or
 space). In this study, the data needed for implementing these methods are
 sketched, as well as the comparison is made showing the advantages and
 disadvantages of each method. The treated ecological data are a sequence
 of pairs of ecological tables, where species abundances and environmental
 variables are measured at different, specified locations, over the course
 of time. 
Journal: Journal of Applied Statistics 
Pages: 979-994 
Issue: 5 
Volume: 39 
Year: 2012 
Month: 10 
X-DOI: 10.1080/02664763.2011.634393 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.634393 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:5:p:979-994




Template-Type: ReDIF-Article 1.0
Author-Name: G&#xF6;ran Kauermann 
Author-X-Name-First: G&#xF6;ran 
Author-X-Name-Last: Kauermann 
Author-Name: Nina Westerheide 
Author-X-Name-First: Nina 
Author-X-Name-Last: Westerheide 
Title: To move or not to move to find a new job: spatial duration time model with dynamic covariate effects 
Abstract:
  The aim of this paper is to show the flexibility and capacity of
 penalized spline smoothing as estimation routine for modelling duration
 time data. We analyse the unemployment behaviour in Germany between 2000
 and 2004 using a massive database from the German Federal Employment
 Agency. To investigate dynamic covariate effects and differences between
 competing job markets depending on the distance between former and recent
 working place, a functional duration time model with competing risks is
 used. It is build upon a competing hazard function where some of the
 smooth covariate effects are allowed to vary with unemployment duration.
 The focus of our analysis is on contrasting the spatial, economic and
 individual covariate effects of the competing job markets and on analysing
 their general influence on the unemployed's re-employment probabilities.
 As a result of our analyses, we reveal differences concerning gender, age
 and education. We also discover an effect between the newly formed and the
 old West German states. Moreover, the spatial pattern between the
 considered job markets differs. 
Journal: Journal of Applied Statistics 
Pages: 995-1009 
Issue: 5 
Volume: 39 
Year: 2012 
Month: 10 
X-DOI: 10.1080/02664763.2011.634394 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.634394 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:5:p:995-1009




Template-Type: ReDIF-Article 1.0
Author-Name: Christopher   H. Morrell 
Author-X-Name-First: Christopher   H. 
Author-X-Name-Last: Morrell 
Author-Name: Larry   J. Brant 
Author-X-Name-First: Larry   J. 
Author-X-Name-Last: Brant 
Author-Name: Shan Sheng 
Author-X-Name-First: Shan 
Author-X-Name-Last: Sheng 
Author-Name: E.   Jeffrey Metter 
Author-X-Name-First: E.   Jeffrey 
Author-X-Name-Last: Metter 
Title: Screening for prostate cancer using multivariate mixed-effects models 
Abstract:
  Using several variables known to be related to prostate cancer, a
 multivariate classification method is developed to predict the onset of
 clinical prostate cancer. A multivariate mixed-effects model is used to
 describe longitudinal changes in prostate-specific antigen (PSA), a free
 testosterone index (FTI), and body mass index (BMI) before any clinical
 evidence of prostate cancer. The patterns of change in these three
 variables are allowed to vary depending on whether the subject develops
 prostate cancer or not and the severity of the prostate cancer at
 diagnosis. An application of Bayes&#x2019; theorem provides posterior
 probabilities that we use to predict whether an individual will develop
 prostate cancer and, if so, whether it is a high-risk or a low-risk
 cancer. The classification rule is applied sequentially one multivariate
 observation at a time until the subject is classified as a cancer case or
 until the last observation has been used. We perform the analyses using
 each of the three variables individually, combined together in pairs, and
 all three variables together in one analysis. We compare the
 classification results among the various analyses and a simulation study
 demonstrates how the sensitivity of prediction changes with respect to the
 number and type of variables used in the prediction process. 
Journal: Journal of Applied Statistics 
Pages: 1151-1175 
Issue: 6 
Volume: 39 
Year: 2012 
Month: 11 
X-DOI: 10.1080/02664763.2011.644523 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.644523 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:6:p:1151-1175




Template-Type: ReDIF-Article 1.0
Author-Name: Liuxia Wang 
Author-X-Name-First: Liuxia 
Author-X-Name-Last: Wang 
Title: Bayesian principal component regression with data-driven component selection 
Abstract:
  Principal component regression (PCR) has two steps: estimating the
 principal components and performing the regression using these components.
 These steps generally are performed sequentially. In PCR, a crucial issue
 is the selection of the principal components to be included in regression.
 In this paper, we build a hierarchical probabilistic PCR model with a
 dynamic component selection procedure. A latent variable is introduced to
 select promising subsets of components based upon the significance of the
 relationship between the response variable and principal components in the
 regression step. We illustrate this model using real and simulated
 examples. The simulations demonstrate that our approach outperforms some
 existing methods in terms of root mean squared error of the regression
 coefficient. 
Journal: Journal of Applied Statistics 
Pages: 1177-1189 
Issue: 6 
Volume: 39 
Year: 2012 
Month: 11 
X-DOI: 10.1080/02664763.2011.644524 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.644524 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:6:p:1177-1189




Template-Type: ReDIF-Article 1.0
Author-Name: Edwin M.M. Ortega 
Author-X-Name-First: Edwin M.M. 
Author-X-Name-Last: Ortega 
Author-Name: Gauss   M. Cordeiro 
Author-X-Name-First: Gauss   M. 
Author-X-Name-Last: Cordeiro 
Author-Name: Michael   W. Kattan 
Author-X-Name-First: Michael   W. 
Author-X-Name-Last: Kattan 
Title: The negative binomial--beta Weibull regression model to predict the cure of prostate cancer 
Abstract:
  In this article, for the first time, we propose the negative
 binomial--beta Weibull (BW) regression model for studying the recurrence
 of prostate cancer and to predict the cure fraction for patients with
 clinically localized prostate cancer treated by open radical
 prostatectomy. The cure model considers that a fraction of the survivors
 are cured of the disease. The survival function for the population of
 patients can be modeled by a cure parametric model using the BW
 distribution. We derive an explicit expansion for the moments of the
 recurrence time distribution for the uncured individuals. The proposed
 distribution can be used to model survival data when the hazard rate
 function is increasing, decreasing, unimodal and bathtub shaped. Another
 advantage is that the proposed model includes as special sub-models some
 of the well-known cure rate models discussed in the literature. We derive
 the appropriate matrices for assessing local influence on the parameter
 estimates under different perturbation schemes. We analyze a real data set
 for localized prostate cancer patients after open radical prostatectomy. 
Journal: Journal of Applied Statistics 
Pages: 1191-1210 
Issue: 6 
Volume: 39 
Year: 2012 
Month: 11 
X-DOI: 10.1080/02664763.2011.644525 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.644525 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:6:p:1191-1210




Template-Type: ReDIF-Article 1.0
Author-Name: Piotr Kulczycki 
Author-X-Name-First: Piotr 
Author-X-Name-Last: Kulczycki 
Author-Name: Malgorzata Charytanowicz 
Author-X-Name-First: Malgorzata 
Author-X-Name-Last: Charytanowicz 
Author-Name: Piotr   A. Kowalski 
Author-X-Name-First: Piotr   A. 
Author-X-Name-Last: Kowalski 
Author-Name: Szymon Lukasik 
Author-X-Name-First: Szymon 
Author-X-Name-Last: Lukasik 
Title: The Complete Gradient Clustering Algorithm: properties in practical applications 
Abstract:
  The aim of this paper is to present a Complete Gradient Clustering
 Algorithm, its applicational aspects and properties, as well as to
 illustrate them with specific practical problems from the subject of
 bioinformatics (the categorization of grains for seed production),
 management (the design of a marketing support strategy for a mobile phone
 operator) and engineering (the synthesis of a fuzzy controller). The main
 property of the Complete Gradient Clustering Algorithm is that it does not
 require strict assumptions regarding the desired number of clusters, which
 allows to better suit its obtained number to a real data structure. In the
 basic version it is possible to provide a complete set of procedures for
 defining the values of all functions and parameters relying on the
 optimization criterions. It is also possible to point out parameters, the
 potential change which implies influence on the size of the number of
 clusters (while still not giving an exact number) and the proportion
 between their numbers in dense and sparse areas of data elements.
 Moreover, the Complete Gradient Clustering Algorithm can be used to
 identify and possibly eliminate atypical elements (outliers). These
 properties proved to be very useful in the presented applications and may
 also be functional in many other practical problems. 
Journal: Journal of Applied Statistics 
Pages: 1211-1224 
Issue: 6 
Volume: 39 
Year: 2012 
Month: 11 
X-DOI: 10.1080/02664763.2011.644526 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.644526 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:6:p:1211-1224




Template-Type: ReDIF-Article 1.0
Author-Name: Florence George 
Author-X-Name-First: Florence 
Author-X-Name-Last: George 
Author-Name: B.   M. Golam Kibria 
Author-X-Name-First: B.   M. 
Author-X-Name-Last: Golam Kibria 
Title: Confidence intervals for estimating the population signal-to-noise ratio: a simulation study 
Abstract:
  This paper considered several confidence intervals for estimating the
 population signal-to-noise ratio based on parametric, non-parametric and
 modified methods. A simulation study has been conducted to compare the
 performance of the interval estimators under both symmetric and skewed
 distributions. We reported coverage probability and average width of the
 interval estimators. Based on the simulation study, we observed that some
 of our proposed interval estimators are performing better in the sense of
 smaller width and coverage probability and have been recommended for the
 researchers. 
Journal: Journal of Applied Statistics 
Pages: 1225-1240 
Issue: 6 
Volume: 39 
Year: 2012 
Month: 11 
X-DOI: 10.1080/02664763.2011.644527 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.644527 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:6:p:1225-1240




Template-Type: ReDIF-Article 1.0
Author-Name: Zhiyong Zhang 
Author-X-Name-First: Zhiyong 
Author-X-Name-Last: Zhang 
Author-Name: John   J. McArdle 
Author-X-Name-First: John   J. 
Author-X-Name-Last: McArdle 
Author-Name: John   R. Nesselroade 
Author-X-Name-First: John   R. 
Author-X-Name-Last: Nesselroade 
Title: Growth rate models: emphasizing growth rate analysis through growth curve modeling 
Abstract:
  To emphasize growth rate analysis, we develop a general method to
 reparametrize growth curve models to analyze rates of growth for a variety
 of growth trajectories, such as quadratic and exponential growth. The
 resulting growth rate models are shown to be related to rotations of
 growth curves. Estimated conveniently through growth curve modeling
 techniques, growth rate models have advantages above and beyond
 traditional growth curve models. The proposed growth rate models are used
 to analyze longitudinal data from the National Longitudinal Study of Youth
 (NLSY) on children's mathematics performance scores including covariates
 of gender and behavioral problems (BPI). Individual differences are found
 in rates of growth from ages 6 to 11. Associations with BPI, gender, and
 their interaction to rates of growth are found to vary with age.
 Implications of the models and the findings are discussed. 
Journal: Journal of Applied Statistics 
Pages: 1241-1262 
Issue: 6 
Volume: 39 
Year: 2012 
Month: 11 
X-DOI: 10.1080/02664763.2011.644528 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.644528 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:6:p:1241-1262




Template-Type: ReDIF-Article 1.0
Author-Name: Andrea Cutillo 
Author-X-Name-First: Andrea 
Author-X-Name-Last: Cutillo 
Author-Name: Claudio Ceccarelli 
Author-X-Name-First: Claudio 
Author-X-Name-Last: Ceccarelli 
Title: The internal relocation premium: are migrants positively or negatively selected? Evidence from Italy 
Abstract:
  This paper analyzes the wage returns from internal migration for recent
 graduates in Italy. We employ a switching regression model that accounts
 for the endogeneity of the individual's choice to relocate to get a job
 after graduation: the omission of this selection decision can lead to
 biased estimates, as there is potential correlation between earnings and
 unobserved traits, exerting an influence on the decision to migrate. The
 empirical results sustain the appropriateness of the estimation technique
 and show that there is a significant pay gap between migrants and
 non-migrants; migrants seem to be positively selected and the migration
 premium is downward biased through OLS estimates. The endogeneity of
 migration shows up both as a negative intercept effect and as a positive
 slope effect, the second being larger than the first: bad knowledge of the
 local labor market and financial constraints lead migrants to accept a low
 basic wage but, due to relevant returns to their characteristics, they
 finally obtain a higher wage than the others. 
Journal: Journal of Applied Statistics 
Pages: 1263-1278 
Issue: 6 
Volume: 39 
Year: 2012 
Month: 11 
X-DOI: 10.1080/02664763.2011.644529 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.644529 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:6:p:1263-1278




Template-Type: ReDIF-Article 1.0
Author-Name: Vlasios Voudouris 
Author-X-Name-First: Vlasios 
Author-X-Name-Last: Voudouris 
Author-Name: Robert Gilchrist 
Author-X-Name-First: Robert 
Author-X-Name-Last: Gilchrist 
Author-Name: Robert Rigby 
Author-X-Name-First: Robert 
Author-X-Name-Last: Rigby 
Author-Name: John Sedgwick 
Author-X-Name-First: John 
Author-X-Name-Last: Sedgwick 
Author-Name: Dimitrios Stasinopoulos 
Author-X-Name-First: Dimitrios 
Author-X-Name-Last: Stasinopoulos 
Title: Modelling skewness and kurtosis with the BCPE density in GAMLSS 
Abstract:
  This paper illustrates the power of modern statistical modelling in
 understanding processes characterised by data that are skewed and have
 heavy tails. Our particular substantive problem concerns film box-office
 revenues. We are able to show that traditional modelling techniques based
 on the Pareto--Levy--Mandelbrot distribution led to what is actually a
 poorly supported conclusion that these data have infinite variance. This
 in turn led to the dominant paradigm of the movie business that
 &#x2018;nobody knows anything&#x2019; and hence that box-office revenues
 cannot be predicted. Using the Box--Cox power exponential distribution
 within the generalized additive models for location, scale and shape
 framework, we are able to model box-office revenues and develop
 probabilistic statements about revenues. 
Journal: Journal of Applied Statistics 
Pages: 1279-1293 
Issue: 6 
Volume: 39 
Year: 2012 
Month: 11 
X-DOI: 10.1080/02664763.2011.644530 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.644530 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:6:p:1279-1293




Template-Type: ReDIF-Article 1.0
Author-Name: Ioannis Vrontos 
Author-X-Name-First: Ioannis 
Author-X-Name-Last: Vrontos 
Title: Evidence for hedge fund predictability from a multivariate Student's <italic>t</italic> full-factor GARCH model 
Abstract:
  Extending previous work on hedge fund return predictability, this paper
 introduces the idea of modelling the conditional distribution of hedge
 fund returns using Student's <italic>t</italic> full-factor multivariate
 GARCH models. This class of models takes into account the stylized facts
 of hedge fund return series, that is, heteroskedasticity, fat tails and
 deviations from normality. For the proposed class of multivariate
 predictive regression models, we derive analytic expressions for the score
 and the Hessian matrix, which can be used within classical and Bayesian
 inferential procedures to estimate the model parameters, as well as to
 compare different predictive regression models. We propose a Bayesian
 approach to model comparison which provides posterior probabilities for
 various predictive models that can be used for model averaging. Our
 empirical application indicates that accounting for fat tails and
 time-varying covariances/correlations provides a more appropriate
 modelling approach of the underlying dynamics of financial series and
 improves our ability to predict hedge fund returns. 
Journal: Journal of Applied Statistics 
Pages: 1295-1321 
Issue: 6 
Volume: 39 
Year: 2012 
Month: 11 
X-DOI: 10.1080/02664763.2011.644771 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.644771 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:6:p:1295-1321




Template-Type: ReDIF-Article 1.0
Author-Name: S. Lalitha 
Author-X-Name-First: S. 
Author-X-Name-Last: Lalitha 
Author-Name: Nirpeksh Kumar 
Author-X-Name-First: Nirpeksh 
Author-X-Name-Last: Kumar 
Title: Multiple outlier test for upper outliers in an exponential sample 
Abstract:
  In this paper, a test statistic for testing upper outliers with a
 slippage alternative, in an exponential sample is proposed. No tables for
 critical values are required as they can be calculated easily for any
 sample size. A simulation study is also carried out to compare the
 performance of the test with the maximum likelihood ratio test and other
 existing tests. 
Journal: Journal of Applied Statistics 
Pages: 1323-1330 
Issue: 6 
Volume: 39 
Year: 2012 
Month: 11 
X-DOI: 10.1080/02664763.2011.645158 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.645158 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:6:p:1323-1330




Template-Type: ReDIF-Article 1.0
Author-Name: Jean-Marc Bardet 
Author-X-Name-First: Jean-Marc 
Author-X-Name-Last: Bardet 
Author-Name: Imen Kammoun 
Author-X-Name-First: Imen 
Author-X-Name-Last: Kammoun 
Author-Name: Veronique Billat 
Author-X-Name-First: Veronique 
Author-X-Name-Last: Billat 
Title: A new process for modeling heartbeat signals during exhaustive run with an adaptive estimator of its fractal parameters 
Abstract:
  This paper is devoted to a new study of the fractal behavior of
 heartbeats during a marathon. Such a case is interesting since it allows
 the examination of heart behavior during a very long exercise in order to
 reach reliable conclusions on the long-term properties of heartbeats.
 Three points of this study can be highlighted. First, the whole race
 heartbeats of each runner are automatically divided into several stages
 where the signal is nearly stationary and these stages are detected with
 an adaptive change points detection method. Secondly, a new process called
 the locally fractional Gaussian noise (LFGN) is proposed to fit such data.
 Finally, a wavelet-based method using a specific mother wavelet provides
 an adaptive procedure for estimating low frequency and high frequency
 fractal parameters as well as the corresponding frequency bandwidths. Such
 an estimator is theoretically proved to converge in the case of LFGNs, and
 simulations confirm this consistency. Moreover, an adaptive chi-squared
 goodness-of-fit test is also built, using this wavelet-based estimator.
 The application of this method to marathon heartbeat series indicates that
 the LFGN fits well data at each stage and that the low frequency fractal
 parameter increases during the race. A detection of a too large low
 frequency fractal parameter during the race could help prevent the too
 frequent heart failures occurring during marathons. 
Journal: Journal of Applied Statistics 
Pages: 1331-1351 
Issue: 6 
Volume: 39 
Year: 2012 
Month: 12 
X-DOI: 10.1080/02664763.2011.646962 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.646962 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:6:p:1331-1351




Template-Type: ReDIF-Article 1.0
Author-Name: Xu-Qing Liu 
Author-X-Name-First: Xu-Qing 
Author-X-Name-Last: Liu 
Author-Name: Bo Li 
Author-X-Name-First: Bo 
Author-X-Name-Last: Li 
Title: General linear estimators under the prediction error sum of squares criterion in a linear regression model 
Abstract:
  In this paper, the notion of the general linear estimator and its
 modified version are introduced using the singular value decomposition
 theorem in the linear regression model <bold>y</bold>=<bold>X</bold>
 <bold>&#x3B2;</bold>+<bold>e</bold> to improve some classical linear
 estimators. The optimal selections of the biasing parameters involved are
 theoretically given under the prediction error sum of squares criterion. A
 numerical example and a simulation study are finally conducted to
 illustrate the superiority of the proposed estimators. 
Journal: Journal of Applied Statistics 
Pages: 1353-1361 
Issue: 6 
Volume: 39 
Year: 2012 
Month: 12 
X-DOI: 10.1080/02664763.2011.646963 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.646963 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:6:p:1353-1361




Template-Type: ReDIF-Article 1.0
Author-Name: Christian   M. Hafner 
Author-X-Name-First: Christian   M. 
Author-X-Name-Last: Hafner 
Title: Cross-correlating wavelet coefficients with applications to high-frequency financial time series 
Abstract:
  This paper uses a new concept in wavelet analysis to explore a financial
 transaction data set including returns, durations, and volume. The concept
 is based on a decomposition of the Allan covariance of two series into
 cross-covariances of wavelet coefficients, which allows a natural
 interpretation of cross-correlations in terms of frequencies. It is
 applied to financial transaction data including returns, durations between
 transactions, and trading volume. At high frequencies, we find significant
 spillover from durations to volume and a strong contemporaneous relation
 between durations and returns, whereas a strong causality between volume
 and volatility exists at various frequencies. 
Journal: Journal of Applied Statistics 
Pages: 1363-1379 
Issue: 6 
Volume: 39 
Year: 2012 
Month: 12 
X-DOI: 10.1080/02664763.2011.649716 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.649716 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:6:p:1363-1379




Template-Type: ReDIF-Article 1.0
Author-Name: Eric   J. Beh 
Author-X-Name-First: Eric   J. 
Author-X-Name-Last: Beh 
Title: Exploratory multivariate analysis by example using R 
Journal: Journal of Applied Statistics 
Pages: 1381-1382 
Issue: 6 
Volume: 39 
Year: 2012 
Month: 6 
X-DOI: 10.1080/02664763.2012.657409 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.657409 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:6:p:1381-1382




Template-Type: ReDIF-Article 1.0
Author-Name: Georgi   N. Boshnakov 
Author-X-Name-First: Georgi   N. 
Author-X-Name-Last: Boshnakov 
Title: Using R for data management, statistical analysis, and graphics 
Journal: Journal of Applied Statistics 
Pages: 1382-1383 
Issue: 6 
Volume: 39 
Year: 2012 
Month: 6 
X-DOI: 10.1080/02664763.2012.657412 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.657412 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:6:p:1382-1383




Template-Type: ReDIF-Article 1.0
Author-Name: Eugenia Stoimenova 
Author-X-Name-First: Eugenia 
Author-X-Name-Last: Stoimenova 
Title: Robust nonparametric statistical methods 
Journal: Journal of Applied Statistics 
Pages: 1383-1384 
Issue: 6 
Volume: 39 
Year: 2012 
Month: 6 
X-DOI: 10.1080/02664763.2012.657414 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.657414 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:6:p:1383-1384




Template-Type: ReDIF-Article 1.0
Author-Name: Eugenia Stoimenova 
Author-X-Name-First: Eugenia 
Author-X-Name-Last: Stoimenova 
Title: Nonparametric statistical inference 
Journal: Journal of Applied Statistics 
Pages: 1384-1385 
Issue: 6 
Volume: 39 
Year: 2012 
Month: 6 
X-DOI: 10.1080/02664763.2012.657415 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.657415 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:6:p:1384-1385




Template-Type: ReDIF-Article 1.0
Author-Name: Shu-Fu Kuo 
Author-X-Name-First: Shu-Fu 
Author-X-Name-Last: Kuo 
Author-Name: Yu-Shan Shih 
Author-X-Name-First: Yu-Shan 
Author-X-Name-Last: Shih 
Title: Variable selection for functional density trees 
Abstract:
  In this paper, the exhaustive search principle used in functional trees
 for classifying densities is shown to select variables with more split
 points. A new variable selection scheme is proposed to correct this bias.
 The Pearson chi-squared tests for associated two-way contingency tables
 are used to select the variables. Through simulation, we show that the new
 method can control bias and is more powerful in selecting split variable. 
Journal: Journal of Applied Statistics 
Pages: 1387-1395 
Issue: 7 
Volume: 39 
Year: 2012 
Month: 12 
X-DOI: 10.1080/02664763.2011.649717 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.649717 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:7:p:1387-1395




Template-Type: ReDIF-Article 1.0
Author-Name: Jin-Hong Park 
Author-X-Name-First: Jin-Hong 
Author-X-Name-Last: Park 
Title: Nonparametric approach to intervention time series modeling 
Abstract:
  Time series are often affected by interventions such as strikes,
 earthquakes, or policy changes. In the current paper, we build a practical
 nonparametric intervention model using the central mean subspace in time
 series. We estimate the central mean subspace for time series taking into
 account known interventions by using the Nadaraya--Watson kernel
 estimator. We use the modified Bayesian information criterion to estimate
 the unknown lag and dimension. Finally, we demonstrate that this
 nonparametric approach for intervened time series performs well in
 simulations and in a real data analysis such as the <italic>Monthly
 average of the oxidant</italic>. 
Journal: Journal of Applied Statistics 
Pages: 1397-1408 
Issue: 7 
Volume: 39 
Year: 2012 
Month: 12 
X-DOI: 10.1080/02664763.2011.650684 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.650684 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:7:p:1397-1408




Template-Type: ReDIF-Article 1.0
Author-Name: I. Ardoino 
Author-X-Name-First: I. 
Author-X-Name-Last: Ardoino 
Author-Name: E.   M. Biganzoli 
Author-X-Name-First: E.   M. 
Author-X-Name-Last: Biganzoli 
Author-Name: C. Bajdik 
Author-X-Name-First: C. 
Author-X-Name-Last: Bajdik 
Author-Name: P.   J. Lisboa 
Author-X-Name-First: P.   J. 
Author-X-Name-Last: Lisboa 
Author-Name: P. Boracchi 
Author-X-Name-First: P. 
Author-X-Name-Last: Boracchi 
Author-Name: F. Ambrogi 
Author-X-Name-First: F. 
Author-X-Name-Last: Ambrogi 
Title: Flexible parametric modelling of the hazard function in breast cancer studies 
Abstract:
  In cancer research, study of the hazard function provides useful insights
 into disease dynamics, as it describes the way in which the (conditional)
 probability of death changes with time. The widely utilized Cox
 proportional hazard model uses a stepwise nonparametric estimator for the
 baseline hazard function, and therefore has a limited utility. The use of
 parametric models and/or other approaches that enables direct estimation
 of the hazard function is often invoked. A recent work by Cox <italic>et
 al</italic>. [6] has stimulated the use of the flexible parametric model
 based on the Generalized Gamma (GG) distribution, supported by the
 development of optimization software. The GG distribution allows
 estimation of different hazard shapes in a single framework. We use the GG
 model to investigate the shape of the hazard function in early breast
 cancer patients. The flexible approach based on a piecewise exponential
 model and the nonparametric additive hazards model are also considered. 
Journal: Journal of Applied Statistics 
Pages: 1409-1421 
Issue: 7 
Volume: 39 
Year: 2012 
Month: 12 
X-DOI: 10.1080/02664763.2011.650685 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.650685 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:7:p:1409-1421




Template-Type: ReDIF-Article 1.0
Author-Name: A.   Mart�n Andr�s 
Author-X-Name-First: A.   Mart�n 
Author-X-Name-Last: Andr�s 
Author-Name: M.   &#xC1;lvarez Hern&#xE1;ndez 
Author-X-Name-First: M.   &#xC1;lvarez 
Author-X-Name-Last: Hern&#xE1;ndez 
Author-Name: I.   Herranz Tejedor 
Author-X-Name-First: I.   Herranz 
Author-X-Name-Last: Tejedor 
Title: Asymptotic two-tailed confidence intervals for the difference of proportions 
Abstract:
  In order to obtain a two-tailed confidence interval for the difference
 between two proportions (independent samples), the current literature on
 the subject has proposed a great number of asymptotic methods. This paper
 assesses 80 classical asymptotic methods (including the best proposals
 made in the literature) and concludes that (1) the best solution consists
 of adding 0.5 to all of the data and inverting the test based on the
 arcsine transformation; (2) a solution which is a little worse than the
 previous one (but much easier and even better when both samples are
 balanced) is a modification of the adjusted Wald method proposed by
 Agresti and Caffo (usually adding <inline-formula> <inline-graphic
 xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_650686_o_ilm0001.gif"/> </inline-formula> to all of the
 data and then applying the classical Wald CI); (3) surprisingly, the
 classical score method is among the worst solutions, since it provides
 excessively liberal results. 
Journal: Journal of Applied Statistics 
Pages: 1423-1435 
Issue: 7 
Volume: 39 
Year: 2012 
Month: 12 
X-DOI: 10.1080/02664763.2011.650686 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.650686 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:7:p:1423-1435




Template-Type: ReDIF-Article 1.0
Author-Name: Carol   Y. Lin 
Author-X-Name-First: Carol   Y. 
Author-X-Name-Last: Lin 
Author-Name: Lance   A. Waller 
Author-X-Name-First: Lance   A. 
Author-X-Name-Last: Waller 
Author-Name: Robert   H. Lyles 
Author-X-Name-First: Robert   H. 
Author-X-Name-Last: Lyles 
Title: The likelihood approach for the comparison of medical diagnostic system with multiple binary tests 
Abstract:
  Detection (diagnosis) techniques play an important role in clinical
 medicine. Early detection of diseases could be life-saving, and the
 consequences of false-positives and false-negatives could be costly. Using
 multiple measurements strategy is a popular tool to increase diagnostic
 accuracy. In addition to the new diagnostic technology, recent advances in
 genomics, proteomics, and other areas have allowed some of these newly
 developed individual biomarkers measured by non-invasive and inexpensive
 procedures (e.g. samples from serum, urine or stool) to progress from
 basic discovery research to assay development. As more tests become
 commercially available, there is an increasing interest for clinicians to
 request combinations of various non-invasive and inexpensive tests to
 increase diagnostic accuracy. Using information regarding individual test
 sensitivities and specificities, we proposed a likelihood approach to
 combine individual test results and to approximate or estimate the
 combined sensitivities and specificities of various tests taking into
 account the conditional correlations to quantify system performance. To
 illustrate this approach, we considered an example using various
 combinations of diagnostic tests to detect bladder cancer. 
Journal: Journal of Applied Statistics 
Pages: 1437-1454 
Issue: 7 
Volume: 39 
Year: 2012 
Month: 12 
X-DOI: 10.1080/02664763.2011.650688 
File-URL: http://hdl.handle.net/10.1080/02664763.2011.650688 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:7:p:1437-1454




Template-Type: ReDIF-Article 1.0
Author-Name: Margaret   R. Donald 
Author-X-Name-First: Margaret   R. 
Author-X-Name-Last: Donald 
Author-Name: Chris Strickland 
Author-X-Name-First: Chris 
Author-X-Name-Last: Strickland 
Author-Name: Clair   L. Alston 
Author-X-Name-First: Clair   L. 
Author-X-Name-Last: Alston 
Author-Name: Rick Young 
Author-X-Name-First: Rick 
Author-X-Name-Last: Young 
Author-Name: Kerrie   L. Mengersen 
Author-X-Name-First: Kerrie   L. 
Author-X-Name-Last: Mengersen 
Title: Comparison of three-dimensional profiles over time 
Abstract:
  In this paper, we describe an analysis for data collected on a
 three-dimensional spatial lattice with treatments applied at the
 horizontal lattice points. Spatial correlation is accounted for using a
 conditional autoregressive model. Observations are defined as neighbours
 only if they are at the same depth. This allows the corresponding variance
 components to vary by depth. We use the Markov chain Monte Carlo method
 with block updating, together with Krylov subspace methods, for efficient
 estimation of the model. The method is applicable to both regular and
 irregular horizontal lattices and hence to data collected at any set of
 horizontal sites for a set of depths or heights, for example, water column
 or soil profile data. The model for the three-dimensional data is applied
 to agricultural trial data for five separate days taken roughly six months
 apart in order to determine possible relationships over time. The purpose
 of the trial is to determine a form of cropping that leads to less moist
 soils in the root zone and beyond. We estimate moisture for each date,
 depth and treatment accounting for spatial correlation and determine
 relationships of these and other parameters over time. 
Journal: Journal of Applied Statistics 
Pages: 1455-1474 
Issue: 7 
Volume: 39 
Year: 2012 
Month: 1 
X-DOI: 10.1080/02664763.2012.654771 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.654771 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:7:p:1455-1474




Template-Type: ReDIF-Article 1.0
Author-Name: Jianbo Li 
Author-X-Name-First: Jianbo 
Author-X-Name-Last: Li 
Author-Name: Minggao Gu 
Author-X-Name-First: Minggao 
Author-X-Name-Last: Gu 
Author-Name: Tao Hu 
Author-X-Name-First: Tao 
Author-X-Name-Last: Hu 
Title: General partially linear varying-coefficient transformation models for ranking data 
Abstract:
  In this paper,we propose a class of general partially linear
 varying-coefficient transformation models for ranking data. In the models,
 the functional coefficients are viewed as nuisance parameters and
 approximated by B-spline smoothing approximation technique. The B-spline
 coefficients and regression parameters are estimated by rank-based maximum
 marginal likelihood method. The three-stage Monte Carlo Markov Chain
 stochastic approximation algorithm based on ranking data is used to
 compute estimates and the corresponding variances for all the B-spline
 coefficients and regression parameters. Through three simulation studies
 and a Hong Kong horse racing data application, the proposed procedure is
 illustrated to be accurate, stable and practical. 
Journal: Journal of Applied Statistics 
Pages: 1475-1488 
Issue: 7 
Volume: 39 
Year: 2012 
Month: 1 
X-DOI: 10.1080/02664763.2012.658357 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.658357 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:7:p:1475-1488




Template-Type: ReDIF-Article 1.0
Author-Name: S�bastien Li-Thiao-T� 
Author-X-Name-First: S�bastien 
Author-X-Name-Last: Li-Thiao-T� 
Author-Name: Daudin Jean-Jacques 
Author-X-Name-First: Daudin 
Author-X-Name-Last: Jean-Jacques 
Author-Name: Robin St�phane 
Author-X-Name-First: Robin 
Author-X-Name-Last: St�phane 
Title: Bayesian model averaging for estimating the number of classes: applications to the total number of species in metagenomics 
Abstract:
  The species abundance distribution and the total number of species are
 fundamental descriptors of the biodiversity of an ecological community.
 This paper focuses on situations where large numbers of rare species are
 not observed in the data set due to insufficient sampling of the
 community, as is the case in metagenomics for the study of microbial
 diversity. We use a truncated mixture model for the observations to
 explicitly tackle the missing data and propose methods to estimate the
 total number of species and, in particular, a Bayesian credibility
 interval for this number. We focus on computationally efficient procedures
 with variational methods and importance sampling as opposed to Markov
 Chain Monte Carlo sampling, and we use Bayesian model averaging as the
 number of components of the mixture model is unknown. 
Journal: Journal of Applied Statistics 
Pages: 1489-1504 
Issue: 7 
Volume: 39 
Year: 2012 
Month: 1 
X-DOI: 10.1080/02664763.2012.658358 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.658358 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:7:p:1489-1504




Template-Type: ReDIF-Article 1.0
Author-Name: Shuyi Jiang 
Author-X-Name-First: Shuyi 
Author-X-Name-Last: Jiang 
Title: Survival in the U.S. petroleum refining industry 
Abstract:
  Of the 324 petroleum refineries operating in the U.S. in 1982, only 149
 were still in the hands of their original owners in 2007. Using duration
 analysis, this paper explores why refineries change ownership or shut
 down. Plants are more likely to &#x2018;survive&#x2019; with their
 original owners if they are older or larger, but less likely if the owner
 is a major integrated firm, or the refinery is a more technologically
 complex one. This latter result differs from existing research on the
 issue. This paper also presents a split population model to relax the
 general assumption of the duration model that all refiners will eventually
 close down; the empirical results show that the split population model
 converges on a standard hazard model; the log-logistic version fits best.
 Finally, a multinomial logit model is estimated to analyze the factors
 that influence the refinery plant's choices of staying open, closing, or
 changing ownership. Plant size, age and technology usage have positive
 impacts on the likelihood that a refinery will stay open, or change
 ownership (rather than close down). 
Journal: Journal of Applied Statistics 
Pages: 1505-1530 
Issue: 7 
Volume: 39 
Year: 2012 
Month: 1 
X-DOI: 10.1080/02664763.2012.658359 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.658359 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:7:p:1505-1530




Template-Type: ReDIF-Article 1.0
Author-Name: Jinook Jeong 
Author-X-Name-First: Jinook 
Author-X-Name-Last: Jeong 
Author-Name: Byunguk Kang 
Author-X-Name-First: Byunguk 
Author-X-Name-Last: Kang 
Title: Wild-bootstrapped variance-ratio test for autocorrelation in the presence of heteroskedasticity 
Abstract:
  The Breusch--Godfrey LM test is one of the most popular tests for
 autocorrelation. However, it has been shown that the LM test may be
 erroneous when there exist heteroskedastic errors in a regression model.
 Recently, remedies have been proposed by Godfrey and Tremayne [9] and Shim
 <italic>et al.</italic> [21]. This paper suggests three wild-bootstrapped
 variance-ratio (WB-VR) tests for autocorrelation in the presence of
 heteroskedasticity. We show through a Monte Carlo simulation that our
 WB-VR tests have better small sample properties and are robust to the
 structure of heteroskedasticity. 
Journal: Journal of Applied Statistics 
Pages: 1531-1542 
Issue: 7 
Volume: 39 
Year: 2012 
Month: 1 
X-DOI: 10.1080/02664763.2012.658360 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.658360 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:7:p:1531-1542




Template-Type: ReDIF-Article 1.0
Author-Name: Federico Palacios-Gonz&#xE1;lez 
Author-X-Name-First: Federico 
Author-X-Name-Last: Palacios-Gonz&#xE1;lez 
Author-Name: Rosa   Mar�a Garc�a-Fern&#xE1;ndez 
Author-X-Name-First: Rosa   Mar�a 
Author-X-Name-Last: Garc�a-Fern&#xE1;ndez 
Title: Interpretation of the coefficient of determination of an ANOVA model as a measure of polarization 
Abstract:
  In this paper, it is demonstrated that coefficient of determination of an
 ANOVA linear model provides a measure of polarization. Taking as the
 starting point the link between polarization and dispersion, we
 reformulate the measure of polarization of Zhang and Kanbur using the
 decomposition of the variance instead of the decomposition of the Theil
 index. We show that the proposed measure is equivalent to the coefficient
 of determination of an ANOVA linear model that explains, for example, the
 income of the households as a function of any population characteristic
 such as education, gender, occupation, etc. This result provides an
 alternative way to analyse polarization by sub-populations characteristics
 and at the same time allows us to compare sub-populations via the
 estimated coefficients of the ANOVA model. 
Journal: Journal of Applied Statistics 
Pages: 1543-1555 
Issue: 7 
Volume: 39 
Year: 2012 
Month: 1 
X-DOI: 10.1080/02664763.2012.658361 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.658361 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:7:p:1543-1555




Template-Type: ReDIF-Article 1.0
Author-Name: Nicholas   T. Longford 
Author-X-Name-First: Nicholas   T. 
Author-X-Name-Last: Longford 
Author-Name: Maria   Grazia Pittau 
Author-X-Name-First: Maria   Grazia 
Author-X-Name-Last: Pittau 
Author-Name: Roberto Zelli 
Author-X-Name-First: Roberto 
Author-X-Name-Last: Zelli 
Author-Name: Riccardo Massari 
Author-X-Name-First: Riccardo 
Author-X-Name-Last: Massari 
Title: Poverty and inequality in European regions 
Abstract:
  The European Union Statistics on Income and Living Conditions (EU-SILC)
 is the main source of information about poverty and economic inequality in
 the member states of the European Union. The sample sizes of its annual
 national surveys are sufficient for reliable estimation at the national
 level but not for inferences at the sub-national level, failing to respond
 to a rising demand from policy-makers and local authorities. We provide a
 comprehensive map of median income, inequality (Gini coefficient and
 Lorenz curve) and poverty (poverty rates) based on the equivalised
 household income in the countries in which the EU-SILC is conducted. We
 study the distribution of income of households (pro-rated to its members),
 not merely its median (or mean), because we regard its dispersion and
 frequency of lower extremes (relative poverty) as important
 characteristics. The estimation for the regions with small sample sizes is
 improved by the small-area methods. The uncertainty of complex nonlinear
 statistics is assessed by bootstrap. Household-level sampling weights are
 taken into account in both the estimates and the associated bootstrap
 standard errors. 
Journal: Journal of Applied Statistics 
Pages: 1557-1576 
Issue: 7 
Volume: 39 
Year: 2012 
Month: 1 
X-DOI: 10.1080/02664763.2012.661705 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.661705 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:7:p:1557-1576




Template-Type: ReDIF-Article 1.0
Author-Name: Alexander   A. Correa 
Author-X-Name-First: Alexander   A. 
Author-X-Name-Last: Correa 
Author-Name: Pere Grima 
Author-X-Name-First: Pere 
Author-X-Name-Last: Grima 
Author-Name: Xavier Tort-Martorell 
Author-X-Name-First: Xavier 
Author-X-Name-Last: Tort-Martorell 
Title: Experimentation order in factorial designs: new findings 
Abstract:
  Under some very reasonable hypotheses, it becomes evident that
 randomizing the run order of a factorial experiment does not always
 neutralize the effect of undesirable factors. Yet, these factors do have
 an influence on the response, depending on the order in which the
 experiments are conducted. On the other hand, changing the factor levels
 is many times costly; therefore it is not reasonable to leave to chance
 the number of changes necessary. For this reason, run orders that offer
 the minimum number of factor level changes and at the same time minimize
 the possible influence of undesirable factors on the experimentation have
 been sought. Sequences which are known to produce the desired properties
 in designs with 8 and 16 experiments can be found in the literature. In
 this paper, we provide the best possible sequences for designs with 32
 experiments, as well as sequences that offer excellent properties for
 designs with 64 and 128 experiments. The method used to find them is based
 on a mixture of algorithmic searches and an augmentation of smaller
 designs. 
Journal: Journal of Applied Statistics 
Pages: 1577-1591 
Issue: 7 
Volume: 39 
Year: 2012 
Month: 1 
X-DOI: 10.1080/02664763.2012.661706 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.661706 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:7:p:1577-1591




Template-Type: ReDIF-Article 1.0
Author-Name: Alan   D. Hutson 
Author-X-Name-First: Alan   D. 
Author-X-Name-Last: Hutson 
Author-Name: Gregory   E. Wilding 
Author-X-Name-First: Gregory   E. 
Author-X-Name-Last: Wilding 
Title: Maintaining the exchangeability assumption for a two-group permutation test in the non-randomized setting 
Abstract:
  In this note, we develop a new two-group bootstrap-permutation test that
 utilizes the tail-extrapolated quantile function estimator for the
 bootstrap component. This test is an extension of the standard two-group
 permutation test, that through its construction is defined to meet the
 exchangeability assumption, and thus it guarantees that the type I error
 is appropriately bounded by definition. This methodology is particularly
 useful in the non-randomized two-group setting for which the
 exchangeability assumption for the traditional two-group permutation test
 is untestable. We develop some theoretical results for the new test,
 followed by a simulation study and an example. 
Journal: Journal of Applied Statistics 
Pages: 1593-1603 
Issue: 7 
Volume: 39 
Year: 2012 
Month: 1 
X-DOI: 10.1080/02664763.2012.661707 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.661707 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:7:p:1593-1603




Template-Type: ReDIF-Article 1.0
Author-Name: Young   Joo Yoon 
Author-X-Name-First: Young   Joo 
Author-X-Name-Last: Yoon 
Author-Name: Cheolwoo Park 
Author-X-Name-First: Cheolwoo 
Author-X-Name-Last: Park 
Author-Name: Erik Hofmeister 
Author-X-Name-First: Erik 
Author-X-Name-Last: Hofmeister 
Author-Name: Sangwook Kang 
Author-X-Name-First: Sangwook 
Author-X-Name-Last: Kang 
Title: Group variable selection in cardiopulmonary cerebral resuscitation data for veterinary patients 
Abstract:
  Cardiopulmonary cerebral resuscitation (CPCR) is a procedure to restore
 spontaneous circulation in patients with cardiopulmonary arrest (CPA).
 While animals with CPA generally have a lower success rate of CPCR than
 people do, CPCR studies in veterinary patients have been limited. In this
 paper, we construct a model for predicting success or failure of CPCR, and
 identifying and evaluating factors that affect the success of CPCR in
 veterinary patients. Due to reparametrization using multiple dummy
 variables or close proximity in nature, many variables in the data form
 groups, and thus a desirable method should take this grouping feature into
 account in variable selection. To accomplish these goals, we propose an
 adaptive group bridge method for a logistic regression model. The
 performance of the proposed method is evaluated under different simulated
 setups and compared with several other regression methods. Using the
 logistic group bridge model, we analyze data from a CPCR study for
 veterinary patients and discuss their implications on the practice of
 veterinary medicine. 
Journal: Journal of Applied Statistics 
Pages: 1605-1621 
Issue: 7 
Volume: 39 
Year: 2012 
Month: 1 
X-DOI: 10.1080/02664763.2012.661929 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.661929 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:7:p:1605-1621




Template-Type: ReDIF-Article 1.0
Author-Name: Thaung Lwin 
Author-X-Name-First: Thaung 
Author-X-Name-Last: Lwin 
Title: Modelling of connected processes 
Abstract:
  The problem of comparing, contrasting and combining information from
 different sets of data is an enduring one in many practical applications
 of statistics. A specific problem of combining information from different
 sources arose in integrating information from three different sets of data
 generated by three different sampling campaigns at the input stage as well
 as at the output stage of a grey-water treatment process. For each stage,
 a common process trend function needs to be estimated to describe the
 input and output material process behaviours. Once the common input and
 output process models are established, it is required to estimate the
 efficiency of the grey-water treatment method. A synthesized tool for
 modelling different sets of process data is created by assembling and
 organizing a number of existing techniques: (i) a mixed model of fixed and
 random effects, extended to allow for a nonlinear fixed effect, (ii)
 variogram modelling, a geostatistical technique, (iii) a weighted least
 squares regression embedded in an iterative maximum-likelihood technique
 to handle linear/nonlinear fixed and random effects and (iv) a formulation
 of a transfer-function model for the input and output processes together
 with a corresponding nonlinear maximum-likelihood method for estimation of
 a transfer function. The synthesized tool is demonstrated, in a new case
 study, to contrast and combine information from connected process models
 and to determine the change in one quality characteristic, namely pH, of
 the input and output materials of a grey-water filtering process. 
Journal: Journal of Applied Statistics 
Pages: 1623-1641 
Issue: 8 
Volume: 39 
Year: 2012 
Month: 1 
X-DOI: 10.1080/02664763.2012.663345 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.663345 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:8:p:1623-1641




Template-Type: ReDIF-Article 1.0
Author-Name: Tatjana Pavlenko 
Author-X-Name-First: Tatjana 
Author-X-Name-Last: Pavlenko 
Author-Name: Anders Bj&#xF6;rkstr&#xF6;m 
Author-X-Name-First: Anders 
Author-X-Name-Last: Bj&#xF6;rkstr&#xF6;m 
Author-Name: Annika Tillander 
Author-X-Name-First: Annika 
Author-X-Name-Last: Tillander 
Title: Covariance structure approximation via gLasso in high-dimensional supervised classification 
Abstract:
  Recent work has shown that the Lasso-based regularization is very useful
 for estimating the high-dimensional inverse covariance matrix. A
 particularly useful scheme is based on penalizing the &#x2113;<sub>1</sub>
 norm of the off-diagonal elements to encourage sparsity. We embed this
 type of regularization into high-dimensional classification. A two-stage
 estimation procedure is proposed which first recovers structural zeros of
 the inverse covariance matrix and then enforces block sparsity by moving
 non-zeros closer to the main diagonal. We show that the block-diagonal
 approximation of the inverse covariance matrix leads to an additive
 classifier, and demonstrate that accounting for the structure can yield
 better performance accuracy. Effect of the block size on classification is
 explored, and a class of asymptotically equivalent structure
 approximations in a high-dimensional setting is specified. We suggest a
 variable selection at the block level and investigate properties of this
 procedure in growing dimension asymptotics. We present a consistency
 result on the feature selection procedure, establish asymptotic lower an
 upper bounds for the fraction of separative blocks and specify constraints
 under which the reliable classification with block-wise feature selection
 can be performed. The relevance and benefits of the proposed approach are
 illustrated on both simulated and real data. 
Journal: Journal of Applied Statistics 
Pages: 1643-1666 
Issue: 8 
Volume: 39 
Year: 2012 
Month: 1 
X-DOI: 10.1080/02664763.2012.663346 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.663346 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:8:p:1643-1666




Template-Type: ReDIF-Article 1.0
Author-Name: Yueqin Zhao 
Author-X-Name-First: Yueqin 
Author-X-Name-Last: Zhao 
Author-Name: Dayanand   N. Naik 
Author-X-Name-First: Dayanand   N. 
Author-X-Name-Last: Naik 
Title: Hypothesis testing with Rao's quadratic entropy and its application to Dinosaur biodiversity 
Abstract:
  Entropy indices, such as Shannon entropy and Gini-Simpson index, have
 been used for analysing biological diversities. However, these entropy
 indices are based on abundance of the species only and they do not take
 differences between the species into consideration. Rao's quadratic
 entropy has found many applications in different fields including ecology.
 Further, the quadratic entropy (QE) index is the only ecological diversity
 index that reflects both the differences and abundances of the species.
 The problem of testing of hypothesis of the equality of QEs is formulated
 as a problem of comparing practical equivalence intervals. Simulation
 experiments are used to compare various equivalence intervals. Previously
 analyzed dinosaur data are used to illustrate the methods for determining
 biodiversity. 
Journal: Journal of Applied Statistics 
Pages: 1667-1680 
Issue: 8 
Volume: 39 
Year: 2012 
Month: 1 
X-DOI: 10.1080/02664763.2012.663347 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.663347 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:8:p:1667-1680




Template-Type: ReDIF-Article 1.0
Author-Name: Anna Klimova 
Author-X-Name-First: Anna 
Author-X-Name-Last: Klimova 
Author-Name: Tam&#xE1;s Rudas 
Author-X-Name-First: Tam&#xE1;s 
Author-X-Name-Last: Rudas 
Title: Coordinate-free analysis of trends in British social mobility 
Abstract:
  This paper is intended to make a contribution to the ongoing debate about
 declining social mobility in Great Britain by analyzing mobility tables
 based on data from the 1991 British Household Panel Survey and the 2005
 General Household Survey. The models proposed here generalize Hauser's
 levels models and allow for a semi-parametric analysis of change in social
 mobility. The cell frequencies are assumed to be equal to the product of
 three effects: the effect of the father's position for the given year, the
 effect of the son's position for the given year, and the mobility effect
 related to the difference between the father's and the son's positions. A
 generalization of the iterative proportional fitting procedure is proposed
 and applied to computing the maximum likelihood estimates of the cell
 frequencies. The standard errors of the estimated parameters are computed
 under the product-multinomial sampling assumption. The results indicate
 opposing trends of mobility between the two timepoints. Fewer steps up or
 down in the society became less likely, while more steps became somewhat
 more likely. 
Journal: Journal of Applied Statistics 
Pages: 1681-1691 
Issue: 8 
Volume: 39 
Year: 2012 
Month: 1 
X-DOI: 10.1080/02664763.2012.663348 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.663348 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:8:p:1681-1691




Template-Type: ReDIF-Article 1.0
Author-Name: Valentina Mameli 
Author-X-Name-First: Valentina 
Author-X-Name-Last: Mameli 
Author-Name: Monica Musio 
Author-X-Name-First: Monica 
Author-X-Name-Last: Musio 
Author-Name: Erik Sauleau 
Author-X-Name-First: Erik 
Author-X-Name-Last: Sauleau 
Author-Name: Annibale Biggeri 
Author-X-Name-First: Annibale 
Author-X-Name-Last: Biggeri 
Title: Large sample confidence intervals for the skewness parameter of the skew-normal distribution based on Fisher's transformation 
Abstract:
  The skew-normal model is a class of distributions that extends the
 Gaussian family by including a skewness parameter. This model presents
 some inferential problems linked to the estimation of the skewness
 parameter. In particular its maximum likelihood estimator can be infinite
 especially for moderate sample sizes and is not clear how to calculate
 confidence intervals for this parameter. In this work, we show how these
 inferential problems can be solved if we are interested in the
 distribution of extreme statistics of two random variables with joint
 normal distribution. Such situations are not uncommon in applications,
 especially in medical and environmental contexts, where it can be relevant
 to estimate the distribution of extreme statistics. A theoretical result,
 found by Loperfido [7], proves that such extreme statistics have a
 skew-normal distribution with skewness parameter that can be expressed as
 a function of the correlation coefficient between the two initial
 variables. It is then possible, using some theoretical results involving
 the correlation coefficient, to find approximate confidence intervals for
 the parameter of skewness. These theoretical intervals are then compared
 with parametric bootstrap intervals by means of a simulation study. Two
 applications are given using real data. 
Journal: Journal of Applied Statistics 
Pages: 1693-1702 
Issue: 8 
Volume: 39 
Year: 2012 
Month: 2 
X-DOI: 10.1080/02664763.2012.668177 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.668177 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:8:p:1693-1702




Template-Type: ReDIF-Article 1.0
Author-Name: Wafaa Benyelles 
Author-X-Name-First: Wafaa 
Author-X-Name-Last: Benyelles 
Author-Name: Tahar Mourid 
Author-X-Name-First: Tahar 
Author-X-Name-Last: Mourid 
Title: On a minimum distance estimate of the period in functional autoregressive processes 
Abstract:
  We consider a continuous time random process with functional
 autoregressive representation. We state statistical results on a mean
 functional estimator determining a minimum distance estimator of the
 period giving consistency and a limit law stated in Mourid and Benyelles
 [13]. Then we discuss their performance on numerical simulations and on
 real data analyzing the cycle of a climatic phenomena. 
Journal: Journal of Applied Statistics 
Pages: 1703-1718 
Issue: 8 
Volume: 39 
Year: 2012 
Month: 2 
X-DOI: 10.1080/02664763.2012.668178 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.668178 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:8:p:1703-1718




Template-Type: ReDIF-Article 1.0
Author-Name: Lan Zhu 
Author-X-Name-First: Lan 
Author-X-Name-Last: Zhu 
Author-Name: Su Chen 
Author-X-Name-First: Su 
Author-X-Name-Last: Chen 
Author-Name: Zhuoxin Jiang 
Author-X-Name-First: Zhuoxin 
Author-X-Name-Last: Jiang 
Author-Name: Zhiwu Zhang 
Author-X-Name-First: Zhiwu 
Author-X-Name-Last: Zhang 
Author-Name: Hung-Chih Ku 
Author-X-Name-First: Hung-Chih 
Author-X-Name-Last: Ku 
Author-Name: Xuesong Li 
Author-X-Name-First: Xuesong 
Author-X-Name-Last: Li 
Author-Name: Melinda McCann 
Author-X-Name-First: Melinda 
Author-X-Name-Last: McCann 
Author-Name: Steve Harris 
Author-X-Name-First: Steve 
Author-X-Name-Last: Harris 
Author-Name: George Lust 
Author-X-Name-First: George 
Author-X-Name-Last: Lust 
Author-Name: Pual Jones 
Author-X-Name-First: Pual 
Author-X-Name-Last: Jones 
Author-Name: Rory Todhunter 
Author-X-Name-First: Rory 
Author-X-Name-Last: Todhunter 
Title: Identification of quantitative trait loci for canine hip dysplasia by two sequential multipoint linkage analyses 
Abstract:
  Canine hip dysplasia (CHD) is characterized by hip laxity and subluxation
 that can lead to hip osteoarthritis. Studies have shown the involvement of
 multiple genetic regions in the expression of CHD. Although we have
 associated some variants in the region of fibrillin 2 with CHD in a subset
 of dogs, no major disease-associated gene has been identified. The focus
 of this study is to identify quantitative trait loci (QTL) associated with
 CHD. Two sequential multipoint linkage analyses based on a reversible jump
 Markov chain Monte Carlo approach were applied on a cross-breed pedigree
 of 366 dogs. Hip radiographic trait (Norberg Angle, NA) on both hips of
 each dog was tested for linkage to 21,455 single nucleotide polymorphisms
 across 39 chromosomes. Putative QTL for the NA was found on 11 chromosomes
 (1, 2, 3, 4, 7, 14, 19, 21, 32, 36, and 39). Identification of genes in
 the QTL region(s) can assist in identification of the aberrant genes and
 biochemical pathways involving hip dysplasia in both dogs and humans. 
Journal: Journal of Applied Statistics 
Pages: 1719-1731 
Issue: 8 
Volume: 39 
Year: 2012 
Month: 3 
X-DOI: 10.1080/02664763.2012.673121 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.673121 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:8:p:1719-1731




Template-Type: ReDIF-Article 1.0
Author-Name: Huiyun Wu 
Author-X-Name-First: Huiyun 
Author-X-Name-Last: Wu 
Author-Name: Qingxia Chen 
Author-X-Name-First: Qingxia 
Author-X-Name-Last: Chen 
Author-Name: Lorraine   B. Ware 
Author-X-Name-First: Lorraine   B. 
Author-X-Name-Last: Ware 
Author-Name: Tatsuki Koyama 
Author-X-Name-First: Tatsuki 
Author-X-Name-Last: Koyama 
Title: A Bayesian approach for generalized linear models with explanatory biomarker measurement variables subject to detection limit: an application to acute lung injury 
Abstract:
  Biomarkers have the potential to improve our understanding of disease
 diagnosis and prognosis. Biomarker levels that fall below the assay
 detection limits (DLs), however, compromise the application of biomarkers
 in research and practice. Most existing methods to handle non-detects
 focus on a scenario in which the response variable is subject to the DL;
 only a few methods consider explanatory variables when dealing with DLs.
 We propose a Bayesian approach for generalized linear models with
 explanatory variables subject to lower, upper, or interval DLs. In
 simulation studies, we compared the proposed Bayesian approach to four
 commonly used methods in a logistic regression model with explanatory
 variable measurements subject to the DL. We also applied the Bayesian
 approach and other four methods in a real study, in which a panel of
 cytokine biomarkers was studied for their association with acute lung
 injury (ALI). We found that IL8 was associated with a moderate increase in
 risk for ALI in the model based on the proposed Bayesian approach. 
Journal: Journal of Applied Statistics 
Pages: 1733-1747 
Issue: 8 
Volume: 39 
Year: 2012 
Month: 3 
X-DOI: 10.1080/02664763.2012.681362 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.681362 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:8:p:1733-1747




Template-Type: ReDIF-Article 1.0
Author-Name: J&#xFA;lia Teles 
Author-X-Name-First: J&#xFA;lia 
Author-X-Name-Last: Teles 
Title: Concordance coefficients to measure the agreement among several sets of ranks 
Abstract:
  In this paper, two measures of agreement among several sets of ranks,
 Kendall's concordance coefficient and top-down concordance coefficient,
 are reviewed. In order to illustrate the utility of these measures, two
 examples, in the fields of health and sports, are presented. A Monte Carlo
 simulation study was carried out to compare the performance of Kendall's
 and top-down concordance coefficients in detecting several types and
 magnitudes of agreements. The data generation scheme was developed in
 order to induce an agreement with different intensities among
 <italic>m</italic> (<italic>m</italic>>2) sets of ranks in non-directional
 and directional rank agreement scenarios. The performance of each
 coefficient was estimated by the proportion of rejected null hypotheses,
 assessed at 5% significance level, when testing whether the underlying
 population concordance coefficient is sufficiently greater than zero. For
 the directional rank agreement scenario, the top-down concordance
 coefficient allowed to achieve a percentage of significant concordances
 that was higher than the one achieved by Kendall's concordance
 coefficient. Mainly, when the degree of agreement was small, the results
 of the simulation study pointed to the advantage of using a weighted rank
 concordance, namely the top-down concordance coefficient, simultaneously
 with Kendall's concordance coefficient, enabling the detection of
 agreement (in a top-down sense) in situations not detected by Kendall's
 concordance coefficient. 
Journal: Journal of Applied Statistics 
Pages: 1749-1764 
Issue: 8 
Volume: 39 
Year: 2012 
Month: 3 
X-DOI: 10.1080/02664763.2012.681460 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.681460 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:8:p:1749-1764




Template-Type: ReDIF-Article 1.0
Author-Name: Ghazi Shukur 
Author-X-Name-First: Ghazi 
Author-X-Name-Last: Shukur 
Author-Name: Zangin Zeebari 
Author-X-Name-First: Zangin 
Author-X-Name-Last: Zeebari 
Title: Median regression for SUR models with the same explanatory variables in each equation 
Abstract:
  In this paper we introduce an interesting feature of the generalized
 least absolute deviations method for seemingly unrelated regression
 equations (SURE) models. Contrary to the collapse of generalized
 leasts-quares parameter estimations of SURE models to the ordinary
 least-squares estimations of the individual equations when the same
 regressors are common between all equations, the estimations of the
 proposed methodology are not identical to the least absolute deviations
 estimations of the individual equations. This is important since contrary
 to the least-squares methods, one can take advantage of efficiency gain
 due to cross-equation correlations even if the system includes the same
 regressors in each equation. 
Journal: Journal of Applied Statistics 
Pages: 1765-1779 
Issue: 8 
Volume: 39 
Year: 2012 
Month: 4 
X-DOI: 10.1080/02664763.2012.682566 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.682566 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:8:p:1765-1779




Template-Type: ReDIF-Article 1.0
Author-Name: Nairanjana Dasgupta 
Author-X-Name-First: Nairanjana 
Author-X-Name-Last: Dasgupta 
Author-Name: Monte   J. Shaffer 
Author-X-Name-First: Monte   J. 
Author-X-Name-Last: Shaffer 
Title: Many-to-one comparison of nonlinear growth curves for Washington's Red Delicious apple 
Abstract:
  In this article, we are interested in comparing growth curves for the Red
 Delicious apple in several locations to that of a reference site. Although
 such multiple comparisons are common for linear models, statistical
 techniques for nonlinear models are not prolific. We theoretically derive
 a test statistic, considering the issues of sample size and design points.
 Under equal sample sizes and same design points, our test statistic is
 based on the maximum of an equi-correlated multivariate chi-square
 distribution. Under unequal sample sizes and design points, we derive a
 general correlation structure, and then utilize the multivariate normal
 distribution to numerically compute critical points for the maximum of the
 multivariate chi-square. We apply this statistical technique to compare
 the growth of Red Delicious apples at six locations to a reference site in
 the state of Washington in 2009. Finally, we perform simulations to verify
 the performance of our proposed procedure for Type I error and marginal
 power. Our proposed method performs well in regard to both. 
Journal: Journal of Applied Statistics 
Pages: 1781-1795 
Issue: 8 
Volume: 39 
Year: 2012 
Month: 4 
X-DOI: 10.1080/02664763.2012.683168 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.683168 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:8:p:1781-1795




Template-Type: ReDIF-Article 1.0
Author-Name: Wenceslao Gonzalez-Manteiga 
Author-X-Name-First: Wenceslao 
Author-X-Name-Last: Gonzalez-Manteiga 
Author-Name: Guillermo Henry 
Author-X-Name-First: Guillermo 
Author-X-Name-Last: Henry 
Author-Name: Daniela Rodriguez 
Author-X-Name-First: Daniela 
Author-X-Name-Last: Rodriguez 
Title: Partly linear models on Riemannian manifolds 
Abstract:
  In partly linear models, the dependence of the response
 <italic>y</italic> on (<bold>x</bold> -super-T, <italic>t</italic>) is
 modeled through the relationship <italic>y</italic>=<bold>x</bold>
 -super-T
 <bold>&#x3B2;</bold>+<italic>g</italic>(<italic>t</italic>)+&#x3F5;, where
 &#x3F5; is independent of (<bold>x</bold> -super-T, <italic>t</italic>).
 We are interested in developing an estimation procedure that allows us to
 combine the flexibility of the partly linear models, studied by several
 authors, but including some variables that belong to a non-Euclidean
 space. The motivating application of this paper deals with the explanation
 of the atmospheric SO<sub>2</sub> pollution incidents using these models
 when some of the predictive variables belong in a cylinder. In this paper,
 the estimators of <bold>&#x3B2;</bold> and <italic>g</italic> are
 constructed when the explanatory variables <italic>t</italic> take values
 on a Riemannian manifold and the asymptotic properties of the proposed
 estimators are obtained under suitable conditions. We illustrate the use
 of this estimation approach using an environmental data set and we explore
 the performance of the estimators through a simulation study. 
Journal: Journal of Applied Statistics 
Pages: 1797-1809 
Issue: 8 
Volume: 39 
Year: 2012 
Month: 4 
X-DOI: 10.1080/02664763.2012.683169 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.683169 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:8:p:1797-1809




Template-Type: ReDIF-Article 1.0
Author-Name: Yu-Jau Lin 
Author-X-Name-First: Yu-Jau 
Author-X-Name-Last: Lin 
Author-Name: Y.   L. Lio 
Author-X-Name-First: Y.   L. 
Author-X-Name-Last: Lio 
Title: Bayesian inference under progressive type-I interval censoring 
Abstract:
  Bayesian estimation for population parameter under progressive type-I
 interval censoring is studied via Markov Chain Monte Carlo (MCMC)
 simulation. Two competitive statistical models, generalized exponential
 and Weibull distributions for modeling a real data set containing 112
 patients with plasma cell myeloma, are studied for illustration. In model
 selection, a novel Bayesian procedure which involves a mixture model is
 proposed. Then the mix proportion is estimated through MCMC and used as
 the model selection criterion. 
Journal: Journal of Applied Statistics 
Pages: 1811-1824 
Issue: 8 
Volume: 39 
Year: 2012 
Month: 4 
X-DOI: 10.1080/02664763.2012.683170 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.683170 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:8:p:1811-1824




Template-Type: ReDIF-Article 1.0
Author-Name: Vasileios Maroulas 
Author-X-Name-First: Vasileios 
Author-X-Name-Last: Maroulas 
Title: Error analysis of stochastic flight trajectory prediction models 
Abstract:
  This paper focuses on the analysis of errors between a flight trajectory
 prediction model and flight data. A novel stochastic prediction flight
 model is compared with the popular fly-by and fly-over turn models. The
 propagated error is measured using either spatial coordinates or angles.
 Depending on the case, the distribution of error is estimated and
 confidence bounds for the linear and directional mean are provided for all
 three stochastic flight models. 
Journal: Journal of Applied Statistics 
Pages: 1825-1841 
Issue: 8 
Volume: 39 
Year: 2012 
Month: 4 
X-DOI: 10.1080/02664763.2012.683171 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.683171 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:8:p:1825-1841




Template-Type: ReDIF-Article 1.0
Author-Name: Han   Lin Shang 
Author-X-Name-First: Han   Lin 
Author-X-Name-Last: Shang 
Title: Graphics for statistics and data analysis with R 
Journal: Journal of Applied Statistics 
Pages: 1843-1844 
Issue: 8 
Volume: 39 
Year: 2012 
Month: 8 
X-DOI: 10.1080/02664763.2012.679355 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.679355 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:8:p:1843-1844




Template-Type: ReDIF-Article 1.0
Author-Name: Ivana Holloway 
Author-X-Name-First: Ivana 
Author-X-Name-Last: Holloway 
Title: Design and analysis of quality of life studies in clinical trials 
Journal: Journal of Applied Statistics 
Pages: 1844-1845 
Issue: 8 
Volume: 39 
Year: 2012 
Month: 8 
X-DOI: 10.1080/02664763.2012.657416 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.657416 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:8:p:1844-1845




Template-Type: ReDIF-Article 1.0
Author-Name: Chris Beeley 
Author-X-Name-First: Chris 
Author-X-Name-Last: Beeley 
Title: Applied Bayesian hierarchical methods 
Journal: Journal of Applied Statistics 
Pages: 1845-1845 
Issue: 8 
Volume: 39 
Year: 2012 
Month: 8 
X-DOI: 10.1080/02664763.2012.657788 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.657788 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:8:p:1845-1845




Template-Type: ReDIF-Article 1.0
Author-Name: John   Paul Gosling 
Author-X-Name-First: John   Paul 
Author-X-Name-Last: Gosling 
Title: Bayesian analysis made simple: An excel GUI for WinBUGS 
Journal: Journal of Applied Statistics 
Pages: 1845-1846 
Issue: 8 
Volume: 39 
Year: 2012 
Month: 8 
X-DOI: 10.1080/02664763.2012.679822 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.679822 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:8:p:1845-1846




Template-Type: ReDIF-Article 1.0
Author-Name: Zhengfeng Guo 
Author-X-Name-First: Zhengfeng 
Author-X-Name-Last: Guo 
Title: Linear model methodology 
Journal: Journal of Applied Statistics 
Pages: 1846-1847 
Issue: 8 
Volume: 39 
Year: 2012 
Month: 8 
X-DOI: 10.1080/02664763.2012.679823 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.679823 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:8:p:1846-1847




Template-Type: ReDIF-Article 1.0
Author-Name: S&#xF8;ren   Feodor Nielsen 
Author-X-Name-First: S&#xF8;ren   Feodor 
Author-X-Name-Last: Nielsen 
Title: An elementary introduction to mathematical finance 
Journal: Journal of Applied Statistics 
Pages: 1847-1848 
Issue: 8 
Volume: 39 
Year: 2012 
Month: 8 
X-DOI: 10.1080/02664763.2012.680688 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.680688 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:8:p:1847-1848




Template-Type: ReDIF-Article 1.0
Author-Name: Derek   S. Young 
Author-X-Name-First: Derek   S. 
Author-X-Name-Last: Young 
Title: Optimal experimental design with <monospace>R</monospace>  
Journal: Journal of Applied Statistics 
Pages: 1848-1849 
Issue: 8 
Volume: 39 
Year: 2012 
Month: 8 
X-DOI: 10.1080/02664763.2012.680689 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.680689 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:8:p:1848-1849




Template-Type: ReDIF-Article 1.0
Author-Name: A.   M. Mosammam 
Author-X-Name-First: A.   M. 
Author-X-Name-Last: Mosammam 
Title: The Oxford handbook of nonlinear filtering 
Journal: Journal of Applied Statistics 
Pages: 1849-1850 
Issue: 8 
Volume: 39 
Year: 2012 
Month: 8 
X-DOI: 10.1080/02664763.2012.680690 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.680690 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:8:p:1849-1850




Template-Type: ReDIF-Article 1.0
Author-Name: &#x15E;ebnem Er 
Author-X-Name-First: &#x15E;ebnem 
Author-X-Name-Last: Er 
Title: Multivariate generalized linear models using R 
Journal: Journal of Applied Statistics 
Pages: 1851-1851 
Issue: 8 
Volume: 39 
Year: 2012 
Month: 8 
X-DOI: 10.1080/02664763.2012.681563 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.681563 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:8:p:1851-1851




Template-Type: ReDIF-Article 1.0
Author-Name: Francesca Martella 
Author-X-Name-First: Francesca 
Author-X-Name-Last: Martella 
Author-Name: Maurizio Vichi 
Author-X-Name-First: Maurizio 
Author-X-Name-Last: Vichi 
Title: Clustering microarray data using model-based double <italic>K</italic>-means 
Abstract:
  The microarray technology allows the measurement of expression levels of
 thousands of genes simultaneously. The dimension and complexity of gene
 expression data obtained by microarrays create challenging data analysis
 and management problems ranging from the analysis of images produced by
 microarray experiments to biological interpretation of results. Therefore,
 statistical and computational approaches are beginning to assume a
 substantial position within the molecular biology area. We consider the
 problem of simultaneously clustering genes and tissue samples (in general
 conditions) of a microarray data set. This can be useful for revealing
 groups of genes involved in the same molecular process as well as groups
 of conditions where this process takes place. The need of finding a subset
 of genes and tissue samples defining a homogeneous block had led to the
 application of <italic>double clustering</italic> techniques on gene
 expression data. Here, we focus on an extension of standard
 <italic>K</italic>-means to simultaneously cluster observations and
 features of a data matrix, namely <italic>double
 <italic>K</italic>-means</italic> introduced by Vichi (2000). We introduce
 this model in a probabilistic framework and discuss the advantages of
 using this approach. We also develop a coordinate ascent algorithm and
 test its performance via simulation studies and real data set. Finally, we
 validate the results obtained on the real data set by building resampling
 confidence intervals for block centroids. 
Journal: Journal of Applied Statistics 
Pages: 1853-1869 
Issue: 9 
Volume: 39 
Year: 2012 
Month: 4 
X-DOI: 10.1080/02664763.2012.683172 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.683172 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:9:p:1853-1869




Template-Type: ReDIF-Article 1.0
Author-Name: F. Lombard 
Author-X-Name-First: F. 
Author-X-Name-Last: Lombard 
Author-Name: R.   K. Maxwell 
Author-X-Name-First: R.   K. 
Author-X-Name-Last: Maxwell 
Title: A cusum procedure to detect deviations from uniformity in angular data 
Abstract:
  We propose a sequential cumulative sum procedure to detect deviations
 from uniformity in angular data. The method is motivated by a problem in
 high-energy astrophysics and is illustrated by an application to data. 
Journal: Journal of Applied Statistics 
Pages: 1871-1880 
Issue: 9 
Volume: 39 
Year: 2012 
Month: 4 
X-DOI: 10.1080/02664763.2012.683857 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.683857 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:9:p:1871-1880




Template-Type: ReDIF-Article 1.0
Author-Name: Pao-Sheng Shen 
Author-X-Name-First: Pao-Sheng 
Author-X-Name-Last: Shen 
Title: Semiparametric mixed-effects models for clustered doubly censored data 
Abstract:
  The Cox proportional frailty model with a random effect has been proposed
 for the analysis of right-censored data which consist of a large number of
 small clusters of correlated failure time observations. For right-censored
 data, Cai <italic>et al.</italic> [3] proposed a class of semiparametric
 mixed-effects models which provides useful alternatives to the Cox model.
 We demonstrate that the approach of Cai <italic>et al.</italic> [3] can be
 used to analyze clustered doubly censored data when both left- and
 right-censoring variables are always observed. The asymptotic properties
 of the proposed estimator are derived. A simulation study is conducted to
 investigate the performance of the proposed estimator. 
Journal: Journal of Applied Statistics 
Pages: 1881-1892 
Issue: 9 
Volume: 39 
Year: 2012 
Month: 4 
X-DOI: 10.1080/02664763.2012.684874 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.684874 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:9:p:1881-1892




Template-Type: ReDIF-Article 1.0
Author-Name: Gikuang   Jeff Chen 
Author-X-Name-First: Gikuang   Jeff 
Author-X-Name-Last: Chen 
Title: A simple way to deal with multicollinearity 
Abstract:
  Despite the long and frustrating history of struggling with the wrong
 signs or other types of implausible estimates under multicollinearity, it
 turns out that the problem can be solved in a surprisingly easy way. This
 paper presents a simple approach that ensures both statistically sound and
 theoretically consistent estimates under multicollinearity. The approach
 is simple in the sense that it requires nothing but basic statistical
 methods plus a piece of <italic>a priori</italic> knowledge. In addition,
 the approach is robust even to the extreme case when the <italic>a
 priori</italic> knowledge is wrong. A simulation test shows astonishingly
 superior performance of the method in repeated samples comparing to the
 OLS, the Ridge Regression and the Dropping-Variable approach. 
Journal: Journal of Applied Statistics 
Pages: 1893-1909 
Issue: 9 
Volume: 39 
Year: 2012 
Month: 5 
X-DOI: 10.1080/02664763.2012.690857 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.690857 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:9:p:1893-1909




Template-Type: ReDIF-Article 1.0
Author-Name: Chung-I Li 
Author-X-Name-First: Chung-I 
Author-X-Name-Last: Li 
Author-Name: Jeh-Nan Pan 
Author-X-Name-First: Jeh-Nan 
Author-X-Name-Last: Pan 
Title: Sample size determination for estimating multivariate process capability indices based on lower confidence limits 
Abstract:
  With the advent of modern technology, manufacturing processes have become
 very sophisticated; a single quality characteristic can no longer reflect
 a product's quality. In order to establish performance measures for
 evaluating the capability of a multivariate manufacturing process, several
 new multivariate capability (NMC) indices, such as NMC<sub>
 <italic>p</italic> </sub> and NMC<sub> <italic>pm</italic> </sub>, have
 been developed over the past few years. However, the sample size
 determination for multivariate process capability indices has not been
 thoroughly considered in previous studies. Generally, the larger the
 sample size, the more accurate an estimation will be. However, too large a
 sample size may result in excessive costs. Hence, the trade-off between
 sample size and precision in estimation is a critical issue. In this
 paper, the lower confidence limits of NMC<sub> <italic>p</italic> </sub>
 and NMC<sub> <italic>pm</italic> </sub> indices are used to determine the
 appropriate sample size. Moreover, a procedure for conducting the
 multivariate process capability study is provided. Finally, two numerical
 examples are given to demonstrate that the proper determination of sample
 size for multivariate process indices can achieve a good balance between
 sampling costs and estimation precision. 
Journal: Journal of Applied Statistics 
Pages: 1911-1920 
Issue: 9 
Volume: 39 
Year: 2012 
Month: 5 
X-DOI: 10.1080/02664763.2012.690858 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.690858 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:9:p:1911-1920




Template-Type: ReDIF-Article 1.0
Author-Name: Ainhoa   Oguiza Tovar 
Author-X-Name-First: Ainhoa   Oguiza 
Author-X-Name-Last: Tovar 
Author-Name: Inmaculada   Gallastegui Zulaica 
Author-X-Name-First: Inmaculada   Gallastegui 
Author-X-Name-Last: Zulaica 
Author-Name: Vicente N&#xFA;&#xF1;ez-Ant&#xF3;n 
Author-X-Name-First: Vicente 
Author-X-Name-Last: N&#xFA;&#xF1;ez-Ant&#xF3;n 
Title: Analysis of pseudo-panel data with dependent samples 
Abstract:
  In this paper, we discuss a model for pseudo-panel data when some but not
 all of the individuals stay in the sample for more than one period. We use
 data on the labor market of the Basque Country from 1993 to 1999 treated
 through FORTRAN 77 programing. We construct economically reasonable age
 cohorts for active population and use gender, qualification and social
 status as explanatory variables in our model. Given the class of data we
 use, we analyze the properties of the random error and estimate the model
 through maximum likelihood, finding significant results from an applied
 point of view. 
Journal: Journal of Applied Statistics 
Pages: 1921-1937 
Issue: 9 
Volume: 39 
Year: 2012 
Month: 5 
X-DOI: 10.1080/02664763.2012.696593 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.696593 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:9:p:1921-1937




Template-Type: ReDIF-Article 1.0
Author-Name: R. L.J. Coetzer 
Author-X-Name-First: R. L.J. 
Author-X-Name-Last: Coetzer 
Author-Name: R.   F Rossouw 
Author-X-Name-First: R.   F 
Author-X-Name-Last: Rossouw 
Author-Name: N.   J. Le Roux 
Author-X-Name-First: N.   J. 
Author-X-Name-Last: Le Roux 
Title: Efficient maximin distance designs for experiments in mixtures 
Abstract:
  In this paper, different dissimilarity measures are investigated to
 construct maximin designs for compositional data. Specifically, the effect
 of different dissimilarity measures on the maximin design criterion for
 two case studies is presented. Design evaluation criteria are proposed to
 distinguish between the maximin designs generated. An optimization
 algorithm is also presented. Divergence is found to be the best
 dissimilarity measure to use in combination with the maximin design
 criterion for creating space-filling designs for mixture variables. 
Journal: Journal of Applied Statistics 
Pages: 1939-1951 
Issue: 9 
Volume: 39 
Year: 2012 
Month: 5 
X-DOI: 10.1080/02664763.2012.697131 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.697131 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:9:p:1939-1951




Template-Type: ReDIF-Article 1.0
Author-Name: Jorge   Alberto Achcar 
Author-X-Name-First: Jorge   Alberto 
Author-X-Name-Last: Achcar 
Author-Name: Edilberto Cepeda-Cuervo 
Author-X-Name-First: Edilberto 
Author-X-Name-Last: Cepeda-Cuervo 
Author-Name: Eliane   R. Rodrigues 
Author-X-Name-First: Eliane   R. 
Author-X-Name-Last: Rodrigues 
Title: Weibull and generalised exponential overdispersion models with an application to ozone air pollution 
Abstract:
  We consider the problem of estimating the mean and variance of the time
 between occurrences of an event of interest (inter-occurrences times)
 where some forms of dependence between two consecutive time intervals are
 allowed. Two basic density functions are taken into account. They are the
 Weibull and the generalised exponential density functions. In order to
 capture the dependence between two consecutive inter-occurrences times, we
 assume that either the shape and/or the scale parameters of the two
 density functions are given by auto-regressive models. The expressions for
 the mean and variance of the inter-occurrences times are presented. The
 models are applied to the ozone data from two regions of Mexico City. The
 estimation of the parameters is performed using a Bayesian point of view
 via Markov chain Monte Carlo (MCMC) methods. 
Journal: Journal of Applied Statistics 
Pages: 1953-1963 
Issue: 9 
Volume: 39 
Year: 2012 
Month: 5 
X-DOI: 10.1080/02664763.2012.697132 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.697132 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:9:p:1953-1963




Template-Type: ReDIF-Article 1.0
Author-Name: E.   Bahrami Samani 
Author-X-Name-First: E.   Bahrami 
Author-X-Name-Last: Samani 
Author-Name: Y. Amirian 
Author-X-Name-First: Y. 
Author-X-Name-Last: Amirian 
Author-Name: M. Ganjali 
Author-X-Name-First: M. 
Author-X-Name-Last: Ganjali 
Title: Likelihood estimation for longitudinal zero-inflated power series regression models 
Abstract:
  In this paper, a zero-inflated power series regression model for
 longitudinal count data with excess zeros is presented. We demonstrate how
 to calculate the likelihood for such data when it is assumed that the
 increment in the cumulative total follows a discrete distribution with a
 location parameter that depends on a linear function of explanatory
 variables. Simulation studies indicate that this method can provide
 improvements in obtaining standard errors of the estimates. We also
 calculate the dispersion index for this model. The influence of a small
 perturbation of the dispersion index of the zero-inflated model on
 likelihood displacement is also studied. The zero-inflated negative
 binomial regression model is illustrated on data regarding joint damage in
 psoriatic arthritis. 
Journal: Journal of Applied Statistics 
Pages: 1965-1974 
Issue: 9 
Volume: 39 
Year: 2012 
Month: 5 
X-DOI: 10.1080/02664763.2012.699951 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.699951 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:9:p:1965-1974




Template-Type: ReDIF-Article 1.0
Author-Name: Loukia Meligkotsidou 
Author-X-Name-First: Loukia 
Author-X-Name-Last: Meligkotsidou 
Author-Name: Elias Tzavalis 
Author-X-Name-First: Elias 
Author-X-Name-Last: Tzavalis 
Author-Name: Ioannis   D. Vrontos 
Author-X-Name-First: Ioannis   D. 
Author-X-Name-Last: Vrontos 
Title: A Bayesian panel data framework for examining the economic growth convergence hypothesis: do the G7 countries converge? 
Abstract:
  In this paper, we suggest a Bayesian panel (longitudinal) data approach
 to test for the economic growth convergence hypothesis. This approach can
 control for possible effects of initial income conditions, observed
 covariates and cross-sectional correlation of unobserved common error
 terms on inference procedures about the unit root hypothesis based on
 panel data dynamic models. Ignoring these effects can lead to spurious
 evidence supporting economic growth divergence. The application of our
 suggested approach to real gross domestic product panel data of the G7
 countries indicates that the economic growth convergence hypothesis is
 supported by the data. Our empirical analysis shows that evidence of
 economic growth divergence for the G7 countries can be attributed to not
 accounting for the presence of exogenous covariates in the model. 
Journal: Journal of Applied Statistics 
Pages: 1975-1990 
Issue: 9 
Volume: 39 
Year: 2012 
Month: 5 
X-DOI: 10.1080/02664763.2012.699952 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.699952 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:9:p:1975-1990




Template-Type: ReDIF-Article 1.0
Author-Name: Hongxia Yang 
Author-X-Name-First: Hongxia 
Author-X-Name-Last: Yang 
Author-Name: Jun Wang 
Author-X-Name-First: Jun 
Author-X-Name-Last: Wang 
Author-Name: Alexsandra Mojslovic 
Author-X-Name-First: Alexsandra 
Author-X-Name-Last: Mojslovic 
Title: Cascade model with Dirichlet process for analyzing multiple dyadic matrices 
Abstract:
  Dyadic matrices are natural data representations in a wide range of
 domains. A dyadic matrix often involves two types of abstract objects and
 is based on observations of pairs of elements with one element from each
 object. Owing to the increasing needs from practical applications, dyadic
 data analysis has recently attracted increasing attention and many
 techniques have been developed. However, most existing approaches, such as
 co-clustering and relational reasoning, only handle a single dyadic table
 and lack flexibility to perform prediction using multiple dyadic matrices.
 In this article, we propose a general nonparametric Bayesian framework
 with a cascaded structure to model multiple dyadic matrices and then
 describe an efficient hybrid Gibbs sampling algorithm for posterior
 inference and analysis. Empirical evaluations using both synthetic data
 and real data show that the proposed model captures the hidden structure
 of data and generalizes the predictive inference in a unique way. 
Journal: Journal of Applied Statistics 
Pages: 1991-2003 
Issue: 9 
Volume: 39 
Year: 2012 
Month: 5 
X-DOI: 10.1080/02664763.2012.699953 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.699953 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:9:p:1991-2003




Template-Type: ReDIF-Article 1.0
Author-Name: Enrico   A. Colosimo 
Author-X-Name-First: Enrico   A. 
Author-X-Name-Last: Colosimo 
Author-Name: Maria   Arlene Fausto 
Author-X-Name-First: Maria   Arlene 
Author-X-Name-Last: Fausto 
Author-Name: Marta   Afonso Freitas 
Author-X-Name-First: Marta   Afonso 
Author-X-Name-Last: Freitas 
Author-Name: Jorge   Andrade Pinto 
Author-X-Name-First: Jorge   Andrade 
Author-X-Name-Last: Pinto 
Title: Practical modeling strategies for unbalanced longitudinal data analysis 
Abstract:
  In practice, data are often measured repeatedly on the same individual at
 several points in time. Main interest often relies in characterizing the
 way the response changes in time, and the predictors of that change.
 Marginal, mixed and transition are frequently considered to be the main
 models for continuous longitudinal data analysis. These approaches are
 proposed primarily for balanced longitudinal design. However, in clinic
 studies, data are usually not balanced and some restrictions are necessary
 in order to use these models. This paper was motivated by a data set
 related to longitudinal height measurements in children of HIV-infected
 mothers that was recorded at the university hospital of the Federal
 University in Minas Gerais, Brazil. This data set is severely unbalanced.
 The goal of this paper is to assess the application of continuous
 longitudinal models for the analysis of unbalanced data set. 
Journal: Journal of Applied Statistics 
Pages: 2005-2013 
Issue: 9 
Volume: 39 
Year: 2012 
Month: 5 
X-DOI: 10.1080/02664763.2012.699954 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.699954 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:9:p:2005-2013




Template-Type: ReDIF-Article 1.0
Author-Name: Bhim Singh 
Author-X-Name-First: Bhim 
Author-X-Name-Last: Singh 
Author-Name: B. V.S. Sisodia 
Author-X-Name-First: B. V.S. 
Author-X-Name-Last: Sisodia 
Author-Name: Anupam Singh 
Author-X-Name-First: Anupam 
Author-X-Name-Last: Singh 
Author-Name: R.   P. Kaushal 
Author-X-Name-First: R.   P. 
Author-X-Name-Last: Kaushal 
Title: A note on the estimation methods of crop production at the block level 
Abstract:
  This paper presents a method of estimation of crop-production statistics
 at smaller geographical levels like a community development block
 (generally referred to as a block) to make area-specific plans for
 agricultural development programmes in India. Using available
 district-level data on crop yield from crop-cutting experiments and data
 on auxiliary variables from various administrative sources, a suitable
 regression model is fitted. The fitted model is then used to predict the
 crop production at the block level. Some scaled estimators are also
 developed using predicted estimates. An empirical study is also carried
 out to judge the merits of the proposed estimators. 
Journal: Journal of Applied Statistics 
Pages: 2015-2027 
Issue: 9 
Volume: 39 
Year: 2012 
Month: 6 
X-DOI: 10.1080/02664763.2012.700449 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.700449 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:9:p:2015-2027




Template-Type: ReDIF-Article 1.0
Author-Name: N.   A. Samat 
Author-X-Name-First: N.   A. 
Author-X-Name-Last: Samat 
Author-Name: D.   F. Percy 
Author-X-Name-First: D.   F. 
Author-X-Name-Last: Percy 
Title: Vector-borne infectious disease mapping with stochastic difference equations: an analysis of dengue disease in Malaysia 
Abstract:
  Few publications consider the estimation of relative risk for
 vector-borne infectious diseases. Most of these articles involve
 exploratory analysis that includes the study of covariates and their
 effects on disease distribution and the study of geographic information
 systems to integrate patient-related information. The aim of this paper is
 to introduce an alternative method of relative risk estimation based on
 discrete time--space stochastic SIR-SI models
 (susceptible--infective--recovered for human populations;
 susceptible--infective for vector populations) for the transmission of
 vector-borne infectious diseases, particularly dengue disease. First, we
 describe deterministic compartmental SIR-SI models that are suitable for
 dengue disease transmission. We then adapt these to develop corresponding
 discrete time--space stochastic SIR-SI models. Finally, we develop an
 alternative method of estimating the relative risk for dengue disease
 mapping based on these models and apply them to analyse dengue data from
 Malaysia. This new approach offers a better model for estimating the
 relative risk for dengue disease mapping compared with the other common
 approaches, because it takes into account the transmission process of the
 disease while allowing for covariates and spatial correlation between
 risks in adjacent regions. 
Journal: Journal of Applied Statistics 
Pages: 2029-2046 
Issue: 9 
Volume: 39 
Year: 2012 
Month: 6 
X-DOI: 10.1080/02664763.2012.700450 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.700450 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:9:p:2029-2046




Template-Type: ReDIF-Article 1.0
Author-Name: Silvia Bacci 
Author-X-Name-First: Silvia 
Author-X-Name-Last: Bacci 
Title: Longitudinal data: different approaches in the context of item-response theory models 
Abstract:
  In this paper, some extended Rasch models are analyzed in the presence of
 longitudinal measurements of a latent variable. Two main approaches,
 multidimensional and multilevel, are compared: we investigate the
 different information that can be obtained from the latent variable, and
 we give advice on the use of the different kinds of models. The
 multidimensional and multilevel approaches are illustrated with a
 simulation study and with a longitudinal study on the health-related
 quality of life in terminal cancer patients. 
Journal: Journal of Applied Statistics 
Pages: 2047-2065 
Issue: 9 
Volume: 39 
Year: 2012 
Month: 6 
X-DOI: 10.1080/02664763.2012.700451 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.700451 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:9:p:2047-2065




Template-Type: ReDIF-Article 1.0
Author-Name: H. Zhang 
Author-X-Name-First: H. 
Author-X-Name-Last: Zhang 
Author-Name: Q. Yu 
Author-X-Name-First: Q. 
Author-X-Name-Last: Yu 
Author-Name: C. Feng 
Author-X-Name-First: C. 
Author-X-Name-Last: Feng 
Author-Name: D. Gunzler 
Author-X-Name-First: D. 
Author-X-Name-Last: Gunzler 
Author-Name: P. Wu 
Author-X-Name-First: P. 
Author-X-Name-Last: Wu 
Author-Name: X.   M. Tu 
Author-X-Name-First: X.   M. 
Author-X-Name-Last: Tu 
Title: A new look at the difference between the GEE and the GLMM when modeling longitudinal count responses 
Abstract:
  Poisson log-linear regression is a popular model for count responses. We
 examine two popular extensions of this model -- the generalized estimating
 equations (GEE) and the generalized linear mixed-effects model (GLMM) --
 to longitudinal data analysis and complement the existing literature on
 characterizing the relationship between the two dueling paradigms in this
 setting. Unlike linear regression, the GEE and the GLMM carry significant
 conceptual and practical implications when applied to modeling count data.
 Our findings shed additional light on the differences between the two
 classes of models when used for count data. Our considerations are
 demonstrated by both real study and simulated data. 
Journal: Journal of Applied Statistics 
Pages: 2067-2079 
Issue: 9 
Volume: 39 
Year: 2012 
Month: 6 
X-DOI: 10.1080/02664763.2012.700452 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.700452 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:9:p:2067-2079




Template-Type: ReDIF-Article 1.0
Author-Name: Meral Ebegil 
Author-X-Name-First: Meral 
Author-X-Name-Last: Ebegil 
Author-Name: Fikri G&#xF6;kp&#x131;nar 
Author-X-Name-First: Fikri 
Author-X-Name-Last: G&#xF6;kp&#x131;nar 
Title: A test statistic to choose between Liu-type and least-squares estimator based on mean square error criteria 
Abstract:
  In this study, the necessary and sufficient conditions for the Liu-type
 (LT) biased estimator are determined. A test for choosing between the LT
 estimator and least-squares estimator is obtained by using these necessary
 and sufficient conditions. Also, a simulation study is carried out to
 compare this estimator against the ridge estimator. Furthermore, a
 numerical example is given for defined test statistic. 
Journal: Journal of Applied Statistics 
Pages: 2081-2096 
Issue: 10 
Volume: 39 
Year: 2012 
Month: 6 
X-DOI: 10.1080/02664763.2012.700453 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.700453 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:10:p:2081-2096




Template-Type: ReDIF-Article 1.0
Author-Name: Jiun-Yi Wang 
Author-X-Name-First: Jiun-Yi 
Author-X-Name-Last: Wang 
Author-Name: Li-Ching Chen 
Author-X-Name-First: Li-Ching 
Author-X-Name-Last: Chen 
Author-Name: Hui-Min Lin 
Author-X-Name-First: Hui-Min 
Author-X-Name-Last: Lin 
Title: Robust methods for detecting familial aggregation of a quantitative trait in matched case--control family studies 
Abstract:
  Assessing familial aggregation of a disease or its underlying
 quantitative traits is often undertaken as the first step in the
 investigation of possible genetic causes. When some major confounding
 variables are known and difficult to be quantified, the matched
 case--control family design provides an opportunity to eliminate biased
 results. In such a design, cases and matched controls are ascertained
 first, with subsequent recruitment of other members in their families. For
 the study of complex diseases, many continuously distributed quantitative
 traits or biomedical evaluations are of primary clinical and health
 significance, and distributions of these continuous outcomes are
 frequently skewed or non-normal. A non-normal distributed outcome may lead
 some standard statistical methods to suffer from loss of substantial
 power. To deal with the problem, in this study, we thus propose a
 rank-based test for detecting familial aggregation of a quantitative trait
 with the use of a within-cluster resampling process. According to our
 simulation studies, the proposed test expresses qualified and robust power
 performance. Specifically, the proposed test is slightly less powerful
 than the generalized estimating equations approach if the trait is
 normally distributed, and it is apparently more powerful if the trait
 distribution is essentially skewed or heavy-tailed. A user-friendly
 R-script and an executable file to perform the proposed test are available
 online to allow its implementation on ordinary research. 
Journal: Journal of Applied Statistics 
Pages: 2097-2111 
Issue: 10 
Volume: 39 
Year: 2012 
Month: 6 
X-DOI: 10.1080/02664763.2012.702204 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.702204 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:10:p:2097-2111




Template-Type: ReDIF-Article 1.0
Author-Name: Sterling McPherson 
Author-X-Name-First: Sterling 
Author-X-Name-Last: McPherson 
Author-Name: Celestina Barbosa-Leiker 
Author-X-Name-First: Celestina 
Author-X-Name-Last: Barbosa-Leiker 
Title: An example of a two-part latent growth curve model for semicontinuous outcomes in the health sciences 
Abstract:
  A new method of modeling coronary artery calcium (CAC) is needed in order
 to properly understand the probability of onset and growth of CAC. CAC
 remains a controversial indicator of cardiovascular disease (CVD) risk,
 but this may be due to ill-equipped methods of specifying CAC during the
 analysis phase of studies reporting an analysis where CAC is the primary
 outcome. The modern method of two-part latent growth modeling may
 represent a strong alternative to the myriad of existing methods for
 modeling CAC. We provide a brief overview of existing methods of analysis
 used for CAC before introducing the general latent growth curve model, how
 it extends into a two-part (semicontinuous) growth model, and how the
 ubiquitous problem of missing data can be effectively handled. We then
 present an example of how to model CAC using this framework. We
 demonstrate that utilizing this type of modeling strategy can result in
 traditional predictors of CAC (e.g. age, gender, and high-density
 lipoprotein cholesterol), exerting a different impact on the two
 different, yet simultaneous, operationalizations of CAC. This method of
 analyzing CAC could inform future analyses of CAC and inform subsequent
 discussions about the nature of its potential to inform long-term CVD risk
 and heart events. 
Journal: Journal of Applied Statistics 
Pages: 2113-2128 
Issue: 10 
Volume: 39 
Year: 2012 
Month: 6 
X-DOI: 10.1080/02664763.2012.702205 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.702205 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:10:p:2113-2128




Template-Type: ReDIF-Article 1.0
Author-Name: Nazif &#xC7;al&#x131;&#x15F; 
Author-X-Name-First: Nazif 
Author-X-Name-Last: &#xC7;al&#x131;&#x15F; 
Author-Name: Hamza Erol 
Author-X-Name-First: Hamza 
Author-X-Name-Last: Erol 
Title: A new per-field classification method using mixture discriminant analysis 
Abstract:
  In this study, a new per-field classification method is proposed for
 supervised classification of remotely sensed multispectral image data of
 an agricultural area using Gaussian mixture discriminant analysis (MDA).
 For the proposed per-field classification method, multivariate Gaussian
 mixture models constructed for control and test fields can have fixed or
 different number of components and each component can have different or
 common covariance matrix structure. The discrimination function and the
 decision rule of this method are established according to the average
 Bhattacharyya distance and the minimum values of the average Bhattacharyya
 distances, respectively. The proposed per-field classification method is
 analyzed for different structures of a covariance matrix with fixed and
 different number of components. Also, we classify the remotely sensed
 multispectral image data using the per-pixel classification method based
 on Gaussian MDA. 
Journal: Journal of Applied Statistics 
Pages: 2129-2140 
Issue: 10 
Volume: 39 
Year: 2012 
Month: 6 
X-DOI: 10.1080/02664763.2012.702263 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.702263 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:10:p:2129-2140




Template-Type: ReDIF-Article 1.0
Author-Name: Youhei Kawasaki 
Author-X-Name-First: Youhei 
Author-X-Name-Last: Kawasaki 
Author-Name: Etsuo Miyaoka 
Author-X-Name-First: Etsuo 
Author-X-Name-Last: Miyaoka 
Title: A Bayesian inference of P(&#x3BB;<sub>1</sub>>&#x3BB;<sub>2</sub>) for two Poisson parameters 
Abstract:
  The statistical inference drawn from the difference between two
 independent Poisson parameters is often discussed in the medical
 literature. However, such discussions are usually based on the frequentist
 viewpoint rather than the Bayesian viewpoint. Here, we propose an index
 &#x3B8;=P(&#x3BB;<sub>1, post</sub>>&#x3BB;<sub>2, post</sub>), where
 &#x3BB;<sub>1, post</sub> and &#x3BB;<sub>2, post</sub> denote Poisson
 parameters following posterior density. We provide an exact and an
 approximate expression for calculating &#x3B8; using the conjugate gamma
 prior and compare the probabilities obtained using the approximate and the
 exact expressions. Moreover, we also show a relation between &#x3B8; and
 the <italic>p</italic>-value. We also highlight the significance of
 &#x3B8; by applying it to the result of actual clinical trials. Our
 findings suggest that &#x3B8; may provide useful information in a clinical
 trial. 
Journal: Journal of Applied Statistics 
Pages: 2141-2152 
Issue: 10 
Volume: 39 
Year: 2012 
Month: 6 
X-DOI: 10.1080/02664763.2012.702264 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.702264 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:10:p:2141-2152




Template-Type: ReDIF-Article 1.0
Author-Name: Jiin-Huarng Guo 
Author-X-Name-First: Jiin-Huarng 
Author-X-Name-Last: Guo 
Title: Optimal sample size planning for the Wilcoxon--Mann--Whitney and van Elteren tests under cost constraints 
Abstract:
  Sampling cost is a crucial factor in sample size planning, particularly
 when the treatment group is more expensive than the control group. To
 either minimize the total cost or maximize the statistical power of the
 test, we used the distribution-free Wilcoxon--Mann--Whitney test for two
 independent samples and the van Elteren test for randomized block design,
 respectively. We then developed approximate sample size formulas when the
 distribution of data is abnormal and/or unknown. This study derived the
 optimal sample size allocation ratio for a given statistical power by
 considering the cost constraints, so that the resulting sample sizes could
 minimize either the total cost or the total sample size. Moreover, for a
 given total cost, the optimal sample size allocation is recommended to
 maximize the statistical power of the test. The proposed formula is not
 only innovative, but also quick and easy. We also applied real data from a
 clinical trial to illustrate how to choose the sample size for a
 randomized two-block design. For nonparametric methods, no existing
 commercial software for sample size planning has considered the cost
 factor, and therefore the proposed methods can provide important insights
 related to the impact of cost constraints. 
Journal: Journal of Applied Statistics 
Pages: 2153-2164 
Issue: 10 
Volume: 39 
Year: 2012 
Month: 6 
X-DOI: 10.1080/02664763.2012.702265 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.702265 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:10:p:2153-2164




Template-Type: ReDIF-Article 1.0
Author-Name: Luigi D'Ambra 
Author-X-Name-First: Luigi 
Author-X-Name-Last: D'Ambra 
Author-Name: Antonello D'Ambra 
Author-X-Name-First: Antonello 
Author-X-Name-Last: D'Ambra 
Author-Name: Pasquale Sarnacchiaro 
Author-X-Name-First: Pasquale 
Author-X-Name-Last: Sarnacchiaro 
Title: Visualizing main effects and interaction in multiple non-symmetric correspondence analysis 
Abstract:
  Non-symmetric correspondence analysis (NSCA) is a useful technique for
 analysing a two-way contingency table. Frequently, the predictor variables
 are more than one; in this paper, we consider two categorical variables as
 predictor variables and one response variable. Interaction represents the
 joint effects of predictor variables on the response variable. When
 interaction is present, the interpretation of the main effects is
 incomplete or misleading. To separate the main effects and the interaction
 term, we introduce a method that, starting from the coordinates of
 multiple NSCA and using a two-way analysis of variance without
 interaction, allows a better interpretation of the impact of the predictor
 variable on the response variable. The proposed method has been applied on
 a well-known three-way contingency table proposed by Bockenholt and
 Bockenholt in which they cross-classify subjects by <italic>person's
 attitude towards abortion, number of years of education</italic> and
 <italic>religion</italic>. We analyse the case where the variables
 <italic>education</italic> and <italic>religion</italic> influence a
 <italic>person's attitude towards abortion</italic>. 
Journal: Journal of Applied Statistics 
Pages: 2165-2175 
Issue: 10 
Volume: 39 
Year: 2012 
Month: 6 
X-DOI: 10.1080/02664763.2012.702266 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.702266 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:10:p:2165-2175




Template-Type: ReDIF-Article 1.0
Author-Name: Yulei He 
Author-X-Name-First: Yulei 
Author-X-Name-Last: He 
Author-Name: Trivellore   E. Raghunathan 
Author-X-Name-First: Trivellore   E. 
Author-X-Name-Last: Raghunathan 
Title: Multiple imputation using multivariate <italic>gh</italic> transformations 
Abstract:
  Multiple imputation has emerged as a popular approach to handling data
 sets with missing values. For incomplete continuous variables, imputations
 are usually produced using multivariate normal models. However, this
 approach might be problematic for variables with a strong non-normal
 shape, as it would generate imputations incoherent with actual
 distributions and thus lead to incorrect inferences. For non-normal data,
 we consider a multivariate extension of Tukey's <italic>gh</italic>
 distribution/transformation [38] to accommodate skewness and/or kurtosis
 and capture the correlation among the variables. We propose an algorithm
 to fit the incomplete data with the model and generate imputations. We
 apply the method to a national data set for hospital performance on
 several standard quality measures, which are highly skewed to the left and
 substantially correlated with each other. We use Monte Carlo studies to
 assess the performance of the proposed approach. We discuss possible
 generalizations and give some advices to practitioners on how to handle
 non-normal incomplete data. 
Journal: Journal of Applied Statistics 
Pages: 2177-2198 
Issue: 10 
Volume: 39 
Year: 2012 
Month: 6 
X-DOI: 10.1080/02664763.2012.702268 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.702268 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:10:p:2177-2198




Template-Type: ReDIF-Article 1.0
Author-Name: H. Fotouhi 
Author-X-Name-First: H. 
Author-X-Name-Last: Fotouhi 
Author-Name: M. Golalizadeh 
Author-X-Name-First: M. 
Author-X-Name-Last: Golalizadeh 
Title: Exploring the variability of DNA molecules via principal geodesic analysis on the shape space 
Abstract:
  Most of the linear statistics deal with data lying in a Euclidean space.
 However, there are many examples, such as DNA molecule topological
 structures, in which the initial or the transformed data lie in a
 non-Euclidean space. To get a measure of variability in these situations,
 the principal component analysis (PCA) is usually performed on a Euclidean
 tangent space as it cannot be directly implemented on a non-Euclidean
 space. Instead, principal geodesic analysis (PGA) is a new tool that
 provides a measure of variability for nonlinear statistics. In this paper,
 the performance of this new tool is compared with that of the PCA using a
 real data set representing a DNA molecular structure. It is shown that due
 to the nonlinearity of space, the PGA explains more variability of the
 data than the PCA. 
Journal: Journal of Applied Statistics 
Pages: 2199-2207 
Issue: 10 
Volume: 39 
Year: 2012 
Month: 6 
X-DOI: 10.1080/02664763.2012.704353 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.704353 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:10:p:2199-2207




Template-Type: ReDIF-Article 1.0
Author-Name: Francisco Louzada 
Author-X-Name-First: Francisco 
Author-X-Name-Last: Louzada 
Author-Name: Vicente   G. Cancho 
Author-X-Name-First: Vicente   G. 
Author-X-Name-Last: Cancho 
Author-Name: Mari Roman 
Author-X-Name-First: Mari 
Author-X-Name-Last: Roman 
Author-Name: Jos�   G. Leite 
Author-X-Name-First: Jos�   G. 
Author-X-Name-Last: Leite 
Title: A new long-term lifetime distribution induced by a latent complementary risk framework 
Abstract:
  In this paper, we proposed a new three-parameter long-term lifetime
 distribution induced by a latent complementary risk framework with
 decreasing, increasing and unimodal hazard function, the long-term
 complementary exponential geometric distribution. The new distribution
 arises from latent competing risk scenarios, where the lifetime associated
 scenario, with a particular risk, is not observable, rather we observe
 only the maximum lifetime value among all risks, and the presence of
 long-term survival. The properties of the proposed distribution are
 discussed, including its probability density function and explicit
 algebraic formulas for its reliability, hazard and quantile functions and
 order statistics. The parameter estimation is based on the usual
 maximum-likelihood approach. A simulation study assesses the performance
 of the estimation procedure. We compare the new distribution with its
 particular cases, as well as with the long-term Weibull distribution on
 three real data sets, observing its potential and competitiveness in
 comparison with some usual long-term lifetime distributions. 
Journal: Journal of Applied Statistics 
Pages: 2209-2222 
Issue: 10 
Volume: 39 
Year: 2012 
Month: 6 
X-DOI: 10.1080/02664763.2012.706264 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.706264 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:10:p:2209-2222




Template-Type: ReDIF-Article 1.0
Author-Name: J�r&#xF4;me Kasparian 
Author-X-Name-First: J�r&#xF4;me 
Author-X-Name-Last: Kasparian 
Author-Name: Antoine Rolland 
Author-X-Name-First: Antoine 
Author-X-Name-Last: Rolland 
Title: OECD's &#x2018;Better Life Index&#x2019;: can any country be well ranked? 
Abstract:
  We critically review the Better Life Index (BLI) recently introduced by
 the Organization for Economic Co-operation and Development (OECD). We
 discuss methodological issues in the definition of the criteria used to
 rank the countries, as well as in their aggregation method. Moreover, we
 explore the unique option offered by the BLI to apply one's own weight set
 to 11 criteria. Although 16 countries can be ranked first by choosing
 <italic>ad hoc</italic> weightings, only Canada, Australia and Sweden do
 so over a substantial fraction of the parameter space defined by all
 possible weight sets. Furthermore, most pairwise comparisons between
 countries are insensitive to the choice of the weights. Therefore, the BLI
 establishes a hierarchy among the evaluated countries, independent of the
 chosen set of weights. 
Journal: Journal of Applied Statistics 
Pages: 2223-2230 
Issue: 10 
Volume: 39 
Year: 2012 
Month: 6 
X-DOI: 10.1080/02664763.2012.706265 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.706265 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:10:p:2223-2230




Template-Type: ReDIF-Article 1.0
Author-Name: Jonathan Gillard 
Author-X-Name-First: Jonathan 
Author-X-Name-Last: Gillard 
Title: A generalised Box--Cox transformation for the parametric estimation of clinical reference intervals 
Abstract:
  Parametric methods for the calculation of reference intervals in clinical
 studies often rely on the identification of a suitable transformation so
 that the transformed data can be assumed to be drawn from a Gaussian
 distribution. In this paper, the two-stage transformation recommended by
 the International Federation for Clinical Chemistry is compared with a
 novel generalised Box--Cox family of transformations. Investigation is
 also made of sample sizes needed to achieve certain criteria of
 reliability in the calculated reference interval. Simulations are used to
 show that the generalised Box--Cox family achieves a lower bias than the
 two-stage transformation. It was found that there is a possibility that
 the two-stage transformation will result in percentile estimates that
 cannot be back-transformed to obtain the required reference intervals, a
 difficulty not observed when using the generalised Box--Cox family
 introduced in this paper. 
Journal: Journal of Applied Statistics 
Pages: 2231-2245 
Issue: 10 
Volume: 39 
Year: 2012 
Month: 6 
X-DOI: 10.1080/02664763.2012.706266 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.706266 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:10:p:2231-2245




Template-Type: ReDIF-Article 1.0
Author-Name: Yungtai Lo 
Author-X-Name-First: Yungtai 
Author-X-Name-Last: Lo 
Title: Estimating the prevalence of low-lumbar spine bone mineral density in older men with or at risk for HIV infection using normal mixture models 
Abstract:
  Bone mineral density decreases naturally as we age because existing bone
 tissue is reabsorbed by the body faster than new bone tissue is
 synthesized. When this occurs, bones lose calcium and other minerals. What
 is normal bone mineral density for men 50 years and older? Suitable
 diagnostic cutoff values for men are less well defined than for women. In
 this paper, we propose using normal mixture models to estimate the
 prevalence of low-lumbar spine bone mineral density in men 50 years and
 older with or at risk for human immunodeficiency virus infection when
 normal values of bone mineral density are not generally known. The
 Box--Cox power transformation is used to determine which transformation
 best suits normal mixture distributions. Parametric bootstrap tests are
 used to determine the number of mixture components and to determine
 whether the mixture components are homoscedastic or heteroscedastic. 
Journal: Journal of Applied Statistics 
Pages: 2247-2258 
Issue: 10 
Volume: 39 
Year: 2012 
Month: 6 
X-DOI: 10.1080/02664763.2012.706267 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.706267 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:10:p:2247-2258




Template-Type: ReDIF-Article 1.0
Author-Name: Wen-Liang Hung 
Author-X-Name-First: Wen-Liang 
Author-X-Name-Last: Hung 
Author-Name: Shou-Jen Chang-Chien 
Author-X-Name-First: Shou-Jen 
Author-X-Name-Last: Chang-Chien 
Author-Name: Miin-Shen Yang 
Author-X-Name-First: Miin-Shen 
Author-X-Name-Last: Yang 
Title: Self-updating clustering algorithm for estimating the parameters in mixtures of von Mises distributions 
Abstract:
  The EM algorithm is the standard method for estimating the parameters in
 finite mixture models. Yang and Pan [25] proposed a generalized
 classification maximum likelihood procedure, called the fuzzy
 <italic>c</italic>-directions (FCD) clustering algorithm, for estimating
 the parameters in mixtures of von Mises distributions. Two main drawbacks
 of the EM algorithm are its slow convergence and the dependence of the
 solution on the initial value used. The choice of initial values is of
 great importance in the algorithm-based literature as it can heavily
 influence the speed of convergence of the algorithm and its ability to
 locate the global maximum. On the other hand, the algorithmic frameworks
 of EM and FCD are closely related. Therefore, the drawbacks of FCD are the
 same as those of the EM algorithm. To resolve these problems, this paper
 proposes another clustering algorithm, which can self-organize local
 optimal cluster numbers without using cluster validity functions. These
 numerical results clearly indicate that the proposed algorithm is superior
 in performance of EM and FCD algorithms. Finally, we apply the proposed
 algorithm to two real data sets. 
Journal: Journal of Applied Statistics 
Pages: 2259-2274 
Issue: 10 
Volume: 39 
Year: 2012 
Month: 6 
X-DOI: 10.1080/02664763.2012.706268 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.706268 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:10:p:2259-2274




Template-Type: ReDIF-Article 1.0
Author-Name: Hong Li 
Author-X-Name-First: Hong 
Author-X-Name-Last: Li 
Author-Name: Wei Ning 
Author-X-Name-First: Wei 
Author-X-Name-Last: Ning 
Title: Multiple comparisons with a control under heteroscedasticity 
Abstract:
  This article investigates three procedures on the multiple comparisons
 with a control in the presence of unequal error variances. The advantages
 of the proposed methods are illustrated through two examples. The
 performance of the proposed methods and other alternative methods is
 compared by simulation studies. The results show that the typical methods
 assuming equal variance will have inflated error rate and may lead to
 erroneous inference when the equal variance assumption fails. In addition,
 the simulation study shows that the proposed approaches always control the
 family-wise error rate at a specified nominal level &#x3B1;, while some
 established methods are liberal and have inflated error rate in some
 scenarios. 
Journal: Journal of Applied Statistics 
Pages: 2275-2283 
Issue: 10 
Volume: 39 
Year: 2012 
Month: 6 
X-DOI: 10.1080/02664763.2012.706269 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.706269 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:10:p:2275-2283




Template-Type: ReDIF-Article 1.0
Author-Name: Taeyoung Park 
Author-X-Name-First: Taeyoung 
Author-X-Name-Last: Park 
Author-Name: Robert   T. Krafty 
Author-X-Name-First: Robert   T. 
Author-X-Name-Last: Krafty 
Author-Name: Alvaro   I. S&#xE1;nchez 
Author-X-Name-First: Alvaro   I. 
Author-X-Name-Last: S&#xE1;nchez 
Title: Bayesian semi-parametric analysis of Poisson change-point regression models: application to policy-making in Cali, Colombia 
Abstract:
  A Poisson regression model with an offset assumes a constant baseline
 rate after accounting for measured covariates, which may lead to biased
 estimates of coefficients in an inhomogeneous Poisson process. To
 correctly estimate the effect of time-dependent covariates, we propose a
 Poisson change-point regression model with an offset that allows a
 time-varying baseline rate. When the non-constant pattern of a log
 baseline rate is modeled with a non-parametric step function, the
 resulting semi-parametric model involves a model component of varying
 dimensions and thus requires a sophisticated varying-dimensional inference
 to obtain the correct estimates of model parameters of a fixed dimension.
 To fit the proposed varying-dimensional model, we devise a
 state-of-the-art Markov chain Monte Carlo-type algorithm based on partial
 collapse. The proposed model and methods are used to investigate the
 association between the daily homicide rates in Cali, Colombia, and the
 policies that restrict the hours during which the legal sale of alcoholic
 beverages is permitted. While simultaneously identifying the latent
 changes in the baseline homicide rate which correspond to the incidence of
 sociopolitical events, we explore the effect of policies governing the
 sale of alcohol on homicide rates and seek a policy that balances the
 economic and cultural dependencies on alcohol sales to the health of the
 public. 
Journal: Journal of Applied Statistics 
Pages: 2285-2298 
Issue: 10 
Volume: 39 
Year: 2012 
Month: 7 
X-DOI: 10.1080/02664763.2012.709227 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.709227 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:10:p:2285-2298




Template-Type: ReDIF-Article 1.0
Author-Name: Alex Karagrigoriou 
Author-X-Name-First: Alex 
Author-X-Name-Last: Karagrigoriou 
Title: Statistical inference: The minimum distance approach 
Journal: Journal of Applied Statistics 
Pages: 2299-2300 
Issue: 10 
Volume: 39 
Year: 2012 
Month: 10 
X-DOI: 10.1080/02664763.2012.681565 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.681565 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:10:p:2299-2300




Template-Type: ReDIF-Article 1.0
Author-Name: Hassan   S. Bakouch 
Author-X-Name-First: Hassan   S. 
Author-X-Name-Last: Bakouch 
Title: Applied time series analysis 
Journal: Journal of Applied Statistics 
Pages: 2300-2301 
Issue: 10 
Volume: 39 
Year: 2012 
Month: 10 
X-DOI: 10.1080/02664763.2012.682445 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.682445 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:10:p:2300-2301




Template-Type: ReDIF-Article 1.0
Author-Name: Eliane   R. Rodrigues 
Author-X-Name-First: Eliane   R. 
Author-X-Name-Last: Rodrigues 
Title: Reversibility and stochastic networks 
Journal: Journal of Applied Statistics 
Pages: 2301-2302 
Issue: 10 
Volume: 39 
Year: 2012 
Month: 10 
X-DOI: 10.1080/02664763.2012.682448 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.682448 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:10:p:2301-2302




Template-Type: ReDIF-Article 1.0
Author-Name: Kassim   S. Mwitondi 
Author-X-Name-First: Kassim   S. 
Author-X-Name-Last: Mwitondi 
Title: Statistical data mining using SAS applications 
Journal: Journal of Applied Statistics 
Pages: 2302-2302 
Issue: 10 
Volume: 39 
Year: 2012 
Month: 10 
X-DOI: 10.1080/02664763.2012.682451 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.682451 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:10:p:2302-2302




Template-Type: ReDIF-Article 1.0
Author-Name: Claire Keeble 
Author-X-Name-First: Claire 
Author-X-Name-Last: Keeble 
Title: The R primer 
Journal: Journal of Applied Statistics 
Pages: 2303-2303 
Issue: 10 
Volume: 39 
Year: 2012 
Month: 10 
X-DOI: 10.1080/02664763.2012.682453 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.682453 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:10:p:2303-2303




Template-Type: ReDIF-Article 1.0
Author-Name: Yiannis Kamarianakis 
Author-X-Name-First: Yiannis 
Author-X-Name-Last: Kamarianakis 
Title: The Oxford handbook of economic forecasting 
Journal: Journal of Applied Statistics 
Pages: 2303-2304 
Issue: 10 
Volume: 39 
Year: 2012 
Month: 10 
X-DOI: 10.1080/02664763.2012.682455 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.682455 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:10:p:2303-2304




Template-Type: ReDIF-Article 1.0
Author-Name: Isaac Dialsingh 
Author-X-Name-First: Isaac 
Author-X-Name-Last: Dialsingh 
Title: Expansions and asymptotics for statistics 
Journal: Journal of Applied Statistics 
Pages: 2304-2305 
Issue: 10 
Volume: 39 
Year: 2012 
Month: 10 
X-DOI: 10.1080/02664763.2012.692541 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.692541 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:10:p:2304-2305




Template-Type: ReDIF-Article 1.0
Author-Name: Isaac Dialsingh 
Author-X-Name-First: Isaac 
Author-X-Name-Last: Dialsingh 
Title: Large-scale inference: empirical Bayes methods for estimation, testing, and prediction 
Journal: Journal of Applied Statistics 
Pages: 2305-2305 
Issue: 10 
Volume: 39 
Year: 2012 
Month: 10 
X-DOI: 10.1080/02664763.2012.692543 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.692543 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:10:p:2305-2305




Template-Type: ReDIF-Article 1.0
Author-Name: P. William Hughes 
Author-X-Name-First: P. 
Author-X-Name-Last: William Hughes 
Title: Biostatistics: a computing approach 
Journal: Journal of Applied Statistics 
Pages: 2306-2306 
Issue: 10 
Volume: 39 
Year: 2012 
Month: 10 
X-DOI: 10.1080/02664763.2012.692545 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.692545 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:10:p:2306-2306




Template-Type: ReDIF-Article 1.0
Author-Name: Rolando de la Cruz 
Author-X-Name-First: Rolando 
Author-X-Name-Last: de la Cruz 
Title: Bayesian ideas and data analysis: An introduction for scientists and statisticians 
Journal: Journal of Applied Statistics 
Pages: 2306-2307 
Issue: 10 
Volume: 39 
Year: 2012 
Month: 10 
X-DOI: 10.1080/17429145.2012.693248 
File-URL: http://hdl.handle.net/10.1080/17429145.2012.693248 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:10:p:2306-2307




Template-Type: ReDIF-Article 1.0
Author-Name: Yves Laberge 
Author-X-Name-First: Yves 
Author-X-Name-Last: Laberge 
Title: The Oxford handbook of quantitative asset management 
Journal: Journal of Applied Statistics 
Pages: 2307-2308 
Issue: 10 
Volume: 39 
Year: 2012 
Month: 10 
X-DOI: 10.1080/02664763.2012.694257 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.694257 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:10:p:2307-2308




Template-Type: ReDIF-Article 1.0
Author-Name: Theofanis Sapatinas 
Author-X-Name-First: Theofanis 
Author-X-Name-Last: Sapatinas 
Title: Statistics for high-dimensional data 
Journal: Journal of Applied Statistics 
Pages: 2308-2309 
Issue: 10 
Volume: 39 
Year: 2012 
Month: 10 
X-DOI: 10.1080/02664763.2012.694258 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.694258 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:10:p:2308-2309




Template-Type: ReDIF-Article 1.0
Author-Name: David   J. Hand 
Author-X-Name-First: David   J. 
Author-X-Name-Last: Hand 
Title: Who's #1? The science of rating and ranking 
Journal: Journal of Applied Statistics 
Pages: 2309-2310 
Issue: 10 
Volume: 39 
Year: 2012 
Month: 10 
X-DOI: 10.1080/02664763.2012.701375 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.701375 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:10:p:2309-2310




Template-Type: ReDIF-Article 1.0
Author-Name: G.   E. Salcedo 
Author-X-Name-First: G.   E. 
Author-X-Name-Last: Salcedo 
Author-Name: R.   F. Porto 
Author-X-Name-First: R.   F. 
Author-X-Name-Last: Porto 
Author-Name: S.   Y. Roa 
Author-X-Name-First: S.   Y. 
Author-X-Name-Last: Roa 
Author-Name: F.   R. Momo 
Author-X-Name-First: F.   R. 
Author-X-Name-Last: Momo 
Title: A wavelet-based time-varying autoregressive model for non-stationary and irregular time series 
Abstract:
  In this work we propose an autoregressive model with parameters varying
 in time applied to irregularly spaced non-stationary time series. We
 expand all the functional parameters in a wavelet basis and estimate the
 coefficients by least squares after truncation at a suitable resolution
 level. We also present some simulations in order to evaluate both the
 estimation method and the model behavior on finite samples. Applications
 to silicates and nitrites irregularly observed data are provided as well. 
Journal: Journal of Applied Statistics 
Pages: 2313-2325 
Issue: 11 
Volume: 39 
Year: 2012 
Month: 6 
X-DOI: 10.1080/02664763.2012.702267 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.702267 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:11:p:2313-2325




Template-Type: ReDIF-Article 1.0
Author-Name: S.   Eftekhari Mahabadi 
Author-X-Name-First: S.   Eftekhari 
Author-X-Name-Last: Mahabadi 
Author-Name: M. Ganjali 
Author-X-Name-First: M. 
Author-X-Name-Last: Ganjali 
Title: An index of local sensitivity to non-ignorability for parametric survival models with potential non-random missing covariate: an application to the SEER cancer registry data 
Abstract:
  Several survival regression models have been developed to assess the
 effects of covariates on failure times. In various settings, including
 surveys, clinical trials and epidemiological studies, missing data may
 often occur due to incomplete covariate data. Most existing methods for
 lifetime data are based on the assumption of missing at random (MAR)
 covariates. However, in many substantive applications, it is important to
 assess the sensitivity of key model inferences to the MAR assumption. The
 index of sensitivity to non-ignorability (ISNI) is a local sensitivity
 tool to measure the potential sensitivity of key model parameters to small
 departures from the ignorability assumption, needless of estimating a
 complicated non-ignorable model. We extend this sensitivity index to
 evaluate the impact of a covariate that is potentially missing, not at
 random in survival analysis, using parametric survival models. The
 approach will be applied to investigate the impact of missing tumor grade
 on post-surgical mortality outcomes in individuals with pancreas-head
 cancer in the Surveillance, Epidemiology, and End Results data set. For
 patients suffering from cancer, tumor grade is an important risk factor.
 Many individuals in these data with pancreas-head cancer have missing
 tumor grade information. Our ISNI analysis shows that the magnitude of
 effect for most covariates (with significant effect on the survival time
 distribution), specifically surgery and tumor grade as some important risk
 factors in cancer studies, highly depends on the missing mechanism
 assumption of the tumor grade. Also a simulation study is conducted to
 evaluate the performance of the proposed index in detecting sensitivity of
 key model parameters. 
Journal: Journal of Applied Statistics 
Pages: 2327-2348 
Issue: 11 
Volume: 39 
Year: 2012 
Month: 7 
X-DOI: 10.1080/02664763.2012.710196 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.710196 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:11:p:2327-2348




Template-Type: ReDIF-Article 1.0
Author-Name: M.   C. Pardo 
Author-X-Name-First: M.   C. 
Author-X-Name-Last: Pardo 
Author-Name: R. Alonso 
Author-X-Name-First: R. 
Author-X-Name-Last: Alonso 
Title: A generalized Q--Q plot for longitudinal data 
Abstract:
  Most biomedical research is carried out using longitudinal studies. The
 method of generalized estimating equations (GEEs) introduced by Liang and
 Zeger [<italic>Longitudinal data analysis using generalized linear
 models</italic>, Biometrika 73 (1986), pp. 13--22] and Zeger and Liang
 [<italic>Longitudinal data analysis for discrete and continuous
 outcomes</italic>, Biometrics 42 (1986), pp. 121--130] has become a
 standard method for analyzing non-normal longitudinal data. Since then, a
 large variety of GEEs have been proposed. However, the model diagnostic
 problem has not been explored intensively. Oh <italic>et al.</italic>
 [<italic>Modeldiagnostic plots for repeated measures data using the
 generalized estimating equations approach</italic>, Comput. Statist. Data
 Anal. 53 (2008), pp. 222--232] proposed residual plots based on the
 quantile--quantile (Q--Q) plots of the &#x3C7;-super-2-distribution for
 repeated-measures data using the GEE methodology. They considered the
 Pearson, Anscombe and deviance residuals. In this work, we propose to
 extend this graphical diagnostic using a generalized residual. A
 simulation study is presented as well as two examples illustrating the
 proposed generalized Q--Q plots. 
Journal: Journal of Applied Statistics 
Pages: 2349-2362 
Issue: 11 
Volume: 39 
Year: 2012 
Month: 7 
X-DOI: 10.1080/02664763.2012.710896 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.710896 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:11:p:2349-2362




Template-Type: ReDIF-Article 1.0
Author-Name: Nicole White 
Author-X-Name-First: Nicole 
Author-X-Name-Last: White 
Author-Name: Helen Johnson 
Author-X-Name-First: Helen 
Author-X-Name-Last: Johnson 
Author-Name: Peter Silburn 
Author-X-Name-First: Peter 
Author-X-Name-Last: Silburn 
Author-Name: Kerrie Mengersen 
Author-X-Name-First: Kerrie 
Author-X-Name-Last: Mengersen 
Title: Dirichlet process mixture models for unsupervised clustering of symptoms in Parkinson's disease 
Abstract:
  In this paper, the goal of identifying disease subgroups based on
 differences in observed symptom profile is considered. Commonly referred
 to as phenotype identification, solutions to this task often involve the
 application of unsupervised clustering techniques. In this paper, we
 investigate the application of a Dirichlet process mixture model for this
 task. This model is defined by the placement of the Dirichlet process on
 the unknown components of a mixture model, allowing for the expression of
 uncertainty about the partitioning of observed data into homogeneous
 subgroups. To exemplify this approach, an application to phenotype
 identification in Parkinson's disease is considered, with symptom profiles
 collected using the Unified Parkinson's Disease Rating Scale. 
Journal: Journal of Applied Statistics 
Pages: 2363-2377 
Issue: 11 
Volume: 39 
Year: 2012 
Month: 7 
X-DOI: 10.1080/02664763.2012.710897 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.710897 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:11:p:2363-2377




Template-Type: ReDIF-Article 1.0
Author-Name: Charles   C. Taylor 
Author-X-Name-First: Charles   C. 
Author-X-Name-Last: Taylor 
Author-Name: Kanti   V. Mardia 
Author-X-Name-First: Kanti   V. 
Author-X-Name-Last: Mardia 
Author-Name: Marco Di Marzio 
Author-X-Name-First: Marco 
Author-X-Name-Last: Di Marzio 
Author-Name: Agnese Panzera 
Author-X-Name-First: Agnese 
Author-X-Name-Last: Panzera 
Title: Validating protein structure using kernel density estimates 
Abstract:
  Measuring the quality of determined protein structures is a very
 important problem in bioinformatics. Kernel density estimation is a
 well-known nonparametric method which is often used for exploratory data
 analysis. Recent advances, which have extended previous linear methods to
 multi-dimensional circular data, give a sound basis for the analysis of
 conformational angles of protein backbones, which lie on the torus. By
 using an energy test, which is based on interpoint distances, we initially
 investigate the dependence of the angles on the amino acid type. Then, by
 computing tail probabilities which are based on amino-acid conditional
 density estimates, a method is proposed which permits inference on a test
 set of data. This can be used, for example, to validate protein
 structures, choose between possible protein predictions and highlight
 unusual residue angles. 
Journal: Journal of Applied Statistics 
Pages: 2379-2388 
Issue: 11 
Volume: 39 
Year: 2012 
Month: 7 
X-DOI: 10.1080/02664763.2012.710898 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.710898 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:11:p:2379-2388




Template-Type: ReDIF-Article 1.0
Author-Name: Manoj   Kumar Rastogi 
Author-X-Name-First: Manoj   Kumar 
Author-X-Name-Last: Rastogi 
Author-Name: Yogesh   Mani Tripathi 
Author-X-Name-First: Yogesh   Mani 
Author-X-Name-Last: Tripathi 
Author-Name: Shuo-Jye Wu 
Author-X-Name-First: Shuo-Jye 
Author-X-Name-Last: Wu 
Title: Estimating the parameters of a bathtub-shaped distribution under progressive type-II censoring 
Abstract:
  We consider the problem of estimating unknown parameters, reliability
 function and hazard function of a two parameter bathtub-shaped
 distribution on the basis of progressive type-II censored sample. The
 maximum likelihood estimators and Bayes estimators are derived for two
 unknown parameters, reliability function and hazard function. The Bayes
 estimators are obtained against squared error, LINEX and entropy loss
 functions. Also, using the Lindley approximation method we have obtained
 approximate Bayes estimators against these loss functions. Some numerical
 comparisons are made among various proposed estimators in terms of their
 mean square error values and some specific recommendations are given.
 Finally, two data sets are analyzed to illustrate the proposed methods. 
Journal: Journal of Applied Statistics 
Pages: 2389-2411 
Issue: 11 
Volume: 39 
Year: 2012 
Month: 7 
X-DOI: 10.1080/02664763.2012.710899 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.710899 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:11:p:2389-2411




Template-Type: ReDIF-Article 1.0
Author-Name: Samuel Iddi 
Author-X-Name-First: Samuel 
Author-X-Name-Last: Iddi 
Author-Name: Geert Molenberghs 
Author-X-Name-First: Geert 
Author-X-Name-Last: Molenberghs 
Title: A joint marginalized multilevel model for longitudinal outcomes 
Abstract:
  The shared-parameter model and its so-called hierarchical or
 random-effects extension are widely used joint modeling approaches for a
 combination of longitudinal continuous, binary, count, missing, and
 survival outcomes that naturally occurs in many clinical and other
 studies. A random effect is introduced and shared or allowed to differ
 between two or more repeated measures or longitudinal outcomes, thereby
 acting as a vehicle to capture association between the outcomes in these
 joint models. It is generally known that parameter estimates in a linear
 mixed model (LMM) for continuous repeated measures or longitudinal
 outcomes allow for a marginal interpretation, even though a hierarchical
 formulation is employed. This is not the case for the generalized linear
 mixed model (GLMM), that is, for non-Gaussian outcomes. The aforementioned
 joint models formulated for continuous and binary or two longitudinal
 binomial outcomes, using the LMM and GLMM, will naturally have marginal
 interpretation for parameters associated with the continuous outcome but a
 subject-specific interpretation for the fixed effects parameters relating
 covariates to binary outcomes. To derive marginally meaningful parameters
 for the binary models in a joint model, we adopt the marginal multilevel
 model (MMM) due to Heagerty [13] and Heagerty and Zeger [14] and formulate
 a joint MMM for two longitudinal responses. This enables to (1) capture
 association between the two responses and (2) obtain parameter estimates
 that have a population-averaged interpretation for both outcomes. The
 model is applied to two sets of data. The results are compared with those
 obtained from the existing approaches such as generalized estimating
 equations, GLMM, and the model of Heagerty [13]. Estimates were found to
 be very close to those from single analysis per outcome but the joint
 model yields higher precision and allows for quantifying the association
 between outcomes. Parameters were estimated using maximum likelihood. The
 model is easy to fit using available tools such as the SAS NLMIXED
 procedure. 
Journal: Journal of Applied Statistics 
Pages: 2413-2430 
Issue: 11 
Volume: 39 
Year: 2012 
Month: 7 
X-DOI: 10.1080/02664763.2012.711302 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.711302 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:11:p:2413-2430




Template-Type: ReDIF-Article 1.0
Author-Name: Mahmoud Torabi 
Author-X-Name-First: Mahmoud 
Author-X-Name-Last: Torabi 
Title: Spatial modeling using frequentist approach for disease mapping 
Abstract:
  In this article, a generalized linear mixed model (GLMM) based on a
 frequentist approach is employed to examine spatial trend of asthma data.
 However, the frequentist analysis of GLMM is computationally difficult. On
 the other hand, the Bayesian analysis of GLMM has been computationally
 convenient due to the advent of Markov chain Monte Carlo algorithms.
 Recently developed data cloning (DC) method, which yields to maximum
 likelihood estimate, provides frequentist approach to complex mixed models
 and equally computationally convenient method. We use DC to conduct
 frequentist analysis of spatial models. The advantages of the DC approach
 are that the answers are independent of the choice of the priors,
 non-estimable parameters are flagged automatically, and the possibility of
 improper posterior distributions is completely avoided. We illustrate this
 approach using a real dataset of asthma visits to hospital in the province
 of Manitoba, Canada, during 2000--2010. The performance of the DC approach
 in our application is also studied through a simulation study. 
Journal: Journal of Applied Statistics 
Pages: 2431-2439 
Issue: 11 
Volume: 39 
Year: 2012 
Month: 7 
X-DOI: 10.1080/02664763.2012.711814 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.711814 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:11:p:2431-2439




Template-Type: ReDIF-Article 1.0
Author-Name: Yan Ma 
Author-X-Name-First: Yan 
Author-X-Name-Last: Ma 
Title: On inference for Kendall's &#x3C4; within a longitudinal data setting 
Abstract:
  Kendall's &#x3C4; is a non-parametric measure of correlation based on
 ranks and is used in a wide range of research disciplines. Although
 methods are available for making inference about Kendall's &#x3C4;, none
 has been extended to modeling multiple Kendall's &#x3C4;s arising in
 longitudinal data analysis. Compounding this problem is the pervasive
 issue of missing data in such study designs. In this article, we develop a
 novel approach to provide inference about Kendall's &#x3C4; within a
 longitudinal study setting under both complete and missing data. The
 proposed approach is illustrated with simulated data and applied to an HIV
 prevention study. 
Journal: Journal of Applied Statistics 
Pages: 2441-2452 
Issue: 11 
Volume: 39 
Year: 2012 
Month: 7 
X-DOI: 10.1080/02664763.2012.712954 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.712954 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:11:p:2441-2452




Template-Type: ReDIF-Article 1.0
Author-Name: Luzia Gon&#xE7;alves 
Author-X-Name-First: Luzia 
Author-X-Name-Last: Gon&#xE7;alves 
Author-Name: M.   Ros&#xE1;rio de Oliveira 
Author-X-Name-First: M.   Ros&#xE1;rio 
Author-X-Name-Last: de Oliveira 
Author-Name: Cl&#xE1;udia Pascoal 
Author-X-Name-First: Cl&#xE1;udia 
Author-X-Name-Last: Pascoal 
Author-Name: Ana Pires 
Author-X-Name-First: Ana 
Author-X-Name-Last: Pires 
Title: Sample size for estimating a binomial proportion: comparison of different methods 
Abstract:
  The poor performance of the Wald method for constructing confidence
 intervals (CIs) for a binomial proportion has been demonstrated in a vast
 literature. The related problem of sample size determination needs to be
 updated and comparative studies are essential to understanding the
 performance of alternative methods. In this paper, the sample size is
 obtained for the Clopper--Pearson, Bayesian (Uniform and Jeffreys priors),
 Wilson, Agresti--Coull, Anscombe, and Wald methods. Two two-step
 procedures are used: one based on the expected length (EL) of the CI and
 another one on its first-order approximation. In the first step, all
 possible solutions that satisfy the optimal criterion are obtained. In the
 second step, a single solution is proposed according to a new criterion
 (e.g. highest coverage probability (CP)). In practice, it is expected a
 sample size reduction, therefore, we explore the behavior of the methods
 admitting 30% and 50% of losses. For all the methods, the ELs are
 inflated, as expected, but the coverage probabilities remain close to the
 original target (with few exceptions). It is not easy to suggest a method
 that is optimal throughout the range (0, 1) for <italic>p</italic>.
 Depending on whether the goal is to achieve CP approximately or above the
 nominal level different recommendations are made. 
Journal: Journal of Applied Statistics 
Pages: 2453-2473 
Issue: 11 
Volume: 39 
Year: 2012 
Month: 7 
X-DOI: 10.1080/02664763.2012.713919 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.713919 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:11:p:2453-2473




Template-Type: ReDIF-Article 1.0
Author-Name: Kanti   V. Mardia 
Author-X-Name-First: Kanti   V. 
Author-X-Name-Last: Mardia 
Author-Name: John   T. Kent 
Author-X-Name-First: John   T. 
Author-X-Name-Last: Kent 
Author-Name: Zhengzheng Zhang 
Author-X-Name-First: Zhengzheng 
Author-X-Name-Last: Zhang 
Author-Name: Charles   C. Taylor 
Author-X-Name-First: Charles   C. 
Author-X-Name-Last: Taylor 
Author-Name: Thomas Hamelryck 
Author-X-Name-First: Thomas 
Author-X-Name-Last: Hamelryck 
Title: Mixtures of concentrated multivariate sine distributions with applications to bioinformatics 
Abstract:
  Motivated by examples in protein bioinformatics, we study a mixture model
 of multivariate angular distributions. The distribution treated here
 (multivariate sine distribution) is a multivariate extension of the
 well-known von Mises distribution on the circle. The density of the sine
 distribution has an intractable normalizing constant and here we propose
 to replace it in the concentrated case by a simple approximation. We study
 the EM algorithm for this distribution and apply it to a practical example
 from protein bioinformatics. 
Journal: Journal of Applied Statistics 
Pages: 2475-2492 
Issue: 11 
Volume: 39 
Year: 2012 
Month: 8 
X-DOI: 10.1080/02664763.2012.719221 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.719221 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:11:p:2475-2492




Template-Type: ReDIF-Article 1.0
Author-Name: Matthew Benigni 
Author-X-Name-First: Matthew 
Author-X-Name-Last: Benigni 
Author-Name: Reinhard Furrer 
Author-X-Name-First: Reinhard 
Author-X-Name-Last: Furrer 
Title: Spatio-temporal improvised explosive device monitoring: improving detection to minimise attacks 
Abstract:
  The improvised explosive device (IED) is a weapon of strategic influence
 on today's battlefield. IED detonations occur predominantly on roads,
 footpaths, or trails. Therefore, locations are best described when
 constrained to the road network, and some spaces on the network are more
 dangerous at specific times of the day. We propose a statistical model
 that reduces the spatial location to one dimension and uses a cyclic time
 as a second dimension. Based on the Poisson process methodology, we
 develop normalised, inhomogeneous, bivariate intensity functions measuring
 the threat of attack to support resourcing decisions. A simulation and an
 analysis of attacks on a main supply route in Baghdad are given to
 illustrate the proposed methods. Additionally, we provide an overview of
 the growing demand for the analysis efforts in support of operations in
 Afghanistan and Iraq, and provide an extensive literature review of
 developments in counter-IED analysis. 
Journal: Journal of Applied Statistics 
Pages: 2493-2508 
Issue: 11 
Volume: 39 
Year: 2012 
Month: 8 
X-DOI: 10.1080/02664763.2012.719222 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.719222 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:11:p:2493-2508




Template-Type: ReDIF-Article 1.0
Author-Name: Alan Kimber 
Author-X-Name-First: Alan 
Author-X-Name-Last: Kimber 
Author-Name: Shah-Jalal Sarker 
Author-X-Name-First: Shah-Jalal 
Author-X-Name-Last: Sarker 
Title: A covariance-based test for shared frailty in multivariate lifetime data 
Abstract:
  We decompose the score statistic for testing for shared finite variance
 frailty in multivariate lifetime data into marginal and covariance-based
 terms. The null properties of the covariance-based statistic are derived
 in the context of parametric lifetime models. Its non-null properties are
 estimated using simulation and compared with those of the score test and
 two likelihood ratio tests when the underlying lifetime distribution is
 Weibull. Some examples are used to illustrate the covariance-based test. A
 case is made for using the covariance-based statistic as a simple
 diagnostic procedure for shared frailty in a parametric exploratory
 analysis of multivariate lifetime data and a link to the bivariate
 Clayton--Oakes copula model is shown. 
Journal: Journal of Applied Statistics 
Pages: 2509-2522 
Issue: 11 
Volume: 39 
Year: 2012 
Month: 8 
X-DOI: 10.1080/02664763.2012.720966 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.720966 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:11:p:2509-2522




Template-Type: ReDIF-Article 1.0
Author-Name: Irina   Chis Ster 
Author-X-Name-First: Irina   Chis 
Author-X-Name-Last: Ster 
Title: Inference for serological surveys investigating past exposures to infections resulting in long-lasting immunity -- an approach using finite mixture models with concomitant information 
Abstract:
  This paper is concerned with developing a latent class mixture modelling
 technique which efficiently exploits data from serological surveys aiming
 to investigate past exposures to infections resulting in long-term or
 life-lasting immunity. Mixture components featured by antibody
 assays&#x2019; distribution are associated with the serological groups in
 the population, whilst the probability mixture that an individual belongs
 to the positive serological group is regarded as an age-dependent
 prevalence. The latter embeds a mechanistic model which explains the
 infection process, accounting for heterogeneities, contact patterns in the
 population and incorporating elements of study design. A Bayesian
 framework for statistical inference using Markov chain Monte Carlo
 estimation methods naturally accommodates missing responses in the data
 and allows straightforward assessement of uncertainties in nonlinear
 models. The applicability of the method is illustrated by investigating
 past exposure to varicella zoster virus infection in pre-school children,
 using data from a large scale UK cohort study which included a
 cross-sectional serological survey based on oral fluid samples. 
Journal: Journal of Applied Statistics 
Pages: 2523-2542 
Issue: 11 
Volume: 39 
Year: 2012 
Month: 8 
X-DOI: 10.1080/02664763.2012.722608 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.722608 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:11:p:2523-2542




Template-Type: ReDIF-Article 1.0
Author-Name: Liu-Cang Wu 
Author-X-Name-First: Liu-Cang 
Author-X-Name-Last: Wu 
Author-Name: Zhong-Zhan Zhang 
Author-X-Name-First: Zhong-Zhan 
Author-X-Name-Last: Zhang 
Author-Name: Deng-Ke Xu 
Author-X-Name-First: Deng-Ke 
Author-X-Name-Last: Xu 
Title: Variable selection in joint mean and variance models of Box--Cox transformation 
Abstract:
  In many applications, a single Box--Cox transformation cannot necessarily
 produce the normality, constancy of variance and linearity of systematic
 effects. In this paper, by establishing a heterogeneous linear regression
 model for the Box--Cox transformed response, we propose a hybrid strategy,
 in which variable selection is employed to reduce the dimension of the
 explanatory variables in joint mean and variance models, and Box--Cox
 transformation is made to remedy the response. We propose a unified
 procedure which can simultaneously select significant variables in the
 joint mean and variance models of Box--Cox transformation which provide a
 useful extension of the ordinary normal linear regression models. With
 appropriate choice of the tuning parameters, we establish the consistency
 of this procedure and the oracle property of the obtained estimators.
 Moreover, we also consider the maximum profile likelihood estimator of the
 Box--Cox transformation parameter. Simulation studies and a real example
 are used to illustrate the application of the proposed methods. 
Journal: Journal of Applied Statistics 
Pages: 2543-2555 
Issue: 12 
Volume: 39 
Year: 2012 
Month: 8 
X-DOI: 10.1080/02664763.2012.722609 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.722609 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:12:p:2543-2555




Template-Type: ReDIF-Article 1.0
Author-Name: Chang-Shuai Li 
Author-X-Name-First: Chang-Shuai 
Author-X-Name-Last: Li 
Title: Analysis of hedging based on co-persistence theory 
Abstract:
  This article analyzes the relationship between co-persistence and
 hedging, which indicates that the co-persistence ratio is just the
 long-term hedging ratio. The new method of exhaustive search algorithm for
 deriving co-persistence ratio is derived in the article. And we also
 develop a new hedging strategy of combining co-persistence with dynamic
 hedging which can enhance the hedging effectiveness and reduce the
 persistence of the hedged portfolio. Finally, our strategy is illustrated
 to study the hedge of JIASHI300 index and HS300 stock index future. 
Journal: Journal of Applied Statistics 
Pages: 2557-2567 
Issue: 12 
Volume: 39 
Year: 2012 
Month: 8 
X-DOI: 10.1080/02664763.2012.722610 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.722610 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:12:p:2557-2567




Template-Type: ReDIF-Article 1.0
Author-Name: Elias Zintzaras 
Author-X-Name-First: Elias 
Author-X-Name-Last: Zintzaras 
Title: The power of generalized odds ratio in assessing association in genetic studies with known mode of inheritance 
Abstract:
  The generalized odds ratio (OR<sub>G</sub>) is a novel model-free
 approach to test the association in genetic studies by estimating the
 overall risk effect based on the complete genotype distribution. However,
 the power of OR<sub>G</sub> has not been explored and, particularly, in a
 setting where the mode of inheritance is known. A population genetics
 model was simulated in order to define the mode of inheritance of a
 pertinent gene--disease association in advance. Then, the power of
 OR<sub>G</sub> was explored based on this model and compared with the
 chi-square test for trend. The model considered bi- and tri-allelic
 gene--disease associations, and deviations from the Hardy--Weinberg
 equilibrium (HWE). The simulations showed that bi- and tri-allelic
 variants have the same pattern of power results. The power of
 OR<sub>G</sub> increases with increase in the frequency of mutant allele
 and the coefficient of selection and, of course, the degree of dominance
 of the mutant allele. The deviation from HWE has a considerable impact on
 power only for small values of the above parameters. The OR<sub>G</sub>
 showed superiority in power compared with the chi-square test for trend
 when there is deviation from HWE; otherwise, the pattern of results was
 similar in both the approaches. 
Journal: Journal of Applied Statistics 
Pages: 2569-2581 
Issue: 12 
Volume: 39 
Year: 2012 
Month: 8 
X-DOI: 10.1080/02664763.2012.722611 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.722611 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:12:p:2569-2581




Template-Type: ReDIF-Article 1.0
Author-Name: Kei Hirose 
Author-X-Name-First: Kei 
Author-X-Name-Last: Hirose 
Author-Name: Tomoyuki Higuchi 
Author-X-Name-First: Tomoyuki 
Author-X-Name-Last: Higuchi 
Title: Creating facial animation of characters via MoCap data 
Abstract:
  We consider the problem of generating 3D facial animation of characters.
 An efficient procedure is realized by using the motion capture data (MoCap
 data), which is obtained by tracking the facial markers from an
 actor/actress. In some cases of artistic animation, the MoCap
 actor/actress and the 3D character facial animation show different
 expressions. For example, from the original facial MoCap data of speaking,
 a user would like to create the character facial animation of speaking
 with a smirk. In this paper, we propose a new easy-to-use system for
 making character facial animation via MoCap data. Our system is based on
 the interpolation: once the character facial expressions of the starting
 and the ending frames are given, the intermediate frames are automatically
 generated by information from the MoCap data. The interpolation procedure
 consists of three stages. First, the time axis of animation is divided
 into several intervals by the fused lasso signal approximator. In the
 second stage, we use the kernel <italic>k</italic>-means clustering to
 obtain control points. Finally, the interpolation is realized by using the
 control points. The user can easily create a wide variety of 3D character
 facial expressions by changing the control points. 
Journal: Journal of Applied Statistics 
Pages: 2583-2597 
Issue: 12 
Volume: 39 
Year: 2012 
Month: 8 
X-DOI: 10.1080/02664763.2012.724391 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.724391 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:12:p:2583-2597




Template-Type: ReDIF-Article 1.0
Author-Name: Vera Hofer 
Author-X-Name-First: Vera 
Author-X-Name-Last: Hofer 
Author-Name: Johannes Leitner 
Author-X-Name-First: Johannes 
Author-X-Name-Last: Leitner 
Title: A bivariate Sarmanov regression model for count data with generalised Poisson marginals 
Abstract:
  We present a bivariate regression model for count data that allows for
 positive as well as negative correlation of the response variables. The
 covariance structure is based on the Sarmanov distribution and consists of
 a product of generalised Poisson marginals and a factor that depends on
 particular functions of the response variables. The closed form of the
 probability function is derived by means of the moment-generating
 function. The model is applied to a large real dataset on health care
 demand. Its performance is compared with alternative models presented in
 the literature. We find that our model is significantly better than or at
 least equivalent to the benchmark models. It gives insights into
 influences on the variance of the response variables. 
Journal: Journal of Applied Statistics 
Pages: 2599-2617 
Issue: 12 
Volume: 39 
Year: 2012 
Month: 8 
X-DOI: 10.1080/02664763.2012.724661 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.724661 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:12:p:2599-2617




Template-Type: ReDIF-Article 1.0
Author-Name: Suzanne   V. Landram 
Author-X-Name-First: Suzanne   V. 
Author-X-Name-Last: Landram 
Author-Name: Frank   G. Landram 
Author-X-Name-First: Frank   G. 
Author-X-Name-Last: Landram 
Title: A computational understanding of partial and part determination coefficients 
Abstract:
  A computational understanding of partial and part determination
 coefficients brings additional insight concerning their interpretations in
 regression. It also enables one to easily identify synergistic
 combinations. Benefits from the practical interpretation of synergism have
 yet to be fully explored and exploited. Hence, this study provides a new
 dimension in the analysis of data. 
Journal: Journal of Applied Statistics 
Pages: 2619-2626 
Issue: 12 
Volume: 39 
Year: 2012 
Month: 8 
X-DOI: 10.1080/02664763.2012.724662 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.724662 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:12:p:2619-2626




Template-Type: ReDIF-Article 1.0
Author-Name: Zangin Zeebari 
Author-X-Name-First: Zangin 
Author-X-Name-Last: Zeebari 
Title: Developing ridge estimation method for median regression 
Abstract:
  In this paper, the ridge estimation method is generalized to the median
 regression. Though the least absolute deviation (LAD) estimation method is
 robust in the presence of non-Gaussian or asymmetric error terms, it can
 still deteriorate into a severe multicollinearity problem when
 non-orthogonal explanatory variables are involved. The proposed method
 increases the efficiency of the LAD estimators by reducing the variance
 inflation and giving more room for the bias to get a smaller mean squared
 error of the LAD estimators. This paper includes an application of the new
 methodology and a simulation study as well. 
Journal: Journal of Applied Statistics 
Pages: 2627-2638 
Issue: 12 
Volume: 39 
Year: 2012 
Month: 8 
X-DOI: 10.1080/02664763.2012.724663 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.724663 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:12:p:2627-2638




Template-Type: ReDIF-Article 1.0
Author-Name: Byron   J. Gajewski 
Author-X-Name-First: Byron   J. 
Author-X-Name-Last: Gajewski 
Author-Name: Robert Lee 
Author-X-Name-First: Robert 
Author-X-Name-Last: Lee 
Author-Name: Nancy Dunton 
Author-X-Name-First: Nancy 
Author-X-Name-Last: Dunton 
Title: Data envelopment analysis in the presence of measurement error: case study from the National Database of Nursing Quality Indicators-super-&#xAE; (NDNQI-super-&#xAE;) 
Abstract:
  Data envelopment analysis (DEA) is the most commonly used approach for
 evaluating healthcare efficiency [B. Hollingsworth, The measurement of
 efficiency and productivity of health care delivery. <italic>Health
 Economics</italic> 17(10) (2008), pp. 1107--1128], but a long-standing
 concern is that DEA assumes that data are measured without error. This is
 quite unlikely, and DEA and other efficiency analysis techniques may yield
 biased efficiency estimates if it is not realized [B.J. Gajewski, R. Lee,
 M. Bott, U. Piamjariyakul, and R.L. Taunton, On estimating the
 distribution of data envelopment analysis efficiency scores: an
 application to nursing homes&#x2019; care planning process.
 <italic>Journal of Applied Statistics</italic> 36(9) (2009), pp. 933--944;
 J. Ruggiero, Data envelopment analysis with stochastic data.
 <italic>Journal of the Operational Research Society</italic> 55 (2004),
 pp. 1008--1012]. We propose to address measurement error systematically
 using a Bayesian method (Bayesian DEA). We will apply Bayesian DEA to data
 from the National Database of Nursing Quality Indicators-super-&#xAE; to
 estimate nursing units&#x2019; efficiency. Several external reliability
 studies inform the posterior distribution of the measurement error on the
 DEA variables. We will discuss the case of generalizing the approach to
 situations where an external reliability study is not feasible. 
Journal: Journal of Applied Statistics 
Pages: 2639-2653 
Issue: 12 
Volume: 39 
Year: 2012 
Month: 8 
X-DOI: 10.1080/02664763.2012.724664 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.724664 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:12:p:2639-2653




Template-Type: ReDIF-Article 1.0
Author-Name: Rand   R. Wilcox 
Author-X-Name-First: Rand   R. 
Author-X-Name-Last: Wilcox 
Author-Name: David   M. Erceg-Hurn 
Author-X-Name-First: David   M. 
Author-X-Name-Last: Erceg-Hurn 
Title: Comparing two dependent groups via quantiles 
Abstract:
  This paper considers two general ways dependent groups might be compared
 based on quantiles. The first compares the quantiles of the marginal
 distributions. The second focuses on the lower and upper quantiles of the
 usual difference scores. Methods for comparing quantiles have been derived
 that typically assume that sampling is from a continuous distribution.
 There are exceptions, but generally, when sampling from a discrete
 distribution where tied values are likely, extant methods can perform
 poorly, even with a large sample size. One reason is that extant methods
 for estimating the standard error can perform poorly. Another is that
 quantile estimators based on a single-order statistic, or a weighted
 average of two-order statistics, are not necessarily asymptotically
 normal. Our main result is that when using the Harrell--Davis estimator,
 good control over the Type I error probability can be achieved in
 simulations via a standard percentile bootstrap method, even when there
 are tied values, provided the sample sizes are not too small. In addition,
 the two methods considered here can have substantially higher power than
 alternative procedures. Using real data, we illustrate how quantile
 comparisons can be used to gain a deeper understanding of how groups
 differ. 
Journal: Journal of Applied Statistics 
Pages: 2655-2664 
Issue: 12 
Volume: 39 
Year: 2012 
Month: 8 
X-DOI: 10.1080/02664763.2012.724665 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.724665 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:12:p:2655-2664




Template-Type: ReDIF-Article 1.0
Author-Name: Mariagiulia Matteucci 
Author-X-Name-First: Mariagiulia 
Author-X-Name-Last: Matteucci 
Author-Name: Stefania Mignani 
Author-X-Name-First: Stefania 
Author-X-Name-Last: Mignani 
Author-Name: Bernard   P. Veldkamp 
Author-X-Name-First: Bernard   P. 
Author-X-Name-Last: Veldkamp 
Title: The use of predicted values for item parameters in item response theory models: an application in intelligence tests 
Abstract:
  In testing, item response theory models are widely used in order to
 estimate item parameters and individual abilities. However, even
 unidimensional models require a considerable sample size so that all
 parameters can be estimated precisely. The introduction of empirical prior
 information about candidates and items might reduce the number of
 candidates needed for parameter estimation. Using data for IQ measurement,
 this work shows how empirical information about items can be used
 effectively for item calibration and in adaptive testing. First, we
 propose multivariate regression trees to predict the item parameters based
 on a set of covariates related to the item-solving process. Afterwards, we
 compare the item parameter estimation when tree-fitted values are included
 in the estimation or when they are ignored. Model estimation is fully
 Bayesian, and is conducted via Markov chain Monte Carlo methods. The
 results are two-fold: (a) in item calibration, it is shown that the
 introduction of prior information is effective with short test lengths and
 small sample sizes and (b) in adaptive testing, it is demonstrated that
 the use of the tree-fitted values instead of the estimated parameters
 leads to a moderate increase in the test length, but provides a
 considerable saving of resources. 
Journal: Journal of Applied Statistics 
Pages: 2665-2683 
Issue: 12 
Volume: 39 
Year: 2012 
Month: 8 
X-DOI: 10.1080/02664763.2012.725034 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.725034 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:12:p:2665-2683




Template-Type: ReDIF-Article 1.0
Author-Name: Osvaldo Venegas 
Author-X-Name-First: Osvaldo 
Author-X-Name-Last: Venegas 
Author-Name: Francisco Rodr�guez 
Author-X-Name-First: Francisco 
Author-X-Name-Last: Rodr�guez 
Author-Name: H�ctor   W. G&#xF3;mez 
Author-X-Name-First: H�ctor   W. 
Author-X-Name-Last: G&#xF3;mez 
Author-Name: Juan   F. Olivares-Pacheco 
Author-X-Name-First: Juan   F. 
Author-X-Name-Last: Olivares-Pacheco 
Author-Name: Heleno Bolfarine 
Author-X-Name-First: Heleno 
Author-X-Name-Last: Bolfarine 
Title: Robust modeling using the generalized epsilon-skew-<italic>t</italic> distribution 
Abstract:
  In this paper, an alternative skew Student-<italic>t</italic> family of
 distributions is studied. It is obtained as an extension of the
 generalized Student-<italic>t</italic> (GS-<italic>t</italic>) family
 introduced by McDonald and Newey [10]. The extension that is obtained can
 be seen as a reparametrization of the skewed GS-<italic>t</italic>
 distribution considered by Theodossiou [14]. A key element in the
 construction of such an extension is that it can be stochastically
 represented as a mixture of an epsilon-skew-power-exponential distribution
 [1] and a generalized-gamma distribution. From this representation, we can
 readily derive theoretical properties and easy-to-implement simulation
 schemes. Furthermore, we study some of its main properties including
 stochastic representation, moments and asymmetry and kurtosis
 coefficients. We also derive the Fisher information matrix, which is shown
 to be nonsingular for some special cases such as when the asymmetry
 parameter is null, that is, at the vicinity of symmetry, and discuss
 maximum-likelihood estimation. Simulation studies for some particular
 cases and real data analysis are also reported, illustrating the
 usefulness of the extension considered. 
Journal: Journal of Applied Statistics 
Pages: 2685-2698 
Issue: 12 
Volume: 39 
Year: 2012 
Month: 8 
X-DOI: 10.1080/02664763.2012.725462 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.725462 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:12:p:2685-2698




Template-Type: ReDIF-Article 1.0
Author-Name: S. Mukhopadhyay 
Author-X-Name-First: S. 
Author-X-Name-Last: Mukhopadhyay 
Author-Name: I. Das 
Author-X-Name-First: I. 
Author-X-Name-Last: Das 
Author-Name: K. Das 
Author-X-Name-First: K. 
Author-X-Name-Last: Das 
Title: Selection of a stroke risk model based on transcranial Doppler ultrasound velocity 
Abstract:
  Increased transcranial Doppler ultrasound (TCD) velocity is an indicator
 of cerebral infarction in children with sickle cell disease (SCD). In this
 article, the parallel genetic algorithm (PGA) is used to select a stroke
 risk model with TCD velocity as the response variable. Development of such
 a stroke risk model leads to the identification of children with SCD who
 are at a higher risk of stroke and their treatment in the early stages.
 Using blood velocity data from SCD patients, it is shown that the PGA is
 an easy-to-use computationally variable selection tool. The results of the
 PGA are also compared with those obtained from the stochastic search
 variable selection method, the Dantzig selector and conventional
 techniques such as stepwise selection and best subset selection. 
Journal: Journal of Applied Statistics 
Pages: 2699-2712 
Issue: 12 
Volume: 39 
Year: 2012 
Month: 8 
X-DOI: 10.1080/02664763.2012.725463 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.725463 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:12:p:2699-2712




Template-Type: ReDIF-Article 1.0
Author-Name: H. E.T. Holgersson 
Author-X-Name-First: H. E.T. 
Author-X-Name-Last: Holgersson 
Author-Name: Peter   S. Karlsson 
Author-X-Name-First: Peter   S. 
Author-X-Name-Last: Karlsson 
Title: Three estimators of the Mahalanobis distance in high-dimensional data 
Abstract:
  This paper treats the problem of estimating the Mahalanobis distance for
 the purpose of detecting outliers in high-dimensional data. Three
 ridge-type estimators are proposed and risk functions for deciding an
 appropriate value of the ridge coefficient are developed. It is argued
 that one of the ridge estimator has particularly tractable properties,
 which is demonstrated through outlier analysis of real and simulated data. 
Journal: Journal of Applied Statistics 
Pages: 2713-2720 
Issue: 12 
Volume: 39 
Year: 2012 
Month: 8 
X-DOI: 10.1080/02664763.2012.725464 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.725464 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:12:p:2713-2720




Template-Type: ReDIF-Article 1.0
Author-Name: Julio   C. Ferreira 
Author-X-Name-First: Julio   C. 
Author-X-Name-Last: Ferreira 
Author-Name: Marta   A. Freitas 
Author-X-Name-First: Marta   A. 
Author-X-Name-Last: Freitas 
Author-Name: Enrico   A. Colosimo 
Author-X-Name-First: Enrico   A. 
Author-X-Name-Last: Colosimo 
Title: Degradation data analysis for samples under unequal operating conditions: a case study on train wheels 
Abstract:
  Traditionally, reliability assessment of devices has been based on life
 tests (LTs) or accelerated life tests (ALTs). However, these approaches
 are not practical for high-reliability devices which are not likely to
 fail in experiments of reasonable length. For these devices, LTs or ALTs
 will end up with a high censoring rate compromising the traditional
 estimation methods. An alternative approach is to monitor the devices for
 a period of time and assess their reliability from the changes in
 performance (degradation) observed during the experiment. In this paper,
 we present a model to evaluate the problem of train wheel degradation,
 which is related to the failure modes of train derailments. We first
 identify the most significant working conditions affecting the wheel wear
 using a nonlinear mixed-effects (NLME) model where the log-rate of wear is
 a linear function of some working conditions such as side, truck and axle
 positions. Next, we estimate the failure time distribution by working
 condition analytically. Point and interval estimates of reliability
 figures by working condition are also obtained. We compare the results of
 the analysis via an NLME to the ones obtained by an approximate
 degradation analysis. 
Journal: Journal of Applied Statistics 
Pages: 2721-2739 
Issue: 12 
Volume: 39 
Year: 2012 
Month: 8 
X-DOI: 10.1080/02664763.2012.725465 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.725465 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:12:p:2721-2739




Template-Type: ReDIF-Article 1.0
Author-Name: Ahmed   A. Soliman 
Author-X-Name-First: Ahmed   A. 
Author-X-Name-Last: Soliman 
Author-Name: A.   H. Abd Ellah 
Author-X-Name-First: A.   H. 
Author-X-Name-Last: Abd Ellah 
Author-Name: N.   A. Abou-Elheggag 
Author-X-Name-First: N.   A. 
Author-X-Name-Last: Abou-Elheggag 
Author-Name: A.   A. Modhesh 
Author-X-Name-First: A.   A. 
Author-X-Name-Last: Modhesh 
Title: Estimation of the coefficient of variation for non-normal model using progressive first-failure-censoring data 
Abstract:
  The coefficient of variation (CV) is extensively used in many areas of
 applied statistics including quality control and sampling. It is regarded
 as a measure of stability or uncertainty, and can indicate the relative
 dispersion of data in the population to the population mean. In this
 article, based on progressive first-failure-censored data, we study the
 behavior of the CV of a random variable that follows a Burr-XII
 distribution. Specifically, we compute the maximum likelihood estimations
 and the confidence intervals of CV based on the observed Fisher
 information matrix using asymptotic distribution of the maximum likelihood
 estimator and also by using the bootstrapping technique. In addition, we
 propose to apply Markov Chain Monte Carlo techniques to tackle this
 problem, which allows us to construct the credible intervals. A numerical
 example based on real data is presented to illustrate the implementation
 of the proposed procedure. Finally, Monte Carlo simulations are performed
 to observe the behavior of the proposed methods. 
Journal: Journal of Applied Statistics 
Pages: 2741-2758 
Issue: 12 
Volume: 39 
Year: 2012 
Month: 8 
X-DOI: 10.1080/02664763.2012.725466 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.725466 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:12:p:2741-2758




Template-Type: ReDIF-Article 1.0
Author-Name: Yinghui Wei 
Author-X-Name-First: Yinghui 
Author-X-Name-Last: Wei 
Author-Name: Peter Neal 
Author-X-Name-First: Peter 
Author-X-Name-Last: Neal 
Author-Name: Sandra Telfer 
Author-X-Name-First: Sandra 
Author-X-Name-Last: Telfer 
Author-Name: Mike Begon 
Author-X-Name-First: Mike 
Author-X-Name-Last: Begon 
Title: Statistical analysis of an endemic disease from a capture--recapture experiment 
Abstract:
  There are a number of statistical techniques for analysing epidemic
 outbreaks. However, many diseases are endemic within populations and the
 analysis of such diseases is complicated by changing population
 demography. Motivated by the spread of cowpox amongst rodent populations,
 a combined mathematical model for population and disease dynamics is
 introduced. A Markov chain Monte Carlo algorithm is then constructed to
 make statistical inference for the model based on data being obtained from
 a capture--recapture experiment. The statistical analysis is used to
 identify the key elements in the spread of the cowpox virus. 
Journal: Journal of Applied Statistics 
Pages: 2759-2773 
Issue: 12 
Volume: 39 
Year: 2012 
Month: 8 
X-DOI: 10.1080/02664763.2012.725467 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.725467 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:39:y:2012:i:12:p:2759-2773




Template-Type: ReDIF-Article 1.0
Author-Name: Robert   G. Aykroyd 
Author-X-Name-First: Robert   G. 
Author-X-Name-Last: Aykroyd 
Title: Editorial 
Journal: Journal of Applied Statistics 
Pages: 1-1 
Issue: 1 
Volume: 40 
Year: 2013 
Month: 1 
X-DOI: 10.1080/02664763.2013.745767 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.745767 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:1:p:1-1




Template-Type: ReDIF-Article 1.0
Author-Name: Russell   J. Bowater 
Author-X-Name-First: Russell   J. 
Author-X-Name-Last: Bowater 
Author-Name: Gabriel Escarela 
Author-X-Name-First: Gabriel 
Author-X-Name-Last: Escarela 
Title: Heterogeneity and study size in random-effects meta-analysis 
Abstract:
  It is well known that heterogeneity between studies in a meta-analysis
 can be either caused by diversity, for example, variations in populations
 and interventions, or caused by bias, that is, variations in design
 quality and conduct of the studies. Heterogeneity that is due to bias is
 difficult to deal with. On the other hand, heterogeneity that is due to
 diversity is taken into account by a standard random-effects model.
 However, such a model generally assumes that heterogeneity does not vary
 according to study-level variables such as the size of the studies in the
 meta-analysis and the type of study design used. This paper develops
 models that allow for this type of variation in heterogeneity and
 discusses the properties of the resulting methods. The models are fitted
 using the maximum-likelihood method and by modifying the Paule--Mandel
 method. Furthermore, a real-world argument is given to support the
 assumption that the inter-study variance is inversely proportional to
 study size. Under this assumption, the corresponding random-effects method
 is shown to be connected with standard fixed-effect meta-analysis in a way
 that may well appeal to many clinicians. The models and methods that are
 proposed are applied to data from two large systematic reviews. 
Journal: Journal of Applied Statistics 
Pages: 2-16 
Issue: 1 
Volume: 40 
Year: 2013
Month: 1
X-DOI: 10.1080/02664763.2012.700448 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.700448 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:1:p:2-16




Template-Type: ReDIF-Article 1.0
Author-Name: Francis Pike 
Author-X-Name-First: Francis 
Author-X-Name-Last: Pike 
Author-Name: Lisa Weissfeld 
Author-X-Name-First: Lisa 
Author-X-Name-Last: Weissfeld 
Title: Joint modeling of censored longitudinal and event time data 
Abstract:
  Censoring of a longitudinal outcome often occurs when data are collected
 in a biomedical study and where the interest is in the survival and or
 longitudinal experiences of a study population. In the setting considered
 herein, we encountered upper and lower censored data as the result of
 restrictions imposed on measurements from a kinetic model producing
 &#x201C;biologically implausible&#x201D; kidney clearances. The goal of
 this paper is to outline the use of a joint model to determine the
 association between a censored longitudinal outcome and a time to event
 endpoint. This paper extends Guo and Carlin's [6] paper to accommodate
 censored longitudinal data, in a commercially available software platform,
 by linking a mixed effects Tobit model to a suitable parametric survival
 distribution. Our simulation results showed that our joint Tobit model
 outperforms a joint model made up of the more na&#xEF;ve or
 &#x201C;fill-in&#x201D; method for the longitudinal component. In this
 case, the upper and/or lower limits of censoring are replaced by the limit
 of detection. We illustrated the use of this approach with example data
 from the hemodialysis (HEMO) study [3] and examined the association
 between doubly censored kidney clearance values and survival. 
Journal: Journal of Applied Statistics 
Pages: 17-27 
Issue: 1 
Volume: 40 
Year: 2013
Month: 1
X-DOI: 10.1080/02664763.2012.725468 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.725468 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:1:p:17-27




Template-Type: ReDIF-Article 1.0
Author-Name: J.   M. Fern&#xE1;ndez-Ponce 
Author-X-Name-First: J.   M. 
Author-X-Name-Last: Fern&#xE1;ndez-Ponce 
Author-Name: F. Palacios-Rodr�guez 
Author-X-Name-First: F. 
Author-X-Name-Last: Palacios-Rodr�guez 
Author-Name: M.   R. Rodr�guez-Gri&#xF1;olo 
Author-X-Name-First: M.   R. 
Author-X-Name-Last: Rodr�guez-Gri&#xF1;olo 
Title: Bayesian influence diagnostics in radiocarbon dating 
Abstract:
  Linear models constitute the primary statistical technique for any
 experimental science. A major topic in this area is the detection of
 influential subsets of data, that is, of observations that are influential
 in terms of their effect on the estimation of parameters in linear
 regression or of the total population parameters. Numerous studies exist
 on radiocarbon dating which propose a value consensus and remove possible
 outliers after the corresponding testing. An influence analysis for the
 value consensus from a Bayesian perspective is developed in this article. 
Journal: Journal of Applied Statistics 
Pages: 28-39 
Issue: 1 
Volume: 40 
Year: 2013
Month: 1
X-DOI: 10.1080/02664763.2012.725531 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.725531 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:1:p:28-39




Template-Type: ReDIF-Article 1.0
Author-Name: Antonio S.M. Arroyo 
Author-X-Name-First: Antonio S.M. 
Author-X-Name-Last: Arroyo 
Author-Name: Antonio Garc�a-Ferrer 
Author-X-Name-First: Antonio 
Author-X-Name-Last: Garc�a-Ferrer 
Author-Name: Ar&#xE1;nzazu de Juan Fern&#xE1;ndez 
Author-X-Name-First: Ar&#xE1;nzazu 
Author-X-Name-Last: de Juan Fern&#xE1;ndez 
Author-Name: Roc�o S&#xE1;nchez-Mangas 
Author-X-Name-First: Roc�o 
Author-X-Name-Last: S&#xE1;nchez-Mangas 
Title: Lower posterior death probabilities from a quick medical response in road traffic accidents 
Abstract:
   <italic>Introduction</italic>: We use data from Spain on roads and
 motorways traffic accidents in May 2004 to quantify the statistical
 association between quick medical response time and mortality rate.
 <italic>Method</italic>: Probit and logit parameters are estimated by a
 Bayesian method in which samples from the posterior densities are obtained
 through an MCMC simulation scheme. We provide posterior credible intervals
 and posterior partial effects of a quick medical response at several time
 levels over which we express our prior beliefs. <italic>Results</italic>:
 A reduction of 5&#xA0;min, from a 25-min response-time level, is
 associated with lower posterior probabilities of death in roads and
 motorways accidents of 24% and 30%, respectively. 
Journal: Journal of Applied Statistics 
Pages: 40-58 
Issue: 1 
Volume: 40 
Year: 2013
Month: 1
X-DOI: 10.1080/02664763.2012.727788 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.727788 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:1:p:40-58




Template-Type: ReDIF-Article 1.0
Author-Name: Paul   H. Garthwaite 
Author-X-Name-First: Paul   H. 
Author-X-Name-Last: Garthwaite 
Author-Name: Shafeeqah   A. Al-Awadhi 
Author-X-Name-First: Shafeeqah   A. 
Author-X-Name-Last: Al-Awadhi 
Author-Name: Fadlalla   G. Elfadaly 
Author-X-Name-First: Fadlalla   G. 
Author-X-Name-Last: Elfadaly 
Author-Name: David   J. Jenkinson 
Author-X-Name-First: David   J. 
Author-X-Name-Last: Jenkinson 
Title: Prior distribution elicitation for generalized linear and piecewise-linear models 
Abstract:
  An elicitation method is proposed for quantifying subjective opinion
 about the regression coefficients of a generalized linear model. Opinion
 between a continuous predictor variable and the dependent variable is
 modelled by a piecewise-linear function, giving a flexible model that can
 represent a wide variety of opinion. To quantify his or her opinions, the
 expert uses an interactive computer program, performing assessment tasks
 that involve drawing graphs and bar-charts to specify medians and other
 quantiles. Opinion about the regression coefficients is represented by a
 multivariate normal distribution whose parameters are determined from the
 assessments. It is practical to use the procedure with models containing a
 large number of parameters. This is illustrated through practical examples
 and the benefit from using prior knowledge is examined through
 cross-validation. 
Journal: Journal of Applied Statistics 
Pages: 59-75 
Issue: 1 
Volume: 40 
Year: 2013
Month: 1
X-DOI: 10.1080/02664763.2012.734794 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.734794 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:1:p:59-75




Template-Type: ReDIF-Article 1.0
Author-Name: Pablo Mart�nez-Camblor 
Author-X-Name-First: Pablo 
Author-X-Name-Last: Mart�nez-Camblor 
Author-Name: Norberto Corral 
Author-X-Name-First: Norberto 
Author-X-Name-Last: Corral 
Author-Name: Jesus Mar�a de la Hera 
Author-X-Name-First: Jesus 
Author-X-Name-Last: Mar�a de la Hera 
Title: Hypothesis test for paired samples in the presence of missing data 
Abstract:
  Missing data are present in almost all statistical analysis. In simple
 paired design tests, when some subject has one of the involved variables
 missing in the so-called <italic>partially overlapping samples</italic>
 scheme, it is usually discarded for the analysis. The lack of consistency
 between the information reported in the univariate and multivariate
 analysis is, perhaps, the main consequence. Although the randomness on the
 missing mechanism (missingness completely at random) is an usual and
 needed assumption for this particular situation, missing data presence
 could lead to serious inconsistencies on the reported conclusions. In this
 paper, the authors develop a simple and direct procedure which allows
 using the whole available information in order to perform paired tests. In
 particular, the proposed methodology is applied to check the equality
 among the means from two paired samples. In addition, the use of two
 different resampling techniques is also explored. Finally, real-world data
 are analysed. 
Journal: Journal of Applied Statistics 
Pages: 76-87 
Issue: 1 
Volume: 40 
Year: 2013
Month: 1
X-DOI: 10.1080/02664763.2012.734795 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.734795 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:1:p:76-87




Template-Type: ReDIF-Article 1.0
Author-Name: Isabella Sulis 
Author-X-Name-First: Isabella 
Author-X-Name-Last: Sulis 
Author-Name: Vincenza Capursi 
Author-X-Name-First: Vincenza 
Author-X-Name-Last: Capursi 
Title: Building up adjusted indicators of students&#x2019; evaluation of university courses using generalized item response models 
Abstract:
  This article advances a proposal for building up adjusted composite
 indicators of the quality of university courses from students&#x2019;
 assessments. The flexible framework of Generalized Item Response Models is
 adopted here for controlling the sources of heterogeneity in the data
 structure that make evaluations across courses not directly comparable.
 Specifically, it allows us to: jointly model students&#x2019; ratings to
 the set of items which define the quality of university courses;
 explicitly consider the dimensionality of the items composing the
 evaluation form; evaluate and remove the effect of potential confounding
 factors which may affect students&#x2019; evaluation; model the
 intra-cluster variability at course level. The approach simultaneously
 deals with: (i) multilevel data structure; (ii) multidimensional latent
 trait; (iii) personal explanatory latent regression models. The paper pays
 attention to the potential of such a flexible approach in the analysis of
 students evaluation of university courses in order to explore both how the
 quality of the different aspects (teaching, management, etc.) is perceived
 by students and how to make meaningful comparisons across them on the
 basis of adjusted indicators. 
Journal: Journal of Applied Statistics 
Pages: 88-102 
Issue: 1 
Volume: 40 
Year: 2013
Month: 1
X-DOI: 10.1080/02664763.2012.734796 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.734796 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:1:p:88-102




Template-Type: ReDIF-Article 1.0
Author-Name: K.   J. Kachiashvili 
Author-X-Name-First: K.   J. 
Author-X-Name-Last: Kachiashvili 
Author-Name: M.   A. Hashmi 
Author-X-Name-First: M.   A. 
Author-X-Name-Last: Hashmi 
Author-Name: A. Mueed 
Author-X-Name-First: A. 
Author-X-Name-Last: Mueed 
Title: Quasi-optimal Bayesian procedures of many hypotheses testing 
Abstract:
  Quasi-optimal procedures of testing many hypotheses are described in this
 paper. They significantly simplify the Bayesian algorithms of hypothesis
 testing and computation of the risk function. The relations allowing for
 obtaining the estimations for the values of average risks in optimum tasks
 are given. The obtained general solutions are reduced to concrete formulae
 for a multivariate normal distribution of probabilities. The methods of
 approximate computation of the risk functions in Bayesian tasks of testing
 many hypotheses are offered. The properties and interrelations of the
 developed methods and algorithms are investigated. On the basis of a
 simulation, the validity of the obtained results and conclusions drawn is
 presented. 
Journal: Journal of Applied Statistics 
Pages: 103-122 
Issue: 1 
Volume: 40 
Year: 2013
Month: 1
X-DOI: 10.1080/02664763.2012.734797 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.734797 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:1:p:103-122




Template-Type: ReDIF-Article 1.0
Author-Name: Yanchun Bao 
Author-X-Name-First: Yanchun 
Author-X-Name-Last: Bao 
Author-Name: Hongsheng Dai 
Author-X-Name-First: Hongsheng 
Author-X-Name-Last: Dai 
Author-Name: Tao Wang 
Author-X-Name-First: Tao 
Author-X-Name-Last: Wang 
Author-Name: Sung-Kiang Chuang 
Author-X-Name-First: Sung-Kiang 
Author-X-Name-Last: Chuang 
Title: A joint modelling approach for clustered recurrent events and death events 
Abstract:
  In dental implant research studies, events such as implant complications
 including pain or infection may be observed recurrently before failure
 events, i.e. the death of implants. It is natural to assume that recurrent
 events and failure events are correlated to each other, since they happen
 on the same implant (subject) and complication times have strong effects
 on the implant survival time. On the other hand, each patient may have
 more than one implant. Therefore these recurrent events or failure events
 are clustered since implant complication times or failure times within the
 same patient (cluster) are likely to be correlated. The overall implant
 survival times and recurrent complication times are both interesting to
 us. In this paper, a joint modelling approach is proposed for modelling
 complication events and dental implant survival times simultaneously. The
 proposed method uses a frailty process to model the correlation within
 cluster and the correlation within subjects. We use Bayesian methods to
 obtain estimates of the parameters. Performance of the joint models are
 shown via simulation studies and data analysis. 
Journal: Journal of Applied Statistics 
Pages: 123-140 
Issue: 1 
Volume: 40 
Year: 2013
Month: 1
X-DOI: 10.1080/02664763.2012.735225 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.735225 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:1:p:123-140




Template-Type: ReDIF-Article 1.0
Author-Name: Eugenia Nissi 
Author-X-Name-First: Eugenia 
Author-X-Name-Last: Nissi 
Author-Name: Annalina Sarra 
Author-X-Name-First: Annalina 
Author-X-Name-Last: Sarra 
Title: A simulation study on the hybrid nature of Tango's index 
Abstract:
  Since the early 1990s, there has been an increasing interest in
 statistical methods for detecting global spatial clustering in data sets.
 Tango's index is one of the most widely used spatial statistics for
 assessing whether spatially distributed disease rates are independent or
 clustered. Interestingly, this statistic can be partitioned into the sum
 of two terms: one term is similar to the usual chi-square statistic, being
 based on deviation patterns between the observed and expected values, and
 the other term, similar to Moran's I, is able to detect the proximity of
 similar values. In this paper, we examine this hybrid nature of Tango's
 index. The goal is to evaluate the possibility of distinguishing the
 spatial sources of clustering: lack of fit or spatial autocorrelation. To
 comply with the aims of the work, a simulation study is performed, by
 which examples of patterns driving the goodness-of-fit and spatial
 autocorrelation components of the statistic are provided. As for the
 latter aspect, it is worth noting that inducing spatial association among
 count data without adding lack of fit is not an easy task. In this
 respect, the overlapping sums method is adopted. The main findings of the
 simulation experiment are illustrated and a comparison with a previous
 research on this topic is also highlighted. 
Journal: Journal of Applied Statistics 
Pages: 141-151 
Issue: 1 
Volume: 40 
Year: 2013
Month: 1
X-DOI: 10.1080/02664763.2012.738189 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.738189 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:1:p:141-151




Template-Type: ReDIF-Article 1.0
Author-Name: Han   Lin Shang 
Author-X-Name-First: Han   Lin 
Author-X-Name-Last: Shang 
Title: Functional time series approach for forecasting very short-term electricity demand 
Abstract:
  This empirical paper presents a number of functional modelling and
 forecasting methods for predicting very short-term (such as
 minute-by-minute) electricity demand. The proposed functional methods
 slice a seasonal univariate time series (TS) into a TS of curves; reduce
 the dimensionality of curves by applying functional principal component
 analysis before using a univariate TS forecasting method and regression
 techniques. As data points in the daily electricity demand are
 sequentially observed, a forecast updating method can greatly improve the
 accuracy of point forecasts. Moreover, we present a non-parametric
 bootstrap approach to construct and update prediction intervals, and
 compare the point and interval forecast accuracy with some naive benchmark
 methods. The proposed methods are illustrated by the half-hourly
 electricity demand from Monday to Sunday in South Australia. 
Journal: Journal of Applied Statistics 
Pages: 152-168 
Issue: 1 
Volume: 40 
Year: 2013 
Month: 1
X-DOI: 10.1080/02664763.2012.740619 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.740619 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:1:p:152-168




Template-Type: ReDIF-Article 1.0
Author-Name: Donald   W. Zimmerman 
Author-X-Name-First: Donald   W. 
Author-X-Name-Last: Zimmerman 
Title: Heterogeneity of variance and biased hypothesis tests 
Abstract:
  This study examined the influence of heterogeneity of variance on Type I
 error rates and power of the independent-samples Student's
 <italic>t</italic>-test of equality of means on samples of scores from
 normal and 10 non-normal distributions. The same test of equality of means
 was performed on corresponding rank-transformed scores. For many
 non-normal distributions, both versions produced anomalous power
 functions, resulting partly from the fact that the hypothesis test was
 <italic>biased</italic>, so that under some conditions, the probability of
 rejecting <italic>H</italic> <sub>0</sub> decreased as the difference
 between means increased. In all cases where bias occurred, the
 <italic>t</italic>-test on ranks exhibited substantially greater bias than
 the <italic>t</italic>-test on scores. This anomalous result was
 independent of the more familiar changes in Type I error rates and power
 attributable to unequal sample sizes combined with unequal variances. 
Journal: Journal of Applied Statistics 
Pages: 169-193 
Issue: 1 
Volume: 40 
Year: 2013
Month: 1
X-DOI: 10.1080/02664763.2012.740620 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.740620 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:1:p:169-193




Template-Type: ReDIF-Article 1.0
Author-Name: S.   H. Lin 
Author-X-Name-First: S.   H. 
Author-X-Name-Last: Lin 
Author-Name: R.   S. Wang 
Author-X-Name-First: R.   S. 
Author-X-Name-Last: Wang 
Title: Modified method on the means for several log-normal distributions 
Abstract:
  Among statistical inferences, one of the main interests is drawing the
 inferences about the log-normal means since the log-normal distribution is
 a well-known candidate model for analyzing positive and right-skewed data.
 In the past, the researchers only focused on one or two log-normal
 populations or used the large sample theory or quadratic procedure to deal
 with several log-normal distributions. In this article, we focus on making
 inferences on several log-normal means based on the modification of the
 quadratic method, in which the researchers often used the vector of the
 generalized variables to deal with the means of the symmetric
 distributions. Simulation studies show that the quadratic method performs
 well only for symmetric distributions. However, the modified procedure
 fits both symmetric and skew distribution. The numerical results show that
 the proposed modified procedure can provide the confidence interval with
 coverage probabilities close to the nominal level and the hypothesis
 testing performed with satisfactory results. 
Journal: Journal of Applied Statistics 
Pages: 194-208 
Issue: 1 
Volume: 40 
Year: 2013
Month: 1
X-DOI: 10.1080/02664763.2012.740622 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.740622 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:1:p:194-208




Template-Type: ReDIF-Article 1.0
Author-Name: Xu-Qing Liu 
Author-X-Name-First: Xu-Qing 
Author-X-Name-Last: Liu 
Author-Name: Feng Gao 
Author-X-Name-First: Feng 
Author-X-Name-Last: Gao 
Author-Name: Zhen-Feng Yu 
Author-X-Name-First: Zhen-Feng 
Author-X-Name-Last: Yu 
Title: Improved ridge estimators in a linear regression model 
Abstract:
  In this paper, the notion of the improved ridge estimator (IRE) is put
 forward in the linear regression model <bold>y</bold>=<bold>X</bold>
 <bold>&#x3B2;</bold>+<bold>e</bold>. The problem arises if augmenting the
 equation
 <bold>0</bold>=<bold>c</bold>&#x2032;<bold>&#x3B1;</bold>+<bold>&#x3B5;</b
 old> instead of <bold>0</bold>=<bold>C</bold>
 <bold>&#x3B1;</bold>+<bold>&#x3F5;</bold> to the model. Three special IREs
 are considered and studied under the mean-squared error criterion and the
 prediction error sum of squares criterion. The simulations demonstrate
 that the proposed estimators are effective and recommendable, especially
 when multicollinearity is severe. 
Journal: Journal of Applied Statistics 
Pages: 209-220 
Issue: 1 
Volume: 40 
Year: 2013 
Month: 1
X-DOI: 10.1080/02664763.2012.740623 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.740623 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:1:p:209-220




Template-Type: ReDIF-Article 1.0
Author-Name: Cesaltina Pires 
Author-X-Name-First: Cesaltina 
Author-X-Name-Last: Pires 
Author-Name: Andreia Dion�sio 
Author-X-Name-First: Andreia 
Author-X-Name-Last: Dion�sio 
Author-Name: Lu�s Coelho 
Author-X-Name-First: Lu�s 
Author-X-Name-Last: Coelho 
Title: Estimating utility functions using generalized maximum entropy 
Abstract:
  This paper estimates von Neumann and Morgenstern utility functions using
 the generalized maximum entropy (GME), applied to data obtained by utility
 elicitation methods. Given the statistical advantages of this approach, we
 provide a comparison of the performance of the GME estimator with ordinary
 least square (OLS) in a real data small sample setup. The results confirm
 the ones obtained for small samples through Monte Carlo simulations. The
 difference between the two estimators is small and it decreases as the
 width of the parameter support vector increases. Moreover, the GME
 estimator is more precise than the OLS one. Overall, the results suggest
 that GME is an interesting alternative to OLS in the estimation of utility
 functions when data are generated by utility elicitation methods. 
Journal: Journal of Applied Statistics 
Pages: 221-234 
Issue: 1 
Volume: 40 
Year: 2013
Month: 1
X-DOI: 10.1080/02664763.2012.740625 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.740625 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:1:p:221-234




Template-Type: ReDIF-Article 1.0
Author-Name: Faping Duan 
Author-X-Name-First: Faping 
Author-X-Name-Last: Duan 
Author-Name: Daniel Ogden 
Author-X-Name-First: Daniel 
Author-X-Name-Last: Ogden 
Author-Name: Ling Xu 
Author-X-Name-First: Ling 
Author-X-Name-Last: Xu 
Author-Name: Kang Liu 
Author-X-Name-First: Kang 
Author-X-Name-Last: Liu 
Author-Name: George Lust 
Author-X-Name-First: George 
Author-X-Name-Last: Lust 
Author-Name: Jody Sandler 
Author-X-Name-First: Jody 
Author-X-Name-Last: Sandler 
Author-Name: Nathan   L. Dykes 
Author-X-Name-First: Nathan   L. 
Author-X-Name-Last: Dykes 
Author-Name: Lan Zhu 
Author-X-Name-First: Lan 
Author-X-Name-Last: Zhu 
Author-Name: Steven Harris 
Author-X-Name-First: Steven 
Author-X-Name-Last: Harris 
Author-Name: Paul Jones 
Author-X-Name-First: Paul 
Author-X-Name-Last: Jones 
Author-Name: Rory   J. Todhunter 
Author-X-Name-First: Rory   J. 
Author-X-Name-Last: Todhunter 
Author-Name: Zhiwu Zhang 
Author-X-Name-First: Zhiwu 
Author-X-Name-Last: Zhang 
Title: Principal component analysis of canine hip dysplasia phenotypes and their statistical power for genome-wide association mapping 
Abstract:
  The aims of this study were to undertake principal component analysis
 (PCA) of hip dysplasia (HD) and to examine the power of the principal
 components (PCs) in genome-wide association studies. A cohort of 278 dogs
 for PCA and that of 369 dogs for genotyping were used. The distraction
 index (DI), the dorsolateral subluxation (DLS) score, the Norberg angle
 (NA), and the extended-hip radiographic (EHR) score were used for the PCA.
 One thousand single-nucleotide polymorphisms (SNPs) (of 23,500) were used
 to simulate genetic locus sharing between the HD phenotypes and 1000 SNPs
 were used to calculate the genetic mapping power of the PCs. The DI and
 the DLS score (first group) reflected hip laxity and the NA and the EHR
 score (second group) reflected the congruency between the femoral head and
 acetabulum. The average hip measurements of the two groups reflected in
 the first PC captured 55% of total radiographic variation. The first four
 PCs captured 90% of the total variation. The PCs had higher statistical
 mapping power to detect pleiotropic quantitative trait loci (QTL) than the
 raw phenotypes. The PCA demonstrated for the first time that HD can be
 reduced mathematically into simpler components essential for its genetic
 dissection. Genes that contribute jointly to all four radiographic hip
 phenotypes can be detected by mapping their first four PCs, while those
 contributing to individual phenotypes can be mapped by association with
 the individual raw phenotype. 
Journal: Journal of Applied Statistics 
Pages: 235-251 
Issue: 2 
Volume: 40 
Year: 2013 
Month: 2 
X-DOI: 10.1080/02664763.2012.740617 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.740617 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:2:p:235-251




Template-Type: ReDIF-Article 1.0
Author-Name: Liyong Fu 
Author-X-Name-First: Liyong 
Author-X-Name-Last: Fu 
Author-Name: Yuancai Lei 
Author-X-Name-First: Yuancai 
Author-X-Name-Last: Lei 
Author-Name: Ram   P. Sharma 
Author-X-Name-First: Ram   P. 
Author-X-Name-Last: Sharma 
Author-Name: Shouzheng Tang 
Author-X-Name-First: Shouzheng 
Author-X-Name-Last: Tang 
Title: Parameter estimation of nonlinear mixed-effects models using first-order conditional linearization and the EM algorithm 
Abstract:
  Nonlinear mixed-effects (NLME) models are flexible enough to handle
 repeated-measures data from various disciplines. In this article, we
 propose both maximum-likelihood and restricted maximum-likelihood
 estimations of NLME models using first-order conditional expansion (FOCE)
 and the expectation--maximization (EM) algorithm. The FOCE-EM algorithm
 implemented in the ForStat procedure <italic>SNLME</italic> is compared
 with the Lindstrom and Bates (LB) algorithm implemented in both the SAS
 macro NLINMIX and the S-Plus/R function <italic>nlme</italic> in terms of
 computational efficiency and statistical properties. Two realworld data
 sets an orange tree data set and a Chinese fir (<italic>Cunninghamia
 lanceolata</italic>) data set, and a simulated data set were used for
 evaluation. FOCE-EM converged for all mixed models derived from the base
 model in the two realworld cases, while LB did not, especially for the
 models in which random effects are simultaneously considered in several
 parameters to account for between-subject variation. However, both
 algorithms had identical estimated parameters and fit statistics for the
 converged models. We therefore recommend using FOCE-EM in NLME models,
 particularly when convergence is a concern in model selection. 
Journal: Journal of Applied Statistics 
Pages: 252-265 
Issue: 2 
Volume: 40 
Year: 2013 
Month: 2 
X-DOI: 10.1080/02664763.2012.740621 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.740621 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:2:p:252-265




Template-Type: ReDIF-Article 1.0
Author-Name: Yuanhua Feng 
Author-X-Name-First: Yuanhua 
Author-X-Name-Last: Feng 
Title: An iterative plug-in algorithm for decomposing seasonal time series using the Berlin Method 
Abstract:
  We propose a fast data-driven procedure for decomposing seasonal time
 series using the Berlin Method, the procedure used, e.g. by the German
 Federal Statistical Office in this context. The formula of the asymptotic
 optimal bandwidth <italic>h</italic> <sub>A</sub> is obtained. Methods for
 estimating the unknowns in <italic>h</italic> <sub>A</sub> are proposed.
 The algorithm is developed by adapting the well-known iterative plug-in
 idea to time series decomposition. Asymptotic behaviour of the proposal is
 investigated. Some computational aspects are discussed in detail. Data
 examples show that the proposal works very well in practice and that
 data-driven bandwidth selection offers new possibilities to improve the
 Berlin Method. Deep insights into the iterative plug-in rule are also
 provided. 
Journal: Journal of Applied Statistics 
Pages: 266-281 
Issue: 2 
Volume: 40 
Year: 2013 
Month: 2 
X-DOI: 10.1080/02664763.2012.740626 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.740626 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:2:p:266-281




Template-Type: ReDIF-Article 1.0
Author-Name: Francesca Greselin 
Author-X-Name-First: Francesca 
Author-X-Name-Last: Greselin 
Author-Name: Leo Pasquazzi 
Author-X-Name-First: Leo 
Author-X-Name-Last: Pasquazzi 
Author-Name: Ri&#x10D;ardas Zitikis 
Author-X-Name-First: Ri&#x10D;ardas 
Author-X-Name-Last: Zitikis 
Title: Contrasting the Gini and Zenga indices of economic inequality 
Abstract:
  The current financial turbulence in Europe inspires and perhaps requires
 researchers to rethink how to measure incomes, wealth, and other
 parameters of interest to policy-makers and others. The noticeable
 increase in disparities between less and more fortunate individuals
 suggests that measures based upon comparing the incomes of less fortunate
 with the mean of the entire population may not be adequate. The classical
 Gini and related indices of economic inequality, however, are based
 exactly on such comparisons. It is because of this reason that in this
 paper we explore and contrast the classical Gini index with a new Zenga
 index, the latter being based on comparisons of the means of less and more
 fortunate sub-populations, irrespectively of the threshold that might be
 used to delineate the two sub-populations. The empirical part of the paper
 is based on the 2001 wave of the European Community Household Panel data
 set provided by EuroStat. Even though sample sizes appear to be large, we
 supplement the estimated Gini and Zenga indices with measures of
 variability in the form of normal, <italic>t</italic>-bootstrap, and
 bootstrap bias-corrected and accelerated confidence intervals. 
Journal: Journal of Applied Statistics 
Pages: 282-297 
Issue: 2 
Volume: 40 
Year: 2013 
Month: 2 
X-DOI: 10.1080/02664763.2012.740627 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.740627 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:2:p:282-297




Template-Type: ReDIF-Article 1.0
Author-Name: Ileana Baldi 
Author-X-Name-First: Ileana 
Author-X-Name-Last: Baldi 
Author-Name: Eva Pagano 
Author-X-Name-First: Eva 
Author-X-Name-Last: Pagano 
Author-Name: Paola Berchialla 
Author-X-Name-First: Paola 
Author-X-Name-Last: Berchialla 
Author-Name: Alessandro Desideri 
Author-X-Name-First: Alessandro 
Author-X-Name-Last: Desideri 
Author-Name: Alberto Ferrando 
Author-X-Name-First: Alberto 
Author-X-Name-Last: Ferrando 
Author-Name: Franco Merletti 
Author-X-Name-First: Franco 
Author-X-Name-Last: Merletti 
Author-Name: Dario Gregori 
Author-X-Name-First: Dario 
Author-X-Name-Last: Gregori 
Title: Modeling healthcare costs in simultaneous presence of asymmetry, heteroscedasticity and correlation 
Abstract:
  Highly skewed outcome distributions observed across clusters are common
 in medical research. The aim of this paper is to understand how regression
 models widely used for accommodating asymmetry fit clustered data under
 heteroscedasticity. In a simulation study, we provide evidence on the
 performance of the Gamma Generalized Linear Mixed Model (GLMM) and
 log-Linear Mixed-Effect (LME) model under a variety of data-generating
 mechanisms. Two case studies from health expenditures literature, the cost
 of strategies after myocardial infarction randomized clinical trial on the
 cost of strategies after myocardial infarction and the European Pressure
 Ulcer Advisory Panel hospital prevalence survey of pressure ulcers, are
 analyzed and discussed. According to simulation results, the log-LME model
 for a Gamma response can lead to estimations that are biased by as much as
 10% of the true value, depending on the error variance. In the Gamma GLMM,
 the bias never exceeds 1%, regardless of the extent of heteroscedasticity,
 and the confidence intervals perform as nominally stated under most
 conditions. The Gamma GLMM with a log link seems to be more robust to both
 Gamma and log-normal generating mechanisms than the log-LME model. 
Journal: Journal of Applied Statistics 
Pages: 298-310 
Issue: 2 
Volume: 40 
Year: 2013
Month: 2
X-DOI: 10.1080/02664763.2012.740628 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.740628 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:2:p:298-310




Template-Type: ReDIF-Article 1.0
Author-Name: Lai Wei 
Author-X-Name-First: Lai 
Author-X-Name-Last: Wei 
Author-Name: Alan   D. Hutson 
Author-X-Name-First: Alan   D. 
Author-X-Name-Last: Hutson 
Title: A comment on sample size calculations for binomial confidence intervals 
Abstract:
  In this article we examine sample size calculations for a binomial
 proportion based on the confidence interval width of the Agresti--Coull,
 Wald and Wilson Score intervals. We pointed out that the commonly used
 methods based on known and fixed standard errors cannot guarantee the
 desired confidence interval width given a hypothesized proportion.
 Therefore, a new adjusted sample size calculation method was introduced,
 which is based on the conditional expectation of the width of the
 confidence interval given the hypothesized proportion. With the reduced
 sample size, the coverage probability can still maintain at the nominal
 level and is very competitive to the converge probability for the original
 sample size. 
Journal: Journal of Applied Statistics 
Pages: 311-319 
Issue: 2 
Volume: 40 
Year: 2013 
Month: 2 
X-DOI: 10.1080/02664763.2012.740629 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.740629 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:2:p:311-319




Template-Type: ReDIF-Article 1.0
Author-Name: J.   Andrew Howe 
Author-X-Name-First: J.   Andrew 
Author-X-Name-Last: Howe 
Author-Name: Hamparsum Bozdogan 
Author-X-Name-First: Hamparsum 
Author-X-Name-Last: Bozdogan 
Title: Robust mixture model cluster analysis using adaptive kernels 
Abstract:
  The traditional mixture model assumes that a dataset is composed of
 several populations of Gaussian distributions. In real life, however, data
 often do not fit the restrictions of normality very well. It is likely
 that data from a single population exhibiting either asymmetrical or
 heavy-tail behavior could be erroneously modeled as two populations,
 resulting in suboptimal decisions. To avoid these pitfalls, we generalize
 the mixture model using adaptive kernel density estimators. Because kernel
 density estimators enforce no functional form, we can adapt to non-normal
 asymmetric, kurtotic, and tail characteristics in each population
 independently. This, in effect, robustifies mixture modeling. We adapt two
 computational algorithms, genetic algorithm with regularized Mahalanobis
 distance and genetic expectation maximization algorithm, to optimize the
 kernel mixture model (KMM) and use results from robust estimation theory
 in order to data-adaptively regularize both. Finally, we likewise extend
 the information criterion ICOMP to score the KMM. We use these tools to
 simultaneously select the best mixture model and classify all observations
 <italic>without making any subjective decisions</italic>. The performance
 of the KMM is demonstrated on two medical datasets; in both cases, we
 recover the clinically determined group structure and substantially
 improve patient classification rates over the Gaussian mixture model. 
Journal: Journal of Applied Statistics 
Pages: 320-336 
Issue: 2 
Volume: 40 
Year: 2013 
Month: 2 
X-DOI: 10.1080/02664763.2012.740630 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.740630 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:2:p:320-336




Template-Type: ReDIF-Article 1.0
Author-Name: Andreas Quatember 
Author-X-Name-First: Andreas 
Author-X-Name-Last: Quatember 
Author-Name: Monika   Cornelia Hausner 
Author-X-Name-First: Monika   Cornelia 
Author-X-Name-Last: Hausner 
Title: A family of methods for statistical disclosure control 
Abstract:
  Statistical disclosure control (SDC) is a balancing act between mandatory
 data protection and the comprehensible demand from researchers for access
 to original data. In this paper, a family of methods is defined to
 &#x2018;mask&#x2019; sensitive variables before data files can be
 released. In the first step, the variable to be masked is
 &#x2018;cloned&#x2019; (C). Then, the duplicated variable as a whole or
 just a part of it is &#x2018;suppressed&#x2019; (S). The masking
 procedure's third step &#x2018;imputes&#x2019; (I) data for these
 artificial missings. Then, the original variable can be deleted and its
 masked substitute has to serve as the basis for the analysis of data. The
 idea of this general &#x2018;CSI framework&#x2019; is to open the wide
 field of imputation methods for SDC. The method applied in the I-step can
 make use of available auxiliary variables including the original variable.
 Different members of this family of methods delivering variance estimators
 are discussed in some detail. Furthermore, a simulation study analyzes
 various methods belonging to the family with respect to both, the quality
 of parameter estimation and privacy protection. Based on the results
 obtained, recommendations are formulated for different estimation tasks. 
Journal: Journal of Applied Statistics 
Pages: 337-346 
Issue: 2 
Volume: 40 
Year: 2013 
Month: 2 
X-DOI: 10.1080/02664763.2012.743975 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.743975 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:2:p:337-346




Template-Type: ReDIF-Article 1.0
Author-Name: Xiting Cao 
Author-X-Name-First: Xiting 
Author-X-Name-Last: Cao 
Author-Name: Baolin Wu 
Author-X-Name-First: Baolin 
Author-X-Name-Last: Wu 
Author-Name: Marshall   I. Hertz 
Author-X-Name-First: Marshall   I. 
Author-X-Name-Last: Hertz 
Title: Empirical null distribution-based modeling of multi-class differential gene expression detection 
Abstract:
  In this paper, we study the multi-class differential gene expression
 detection for microarray data. We propose a likelihood-based approach to
 estimating an empirical null distribution to incorporate gene interactions
 and provide a more accurate false-positive control than the commonly used
 permutation or theoretical null distribution-based approach. We propose to
 rank important genes by <italic>p</italic>-values or local false discovery
 rate based on the estimated empirical null distribution. Through
 simulations and application to lung transplant microarray data, we
 illustrate the competitive performance of the proposed method. 
Journal: Journal of Applied Statistics 
Pages: 347-357 
Issue: 2 
Volume: 40 
Year: 2013 
Month: 2
X-DOI: 10.1080/02664763.2012.743976 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.743976 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:2:p:347-357




Template-Type: ReDIF-Article 1.0
Author-Name: Baolin Wu 
Author-X-Name-First: Baolin 
Author-X-Name-Last: Wu 
Title: Sparse cluster analysis of large-scale discrete variables with application to single nucleotide polymorphism data 
Abstract:
  Currently, extreme large-scale genetic data present significant
 challenges for cluster analysis. Most of the existing clustering methods
 are typically built on the Euclidean distance and geared toward analyzing
 continuous response. They work well for clustering, e.g. microarray gene
 expression data, but often perform poorly for clustering, e.g. large-scale
 single nucleotide polymorphism (SNP) data. In this paper, we study the
 penalized latent class model for clustering extremely large-scale discrete
 data. The penalized latent class model takes into account the discrete
 nature of the response using appropriate generalized linear models and
 adopts the lasso penalized likelihood approach for simultaneous model
 estimation and selection of important covariates. We develop very
 efficient numerical algorithms for model estimation based on the iterative
 coordinate descent approach and further develop the
 expectation--maximization algorithm to incorporate and model missing
 values. We use simulation studies and applications to the international
 HapMap SNP data to illustrate the competitive performance of the penalized
 latent class model. 
Journal: Journal of Applied Statistics 
Pages: 358-367 
Issue: 2 
Volume: 40 
Year: 2013 
Month: 2 
X-DOI: 10.1080/02664763.2012.743977 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.743977 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:2:p:358-367




Template-Type: ReDIF-Article 1.0
Author-Name: Dongliang Wang 
Author-X-Name-First: Dongliang 
Author-X-Name-Last: Wang 
Author-Name: Alan   D. Hutson 
Author-X-Name-First: Alan   D. 
Author-X-Name-Last: Hutson 
Title: Joint confidence region estimation of L-moment ratios with an extension to right censored data 
Abstract:
  L-moments, defined as specific linear combinations of expectations of
 order statistics, have been advocated by Hosking 7 and others in the
 literature as meaningful replacements to that of classic moments in a wide
 variety of applications. One particular use of L-moments is to classify
 distributions based on the so-called L-skewness and L-kurtosis measures
 and given by an L-moment ratio diagram. This method parallels the classic
 moment-based plot of skewness and kurtosis corresponding to the Pearson
 system of distributions. In general, these methods have been more
 descriptive in nature and failed to consider the corresponding variation
 and covariance of the point estimators. In this note, we propose two
 procedures to estimate the 100(1&#x2212;&#x3B1;)% joint confidence region
 of L-skewness and L-kurtosis, given both complete and censored data. The
 procedures are derived based on asymptotic normality of L-moment
 estimators or through a novel empirical characteristic function (c.f.)
 approach. Simulation results are provided for comparing the performance of
 these procedures in terms of their respective coverage probabilities. The
 new and novel c.f.-based confidence region provided superior coverage
 probability as compared to the standard bootstrap procedure across all
 parameter settings. The proposed methods are illustrated via an
 application to a complete Buffalo snow fall data set and to a censored
 breast cancer data set, respectively. 
Journal: Journal of Applied Statistics 
Pages: 368-379 
Issue: 2 
Volume: 40 
Year: 2013 
Month: 2 
X-DOI: 10.1080/02664763.2012.744386 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.744386 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:2:p:368-379




Template-Type: ReDIF-Article 1.0
Author-Name: Peixin Zhao 
Author-X-Name-First: Peixin 
Author-X-Name-Last: Zhao 
Author-Name: Liugen Xue 
Author-X-Name-First: Liugen 
Author-X-Name-Last: Xue 
Title: Instrumental variable-based empirical likelihood inferences for varying-coefficient models with error-prone covariates 
Abstract:
  This paper presents the empirical likelihood inferences for a class of
 varying-coefficient models with error-prone covariates. We focus on the
 case that the covariance matrix of the measurement errors is unknown and
 neither repeated measurements nor validation data are available. We
 propose an instrumental variable-based empirical likelihood inference
 method and show that the proposed empirical log-likelihood ratio is
 asymptotically chi-squared. Then, the confidence intervals for the
 varying-coefficient functions are constructed. Some simulation studies and
 a real data application are used to assess the finite sample performance
 of the proposed empirical likelihood procedure. 
Journal: Journal of Applied Statistics 
Pages: 380-396 
Issue: 2 
Volume: 40 
Year: 2013 
Month: 2 
X-DOI: 10.1080/02664763.2012.744810 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.744810 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:2:p:380-396




Template-Type: ReDIF-Article 1.0
Author-Name: Ilhan Usta 
Author-X-Name-First: Ilhan 
Author-X-Name-Last: Usta 
Title: Different estimation methods for the parameters of the extended Burr XII distribution 
Abstract:
  The extended three-parameter Burr XII (EBXII) distribution has recently
 attracted considerable attention for modeling data from various scientific
 fields since it yields a wide range of skewness and kurtosis values.
 However, it is well known that the parameter estimates have significant
 effects on the success of a distribution in real-life applications. In
 this study, modified moment estimators (MMEs) and modified
 probability-weighted moments estimators (MPWMEs) are used to estimate the
 parameters of the EBXII distribution. These two considered estimators are
 also compared with the commonly used maximum-likelihood, percentiles,
 least-squares and weighted least-squares estimators in terms of bias and
 efficiency via an extensive numerical simulation. The MMEs and MPWMEs are
 observed to perform well in varying sample cases, and the simulation
 results are supported with application through a real-life data set. 
Journal: Journal of Applied Statistics 
Pages: 397-414 
Issue: 2 
Volume: 40 
Year: 2013 
Month: 2 
X-DOI: 10.1080/02664763.2012.743974 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.743974 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:2:p:397-414




Template-Type: ReDIF-Article 1.0
Author-Name: Elena Abascal 
Author-X-Name-First: Elena 
Author-X-Name-Last: Abascal 
Author-Name: Vidal   D�az de Rada 
Author-X-Name-First: Vidal   D�az 
Author-X-Name-Last: de Rada 
Author-Name: Ignacio   Garc�a Lautre 
Author-X-Name-First: Ignacio   Garc�a 
Author-X-Name-Last: Lautre 
Author-Name: M.   Isabel Landaluce 
Author-X-Name-First: M.   Isabel 
Author-X-Name-Last: Landaluce 
Title: Extending dual multiple factor analysis to categorical tables 
Abstract:
  This paper describes a proposal for the extension of the dual multiple
 factor analysis (DMFA) method developed by L&#xEA; and Pag&#xE8;s 15 to
 the analysis of categorical tables in which the same set of variables is
 measured on different sets of individuals. The extension of DMFA is based
 on the transformation of categorical variables into properly weighted
 indicator variables, in a way analogous to that used in the multiple
 factor analysis of categorical variables. The DMFA of categorical
 variables enables visual comparison of the association structures between
 categories over the sample as a whole and in the various subsamples (sets
 of individuals). For each category, DMFA allows us to obtain its global
 (considering all the individuals) and partial (considering each set of
 individuals) coordinates in a factor space. This visual analysis allows us
 to compare the set of individuals to identify their similarities and
 differences. The suitability of the technique is illustrated through two
 applications: one using simulated data for two groups of individuals with
 very different association structures and the other using real data from a
 voting intention survey in which some respondents were interviewed by
 telephone and others face to face. The results indicate that the two data
 collection methods, while similar, are not entirely equivalent. 
Journal: Journal of Applied Statistics 
Pages: 415-428 
Issue: 2 
Volume: 40 
Year: 2013 
Month: 2 
X-DOI: 10.1080/02664763.2012.745836 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.745836 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:2:p:415-428




Template-Type: ReDIF-Article 1.0
Author-Name: P. Angelopoulos 
Author-X-Name-First: P. 
Author-X-Name-Last: Angelopoulos 
Author-Name: K. Drosou 
Author-X-Name-First: K. 
Author-X-Name-Last: Drosou 
Author-Name: C. Koukouvinos 
Author-X-Name-First: C. 
Author-X-Name-Last: Koukouvinos 
Title: An orthogonal arrays approach to robust parameter designs methodology 
Abstract:
  Robust parameter design methodology was originally introduced by Taguchi
 [14] as an engineering methodology for quality improvement of products and
 processes. A robust design of a system is one in which two different types
 of factors are varied; control factors and noise factors. Control factors
 are variables with levels that are adjustable, whereas noise factors are
 variables with levels that are hard or impossible to control during normal
 conditions, such as environmental conditions and raw-material properties.
 Robust parameter design aims at the reduction of process variation by
 properly selecting the levels of control factors so that the process
 becomes insensitive to changes in noise factors. Taguchi [1415] proposed
 the use of crossed arrays (inner--outer arrays) for robust parameter
 design. A crossed array is the cross-product of an orthogonal array (OA)
 involving control factors (inner array) and an OA involving noise factors
 (outer array). Objecting to the run size and the flexibility of crossed
 arrays, several authors combined control and noise factors in a single
 design matrix, which is called a combined array, instead of crossed
 arrays. In this framework, we present the use of OAs in Taguchi's
 methodology as a useful tool for designing robust parameter designs with
 economical run size. 
Journal: Journal of Applied Statistics 
Pages: 429-437 
Issue: 2 
Volume: 40 
Year: 2012 
Month: 2 
X-DOI: 10.1080/02664763.2012.745838 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.745838 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:2:p:429-437




Template-Type: ReDIF-Article 1.0
Author-Name: Vasyl Golosnoy 
Author-X-Name-First: Vasyl 
Author-X-Name-Last: Golosnoy 
Author-Name: Jens Hogrefe 
Author-X-Name-First: Jens 
Author-X-Name-Last: Hogrefe 
Title: Signaling NBER turning points: a sequential approach 
Abstract:
  The dates of the U.S. business cycle are reported by the National Bureau
 of Economic Research with a considerable delay, so an early notion of
 turning points is of particular interest. This paper proposes a novel
 sequential classification approach designed for timely signaling these
 turning points, using the time series of coincident economic indicators.
 The approach exhibits a range of theoretical optimality properties for
 early signaling, moreover, it is transparent and easy to implement. The
 empirical study evaluates the signaling ability of the proposed
 methodology. 
Journal: Journal of Applied Statistics 
Pages: 438-448 
Issue: 2 
Volume: 40 
Year: 2013 
Month: 2 
X-DOI: 10.1080/02664763.2012.748017 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.748017 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:2:p:438-448




Template-Type: ReDIF-Article 1.0
Author-Name: Hea-Jung Kim 
Author-X-Name-First: Hea-Jung 
Author-X-Name-Last: Kim 
Title: Optimal asymmetric classification procedures for interval-screened normal data 
Abstract:
  Statistical methods for an asymmetric normal classification do not adapt
 well to the situations where the population distributions are perturbed by
 an interval-screening scheme. This paper explores methods for providing an
 optimal classification of future samples in this situation. The properties
 of the screened population distributions are considered and two optimal
 regions for classifying the future samples are obtained. These
 developments yield yet other rules for the interval-screened asymmetric
 normal classification. The rules are studied from several aspects such as
 the probability of misclassification, robustness, and estimation of the
 rules. The investigation of the performance of the rules as well as the
 illustration of the screened classification idea, using two numerical
 examples, is also considered. 
Journal: Journal of Applied Statistics 
Pages: 449-462 
Issue: 2 
Volume: 40 
Year: 2013 
Month: 2 
X-DOI: 10.1080/02664763.2012.748018 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.748018 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:2:p:449-462




Template-Type: ReDIF-Article 1.0
Author-Name: Shuangzhe Liu 
Author-X-Name-First: Shuangzhe 
Author-X-Name-Last: Liu 
Title: Econometric methods for labour economics 
Journal: Journal of Applied Statistics 
Pages: 463-464 
Issue: 2 
Volume: 40 
Year: 2013 
Month: 2 
X-DOI: 10.1080/02664763.2012.749026 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.749026 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:2:p:463-464




Template-Type: ReDIF-Article 1.0
Author-Name: Kassim   S. Mwitondi 
Author-X-Name-First: Kassim   S. 
Author-X-Name-Last: Mwitondi 
Title: Data mining with Rattle and R 
Journal: Journal of Applied Statistics 
Pages: 464-464 
Issue: 2 
Volume: 40 
Year: 2013 
Month: 2 
X-DOI: 10.1080/02664763.2012.749050 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.749050 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:2:p:464-464




Template-Type: ReDIF-Article 1.0
Author-Name: Stephan Stahlschmidt 
Author-X-Name-First: Stephan 
Author-X-Name-Last: Stahlschmidt 
Author-Name: Helmut Tausendteufel 
Author-X-Name-First: Helmut 
Author-X-Name-Last: Tausendteufel 
Author-Name: Wolfgang K. H&#xE4;rdle 
Author-X-Name-First: Wolfgang K. 
Author-X-Name-Last: H&#xE4;rdle 
Title: Bayesian networks for sex-related homicides: structure learning and prediction 
Abstract:
	      Sex-related homicides tend to arouse wide media coverage and
 thus raise the urgency to find the responsible offender. However, due to
 the low frequency of such crimes, domain knowledge lacks completeness. We
 have therefore accumulated a large data-set and apply several structural
 learning algorithms to the data in order to combine their results into a
 single general graphic model. The graphical model broadly presents a
 distinction between an offender and a situation-driven crime. A
 situation-driven crime may be characterised by, amongst others, an
 offender lacking preparation and typically attacking a known victim in
 familiar surroundings. On the other hand, offender-driven crimes may be
 identified by the high level of forensic awareness demonstrated by the
 offender and the sophisticated measures applied to control the victim. The
 prediction performance of the graphical model is evaluated via a model
 averaging approach on the outcome variable offender's age. The combined
 graph undercuts the error rate of the single algorithms and an appropriate
 threshold results in an error rate of less than 10%, which describes a
 promising level for an actual implementation by the police.	      
Journal: Journal of Applied Statistics 
Pages: 1155-1171 
Issue: 6 
Volume: 40 
Year: 2013 
Month: 6 
X-DOI: 10.1080/02664763.2013.780235 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.780235 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:6:p:1155-1171




Template-Type: ReDIF-Article 1.0
Author-Name: Raffaella Calabrese 
Author-X-Name-First: Raffaella 
Author-X-Name-Last: Calabrese 
Author-Name: Silvia Angela Osmetti 
Author-X-Name-First: Silvia Angela 
Author-X-Name-Last: Osmetti 
Title: Modelling small and medium enterprise loan defaults as rare events: the generalized extreme value regression model 
Abstract:
	      A pivotal characteristic of credit defaults that is ignored
 by most credit scoring models is the rarity of the event. The most widely
 used model to estimate the probability of default is the logistic
 regression model. Since the dependent variable represents a rare event,
 the logistic regression model shows relevant drawbacks, for example,
 underestimation of the default probability, which could be very risky for
 banks. In order to overcome these drawbacks, we propose the generalized
 extreme value regression model. In particular, in a generalized linear
 model (GLM) with the binary-dependent variable we suggest the quantile
 function of the GEV distribution as link function, so our attention is
 focused on the tail of the response curve for values close to one. The
 estimation procedure used is the maximum-likelihood method. This model
 accommodates skewness and it presents a generalisation of GLMs with
 complementary log--log link function. We analyse its performance by
 simulation studies. Finally, we apply the proposed model to empirical data
 on Italian small and medium enterprises.	   
Journal: Journal of Applied Statistics 
Pages: 1172-1188 
Issue: 6 
Volume: 40 
Year: 2013 
Month: 6 
X-DOI: 10.1080/02664763.2013.784894 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.784894 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:6:p:1172-1188




Template-Type: ReDIF-Article 1.0
Author-Name: Wan-Min Tsai 
Author-X-Name-First: Wan-Min 
Author-X-Name-Last: Tsai 
Author-Name: Albert Vexler 
Author-X-Name-First: Albert 
Author-X-Name-Last: Vexler 
Author-Name: Gregory Gurevich 
Author-X-Name-First: Gregory 
Author-X-Name-Last: Gurevich 
Title: An extensive power evaluation of a novel two-sample density-based empirical likelihood ratio test for paired data with an application to a treatment study of attention-deficit/hyperactivity disorder and severe mood dysregulation 
Abstract:
	      In many case-control studies, it is common to utilize paired
 data when treatments are being evaluated. In this article, we propose and
 examine an efficient distribution-free test to compare two independent
 samples, where each is based on paired observations. We extend and modify
 the density-based empirical likelihood ratio test presented by Gurevich
 and Vexler [7] to formulate an appropriate parametric likelihood ratio
 test statistic corresponding to the hypothesis of our interest and then to
 approximate the test statistic nonparametrically. We conduct an extensive
 Monte Carlo study to evaluate the proposed test. The results of the
 performed simulation study demonstrate the robustness of the proposed test
 with respect to values of test parameters. Furthermore, an extensive power
 analysis via Monte Carlo simulations confirms that the proposed method
 outperforms the classical and general procedures in most cases related to
 a wide class of alternatives. An application to a real paired data study
 illustrates that the proposed test can be efficiently implemented in
 practice.	    
Journal: Journal of Applied Statistics 
Pages: 1189-1208 
Issue: 6 
Volume: 40 
Year: 2013 
Month: 6 
X-DOI: 10.1080/02664763.2013.784895 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.784895 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:6:p:1189-1208




Template-Type: ReDIF-Article 1.0
Author-Name: Donatella Vicari 
Author-X-Name-First: Donatella 
Author-X-Name-Last: Vicari 
Author-Name: Maurizio Vichi 
Author-X-Name-First: Maurizio 
Author-X-Name-Last: Vichi 
Title: Multivariate linear regression for heterogeneous data 
Abstract:
	      The problem of multivariate regression modelling in the
 presence of heterogeneous data is dealt to address the relevant issue of
 the influence of such heterogeneity in assessing the linear relations
 between responses and explanatory variables. In spite of its popularity,
 clusterwise regression is not designed to identify the linear
 relationships within &#x2018;homogeneous&#x2019; clusters exhibiting
 internal cohesion and external separation. A within-clusterwise regression
 is introduced to achieve this aim and, since the possible presence of a
 linear relation &#x2018;between&#x2019; clusters should be also taken into
 account, a general regression model is introduced to account for both the
 between-cluster and the within-cluster regression variation. Some
 decompositions of the variance of the responses accounted for are also
 given, the least-squares estimation of the parameters is derived, together
 with an appropriate coordinate descent algorithms and the performance of
 the proposed methodology is evaluated in different datasets.	       
Journal: Journal of Applied Statistics 
Pages: 1209-1230 
Issue: 6 
Volume: 40 
Year: 2013 
Month: 6 
X-DOI: 10.1080/02664763.2013.784896 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.784896 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:6:p:1209-1230




Template-Type: ReDIF-Article 1.0
Author-Name: Hyokyoung Grace Hong 
Author-X-Name-First: Hyokyoung Grace 
Author-X-Name-Last: Hong 
Author-Name: Jianhui Zhou 
Author-X-Name-First: Jianhui 
Author-X-Name-Last: Zhou 
Title: A multi-index model for quantile regression with ordinal data 
Abstract:
	      In this paper, we propose a quantile approach to the
 multi-index semiparametric model for an ordinal response variable.
 Permitting non-parametric transformation of the response, the proposed
 method achieves a root-<italic>n</italic> rate of convergence and has
 attractive robustness properties. Further, the proposed model allows
 additional indices to model the remaining correlations between covariates
 and the residuals from the single-index, considerably reducing the error
 variance and thus leading to more efficient prediction intervals (PIs).
 The utility of the model is demonstrated by estimating PIs for functional
 status of the elderly based on data from the second longitudinal study of
 aging. It is shown that the proposed multi-index model provides
 significantly narrower PIs than competing models. Our approach can be
 applied to other areas in which the distribution of future observations
 must be predicted from ordinal response data.		
Journal: Journal of Applied Statistics 
Pages: 1231-1245 
Issue: 6 
Volume: 40 
Year: 2013 
Month: 6 
X-DOI: 10.1080/02664763.2013.785489 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.785489 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:6:p:1231-1245




Template-Type: ReDIF-Article 1.0
Author-Name: Hua Jin 
Author-X-Name-First: Hua 
Author-X-Name-Last: Jin 
Author-Name: Qi Mo 
Author-X-Name-First: Qi 
Author-X-Name-Last: Mo 
Title: Hip fracture prediction from a new classification algorithm based on recursive partitioning methods 
Abstract:
	      Classification and regression tree has been useful in medical
 research to construct algorithms for disease diagnosis or prognostic
 prediction. Jin <italic>et al.</italic>7 developed a robust and
 cost-saving tree (RACT) algorithm with application in classification of
 hip fracture risk after 5-year follow-up based on the data from the Study
 of Osteoporotic Fractures (SOF). Although conventional recursive
 partitioning algorithms have been well developed, they still have some
 limitations. Binary splits may generate a big tree with many layers, but
 trinary splits may produce too many nodes. In this paper, we propose a
 classification approach combining trinary splits and binary splits to
 generate a trinary--binary tree. A new non-inferiority test of entropy is
 used to select the binary or trinary splits. We apply the modified method
 in SOF to construct a trinary--binary classification rule for predicting
 risk of osteoporotic hip fracture. Our new classification tree has good
 statistical utility: it is statistically non-inferior to the optimum
 binary tree and the RACT based on the testing sample and is also
 cost-saving. It may be useful in clinical applications: femoral neck bone
 mineral density, age, height loss and weight gain since age 25 can
 identify subjects with elevated 5-year hip fracture risk without loss of
 statistical efficiency.	  
Journal: Journal of Applied Statistics 
Pages: 1246-1253 
Issue: 6 
Volume: 40 
Year: 2013 
Month: 6 
X-DOI: 10.1080/02664763.2013.785490 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.785490 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:6:p:1246-1253




Template-Type: ReDIF-Article 1.0
Author-Name: Ferra Yanuar 
Author-X-Name-First: Ferra 
Author-X-Name-Last: Yanuar 
Author-Name: Kamarulzaman Ibrahim 
Author-X-Name-First: Kamarulzaman 
Author-X-Name-Last: Ibrahim 
Author-Name: Abdul Aziz Jemain 
Author-X-Name-First: Abdul Aziz 
Author-X-Name-Last: Jemain 
Title: Bayesian structural equation modeling for the health index 
Abstract:
	      There are many factors which could influence the level of
 health of an individual. These factors are interactive and their overall
 effects on health are usually measured by an index which is called as
 health index. The health index could also be used as an indicator to
 describe the health level of a community. Since the health index is
 important, many research have been done to study its determinant. The main
 purpose of this study is to model the health index of an individual based
 on classical structural equation modeling (SEM) and Bayesian SEM. For
 estimation of the parameters in the measurement and structural equation
 models, the classical SEM applies the robust-weighted least-square
 approach, while the Bayesian SEM implements the Gibbs sampler algorithm.
 The Bayesian SEM approach allows the user to use the prior information for
 updating the current information on the parameter. Both methods are
 applied to the data gathered from a survey conducted in Hulu Langat, a
 district in Malaysia. Based on the classical and the Bayesian SEM, it is
 found that demographic status and lifestyle are significantly related to
 the health index. However, mental health has no significant relation to
 the health index.	    
Journal: Journal of Applied Statistics 
Pages: 1254-1269 
Issue: 6 
Volume: 40 
Year: 2013 
Month: 6 
X-DOI: 10.1080/02664763.2013.785491 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.785491 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:6:p:1254-1269




Template-Type: ReDIF-Article 1.0
Author-Name: Sebastian Kurtek 
Author-X-Name-First: Sebastian 
Author-X-Name-Last: Kurtek 
Author-Name: Wei Wu 
Author-X-Name-First: Wei 
Author-X-Name-Last: Wu 
Author-Name: Gary E. Christensen 
Author-X-Name-First: Gary E. 
Author-X-Name-Last: Christensen 
Author-Name: Anuj Srivastava 
Author-X-Name-First: Anuj 
Author-X-Name-Last: Srivastava 
Title: Segmentation, alignment and statistical analysis of biosignals with application to disease classification 
Abstract:
	      We present a novel methodology for a comprehensive
 statistical analysis of approximately periodic biosignal data. There are
 two main challenges in such analysis: (1) the automatic extraction
 (segmentation) of cycles from long, cyclostationary biosignals and (2) the
 subsequent statistical analysis, which in many cases involves the
 separation of temporal and amplitude variabilities. The proposed framework
 provides a principled approach for statistical analysis of such signals,
 which in turn allows for an efficient cycle segmentation algorithm. This
 is achieved using a convenient representation of functions called the
 square-root velocity function (SRVF). The segmented cycles, represented by
 SRVFs, are temporally aligned using the notion of the Karcher mean, which
 in turn allows for more efficient statistical summaries of signals. We
 show the strengths of this method through various disease classification
 experiments. In the case of myocardial infarction detection and
 localization, we show that our method compares favorably to methods
 described in the current literature.	       
Journal: Journal of Applied Statistics 
Pages: 1270-1288 
Issue: 6 
Volume: 40 
Year: 2013 
Month: 6 
X-DOI: 10.1080/02664763.2013.785492 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.785492 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:6:p:1270-1288




Template-Type: ReDIF-Article 1.0
Author-Name: Debasis Kundu 
Author-X-Name-First: Debasis 
Author-X-Name-Last: Kundu 
Title: Bayesian analysis for partially complete time and type of failure data 
Abstract:
	      In this paper, we consider the Bayesian analysis of competing
 risks data, when the data are partially complete in both time and type of
 failures. It is assumed that the latent cause of failures have independent
 Weibull distributions with the common shape parameter, but different scale
 parameters. When the shape parameter is known, it is assumed that the
 scale parameters have Beta--Gamma priors. In this case, the Bayes
 estimates and the associated credible intervals can be obtained in
 explicit forms. When the shape parameter is also unknown, it is assumed
 that it has a very flexible log-concave prior density functions. When the
 common shape parameter is unknown, the Bayes estimates of the unknown
 parameters and the associated credible intervals cannot be obtained in
 explicit forms. We propose to use Markov Chain Monte Carlo sampling
 technique to compute Bayes estimates and also to compute associated
 credible intervals. We further consider the case when the covariates are
 also present. The analysis of two competing risks data sets, one with
 covariates and the other without covariates, have been performed for
 illustrative purposes. It is observed that the proposed model is very
 flexible, and the method is very easy to implement in practice.	  
Journal: Journal of Applied Statistics 
Pages: 1289-1300 
Issue: 6 
Volume: 40 
Year: 2013 
Month: 6 
X-DOI: 10.1080/02664763.2013.785493 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.785493 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:6:p:1289-1300




Template-Type: ReDIF-Article 1.0
Author-Name: Seyed Taghi Akhavan Niaki 
Author-X-Name-First: Seyed Taghi Akhavan 
Author-X-Name-Last: Niaki 
Author-Name: Paravaneh Jahani 
Author-X-Name-First: Paravaneh 
Author-X-Name-Last: Jahani 
Title: The economic design of multivariate binomial EWMA VSSI control charts 
Abstract:
	      Since multi-attribute control charts have received little
 attention compared with multivariate variable control charts, this
 research is concerned with developing a new methodology to employ the
 multivariate exponentially weighted moving average (MEWMA) charts for
 <italic>m</italic>-attribute binomial processes; the attributes being the
 number of nonconforming items. Moreover, since the variable sample size
 and sampling interval (VSSI) MEWMA charts detect small process mean shifts
 faster than the traditional MEWMA, an economic design of the VSSI MEWMA
 chart is proposed to obtain the optimum design parameters of the chart.
 The sample size, the sampling interval, and the warning/action limit
 coefficients are obtained using a genetic algorithm such that the expected
 total cost per hour is minimized. At the end, a sensitivity analysis has
 been carried out to investigate the effects of the cost and the model
 parameters on the solution of the economic design of the VSSI MEWMA chart. 
Journal: Journal of Applied Statistics 
Pages: 1301-1318 
Issue: 6 
Volume: 40 
Year: 2013 
Month: 6 
X-DOI: 10.1080/02664763.2013.785494 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.785494 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:6:p:1301-1318




Template-Type: ReDIF-Article 1.0
Author-Name: Miler Jerkovic Vera 
Author-X-Name-First: Miler Jerkovic 
Author-X-Name-Last: Vera 
Author-Name: Bojanic Dubravka 
Author-X-Name-First: Bojanic 
Author-X-Name-Last: Dubravka 
Author-Name: Jorgovanovic Nikola 
Author-X-Name-First: Jorgovanovic 
Author-X-Name-Last: Nikola 
Author-Name: Ilic Vojin 
Author-X-Name-First: Ilic 
Author-X-Name-Last: Vojin 
Author-Name: Petrovacki-Balj Bojana 
Author-X-Name-First: Petrovacki-Balj 
Author-X-Name-Last: Bojana 
Title: Detecting and removing outlier(s) in electromyographic gait-related patterns 
Abstract:
	      In this paper, we propose a method for outlier detection and
 removal in electromyographic gait-related patterns (EMG-GRPs). The goal
 was to detect and remove EMG-GRPs that reduce the quality of gait data
 while preserving natural biological variations in EMG-GRPs. The proposed
 procedure consists of general statistical tests and is simple to use. The
 Friedman test with multiple comparisons was used to find particular
 EMG-GRPs that are extremely different from others. Next, outlying
 observations were calculated for each suspected stride waveform by
 applying the generalized extreme studentized deviate test. To complete the
 analysis, we applied different outlier criteria. The results suggest that
 an EMG-GRP is an outlier if it differs from at least 50% of the other
 stride waveforms and contains at least 20% of the outlying observations.
 The EMG signal remains a realistic representation of muscle activity and
 demonstrates step-by-step variability once the outliers, as defined here,
 are removed.	       
Journal: Journal of Applied Statistics 
Pages: 1319-1332 
Issue: 6 
Volume: 40 
Year: 2013 
Month: 6 
X-DOI: 10.1080/02664763.2013.785495 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.785495 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:6:p:1319-1332




Template-Type: ReDIF-Article 1.0
Author-Name: Sophie Bercu 
Author-X-Name-First: Sophie 
Author-X-Name-Last: Bercu 
Author-Name: Fr�d�ric Pro&#xEF;a 
Author-X-Name-First: Fr�d�ric 
Author-X-Name-Last: Pro&#xEF;a 
Title: A SARIMAX coupled modelling applied to individual load curves intraday forecasting 
Abstract:
	      A dynamic coupled modelling is investigated to take
 temperature into account in the individual energy consumption forecasting.
 The objective is both to avoid the inherent complexity of exhaustive
 SARIMAX models and to take advantage of the usual linear relation between
 energy consumption and temperature for thermosensitive customers. We first
 recall some issues related to individual load curves forecasting. Then, we
 propose and study the properties of a dynamic coupled modelling taking
 temperature into account as an exogenous contribution and its application
 to the intraday prediction of energy consumption. Finally, these
 theoretical results are illustrated on a real individual load curve. The
 authors discuss the relevance of such an approach and anticipate that it
 could form a substantial alternative to the commonly used methods for
 energy consumption forecasting of individual customers.	  
Journal: Journal of Applied Statistics 
Pages: 1333-1348 
Issue: 6 
Volume: 40 
Year: 2013 
Month: 6 
X-DOI: 10.1080/02664763.2013.785496 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.785496 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:6:p:1333-1348




Template-Type: ReDIF-Article 1.0
Author-Name: Erhard Reschenhofer 
Author-X-Name-First: Erhard 
Author-X-Name-Last: Reschenhofer 
Title: Robust testing for stationarity of global surface temperature 
Abstract:
	      Surface temperature is a major indicator of climate change.
 To test for the presence of an upward trend in surface-temperature (global
 warming), sophisticated statistical methods are typically used which
 depend on implausible and/or unverifiable assumptions, in particular on
 the availability of a very large number of measurements. In this paper,
 the validity of these methods is challenged. It is argued that the
 available series are simply not long enough to justify the use of methods
 which are based on asymptotic arguments, because only a small fraction of
 the information contained in the data is utilizable to distinguish between
 a trend and natural variability. Thus, a simple frequency-domain test is
 proposed for the case when all but a very small number of frequencies may
 be corrupted by transitory fluctuations. Simulations confirm its
 robustness against short-term autocorrelation. When applied to a global
 surface-temperature series, significance can be achieved with far fewer
 frequencies than required by conventional tests.	   
Journal: Journal of Applied Statistics 
Pages: 1349-1361 
Issue: 6 
Volume: 40 
Year: 2013 
Month: 6 
X-DOI: 10.1080/02664763.2013.785497 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.785497 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:6:p:1349-1361




Template-Type: ReDIF-Article 1.0
Author-Name: K. Suresh Chandra 
Author-X-Name-First: K. Suresh 
Author-X-Name-Last: Chandra 
Author-Name: G. Gopal 
Author-X-Name-First: G. 
Author-X-Name-Last: Gopal 
Author-Name: M. Ramadurai 
Author-X-Name-First: M. 
Author-X-Name-Last: Ramadurai 
Title: A stochastic frontier approach to survival analysis 
Abstract:
	      In spite of the best set of covariates and statistical tools
 for the survival analysis, there are instances when experts do not rule
 out the existence of many <italic>non-observable factors</italic> that
 could influence the survival probability of an individual. The fact that
 every human body, sick or otherwise, strives to maximize time to death,
 renders the stochastic frontier analysis (vide 2) as a meaningful tool to
 measure the unobservable individual-specific deficiency factor that
 accounts for the difference between the optimal and observed survival
 times. In this paper, given the survival data, an attempt is made to
 measure the deficiency factor for each individual in the data on adopting
 the stochastic frontier analysis. Such an attempt to quantify the effect
 of these unobservable factors can provide ample scope for further research
 in bio-medical studies. The utility of these estimates in the survival
 analysis is also highlighted using a real-life data.	       
Journal: Journal of Applied Statistics 
Pages: 1362-1371 
Issue: 6 
Volume: 40 
Year: 2013 
Month: 6 
X-DOI: 10.1080/02664763.2013.785498 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.785498 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:6:p:1362-1371




Template-Type: ReDIF-Article 1.0
Author-Name: Jiaqi Yang 
Author-X-Name-First: Jiaqi 
Author-X-Name-Last: Yang 
Author-Name: Wei Zhang 
Author-X-Name-First: Wei 
Author-X-Name-Last: Zhang 
Author-Name: Baolin Wu 
Author-X-Name-First: Baolin 
Author-X-Name-Last: Wu 
Title: A note on statistical method for genotype calling of high-throughput single-nucleotide polymorphism arrays 
Abstract:
	      We study the genotype calling algorithms for the
 high-throughput single-nucleotide polymorphism (SNP) arrays. Building upon
 the novel SNP-robust multi-chip average preprocessing approach and the
 state-of-the-art corrected robust linear model with Mahalanobis distance
 (CRLMM) approach for genotype calling, we propose a simple modification to
 better model and combine the information across multiple SNPs with
 empirical Bayes modeling, which could often significantly improve the
 genotype calling of CRLMM. Through applications to the HapMap Trio data
 set and a non-HapMap test set of high quality SNP chips, we illustrate the
 competitive performance of the proposed method.	  
Journal: Journal of Applied Statistics 
Pages: 1372-1381 
Issue: 6 
Volume: 40 
Year: 2013 
Month: 6 
X-DOI: 10.1080/02664763.2013.785499 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.785499 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:6:p:1372-1381




Template-Type: ReDIF-Article 1.0
Author-Name: Jing Xu 
Author-X-Name-First: Jing 
Author-X-Name-Last: Xu 
Title: Expect the unexpected: a first course in biostatistics 
Journal: Journal of Applied Statistics 
Pages: 1382-1383 
Issue: 6 
Volume: 40 
Year: 2013 
Month: 6 
X-DOI: 10.1080/02664763.2012.760781 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.760781 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:6:p:1382-1383




Template-Type: ReDIF-Article 1.0
Author-Name: Jing Xu 
Author-X-Name-First: Jing 
Author-X-Name-Last: Xu 
Title: Introduction to probability with Texas Hold'em examples 
Journal: Journal of Applied Statistics 
Pages: 1383-1384 
Issue: 6 
Volume: 40 
Year: 2013 
Month: 6 
X-DOI: 10.1080/02664763.2012.760782 
File-URL: http://hdl.handle.net/10.1080/02664763.2012.760782 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:6:p:1383-1384




Template-Type: ReDIF-Article 1.0
Author-Name: Abhay Kumar Tiwari 
Author-X-Name-First: Abhay Kumar 
Author-X-Name-Last: Tiwari 
Title: Economic time series 
Journal: Journal of Applied Statistics 
Pages: 1384-1385 
Issue: 6 
Volume: 40 
Year: 2013 
Month: 6 
X-DOI: 10.1080/02664763.2013.767416 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.767416 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:6:p:1384-1385




Template-Type: ReDIF-Article 1.0
Author-Name: Ying Chen 
Author-X-Name-First: Ying 
Author-X-Name-Last: Chen 
Title: A powerful test method for analyzing unreplicated factorials 
Abstract:
	      In this paper, a new test method for analyzing unreplicated
 factorial designs is proposed. The proposed method is illustrated by some
 examples. An extensive simulation with the standard 16-run designs was
 carried out to compare the proposed method with three another existing
 methods. Besides the usual power criterion, another three versions of
 power, Power I--III, were also used to evaluate the performance of the
 compared methods. The simulation study shows that the proposed method has
 higher ability than the remaining three compared methods to identify all
 active effects without misidentifying any inactive effects as active.	    
Journal: Journal of Applied Statistics 
Pages: 1387-1401 
Issue: 7 
Volume: 40 
Year: 2013 
Month: 7 
X-DOI: 10.1080/02664763.2013.785500 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.785500 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:7:p:1387-1401




Template-Type: ReDIF-Article 1.0
Author-Name: Saheli Datta 
Author-X-Name-First: Saheli 
Author-X-Name-Last: Datta 
Author-Name: Raquel Prado 
Author-X-Name-First: Raquel 
Author-X-Name-Last: Prado 
Author-Name: Abel Rodr�guez 
Author-X-Name-First: Abel 
Author-X-Name-Last: Rodr�guez 
Title: Bayesian factor models in characterizing molecular adaptation 
Abstract:
	      Assessing the selective influence of amino acid properties is
 important in understanding evolution at the molecular level. A collection
 of methods and models has been developed in recent years to determine if
 amino acid sites in a given DNA sequence alignment display substitutions
 that are altering or conserving a prespecified set of amino acid
 properties. Residues showing an elevated number of substitutions that
 favorably alter a physicochemical property are considered targets of
 positive natural selection. Such approaches usually perform independent
 analyses for each amino acid property under consideration, without taking
 into account the fact that some of the properties may be highly
 correlated. We propose a Bayesian hierarchical regression model with
 latent factor structure that allows us to determine which sites display
 substitutions that conserve or radically change a set of amino acid
 properties, while accounting for the correlation structure that may be
 present across such properties. We illustrate our approach by analyzing
 simulated data sets and an alignment of lysin sperm DNA.	   
Journal: Journal of Applied Statistics 
Pages: 1402-1424 
Issue: 7 
Volume: 40 
Year: 2013 
Month: 7 
X-DOI: 10.1080/02664763.2013.785652 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.785652 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:7:p:1402-1424




Template-Type: ReDIF-Article 1.0
Author-Name: Z. Rezaei Ghahroodi 
Author-X-Name-First: Z. Rezaei 
Author-X-Name-Last: Ghahroodi 
Author-Name: M. Ganjali 
Author-X-Name-First: M. 
Author-X-Name-Last: Ganjali 
Title: A Bayesian approach for analysing longitudinal nominal outcomes using random coefficients transitional generalized logit model: an application to the labour force survey data 
Abstract:
	      A random-effects transition model is proposed to model the
 economic activity status of household members. This model is introduced to
 take into account two kinds of correlations; one due to the longitudinal
 nature of the study, which will be considered using a transition
 parameter, and the other due to the existing correlation between responses
 of members of the same household which is taken into account by
 introducing random coefficients into the model. The results are presented
 based on the homogeneous (all parameters are not changed by time) and
 non-homogeneous Markov models with random coefficients. A Bayesian
 approach via the Gibbs sampling is used to perform parameter estimation.
 Results of using random-effects transition model are compared, using
 deviance information criterion, with those of three other models which
 exclude random effects and/or transition effects. It is shown that the
 full model gains more precision due to the consideration of all aspects of
 the process which generated the data. To illustrate the utility of the
 proposed model, a longitudinal data set which is extracted from the
 Iranian Labour Force Survey is analysed to explore the simultaneous effect
 of some covariates on the current economic activity as a nominal response.
 Also, some sensitivity analyses are performed to assess the robustness of
 the posterior estimation of the transition parameters to the perturbations
 of the prior parameters.	   
Journal: Journal of Applied Statistics 
Pages: 1425-1445 
Issue: 7 
Volume: 40 
Year: 2013 
Month: 7 
X-DOI: 10.1080/02664763.2013.785653 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.785653 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:7:p:1425-1445




Template-Type: ReDIF-Article 1.0
Author-Name: Kouji Yamamoto 
Author-X-Name-First: Kouji 
Author-X-Name-Last: Yamamoto 
Author-Name: Shota Murakami 
Author-X-Name-First: Shota 
Author-X-Name-Last: Murakami 
Author-Name: Sadao Tomizawa 
Author-X-Name-First: Sadao 
Author-X-Name-Last: Tomizawa 
Title: Point-symmetry models and decomposition for collapsed square contingency tables 
Abstract:
	      For square contingency tables with ordered categories, there
 may be some cases that one wants to analyze them by considering collapsed
 tables with some adjacent categories combined in the original table. This
 paper proposes three kinds of new models which have the structure of
 point-symmetry (PS), quasi point-symmetry and marginal point-symmetry for
 collapsed square tables. This paper also gives a decomposition of the PS
 model for collapsed square tables. The father's and his daughter's
 occupational mobility data are analyzed using new models.	    
Journal: Journal of Applied Statistics 
Pages: 1446-1452 
Issue: 7 
Volume: 40 
Year: 2013 
Month: 7 
X-DOI: 10.1080/02664763.2013.786028 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.786028 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:7:p:1446-1452




Template-Type: ReDIF-Article 1.0
Author-Name: Anouar BenMabrouk 
Author-X-Name-First: Anouar 
Author-X-Name-Last: BenMabrouk 
Author-Name: Olfa Zaafrane 
Author-X-Name-First: Olfa 
Author-X-Name-Last: Zaafrane 
Title: Wavelet fuzzy hybrid model for physico-financial signals 
Abstract:
	      In the present paper, a fuzzy logic-based method is combined
 with wavelet decomposition to develop a step-by-step dynamic hybrid model
 to analyze and approximate one-dimensional physico-financial signals
 characterized by fuzzy values. Computational tests based on a well-known
 signal and conducted with the pure fuzzy model, the wavelet one and the
 new hybrid model, are developed and result in an efficient hybrid one.     
Journal: Journal of Applied Statistics 
Pages: 1453-1463 
Issue: 7 
Volume: 40 
Year: 2013 
Month: 7 
X-DOI: 10.1080/02664763.2013.786690 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.786690 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:7:p:1453-1463




Template-Type: ReDIF-Article 1.0
Author-Name: Conghua Cheng 
Author-X-Name-First: Conghua 
Author-X-Name-Last: Cheng 
Author-Name: Jinyuan Chen 
Author-X-Name-First: Jinyuan 
Author-X-Name-Last: Chen 
Author-Name: Jianming Bai 
Author-X-Name-First: Jianming 
Author-X-Name-Last: Bai 
Title: Exact inferences of the two-parameter exponential distribution and Pareto distribution with censored data 
Abstract:
	      We develop an exact inference for the location and the scale
 parameters of the two-exponential distribution and the Pareto distribution
 based on their maximum-likelihood estimators from the doubly Type-II and
 the progressive Type-II censored sample. Based on some pivotal quantities,
 exact confidence intervals and tests of hypotheses are constructed. Exact
 distributions of the pivotal quantities are expressed as mixtures of
 linear combinations and of ratios of linear combinations of standard
 exponential random variables, which facilitates the computation of
 quantiles of these pivotal quantities. We also provide a bootstrap method
 for constructing a confidence interval. Some simulation studies are
 carried out to assess their performances. Using the exact distribution of
 the scale parameter, we establish an acceptance sampling procedure based
 on the lifetime of the unit. Some numerical results are tabulated for the
 illustration. One biometrical example is also given to illustrate the
 proposed methods.	    
Journal: Journal of Applied Statistics 
Pages: 1464-1479 
Issue: 7 
Volume: 40 
Year: 2013 
Month: 7 
X-DOI: 10.1080/02664763.2013.788613 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.788613 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:7:p:1464-1479




Template-Type: ReDIF-Article 1.0
Author-Name: Mohammad Z. Raqab 
Author-X-Name-First: Mohammad Z. 
Author-X-Name-Last: Raqab 
Title: Discriminating between the generalized Rayleigh and Weibull distributions 
Abstract:
	      Generalized Rayleigh (GR) and Weibull (WE) distributions are
 used quite effectively for analysing skewed lifetime data. In this paper,
 we consider the problem of selecting either GR or WE distribution as a
 more appropriate fitting model for a given data set. We use the ratio of
 maximized likelihoods (RML) for discriminating between the two
 distributions. The asymptotic and simulated distributions of the logarithm
 of the RML are applied to determine the probability of correctly selecting
 between these two families of distributions. It is examined numerically
 that the asymptotic results work quite well even for small sample sizes. A
 real data set involving the annual rainfall recorded at Los Angeles Civic
 Center during 25 years is analysed to illustrate the procedures developed
 here.		
Journal: Journal of Applied Statistics 
Pages: 1480-1493 
Issue: 7 
Volume: 40 
Year: 2013 
Month: 7 
X-DOI: 10.1080/02664763.2013.788614 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.788614 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:7:p:1480-1493




Template-Type: ReDIF-Article 1.0
Author-Name: K. H. Makambi 
Author-X-Name-First: K. H. 
Author-X-Name-Last: Makambi 
Title: Extended tests for non-zero between-study variance 
Abstract:
	      The ANOVA <italic>F</italic>-test, James tests and
 generalized <italic>F</italic>-test are extended to test hypotheses on the
 between-study variance for values greater than zero. Using simulations, we
 compare the performance of extended test procedures with respect to the
 actual attained type I error rate. Examples are provided to demonstrate
 the application of the procedures in ANOVA models and meta-analysis.	    
Journal: Journal of Applied Statistics 
Pages: 1494-1505 
Issue: 7 
Volume: 40 
Year: 2013 
Month: 7 
X-DOI: 10.1080/02664763.2013.788616 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.788616 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:7:p:1494-1505




Template-Type: ReDIF-Article 1.0
Author-Name: Y. Sertdemir 
Author-X-Name-First: Y. 
Author-X-Name-Last: Sertdemir 
Author-Name: H. R. Burgut 
Author-X-Name-First: H. R. 
Author-X-Name-Last: Burgut 
Author-Name: Z. N. Alparslan 
Author-X-Name-First: Z. N. 
Author-X-Name-Last: Alparslan 
Author-Name: I. Unal 
Author-X-Name-First: I. 
Author-X-Name-Last: Unal 
Author-Name: S. Gunasti 
Author-X-Name-First: S. 
Author-X-Name-Last: Gunasti 
Title: Comparing the methods of measuring multi-rater agreement on an ordinal rating scale: a simulation study with an application to real data 
Abstract:
	      Agreement among raters is an important issue in medicine, as
 well as in education and psychology. The agreement among two raters on a
 nominal or ordinal rating scale has been investigated in many articles.
 The multi-rater case with normally distributed ratings has also been
 explored at length. However, there is a lack of research on multiple
 raters using an ordinal rating scale. In this simulation study, several
 methods were compared with analyze rater agreement. The special case that
 was focused on was the multi-rater case using a bounded ordinal rating
 scale. The proposed methods for agreement were compared within different
 settings. Three main ordinal data simulation settings were used (normal,
 skewed and shifted data). In addition, the proposed methods were applied
 to a real data set from dermatology. The simulation results showed that
 the Kendall's <italic>W</italic> and mean gamma highly overestimated the
 agreement in data sets with shifts in data. ICC<sub>4</sub> for bounded
 data should be avoided in agreement studies with rating scales>5, where
 this method highly overestimated the simulated agreement. The difference
 in bias for all methods under study, except the mean gamma and Kendall's
 <italic>W</italic>, decreased as the rating scale increased. The bias of
 ICC<sub>3</sub> was consistent and small for nearly all simulation
 settings except the low agreement setting in the shifted data set.
 Researchers should be careful in selecting agreement methods, especially
 if shifts in ratings between raters exist and may apply more than one
 method before any conclusions are made.	  
Journal: Journal of Applied Statistics 
Pages: 1506-1519 
Issue: 7 
Volume: 40 
Year: 2013 
Month: 7 
X-DOI: 10.1080/02664763.2013.788617 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.788617 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:7:p:1506-1519




Template-Type: ReDIF-Article 1.0
Author-Name: Pao-Sheng Shen 
Author-X-Name-First: Pao-Sheng 
Author-X-Name-Last: Shen 
Title: Additive hazards model with truncated and doubly censored data 
Abstract:
	      In longitudinal studies, the additive hazard model is often
 used to analyze covariate effects on the duration time, defined as the
 elapsed time between the first and the second event. In this article, we
 consider the situation when the first event suffers partly interval
 censoring and the second event suffers left truncation and
 right-censoring. We proposed a two-step estimation procedure for
 estimating the regression coefficients of the additive hazards model. A
 simulation study is conducted to investigate the performance of the
 proposed estimator. The proposed method is applied to the Centers for
 Disease Control acquired immune deficiency syndrome blood transfusion
 data.		
Journal: Journal of Applied Statistics 
Pages: 1520-1532 
Issue: 7 
Volume: 40 
Year: 2013 
Month: 7 
X-DOI: 10.1080/02664763.2013.788618 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.788618 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:7:p:1520-1532




Template-Type: ReDIF-Article 1.0
Author-Name: Leonardo Soares Bastos 
Author-X-Name-First: Leonardo Soares 
Author-X-Name-Last: Bastos 
Author-Name: Joel Mauricio Correa da Rosa 
Author-X-Name-First: Joel Mauricio Correa 
Author-X-Name-Last: da Rosa 
Title: Predicting probabilities for the 2010 FIFA World Cup games using a Poisson-Gamma model 
Abstract:
	      In this paper, we provide probabilistic predictions for
 soccer games of the 2010 FIFA World Cup modelling the number of goals
 scored in a game by each team. We use a Poisson distribution for the
 number of goals for each team in a game, where the scoring rate is
 considered unknown. We use a Gamma distribution for the scoring rate and
 the Gamma parameters are chosen using historical data and difference among
 teams defined by a strength factor for each team. The strength factor is a
 measure of discrimination among the national teams obtained from their
 memberships to fuzzy clusters. The clusters are obtained with the use of
 the Fuzzy C-means algorithm applied to a vector of variables, most of them
 available on the official FIFA website. Static and dynamic models were
 used to predict the World Cup outcomes and the performance of our
 predictions was evaluated using two comparison methods.	  
Journal: Journal of Applied Statistics 
Pages: 1533-1544 
Issue: 7 
Volume: 40 
Year: 2013 
Month: 7 
X-DOI: 10.1080/02664763.2013.788619 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.788619 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:7:p:1533-1544




Template-Type: ReDIF-Article 1.0
Author-Name: Sukru Acitas 
Author-X-Name-First: Sukru 
Author-X-Name-Last: Acitas 
Author-Name: Pelin Kasap 
Author-X-Name-First: Pelin 
Author-X-Name-Last: Kasap 
Author-Name: Birdal Senoglu 
Author-X-Name-First: Birdal 
Author-X-Name-Last: Senoglu 
Author-Name: Olcay Arslan 
Author-X-Name-First: Olcay 
Author-X-Name-Last: Arslan 
Title: One-step <italic>M</italic>-estimators: Jones and Faddy's skewed <italic>t</italic>-distribution 
Abstract:
	      One-step <italic>M</italic> (OSM)-estimator needs some
 initial/preliminary estimates at the beginning of the calculation process.
 In this study, we propose to use new initial estimates for the calculation
 of the OSM-estimator. We consider simple location and simple linear
 regression models when the distribution of the error terms is Jones and
 Faddy's skewed <italic>t</italic>. Monte-Carlo simulation study shows that
 the OSM estimator(s) based on the proposed initial estimates is/are more
 efficient than the OSM estimator(s) based on the traditional initial
 estimates especially for the skewed cases. We also analyze some real data
 sets taken from the literature at the end of the paper.	  
Journal: Journal of Applied Statistics 
Pages: 1545-1560 
Issue: 7 
Volume: 40 
Year: 2013 
Month: 7 
X-DOI: 10.1080/02664763.2013.788620 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.788620 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:7:p:1545-1560




Template-Type: ReDIF-Article 1.0
Author-Name: L. I. Pettit 
Author-X-Name-First: L. I. 
Author-X-Name-Last: Pettit 
Author-Name: N. Sothinathan 
Author-X-Name-First: N. 
Author-X-Name-Last: Sothinathan 
Title: Effect of individual observations on the Box--Cox transformation 
Abstract:
	      In this paper, we consider the influence of individual
 observations on inferences about the Box--Cox power transformation
 parameter from a Bayesian point of view. We compare Bayesian diagnostic
 measures with the 'forward' method of analysis due to Riani and Atkinson.
 In particular, we look at the effect of omitting observations on the
 inference by comparing particular choices of transformation using the
 conditional predictive ordinate and the <italic>k</italic>		  
 <sub>			 <italic>d</italic>		   </sub> measure
 of Pettit and Young. We illustrate the methods using a designed
 experiment. We show that a group of masked outliers can be detected using
 these single deletion diagnostics. Also, we show that Bayesian diagnostic
 measures are simpler to use to investigate the effect of observations on
 transformations than the forward search method.	  
Journal: Journal of Applied Statistics 
Pages: 1561-1571 
Issue: 7 
Volume: 40 
Year: 2013 
Month: 7 
X-DOI: 10.1080/02664763.2013.789007 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.789007 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:7:p:1561-1571




Template-Type: ReDIF-Article 1.0
Author-Name: Jorge Gonz�lez Chapela 
Author-X-Name-First: Jorge 
Author-X-Name-Last: Gonz�lez Chapela 
Title: Things that make us different: analysis of deviance with time-use data 
Abstract:
	      The constrained, non-normal nature of time-use data poses a
 challenge to ordinary analysis of variance. This paper investigates a
 computationally simple variance decomposition technique suitable for those
 data. As a by-product of the analysis, a measure of fit for systems of
 time-demand equations is proposed that possesses several useful
 properties.	      
Journal: Journal of Applied Statistics 
Pages: 1572-1585 
Issue: 7 
Volume: 40 
Year: 2013 
Month: 7 
X-DOI: 10.1080/02664763.2013.789097 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.789097 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:7:p:1572-1585




Template-Type: ReDIF-Article 1.0
Author-Name: Mariana Rodrigues-Motta 
Author-X-Name-First: Mariana 
Author-X-Name-Last: Rodrigues-Motta 
Author-Name: Hildete P. Pinheiro 
Author-X-Name-First: Hildete P. 
Author-X-Name-Last: Pinheiro 
Author-Name: Eduardo G. Martins 
Author-X-Name-First: Eduardo G. 
Author-X-Name-Last: Martins 
Author-Name: M�rcio S. Ara&uacute;jo 
Author-X-Name-First: M�rcio S. 
Author-X-Name-Last: Ara&uacute;jo 
Author-Name: S�rgio F. dos Reis 
Author-X-Name-First: S�rgio F. 
Author-X-Name-Last: dos Reis 
Title: Multivariate models for correlated count data 
Abstract:
	      In this study, we deal with the problem of overdispersion
 beyond extra zeros for a collection of counts that can be correlated.
 Poisson, negative binomial, zero-inflated Poisson and zero-inflated
 negative binomial distributions have been considered. First, we propose a
 multivariate count model in which all counts follow the same distribution
 and are correlated. Then we extend this model in a sense that correlated
 counts may follow different distributions. To accommodate correlation
 among counts, we have considered correlated random effects for each
 individual in the mean structure, thus inducing dependency among common
 observations to an individual. The method is applied to real data to
 investigate variation in food resources use in a species of marsupial in a
 locality of the Brazilian Cerrado biome.	   
Journal: Journal of Applied Statistics 
Pages: 1586-1596 
Issue: 7 
Volume: 40 
Year: 2013 
Month: 7 
X-DOI: 10.1080/02664763.2013.789098 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.789098 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:7:p:1586-1596




Template-Type: ReDIF-Article 1.0
Author-Name: Sanjay Kumar Singh 
Author-X-Name-First: Sanjay Kumar 
Author-X-Name-Last: Singh 
Author-Name: Umesh Singh 
Author-X-Name-First: Umesh 
Author-X-Name-Last: Singh 
Author-Name: Dinesh Kumar 
Author-X-Name-First: Dinesh 
Author-X-Name-Last: Kumar 
Title: Bayesian estimation of parameters of inverse Weibull distribution 
Abstract:
	      The present paper describes the Bayes estimators of
 parameters of inverse Weibull distribution for complete, type I and type
 II censored samples under general entropy and squared error loss
 functions. The proposed estimators have been compared on the basis of
 their simulated risks (average loss over sample space). A real-life data
 set is used to illustrate the results. 	 
Journal: Journal of Applied Statistics 
Pages: 1597-1607 
Issue: 7 
Volume: 40 
Year: 2013 
Month: 7 
X-DOI: 10.1080/02664763.2013.789492 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.789492 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:7:p:1597-1607




Template-Type: ReDIF-Article 1.0
Author-Name: Saad T. Bakir 
Author-X-Name-First: Saad T. 
Author-X-Name-Last: Bakir 
Title: A subset selection procedure for multinomial distributions 
Abstract:
	      A subset selection procedure is developed for selecting a
 subset containing the multinomial population that has the highest value of
 a certain linear combination of the multinomial cell probabilities; such
 population is called the 'best' The multivariate normal large sample
 approximation to the multinomial distribution is used to derive
 expressions for the probability of a correct selection, and for the
 threshold constant involved in the procedure. The procedure guarantees
 that the probability of a correct selection is at least at a pre-assigned
 level. The proposed procedure is an extension of Gupta and Sobel's [14]
 selection procedure for binomials and of Bakir's [2] restrictive selection
 procedure for multinomials. One illustration of the procedure concerns
 population income mobility in four countries: Peru, Russia, South Africa
 and the USA. Analysis indicates that Russia and Peru fall in the selected
 subset containing the best population with respect to income mobility from
 poverty to a higher-income status. The procedure is also applied to data
 concerning grade distribution for students in a certain freshman class.    
Journal: Journal of Applied Statistics 
Pages: 1608-1618 
Issue: 7 
Volume: 40 
Year: 2013 
Month: 7 
X-DOI: 10.1080/02664763.2013.789493 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.789493 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:7:p:1608-1618




Template-Type: ReDIF-Article 1.0
Author-Name: Claude Mant� 
Author-X-Name-First: Claude 
Author-X-Name-Last: Mant� 
Author-Name: Guillaume Bernard 
Author-X-Name-First: Guillaume 
Author-X-Name-Last: Bernard 
Author-Name: Patrick Bonhomme 
Author-X-Name-First: Patrick 
Author-X-Name-Last: Bonhomme 
Author-Name: David Nerini 
Author-X-Name-First: David 
Author-X-Name-Last: Nerini 
Title: Application of ordinal correspondence analysis for submerged aquatic vegetation monitoring 
Abstract:
			      <italic>The European Water Framework states
 that macrophyte</italic> communities (seaweeds and seagrass) are key
 indicators of the ecological health of lagoons. Furthermore, the
 restoration of these communities, especially the <italic>Zostera</italic>
 meadows, is one of the main objectives of the Berre lagoon restoration
 plan. Consequently, a monitoring programme of the main
 <italic>macrophyte</italic> species still present in the lagoon was
 initiated in 1996. This monitoring resulted in a sequence of 11 spatially
 structured annual tables consisting of the observed density of these
 species. These tables are processed in this study. First, we specify the
 principles of Beh's ordinal correspondence analysis (OCA), designed for
 ordered row/column categories, and compare this method to classical
 correspondence analysis (CA). Then, we show that OCA is straightforwardly
 adaptable for processing a sequence of ordered contingency tables like
 ours. Both OCA and CA are afterwards used to reveal and test the main
 patterns of spatio-temporal changes of two <italic>macrophyte</italic>
 species in the Berre lagoon: <italic>Ulva</italic> and
 <italic>Zostera</italic>. The results we obtained are compared and
 discussed.	     
Journal: Journal of Applied Statistics 
Pages: 1619-1638 
Issue: 8 
Volume: 40 
Year: 2013 
Month: 8 
X-DOI: 10.1080/02664763.2013.789494 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.789494 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:8:p:1619-1638




Template-Type: ReDIF-Article 1.0
Author-Name: Syed Mohsin Ali Kazmi 
Author-X-Name-First: Syed Mohsin Ali 
Author-X-Name-Last: Kazmi 
Author-Name: Muhammad Aslam 
Author-X-Name-First: Muhammad 
Author-X-Name-Last: Aslam 
Author-Name: Sajid Ali 
Author-X-Name-First: Sajid 
Author-X-Name-Last: Ali 
Author-Name: Nasir Abbas 
Author-X-Name-First: Nasir 
Author-X-Name-Last: Abbas 
Title: Selection of suitable prior for the Bayesian mixture of a class of lifetime distributions under type-I censored datasets 
Abstract:
	      This paper explores the study on mixture of a class of
 probability density functions under type-I censoring scheme. In this
 paper, we mold a heterogeneous population by means of a two-component
 mixture of the class of probability density functions. The parameters of
 the class of mixture density functions are estimated and compared using
 the Bayes estimates under the squared-error and precautionary loss
 functions. A censored mixture dataset is simulated by probabilistic mixing
 for the computational purpose considering particular case of the Maxwell
 distribution. Closed-form expressions for the Bayes estimators along with
 their posterior risks are derived for censored as well as complete
 samples. Some stimulating comparisons and properties of the estimates are
 presented here. A factual dataset has also been for illustration.	    
Journal: Journal of Applied Statistics 
Pages: 1639-1658 
Issue: 8 
Volume: 40 
Year: 2013 
Month: 8 
X-DOI: 10.1080/02664763.2013.789831 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.789831 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:8:p:1639-1658




Template-Type: ReDIF-Article 1.0
Author-Name: Akiko Kada 
Author-X-Name-First: Akiko 
Author-X-Name-Last: Kada 
Author-Name: Zhihong Cai 
Author-X-Name-First: Zhihong 
Author-X-Name-Last: Cai 
Author-Name: Manabu Kuroki 
Author-X-Name-First: Manabu 
Author-X-Name-Last: Kuroki 
Title: Medical diagnostic test based on the potential test result approach: bounds and identification 
Abstract:
	      Evaluating the performance of a medical diagnostic test is an
 important issue in disease diagnosis. Youden [<italic>Index for rating
 diagnostic tests</italic>, Cancer 3 (1950), pp. 32--35] stated that the
 ideal measure of performance is to ensure that the control group resembles
 the diseased group as closely as possible in all respects except for the
 presence of the disease. To achieve this aim, this paper introduces the
 potential test result approach and proposes a new measure to evaluate the
 performance of medical diagnostic tests. This proposed measure, denoted as
 <inline-formula>		    <inline-graphic
 xmlns:xlink="http://www.w3.org/1999/xlink"				   
   xlink:href="cjas_a_789832_o_ilm0001.gif"/>		    
 </inline-formula>, can be interpreted as a probability that a test result
 <italic>T</italic> would respond to a disease status <italic>D</italic>
 (<italic>d</italic> is an element of {<italic>d</italic>		
 <sub>0</sub>, <italic>d</italic>		 <sub>1</sub>}) for a given
 threshold <italic>t</italic>, and therefore evaluates both the sufficiency
 and necessity of the performance of a medical diagnostic test. This new
 measure provides a total different interpretation for the Youden index and
 thus helps us to better understand the essence of the Youden index and its
 properties. We further propose non-parametric bounds on the proposed
 measure based on a variety of assumptions and illustrate our results with
 an example from the neonatal audiology study.		
Journal: Journal of Applied Statistics 
Pages: 1659-1672 
Issue: 8 
Volume: 40 
Year: 2013 
Month: 8 
X-DOI: 10.1080/02664763.2013.789832 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.789832 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:8:p:1659-1672




Template-Type: ReDIF-Article 1.0
Author-Name: Ting-Ting Gang 
Author-X-Name-First: Ting-Ting 
Author-X-Name-Last: Gang 
Author-Name: Jun Yang 
Author-X-Name-First: Jun 
Author-X-Name-Last: Yang 
Author-Name: Yu Zhao 
Author-X-Name-First: Yu 
Author-X-Name-Last: Zhao 
Title: Multivariate control chart based on the highest possibility region 
Abstract:
	      The <italic>T</italic>		    -super-2 control chart
 is widely adopted in multivariate statistical process control. However,
 when dealing with asymmetrical or multimodal distributions using the
 traditional <italic>T</italic> 	       -super-2 control chart, some
 points with relatively high occurrence possibility might be excluded,
 while some points with relatively low occurrence possibility might be
 accepted. Motived by the thought of the highest posterior density credible
 region, we develop a control chart based on the highest possibility region
 to solve this problem. It is shown that the proposed multivariate control
 chart will not only meet the false alarm requirement, but also ensure that
 all the in-control points are with relatively high occurrence possibility.
 The advantages and effectiveness of the proposed control chart are
 demonstrated by some numerical examples in the end.	      
Journal: Journal of Applied Statistics 
Pages: 1673-1681 
Issue: 8 
Volume: 40 
Year: 2013 
Month: 8 
X-DOI: 10.1080/02664763.2013.790007 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.790007 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:8:p:1673-1681




Template-Type: ReDIF-Article 1.0
Author-Name: Luca De Angelis 
Author-X-Name-First: Luca 
Author-X-Name-Last: De Angelis 
Author-Name: Leonard J. Paas 
Author-X-Name-First: Leonard J. 
Author-X-Name-Last: Paas 
Title: A dynamic analysis of stock markets using a hidden Markov model 
Abstract:
	      This paper proposes a framework to detect financial crises,
 pinpoint the end of a crisis in stock markets and support investment
 decision-making processes. This proposal is based on a hidden Markov model
 (HMM) and allows for a specific focus on conditional mean returns. By
 analysing weekly changes in the US stock market indexes over a period of
 20 years, this study obtains an accurate detection of stable and turmoil
 periods and a probabilistic measure of switching between different stock
 market conditions. The results contribute to the discussion of the
 capabilities of Markov-switching models of analysing stock market
 behaviour. In particular, we find evidence that HMM outperforms threshold
 GARCH model with Student-<italic>t</italic> innovations both in-sample and
 out-of-sample, giving financial operators some appealing investment
 strategies.	      
Journal: Journal of Applied Statistics 
Pages: 1682-1700 
Issue: 8 
Volume: 40 
Year: 2013 
Month: 8 
X-DOI: 10.1080/02664763.2013.793302 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.793302 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:8:p:1682-1700




Template-Type: ReDIF-Article 1.0
Author-Name: Laura Barbieri 
Author-X-Name-First: Laura 
Author-X-Name-Last: Barbieri 
Title: Causality and interdependence analysis in linear econometric models with an application to fertility 
Abstract:
	      This paper is an applied analysis of the causal structure of
 linear multi-equational econometric models. Its aim is to identify the
 kind of relationships linking the endogenous variables of the model,
 distinguishing between causal links and feedback loops. The investigation
 is first carried out within a deterministic framework and then moves on to
 show how the results may change inside a more realistic stochastic
 context. The causal analysis is then specifically applied to a linear
 simultaneous equation model explaining fertility rates. The analysis is
 carried out by means of a specific RATS programming code designed to show
 the specific nature of the relationships within the model.	     
Journal: Journal of Applied Statistics 
Pages: 1701-1716 
Issue: 8 
Volume: 40 
Year: 2013 
Month: 8 
X-DOI: 10.1080/02664763.2013.793660 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.793660 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:8:p:1701-1716




Template-Type: ReDIF-Article 1.0
Author-Name: A. Asgharzadeh 
Author-X-Name-First: A. 
Author-X-Name-Last: Asgharzadeh 
Author-Name: Hassan S. Bakouch 
Author-X-Name-First: Hassan S. 
Author-X-Name-Last: Bakouch 
Author-Name: L. Esmaeili 
Author-X-Name-First: L. 
Author-X-Name-Last: Esmaeili 
Title: Pareto Poisson--Lindley distribution with applications 
Abstract:
	      A new lifetime distribution is introduced based on
 compounding Pareto and Poisson--Lindley distributions. Several statistical
 properties of the distribution are established, including behavior of the
 probability density function and the failure rate function, heavy- and
 long-right tailedness, moments, the Laplace transform, quantiles, order
 statistics, moments of residual lifetime, conditional moments, conditional
 moment generating function, stress--strength parameter, R�nyi entropy and
 Song's measure. We get maximum-likelihood estimators of the distribution
 parameters and investigate the asymptotic distribution of the estimators
 via Fisher's information matrix. Applications of the distribution using
 three real data sets are presented and it is shown that the distribution
 fits better than other related distributions in practical uses.	  
Journal: Journal of Applied Statistics 
Pages: 1717-1734 
Issue: 8 
Volume: 40 
Year: 2013 
Month: 8 
X-DOI: 10.1080/02664763.2013.793886 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.793886 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:8:p:1717-1734




Template-Type: ReDIF-Article 1.0
Author-Name: Hasan Ertas 
Author-X-Name-First: Hasan 
Author-X-Name-Last: Ertas 
Author-Name: Murat Erisoglu 
Author-X-Name-First: Murat 
Author-X-Name-Last: Erisoglu 
Author-Name: Selahattin Kaciranlar 
Author-X-Name-First: Selahattin 
Author-X-Name-Last: Kaciranlar 
Title: Detecting influential observations in Liu and modified Liu estimators 
Abstract:
	      In regression, detecting anomalous observations is a
 significant step for model-building process. Various influence measures
 based on different motivational arguments are designed to measure the
 influence of observations through different aspects of various regression
 models. The presence of influential observations in the data is
 complicated by the existence of multicollinearity. The purpose of this
 paper is to assess the influence of observations in the Liu [9] and
 modified Liu [15] estimators by using the method of approximate case
 deletion formulas suggested by Walker and Birch [14]. A numerical example
 using a real data set used by Longley [10] and a Monte Carlo simulation
 are given to illustrate the theoretical results.	   
Journal: Journal of Applied Statistics 
Pages: 1735-1745 
Issue: 8 
Volume: 40 
Year: 2013 
Month: 8 
X-DOI: 10.1080/02664763.2013.794203 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.794203 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:8:p:1735-1745




Template-Type: ReDIF-Article 1.0
Author-Name: Z. I. Kalaylioglu 
Author-X-Name-First: Z. I. 
Author-X-Name-Last: Kalaylioglu 
Author-Name: O. Ozturk 
Author-X-Name-First: O. 
Author-X-Name-Last: Ozturk 
Title: Bayesian semiparametric models for nonignorable missing mechanisms in generalized linear models 
Abstract:
	      Semiparametric models provide a more flexible form for
 modeling the relationship between the response and the explanatory
 variables. On the other hand in the literature of modeling for the missing
 variables, canonical form of the probability of the variable being missing
 (<italic>p</italic>) is modeled taking a fully parametric approach. Here
 we consider a regression spline based semiparametric approach to model the
 missingness mechanism of nonignorably missing covariates. In this model
 the relationship between the suitable canonical form of <italic>p</italic>
 (e.g. probit <italic>p</italic>) and the missing covariate is modeled
 through several splines. A Bayesian procedure is developed to efficiently
 estimate the parameters. A computationally advantageous prior construction
 is proposed for the parameters of the semiparametric part. A WinBUGS code
 is constructed to apply Gibbs sampling to obtain the posterior
 distributions. We show through an extensive Monte Carlo simulation
 experiment that response model coefficent estimators maintain better (when
 the true missingness mechanism is nonlinear) or equivalent (when the true
 missingness mechanism is linear) bias and efficiency properties with the
 use of proposed semiparametric missingness model compared to the
 conventional model.	      
Journal: Journal of Applied Statistics 
Pages: 1746-1763 
Issue: 8 
Volume: 40 
Year: 2013 
Month: 8 
X-DOI: 10.1080/02664763.2013.794329 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.794329 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:8:p:1746-1763




Template-Type: ReDIF-Article 1.0
Author-Name: Mark Trede 
Author-X-Name-First: Mark 
Author-X-Name-Last: Trede 
Author-Name: Cornelia Savu 
Author-X-Name-First: Cornelia 
Author-X-Name-Last: Savu 
Title: Do stock returns have an Archimedean copula? 
Abstract:
	      The flexible class of Archimedean copulas plays an important
 role in multivariate statistics. While there is a large number of
 goodness-of-fit tests for copulas and parametric families of copulas, the
 question if a given data set belongs to an arbitrary Archimedean copula or
 not has not yet received much attention in the literature. This paper
 suggests a new, straightforward method to test whether a copula is an
 Archimedean copula without the need to specify its parametric family. We
 conduct Monte Carlo simulations to assess the power of the test. The
 approach is applied to (bivariate) joint distributions of stock asset
 returns. We find that, in general, stock returns may have Archimedean
 copulas.	   
Journal: Journal of Applied Statistics 
Pages: 1764-1778 
Issue: 8 
Volume: 40 
Year: 2013 
Month: 8 
X-DOI: 10.1080/02664763.2013.794330 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.794330 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:8:p:1764-1778




Template-Type: ReDIF-Article 1.0
Author-Name: Zhiyong Zhang 
Author-X-Name-First: Zhiyong 
Author-X-Name-Last: Zhang 
Title: Bayesian growth curve models with the generalized error distribution 
Abstract:
	      To deal with the longitudinal data with both leptokurtic and
 platykurtic errors, we extend growth curve models using the generalized
 error distribution (GED) model. The Metropolis--Hastings algorithm is used
 to estimate the GED model parameters in the Bayesian framework. The
 application of the GED model is illustrated through the analysis of
 mathematical development data. Results show that the GED model can
 correctly identify the deviation from normal of the error distributions.   
Journal: Journal of Applied Statistics 
Pages: 1779-1795 
Issue: 8 
Volume: 40 
Year: 2013 
Month: 8 
X-DOI: 10.1080/02664763.2013.796348 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.796348 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:8:p:1779-1795




Template-Type: ReDIF-Article 1.0
Author-Name: Guillermo Ferreira 
Author-X-Name-First: Guillermo 
Author-X-Name-Last: Ferreira 
Author-Name: Luis M. Castro 
Author-X-Name-First: Luis M. 
Author-X-Name-Last: Castro 
Author-Name: Victor H. Lachos 
Author-X-Name-First: Victor H. 
Author-X-Name-Last: Lachos 
Author-Name: Ronaldo Dias 
Author-X-Name-First: Ronaldo 
Author-X-Name-Last: Dias 
Title: Bayesian modeling of autoregressive partial linear models with scale mixture of normal errors 
Abstract:
	      Normality and independence of error terms are typical
 assumptions for partial linear models. However, these assumptions may be
 unrealistic in many fields, such as economics, finance and biostatistics.
 In this paper, a Bayesian analysis for partial linear model with
 first-order autoregressive errors belonging to the class of the scale
 mixtures of normal distributions is studied in detail. The proposed model
 provides a useful generalization of the symmetrical linear regression
 model with independent errors, since the distribution of the error term
 covers both correlated and thick-tailed distributions, and has a
 convenient hierarchical representation allowing easy implementation of a
 Markov chain Monte Carlo scheme. In order to examine the robustness of the
 model against outlying and influential observations, a Bayesian case
 deletion influence diagnostics based on the Kullback--Leibler (K--L)
 divergence is presented. The proposed method is applied to monthly and
 daily returns of two Chilean companies.	  
Journal: Journal of Applied Statistics 
Pages: 1796-1816 
Issue: 8 
Volume: 40 
Year: 2013 
Month: 8 
X-DOI: 10.1080/02664763.2013.796349 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.796349 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:8:p:1796-1816




Template-Type: ReDIF-Article 1.0
Author-Name: Vishal Maurya 
Author-X-Name-First: Vishal 
Author-X-Name-Last: Maurya 
Author-Name: Amar Nath Gill 
Author-X-Name-First: Amar Nath 
Author-X-Name-Last: Gill 
Author-Name: Parminder Singh 
Author-X-Name-First: Parminder 
Author-X-Name-Last: Singh 
Title: Multiple comparisons with a control for exponential location parameters under heteroscedasticity 
Abstract:
	      In this paper, a new design-oriented two-stage two-sided
 simultaneous confidence intervals, for comparing several exponential
 populations with control population in terms of location parameters under
 heteroscedasticity, are proposed. If there is a prior information that the
 location parameter of <italic>k</italic> exponential populations are not
 less than the location parameter of control population, one-sided
 simultaneous confidence intervals provide more inferential sensitivity
 than two-sided simultaneous confidence intervals. But the two-sided
 simultaneous confidence intervals have advantages over the one-sided
 simultaneous confidence intervals as they provide both lower and upper
 bounds for the parameters of interest. The proposed design-oriented
 two-stage two-sided simultaneous confidence intervals provide the benefits
 of both the two-stage one-sided and two-sided simultaneous confidence
 intervals. When the additional sample at the second stage may not be
 available due to the experimental budget shortage or other factors in an
 experiment, one-stage two-sided confidence intervals are proposed, which
 combine the advantages of one-stage one-sided and two-sided simultaneous
 confidence intervals. The critical constants are obtained using the
 techniques given in Lam [9,10]. These critical constant are compared with
 the critical constants obtained by Bonferroni inequality techniques and
 found that critical constant obtained by Lam [9,10] are less conservative
 than critical constants computed from the Bonferroni inequality technique.
 Implementation of the proposed simultaneous confidence intervals is
 demonstrated by a numerical example.	       
Journal: Journal of Applied Statistics 
Pages: 1817-1830 
Issue: 8 
Volume: 40 
Year: 2013 
Month: 8 
X-DOI: 10.1080/02664763.2013.796350 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.796350 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:8:p:1817-1830




Template-Type: ReDIF-Article 1.0
Author-Name: Paola Annoni 
Author-X-Name-First: Paola 
Author-X-Name-Last: Annoni 
Author-Name: Dorota Weziak-Bialowolska 
Author-X-Name-First: Dorota 
Author-X-Name-Last: Weziak-Bialowolska 
Author-Name: Hania Farhan 
Author-X-Name-First: Hania 
Author-X-Name-Last: Farhan 
Title: Measuring the impact of the Web: Rasch modelling for survey evaluation 
Abstract:
	      In 2012, the World Wide Web Foundation launched for the first
 time the Web Index (WI), which combines the existing secondary data with
 new primary data to rank countries according to their progress and use of
 the Web. Primary data are gathered via a multi-country specifically
 designed questionnaire. The aim of our analysis is (1) to evaluate the
 measurement properties of the expert assessment survey and to provide
 survey designers with some insights into possible problematic questions
 and/or unexpectedly behaving countries and (2) to assess the experts'
 perception of the state and the value of the Web. To do so the Rating
 Scale Rasch model is employed. Results show that about 10% of survey
 questions are detected as misfitting and need to be reworded. Possible
 reasons are: counter-orientation with respect to the WI polarity,
 difficulty in understanding the question's words or binary instead of the
 multiple response scale. Country analysis shows that no country can be
 considered as an outlier due to notable unexpected pattern of answers.
 Since the survey is to be expanded in future editions of the WI, the
 results of our analysis are very important in pin-pointing the questions
 most in need of refinement for the next edition of the Index.		
Journal: Journal of Applied Statistics 
Pages: 1831-1851 
Issue: 8 
Volume: 40 
Year: 2013 
Month: 8 
X-DOI: 10.1080/02664763.2013.796351 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.796351 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:8:p:1831-1851




Template-Type: ReDIF-Article 1.0
Author-Name: David Canning 
Author-X-Name-First: David 
Author-X-Name-Last: Canning 
Author-Name: Declan French 
Author-X-Name-First: Declan 
Author-X-Name-Last: French 
Author-Name: Michael Moore 
Author-X-Name-First: Michael 
Author-X-Name-Last: Moore 
Title: Non-parametric estimation of data dimensionality prior to data compression: the case of the human development index 
Abstract:
	      In many applications in applied statistics, researchers
 reduce the complexity of a data set by combining a group of variables into
 a single measure using a factor analysis or an index number. We argue that
 such compression loses information if the data actually have high
 dimensionality. We advocate the use of a non-parametric estimator,
 commonly used in physics (the <italic>Takens estimator</italic>), to
 estimate the correlation dimension of the data prior to compression. The
 advantage of this approach over traditional linear data compression
 approaches is that the data do not have to be linearised. Applying our
 ideas to the United Nations Human Development Index, we find that the four
 variables that are used in its construction have dimension 3 and the index
 loses information.	     
Journal: Journal of Applied Statistics 
Pages: 1853-1863 
Issue: 9 
Volume: 40 
Year: 2013 
Month: 9 
X-DOI: 10.1080/02664763.2013.798629 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.798629 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:9:p:1853-1863




Template-Type: ReDIF-Article 1.0
Author-Name: Jorge Alberto Achcar 
Author-X-Name-First: Jorge Alberto 
Author-X-Name-Last: Achcar
Author-Name: Em�lio Augusto Coelho-Barros 
Author-X-Name-First: Em�lio Augusto 
Author-X-Name-Last: Coelho-Barros 
Author-Name: Josmar Mazucheli 
Author-X-Name-First: Josmar 
Author-X-Name-Last: Mazucheli 
Title: Block and Basu bivariate lifetime distribution in the presence of cure fraction 
Abstract:
	      This paper presents estimates for the parameters included in
 the Block and Basu bivariate lifetime distributions in the presence of
 covariates and cure fraction, applied to analyze survival data when some
 individuals may never experience the event of interest and two lifetimes
 are associated with each unit. A Bayesian procedure is used to get point
 and confidence intervals for the unknown parameters. Posterior summaries
 of interest are obtained using standard Markov Chain Monte Carlo methods
 in <italic>rjags</italic> package for <italic>R</italic> software. An
 illustration of the proposed methodology is given for a Diabetic
 Retinopathy Study data set.	      
Journal: Journal of Applied Statistics 
Pages: 1864-1874 
Issue: 9 
Volume: 40 
Year: 2013 
Month: 9 
X-DOI: 10.1080/02664763.2013.798630 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.798630 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:9:p:1864-1874




Template-Type: ReDIF-Article 1.0
Author-Name: Wen-Liang Hung 
Author-X-Name-First: Wen-Liang 
Author-X-Name-Last: Hung 
Author-Name: De-Hua Chen 
Author-X-Name-First: De-Hua 
Author-X-Name-Last: Chen 
Title: Clustering algorithm for proximity-relation matrix and its applications 
Abstract:
	      In this paper, we present a new algorithm for clustering
 proximity-relation matrix that does not require the transitivity property.
 The proposed algorithm is first inspired by the idea of Yang and Wu [16]
 then turned into a self-organizing process that is built upon the
 intuition behind clustering. At the end of the process subjects belonging
 to be the same cluster should converge to the same point, which represents
 the cluster center. However, the performance of Yang and Wu's algorithm
 depends on parameter selection. In this paper, we use the partition
 entropy (PE) index to choose it. Numerical result illustrates that the
 proposed method does not only solve the parameter selection problem but
 also obtains an optimal clustering result. Finally, we apply the proposed
 algorithm to three applications. One is to evaluate the performance of
 higher education in Taiwan, another is machine--parts grouping in cellular
 manufacturing systems, and the other is to cluster probability density
 functions.	     
Journal: Journal of Applied Statistics 
Pages: 1875-1892 
Issue: 9 
Volume: 40 
Year: 2013 
Month: 9 
X-DOI: 10.1080/02664763.2013.799126 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.799126 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:9:p:1875-1892




Template-Type: ReDIF-Article 1.0
Author-Name: Rui Fragoso 
Author-X-Name-First: Rui 
Author-X-Name-Last: Fragoso 
Author-Name: Maria Leonor da Silva Carvalho 
Author-X-Name-First: Maria Leonor da Silva 
Author-X-Name-Last: Carvalho 
Title: Estimation of cost allocation coefficients at the farm level using an entropy approach 
Abstract:
	      This paper aims to estimate the farm cost allocation
 coefficients from whole-farm input costs. An entropy approach was
 developed under a Tobit formulation and was applied to a sample of farms
 from the 2004 Farm Accountancy Data Network data base for Alentejo region,
 Southern Portugal. A Generalized Maximum Entropy model and Cross
 Generalized Entropy model were developed to the sample conditions and were
 tested. Model results were assessed in terms of their precision and
 estimation power and were compared with the observed data. The entropy
 approach showed to be a flexible and valid tool to estimate incomplete
 information, namely regarding farm costs.	    
Journal: Journal of Applied Statistics 
Pages: 1893-1906 
Issue: 9 
Volume: 40 
Year: 2013 
Month: 9 
X-DOI: 10.1080/02664763.2013.799127 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.799127 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:9:p:1893-1906




Template-Type: ReDIF-Article 1.0
Author-Name: R. M. Green 
Author-X-Name-First: R. M. 
Author-X-Name-Last: Green 
Author-Name: M. S. Bebbington 
Author-X-Name-First: M. S. 
Author-X-Name-Last: Bebbington 
Title: A longitudinal analysis of infant and senescent mortality using mixture models 
Abstract:
	      We construct a mixture distribution including infant,
 exogenous and Gompertzian/non-Gompertzian senescent mortality. Using
 mortality data from Swedish females 1751--, we show that this outperforms
 models without these features, and compare its trends in cohort and period
 mortality over time. We find an almost complete disappearance of exogenous
 mortality within the last century of period mortality, with cohort
 mortality approaching the same limits. Both Gompertzian and
 non-Gompertzian senescent mortality are consistently present, with the
 estimated balance between them oscillating constantly. While the
 parameters of the latter appear to be trending over time, the parameters
 of the former do not.		
Journal: Journal of Applied Statistics 
Pages: 1907-1920 
Issue: 9 
Volume: 40 
Year: 2013 
Month: 9 
X-DOI: 10.1080/02664763.2013.800032 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.800032 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:9:p:1907-1920




Template-Type: ReDIF-Article 1.0
Author-Name: Arindam Gupta 
Author-X-Name-First: Arindam 
Author-X-Name-Last: Gupta 
Author-Name: Samba Siva Rao Pasupuleti 
Author-X-Name-First: Samba Siva Rao 
Author-X-Name-Last: Pasupuleti 
Title: A new behavioural model for fertility schedules 
Abstract:
	      Modelling age-specific fertility rates is of great importance
 in demography because of their influence on population growth. Although we
 have a variety of fertility models in the demographic literature, most of
 them do not have any demographic interpretation for their parameters. It
 is generally expected that models with behavioural interpretation are more
 universal than those without any interpretation. Even though the famous
 Gompertz model has some behavioural interpretation it suffers from other
 drawbacks. In the present work, we propose a new fertility model, which
 has its genesis in the generalization of logistic law. The proposed model
 has good behavioural interpretation, alongside having nice parameter
 interpretations.	   
Journal: Journal of Applied Statistics 
Pages: 1921-1930 
Issue: 9 
Volume: 40 
Year: 2013 
Month: 9 
X-DOI: 10.1080/02664763.2013.800033 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.800033 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:9:p:1921-1930




Template-Type: ReDIF-Article 1.0
Author-Name: Christian H. Wei&szlig; 
Author-X-Name-First: Christian H. 
Author-X-Name-Last: Wei&szlig; 
Title: Integer-valued autoregressive models for counts showing underdispersion 
Abstract:
	      The Poisson distribution is a simple and popular model for
 count-data random variables, but it suffers from the equidispersion
 requirement, which is often not met in practice. While models for
 overdispersed counts have been discussed intensively in the literature,
 the opposite phenomenon, underdispersion, has received only little
 attention, especially in a time series context. We start with a detailed
 survey of distribution models allowing for underdispersion, discuss their
 properties and highlight possible disadvantages. After having identified
 two model families with attractive properties as well as only two model
 parameters, we combine these models with the INAR(1) model
 (<italic>in</italic>teger-valued
 <italic>a</italic>uto<italic>r</italic>egressive), which is particularly
 well suited to obtain auotocorrelated counts with underdispersion.
 Properties of the resulting stationary INAR(1) models and approaches for
 parameter estimation are considered, as well as possible extensions to
 higher order autoregressions. Three real-data examples illustrate the
 application of the models in practice. 	 
Journal: Journal of Applied Statistics 
Pages: 1931-1948 
Issue: 9 
Volume: 40 
Year: 2013 
Month: 9 
X-DOI: 10.1080/02664763.2013.800034 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.800034 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:9:p:1931-1948




Template-Type: ReDIF-Article 1.0
Author-Name: Tiejun Tong 
Author-X-Name-First: Tiejun 
Author-X-Name-Last: Tong 
Author-Name: Zeny Feng 
Author-X-Name-First: Zeny 
Author-X-Name-Last: Feng 
Author-Name: Julia S. Hilton 
Author-X-Name-First: Julia S. 
Author-X-Name-Last: Hilton 
Author-Name: Hongyu Zhao 
Author-X-Name-First: Hongyu 
Author-X-Name-Last: Zhao 
Title: Estimating the proportion of true null hypotheses using the pattern of observed <italic>p</italic>-values 
Abstract:
	      Estimating the proportion of true null hypotheses,
 &pi;<sub>0</sub>, has attracted much attention in the recent statistical
 literature. Besides its apparent relevance for a set of specific
 scientific hypotheses, an accurate estimate of this parameter is key for
 many multiple testing procedures. Most existing methods for estimating
 &pi;<sub>0</sub> in the literature are motivated from the independence
 assumption of test statistics, which is often not true in reality.
 Simulations indicate that most existing estimators in the presence of the
 dependence among test statistics can be poor, mainly due to the increase
 of variation in these estimators. In this paper, we propose several
 data-driven methods for estimating &pi;<sub>0</sub> by incorporating the
 distribution pattern of the observed <italic>p</italic>-values as a
 practical approach to address potential dependence among test statistics.
 Specifically, we use a linear fit to give a data-driven estimate for the
 proportion of true-null <italic>p</italic>-values in (&lambda;, 1] over
 the whole range [0, 1] instead of using the expected proportion at 1 -
 &lambda;. We find that the proposed estimators may substantially decrease
 the variance of the estimated true null proportion and thus improve the
 overall performance.	       
Journal: Journal of Applied Statistics 
Pages: 1949-1964 
Issue: 9 
Volume: 40 
Year: 2013 
Month: 9 
X-DOI: 10.1080/02664763.2013.800035 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.800035 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:9:p:1949-1964




Template-Type: ReDIF-Article 1.0
Author-Name: Donatella Vicari 
Author-X-Name-First: Donatella 
Author-X-Name-Last: Vicari 
Author-Name: Johan Ren� van Dorp 
Author-X-Name-First: Johan Ren� 
Author-X-Name-Last: van Dorp 
Title: On a bounded bimodal two-sided distribution fitted to the Old-Faithful geyser data 
Abstract:
	      In this paper, we shall develop a novel family of bimodal
 univariate distributions (also allowing for unimodal shapes) and
 demonstrate its use utilizing the well-known and almost classical data set
 involving durations and waiting times of eruptions of the Old-Faithful
 geyser in Yellowstone park. Specifically, we shall analyze the
 Old-Faithful data set with 272 data points provided in Dekking <italic>et
 al.</italic> [3]. In the process, we develop a bivariate distribution
 using a copula technique and compare its fit to a mixture of bivariate
 normal distributions also fitted to the same bivariate data set. We
 believe the fit-analysis and comparison is primarily illustrative from an
 educational perspective for distribution theory modelers, since in the
 process a variety of statistical techniques are demonstrated. We do not
 claim one model as preferred over the other.	       
Journal: Journal of Applied Statistics 
Pages: 1965-1978 
Issue: 9 
Volume: 40 
Year: 2013 
Month: 9 
X-DOI: 10.1080/02664763.2013.800036 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.800036 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:9:p:1965-1978




Template-Type: ReDIF-Article 1.0
Author-Name: Edgard M. Maboudou-Tchao 
Author-X-Name-First: Edgard M. 
Author-X-Name-Last: Maboudou-Tchao 
Author-Name: Douglas M. Hawkins 
Author-X-Name-First: Douglas M. 
Author-X-Name-Last: Hawkins 
Title: Detection of multiple change-points in multivariate data 
Abstract:
	      The statistical analysis of change-point detection and
 estimation has received much attention recently. A time point such that
 observations follow a certain statistical distribution up to that point
 and a different distribution -- commonly of the same functional form but
 different parameters after that point -- is called a change-point.
 Multiple change-point problems arise when we have more than one
 change-point. This paper develops a method for multivariate normally
 distributed data to detect change-points and estimate within-segment
 parameters using maximum likelihood estimation.	  
Journal: Journal of Applied Statistics 
Pages: 1979-1995 
Issue: 9 
Volume: 40 
Year: 2013 
Month: 9 
X-DOI: 10.1080/02664763.2013.800471 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.800471 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:9:p:1979-1995




Template-Type: ReDIF-Article 1.0
Author-Name: Yanqing Yi 
Author-X-Name-First: Yanqing 
Author-X-Name-Last: Yi 
Author-Name: Yuan Yuan 
Author-X-Name-First: Yuan 
Author-X-Name-Last: Yuan 
Title: An optimal allocation for response-adaptive designs 
Abstract:
	      A new allocation proportion is derived by using differential
 equation methods for response-adaptive designs. This new allocation is
 compared with the balanced and the Neyman allocations and the optimal
 allocation proposed by Rosenberger, Stallard, Ivanova, Harper and Ricks
 (RSIHR) from an ethical point of view and statistical power performance.
 The new allocation has the ethical advantages of allocating more than 50%
 of patients to the better treatment. It also allocates higher proportion
 of patients to the better treatment than the RSIHR optimal allocation for
 success probabilities larger than 0.5. The statistical power under the
 proposed allocation is compared with these under the balanced, the Neyman
 and Rosenberger's optimal allocations through simulation. The simulation
 results indicate that the statistical power under the proposed allocation
 proportion is similar as to those under the balanced, the Neyman and the
 RSIHR allocations.	     
Journal: Journal of Applied Statistics 
Pages: 1996-2008 
Issue: 9 
Volume: 40 
Year: 2013 
Month: 9 
X-DOI: 10.1080/02664763.2013.800846 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.800846 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:9:p:1996-2008




Template-Type: ReDIF-Article 1.0
Author-Name: Laura Barbieri 
Author-X-Name-First: Laura 
Author-X-Name-Last: Barbieri 
Author-Name: Mario Faliva 
Author-X-Name-First: Mario 
Author-X-Name-Last: Faliva 
Author-Name: Maria Grazia Zoia 
Author-X-Name-First: Maria Grazia 
Author-X-Name-Last: Zoia 
Title: Band-limited component estimation in time-limited economic series 
Abstract:
	      This paper tackles the issue of economic time-series modeling
 from a joint time and frequency-domain standpoint, with the objective of
 estimating the latent trend-cycle component. Since time-series records are
 data strings over a finite time span, they read as samples of contiguous
 data drawn from realizations of stochastic processes aligned with the time
 arrow. This accounts for the interpretation of time series as time-limited
 signals. Economic time series (up to a disturbance term) result from
 latent components known as trend, cycle, and seasonality, whose generating
 stochastic processes are harmonizable on a finite average-power argument.
 In addition, since trend is associated with long-run regular movements,
 and cycle with medium-term economic fluctuation, both of these turn out to
 be band-limited components. Recognizing such a frequency-domain location
 permits a filter-based approach to component estimation. This is
 accomplished through a Toeplitz matrix operator with sinc functions as
 entries, mirroring the ideal low-pass filter impulse response. The notion
 of virtual transfer function is developed and its closed-form expression
 derived in order to evaluate the filter features. The paper is completed
 by applying this filter to quarterly data from Italian industrial
 production, thus shedding light on the performance of the estimation
 procedure.	     
Journal: Journal of Applied Statistics 
Pages: 2009-2023 
Issue: 9 
Volume: 40 
Year: 2013 
Month: 9 
X-DOI: 10.1080/02664763.2013.801408 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.801408 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:9:p:2009-2023




Template-Type: ReDIF-Article 1.0
Author-Name: Weihua Zhao 
Author-X-Name-First: Weihua 
Author-X-Name-Last: Zhao 
Author-Name: Riquan Zhang 
Author-X-Name-First: Riquan 
Author-X-Name-Last: Zhang 
Author-Name: Jicai Liu 
Author-X-Name-First: Jicai 
Author-X-Name-Last: Liu 
Title: Robust variable selection for the varying coefficient model based on composite <italic>L</italic>                <sub>1</sub>--<italic>L</italic>                <sub>2</sub> regression 
Abstract:
	      The varying coefficient model (VCM) is an important
 generalization of the linear regression model and many existing estimation
 procedures for VCM were built on <italic>L</italic>		   
 <sub>2</sub> loss, which is popular for its mathematical beauty but is not
 robust to non-normal errors and outliers. In this paper, we address the
 problem of both robustness and efficiency of estimation and variable
 selection procedure based on the convex combined loss of
 <italic>L</italic>		   <sub>1</sub> and <italic>L</italic>	   
	   <sub>2</sub> instead of only quadratic loss for VCM. By using
 local linear modeling method, the asymptotic normality of estimation is
 driven and a useful selection method is proposed for the weight of
 composite <italic>L</italic>		     <sub>1</sub> and
 <italic>L</italic>		   <sub>2</sub>. Then the variable
 selection procedure is given by combining local kernel smoothing with
 adaptive group LASSO. With appropriate selection of tuning parameters by
 Bayesian information criterion (BIC) the theoretical properties of the new
 procedure, including consistency in variable selection and the oracle
 property in estimation, are established. The finite sample performance of
 the new method is investigated through simulation studies and the analysis
 of body fat data. Numerical studies show that the new method is better
 than or at least as well as the least square-based method in terms of both
 robustness and efficiency for variable selection.	    
Journal: Journal of Applied Statistics 
Pages: 2024-2040 
Issue: 9 
Volume: 40 
Year: 2013 
Month: 9 
X-DOI: 10.1080/02664763.2013.804040 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.804040 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:9:p:2024-2040




Template-Type: ReDIF-Article 1.0
Author-Name: Nicholas T. Longford 
Author-X-Name-First: Nicholas T. 
Author-X-Name-Last: Longford 
Title: Searching for contaminants 
Abstract:
	      Decision theory is applied to the problem of identifying a
 small fraction of observations that contaminate a random sample from a
 specified distribution. The uncertainty about the parameters that
 characterise the contamination is addressed by sensitivity analysis. The
 analyst's (or the client's) perspective and priorities are incorporated in
 the analysis by ranges of plausible loss functions. An application to
 fraud detection is presented.		
Journal: Journal of Applied Statistics 
Pages: 2041-2055 
Issue: 9 
Volume: 40 
Year: 2013 
Month: 9 
X-DOI: 10.1080/02664763.2013.804041 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.804041 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:9:p:2041-2055




Template-Type: ReDIF-Article 1.0
Author-Name: Hossein Zamani 
Author-X-Name-First: Hossein 
Author-X-Name-Last: Zamani 
Author-Name: Noriszura Ismail 
Author-X-Name-First: Noriszura 
Author-X-Name-Last: Ismail 
Title: Score test for testing zero-inflated Poisson regression against zero-inflated generalized Poisson alternatives 
Abstract:
	      In several cases, count data often have excessive number of
 zero outcomes. This zero-inflated phenomenon is a specific cause of
 overdispersion, and zero-inflated Poisson regression model (ZIP) has been
 proposed for accommodating zero-inflated data. However, if the data
 continue to suggest additional overdispersion, zero-inflated negative
 binomial (ZINB) and zero-inflated generalized Poisson (ZIGP) regression
 models have been considered as alternatives. This study proposes the score
 test for testing ZIP regression model against ZIGP alternatives and proves
 that it is equal to the score test for testing ZIP regression model
 against ZINB alternatives. The advantage of using the score test over
 other alternative tests such as likelihood ratio and Wald is that the
 score test can be used to determine whether a more complex model is
 appropriate without fitting the more complex model. Applications of the
 proposed score test on several datasets are also illustrated.		
Journal: Journal of Applied Statistics 
Pages: 2056-2068 
Issue: 9 
Volume: 40 
Year: 2013 
Month: 9 
X-DOI: 10.1080/02664763.2013.804904 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.804904 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:9:p:2056-2068




Template-Type: ReDIF-Article 1.0
Author-Name: Riccardo Borgoni 
Author-X-Name-First: Riccardo 
Author-X-Name-Last: Borgoni 
Author-Name: Valeria Tritto 
Author-X-Name-First: Valeria 
Author-X-Name-Last: Tritto 
Author-Name: Daniela de Bartolo 
Author-X-Name-First: Daniela 
Author-X-Name-Last: de Bartolo 
Title: Identifying radon-prone building typologies by marginal modelling 
Abstract:
	      Radon is a naturally occurring decay product of uranium known
 to be the main contributor to natural background radiation exposure. It
 has been established that the health risk related to radon exposure is
 lung cancer. In fact, radon is considered to be a major leading cause of
 lung cancer, second only to smoking. In this paper, we identified building
 typologies that affect the probability of detecting indoor radon
 concentration above reference values, using the data collected within two
 monitoring campaigns recently conducted in Northern Italy. This
 information is fundamental both in prevention, i.e. when the construction
 of a new building is planned and in mitigation, i.e. when a high
 concentration detected inside buildings has to be reduced. A spatial
 regression approach for binary data was adopted for this goal where some
 relevant covariates on the soil were retrieved by linking external spatial
 databases.	     
Journal: Journal of Applied Statistics 
Pages: 2069-2086 
Issue: 9 
Volume: 40 
Year: 2013 
Month: 9 
X-DOI: 10.1080/02664763.2013.804906 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.804906 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:9:p:2069-2086




Template-Type: ReDIF-Article 1.0
Author-Name: Marcelo Justus dos Santos 
Author-X-Name-First: Marcelo Justus 
Author-X-Name-Last: dos Santos 
Author-Name: Ana L&uacute;cia Kassouf 
Author-X-Name-First: Ana L&uacute;cia 
Author-X-Name-Last: Kassouf 
Title: A cointegration analysis of crime, economic activity, and police performance in S&atilde;o Paulo city 
Abstract:
	      The main objective of this paper is to investigate possible
 causes for the significant reduction observed in crime rates in S&atilde;o
 Paulo city. By applying a cointegration analysis, we observed long-run
 relationships between crime, economic activity, and police performance.
 The results indicate that the lethal crime rate is positively related to
 unemployment and negatively related to real wages and to the results of
 law-enforcement activities, specifically arrests and seizure of firearms.
 Moreover, the hypothesis that the Disarmament Statute led to a reduction
 in the lethal crime rate is not rejected.	    
Journal: Journal of Applied Statistics 
Pages: 2087-2109 
Issue: 10 
Volume: 40 
Year: 2013 
Month: 10 
X-DOI: 10.1080/02664763.2013.804905 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.804905 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:10:p:2087-2109




Template-Type: ReDIF-Article 1.0
Author-Name: Wantanee Surapaitoolkorn 
Author-X-Name-First: Wantanee 
Author-X-Name-Last: Surapaitoolkorn 
Title: Variable dimension via stochastic volatility model using FX rates 
Abstract:
	      In this paper, changepoint analysis is applied to stochastic
 volatility (SV) models which aim to understand the locations and movements
 of high frequency FX financial time series. Bayesian inference using the
 Markov Chain Monte Carlo method is performed using a process called
 <italic>variable dimension</italic> for SV parameters. Interesting results
 are that FX series have locations where one or more positions of the
 sequence correspond to systemic changes, and overall non-stationarity, in
 the returns process. Furthermore, we found that the changepoint locations
 provide an informative estimate for all FX series. Importantly in most
 cases, the detected changepoints can be identified with economic factors
 relevant to the country concerned. This helps support the fact that
 macroeconomics news and the movement in financial price are positively
 related.	   
Journal: Journal of Applied Statistics 
Pages: 2110-2128 
Issue: 10 
Volume: 40 
Year: 2013 
Month: 10 
X-DOI: 10.1080/02664763.2013.807330 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.807330 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:10:p:2110-2128




Template-Type: ReDIF-Article 1.0
Author-Name: Jose R.S. Santos 
Author-X-Name-First: Jose R.S. 
Author-X-Name-Last: Santos 
Author-Name: Caio L.N. Azevedo 
Author-X-Name-First: Caio L.N. 
Author-X-Name-Last: Azevedo 
Author-Name: Heleno Bolfarine 
Author-X-Name-First: Heleno 
Author-X-Name-Last: Bolfarine 
Title: A multiple group item response theory model with centered skew-normal latent trait distributions under a Bayesian framework 
Abstract:
	      Very often, in psychometric research, as in educational
 assessment, it is necessary to analyze item response from clustered
 respondents. The multiple group item response theory (IRT) model proposed
 by Bock and Zimowski [12] provides a useful framework for analyzing such
 type of data. In this model, the selected groups of respondents are of
 specific interest such that group-specific population distributions need
 to be defined. The usual assumption for parameter estimation in this
 model, which is that the latent traits are random variables following
 different symmetric normal distributions, has been questioned in many
 works found in the IRT literature. Furthermore, when this assumption does
 not hold, misleading inference can result. In this paper, we consider that
 the latent traits for each group follow different skew-normal
 distributions, under the centered parameterization. We named it skew
 multiple group IRT model. This modeling extends the works of Azevedo
 <italic>et al</italic>. [4], Baz�n <italic>et al</italic>. [11] and Bock
 and Zimowski [12] (concerning the latent trait distribution). Our approach
 ensures that the model is identifiable. We propose and compare, concerning
 convergence issues, two Monte Carlo Markov Chain (MCMC) algorithms for
 parameter estimation. A simulation study was performed in order to
 evaluate parameter recovery for the proposed model and the selected
 algorithm concerning convergence issues. Results reveal that the proposed
 algorithm recovers properly all model parameters. Furthermore, we analyzed
 a real data set which presents asymmetry concerning the latent traits
 distribution. The results obtained by using our approach confirmed the
 presence of negative asymmetry for some latent trait distributions.	    
Journal: Journal of Applied Statistics 
Pages: 2129-2149 
Issue: 10 
Volume: 40 
Year: 2013 
Month: 10 
X-DOI: 10.1080/02664763.2013.807331 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.807331 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:10:p:2129-2149




Template-Type: ReDIF-Article 1.0
Author-Name: S. Faria 
Author-X-Name-First: S. 
Author-X-Name-Last: Faria 
Author-Name: F. Gon�alves 
Author-X-Name-First: F. 
Author-X-Name-Last: Gon�alves 
Title: Financial data modeling by Poisson mixture regression 
Abstract:
	      In many financial applications, Poisson mixture regression
 models are commonly used to analyze heterogeneous count data. When fitting
 these models, the observed counts are supposed to come from two or more
 subpopulations and parameter estimation is typically performed by means of
 maximum likelihood via the Expectation--Maximization algorithm. In this
 study, we discuss briefly the procedure for fitting Poisson mixture
 regression models by means of maximum likelihood, the model selection and
 goodness-of-fit tests. These models are applied to a real data set for
 credit-scoring purposes. We aim to reveal the impact of demographic and
 financial variables in creating different groups of clients and to predict
 the group to which each client belongs, as well as his expected number of
 defaulted payments. The model's conclusions are very interesting,
 revealing that the population consists of three groups, contrasting with
 the traditional good versus bad categorization approach of the
 credit-scoring systems.	  
Journal: Journal of Applied Statistics 
Pages: 2150-2162 
Issue: 10 
Volume: 40 
Year: 2013 
Month: 10 
X-DOI: 10.1080/02664763.2013.807332 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.807332 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:10:p:2150-2162




Template-Type: ReDIF-Article 1.0
Author-Name: Jacques B�nass�ni 
Author-X-Name-First: Jacques 
Author-X-Name-Last: B�nass�ni 
Title: A concentration approach to sensitivity studies in statistical estimation problems 
Abstract:
	      It is shown that the concept of concentration is of potential
 interest in the sensitivity study of some parameters and related
 estimators. Basic ideas are introduced for a real parameter &theta;>0
 together with graphical representations using Lorenz curves of
 concentration. Examples based on the mean, standard deviation and variance
 are provided for some classical distributions. This concentration approach
 is also discussed in relation with influence functions. Special emphasis
 is given to the average concentration of an estimator which provides a
 sensitivity measure allowing one to compare several estimators of the same
 parameter. Properties of this measure are investigated through simulation
 studies and its practical interest is illustrated by examples based on the
 trimmed mean and the Winsorized variance.	    
Journal: Journal of Applied Statistics 
Pages: 2163-2180 
Issue: 10 
Volume: 40 
Year: 2013 
Month: 10 
X-DOI: 10.1080/02664763.2013.808318 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.808318 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:10:p:2163-2180




Template-Type: ReDIF-Article 1.0
Author-Name: Vesna &Cacute;ojba&scaron;i&cacute; Raji&cacute; 
Author-X-Name-First: Vesna &Cacute;ojba&scaron;i&cacute; 
Author-X-Name-Last: Raji&cacute; 
Author-Name: J. Stanojevi&cacute; 
Author-X-Name-First: J. 
Author-X-Name-Last: Stanojevi&cacute; 
Title: Confidence intervals for the ratio of two variances 
Abstract:
	      In this paper we consider confidence intervals for the ratio
 of two population variances. We propose a confidence interval for the
 ratio of two variances based on the <italic>t</italic>-statistic by
 deriving its Edgeworth expansion and considering Hall's and Johnson's
 transformations. Then, we consider the coverage accuracy of suggested
 intervals and intervals based on the <italic>F</italic>-statistic for some
 distributions. 	 
Journal: Journal of Applied Statistics 
Pages: 2181-2187 
Issue: 10 
Volume: 40 
Year: 2013 
Month: 10 
X-DOI: 10.1080/02664763.2013.808319 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.808319 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:10:p:2181-2187




Template-Type: ReDIF-Article 1.0
Author-Name: Hongli Niu 
Author-X-Name-First: Hongli 
Author-X-Name-Last: Niu 
Author-Name: Jun Wang 
Author-X-Name-First: Jun 
Author-X-Name-Last: Wang 
Title: Power-law scaling behavior analysis of financial time series model by voter interacting dynamic system 
Abstract:
	      We investigate the power-law scaling behaviors of returns for
 a financial price process which is developed by the voter interacting
 dynamic system in comparison with the real financial market index
 (Shanghai Composite Index). The voter system is a continuous time Markov
 process, which originally represents a voter's attitude on a particular
 topic, that is, voters reconsider their opinions at times distributed
 according to independent exponential random variables. In this paper, the
 detrended fluctuation analysis method is employed to explore the long
 range power-law correlations of return time series for different values of
 parameters in the financial model. The findings show no indication or very
 weak long-range power-law correlations for the simulated returns but
 strong long-range dependence for the absolute returns. The multiplier
 distribution is studied to demonstrate directly the existence of scale
 invariance in the actual data of the Shanghai Stock Exchange and the
 simulation data of the model by comparison. Moreover, the Zipf analysis is
 applied to investigate the statistical behaviors of frequency functions
 and the distributions of the returns. By a comparative study, the
 simulation data for our constructed price model exhibits very similar
 behaviors to the real stock index, this indicates somewhat rationality of
 our model to the market application.	       
Journal: Journal of Applied Statistics 
Pages: 2188-2203 
Issue: 10 
Volume: 40 
Year: 2013 
Month: 10 
X-DOI: 10.1080/02664763.2013.809515 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.809515 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:10:p:2188-2203




Template-Type: ReDIF-Article 1.0
Author-Name: Saima Altaf 
Author-X-Name-First: Saima 
Author-X-Name-Last: Altaf 
Author-Name: Muhammad Aslam 
Author-X-Name-First: Muhammad 
Author-X-Name-Last: Aslam 
Author-Name: Muhammad Aslam 
Author-X-Name-First: Muhammad 
Author-X-Name-Last: Aslam 
Title: Analysis of the amended Davidson model with order effect for paired comparison in the Bayesian paradigm 
Abstract:
	      We commonly observe many types of paired nature of
 competitions in which the objects are compared by the respondents pairwise
 in a subjective manner. The Bayesian statistics, contrary to the classical
 statistics, presents a generic tool to incorporate new experimental
 evidence and update the existing information. These and other properties
 have ushered the statisticians to focus their attention on the Bayesian
 analysis of different paired comparison models. The present article
 focuses on the amended Davidson model for paired comparison in which an
 amendment has been introduced that accommodates the option of not
 distinguishing the effects of two treatments when they are compared
 pairwise. However, Bayesian analysis of the amended Davidson model is
 performed using the noninformative priors after making another small
 modification of incorporating the parameter of order effect factor. The
 joint and marginal posterior distributions of the parameters, their
 posterior estimates, predictive and posterior probabilities to compare the
 treatment parameters are obtained.	     
Journal: Journal of Applied Statistics 
Pages: 2204-2218 
Issue: 10 
Volume: 40 
Year: 2013 
Month: 10 
X-DOI: 10.1080/02664763.2013.809516 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.809516 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:10:p:2204-2218




Template-Type: ReDIF-Article 1.0
Author-Name: M. Revan &Ouml;zkale 
Author-X-Name-First: M. Revan 
Author-X-Name-Last: &Ouml;zkale 
Title: Influence measures in affine combination type regression 
Abstract:
	      The detection of outliers and influential observations has
 received a great deal of attention in the statistical literature in the
 context of least-squares (LS) regression. However, the explanatory
 variables can be correlated with each other and alternatives to LS come
 out to address outliers/influential observations and multicollinearity,
 simultaneously. This paper proposes new influence measures based on the
 affine combination type regression for the detection of influential
 observations in the linear regression model when multicollinearity exists.
 Approximate influence measures are also proposed for the affine
 combination type regression. Since the affine combination type regression
 includes the ridge, the Liu and the shrunken regressions as special cases,
 influence measures under the ridge, the Liu and the shrunken regressions
 are also examined to see the possible effect that multicollinearity can
 have on the influence of an observation. The Longley data set is given
 illustrating the influence measures in affine combination type regression
 and also in ridge, Liu and shrunken regressions so that the performance of
 different biased regressions on detecting and assessing the influential
 observations is examined.	    
Journal: Journal of Applied Statistics 
Pages: 2219-2243 
Issue: 10 
Volume: 40 
Year: 2013 
Month: 10 
X-DOI: 10.1080/02664763.2013.809568 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.809568 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:10:p:2219-2243




Template-Type: ReDIF-Article 1.0
Author-Name: Yangxin Huang 
Author-X-Name-First: Yangxin 
Author-X-Name-Last: Huang 
Author-Name: Getachew A. Dagne 
Author-X-Name-First: Getachew A. 
Author-X-Name-Last: Dagne 
Author-Name: Jeong-Gun Park 
Author-X-Name-First: Jeong-Gun 
Author-X-Name-Last: Park 
Title: Segmental modeling of changing immunologic response for CD4 data with skewness, missingness and dropout 
Abstract:
	      In clinical practice, the profile of each subject's CD4
 response from a longitudinal study may follow a 'broken stick' like
 trajectory, indicating multiple phases of increase and/or decline in
 response. Such multiple phases (changepoints) may be important indicators
 to help quantify treatment effect and improve management of patient care.
 Although it is a common practice to analyze complex AIDS longitudinal data
 using nonlinear mixed-effects (NLME) or nonparametric mixed-effects (NPME)
 models in the literature, NLME or NPME models become a challenge to
 estimate changepoint due to complicated structures of model formulations.
 In this paper, we propose a changepoint mixed-effects model with random
 subject-specific parameters, including the changepoint for the analysis of
 longitudinal CD4 cell counts for HIV infected subjects following highly
 active antiretroviral treatment. The longitudinal CD4 data in this study
 may exhibit departures from symmetry, may encounter missing observations
 due to various reasons, which are likely to be non-ignorable in the sense
 that missingness may be related to the missing values, and may be censored
 at the time of the subject going off study-treatment, which is a
 potentially informative dropout mechanism. Inferential procedures can be
 complicated dramatically when longitudinal CD4 data with asymmetry
 (skewness), incompleteness and informative dropout are observed in
 conjunction with an unknown changepoint. Our objective is to address the
 simultaneous impact of skewness, missingness and informative censoring by
 jointly modeling the CD4 response and dropout time processes under a
 Bayesian framework. The method is illustrated using a real AIDS data set
 to compare potential models with various scenarios, and some interested
 results are presented. 	 
Journal: Journal of Applied Statistics 
Pages: 2244-2258 
Issue: 10 
Volume: 40 
Year: 2013 
Month: 10 
X-DOI: 10.1080/02664763.2013.809569 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.809569 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:10:p:2244-2258




Template-Type: ReDIF-Article 1.0
Author-Name: Min Wang 
Author-X-Name-First: Min 
Author-X-Name-Last: Wang 
Author-Name: Jing Zhao 
Author-X-Name-First: Jing 
Author-X-Name-Last: Zhao 
Author-Name: Xiaoqian Sun 
Author-X-Name-First: Xiaoqian 
Author-X-Name-Last: Sun 
Author-Name: Chanseok Park 
Author-X-Name-First: Chanseok 
Author-X-Name-Last: Park 
Title: Robust explicit estimation of the two-parameter Birnbaum--Saunders distribution 
Abstract:
	      The two-parameter Birnbaum--Saunders distribution is widely
 applicable to model failure times of fatiguing materials. Its
 maximum-likelihood estimators (MLEs) are very sensitive to outliers and
 also have no closed-form expressions. This motivates us to develop some
 alternative estimators. In this paper, we develop two robust estimators,
 which are also explicit functions of sample observations and are thus easy
 to compute. We derive their breakdown points and carry out extensive Monte
 Carlo simulation experiments to compare the performance of all the
 estimators under consideration. It has been observed from the simulation
 results that the proposed estimators outperform in a manner that is
 approximately comparable with the MLEs, whereas they are far superior in
 the presence of data contamination that often occurs in practical
 situations. A simple bias-reduction technique is presented to reduce the
 bias of the recommended estimators. Finally, the practical application of
 the developed procedures is illustrated with a real-data example.	    
Journal: Journal of Applied Statistics 
Pages: 2259-2274 
Issue: 10 
Volume: 40 
Year: 2013 
Month: 10 
X-DOI: 10.1080/02664763.2013.809570 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.809570 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:10:p:2259-2274




Template-Type: ReDIF-Article 1.0
Author-Name: Mauro Costantini 
Author-X-Name-First: Mauro 
Author-X-Name-Last: Costantini 
Title: Forecasting the industrial production using alternative factor models and business survey data 
Abstract:
	      This paper compares the forecasting performance of three
 alternative factor models based on business survey data for the industrial
 production in Italy. The first model uses static principal component
 analysis, while the other two apply dynamic principal component analysis
 in frequency domain and subspace algorithms for state-space
 representation, respectively. Once the factors are extracted from the
 business survey data, then they are included into a single equation to
 predict the industrial production index. The forecast results show that
 the three factor models have a better performance than that of a simple
 autoregressive benchmark model regardless of the specification and
 estimation methods. Furthermore, the state-space model yields superior
 forecasts amongst the factor models.	       
Journal: Journal of Applied Statistics 
Pages: 2275-2289 
Issue: 10 
Volume: 40 
Year: 2013 
Month: 10 
X-DOI: 10.1080/02664763.2013.809870 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.809870 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:10:p:2275-2289




Template-Type: ReDIF-Article 1.0
Author-Name: Hossein Hassani 
Author-X-Name-First: Hossein 
Author-X-Name-Last: Hassani 
Author-Name: Saeed Heravi 
Author-X-Name-First: Saeed 
Author-X-Name-Last: Heravi 
Author-Name: Gary Brown 
Author-X-Name-First: Gary 
Author-X-Name-Last: Brown 
Author-Name: Daniel Ayoubkhani 
Author-X-Name-First: Daniel 
Author-X-Name-Last: Ayoubkhani 
Title: Forecasting before, during, and after recession with singular spectrum analysis 
Abstract:
	      The aim of this research is to apply the singular spectrum
 analysis (SSA) technique, which is a relatively new and powerful technique
 in time series analysis and forecasting, to forecast the 2008 UK
 recession, using eight economic time series. These time series were
 selected as they represent the most important economic indicators in the
 UK. The ability to understand the underlying structure of these series and
 to quickly identify turning points such as the on-set of the recent
 recession is of key interest to users. In recent years, the SSA technique
 has been further developed and applied to many practical problems. Hence,
 these series will provide an ideal practical test of the potential
 benefits from SSA during one of the most challenging periods for
 econometric analyses of recent years. The results are compared with those
 obtained using the ARIMA and Holt--Winters models as these methods are
 currently used as standard forecasting methods in the Office for National
 Statistics in the UK.		
Journal: Journal of Applied Statistics 
Pages: 2290-2302 
Issue: 10 
Volume: 40 
Year: 2013 
Month: 10 
X-DOI: 10.1080/02664763.2013.810193 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.810193 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:10:p:2290-2302




Template-Type: ReDIF-Article 1.0
Author-Name: Haydar Demirhan 
Author-X-Name-First: Haydar 
Author-X-Name-Last: Demirhan 
Title: Bayesian estimation of log odds ratios over two-way contingency tables with intraclass correlated cells 
Abstract:
	      In this article, a Bayesian approach is proposed for the
 estimation of log odds ratios and intraclass correlations over a two-way
 contingency table, including intraclass correlated cells. Required
 likelihood functions of log odds ratios are obtained, and determination of
 prior structures is discussed. Hypothesis testing for log odds ratios and
 intraclass correlations by using the posterior simulations is outlined.
 Because the proposed approach includes no asymptotic theory, it is useful
 for the estimation and hypothesis testing of log odds ratios in the
 presence of certain intraclass correlation patterns. A family health
 status and limitations data set is analyzed by using the proposed approach
 in order to figure out the impact of intraclass correlations on the
 estimates and hypothesis tests of log odds ratios. Although intraclass
 correlations are small in the data set, we obtain that even small
 intraclass correlations can significantly affect the estimates and test
 results, and our approach is useful for the estimation and testing of log
 odds ratios in the presence of intraclass correlations.	  
Journal: Journal of Applied Statistics 
Pages: 2303-2316 
Issue: 10 
Volume: 40 
Year: 2013 
Month: 10 
X-DOI: 10.1080/02664763.2013.810196 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.810196 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:10:p:2303-2316




Template-Type: ReDIF-Article 1.0
Author-Name: Ludovic Seifert 
Author-X-Name-First: Ludovic 
Author-X-Name-Last: Seifert 
Author-Name: Jean-Fran�ois Coeurjolly 
Author-X-Name-First: Jean-Fran�ois 
Author-X-Name-Last: Coeurjolly 
Author-Name: Romain H�rault 
Author-X-Name-First: Romain 
Author-X-Name-Last: H�rault 
Author-Name: L�o Wattebled 
Author-X-Name-First: L�o 
Author-X-Name-Last: Wattebled 
Author-Name: Keith Davids 
Author-X-Name-First: Keith 
Author-X-Name-Last: Davids 
Title: Temporal dynamics of inter-limb coordination in ice climbing revealed through change-point analysis of the geodesic mean of circular data 
Abstract:
	      This study examined the temporal dynamics of the inter-limb
 angles of skilled and less skilled ice climbers to determine how they
 explored ice fall properties to adapt their coordination patterns during
 performance. We observed two circular time series corresponding to the
 upper- and lower-limbs of seven expert and eight inexperienced ice
 climbers. We analyzed these data through a multiple change-point analysis
 of the geodesic (or Fr�chet) mean on the circle. Guided by the nature of
 the geodesic mean obtained by an optimization procedure, we extended the
 filtered derivative method, known to be computationally very cheap and
 fast, to circular data. Local estimation of the variability was assessed
 through the number of change-points computed via the filtered derivatives
 with <italic>p</italic>-value method for the time series and integrated
 squared error (ISE). Results of this change-point analysis did not reveal
 significant differences of the number of change-points between groups but
 indicated higher ISE that supported the existence of plateaux for
 beginners. These results emphasized higher local variability of limb
 angles for experts than for beginners suggesting greater dependence on the
 properties of the performance environment and adaptive behaviors in the
 former. Conversely, the lower local variance of limb angles assessed in
 beginners may reflect their independence of the environmental constraints,
 as they focused mainly on controlling body equilibrium.	  
Journal: Journal of Applied Statistics 
Pages: 2317-2331 
Issue: 11 
Volume: 40 
Year: 2013 
Month: 11 
X-DOI: 10.1080/02664763.2013.810194 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.810194 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:11:p:2317-2331




Template-Type: ReDIF-Article 1.0
Author-Name: Chor Foon Tang 
Author-X-Name-First: Chor Foon 
Author-X-Name-Last: Tang 
Title: A revisitation of the export-led growth hypothesis in Malaysia using the leveraged bootstrap simulation and rolling causality techniques 
Abstract:
	      According to the neoclassical growth theory, export expansion
 could stimulate economic growth because it promotes specialisation and
 raises factor productivity. Thus, many developing countries depend heavily
 on export-orientated businesses to accelerate economic growth.
 Nevertheless, the causality evidences on the export-led growth hypothesis
 remain elusive and controversial. Two primary empirical questions emerged
 in the international trade and development literatures are: (a) Does the
 export-led growth hypothesis still valid? (b) Why causality evidences are
 inconsistent among studies? In light of these, the present study attempts
 to contribute to the export-led growth literature by using the Malaysian
 data set. This study covers the monthly data set from January 1975 to
 August 2010. To achieve the objectives of this study, we employ the
 leveraged bootstrap simulation causality test and also the rolling
 regression-based causality tests. The leveraged bootstrap simulation
 causality results suggest that exports and output growth are bilateral
 causality in nature. However, the rolling causality results demonstrate
 that the causality inferences for export-led growth hypothesis are
 unstable over time. For this reason, policy initiative to promote exports
 may not always stimulate economic growth and development in Malaysia.
 Therefore, balancing policy is urged to ensure that the economic growth in
 Malaysia can be materialised.		
Journal: Journal of Applied Statistics 
Pages: 2332-2340 
Issue: 11 
Volume: 40 
Year: 2013 
Month: 11 
X-DOI: 10.1080/02664763.2013.810195 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.810195 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:11:p:2332-2340




Template-Type: ReDIF-Article 1.0
Author-Name: Jing-Er Chiu 
Author-X-Name-First: Jing-Er 
Author-X-Name-Last: Chiu 
Author-Name: Chih-Hsin Tsai 
Author-X-Name-First: Chih-Hsin 
Author-X-Name-Last: Tsai 
Title: Properties and performance of one-sided cumulative count of conforming chart with parameter estimation in high-quality processes 
Abstract:
	      The one-sided cumulative count of conforming (CCC) chart is a
 useful method to monitor nonconforming fraction in high-quality
 manufacturing processes. The nonconforming fraction parameter is assumed
 to be known when implementing a one-sided CCC chart. In this study, we
 investigated the impact of estimated nonconforming fraction,
 <italic>[pcirc]</italic>		 <sub>0</sub>, in a one-sided CCC
 chart. The run length distribution is derived as well as the conditional
 probability of a false alarm rate (CFAR), conditional average run length
 (CARL) and its standard deviation (CSDRL). Simulation results are
 conducted to evaluate the effect of <italic>[pcirc]</italic>		   
  <sub>0</sub> in a one-sided CCC chart. The results show that values of
 CFAR, CARL and CSDRL are close to the nominal values for a large sample.
 The impact of estimation errors was also studied. We find that CFAR
 decreases for large <italic>[pcirc]</italic>		     <sub>0</sub>.
 Thus, a large value of <italic>[pcirc]</italic>	       
 <sub>0</sub> is suggested for fewer false alarms.	    
Journal: Journal of Applied Statistics 
Pages: 2341-2353 
Issue: 11 
Volume: 40 
Year: 2013 
Month: 11 
X-DOI: 10.1080/02664763.2013.811479 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.811479 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:11:p:2341-2353




Template-Type: ReDIF-Article 1.0
Author-Name: Filidor Vilca 
Author-X-Name-First: Filidor 
Author-X-Name-Last: Vilca 
Author-Name: Mariana Rodrigues-Motta 
Author-X-Name-First: Mariana 
Author-X-Name-Last: Rodrigues-Motta 
Author-Name: V�ctor Leiva 
Author-X-Name-First: V�ctor 
Author-X-Name-Last: Leiva 
Title: On a variance stabilizing model and its application to genomic data 
Abstract:
	      In this paper, we propose a model based on a class of
 symmetric distributions, which avoids the transformation of data,
 stabilizes the variance of the observations, and provides robust
 estimation of parameters and high flexibility for modeling different types
 of data. Probabilistic and statistical aspects of this new model are
 developed throughout the article, which include mathematical properties,
 estimation of parameters and inference. The obtained results are
 illustrated by means of real genomic data.	     
Journal: Journal of Applied Statistics 
Pages: 2354-2371 
Issue: 11 
Volume: 40 
Year: 2013 
Month: 11 
X-DOI: 10.1080/02664763.2013.811480 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.811480 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:11:p:2354-2371




Template-Type: ReDIF-Article 1.0
Author-Name: Lei Wang 
Author-X-Name-First: Lei 
Author-X-Name-Last: Wang 
Author-Name: Yukun Liu 
Author-X-Name-First: Yukun 
Author-X-Name-Last: Liu 
Author-Name: Wei Wu 
Author-X-Name-First: Wei 
Author-X-Name-Last: Wu 
Author-Name: Xiaolong Pu 
Author-X-Name-First: Xiaolong 
Author-X-Name-Last: Pu 
Title: Sequential LND sensitivity test for binary response data 
Abstract:
	      Sensitivity tests are used to make inferences about a
 sensitivity, a characteristic property of some products that cannot be
 observed directly. For binary response sensitivity data (dead or alive,
 explode or unexplode), the Langlie and Neyer are two well-known
 sensitivity tests. The priorities of the Langlie and Neyer tests are
 investigated in this paper. It is shown that the Langlie test has an
 advantage in getting an overlap, while the Neyer test has better
 estimation precision. Aiming at improving both the speed of getting an
 overlap and the estimation precision, we propose a new sensitivity test
 which replaces the first part of the Neyer test with the Langlie test. Our
 simulation studies indicate that the proposed test outperforms the
 Langlie, Neyer and Dror and Steinberg tests from the viewpoints of
 estimation precision and probability of obtaining an overlap.		
Journal: Journal of Applied Statistics 
Pages: 2372-2384 
Issue: 11 
Volume: 40 
Year: 2013 
Month: 11 
X-DOI: 10.1080/02664763.2013.817546 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.817546 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:11:p:2372-2384




Template-Type: ReDIF-Article 1.0
Author-Name: Lee Fawcett 
Author-X-Name-First: Lee 
Author-X-Name-Last: Fawcett 
Author-Name: Neil Thorpe 
Author-X-Name-First: Neil 
Author-X-Name-Last: Thorpe 
Title: Mobile safety cameras: estimating casualty reductions and the demand for secondary healthcare 
Abstract:
	      We consider a fully Bayesian analysis of road casualty data
 at 56 designated mobile safety camera sites in the Northumbria Police
 Force area in the UK. It is well documented that regression to the mean
 (RTM) can exaggerate the effectiveness of road safety measures and, since
 the 1980s, an empirical Bayes (EB) estimation framework has become the
 gold standard for separating real treatment effects from those of RTM. In
 this paper we suggest some diagnostics to check the assumptions
 underpinning the standard estimation framework. We also show that,
 relative to a fully Bayesian treatment, the EB method is over-optimistic
 when quantifying the variability of estimates of casualty frequency.
 Implementing a fully Bayesian analysis via Markov chain Monte Carlo also
 provides a more flexible and complete inferential procedure. We assess the
 sensitivity of estimates of treatment effectiveness, as well as the
 expected monetary value of prevention owing to the implementation of the
 safety cameras, to different model specifications, which include the
 estimation of trend and the construction of informative priors for some
 parameters.	      
Journal: Journal of Applied Statistics 
Pages: 2385-2406 
Issue: 11 
Volume: 40 
Year: 2013 
Month: 11 
X-DOI: 10.1080/02664763.2013.817547 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.817547 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:11:p:2385-2406




Template-Type: ReDIF-Article 1.0
Author-Name: M. H. Lee 
Author-X-Name-First: M. H. 
Author-X-Name-Last: Lee 
Author-Name: H. J. Sadaei 
Author-X-Name-First: H. J. 
Author-X-Name-Last: Sadaei 
Author-Name:  Suhartono 
Author-X-Name-First:  
Author-X-Name-Last: Suhartono 
Title: Improving TAIEX forecasting using fuzzy time series with Box--Cox power transformation 
Abstract:
	      Box--Cox together with our newly proposed transformation were
 implemented in three different real world empirical problems to alleviate
 noisy and the volatility effect of them. Consequently, a new domain was
 constructed. Subsequently, universe of discourse for transformed data was
 established and an approach for calculating effective length of the
 intervals was then proposed. Considering the steps above, the initial
 forecasts were performed using frequently used fuzzy time series (FTS)
 methods on transformed data. Final forecasts were retrieved from initial
 forecasted values by proper inverse operation. Comparisons of the results
 demonstrate that the proposed method produced more accurate forecasts
 compared with existing FTS on original data.	       
Journal: Journal of Applied Statistics 
Pages: 2407-2422 
Issue: 11 
Volume: 40 
Year: 2013 
Month: 11 
X-DOI: 10.1080/02664763.2013.817548 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.817548 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:11:p:2407-2422




Template-Type: ReDIF-Article 1.0
Author-Name: Therese Graversen 
Author-X-Name-First: Therese 
Author-X-Name-Last: Graversen 
Author-Name: Steffen Lauritzen 
Author-X-Name-First: Steffen 
Author-X-Name-Last: Lauritzen 
Title: Estimation of parameters in DNA mixture analysis 
Abstract:
	      In [7], a Bayesian network for analysis of mixed traces of
 DNA was presented using gamma distributions for modelling peak sizes in
 the electropherogram. It was demonstrated that the analysis was sensitive
 to the choice of a variance factor and hence this should be adapted to any
 new trace analysed. In this paper, we discuss how the variance parameter
 can be estimated by maximum likelihood to achieve this. The unknown
 proportions of DNA from each contributor can similarly be estimated by
 maximum likelihood jointly with the variance parameter. Furthermore, we
 discuss how to incorporate prior knowledge about the parameters in a
 Bayesian analysis. The proposed estimation methods are illustrated through
 a few examples of applications for calculating evidential value in
 casework and for mixture deconvolution.	  
Journal: Journal of Applied Statistics 
Pages: 2423-2436 
Issue: 11 
Volume: 40 
Year: 2013 
Month: 11 
X-DOI: 10.1080/02664763.2013.817549 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.817549 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:11:p:2423-2436




Template-Type: ReDIF-Article 1.0
Author-Name: Coskun Kus 
Author-X-Name-First: Coskun 
Author-X-Name-Last: Kus 
Author-Name: Yunus Akdogan 
Author-X-Name-First: Yunus 
Author-X-Name-Last: Akdogan 
Author-Name: Shuo-Jye Wu 
Author-X-Name-First: Shuo-Jye 
Author-X-Name-Last: Wu 
Title: Optimal progressive group censoring scheme under cost considerations for pareto distribution 
Abstract:
	      In this article, optimal design under the restriction of
 pre-determined budget of experiment is developed for the Pareto
 distribution when the life test is progressively group censored. We use
 the maximum-likelihood method to obtain the point estimator of the Pareto
 parameter. We propose two approaches to decide the number of test units,
 the number of inspections, and the length of inspection interval under
 limited budget such that the asymptotic variance of estimator of Pareto
 parameter is minimum. A numerical example is given to illustrate the
 proposed method. Some sensitivity analysis is also studied.	      
Journal: Journal of Applied Statistics 
Pages: 2437-2450 
Issue: 11 
Volume: 40 
Year: 2013 
Month: 11 
X-DOI: 10.1080/02664763.2013.818107 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.818107 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:11:p:2437-2450




Template-Type: ReDIF-Article 1.0
Author-Name: �lk� Erisoglu 
Author-X-Name-First: �lk� 
Author-X-Name-Last: Erisoglu 
Author-Name: Murat Erisoglu 
Author-X-Name-First: Murat 
Author-X-Name-Last: Erisoglu 
Author-Name: Nazif &Ccedil;alis 
Author-X-Name-First: Nazif 
Author-X-Name-Last: &Ccedil;alis 
Title: Heterogeneous data modeling with two-component Weibull--Poisson distribution 
Abstract:
	      The mixture distribution models are more useful than pure
 distributions in modeling of heterogeneous data sets. The aim of this
 paper is to propose mixture of Weibull--Poisson (WP) distributions to
 model heterogeneous data sets for the first time. So, a powerful
 alternative mixture distribution is created for modeling of the
 heterogeneous data sets. In the study, many features of the proposed
 mixture of WP distributions are examined. Also, the expectation
 maximization (EM) algorithm is used to determine the maximum-likelihood
 estimates of the parameters, and the simulation study is conducted for
 evaluating the performance of the proposed EM scheme. Applications for two
 real heterogeneous data sets are given to show the flexibility and
 potentiality of the new mixture distribution.		
Journal: Journal of Applied Statistics 
Pages: 2451-2461 
Issue: 11 
Volume: 40 
Year: 2013 
Month: 11 
X-DOI: 10.1080/02664763.2013.818108 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.818108 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:11:p:2451-2461




Template-Type: ReDIF-Article 1.0
Author-Name: Robert Schall 
Author-X-Name-First: Robert 
Author-X-Name-Last: Schall 
Author-Name: Dianne Weatherall 
Author-X-Name-First: Dianne 
Author-X-Name-Last: Weatherall 
Title: Accuracy and fairness of rain rules for interrupted one-day cricket matches 
Abstract:
	      In this paper, we investigate the relative merits of rain
 rules for one-day cricket matches. We suggest that interrupted one-day
 matches present a missing data problem: the outcome of the complete match
 cannot be observed, and instead the outcome of the interrupted match, as
 determined at least in part by the rain rule in question, is observed.
 Viewing the outcome of the interrupted match as an imputation of the
 missing outcome of the complete match, standard characteristics to assess
 the performance of classification tests can be used to assess the
 performance of a rain rule. In particular, we consider the overall and
 conditional accuracy and the predictive value of a rain rule. We propose
 two requirements for a 'fair' rain rule, and show that a fair rain rule
 must satisfy an identity involving its conditional accuracies. Estimating
 the performance characteristics of various rain rules from a sample of
 complete one-day matches our results suggest that the Duckworth--Lewis
 method, currently adopted by the International Cricket Council, is
 essentially as accurate as and somewhat more fair than its best
 competitors. A rain rule based on the iso-probability principle also
 performs well but might benefit from re-calibration using a more
 representative data base.	    
Journal: Journal of Applied Statistics 
Pages: 2462-2479 
Issue: 11 
Volume: 40 
Year: 2013 
Month: 11 
X-DOI: 10.1080/02664763.2013.818623 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.818623 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:11:p:2462-2479




Template-Type: ReDIF-Article 1.0
Author-Name: Alexandros E. Milionis 
Author-X-Name-First: Alexandros E. 
Author-X-Name-Last: Milionis 
Author-Name: Evangelia Papanagiotou 
Author-X-Name-First: Evangelia 
Author-X-Name-Last: Papanagiotou 
Title: Decomposing the predictive performance of the moving average trading rule of technical analysis: the contribution of linear and non-linear dependencies in stock returns 
Abstract:
	      The main purpose of this work is to decompose the predictive
 performance of the moving average (MA) trading rule and find out the
 portion that could be attributed to the possible exploitation of linear
 and non-linear dependencies in stock returns. Data from the General Index
 of the Athens Stock Exchange, from the Standard and Poor-500 Index of the
 New York Stock Exchange and from the Austrian Traded Index of the Vienna
 Stock Exchange are filtered by linear filters so as the resulting
 simulated 'returns' exhibit no serial correlation. Applying MA trading
 rules to both the original and the simulated indices and using a new
 statistical testing procedure that takes into account the sensitivity of
 the performance of the trading rule as a function of the length of the MA
 it is found that the predictive performance of the trading rule is clearly
 weakened when applied to the simulated indices indicating that a
 substantial part of the rule's predictive performance is due to the
 exploitation of linear dependencies in stock returns. This weakening is
 uneven; in general the shorter the MA length the more pronounced the
 attenuation.	       
Journal: Journal of Applied Statistics 
Pages: 2480-2494 
Issue: 11 
Volume: 40 
Year: 2013 
Month: 11 
X-DOI: 10.1080/02664763.2013.818624 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.818624 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:11:p:2480-2494




Template-Type: ReDIF-Article 1.0
Author-Name: David A. Wooff 
Author-X-Name-First: David A. 
Author-X-Name-Last: Wooff 
Author-Name: Amin Jamalzadeh 
Author-X-Name-First: Amin 
Author-X-Name-Last: Jamalzadeh 
Title: Robust and scale-free effect sizes for non-Normal two-sample comparisons, with applications in e-commerce 
Abstract:
	      The <italic>effect size</italic> (ES) has been mainly
 introduced and investigated for changes in location under an assumption of
 Normality for the underlying population. However, there are many
 circumstances where populations are non-Normal, or depend on scale and
 shape and not just a location parameter. Our motivating application from
 e-commerce requires an ES which is appropriate for long-tailed
 distributions. We review some common ES measures. We then introduce two
 novel alternative ES for two-sample comparisons, one scale-free and one on
 the original scale of measurement, and analyse some theoretical
 properties. We examine these ES for two-sample comparison studies under an
 assumption of Normality and investigate what happens when both location
 and scale parameters differ. We explore ES for phenomena for non-Normal
 situations, using the Weibull family for illustration. Finally, for an
 application, we assess differences in customer behaviour when browsing
 E-commerce websites.	       
Journal: Journal of Applied Statistics 
Pages: 2495-2515 
Issue: 11 
Volume: 40 
Year: 2013 
Month: 11 
X-DOI: 10.1080/02664763.2013.818625 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.818625 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:11:p:2495-2515




Template-Type: ReDIF-Article 1.0
Author-Name: Gang Han 
Author-X-Name-First: Gang 
Author-X-Name-Last: Han 
Author-Name: Yangxin Huang 
Author-X-Name-First: Yangxin 
Author-X-Name-Last: Huang 
Author-Name: Qizhai Li 
Author-X-Name-First: Qizhai 
Author-X-Name-Last: Li 
Author-Name: Lili Chen 
Author-X-Name-First: Lili 
Author-X-Name-Last: Chen 
Author-Name: Xi Zhang 
Author-X-Name-First: Xi 
Author-X-Name-Last: Zhang 
Title: Hybrid Bayesian inference on HIV viral dynamic models 
Abstract:
	      Modelling of HIV dynamics in AIDS research has greatly
 improved our understanding of the pathogenesis of HIV-1 infection and
 guided for the treatment of AIDS patients and evaluation of antiretroviral
 therapies. Some of the model parameters may have practical meanings with
 prior knowledge available, but others might not have prior knowledge.
 Incorporating priors can improve the statistical inference. Although there
 have been extensive Bayesian and frequentist estimation methods for the
 viral dynamic models, little work has been done on making simultaneous
 inference about the Bayesian and frequentist parameters. In this article,
 we propose a hybrid Bayesian inference approach for viral dynamic
 nonlinear mixed-effects models using the Bayesian frequentist hybrid
 theory developed in Yuan [<italic>Bayesian frequentist hybrid
 inference</italic>, Ann. Statist. 37 (2009), pp. 2458--2501]. Compared
 with frequentist inference in a real example and two simulation examples,
 the hybrid Bayesian approach is able to improve the inference accuracy
 without compromising the computational load.	       
Journal: Journal of Applied Statistics 
Pages: 2516-2532 
Issue: 11 
Volume: 40 
Year: 2013 
Month: 11 
X-DOI: 10.1080/02664763.2013.818626 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.818626 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:11:p:2516-2532




Template-Type: ReDIF-Article 1.0
Author-Name: Yao Zhang 
Author-X-Name-First: Yao 
Author-X-Name-Last: Zhang 
Author-Name: Eric T. Bradlow 
Author-X-Name-First: Eric T. 
Author-X-Name-Last: Bradlow 
Author-Name: Dylan S. Small 
Author-X-Name-First: Dylan S. 
Author-X-Name-Last: Small 
Title: New measures of clumpiness for incidence data 
Abstract:
	      In recent years, growing attention has been placed on the
 increasing pattern of 'clumpy data' in many empirical areas such as
 financial market microstructure, criminology and seismology, and digital
 media consumption to name just a few; but a well-defined and careful
 measurement of clumpiness has remained somewhat elusive. The related 'hot
 hand' effect has long been a widespread belief in sports, and has
 triggered a branch of interesting research which could shed some light on
 this domain. However, since many concerns have been raised about the low
 power of the existing 'hot hand' significance tests, we propose a new
 class of clumpiness measures which are shown to have higher statistical
 power in extensive simulations under a wide variety of statistical models
 for repeated outcomes. Finally, an empirical study is provided by using a
 unique dataset obtained from Hulu.com, an increasingly popular video
 streaming provider. Our results provide evidence that the 'clumpiness
 phenomena' is widely prevalent in digital content consumption, which
 supports the lore of 'bingeability' of online content believed to exist
 today. 	 
Journal: Journal of Applied Statistics 
Pages: 2533-2548 
Issue: 11 
Volume: 40 
Year: 2013 
Month: 11 
X-DOI: 10.1080/02664763.2013.818627 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.818627 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:11:p:2533-2548




Template-Type: ReDIF-Article 1.0
Author-Name: Jiin-Huarng Guo 
Author-X-Name-First: Jiin-Huarng 
Author-X-Name-Last: Guo 
Author-Name: Wei-Ming Luh 
Author-X-Name-First: Wei-Ming 
Author-X-Name-Last: Luh 
Title: Efficient sample size allocation with cost constraints for heterogeneous-variance group comparison 
Abstract:
				 When conducting research with controlled
 experiments, sample size planning is one of the important decisions that
 researchers have to make. However, current methods do not adequately
 address this issue with regard to variance heterogeneity with some cost
 constraints for comparing several treatment means. This paper proposes a
 sample size allocation ratio in the fixed-effect heterogeneous analysis of
 variance when group variances are unequal and in cases where the sampling
 and/or variable cost has some constraints. The efficient sample size
 allocation is determined for the purpose of minimizing total cost with a
 designated power or maximizing the power with a given total cost. Finally,
 the proposed method is verified by using the index of relative efficiency
 and the corresponding total cost and the total sample size needed. We also
 apply our method in a pain management trial to decide an efficient sample
 size. Simulation studies also show that the proposed sample size formulas
 are efficient in terms of statistical power. SAS and R codes are provided
 in the appendix for easy application.			
Journal: Journal of Applied Statistics 
Pages: 2549-2563 
Issue: 12 
Volume: 40 
Year: 2013 
Month: 12 
X-DOI: 10.1080/02664763.2013.819417 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.819417 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:12:p:2549-2563




Template-Type: ReDIF-Article 1.0
Author-Name: E. Androulakis 
Author-X-Name-First: E. 
Author-X-Name-Last: Androulakis 
Author-Name: C. Koukouvinos 
Author-X-Name-First: C. 
Author-X-Name-Last: Koukouvinos 
Title: A new variable selection method for uniform designs 
Abstract:
				 As an important class of space-filling
 designs, uniform designs (UDs) choose a set of points over a certain
 domain such that these points are uniformly scattered, under a specific
 discrepancy measure. They have been applied successfully in many
 industrial and scientific experiments since they appeared in 1980. A
 noteworthy and practical advantage is their ability to investigate a large
 number of high-level factors simultaneously with a fairly economical set
 of experimental runs. As a result, UDs can be properly used as
 experimental plans that are intended to derive the significant factors
 from a list of many potential ones. To this end, a new screening procedure
 is introduced via penalized least squares. A simulation study is conducted
 to support the proposed method, which reveals that it can be considered
 quite promising and expedient, as judged in terms of Type I and Type II
 error rates.		       
Journal: Journal of Applied Statistics 
Pages: 2564-2578 
Issue: 12 
Volume: 40 
Year: 2013 
Month: 12 
X-DOI: 10.1080/02664763.2013.819568 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.819568 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:12:p:2564-2578




Template-Type: ReDIF-Article 1.0
Author-Name: Ivo Alberink 
Author-X-Name-First: Ivo 
Author-X-Name-Last: Alberink 
Author-Name: Annabel Bolck 
Author-X-Name-First: Annabel 
Author-X-Name-Last: Bolck 
Author-Name: Sonja Menges 
Author-X-Name-First: Sonja 
Author-X-Name-Last: Menges 
Title: Posterior likelihood ratios for evaluation of forensic trace evidence given a two-level model on the data 
Abstract:
				 In forensic science, in order to determine
 whether sets of traces are from the same source or not, it is widely
 advocated to evaluate evidential value of similarity of the traces by
 likelihood ratios (LRs). If traces are expressed by measurements following
 a two-level model with random effects and known variances, closed LR
 formulas are available given normality, or kernel density distributions,
 on the effects. For the known variances estimators are used though, which
 leads to uncertainty on the resulting LRs which is hard to quantify. The
 above is analyzed in an approach in which both effects and variances are
 random, following standard prior distributions on univariate data, leading
 to posterior LRs. For non-informative and conjugate priors, closed LR
 formulas are obtained that are interesting in structure and generalize a
 known result given fixed variance. A semi-conjugate prior on the model
 seems usable in many applications. It is described how to obtain credible
 intervals using Monte Carlo Markov Chain and regular simulation, and an
 example is described for comparison of XTC tablets based on MDMA content.
 In this way, uncertainty on LR estimation is expressed more clearly which
 makes the evidential value more transparent in a judicial context.	    
Journal: Journal of Applied Statistics 
Pages: 2579-2600 
Issue: 12 
Volume: 40 
Year: 2013 
Month: 12 
X-DOI: 10.1080/02664763.2013.822056 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.822056 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:12:p:2579-2600




Template-Type: ReDIF-Article 1.0
Author-Name: A.H.M. Rahmatullah Imon 
Author-X-Name-First: A.H.M. Rahmatullah 
Author-X-Name-Last: Imon 
Author-Name: Ali S. Hadi 
Author-X-Name-First: Ali S. 
Author-X-Name-Last: Hadi 
Title: Identification of multiple high leverage points in logistic regression 
Abstract:
				 Leverage values are being used in
 regression diagnostics as measures of unusual observations in the
 <italic>X</italic>-space. Detection of high leverage observations or
 points is crucial due to their responsibility for masking outliers. In
 linear regression, high leverage points (HLP) are those that stand far
 apart from the center (mean) of the data and hence the most extreme points
 in the covariate space get the highest leverage. But Hosemer and Lemeshow
 [<italic>Applied logistic regression</italic>, Wiley, New York, 1980]
 pointed out that in logistic regression, the leverage measure contains a
 component which can make the leverage values of genuine HLP misleadingly
 very small and that creates problem in the correct identification of the
 cases. Attempts have been made to identify the HLP based on the median
 distances from the mean, but since they are designed for the
 identification of a single high leverage point they may not be very
 effective in the presence of multiple HLP due to their masking
 (false--negative) and swamping (false--positive) effects. In this paper we
 propose a new method for the identification of multiple HLP in logistic
 regression where the suspect cases are identified by a robust group
 deletion technique and they are confirmed using diagnostic techniques. The
 usefulness of the proposed method is then investigated through several
 well-known examples and a Monte Carlo simulation.		    
Journal: Journal of Applied Statistics 
Pages: 2601-2616 
Issue: 12 
Volume: 40 
Year: 2013 
Month: 12 
X-DOI: 10.1080/02664763.2013.822057 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.822057 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:12:p:2601-2616




Template-Type: ReDIF-Article 1.0
Author-Name: B. M. Golam Kibria 
Author-X-Name-First: B. M. Golam 
Author-X-Name-Last: Kibria 
Author-Name: Shipra Banik 
Author-X-Name-First: Shipra 
Author-X-Name-Last: Banik 
Title: Parametric and nonparametric confidence intervals for estimating the difference of means of two skewed populations 
Abstract:
				 In this paper, we have reviewed and
 proposed several interval estimators for estimating the difference of
 means of two skewed populations. Estimators include the
 ordinary-<italic>t</italic>, two versions proposed by Welch [17] and
 Satterthwaite [15], three versions proposed by Zhou and Dinh [18], Johnson
 [9], Hall [8], empirical likelihood (EL), bootstrap version of EL, median
 <italic>t</italic> proposed by Baklizi and Kibria [2] and bootstrap
 version of median <italic>t</italic>. A Monte Carlo simulation study has
 been conducted to compare the performance of the proposed interval
 estimators. Some real life health related data have been considered to
 illustrate the application of the paper. Based on our findings, some
 possible good interval estimators for estimating the mean difference of
 two populations have been recommended for the researchers.		    
Journal: Journal of Applied Statistics 
Pages: 2617-2636 
Issue: 12 
Volume: 40 
Year: 2013 
Month: 12 
X-DOI: 10.1080/02664763.2013.822478 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.822478 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:12:p:2617-2636




Template-Type: ReDIF-Article 1.0
Author-Name: Sanying Feng 
Author-X-Name-First: Sanying 
Author-X-Name-Last: Feng 
Author-Name: Liugen Xue 
Author-X-Name-First: Liugen 
Author-X-Name-Last: Xue 
Title: Variable selection for partially varying coefficient single-index model 
Abstract:
				 In this paper, we consider the problem of
 variable selection for partially varying coefficient single-index model,
 and present a regularized variable selection procedure by combining basis
 function approximations with smoothly clipped absolute deviation penalty.
 The proposed procedure simultaneously selects significant variables in the
 single-index parametric components and the nonparametric coefficient
 function components. With appropriate selection of the tuning parameters,
 the consistency of the variable selection procedure and the oracle
 property of the estimators are established. Finite sample performance of
 the proposed method is illustrated by a simulation study and real data
 analysis.		     
Journal: Journal of Applied Statistics 
Pages: 2637-2652 
Issue: 12 
Volume: 40 
Year: 2013 
Month: 12 
X-DOI: 10.1080/02664763.2013.823919 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.823919 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:12:p:2637-2652




Template-Type: ReDIF-Article 1.0
Author-Name: Akanksha S. Kashikar 
Author-X-Name-First: Akanksha S. 
Author-X-Name-Last: Kashikar 
Author-Name: Neelabh Rohan 
Author-X-Name-First: Neelabh 
Author-X-Name-Last: Rohan 
Author-Name: T.V. Ramanathan 
Author-X-Name-First: T.V. 
Author-X-Name-Last: Ramanathan 
Title: Integer autoregressive models with structural breaks 
Abstract:
				 Even though integer-valued time series are
 common in practice, the methods for their analysis have been developed
 only in recent past. Several models for stationary processes with discrete
 marginal distributions have been proposed in the literature. Such
 processes assume the parameters of the model to remain constant throughout
 the time period. However, this need not be true in practice. In this
 paper, we introduce non-stationary integer-valued autoregressive (INAR)
 models with structural breaks to model a situation, where the parameters
 of the INAR process do not remain constant over time. Such models are
 useful while modelling count data time series with structural breaks. The
 Bayesian and Markov Chain Monte Carlo (MCMC) procedures for the estimation
 of the parameters and break points of such models are discussed. We
 illustrate the model and estimation procedure with the help of a
 simulation study. The proposed model is applied to the two real
 biometrical data sets. 		 
Journal: Journal of Applied Statistics 
Pages: 2653-2669 
Issue: 12 
Volume: 40 
Year: 2013 
Month: 12 
X-DOI: 10.1080/02664763.2013.823920 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.823920 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:12:p:2653-2669




Template-Type: ReDIF-Article 1.0
Author-Name: Sharif Mahmood 
Author-X-Name-First: Sharif 
Author-X-Name-Last: Mahmood 
Author-Name: Begum Zainab 
Author-X-Name-First: Begum 
Author-X-Name-Last: Zainab 
Author-Name: A.H.M. Mahbub Latif 
Author-X-Name-First: A.H.M. Mahbub 
Author-X-Name-Last: Latif 
Title: Frailty modeling for clustered survival data: an application to birth interval in Bangladesh 
Abstract:
				 The present work demonstrates an
 application of random effects model for analyzing birth intervals that are
 clustered into geographical regions. Observations from the same cluster
 are assumed to be correlated because usually they share certain unobserved
 characteristics between them. Ignoring the correlations among the
 observations may lead to incorrect standard errors of the estimates of
 parameters of interest. Beside making the comparisons between Cox's
 proportional hazards model and random effects model for analyzing
 geographically clustered time-to-event data, important demographic and
 socioeconomic factors that may affect the length of birth intervals of
 Bangladeshi women are also reported in this paper.		     
Journal: Journal of Applied Statistics 
Pages: 2670-2680 
Issue: 12 
Volume: 40 
Year: 2013 
Month: 12 
X-DOI: 10.1080/02664763.2013.825702 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.825702 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:12:p:2670-2680




Template-Type: ReDIF-Article 1.0
Author-Name: Johannes Forkman 
Author-X-Name-First: Johannes 
Author-X-Name-Last: Forkman 
Title: The use of a reference variety for comparisons in incomplete series of crop variety trials 
Abstract:
				 In a series of crop variety trials, 'test
 varieties' are compared with one another and with a 'reference' variety
 that is included in all trials. The series is typically analyzed with a
 linear mixed model and the method of generalized least squares. Usually,
 the estimates of the expected differences between the test varieties and
 the reference variety are presented. When the series is incomplete, i.e.
 when all test varieties were not included in all trials, the method of
 generalized least squares may give estimates of expected differences to
 the reference variety that do not appear to accord with observed
 differences. The present paper draws attention to this phenomenon and
 explores the recurrent idea of comparing test varieties indirectly through
 the use of the reference. A new 'reference treatment method' was specified
 and compared with the method of generalized least squares when applied to
 a five-year series of 85 spring wheat trials. The reference treatment
 method provided estimates of differences to the reference variety that
 agreed with observed differences, but was considerably less efficient than
 the method of generalized least squares.		      
Journal: Journal of Applied Statistics 
Pages: 2681-2698 
Issue: 12 
Volume: 40 
Year: 2013 
Month: 12 
X-DOI: 10.1080/02664763.2013.825703 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.825703 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:12:p:2681-2698




Template-Type: ReDIF-Article 1.0
Author-Name: Le Kang 
Author-X-Name-First: Le 
Author-X-Name-Last: Kang 
Author-Name: Randy Carter 
Author-X-Name-First: Randy 
Author-X-Name-Last: Carter 
Author-Name: Kathleen Darcy 
Author-X-Name-First: Kathleen 
Author-X-Name-Last: Darcy 
Author-Name: James Kauderer 
Author-X-Name-First: James 
Author-X-Name-Last: Kauderer 
Author-Name: Shu-Yuan Liao 
Author-X-Name-First: Shu-Yuan 
Author-X-Name-Last: Liao 
Title: A fast Monte Carlo expectation--maximization algorithm for estimation in latent class model analysis with an application to assess diagnostic accuracy for cervical neoplasia in women with atypical glandular cells 
Abstract:
				 In this article, we use a latent class
 model (LCM) with prevalence modeled as a function of covariates to assess
 diagnostic test accuracy in situations where the true disease status is
 not observed, but observations on three or more conditionally independent
 diagnostic tests are available. A fast Monte Carlo
 expectation--maximization (MCEM) algorithm with binary (disease)
 diagnostic data is implemented to estimate parameters of interest; namely,
 sensitivity, specificity, and prevalence of the disease as a function of
 covariates. To obtain standard errors for confidence interval construction
 of estimated parameters, the missing information principle is applied to
 adjust information matrix estimates. We compare the adjusted information
 matrix-based standard error estimates with the bootstrap standard error
 estimates both obtained using the fast MCEM algorithm through an extensive
 Monte Carlo study. Simulation demonstrates that the adjusted information
 matrix approach estimates the standard error similarly with the bootstrap
 methods under certain scenarios. The bootstrap percentile intervals have
 satisfactory coverage probabilities. We then apply the LCM analysis to a
 real data set of 122 subjects from a Gynecologic Oncology Group study of
 significant cervical lesion diagnosis in women with atypical glandular
 cells of undetermined significance to compare the diagnostic accuracy of a
 histology-based evaluation, a carbonic anhydrase-IX biomarker-based test
 and a human papillomavirus DNA test.			     
Journal: Journal of Applied Statistics 
Pages: 2699-2719 
Issue: 12 
Volume: 40 
Year: 2013 
Month: 12 
X-DOI: 10.1080/02664763.2013.825704 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.825704 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:12:p:2699-2719




Template-Type: ReDIF-Article 1.0
Author-Name: Saieed F. Ateya 
Author-X-Name-First: Saieed F. 
Author-X-Name-Last: Ateya 
Title: Estimation under modified Weibull distribution based on right censored generalized order statistics 
Abstract:
				 In this paper, the maximum likelihood (ML)
 and Bayes, by using Markov chain Monte Carlo (MCMC), methods are
 considered to estimate the parameters of three-parameter modified Weibull
 distribution (MWD(&beta;, &tau;, &lambda;)) based on a right censored
 sample of generalized order statistics (gos). Simulation experiments are
 conducted to demonstrate the efficiency of the proposed methods. Some
 comparisons are carried out between the ML and Bayes methods by computing
 the mean squared errors (MSEs), Akaike's information criteria (AIC) and
 Bayesian information criteria (BIC) of the estimates to illustrate the
 paper. Three real data sets from Weibull(&alpha;, &beta;) distribution are
 introduced and analyzed using the MWD(&beta;, &tau;, &lambda;) and also
 using the Weibull(&alpha;, &beta;) distribution. A comparison is carried
 out between the mentioned models based on the corresponding
 Kolmogorov--Smirnov (<italic>K</italic>--<italic>S</italic>) test
 statistic, {AIC and BIC} to emphasize that the MWD(&beta;, &tau;,
 &lambda;) fits the data better than the other distribution. All parameters
 are estimated based on type-II censored sample, censored upper record
 values and progressively type-II censored sample which are generated from
 the real data sets.		      
Journal: Journal of Applied Statistics 
Pages: 2720-2734 
Issue: 12 
Volume: 40 
Year: 2013 
Month: 12 
X-DOI: 10.1080/02664763.2013.825705 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.825705 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:12:p:2720-2734




Template-Type: ReDIF-Article 1.0
Author-Name: Abdul Aziz Karia 
Author-X-Name-First: Abdul Aziz 
Author-X-Name-Last: Karia 
Author-Name: Imbarine Bujang 
Author-X-Name-First: Imbarine 
Author-X-Name-Last: Bujang 
Author-Name: Ismail Ahmad 
Author-X-Name-First: Ismail 
Author-X-Name-Last: Ahmad 
Title: Fractionally integrated ARMA for crude palm oil prices prediction: case of potentially overdifference 
Abstract:
				 Dealing with stationarity remains an
 unsolved problem. Some of the time series data, especially crude palm oil
 (CPO) prices persist towards nonstationarity in the long-run data. This
 dilemma forces the researchers to conduct first-order difference. The
 basic idea is that to obtain the stationary data that is considered as a
 good strategy to overcome the nonstationary counterparts. An opportune
 remark as it is, this proxy may lead to overdifference. The CPO prices
 trend elements have not been attenuated but nearly annihilated. Therefore,
 this paper presents the usefulness of autoregressive fractionally
 integrated moving average (ARFIMA) model as the solution towards the
 nonstationary persistency of CPO prices in the long-run data. In this
 study, we employed daily historical Free-on-Board CPO prices in Malaysia.
 A comparison was made between the ARFIMA over the existing
 autoregressive-integrated moving average (ARIMA) model. Here, we employed
 three statistical evaluation criteria in order to measure the performance
 of the applied models. The general conclusion that can be derived from
 this paper is that the usefulness of the ARFIMA model outperformed the
 existing ARIMA model.			      
Journal: Journal of Applied Statistics 
Pages: 2735-2748 
Issue: 12 
Volume: 40 
Year: 2013 
Month: 12 
X-DOI: 10.1080/02664763.2013.825706 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.825706 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:12:p:2735-2748




Template-Type: ReDIF-Article 1.0
Author-Name: Gang Wang 
Author-X-Name-First: Gang 
Author-X-Name-Last: Wang 
Author-Name: Jun Wang 
Author-X-Name-First: Jun 
Author-X-Name-Last: Wang 
Author-Name: Mingyu Wang 
Author-X-Name-First: Mingyu 
Author-X-Name-Last: Wang 
Title: Modular-transform based clustering 
Abstract:
				 Spectral clustering uses eigenvectors of
 the Laplacian of the similarity matrix. It is convenient to solve binary
 clustering problems. When applied to multi-way clustering, either the
 binary spectral clustering is recursively applied or an embedding to
 spectral space is done and some other methods, such as K-means clustering,
 are used to cluster the points. Here we propose and study a K-way
 clustering algorithm -- spectral modular transformation, based on the fact
 that the graph Laplacian has an equivalent representation, which has a
 diagonal modular structure. The method first transforms the original
 similarity matrix into a new one, which is nearly disconnected and reveals
 a cluster structure clearly, then we apply linearized cluster assignment
 algorithm to split the clusters. In this way, we can find some samples for
 each cluster recursively using the divide and conquer method. To get the
 overall clustering results, we apply the cluster assignment obtained in
 the previous step as the initialization of multiplicative update method
 for spectral clustering. Examples show that our method outperforms
 spectral clustering using other initializations.		     
Journal: Journal of Applied Statistics 
Pages: 2749-2759 
Issue: 12 
Volume: 40 
Year: 2013 
Month: 12 
X-DOI: 10.1080/02664763.2013.826638 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.826638 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:12:p:2749-2759




Template-Type: ReDIF-Article 1.0
Author-Name: Emilio G&#xf3;mez D&#xe9;niz 
Author-X-Name-First: Emilio G&#xf3;mez 
Author-X-Name-Last: D&#xe9;niz 
Title: A new discrete distribution: properties and applications in medical care 
Abstract:
				 This paper proposes a simple and flexible
 count data regression model which is able to incorporate overdispersion
 (the variance is greater than the mean) and which can be considered a
 competitor to the Poisson model. As is well known, this classical model
 imposes the restriction that the conditional mean of each count variable
 must equal the conditional variance. Nevertheless, for the common case of
 well-dispersed counts the Poisson regression may not be appropriate, while
 the count regression model proposed here is potentially useful. We
 consider an application to model counts of medical care utilization by the
 elderly in the USA using a well-known data set from the National Medical
 Expenditure Survey (1987), where the dependent variable is the number of
 stays after hospital admission, and where 10 explanatory variables are
 analysed.		    
Journal: Journal of Applied Statistics 
Pages: 2760-2770 
Issue: 12 
Volume: 40 
Year: 2013 
Month: 12 
X-DOI: 10.1080/02664763.2013.827161 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.827161 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:12:p:2760-2770




Template-Type: ReDIF-Article 1.0
Author-Name: Mikhail Moklyachuk 
Author-X-Name-First: Mikhail 
Author-X-Name-Last: Moklyachuk 
Title: Advances in time series forecasting 
Journal: Journal of Applied Statistics 
Pages: 2771-2772 
Issue: 12 
Volume: 40 
Year: 2013 
Month: 12 
X-DOI: 10.1080/02664763.2013.816023 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.816023 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:12:p:2771-2772




Template-Type: ReDIF-Article 1.0
Author-Name: Yves Laberge 
Author-X-Name-First: Yves 
Author-X-Name-Last: Laberge 
Title: Handbook of statistics in clinical oncology 
Journal: Journal of Applied Statistics 
Pages: 2772-2773 
Issue: 12 
Volume: 40 
Year: 2013 
Month: 12 
X-DOI: 10.1080/02664763.2013.816026 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.816026 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:12:p:2772-2773




Template-Type: ReDIF-Article 1.0
Author-Name: Eugenia Stoimenova 
Author-X-Name-First: Eugenia 
Author-X-Name-Last: Stoimenova 
Title: Methodology in robust and nonparametric statistics 
Journal: Journal of Applied Statistics 
Pages: 2773-2773 
Issue: 12 
Volume: 40 
Year: 2013 
Month: 12 
X-DOI: 10.1080/02664763.2013.816029 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.816029 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:12:p:2773-2773




Template-Type: ReDIF-Article 1.0
Author-Name: Mark Webster 
Author-X-Name-First: Mark 
Author-X-Name-Last: Webster 
Title: Bayesian statistics 
Journal: Journal of Applied Statistics 
Pages: 2773-2774 
Issue: 12 
Volume: 40 
Year: 2013 
Month: 12 
X-DOI: 10.1080/02664763.2013.816049 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.816049 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:12:p:2773-2774




Template-Type: ReDIF-Article 1.0
Author-Name: Han Lin Shang 
Author-X-Name-First: Han Lin 
Author-X-Name-Last: Shang 
Title: The BUGS book: a practical introduction to Bayesian analysis 
Journal: Journal of Applied Statistics 
Pages: 2774-2775 
Issue: 12 
Volume: 40 
Year: 2013 
Month: 12 
X-DOI: 10.1080/02664763.2013.816061 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.816061 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:12:p:2774-2775




Template-Type: ReDIF-Article 1.0
Author-Name: Pieter Bastiaan Ober 
Author-X-Name-First: Pieter Bastiaan 
Author-X-Name-Last: Ober 
Title: Introduction to linear regression analysis 
Journal: Journal of Applied Statistics 
Pages: 2775-2776 
Issue: 12 
Volume: 40 
Year: 2013 
Month: 12 
X-DOI: 10.1080/02664763.2013.816069 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.816069 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:12:p:2775-2776




Template-Type: ReDIF-Article 1.0
Author-Name: Giovanni C. Porzio 
Author-X-Name-First: Giovanni C. 
Author-X-Name-Last: Porzio 
Title: Regression analysis by example 
Journal: Journal of Applied Statistics 
Pages: 2776-2777 
Issue: 12 
Volume: 40 
Year: 2013 
Month: 12 
X-DOI: 10.1080/02664763.2013.817041 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.817041 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:12:p:2776-2777




Template-Type: ReDIF-Article 1.0
Author-Name: Claire Keeble 
Author-X-Name-First: Claire 
Author-X-Name-Last: Keeble 
Title: Maximum-likelihood estimation for sample surveys 
Journal: Journal of Applied Statistics 
Pages: 2777-2777 
Issue: 12 
Volume: 40 
Year: 2013 
Month: 12 
X-DOI: 10.1080/02664763.2013.820437 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.820437 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:12:p:2777-2777




Template-Type: ReDIF-Article 1.0
Author-Name: Mariano Ruiz Espejo 
Author-X-Name-First: Mariano 
Author-X-Name-Last: Ruiz Espejo 
Title: Confidence intervals for proportions and related measures of effect size 
Journal: Journal of Applied Statistics 
Pages: 2778-2778 
Issue: 12 
Volume: 40 
Year: 2013 
Month: 12 
X-DOI: 10.1080/02664763.2013.820444 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.820444 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:12:p:2778-2778




Template-Type: ReDIF-Article 1.0
Author-Name: Mariano Ruiz Espejo 
Author-X-Name-First: Mariano 
Author-X-Name-Last: Ruiz Espejo 
Title: Design and analysis of experiments in the health sciences 
Journal: Journal of Applied Statistics 
Pages: 2778-2779 
Issue: 12 
Volume: 40 
Year: 2013 
Month: 12 
X-DOI: 10.1080/02664763.2013.820452 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.820452 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:40:y:2013:i:12:p:2778-2779




Template-Type: ReDIF-Article 1.0
Author-Name: Robert G. Aykroyd 
Author-X-Name-First: Robert G. 
Author-X-Name-Last: Aykroyd 
Title: Editorial 
Journal: Journal of Applied Statistics 
Pages: 1-1 
Issue: 1 
Volume: 41 
Year: 2014 
Month: 1 
X-DOI: 10.1080/02664763.2014.859354 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.859354 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:1:p:1-1




Template-Type: ReDIF-Article 1.0
Author-Name: Nalan G�lp&#x131;nar 
Author-X-Name-First: Nalan 
Author-X-Name-Last: G�lp&#x131;nar 
Author-Name: Kabir Katata 
Author-X-Name-First: Kabir 
Author-X-Name-Last: Katata 
Title: Modelling oil and gas supply disruption risks using extreme-value theory and copula 
Abstract:
				 In this paper, we are concerned with
 modelling oil and gas supply disruption risks using extreme-value theory
 and copula. We analyse financial and volumetric losses due to both oil and
 gas supply disruptions and investigate their dependence structure using
 real data. In order to illustrate the impact of crude oil and natural gas
 supply disruptions on an energy-dependent economy, Nigeria is considered
 as a case study. Computational studies illustrate that the generalized
 extreme-value distribution anticipates higher financial losses and
 extreme-value copulas produce the best fit for financial and volumetric
 losses compared with normal copulas. Moreover, multivariate financial
 losses exhibit stronger positive dependence than volumetric losses.	    
Journal: Journal of Applied Statistics 
Pages: 2-25 
Issue: 1 
Volume: 41 
Year: 2014 
Month: 1 
X-DOI: 10.1080/02664763.2013.827160 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.827160 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:1:p:2-25




Template-Type: ReDIF-Article 1.0
Author-Name: Sung Wan Han 
Author-X-Name-First: Sung Wan 
Author-X-Name-Last: Han 
Author-Name: Rickson C. Mesquita 
Author-X-Name-First: Rickson C. 
Author-X-Name-Last: Mesquita 
Author-Name: Theresa M. Busch 
Author-X-Name-First: Theresa M. 
Author-X-Name-Last: Busch 
Author-Name: Mary E. Putt 
Author-X-Name-First: Mary E. 
Author-X-Name-Last: Putt 
Title: A method for choosing the smoothing parameter in a semi-parametric model for detecting change-points in blood flow 
Abstract:
				 In a smoothing spline model with unknown
 change-points, the choice of the smoothing parameter strongly influences
 the estimation of the change-point locations and the function at the
 change-points. In a tumor biology example, where change-points in blood
 flow in response to treatment were of interest, choosing the smoothing
 parameter based on minimizing generalized cross-validation (GCV) gave
 unsatisfactory estimates of the change-points. We propose a new method,
 aGCV, that re-weights the residual sum of squares and generalized degrees
 of freedom terms from GCV. The weight is chosen to maximize the decrease
 in the generalized degrees of freedom as a function of the weight value,
 while simultaneously minimizing aGCV as a function of the smoothing
 parameter and the change-points. Compared with GCV, simulation studies
 suggest that the aGCV method yields improved estimates of the change-point
 and the value of the function at the change-point.			   
Journal: Journal of Applied Statistics 
Pages: 26-45 
Issue: 1 
Volume: 41 
Year: 2014 
Month: 1 
X-DOI: 10.1080/02664763.2013.830085 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.830085 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:1:p:26-45




Template-Type: ReDIF-Article 1.0
Author-Name: Xiaowei Yang 
Author-X-Name-First: Xiaowei 
Author-X-Name-Last: Yang 
Author-Name: Bin Peng 
Author-X-Name-First: Bin 
Author-X-Name-Last: Peng 
Author-Name: Rongqi Chen 
Author-X-Name-First: Rongqi 
Author-X-Name-Last: Chen 
Author-Name: Qian Zhang 
Author-X-Name-First: Qian 
Author-X-Name-Last: Zhang 
Author-Name: Dianwen Zhu 
Author-X-Name-First: Dianwen 
Author-X-Name-Last: Zhu 
Author-Name: Qing J. Zhang 
Author-X-Name-First: Qing J. 
Author-X-Name-Last: Zhang 
Author-Name: Fuzhong Xue 
Author-X-Name-First: Fuzhong 
Author-X-Name-Last: Xue 
Author-Name: Lihong Qi 
Author-X-Name-First: Lihong 
Author-X-Name-Last: Qi 
Title: Statistical profiling methods with hierarchical logistic regression for healthcare providers with binary outcomes 
Abstract:
				 Within the context of California's public
 report of <italic>coronary artery bypass graft</italic> (CABG) surgery
 outcomes, we first thoroughly review popular statistical methods for
 profiling healthcare providers. Extensive simulation studies are then
 conducted to compare profiling schemes based on hierarchical logistic
 regression (LR) modeling under various conditions. Both Bayesian and
 frequentist's methods are evaluated in classifying hospitals into
 'better', 'normal' or 'worse' service providers. The simulation results
 suggest that no single method would dominate others on all accounts.
 Traditional schemes based on LR tend to identify too many false outliers,
 while those based on hierarchical modeling are relatively conservative.
 The issue of over shrinkage in hierarchical modeling is also investigated
 using the 2005--2006 California CABG data set. The article provides
 theoretical and empirical evidence in choosing the right methodology for
 provider profiling.			  
Journal: Journal of Applied Statistics 
Pages: 46-59 
Issue: 1 
Volume: 41 
Year: 2014 
Month: 1 
X-DOI: 10.1080/02664763.2013.830086 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.830086 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:1:p:46-59




Template-Type: ReDIF-Article 1.0
Author-Name: Abidemi K. Adeniji 
Author-X-Name-First: Abidemi K. 
Author-X-Name-Last: Adeniji 
Author-Name: Steven H. Belle 
Author-X-Name-First: Steven H. 
Author-X-Name-Last: Belle 
Author-Name: Abdus S. Wahed 
Author-X-Name-First: Abdus S. 
Author-X-Name-Last: Wahed 
Title: Incorporating diagnostic accuracy into the estimation of discrete survival function 
Abstract:
				 Empirical distribution function (EDF) is a
 commonly used estimator of population cumulative distribution function.
 Survival function is estimated as the complement of EDF. However, clinical
 diagnosis of an event is often subjected to misclassification, by which
 the outcome is given with some uncertainty. In the presence of such
 errors, the true distribution of the time to first event is unknown. We
 develop a method to estimate the true survival distribution by
 incorporating negative predictive values and positive predictive values of
 the prediction process into a product-limit style construction. This will
 allow us to quantify the bias of the EDF estimates due to the presence of
 misclassified events in the observed data. We present an unbiased
 estimator of the true survival rates and its variance. Asymptotic
 properties of the proposed estimators are provided and these properties
 are examined through simulations. We evaluate our methods using data from
 the VIRAHEP-C study.			   
Journal: Journal of Applied Statistics 
Pages: 60-72 
Issue: 1 
Volume: 41 
Year: 2014 
Month: 1 
X-DOI: 10.1080/02664763.2013.830087 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.830087 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:1:p:60-72




Template-Type: ReDIF-Article 1.0
Author-Name: Xavier Puig 
Author-X-Name-First: Xavier 
Author-X-Name-Last: Puig 
Author-Name: Josep Ginebra 
Author-X-Name-First: Josep 
Author-X-Name-Last: Ginebra 
Title: A Bayesian cluster analysis of election results 
Abstract:
				 A Bayesian cluster analysis for the
 results of an election based on multinomial mixture models is proposed.
 The number of clusters is chosen based on the careful comparison of the
 results with predictive simulations from the models, and by checking
 whether models capture most of the spatial dependence in the results. By
 implementing the analysis on five recent elections in Barcelona, the
 reader is walked through the choice of the best statistics and graphical
 displays to help chose a model and present the results. Even though the
 models do not use any information about the location of the areas in which
 the results are broken into, in the example they uncover a four-cluster
 structure with a strong spatial dependence, that is very stable over time
 and relates to the demographic composition.			    
Journal: Journal of Applied Statistics 
Pages: 73-94 
Issue: 1 
Volume: 41 
Year: 2014 
Month: 1 
X-DOI: 10.1080/02664763.2013.830088 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.830088 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:1:p:73-94




Template-Type: ReDIF-Article 1.0
Author-Name: Weihua Zhao 
Author-X-Name-First: Weihua 
Author-X-Name-Last: Zhao 
Author-Name: Riquan Zhang 
Author-X-Name-First: Riquan 
Author-X-Name-Last: Zhang 
Author-Name: Yazhao Lv 
Author-X-Name-First: Yazhao 
Author-X-Name-Last: Lv 
Author-Name: Jicai Liu 
Author-X-Name-First: Jicai 
Author-X-Name-Last: Liu 
Title: Variable selection for varying dispersion beta regression model 
Abstract:
				 The beta regression models are commonly
 used by practitioners to model variables that assume values in the
 standard unit interval (0, 1). In this paper, we consider the issue of
 variable selection for beta regression models with varying dispersion
 (VBRM), in which both the mean and the dispersion depend upon predictor
 variables. Based on a penalized likelihood method, the consistency and the
 oracle property of the penalized estimators are established. Following the
 coordinate descent algorithm idea of generalized linear models, we develop
 new variable selection procedure for the VBRM, which can efficiently
 simultaneously estimate and select important variables in both mean model
 and dispersion model. Simulation studies and body fat data analysis are
 presented to illustrate the proposed methods.			 
Journal: Journal of Applied Statistics 
Pages: 95-108 
Issue: 1 
Volume: 41 
Year: 2014 
Month: 1 
X-DOI: 10.1080/02664763.2013.830284 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.830284 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:1:p:95-108




Template-Type: ReDIF-Article 1.0
Author-Name: Hani M. Samawi 
Author-X-Name-First: Hani M. 
Author-X-Name-Last: Samawi 
Author-Name: Robert Vogel 
Author-X-Name-First: Robert 
Author-X-Name-Last: Vogel 
Title: Notes on two sample tests for partially correlated (paired) data 
Abstract:
				 We provide several methods to compare two
 Gaussian distributed means in the two sample location problems under the
 assumption of partially dependent observations. Simulation studies
 indicate that our test procedure is frequently more powerful than other
 methods depending on the ratio of the unpaired data and the strength and
 direction of the correlation between the two variables. The tests used in
 our comparative study are illustrated with an example based on data from a
 small gynecological study.		     
Journal: Journal of Applied Statistics 
Pages: 109-117 
Issue: 1 
Volume: 41 
Year: 2014 
Month: 1 
X-DOI: 10.1080/02664763.2013.830285 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.830285 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:1:p:109-117




Template-Type: ReDIF-Article 1.0
Author-Name: E.D. Lozano-Aguilera, 
Author-X-Name-First: E.D. 
Author-X-Name-Last: Lozano-Aguilera, 
Author-Name: Mar�a Dolores Estudillo-Mart�nez 
Author-X-Name-First: Mar�a Dolores 
Author-X-Name-Last: Estudillo-Mart�nez 
Author-Name: Sonia Castillo-Guti�rrez 
Author-X-Name-First: Sonia 
Author-X-Name-Last: Castillo-Guti�rrez 
Title: A proposal for plotting positions in probability plots 
Abstract:
				 Probability plots allow us to determine
 whether a set of sample observations is distributed according to a
 theoretical distribution. Plotting positions are fundamental elements in
 statistics and, in particular, for the construction of probability plots.
 In this paper, a new plotting position to construct different probability
 plots, such as Q--Q Plot, P--P Plot and S--P Plot, is proposed. The
 proposed definition is based on the median of the <italic>i</italic>th
 order statistic of the theoretical distribution considered. The main
 feature of this plotting position formula is that it is independent of the
 theoretical distribution selected. Moreover, the procedure developed is
 'almost' exact, reaching, without a high cost of time, an accuracy as
 great as we want, which avoids using approximations (proposed by other
 authors).			 
Journal: Journal of Applied Statistics 
Pages: 118-126 
Issue: 1 
Volume: 41 
Year: 2014 
Month: 1 
X-DOI: 10.1080/02664763.2013.831814 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.831814 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:1:p:118-126




Template-Type: ReDIF-Article 1.0
Author-Name: Stacia M. DeSantis 
Author-X-Name-First: Stacia M. 
Author-X-Name-Last: DeSantis 
Author-Name: Christos Lazaridis 
Author-X-Name-First: Christos 
Author-X-Name-Last: Lazaridis 
Author-Name: Shuang Ji 
Author-X-Name-First: Shuang 
Author-X-Name-Last: Ji 
Author-Name: Francis G. Spinale 
Author-X-Name-First: Francis G. 
Author-X-Name-Last: Spinale 
Title: Analyzing propensity matched zero-inflated count outcomes in observational studies 
Abstract:
				 Determining the effectiveness of different
 treatments from observational data, which are characterized by imbalance
 between groups due to lack of randomization, is challenging. Propensity
 matching is often used to rectify imbalances among prognostic variables.
 However, there are no guidelines on how appropriately to analyze group
 matched data when the outcome is a zero-inflated count. In addition, there
 is debate over whether to account for correlation of responses induced by
 matching and/or whether to adjust for variables used in generating the
 propensity score in the final analysis. The aim of this research is to
 compare covariate unadjusted and adjusted zero-inflated Poisson models
 that do and do not account for the correlation. A simulation study is
 conducted, demonstrating that it is necessary to adjust for potential
 residual confounding, but that accounting for correlation is less
 important. The methods are applied to a biomedical research data set.	    
Journal: Journal of Applied Statistics 
Pages: 127-141 
Issue: 1 
Volume: 41 
Year: 2014 
Month: 1 
X-DOI: 10.1080/02664763.2013.834296 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.834296 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:1:p:127-141




Template-Type: ReDIF-Article 1.0
Author-Name: M�rcio Poletti Laurini 
Author-X-Name-First: M�rcio Poletti 
Author-X-Name-Last: Laurini 
Title: Dynamic functional data analysis with non-parametric state space models 
Abstract:
				 In this article, we introduce a new method
 for modelling curves with dynamic structures, using a non-parametric
 approach formulated as a state space model. The non-parametric approach is
 based on the use of penalised splines, represented as a dynamic mixed
 model. This formulation can capture the dynamic evolution of curves using
 a limited number of latent factors, allowing an accurate fit with a small
 number of parameters. We also present a new method to determine the
 optimal smoothing parameter through an adaptive procedure, using a
 formulation analogous to a model of stochastic volatility (SV). The
 non-parametric state space model allows unifying different methods applied
 to data with a functional structure in finance. We present the advantages
 and limitations of this method through simulation studies and also by
 comparing its predictive performance with other parametric and
 non-parametric methods used in financial applications using data on the
 term structure of interest rates.		       
Journal: Journal of Applied Statistics 
Pages: 142-163 
Issue: 1 
Volume: 41 
Year: 2014 
Month: 1 
X-DOI: 10.1080/02664763.2013.838663 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.838663 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:1:p:142-163




Template-Type: ReDIF-Article 1.0
Author-Name: Boudewijn F. Roukema 
Author-X-Name-First: Boudewijn F. 
Author-X-Name-Last: Roukema 
Title: A first-digit anomaly in the 2009 Iranian presidential election 
Abstract:
				 A local bootstrap method is proposed for
 the analysis of electoral vote-count first-digit frequencies,
 complementing the Benford's Law limit. The method is calibrated on five
 presidential-election first rounds (2002--2006) and applied to the 2009
 Iranian presidential-election first round. Candidate K has a highly
 significant (<italic>p</italic>>0.15% ) excess of vote counts starting
 with the digit 7. This leads to other anomalies, two of which are
 individually significant at <italic>p</italic>&sim; 0.1% and one at
 <italic>p</italic>&sim; 1%. Independently, Iranian pre-election opinion
 polls significantly reject the official results unless the five polls
 favouring candidate A are considered alone. If the latter represent
 normalised data and a linear, least-squares, equal-weighted fit is used,
 then either candidates R and K suffered a sudden, dramatic (70%&plusmn;15%
 ) loss of electoral support just prior to the election, or the official
 results are rejected (<italic>p</italic>&sim; 0.01% ). 		   
Journal: Journal of Applied Statistics 
Pages: 164-199 
Issue: 1 
Volume: 41 
Year: 2014 
Month: 1 
X-DOI: 10.1080/02664763.2013.838664 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.838664 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:1:p:164-199




Template-Type: ReDIF-Article 1.0
Author-Name: Aamir Saghir 
Author-X-Name-First: Aamir 
Author-X-Name-Last: Saghir 
Author-Name: Zhengyan Lin 
Author-X-Name-First: Zhengyan 
Author-X-Name-Last: Lin 
Title: Control chart for monitoring multivariate COM-Poisson attributes 
Abstract:
				 Statistical process control of
 multi-attribute count data has received much attention with modern
 data-acquisition equipment and online computers. The multivariate Poisson
 distribution is often used to monitor multivariate attributes count data.
 However, little work has been done so far on under- or over-dispersed
 multivariate count data, which is common in many industrial processes,
 with positive or negative correlation. In this study, a Shewhart-type
 multivariate control chart is constructed to monitor such kind of data,
 namely the multivariate COM-Poisson (MCP) chart, based on the MCP
 distribution. The performance of the MCP chart is evaluated by the average
 run length in simulation. The proposed chart generalizes some existing
 multivariate attribute charts as its special cases. A real-life bivariate
 process and a simulated trivariate Poisson process are used to illustrate
 the application of the MCP chart.			
Journal: Journal of Applied Statistics 
Pages: 200-214 
Issue: 1 
Volume: 41 
Year: 2014 
Month: 1 
X-DOI: 10.1080/02664763.2013.838666 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.838666 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:1:p:200-214




Template-Type: ReDIF-Article 1.0
Author-Name: Hanieh Panahi 
Author-X-Name-First: Hanieh 
Author-X-Name-Last: Panahi 
Author-Name: Abdolreza Sayyareh 
Author-X-Name-First: Abdolreza 
Author-X-Name-Last: Sayyareh 
Title: Parameter estimation and prediction of order statistics for the Burr Type XII distribution with Type II censoring 
Abstract:
				 This article deals with the statistical
 inference and prediction on Burr Type XII parameters based on Type II
 censored sample. It is observed that the maximum likelihood estimators
 (MLEs) cannot be obtained in closed form. We use the
 expectation-maximization algorithm to compute the MLEs. We also obtain the
 Bayes estimators under symmetric and asymmetric loss functions such as
 squared error and Linex By applying Lindley's approximation and Markov
 chain Monte Carlo (MCMC) technique. Further, MCMC samples are used to
 calculate the highest posterior density credible intervals. Monte Carlo
 simulation study and two real-life data-sets are presented to illustrate
 all of the methods developed here. Furthermore, we obtain a prediction of
 future order statistics based on the observed ordered because of its
 important application in different fields such as medical and engineering
 sciences. A numerical example carried out to illustrate the procedures
 obtained for prediction of future order statistics.		       
Journal: Journal of Applied Statistics 
Pages: 215-232 
Issue: 1 
Volume: 41 
Year: 2014 
Month: 1 
X-DOI: 10.1080/02664763.2013.838668 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.838668 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:1:p:215-232




Template-Type: ReDIF-Article 1.0
Author-Name: H.E.T. Holgersson 
Author-X-Name-First: H.E.T. 
Author-X-Name-Last: Holgersson 
Author-Name: L. Nordstr�m 
Author-X-Name-First: L. 
Author-X-Name-Last: Nordstr�m 
Author-Name: &Ouml;. &Ouml;ner 
Author-X-Name-First: &Ouml;. 
Author-X-Name-Last: &Ouml;ner 
Title: Dummy variables vs. category-wise models 
Abstract:
				 Empirical research frequently involves
 regression analysis with binary categorical variables, which are
 traditionally handled through dummy explanatory variables. This paper
 argues that separate category-wise models may provide a more logical and
 comprehensive tool for analysing data with binary categories. Exploring
 different aspects of both methods, we contrast the two with a Monte Carlo
 simulation and an empirical example to provide a practical insight.	    
Journal: Journal of Applied Statistics 
Pages: 233-241 
Issue: 2 
Volume: 41 
Year: 2014 
Month: 2 
X-DOI: 10.1080/02664763.2013.838665 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.838665 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:2:p:233-241




Template-Type: ReDIF-Article 1.0
Author-Name: Ting-Li Chen 
Author-X-Name-First: Ting-Li 
Author-X-Name-Last: Chen 
Author-Name: Stuart Geman 
Author-X-Name-First: Stuart 
Author-X-Name-Last: Geman 
Title: Image warping using radial basis functions 
Abstract:
				 Image warping is the process of deforming
 an image through a transformation of its domain, which is typically a
 subset of <italic>R</italic>					 -super-2.
 Given the destination of a collection of points, the problem becomes one
 of finding a suitable smooth interpolation for the destinations of the
 remaining points of the domain. A common solution is to use the thin plate
 spline (TPS). We find that the TPS often introduces unintended distortions
 of image structures. In this paper, we will analyze interpolation by TPS,
 experiment with other radial basis functions, and suggest two alternative
 functions that provide better results. 		     
Journal: Journal of Applied Statistics 
Pages: 242-258 
Issue: 2 
Volume: 41 
Year: 2014 
Month: 2 
X-DOI: 10.1080/02664763.2013.838667 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.838667 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:2:p:242-258




Template-Type: ReDIF-Article 1.0
Author-Name: S.H. Lin 
Author-X-Name-First: S.H. 
Author-X-Name-Last: Lin 
Title: Comparing the mean vectors of two independent multivariate log-normal distributions 
Abstract:
				 The multivariate log-normal distribution
 is a good candidate to describe data that are not only positive and
 skewed, but also contain many characteristic values. In this study, we
 apply the generalized variable method to compare the mean vectors of two
 independent multivariate log-normal populations that display
 heteroscedasticity. Two generalized pivotal quantities are derived for
 constructing the generalized confidence region and for testing the
 difference between two mean vectors. Simulation results indicate that the
 proposed procedures exhibit satisfactory performance regardless of the
 sample sizes and heteroscedasticity. The type I error rates obtained are
 consistent with expectations and the coverage probabilities are close to
 the nominal level when compared with the other method which is currently
 available. These features make the proposed method a worthy alternative
 for inferential analysis of problems involving multivariate log-normal
 means. The results are illustrated using three examples.		    
Journal: Journal of Applied Statistics 
Pages: 259-274 
Issue: 2 
Volume: 41 
Year: 2014 
Month: 2 
X-DOI: 10.1080/02664763.2013.838669 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.838669 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:2:p:259-274




Template-Type: ReDIF-Article 1.0
Author-Name: Arash Nademi 
Author-X-Name-First: Arash 
Author-X-Name-Last: Nademi 
Author-Name: Rahman Farnoosh 
Author-X-Name-First: Rahman 
Author-X-Name-Last: Farnoosh 
Title: Mixtures of autoregressive-autoregressive conditionally heteroscedastic models: semi-parametric approach 
Abstract:
				 We propose data generating structures
 which can be represented as a mixture of autoregressive-autoregressive
 conditionally heteroscedastic models. The switching between the states is
 governed by a hidden Markov chain. We investigate semi-parametric
 estimators for estimating the functions based on the quasi-maximum
 likelihood approach and provide sufficient conditions for geometric
 ergodicity of the process. We also present an expectation--maximization
 algorithm for calculating the estimates numerically.			 
Journal: Journal of Applied Statistics 
Pages: 275-293 
Issue: 2 
Volume: 41 
Year: 2014 
Month: 2 
X-DOI: 10.1080/02664763.2013.839129 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.839129 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:2:p:275-293




Template-Type: ReDIF-Article 1.0
Author-Name: Tsukasa Hokimoto 
Author-X-Name-First: Tsukasa 
Author-X-Name-Last: Hokimoto 
Author-Name: Kunio Shimizu 
Author-X-Name-First: Kunio 
Author-X-Name-Last: Shimizu 
Title: A non-homogeneous hidden Markov model for predicting the distribution of sea surface elevation 
Abstract:
				 The prediction problem of sea state based
 on the field measurements of wave and meteorological factors is a topic of
 interest from the standpoints of navigation safety and fisheries. Various
 statistical methods have been considered for the prediction of the
 distribution of sea surface elevation. However, prediction of sea state in
 the transitional situation when waves are developing by blowing wind has
 been a difficult problem until now, because the statistical expression of
 the dynamic mechanism during this situation is very complicated. In this
 article, we consider this problem through the development of a statistical
 model. More precisely, we develop a model for the prediction of the
 time-varying distribution of sea surface elevation, taking into account a
 non-homogeneous hidden Markov model in which the time-varying structures
 are influenced by wind speed and wind direction. Our prediction
 experiments suggest the possibility that the proposed model contributes to
 an improvement of the prediction accuracy by using a homogenous hidden
 Markov model. Furthermore, we found that the prediction accuracy is
 influenced by the circular distribution of the circular hidden Markov
 model for the directional time series wind direction data.		    
Journal: Journal of Applied Statistics 
Pages: 294-319 
Issue: 2 
Volume: 41 
Year: 2014 
Month: 2 
X-DOI: 10.1080/02664763.2013.839634 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.839634 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:2:p:294-319




Template-Type: ReDIF-Article 1.0
Author-Name: Jos� A. Fioruci 
Author-X-Name-First: Jos� A. 
Author-X-Name-Last: Fioruci 
Author-Name: Ricardo S. Ehlers 
Author-X-Name-First: Ricardo S. 
Author-X-Name-Last: Ehlers 
Author-Name: Marinho G. Andrade Filho 
Author-X-Name-First: Marinho G. 
Author-X-Name-Last: Andrade Filho 
Title: Bayesian multivariate GARCH models with dynamic correlations and asymmetric error distributions 
Abstract:
				 The main goal in this paper is to develop
 and apply stochastic simulation techniques for GARCH models with
 multivariate skewed distributions using the Bayesian approach. Both
 parameter estimation and model comparison are not trivial tasks and
 several approximate and computationally intensive methods (Markov chain
 Monte Carlo) will be used to this end. We consider a flexible class of
 multivariate distributions which can model both skewness and heavy tails.
 Also, we do not fix tail behaviour when dealing with fat tail
 distributions but leave it subject to inference.		       
Journal: Journal of Applied Statistics 
Pages: 320-331 
Issue: 2 
Volume: 41 
Year: 2014 
Month: 2 
X-DOI: 10.1080/02664763.2013.839635 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.839635 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:2:p:320-331




Template-Type: ReDIF-Article 1.0
Author-Name: Michael McCullough 
Author-X-Name-First: Michael 
Author-X-Name-Last: McCullough 
Author-Name: Thomas L Marsh 
Author-X-Name-First: Thomas L 
Author-X-Name-Last: Marsh 
Author-Name: Ron C Mittelhammer 
Author-X-Name-First: Ron C 
Author-X-Name-Last: Mittelhammer 
Title: Reconstructing nonlinear structure in regression residuals 
Abstract:
				 Phase space reconstruction is investigated
 as a diagnostic tool for uncovering structure of nonlinear processes in
 regression residuals. Results in the form of phase portraits (e.g. scatter
 plots of reconstructed dynamical systems) and descriptive statistics
 provide information that can identify underlying structural components
 from stochastic data outcomes, even in cases where such data appear
 essentially random, and provide insights categorizing structural
 components into functional classes to inform econometric/time series
 modeling efforts. Empirical evidence supporting this approach is provided
 using simulations from an Ikeda mapping. An application to US hops exports
 is used to illustrate the application of the approach. 		    
Journal: Journal of Applied Statistics 
Pages: 332-350 
Issue: 2 
Volume: 41 
Year: 2014 
Month: 2 
X-DOI: 10.1080/02664763.2013.839636 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.839636 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:2:p:332-350




Template-Type: ReDIF-Article 1.0
Author-Name: Ming Zhou 
Author-X-Name-First: Ming 
Author-X-Name-Last: Zhou 
Author-Name: Yongzhao Shao 
Author-X-Name-First: Yongzhao 
Author-X-Name-Last: Shao 
Title: A powerful test for multivariate normality 
Abstract:
				 This paper investigates a new test for
 normality that is easy for biomedical researchers to understand and easy
 to implement in all dimensions. In terms of power comparison against a
 broad range of alternatives, the new test outperforms the best known
 competitors in the literature as demonstrated by simulation results. In
 addition, the proposed test is illustrated using data from real biomedical
 studies.			  
Journal: Journal of Applied Statistics 
Pages: 351-363 
Issue: 2 
Volume: 41 
Year: 2014 
Month: 2 
X-DOI: 10.1080/02664763.2013.839637 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.839637 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:2:p:351-363




Template-Type: ReDIF-Article 1.0
Author-Name: A. Asrat Atsedeweyn 
Author-X-Name-First: A. Asrat 
Author-X-Name-Last: Atsedeweyn 
Author-Name: K. Srinivasa Rao 
Author-X-Name-First: K. 
Author-X-Name-Last: Srinivasa Rao 
Title: Linear regression model with new symmetric distributed errors 
Abstract:
				 Regression models play a dominant role in
 analyzing several data sets arising from areas like agricultural
 experiment, space experiment, biological experiment, financial modeling,
 etc. One of the major strings in developing the regression models is the
 assumption of the distribution of the error terms. It is customary to
 consider that the error terms follow the Gaussian distribution. However,
 there are some drawbacks of Gaussian errors such as the distribution being
 mesokurtic having kurtosis three. In many practical situations the
 variables under study may not be having mesokurtic but they are
 platykurtic. Hence, to analyze these sorts of platykurtic variables, a
 two-variable regression model with new symmetric distributed errors is
 developed and analyzed. The maximum likelihood (ML) estimators of the
 model parameters are derived. The properties of the ML estimators with
 respect to the new symmetrically distributed errors are also discussed. A
 simulation study is carried out to compare the proposed model with that of
 Gaussian errors and found that the proposed model performs better when the
 variables are platykurtic. Some applications of the developed model are
 also pointed out.		    
Journal: Journal of Applied Statistics 
Pages: 364-381 
Issue: 2 
Volume: 41 
Year: 2014 
Month: 2 
X-DOI: 10.1080/02664763.2013.839638 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.839638 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:2:p:364-381




Template-Type: ReDIF-Article 1.0
Author-Name: Ji-Liang Shiu 
Author-X-Name-First: Ji-Liang 
Author-X-Name-Last: Shiu 
Author-Name: Chia-Hung D. Sun 
Author-X-Name-First: Chia-Hung D. 
Author-X-Name-Last: Sun 
Title: The determinants of price in online auctions: more evidence from unbalanced panel data 
Abstract:
				 This study provides an alternative
 approach that takes account of the unobserved effects of each seller under
 a sample selection framework while using online auction data. We use data
 collected from Yahoo! Kimo Auction (Taiwan) to demonstrate that earlier
 empirical results of online auction studies may be biased due to violating
 the assumption of independence of the error terms between sample
 observations. Empirical findings show that seller reputation is no longer
 as the most important factor for buyers to bid on items, while the sample
 data confirm the unobserved heterogeneity of sellers and sample selection
 problem.		       
Journal: Journal of Applied Statistics 
Pages: 382-392 
Issue: 2 
Volume: 41 
Year: 2014 
Month: 2 
X-DOI: 10.1080/02664763.2013.839639 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.839639 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:2:p:382-392




Template-Type: ReDIF-Article 1.0
Author-Name: Xiaoguang Wang 
Author-X-Name-First: Xiaoguang 
Author-X-Name-Last: Wang 
Author-Name: Junhui Fan 
Author-X-Name-First: Junhui 
Author-X-Name-Last: Fan 
Title: Variable selection for multivariate generalized linear models 
Abstract:
				 Generalized linear models (GLMs) are
 widely studied to deal with complex response variables. For the analysis
 of categorical dependent variables with more than two response categories,
 multivariate GLMs are presented to build the relationship between this
 polytomous response and a set of regressors. Traditional variable
 selection approaches have been proposed for the multivariate GLM with a
 canonical link function when the number of parameters is fixed in the
 literature. However, in many model selection problems, the number of
 parameters may be large and grow with the sample size. In this paper, we
 present a new selection criterion to the model with a diverging number of
 parameters. Under suitable conditions, the criterion is shown to be model
 selection consistent. A simulation study and a real data analysis are
 conducted to support theoretical findings.			 
Journal: Journal of Applied Statistics 
Pages: 393-406 
Issue: 2 
Volume: 41 
Year: 2014 
Month: 2 
X-DOI: 10.1080/02664763.2013.839640 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.839640 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:2:p:393-406




Template-Type: ReDIF-Article 1.0
Author-Name: Saeid Amiri 
Author-X-Name-First: Saeid 
Author-X-Name-Last: Amiri 
Title: The resampling of entropies with the application of biodiversity 
Abstract:
				 This paper discusses the bootstrap test of
 entropies. Since the comparison of entropies is of prime interest in
 applied fields, finding an appropriate way to carry out such a comparison
 is of utmost importance. This paper presents how resampling should be
 performed to obtain an accurate <italic>p</italic>-value. Although the
 test using a pair-wise bootstrap confidence interval (CI) has already been
 dealt with in few works, here the bootstrap tests are studied because it
 may demand quite a different resampling algorithm compared with the CI.
 Moreover, the multiple test is studied. The proposed tests appear to yield
 several appreciable advantages. The easy implementation and the power of
 the proposed test can be considered as advantages. Here the entropy of the
 discrete variable is studied. The proposed tests are examined using Monte
 Carlo investigations and also evaluated using various distributions.	    
Journal: Journal of Applied Statistics 
Pages: 407-422 
Issue: 2 
Volume: 41 
Year: 2014 
Month: 2 
X-DOI: 10.1080/02664763.2013.840052 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.840052 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:2:p:407-422




Template-Type: ReDIF-Article 1.0
Author-Name: Paul J. Plummer 
Author-X-Name-First: Paul J. 
Author-X-Name-Last: Plummer 
Author-Name: Jie Chen 
Author-X-Name-First: Jie 
Author-X-Name-Last: Chen 
Title: A Bayesian approach for locating change points in a compound Poisson process with application to detecting DNA copy number variations 
Abstract:
				 This work examines the problem of locating
 changes in the distribution of a Compound Poisson Process where the
 variables being summed are iid normal and the number of variable follows
 the Poisson distribution. A Bayesian approach is developed to identify the
 location of significant changes in any of the parameters of the
 distribution, and a sliding window algorithm is used to identify multiple
 change points. These results can be applied in any field of study where an
 interest in locating changes not only in the parameter of a normally
 distributed data set but also in the rate of their occurrence. It has
 direct application to the study of DNA copy number variations in cancer
 research, where it is known that the distances between the genes can
 affect their intensity level.			 
Journal: Journal of Applied Statistics 
Pages: 423-438 
Issue: 2 
Volume: 41 
Year: 2014 
Month: 2 
X-DOI: 10.1080/02664763.2013.840272 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.840272 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:2:p:423-438




Template-Type: ReDIF-Article 1.0
Author-Name: Krishna K. Saha 
Author-X-Name-First: Krishna K. 
Author-X-Name-Last: Saha 
Author-Name: Roger Bilisoly 
Author-X-Name-First: Roger 
Author-X-Name-Last: Bilisoly 
Author-Name: Darius M. Dziuda 
Author-X-Name-First: Darius M. 
Author-X-Name-Last: Dziuda 
Title: Hybrid-based confidence intervals for the ratio of two treatment means in the over-dispersed Poisson data 
Abstract:
				 In many clinical trials and
 epidemiological studies, comparing the mean count response of an exposed
 group to a control group is often of interest. This type of data is often
 over-dispersed with respect to Poisson variation, and previous studies
 usually compared groups using confidence intervals (CIs) of the difference
 between the two means. However, in some situations, especially when the
 means are small, interval estimation of the mean ratio (MR) is preferable.
 Moreover, Cox and Lewis [4] pointed out many other situations where the MR
 is more relevant than the difference of means. In this paper, we consider
 CI construction for the ratio of means between two treatments for
 over-dispersed Poisson data. We develop several CIs for the situation by
 hybridizing two separate CIs for two individual means. Extensive
 simulations show that all hybrid-based CIs perform reasonably well in
 terms of coverage. However, the CIs based on the delta method using the
 logarithmic transformation perform better than other intervals in the
 sense that they have slightly shorter interval lengths and show better
 balance of tail errors. These proposed CIs are illustrated with three real
 data examples. 		      
Journal: Journal of Applied Statistics 
Pages: 439-453 
Issue: 2 
Volume: 41 
Year: 2014 
Month: 2 
X-DOI: 10.1080/02664763.2013.840273 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.840273 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:2:p:439-453




Template-Type: ReDIF-Article 1.0
Author-Name: Bulent Tutmez 
Author-X-Name-First: Bulent 
Author-X-Name-Last: Tutmez 
Title: Analyzing non-stationarity in cement stone pit by median polish interpolation: a case study 
Abstract:
				 The raw materials utilized in the
 manufacture of cement comprise mainly of lime, silica, alumina and iron
 oxide. Spatial evaluation of these main chemical constituents of cement
 has crucial importance for providing effective production. Because these
 components are composed of some raw materials such as limestone and marl,
 the spatial relationships in a calcareous marl stone pit was taken into
 consideration. In practice, spatial field data taken from a cement quarry
 may include some variations and trends. For modeling and removing spatial
 trend in a cement raw material quarry as well as providing unbiased
 estimates, median polish kriging was used. By using the variation of the
 data itself, some approximations and interpolations were carried out. It
 was recorded that the method obtained outlier-resistant estimation of
 spatial trend without needing an external exploratory variable. In
 addition, it provided very effective estimations and additional
 information for analyzing spatial non-stationary data. 		 
Journal: Journal of Applied Statistics 
Pages: 454-466 
Issue: 2 
Volume: 41 
Year: 2014 
Month: 2 
X-DOI: 10.1080/02664763.2013.840274 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.840274 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:2:p:454-466




Template-Type: ReDIF-Article 1.0
Author-Name: Guo-Liang Tian 
Author-X-Name-First: Guo-Liang 
Author-X-Name-Last: Tian 
Author-Name: Mingqiu Wang 
Author-X-Name-First: Mingqiu 
Author-X-Name-Last: Wang 
Author-Name: Lixin Song 
Author-X-Name-First: Lixin 
Author-X-Name-Last: Song 
Title: Variable selection in the high-dimensional continuous generalized linear model with current status data 
Abstract:
				 In survival studies, current status data
 are frequently encountered when some individuals in a study are not
 successively observed. This paper considers the problem of simultaneous
 variable selection and parameter estimation in the high-dimensional
 continuous generalized linear model with current status data. We apply the
 penalized likelihood procedure with the smoothly clipped absolute
 deviation penalty to select significant variables and estimate the
 corresponding regression coefficients. With a proper choice of tuning
 parameters, the resulting estimator is shown to be a root
 <italic>n</italic>/<italic>p</italic>				       
 <sub>						 <italic>n</italic>	   
			      </sub>-consistent estimator under some mild
 conditions. In addition, we show that the resulting estimator has the same
 asymptotic distribution as the estimator obtained when the true model is
 known. The finite sample behavior of the proposed estimator is evaluated
 through simulation studies and a real example. 			
Journal: Journal of Applied Statistics 
Pages: 467-483 
Issue: 3 
Volume: 41 
Year: 2014 
Month: 3 
X-DOI: 10.1080/02664763.2013.840271 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.840271 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:3:p:467-483




Template-Type: ReDIF-Article 1.0
Author-Name: Alok Kumar Dwivedi 
Author-X-Name-First: Alok Kumar 
Author-X-Name-Last: Dwivedi 
Author-Name: Indika Mallawaarachchi 
Author-X-Name-First: Indika 
Author-X-Name-Last: Mallawaarachchi 
Author-Name: Soyoung Lee 
Author-X-Name-First: Soyoung 
Author-X-Name-Last: Lee 
Author-Name: Patrick Tarwater 
Author-X-Name-First: Patrick 
Author-X-Name-Last: Tarwater 
Title: Methods for estimating relative risk in studies of common binary outcomes 
Abstract:
				 Studying the effect of exposure or
 intervention on a dichotomous outcome is very common in medical research.
 Logistic regression (LR) is often used to determine such association which
 provides odds ratio (OR). OR often overestimates the effect size for
 prevalent outcome data. In such situations, use of relative risk (RR) has
 been suggested. We propose modifications in Zhang and Yu and Diaz-Quijano
 methods. These methods were compared with stratified Mantel Haenszel
 method, LR, log binomial regression (LBR), Zhang and Yu method,
 Poisson/Cox regression, modified Poisson/Cox regression, marginal
 probability method, COPY method, inverse probability of treatment weighted
 LBR, and Diaz-Quijano method. Our proposed modified Diaz-Quijano (MDQ)
 method provides RR and its confidence interval similar to those estimated
 by modified Poisson/Cox and LBRs. The proposed modifications in Zhang and
 Yu method provides better estimate of RR and its standard error as
 compared to Zhang and Yu method in a variety of situations with prevalent
 outcome. The MDQ method can be used easily to estimate the RR and its
 confidence interval in the studies which require reporting of RRs.
 Regression models which directly provide the estimate of RR without
 convergence problems such as the MDQ method and modified Poisson/Cox
 regression should be preferred.		    
Journal: Journal of Applied Statistics 
Pages: 484-500 
Issue: 3 
Volume: 41 
Year: 2014 
Month: 3 
X-DOI: 10.1080/02664763.2013.840772 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.840772 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:3:p:484-500




Template-Type: ReDIF-Article 1.0
Author-Name: P. Niloofar 
Author-X-Name-First: P. 
Author-X-Name-Last: Niloofar 
Author-Name: M. Ganjali 
Author-X-Name-First: M. 
Author-X-Name-Last: Ganjali 
Title: A new multivariate imputation method based on Bayesian networks 
Abstract:
				 Dealing with incomplete data is a
 pervasive problem in statistical surveys. Bayesian networks have been
 recently used in missing data imputation. In this research, we propose a
 new methodology for the multivariate imputation of missing data using
 discrete Bayesian networks and conditional Gaussian Bayesian networks.
 Results from imputing missing values in coronary artery disease data set
 and milk composition data set as well as a simulation study from
 cancer-neapolitan network are presented to demonstrate and compare the
 performance of three Bayesian network-based imputation methods with those
 of multivariate imputation by chained equations (MICE) and the classical
 hot-deck imputation method. To assess the effect of the structure learning
 algorithm on the performance of the Bayesian network-based methods, two
 methods called Peter-Clark algorithm and greedy search-and-score have been
 applied. Bayesian network-based methods are: first, the method introduced
 by Di Zio <italic>et al.</italic> [<italic>Bayesian networks for
 imputation</italic>, J. R. Stat. Soc. Ser. A 167 (2004), 309--322] in
 which, each missing item of a variable is imputed using the information
 given in the parents of that variable; second, the method of Di Zio
 <italic>et al.</italic> [<italic>Multivariate techniques for imputation
 based on Bayesian networks</italic>, Neural Netw. World 15 (2005),
 303--310] which uses the information in the Markov blanket set of the
 variable to be imputed and finally, our new proposed method which applies
 the whole available knowledge of all variables of interest, consisting the
 Markov blanket and so the parent set, to impute a missing item. Results
 indicate the high quality of our new proposed method especially in the
 presence of high missingness percentages and more connected networks. Also
 the new method have shown to be more efficient than the MICE method for
 small sample sizes with high missing rates.			    
Journal: Journal of Applied Statistics 
Pages: 501-518 
Issue: 3 
Volume: 41 
Year: 2014 
Month: 3 
X-DOI: 10.1080/02664763.2013.842960 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.842960 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:3:p:501-518




Template-Type: ReDIF-Article 1.0
Author-Name: &#xc7;i&#x11f;dem Ar&#x131;c&#x131;gil &#xc7;ilan 
Author-X-Name-First: &#xc7;i&#x11f;dem 
Author-X-Name-Last: Ar&#x131;c&#x131;gil &#xc7;ilan 
Title: Latent class analysis for measuring Turkish People's future expectations for Turkey 
Abstract:
				 The aim of this study is to classify the
 Turkish People and measure the probability of their positive or negative
 expectations according to their 5-year expectations on Turkish Economy,
 Social Rights and Freedom, Rendering of the Public Services, Government
 Transparency and Turkey's Reputation. For this purpose latest data from
 the Turkish Statistical Institute's Life Satisfaction Survey 2011 was used
 and latent class analysis (LCA) was utilized on this data. For this study,
 unrestricted and restricted models of LCAs were performed, and it is
 observed that the three-class unrestricted model was found to be the best
 fit. Latent Class probabilities were interpreted and each class was named
 based on the calculated conditional probabilities.			
Journal: Journal of Applied Statistics 
Pages: 519-529 
Issue: 3 
Volume: 41 
Year: 2014 
Month: 3 
X-DOI: 10.1080/02664763.2013.842961 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.842961 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:3:p:519-529




Template-Type: ReDIF-Article 1.0
Author-Name: Wenlin Dai 
Author-X-Name-First: Wenlin 
Author-X-Name-Last: Dai 
Author-Name: Tiejun Tong 
Author-X-Name-First: Tiejun 
Author-X-Name-Last: Tong 
Title: Variance estimation in nonparametric regression with jump discontinuities 
Abstract:
				 Variance estimation is an important topic
 in nonparametric regression. In this paper, we propose a pairwise
 regression method for estimating the residual variance. Specifically, we
 regress the squared difference between observations on the squared
 distance between design points, and then estimate the residual variance as
 the intercept. Unlike most existing difference-based estimators that
 require a smooth regression function, our method applies to regression
 models with jump discontinuities. Our method also applies to the
 situations where the design points are unequally spaced. Finally, we
 conduct extensive simulation studies to evaluate the finite-sample
 performance of the proposed method and compare it with some existing
 competitors.			    
Journal: Journal of Applied Statistics 
Pages: 530-545 
Issue: 3 
Volume: 41 
Year: 2014 
Month: 3 
X-DOI: 10.1080/02664763.2013.842962 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.842962 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:3:p:530-545




Template-Type: ReDIF-Article 1.0
Author-Name: Wali Ullah 
Author-X-Name-First: Wali 
Author-X-Name-Last: Ullah 
Author-Name: Yasumasa Matsuda 
Author-X-Name-First: Yasumasa 
Author-X-Name-Last: Matsuda 
Author-Name: Yoshihiko Tsukuda 
Author-X-Name-First: Yoshihiko 
Author-X-Name-Last: Tsukuda 
Title: Dynamics of the term structure of interest rates and monetary policy: is monetary policy effective during zero interest rate policy? 
Abstract:
				 The monetary policy targets the short
 rates; however, during zero interest rate policy (ZIRP), the short end of
 the yield curve cannot serve as a policy instrument. Relying on the joint
 yields-macro latent factors model, this study empirically examines the
 effect of monetary policy stances on term structure and the possible
 feedback effect on the real sector using the Japanese experience of ZIRP.
 The analysis indicates that it is the entire term structure that transmits
 the policy shocks to the real economy rather than the yield spread only.
 The monetary policy signals pass through the yield curve level and slope
 factors to stimulate the economic activity. The curvature factor, besides
 reflecting the cyclical fluctuations of the economy, acts as a leading
 indicator for future inflation. In addition, policy influence tends to be
 low as the short end becomes segmented toward medium/long-term of the
 yield curve. Furthermore, volatility in bond markets is found to be
 asymmetrically affected by positive and negative shocks and long end tends
 to be less sensitive to stochastic shocks than the short maturities. The
 expectation hypothesis of the term structure does not hold during the ZIRP
 period.			
Journal: Journal of Applied Statistics 
Pages: 546-572 
Issue: 3 
Volume: 41 
Year: 2014 
Month: 3 
X-DOI: 10.1080/02664763.2013.845142 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.845142 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:3:p:546-572




Template-Type: ReDIF-Article 1.0
Author-Name: Insuk Sohn 
Author-X-Name-First: Insuk 
Author-X-Name-Last: Sohn 
Author-Name: Jooyong Shim 
Author-X-Name-First: Jooyong 
Author-X-Name-Last: Shim 
Author-Name: Changha Hwang 
Author-X-Name-First: Changha 
Author-X-Name-Last: Hwang 
Author-Name: Sujong Kim 
Author-X-Name-First: Sujong 
Author-X-Name-Last: Kim 
Author-Name: Jae Won Lee 
Author-X-Name-First: Jae Won 
Author-X-Name-Last: Lee 
Title: Transcription factor-binding site identification and gene classification via fusion of the supervised-weighted discrete kernel clustering and support vector machine 
Abstract:
				 The genetic regulatory mechanism heavily
 influences a substantial portion of biological functions and processes
 needed to sustain life. For a comprehensive mechanistic understanding of
 biological processes, it is important to identify the common transcription
 factor (TF) binding sites (TFBSs) from a set of promoter sequences of
 co-regulated genes and classify genes that are co-regulated by certain
 TFs, therefore to provide an insight into the mechanism that underlies the
 interaction among the co-regulated genes and complicate genetic
 regulation. We propose a new supervised-weighted discrete kernel
 clustering (SWDKC) classification method for the identification of TFBS
 and the classification of gene. Our SWDKC method gave smaller
 misclassification error rate than the other methods on both the simulated
 data and the real NF-&kappa;B data. We verify that the selected
 over-represented TFBSs serve informative TFBSs from a biological point of
 view.			  
Journal: Journal of Applied Statistics 
Pages: 573-581 
Issue: 3 
Volume: 41 
Year: 2014 
Month: 3 
X-DOI: 10.1080/02664763.2013.845143 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.845143 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:3:p:573-581




Template-Type: ReDIF-Article 1.0
Author-Name: A.B. Schmiedt 
Author-X-Name-First: A.B. 
Author-X-Name-Last: Schmiedt 
Author-Name: H.H. Dickert 
Author-X-Name-First: H.H. 
Author-X-Name-Last: Dickert 
Author-Name: W. Bleck 
Author-X-Name-First: W. 
Author-X-Name-Last: Bleck 
Author-Name: U. Kamps 
Author-X-Name-First: U. 
Author-X-Name-Last: Kamps 
Title: Multivariate extreme value analysis and its relevance in a metallographical application 
Abstract:
				 Motivated from extreme value (EV) analysis
 for large non-metallic inclusions in engineering steels and a real data
 set, the benefit of choosing a multivariate EV approach is discussed. An
 extensive simulation study shows that the common univariate setup may lead
 to a high proportion of mis-specifications of the true EV distribution, as
 well as that the statistical analysis is considerably improved when being
 based on the respective data of <italic>r</italic> largest observations,
 with <italic>r</italic> appropriately chosen. Results for several
 underlying distributions and various values of <italic>r</italic> are
 presented along with effects on estimators for the parameters of the
 generalized EV family of distributions.		       
Journal: Journal of Applied Statistics 
Pages: 582-595 
Issue: 3 
Volume: 41 
Year: 2014 
Month: 3 
X-DOI: 10.1080/02664763.2013.845872 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.845872 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:3:p:582-595




Template-Type: ReDIF-Article 1.0
Author-Name: Giancarlo Diana 
Author-X-Name-First: Giancarlo 
Author-X-Name-Last: Diana 
Author-Name: Saba Riaz 
Author-X-Name-First: Saba 
Author-X-Name-Last: Riaz 
Author-Name: Javid Shabbir 
Author-X-Name-First: Javid 
Author-X-Name-Last: Shabbir 
Title: Hansen and Hurwitz estimator with scrambled response on the second call 
Abstract:
				 In this paper we propose a modified
 version of the estimator of Hansen and Hurwitz [12] in the case of
 quantitative sensitive variable and consider a randomization mechanism on
 the second call that provides privacy protection to the respondents to get
 truthful information. We use variance of the modified estimator as a tool
 to measure privacy protection and it is observed that the higher is the
 variance, the lower is the efficiency but the higher is the privacy
 protection. To overcome this efficiency loss, we consider a linear
 regression estimator using known non-sensitive auxiliary information. With
 consideration of four scrambled models, we try to make a trade-off between
 efficiency and privacy protection. To show this compromise, analytical and
 numerical comparisons are obtained.			   
Journal: Journal of Applied Statistics 
Pages: 596-611 
Issue: 3 
Volume: 41 
Year: 2014 
Month: 3 
X-DOI: 10.1080/02664763.2013.846305 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.846305 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:3:p:596-611




Template-Type: ReDIF-Article 1.0
Author-Name: Mahmoud Torabi 
Author-X-Name-First: Mahmoud 
Author-X-Name-Last: Torabi 
Title: Hierarchical Bayesian bivariate disease mapping: analysis of children and adults asthma visits to hospital 
Abstract:
				 In spatial epidemiology, detecting areas
 with high ratio of disease is important as it may lead to identifying risk
 factors associated with disease. This in turn may lead to further
 epidemiological investigations into the nature of disease. Disease mapping
 studies have been widely performed with considering only one disease in
 the estimated models. Simultaneous modelling of different diseases can
 also be a valuable tool both from the epidemiological and also from the
 statistical point of view. In particular, when we have several
 measurements recorded at each spatial location, one can consider
 multivariate models in order to handle the dependence among the
 multivariate components and the spatial dependence between locations. In
 this paper, spatial models that use multivariate conditionally
 autoregressive smoothing across the spatial dimension are considered. We
 study the patterns of incidence ratios and identify areas with
 consistently high ratio estimates as areas for further investigation. A
 hierarchical Bayesian approach using Markov chain Monte Carlo techniques
 is employed to simultaneously examine spatial trends of asthma visits by
 children and adults to hospital in the province of Manitoba, Canada,
 during 2000--2010.			  
Journal: Journal of Applied Statistics 
Pages: 612-621 
Issue: 3 
Volume: 41 
Year: 2014 
Month: 3 
X-DOI: 10.1080/02664763.2013.847066 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.847066 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:3:p:612-621




Template-Type: ReDIF-Article 1.0
Author-Name: Francisco Louzada 
Author-X-Name-First: Francisco 
Author-X-Name-Last: Louzada 
Author-Name: M�rio de Castro 
Author-X-Name-First: M�rio 
Author-X-Name-Last: de Castro 
Author-Name: Vera Tomazella 
Author-X-Name-First: Vera 
Author-X-Name-Last: Tomazella 
Author-Name: Jhon F.B. Gonzales 
Author-X-Name-First: Jhon F.B. 
Author-X-Name-Last: Gonzales 
Title: Modeling categorical covariates for lifetime data in the presence of cure fraction by Bayesian partition structures 
Abstract:
				 In this paper, we propose a Bayesian
 partition modeling for lifetime data in the presence of a cure fraction by
 considering a local structure generated by a tessellation which depends on
 covariates. In this modeling we include information of nominal qualitative
 variables with more than two categories or ordinal qualitative variables.
 The proposed modeling is based on a promotion time cure model structure
 but assuming that the number of competing causes follows a geometric
 distribution. It is an alternative modeling strategy to the conventional
 survival regression modeling generally used for modeling lifetime data in
 the presence of a cure fraction, which models the cure fraction through a
 (generalized) linear model of the covariates. An advantage of our approach
 is its ability to capture the effects of covariates in a local structure.
 The flexibility of having a local structure is crucial to capture local
 effects and features of the data. The modeling is illustrated on two real
 melanoma data sets.			
Journal: Journal of Applied Statistics 
Pages: 622-634 
Issue: 3 
Volume: 41 
Year: 2014 
Month: 3 
X-DOI: 10.1080/02664763.2013.847067 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.847067 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:3:p:622-634




Template-Type: ReDIF-Article 1.0
Author-Name: Najeh Cha&#xe2;bane 
Author-X-Name-First: Najeh 
Author-X-Name-Last: Cha&#xe2;bane 
Title: A novel auto-regressive fractionally integrated moving average--least-squares support vector machine model for electricity spot prices prediction 
Abstract:
				 In the framework of competitive
 electricity market, prices forecasting has become a real challenge for all
 market participants. However, forecasting is a rather complex task since
 electricity prices involve many features comparably with those in
 financial markets. Electricity markets are more unpredictable than other
 commodities referred to as extreme volatile. Therefore, the choice of the
 forecasting model has become even more important. In this paper, a new
 hybrid model is proposed. This model exploits the feature and strength of
 the auto-regressive fractionally integrated moving average model as well
 as least-squares support vector machine model. The expected prediction
 combination takes advantage of each model's strength or unique capability.
 The proposed model is examined by using data from the Nordpool electricity
 market. Empirical results showed that the proposed method has the best
 prediction accuracy compared to other methods. 		  
Journal: Journal of Applied Statistics 
Pages: 635-651 
Issue: 3 
Volume: 41 
Year: 2014 
Month: 3 
X-DOI: 10.1080/02664763.2013.847068 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.847068 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:3:p:635-651




Template-Type: ReDIF-Article 1.0
Author-Name: Edgard Nyssen 
Author-X-Name-First: Edgard 
Author-X-Name-Last: Nyssen 
Author-Name: Wolfgang Jacquet 
Author-X-Name-First: Wolfgang 
Author-X-Name-Last: Jacquet 
Title: A statistical testing framework for evaluating the quality of measurement processes 
Abstract:
				 In this paper we address the evaluation of
 measurement process quality. We mainly focus on the evaluation procedure,
 as far as it is based on the numerical outcomes for the measurement of a
 single physical quantity. We challenge the approach where the 'exact'
 value of the observed quantity is compared with the error interval
 obtained from the measurements under test and we propose a procedure where
 reference measurements are used as 'gold standard'. To this purpose, we
 designed a specific <italic>t</italic>-test procedure, explained here. We
 also describe and discuss a numerical simulation experiment demonstrating
 the behaviour of our procedure.		   
Journal: Journal of Applied Statistics 
Pages: 652-659 
Issue: 3 
Volume: 41 
Year: 2014 
Month: 3 
X-DOI: 10.1080/02664763.2013.847069 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.847069 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:3:p:652-659




Template-Type: ReDIF-Article 1.0
Author-Name: Yuzhu Tian 
Author-X-Name-First: Yuzhu 
Author-X-Name-Last: Tian 
Author-Name: Qianqian Zhu 
Author-X-Name-First: Qianqian 
Author-X-Name-Last: Zhu 
Author-Name: Maozai Tian 
Author-X-Name-First: Maozai 
Author-X-Name-Last: Tian 
Title: Inference for mixed generalized exponential distribution under progressively type-II censored samples 
Abstract:
				 In industrial life tests, reliability
 analysis and clinical trials, the type-II progressive censoring
 methodology, which allows for random removals of the remaining survival
 units at each failure time, has become quite popular for analyzing
 lifetime data. Parameter estimation under progressively type-II censored
 samples for many common lifetime distributions has been investigated
 extensively. However, how to estimate unknown parameters of the mixed
 distribution models under progressive type-II censoring schemes is still a
 challenging and interesting problem. Based on progressively type-II
 censored samples, this paper addresses the estimation problem of mixed
 generalized exponential distributions. In addition, it is observed that
 the maximum-likelihood estimates (MLEs) cannot be easily obtained in
 closed form due to the complexity of the likelihood function. Thus, we
 make good use of the expectation-maximization algorithm to obtain the
 MLEs. Finally, some simulations are implemented in order to show the
 performance of the proposed method under finite samples and a case
 analysis is illustrated.		    
Journal: Journal of Applied Statistics 
Pages: 660-676 
Issue: 3 
Volume: 41 
Year: 2014 
Month: 3 
X-DOI: 10.1080/02664763.2013.847070 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.847070 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:3:p:660-676




Template-Type: ReDIF-Article 1.0
Author-Name: Edilberto Cepeda-Cuervo 
Author-X-Name-First: Edilberto 
Author-X-Name-Last: Cepeda-Cuervo 
Author-Name: Jorge Alberto Achcar 
Author-X-Name-First: Jorge Alberto 
Author-X-Name-Last: Achcar 
Author-Name: Liliana Garrido Lopera 
Author-X-Name-First: Liliana Garrido 
Author-X-Name-Last: Lopera 
Title: Bivariate beta regression models: joint modeling of the mean, dispersion and association parameters 
Abstract:
				 In this paper a bivariate beta regression
 model with joint modeling of the mean and dispersion parameters is
 proposed, defining the bivariate beta distribution from
 Farlie--Gumbel--Morgenstern (FGM) copulas. This model, that can be
 generalized using other copulas, is a good alternative to analyze
 non-independent pairs of proportions and can be fitted applying standard
 Markov chain Monte Carlo methods. Results of two applications of the
 proposed model in the analysis of structural and real data set are
 included.		      
Journal: Journal of Applied Statistics 
Pages: 677-687 
Issue: 3 
Volume: 41 
Year: 2014 
Month: 3 
X-DOI: 10.1080/02664763.2013.847071 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.847071 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:3:p:677-687




Template-Type: ReDIF-Article 1.0
Author-Name: Yi Zhang 
Author-X-Name-First: Yi 
Author-X-Name-Last: Zhang 
Author-Name: Haitao Chu 
Author-X-Name-First: Haitao 
Author-X-Name-Last: Chu 
Author-Name: Donglin Zeng 
Author-X-Name-First: Donglin 
Author-X-Name-Last: Zeng 
Title: Evaluation of incomplete multiple diagnostic tests, with an application in the colon cancer family registry study 
Abstract:
				 Accurate diagnosis of a molecularly
 defined subtype of cancer is often an important step toward its effective
 control and treatment. For the diagnosis of some subtypes of a cancer, a
 gold standard with perfect sensitivity and specificity may be unavailable.
 In those scenarios, tumor subtype status is commonly measured by multiple
 imperfect diagnostic markers. Additionally, in many such studies, some
 subjects are only measured by a subset of diagnostic tests and the missing
 probabilities may depend on the unknown disease status. In this paper, we
 present statistical methods based on the EM algorithm to evaluate
 incomplete multiple imperfect diagnostic tests under a missing at random
 assumption and one missing not at random scenario. We apply the proposed
 methods to a real data set from the National Cancer Institute (NCI) colon
 cancer family registry on diagnosing microsatellite instability for
 hereditary non-polyposis colorectal cancer to estimate diagnostic accuracy
 parameters (i.e. sensitivities and specificities), prevalence, and
 potential differential missing probabilities for 11 biomarker tests.
 Simulations are also conducted to evaluate the small-sample performance of
 our methods.			 
Journal: Journal of Applied Statistics 
Pages: 688-700 
Issue: 3 
Volume: 41 
Year: 2014 
Month: 3 
X-DOI: 10.1080/02664763.2013.849231 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.849231 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:3:p:688-700




Template-Type: ReDIF-Article 1.0
Author-Name: Andrew Cron 
Author-X-Name-First: Andrew 
Author-X-Name-Last: Cron 
Author-Name: Liang Zhang 
Author-X-Name-First: Liang 
Author-X-Name-Last: Zhang 
Author-Name: Deepak Agarwal 
Author-X-Name-First: Deepak 
Author-X-Name-Last: Agarwal 
Title: Collaborative filtering for massive multinomial data 
Abstract:
				 Content recommendation on a webpage
 involves recommending content links (items) on multiple slots for each
 user visit to maximize some objective function, typically the
 click-through rate (CTR) which is the probability of clicking on an item
 for a given user visit. Most existing approaches to this problem assume
 user's response (click/no click) on different slots are independent of
 each other. This is problematic since in many scenarios CTR on a slot may
 depend on externalities like items recommended on other slots.
 Incorporating the effects of such externalities in the modeling process is
 important to better predictive accuracy. We therefore propose a
 hierarchical model that assumes a multinomial response for each visit to
 incorporate competition among slots and models complex interactions among
 (user, item, slot) combinations through factor models via a tensor
 approach. In addition, factors in our model are drawn with means that are
 based on regression functions of user/item covariates, which helps us
 obtain better estimates for users/items that are relatively new with
 little past activity. We show marked gains in predictive accuracy by
 various metrics.		    
Journal: Journal of Applied Statistics 
Pages: 701-715 
Issue: 4 
Volume: 41 
Year: 2014 
Month: 4 
X-DOI: 10.1080/02664763.2013.847072 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.847072 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:4:p:701-715




Template-Type: ReDIF-Article 1.0
Author-Name: Jean-Paul Lucas 
Author-X-Name-First: Jean-Paul 
Author-X-Name-Last: Lucas 
Author-Name: V�ronique S�bille 
Author-X-Name-First: V�ronique 
Author-X-Name-Last: S�bille 
Author-Name: Alain Le Tertre 
Author-X-Name-First: Alain 
Author-X-Name-Last: Le Tertre 
Author-Name: Yann Le Strat 
Author-X-Name-First: Yann 
Author-X-Name-Last: Le Strat 
Author-Name: Lise Bellanger 
Author-X-Name-First: Lise 
Author-X-Name-Last: Bellanger 
Title: Multilevel modelling of survey data: impact of the two-level weights used in the pseudolikelihood 
Abstract:
				 Approaches that use the pseudolikelihood
 to perform multilevel modelling on survey data have been presented in the
 literature. To avoid biased estimates due to unequal selection
 probabilities, conditional weights can be introduced at each level.
 Less-biased estimators can also be obtained in a two-level linear model if
 the level-1 weights are scaled. In this paper, we studied several level-2
 weights that can be introduced into the pseudolikelihood when the sampling
 design and the hierarchical structure of the multilevel model do not
 match. Two-level and three-level models were studied. The present work was
 motivated by a study that aims to estimate the contributions of lead
 sources to polluting the interior floor dust of the rooms within
 dwellings. We performed a simulation study using the real data collected
 from a French survey to achieve our objective. We conclude that it is
 preferable to use unweighted analyses or, at the most, to use conditional
 level-2 weights in a two-level or a three-level model. We state some
 warnings and make some recommendations.		       
Journal: Journal of Applied Statistics 
Pages: 716-732 
Issue: 4 
Volume: 41 
Year: 2014 
Month: 4 
X-DOI: 10.1080/02664763.2013.847404 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.847404 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:4:p:716-732




Template-Type: ReDIF-Article 1.0
Author-Name: Ahmed N Albatineh 
Author-X-Name-First: Ahmed N 
Author-X-Name-Last: Albatineh 
Author-Name: B.M. Golam Kibria 
Author-X-Name-First: B.M. Golam 
Author-X-Name-Last: Kibria 
Author-Name: Meredith L Wilcox 
Author-X-Name-First: Meredith L 
Author-X-Name-Last: Wilcox 
Author-Name: Bashar Zogheib 
Author-X-Name-First: Bashar 
Author-X-Name-Last: Zogheib 
Title: Confidence interval estimation for the population coefficient of variation using ranked set sampling: a simulation study 
Abstract:
				 In this paper, an evaluation of the
 performance of several confidence interval estimators of the population
 coefficient of variation (&tau;) using ranked set sampling compared to
 simple random sampling is performed. Two performance measures are used to
 assess the confidence intervals for &tau;, namely: width and coverage
 probabilities. Simulated data were generated from normal, log-normal, skew
 normal, Gamma, and Weibull distributions with specified population
 parameters so that the same values of &tau; are obtained for each
 distribution, with sample sizes <italic>n</italic>=15, 20, 25, 50, 100. A
 real data example representing birth weight of 189 newborns is used for
 illustration and performance comparison.		    
Journal: Journal of Applied Statistics 
Pages: 733-751 
Issue: 4 
Volume: 41 
Year: 2014 
Month: 4 
X-DOI: 10.1080/02664763.2013.847405 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.847405 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:4:p:733-751




Template-Type: ReDIF-Article 1.0
Author-Name: Essam A. Ahmed 
Author-X-Name-First: Essam A. 
Author-X-Name-Last: Ahmed 
Title: Bayesian estimation based on progressive Type-II censoring from two-parameter bathtub-shaped lifetime model: an Markov chain Monte Carlo approach 
Abstract:
				 In this paper, maximum likelihood and
 Bayes estimators of the parameters, reliability and hazard functions have
 been obtained for two-parameter bathtub-shaped lifetime distribution when
 sample is available from progressive Type-II censoring scheme. The Markov
 chain Monte Carlo (MCMC) method is used to compute the Bayes estimates of
 the model parameters. It has been assumed that the parameters have gamma
 priors and they are independently distributed. Gibbs within the
 Metropolis--Hasting algorithm has been applied to generate MCMC samples
 from the posterior density function. Based on the generated samples, the
 Bayes estimates and highest posterior density credible intervals of the
 unknown parameters as well as reliability and hazard functions have been
 computed. The results of Bayes estimators are obtained under both the
 balanced-squared error loss and balanced linear-exponential (BLINEX) loss.
 Moreover, based on the asymptotic normality of the maximum likelihood
 estimators the approximate confidence intervals (CIs) are obtained. In
 order to construct the asymptotic CI of the reliability and hazard
 functions, we need to find the variance of them, which are approximated by
 delta and Bootstrap methods. Two real data sets have been analyzed to
 demonstrate how the proposed methods can be used in practice.		    
Journal: Journal of Applied Statistics 
Pages: 752-768 
Issue: 4 
Volume: 41 
Year: 2014 
Month: 4 
X-DOI: 10.1080/02664763.2013.847907 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.847907 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:4:p:752-768




Template-Type: ReDIF-Article 1.0
Author-Name: Lingsong Zhang 
Author-X-Name-First: Lingsong 
Author-X-Name-Last: Zhang 
Author-Name: Zhengyuan Zhu 
Author-X-Name-First: Zhengyuan 
Author-X-Name-Last: Zhu 
Author-Name: J. S. Marron 
Author-X-Name-First: J. S. 
Author-X-Name-Last: Marron 
Title: Multiresolution anomaly detection method for fractional Gaussian noise 
Abstract:
				 Driven by network intrusion detection, we
 propose a MultiResolution Anomaly Detection (MRAD) method, which
 effectively utilizes the multiscale properties of Internet features and
 network anomalies. In this paper, several theoretical properties of the
 MRAD method are explored. A major new result is the mathematical
 formulation of the notion that a two-scaled MRAD method has larger power
 than the average power of the detection method based on the given two
 scales. Test threshold is also developed. Comparisons between MRAD method
 and other classical outlier detectors in time series are reported as well. 
Journal: Journal of Applied Statistics 
Pages: 769-784 
Issue: 4 
Volume: 41 
Year: 2014 
Month: 4 
X-DOI: 10.1080/02664763.2013.850065 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.850065 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:4:p:769-784




Template-Type: ReDIF-Article 1.0
Author-Name: Show-Lin Chen 
Author-X-Name-First: Show-Lin 
Author-X-Name-Last: Chen 
Author-Name: Nen-Jing Chen 
Author-X-Name-First: Nen-Jing 
Author-X-Name-Last: Chen 
Author-Name: Rwei-Ju Chuang 
Author-X-Name-First: Rwei-Ju 
Author-X-Name-Last: Chuang 
Title: An empirical study on technical analysis: GARCH (1, 1) model 
Abstract:
				 One of the deficits of the common
 Bollinger band is that it fails to consider the fat tails/leptokurtosis
 often exists in financial time series. An adjusted Bollinger band
 generated by rolling GARCH regression method is proposed in this study.
 The performance of the adjusted Bollinger band strategy on EUR, GBP, JPY,
 and AUD vs. USD foreign exchange trading is evaluated. Results show that
 in general, the adjusted Bollinger band performs better than the
 traditional one in terms of success ratios, net successes, and profit. In
 addition, no matter there is transaction cost or not, only adjusted
 Bollinger strategies are recommended for investors. Adjusted Bollinger
 band strategies with MA 5 or 10 are recommended for EUR, GBP, and JPY.
 Adjusted Bollinger strategy with MA 20 is the recommended strategies for
 AUD.			    
Journal: Journal of Applied Statistics 
Pages: 785-801 
Issue: 4 
Volume: 41 
Year: 2014 
Month: 4 
X-DOI: 10.1080/02664763.2013.856383 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.856383 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:4:p:785-801




Template-Type: ReDIF-Article 1.0
Author-Name: Hisham Hilow 
Author-X-Name-First: Hisham 
Author-X-Name-Last: Hilow 
Title: Minimum cost linear trend-free 12-run fractional factorial designs 
Abstract:
				 Time trend resistant fractional factorial
 experiments have often been based on regular fractionated designs where
 several algorithms exist for sequencing their runs in minimum number of
 factor-level changes (i.e. minimum cost) such that main effects and/or
 two-factor interactions are orthogonal to and free from aliasing with the
 time trend, which may be present in the sequentially generated responses.
 On the other hand, only one algorithm exists for sequencing runs of the
 more economical non-regular fractional factorial experiments, namely
 Angelopoulos <italic>et al.</italic> [1]. This research studies sequential
 factorial experimentation under non-regular fractionated designs and
 constructs a catalog of 8 minimum cost linear trend-free 12-run designs
 (of resolution III) in 4 up to 11 two-level factors by applying the
 interactions-main effects assignment technique of Cheng and Jacroux [3] on
 the standard 12-run Plackett--Burman design, where factor-level changes
 between runs are minimal and where main effects are orthogonal to the
 linear time trend. These eight 12-run designs are non-orthogonal but are
 more economical than the linear trend-free designs of Angelopoulos
 <italic>et al.</italic> [1], where they can accommodate larger number of
 two-level factors in smaller number of experimental runs. These
 non-regular designs are also more economical than many regular trend-free
 designs. The following will be provided for each proposed systematic
 design:				      <list list-type="simple">    
					   <list-item>			   
				(1) The run order in minimum number of
 factor-level changes.						 
 </list-item>						 <list-item>	   
					      (2) The total number of
 factor-level changes between the 12 runs (i.e. the cost).		   
			      </list-item>				   
	   <list-item>							  
 (3) The closed-form least-squares contrast estimates for all main effects
 as well as their closed-form variance--covariance structure.		   
			    </list-item>				   
 </list>				 In addition, combined designs of
 each of these 8 designs that can be generated by either complete or
 partial foldover allow for the estimation of two-factor interactions
 involving one of the factors (i.e. the most influential).		    
Journal: Journal of Applied Statistics 
Pages: 802-816 
Issue: 4 
Volume: 41 
Year: 2014 
Month: 4  
X-DOI: 10.1080/02664763.2013.856384 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.856384 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:4:p:802-816




Template-Type: ReDIF-Article 1.0
Author-Name: Keya Rani Das 
Author-X-Name-First: Keya Rani 
Author-X-Name-Last: Das 
Author-Name: A.H.M. Rahmatullah Imon 
Author-X-Name-First: A.H.M. Rahmatullah 
Author-X-Name-Last: Imon 
Title: Geometric median and its application in the identification of multiple outliers 
Abstract:
				 Geometric mean (GM) is having growing and
 wider applications in statistical data analysis as a measure of central
 tendency. It is generally believed that GM is less sensitive to outliers
 than the arithmetic mean (AM) but we suspect likewise the AM the GM may
 also suffer a huge set back in the presence of outliers, especially when
 multiple outliers occur in a data. So far as we know, not much work has
 been done on the robustness issue of GM. In quest of a simple robust
 measure of central tendency, we propose the geometric median (GMed) in
 this paper. We show that the classical GM has only 0% breakdown point
 while it is 50% for the proposed GMed. Numerical examples also support our
 claim that the proposed GMed is unaffected in the presence of multiple
 outliers and can maintain the highest possible 50% breakdown. Later we
 develop a new method for the identification of multiple outliers based on
 this proposed GMed. A variety of numerical examples show that the proposed
 method can successfully identify all potential outliers while the
 traditional GM fails to do so. 		   
Journal: Journal of Applied Statistics 
Pages: 817-831 
Issue: 4 
Volume: 41 
Year: 2014 
Month: 4 
X-DOI: 10.1080/02664763.2013.856385 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.856385 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:4:p:817-831




Template-Type: ReDIF-Article 1.0
Author-Name: Sajid Ali 
Author-X-Name-First: Sajid 
Author-X-Name-Last: Ali 
Author-Name: Muhammad Riaz 
Author-X-Name-First: Muhammad 
Author-X-Name-Last: Riaz 
Title: On the generalized process capability under simple and mixture models 
Abstract:
				 Process capability (PC) indices measure
 the ability of a process of interest to meet the desired specifications
 under certain restrictions. There are a variety of capability indices
 available in literature for different interest variables such as weights,
 lengths, thickness, and the life time of items among many others. The goal
 of this article is to study the generalized capability indices from the
 Bayesian view point under different symmetric and asymmetric loss
 functions for the simple and mixture of generalized lifetime models. For
 our study purposes, we have covered a simple and two component mixture of
 Maxwell distribution as a special case of the generalized class of models.
 A comparative discussion of the PC with the mixture models under Laplace
 and inverse Rayleigh are also included. Bayesian point estimation of
 maintenance performance of the system is also part of the study
 (considering the Maxwell failure lifetime model and the repair time
 model). A real-life example is also included to illustrate the procedural
 details of the proposed method.		  
Journal: Journal of Applied Statistics 
Pages: 832-852 
Issue: 4 
Volume: 41 
Year: 2014 
Month: 4 
X-DOI: 10.1080/02664763.2013.856386 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.856386 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:4:p:832-852




Template-Type: ReDIF-Article 1.0
Author-Name: Sharmishtha Mitra 
Author-X-Name-First: Sharmishtha 
Author-X-Name-Last: Mitra 
Author-Name: Amit Mitra 
Author-X-Name-First: Amit 
Author-X-Name-Last: Mitra 
Title: M-estimator-based robust estimation of the number of components of a superimposed sinusoidal signal model 
Abstract:
				 In this paper, we consider the problem of
 estimating the number of components of a superimposed nonlinear sinusoids
 model of a signal in the presence of additive noise. We propose and
 provide a detailed empirical comparison of robust methods for estimation
 of the number of components. The proposed methods, which are robust
 modifications of the commonly used information theoretic criteria, are
 based on various M-estimator approaches and are robust with respect to
 outliers present in the data and heavy-tailed noise. The proposed methods
 are compared with the usual non-robust methods through extensive
 simulations under varied model scenarios. We also present real signal
 analysis of two speech signals to show the usefulness of the proposed
 methodology.		       
Journal: Journal of Applied Statistics 
Pages: 853-878 
Issue: 4 
Volume: 41 
Year: 2014 
Month: 4 
X-DOI: 10.1080/02664763.2013.856387 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.856387 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:4:p:853-878




Template-Type: ReDIF-Article 1.0
Author-Name: Ping Zeng 
Author-X-Name-First: Ping 
Author-X-Name-Last: Zeng 
Author-Name: Yongyue Wei 
Author-X-Name-First: Yongyue 
Author-X-Name-Last: Wei 
Author-Name: Yang Zhao 
Author-X-Name-First: Yang 
Author-X-Name-Last: Zhao 
Author-Name: Jin Liu 
Author-X-Name-First: Jin 
Author-X-Name-Last: Liu 
Author-Name: Liya Liu 
Author-X-Name-First: Liya 
Author-X-Name-Last: Liu 
Author-Name: Ruyang Zhang 
Author-X-Name-First: Ruyang 
Author-X-Name-Last: Zhang 
Author-Name: Jianwei Gou 
Author-X-Name-First: Jianwei 
Author-X-Name-Last: Gou 
Author-Name: Shuiping Huang 
Author-X-Name-First: Shuiping 
Author-X-Name-Last: Huang 
Author-Name: Feng Chen 
Author-X-Name-First: Feng 
Author-X-Name-Last: Chen 
Title: Variable selection approach for zero-inflated count data via adaptive lasso 
Abstract:
				 This article proposes a variable selection
 approach for zero-inflated count data analysis based on the adaptive lasso
 technique. Two models including the zero-inflated Poisson and the
 zero-inflated negative binomial are investigated. An efficient algorithm
 is used to minimize the penalized log-likelihood function in an
 approximate manner. Both the generalized cross-validation and Bayesian
 information criterion procedures are employed to determine the optimal
 tuning parameter, and a consistent sandwich formula of standard errors for
 nonzero estimates is given based on local quadratic approximation. We
 evaluate the performance of the proposed adaptive lasso approach through
 extensive simulation studies, and apply it to analyze real-life data about
 doctor visits. 		     
Journal: Journal of Applied Statistics 
Pages: 879-894 
Issue: 4 
Volume: 41 
Year: 2014 
Month: 4 
X-DOI: 10.1080/02664763.2013.858672 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.858672 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:4:p:879-894




Template-Type: ReDIF-Article 1.0
Author-Name:  Ellinor Fackle-Fornius 
Author-X-Name-First:  Ellinor 
Author-X-Name-Last: Fackle-Fornius 
Author-Name: Linda Anna W�nstr�m 
Author-X-Name-First: Linda Anna 
Author-X-Name-Last: W�nstr�m 
Title: Minimax D-optimal designs of contingent valuation experiments: willingness to pay for environmentally friendly clothes 
Abstract:
				 This paper demonstrates how to plan a
 contingent valuation experiment to assess the value of ecologically
 produced clothes. First, an appropriate statistical model (the trinomial
 spike model) that describes the probability that a randomly selected
 individual will accept any positive bid, and if so, will accept the bid A,
 is defined. Secondly, an optimization criterion that is a function of the
 variances of the parameter estimators is chosen. However, the variances of
 the parameter estimators in this model depend on the true parameter
 values. Pilot study data are therefore used to obtain estimates of the
 parameter values and a locally optimal design is found. Because this
 design is only optimal given that the estimated parameter values are
 correct, a design that minimizes the maximum of the criterion function
 over a plausable parameter region (i.e. a minimax design) is then found.   
Journal: Journal of Applied Statistics 
Pages: 895-908 
Issue: 4 
Volume: 41 
Year: 2014 
Month: 4 
X-DOI: 10.1080/02664763.2013.858670 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.858670 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:4:p:895-908




Template-Type: ReDIF-Article 1.0
Author-Name: Jonathan Gillard 
Author-X-Name-First: Jonathan 
Author-X-Name-Last: Gillard 
Title: The R book, second edition 
Journal: Journal of Applied Statistics 
Pages: 909-909 
Issue: 4 
Volume: 41 
Year: 2014 
Month: 4 
X-DOI: 10.1080/02664763.2013.853909 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.853909 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:4:p:909-909




Template-Type: ReDIF-Article 1.0
Author-Name: Isaac Dialsingh 
Author-X-Name-First: Isaac 
Author-X-Name-Last: Dialsingh 
Title: Risk assessment and decision analysis with Bayesian networks 
Journal: Journal of Applied Statistics 
Pages: 910-910 
Issue: 4 
Volume: 41 
Year: 2014 
Month: 4 
X-DOI: 10.1080/02664763.2013.853911 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.853911 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:4:p:910-910




Template-Type: ReDIF-Article 1.0
Author-Name: Isaac Dialsingh 
Author-X-Name-First: Isaac 
Author-X-Name-Last: Dialsingh 
Title: Computational statistics, second edition 
Journal: Journal of Applied Statistics 
Pages: 910-911 
Issue: 4 
Volume: 41 
Year: 2014 
Month: 4 
X-DOI: 10.1080/02664763.2013.853912 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.853912 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:4:p:910-911




Template-Type: ReDIF-Article 1.0
Author-Name: Mariano Ruiz Espejo 
Author-X-Name-First: Mariano 
Author-X-Name-Last: Ruiz Espejo 
Title: Medical biostatistics, third edition 
Journal: Journal of Applied Statistics 
Pages: 911-911 
Issue: 4 
Volume: 41 
Year: 2014 
Month: 4 
X-DOI: 10.1080/02664763.2013.853918 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.853918 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:4:p:911-911




Template-Type: ReDIF-Article 1.0
Author-Name: Anshuman Sahu 
Author-X-Name-First: Anshuman 
Author-X-Name-Last: Sahu 
Title: Statistical methods in customer relationship management 
Journal: Journal of Applied Statistics 
Pages: 912-912 
Issue: 4 
Volume: 41 
Year: 2014 
Month: 4 
X-DOI: 10.1080/02664763.2013.853920 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.853920 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:4:p:912-912




Template-Type: ReDIF-Article 1.0
Author-Name: A.M. Mosammam 
Author-X-Name-First: A.M. 
Author-X-Name-Last: Mosammam 
Title: Spatio-temporal design: advances in efficient data acquisition 
Journal: Journal of Applied Statistics 
Pages: 912-913 
Issue: 4 
Volume: 41 
Year: 2014 
Month: 4 
X-DOI: 10.1080/02664763.2013.853924 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.853924 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:4:p:912-913




Template-Type: ReDIF-Article 1.0
Author-Name: Hassan S. Bakouch 
Author-X-Name-First: Hassan S. 
Author-X-Name-Last: Bakouch 
Title: Using the Weibull distribution: Reliability, modeling and inference 
Journal: Journal of Applied Statistics 
Pages: 913-914 
Issue: 4 
Volume: 41 
Year: 2014 
Month: 4 
X-DOI: 10.1080/02664763.2013.853927 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.853927 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:4:p:913-914




Template-Type: ReDIF-Article 1.0
Author-Name: Bo&#x17e;idar V. Popovi&cacute; 
Author-X-Name-First: Bo&#x17e;idar V. 
Author-X-Name-Last: Popovi&cacute; 
Title: A course on statistics for finance 
Journal: Journal of Applied Statistics 
Pages: 914-915 
Issue: 4 
Volume: 41 
Year: 2014 
Month: 4 
X-DOI: 10.1080/02664763.2013.853931 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.853931 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:4:p:914-915




Template-Type: ReDIF-Article 1.0
Author-Name: Isaac Dialsingh 
Author-X-Name-First: Isaac 
Author-X-Name-Last: Dialsingh 
Title: Applied categorical and count data analysis 
Journal: Journal of Applied Statistics 
Pages: 915-915 
Issue: 4 
Volume: 41 
Year: 2014 
Month: 4 
X-DOI: 10.1080/02664763.2013.853934 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.853934 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:4:p:915-915




Template-Type: ReDIF-Article 1.0
Author-Name: Cl�udia Neves 
Author-X-Name-First: Cl�udia 
Author-X-Name-Last: Neves 
Title: Categorical data analysis, third edition 
Journal: Journal of Applied Statistics 
Pages: 915-916 
Issue: 4 
Volume: 41 
Year: 2014 
Month: 4 
X-DOI: 10.1080/02664763.2013.854979 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.854979 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:4:p:915-916




Template-Type: ReDIF-Article 1.0
Author-Name: Mikhail Moklyachuk 
Author-X-Name-First: Mikhail 
Author-X-Name-Last: Moklyachuk 
Title: Exercises in probability, second edition 
Journal: Journal of Applied Statistics 
Pages: 916-917 
Issue: 4 
Volume: 41 
Year: 2014 
Month: 4 
X-DOI: 10.1080/02664763.2013.854981 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.854981 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:4:p:916-917




Template-Type: ReDIF-Article 1.0
Author-Name: Shang-Ling Ou 
Author-X-Name-First: Shang-Ling 
Author-X-Name-Last: Ou 
Author-Name: Li-yu Daisy Liu 
Author-X-Name-First: Li-yu Daisy 
Author-X-Name-Last: Liu 
Author-Name: Yih-Chang Ou 
Author-X-Name-First: Yih-Chang 
Author-X-Name-Last: Ou 
Title: Using a genetic algorithm-based RAROC model for the performance and persistence of the funds 
Abstract:
				 Assisting fund investors in making better
 investment decisions when faced with market climate change is an important
 subject. For this purpose, we adopt a genetic algorithm (GA) to search for
 an optimal decay factor for an exponential weighted moving average model,
 which is used to calculate the value at risk combined with risk-adjusted
 return on capital (RAROC). We then propose a GA-based RAROC model. Next,
 using the model we find the optimal decay factor and investigate the
 performance and persistence of 31 Taiwanese open-end equity mutual funds
 over the period from November 2006 to October 2009, divided into three
 periods: November 2006--October 2007, November 2007--October 2008, and
 November 2008--October 2009, which includes the global financial crisis.
 We find that for three periods, the optimal decay factors are 0.999,
 0.951, and 0.990, respectively. The rankings of funds between bull and
 bear markets are quite different. Moreover, the proposed model improves
 performance persistence. That is, a fund's past performance will continue
 into the future.			  
Journal: Journal of Applied Statistics 
Pages: 929-943 
Issue: 5 
Volume: 41 
Year: 2014 
Month: 5 
X-DOI: 10.1080/02664763.2013.856870 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.856870 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:5:p:929-943




Template-Type: ReDIF-Article 1.0
Author-Name: K. Fa&#x10d;evicov� 
Author-X-Name-First: K. 
Author-X-Name-Last: Fa&#x10d;evicov� 
Author-Name: K. Hron 
Author-X-Name-First: K. 
Author-X-Name-Last: Hron 
Author-Name: V. Todorov 
Author-X-Name-First: V. 
Author-X-Name-Last: Todorov 
Author-Name: D. Guo 
Author-X-Name-First: D. 
Author-X-Name-Last: Guo 
Author-Name: M. Templ 
Author-X-Name-First: M. 
Author-X-Name-Last: Templ 
Title: Logratio approach to statistical analysis of 2&times;2 compositional tables 
Abstract:
				 Compositional tables represent a
 continuous counterpart to well-known contingency tables. Their cells
 contain quantitatively expressed relative contributions of a whole,
 carrying exclusively relative information and are popularly represented in
 proportions or percentages. The resulting factors, corresponding to rows
 and columns of the table, can be inspected similarly as with contingency
 tables, e.g. for their mutual independent behaviour. The nature of
 compositional tables requires a specific geometrical treatment,
 represented by the Aitchison geometry on the simplex. The properties of
 the Aitchison geometry allow a decomposition of the original table into
 its independent and interactive parts. Moreover, the specific case of
 2&times;2 compositional tables allows the construction of easily
 interpretable orthonormal coordinates (resulting from the isometric
 logratio transformation) for the original table and its decompositions.
 Consequently, for a sample of compositional tables both explorative
 statistical analysis like graphical inspection of the independent and
 interactive parts or any statistical inference (odds-ratio-like testing of
 independence) can be performed. Theoretical advancements of the presented
 approach are demonstrated using two economic applications.		    
Journal: Journal of Applied Statistics 
Pages: 944-958 
Issue: 5 
Volume: 41 
Year: 2014 
Month: 5 
X-DOI: 10.1080/02664763.2013.856871 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.856871 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:5:p:944-958




Template-Type: ReDIF-Article 1.0
Author-Name: Lee Fawcett 
Author-X-Name-First: Lee 
Author-X-Name-Last: Fawcett 
Author-Name: David Walshaw 
Author-X-Name-First: David 
Author-X-Name-Last: Walshaw 
Title: Estimating the probability of simultaneous rainfall extremes within a region: a spatial approach 
Abstract:
				 In this paper we investigate the impact of
 model mis-specification, in terms of the dependence structure in the
 extremes of a spatial process, on the estimation of key quantities that
 are of interest to hydrologists and engineers. For example, it is often
 the case that severe flooding occurs as a result of the observation of
 rainfall extremes at several locations in a region simultaneously. Thus,
 practitioners might be interested in estimates of the joint exceedance
 probability of some high levels across these locations. It is likely that
 there will be spatial dependence present between the extremes, and this
 should be properly accounted for when estimating such probabilities. We
 compare the use of standard models from the geostatistics literature with
 max-stables models from extreme value theory. We find that, in some
 situations, using an incorrect spatial model for our extremes results in a
 significant under-estimation of these probabilities which -- in flood
 defence terms -- could lead to substantial under-protection.		    
Journal: Journal of Applied Statistics 
Pages: 959-976 
Issue: 5 
Volume: 41 
Year: 2014 
Month: 5 
X-DOI: 10.1080/02664763.2013.856872 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.856872 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:5:p:959-976




Template-Type: ReDIF-Article 1.0
Author-Name: Martin X. Dunbar 
Author-X-Name-First: Martin X. 
Author-X-Name-Last: Dunbar 
Author-Name: Hani M. Samawi 
Author-X-Name-First: Hani M. 
Author-X-Name-Last: Samawi 
Author-Name: Robert Vogel 
Author-X-Name-First: Robert 
Author-X-Name-Last: Vogel 
Author-Name: Lili Yu 
Author-X-Name-First: Lili 
Author-X-Name-Last: Yu 
Title: Steady-state Gibbs sampler estimation for lung cancer data 
Abstract:
				 This paper is based on the application of
 a Bayesian model to a clinical trial study to determine a more effective
 treatment to lower mortality rates and consequently to increase survival
 times among patients with lung cancer. In this study, Qian <italic>et
 al.</italic> [13] strived to determine if a Weibull survival model can be
 used to decide whether to stop a clinical trial. The traditional Gibbs
 sampler was used to estimate the model parameters. This paper proposes to
 use the independent steady-state Gibbs sampling (ISSGS) approach,
 introduced by Dunbar <italic>et al.</italic> [3], to improve the original
 Gibbs sampler in multidimensional problems. It is demonstrated that ISSGS
 provides accuracy with unbiased estimation and improves the performance
 and convergence of the Gibbs sampler in this application.		    
Journal: Journal of Applied Statistics 
Pages: 977-988 
Issue: 5 
Volume: 41 
Year: 2014 
Month: 5 
X-DOI: 10.1080/02664763.2013.858671 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.858671 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:5:p:977-988




Template-Type: ReDIF-Article 1.0
Author-Name: Kun Li 
Author-X-Name-First: Kun 
Author-X-Name-Last: Li 
Author-Name: YongSheng Qian 
Author-X-Name-First: YongSheng 
Author-X-Name-Last: Qian 
Author-Name: WenBo Zhao 
Author-X-Name-First: WenBo 
Author-X-Name-Last: Zhao 
Title: An auxiliary function approach for Lasso in music composition using cellular automata 
Abstract:
				 In this paper, we present an auxiliary
 function approach to solve the overlap group Lasso problem. Our goal is to
 solve a more general structure with overlapping groups, which is suitable
 to be used in cellular automata (CA). The CA were introduced to the
 algorithmic composition which is based on the development and
 classification. At the same time, concrete algorithm and mapping from CA
 to music series are given. Experimental simulations show the effectiveness
 of our algorithms, and using the auxiliary function approach to solve
 Lasso with CA is a potentially useful music automatic-generation
 algorithm.			
Journal: Journal of Applied Statistics 
Pages: 989-997 
Issue: 5 
Volume: 41 
Year: 2014 
Month: 5 
X-DOI: 10.1080/02664763.2013.859233 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.859233 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:5:p:989-997




Template-Type: ReDIF-Article 1.0
Author-Name: M. Revan &Ouml;zkale 
Author-X-Name-First: M. Revan 
Author-X-Name-Last: &Ouml;zkale 
Title: The relative efficiency of the restricted estimators in linear regression models 
Abstract:
				 This paper deals with the problem of
 multicollinearity in a multiple linear regression model with linear
 equality restrictions. The restricted two parameter estimator which was
 proposed in case of multicollinearity satisfies the restrictions. The
 performance of the restricted two parameter estimator over the restricted
 least squares (RLS) estimator and the ordinary least squares (OLS)
 estimator is examined under the mean square error (MSE) matrix criterion
 when the restrictions are correct and not correct. The necessary and
 sufficient conditions for the restricted ridge regression, restricted Liu
 and restricted shrunken estimators, which are the special cases of the
 restricted two parameter estimator, to have a smaller MSE matrix than the
 RLS and the OLS estimators are derived when the restrictions hold true and
 do not hold true. Theoretical results are illustrated with numerical
 examples based on Webster, Gunst and Mason data and Gorman and Toman data.
 We conduct a final demonstration of the performance of the estimators by
 running a Monte Carlo simulation which shows that when the variance of the
 error term and the correlation between the explanatory variables are
 large, the restricted two parameter estimator performs better than the RLS
 estimator and the OLS estimator under the configurations examined.	    
Journal: Journal of Applied Statistics 
Pages: 998-1027 
Issue: 5 
Volume: 41 
Year: 2014 
Month: 5 
X-DOI: 10.1080/02664763.2013.859234 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.859234 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:5:p:998-1027




Template-Type: ReDIF-Article 1.0
Author-Name: Nanhua Zhang 
Author-X-Name-First: Nanhua 
Author-X-Name-Last: Zhang 
Author-Name: Henian Chen 
Author-X-Name-First: Henian 
Author-X-Name-Last: Chen 
Author-Name: Yuanshu Zou 
Author-X-Name-First: Yuanshu 
Author-X-Name-Last: Zou 
Title: A joint model of binary and longitudinal data with non-ignorable missingness, with application to marital stress and late-life major depression in women 
Abstract:
				 Understanding how long-term marital stress
 affects major depressive disorder (MDD) in older women has clinical
 implications for the treatment of women at risk. In this paper, we
 consider the problem of predicting MDD in older women (mean age 60) from a
 marital stress scale administered four times during the preceding 20-year
 period, with a greater dropout by women experiencing marital stress or
 MDD. To analyze these data, we propose a Bayesian joint model consisting
 of: (1) a linear mixed effects model for the longitudinal measurements,
 (2) a generalized linear model for the binary primary endpoint, and (3) a
 shared parameter model for the missing data mechanism. Our analysis
 indicates that MDD in older women is significantly associated with higher
 levels of prior marital stress and increasing marital stress over time,
 although there is a generally decreasing trend in marital stress. This is
 the first study to propose a joint model for incompletely observed
 longitudinal measurements, a binary primary endpoint, and non-ignorable
 missing data; a comparison shows that the joint model yields better
 predictive accuracy than a two-stage model. These findings suggest that
 women who experience marital stress in mid-life need treatment to help
 prevent late-life MDD, which has serious consequences for older persons.   
Journal: Journal of Applied Statistics 
Pages: 1028-1039 
Issue: 5 
Volume: 41 
Year: 2014 
Month: 5 
X-DOI: 10.1080/02664763.2013.859235 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.859235 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:5:p:1028-1039




Template-Type: ReDIF-Article 1.0
Author-Name: Melody S. Goodman 
Author-X-Name-First: Melody S. 
Author-X-Name-Last: Goodman 
Author-Name: Yi Li 
Author-X-Name-First: Yi 
Author-X-Name-Last: Li 
Author-Name: Anne M. Stoddard 
Author-X-Name-First: Anne M. 
Author-X-Name-Last: Stoddard 
Author-Name: Glorian Sorensen 
Author-X-Name-First: Glorian 
Author-X-Name-Last: Sorensen 
Title: Analysis of ordinal outcomes with longitudinal covariates subject to missingness 
Abstract:
				 We propose a mixture model for data with
 an ordinal outcome and a longitudinal covariate that is subject to
 missingness. Data from a tailored telephone delivered, smoking cessation
 intervention for construction laborers are used to illustrate the method,
 which considers as an outcome a categorical measure of smoking cessation,
 and evaluates the effectiveness of the motivational telephone interviews
 on this outcome. We propose two model structures for the longitudinal
 covariate, for the case when the missing data are missing at random, and
 when the missing data mechanism is non-ignorable. A generalized EM
 algorithm is used to obtain maximum likelihood estimates.		    
Journal: Journal of Applied Statistics 
Pages: 1040-1052 
Issue: 5 
Volume: 41 
Year: 2014 
Month: 5 
X-DOI: 10.1080/02664763.2013.859236 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.859236 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:5:p:1040-1052




Template-Type: ReDIF-Article 1.0
Author-Name: M. Templ 
Author-X-Name-First: M. 
Author-X-Name-Last: Templ 
Author-Name: P. Filzmoser 
Author-X-Name-First: P. 
Author-X-Name-Last: Filzmoser 
Title: Simulation and quality of a synthetic close-to-reality employer--employee population 
Abstract:
				 It is of essential importance that
 researchers have access to linked employer--employee data, but such data
 sets are rarely available for researchers or the public. Even in case that
 survey data have been made available, the evaluation of estimation methods
 is usually done by complex design-based simulation studies. For this aim,
 data on population level are needed to know the true parameters that are
 compared with the estimations derived from complex samples. These samples
 are usually drawn from the population under various sampling designs,
 missing values and outlier scenarios. The structural earnings statistics
 sample survey proposes accurate and harmonized data on the level and
 structure of remuneration of employees, their individual characteristics
 and the enterprise or place of employment to which they belong in EU
 member states and candidate countries. At the basis of this data set, we
 show how to simulate a synthetic close-to-reality population representing
 the employer and employee structure of Austria. The proposed simulation is
 based on work of A. Alfons, S. Kraft, M. Templ, and P. Filzmoser [{\em On
 the simulation of complex universes in the case of applying the German
 microcensus}, DACSEIS research paper series No. 4, University of T�bingen,
 2003] and R. M�nnich and J. Sch�rle [{\em Simulation of close-to-reality
 population data for household surveys with application to EU-SILC},
 Statistical Methods & Applications 20(3) (2011c), pp. 383--407]. However,
 new challenges are related to consider the special structure of
 employer--employee data and the complexity induced with the underlying
 two-stage design of the survey. By using quality measures in form of
 simple summary statistics, benchmarking indicators and visualizations, the
 simulated population is analysed and evaluated. An accompanying study on
 literature has been made to select the most important benchmarking
 indicators.			    
Journal: Journal of Applied Statistics 
Pages: 1053-1072 
Issue: 5 
Volume: 41 
Year: 2014 
Month: 5 
X-DOI: 10.1080/02664763.2013.859237 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.859237 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:5:p:1053-1072




Template-Type: ReDIF-Article 1.0
Author-Name: Matthias Borowski 
Author-X-Name-First: Matthias 
Author-X-Name-Last: Borowski 
Author-Name: Nikolaus Rudak 
Author-X-Name-First: Nikolaus 
Author-X-Name-Last: Rudak 
Author-Name: Birger Hussong 
Author-X-Name-First: Birger 
Author-X-Name-Last: Hussong 
Author-Name: Dominik Wied 
Author-X-Name-First: Dominik 
Author-X-Name-Last: Wied 
Author-Name: Sonja Kuhnt 
Author-X-Name-First: Sonja 
Author-X-Name-Last: Kuhnt 
Author-Name: Wolfgang Tillmann 
Author-X-Name-First: Wolfgang 
Author-X-Name-Last: Tillmann 
Title: On- and offline detection of structural breaks in thermal spraying processes 
Abstract:
				 We investigate and develop methods for
 structural break detection, considering time series from thermal spraying
 process monitoring. Since engineers induce technical malfunctions during
 the processes, the time series exhibit structural breaks at known time
 points, giving us valuable information to conduct the investigations.
 First, we consider a recently developed robust <italic>online</italic>
 (also <italic>real-time</italic>) filtering (i.e. smoothing) procedure
 that comprises a test for local linearity. This test rejects when jumps
 and trend changes are present, so that it can also be useful to detect
 such structural breaks online. Second, based on the filtering procedure we
 develop a robust method for the online detection of ongoing trends. We
 investigate these two methods as to the online detection of structural
 breaks by simulations and applications to the time series from the
 manipulated spraying processes. Third, we consider a recently developed
 fluctuation test for constant variances that can be applied
 <italic>offline</italic>, i.e. after the whole time series has been
 observed, to control the spraying results. Since this test is not reliable
 when jumps are present in the time series, we suggest data transformation
 based on filtering and demonstrate that this transformation makes the test
 applicable.			 
Journal: Journal of Applied Statistics 
Pages: 1073-1090 
Issue: 5 
Volume: 41 
Year: 2014 
Month: 5 
X-DOI: 10.1080/02664763.2013.860957 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.860957 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:5:p:1073-1090




Template-Type: ReDIF-Article 1.0
Author-Name: &#x130;lker �nal 
Author-X-Name-First: &#x130;lker 
Author-X-Name-Last: �nal 
Author-Name: H. Refik Burgut 
Author-X-Name-First: H. Refik 
Author-X-Name-Last: Burgut 
Title: Verification bias on sensitivity and specificity measurements in diagnostic medicine: a comparison of some approaches used for correction 
Abstract:
				 Verification bias may occur when the test
 results of not all subjects are verified by using a gold standard. The
 correction for this bias can be made using different approaches depending
 on whether missing gold standard test results are random or not. Some of
 these approaches with binary test and gold standard results include the
 correction method by Begg and Greenes, lower and upper limits for
 diagnostic measurements by Zhou, logistic regression method, multiple
 imputation method, and neural networks. In this study, all these
 approaches are compared by employing a real and simulated data under
 different conditions.			       
Journal: Journal of Applied Statistics 
Pages: 1091-1104 
Issue: 5 
Volume: 41 
Year: 2014 
Month: 5 
X-DOI: 10.1080/02664763.2013.862217 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.862217 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:5:p:1091-1104




Template-Type: ReDIF-Article 1.0
Author-Name: S. Mostafa Mokhtari 
Author-X-Name-First: S. Mostafa 
Author-X-Name-Last: Mokhtari 
Author-Name: Hamid Alinejad-Rokny 
Author-X-Name-First: Hamid 
Author-X-Name-Last: Alinejad-Rokny 
Author-Name: Hossein Jalalifar 
Author-X-Name-First: Hossein 
Author-X-Name-Last: Jalalifar 
Title: Selection of the best well control system by using fuzzy multiple-attribute decision-making methods 
Abstract:
				 There are numerous difficulties involved
 in drilling operations of an oil well, one of the most important of them
 being well control. Well control systems are applied when we have
 irruption of liquids or unwanted intrusion of the reservoir's liquid (oil,
 gas or brine) into the well, during drilling when the pressure of well
 fluid column is less than formation pressure, and the permeability of the
 reservoir has a value that is able to pass the liquid through. For this
 purpose, a variety of methods including Driller, wait and weight, and the
 concurrent methods were used to control the well at different drilling
 sites. In this study, we investigate the optimum method for well control
 using a fussy method based on many parameters, including technical factors
 (mud weight, drilling rate, blockage of pipes, sensitivity to drilling
 network changes, etc.) and security factors (existence of effervescent
 mud, drilling circuit control, etc.), and cost of selection, which is one
 of the most important decisions that are made under critical conditions
 such as irruption. Till now, these methods were selected based on the
 experience of field personnel in drilling sites. The technical criteria
 and standards were influenced by experience, so the soft computerizing
 system (fuzzy method) was used. Thus, both these criteria and standards
 would be of greater importance and indicate whether the optimum numerical
 method is the same one that is expressed by human experience. The
 concurrent method was selected as the best for well control, using the
 fuzzy method at the end of the evaluation, while field personnel
 experience suggests the Driller method.		   
Journal: Journal of Applied Statistics 
Pages: 1105-1121 
Issue: 5 
Volume: 41 
Year: 2014 
Month: 5 
X-DOI: 10.1080/02664763.2013.862218 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.862218 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:5:p:1105-1121




Template-Type: ReDIF-Article 1.0
Author-Name: A. Parchami 
Author-X-Name-First: A. 
Author-X-Name-Last: Parchami 
Author-Name: B. Sadeghpour-Gildeh 
Author-X-Name-First: B. 
Author-X-Name-Last: Sadeghpour-Gildeh 
Author-Name: M. Nourbakhsh 
Author-X-Name-First: M. 
Author-X-Name-Last: Nourbakhsh 
Author-Name: M. Mashinchi 
Author-X-Name-First: M. 
Author-X-Name-Last: Mashinchi 
Title: A new generation of process capability indices based on fuzzy measurements 
Abstract:
				 Process capability indices (PCIs) provide
 numerical measures on whether a process conforms to the defined
 manufacturing capability prerequisite. These have been successfully
 applied by companies to compete with and to lead high-profit markets by
 evaluating the quality and productivity performance. The PCI
 <italic>C</italic>				      <sub>p</sub> compares
 the output of a process to the specification limits (SLs) by forming the
 ratio of the width between the process SLs with the width of the natural
 tolerance limits which is measured by six process standard deviation
 units. As another common PCI, <italic>C</italic>			   
	 <sub>pm</sub> incorporates two variation components which are
 variation to the process mean and deviation of the process mean from the
 target. A meaningful generalized version of above PCIs is introduced in
 this paper which is able to handle in a fuzzy environment. These
 generalized PCIs are able to measure the capability of a fuzzy-valued
 process in producing products on the basis of a fuzzy quality. Fast
 computing formulas for the generalized PCIs are computed for normal and
 symmetric triangular fuzzy observations, where the fuzzy quality is
 defined by linear and exponential fuzzy SLs. A practical example is
 presented to show the performance of proposed indices. 		    
Journal: Journal of Applied Statistics 
Pages: 1122-1136 
Issue: 5 
Volume: 41 
Year: 2014 
Month: 5 
X-DOI: 10.1080/02664763.2013.862219 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.862219 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:5:p:1122-1136




Template-Type: ReDIF-Article 1.0
Author-Name: Feridun Tasdan 
Author-X-Name-First: Feridun 
Author-X-Name-Last: Tasdan 
Author-Name: Meral Cetin 
Author-X-Name-First: Meral 
Author-X-Name-Last: Cetin 
Title: A simulation study on the influence of ties on uniform scores test for circular data 
Abstract:
				 Uniform scores test is a rank-based method
 that tests the homogeneity of <italic>k</italic>-populations in circular
 data problems. The influence of ties on the uniform scores test has been
 emphasized by several authors in several articles and books. Moreover, it
 is suggested that the uniform scores test should be used with caution if
 ties are present in the data. This paper investigates the influence of
 ties on the uniform scores test by computing the power of the test using
 average, randomization, permutation, minimum, and maximum methods to break
 ties. Monte Carlo simulation is performed to compute the power of the test
 under several scenarios such as having 5% or 10% of ties and tie group
 structures in the data. The simulation study shows no significant
 difference among the methods under the existence of ties but the test
 loses its power when there are many ties or complicated group structures.
 Thus, randomization or average methods are equally powerful to break ties
 when applying uniform scores test. Also, it can be concluded that
 <italic>k</italic>-sample uniform scores test can be used safely without
 sacrificing the power if there are only less than 5% of ties or at most
 two groups of a few ties.		     
Journal: Journal of Applied Statistics 
Pages: 1137-1146 
Issue: 5 
Volume: 41 
Year: 2014 
Month: 5 
X-DOI: 10.1080/02664763.2013.862224 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.862224 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:5:p:1137-1146




Template-Type: ReDIF-Article 1.0
Author-Name: Feridun Tasdan 
Author-X-Name-First: Feridun 
Author-X-Name-Last: Tasdan 
Author-Name: Ozgur Yeniay 
Author-X-Name-First: Ozgur 
Author-X-Name-Last: Yeniay 
Title: A shift parameter estimation based on smoothed Kolmogorov--Smirnov 
Abstract:
				 A new procedure of shift parameter
 estimation in the two-sample location problem is investigated and compared
 with existing estimators. The proposed procedure smooths the empirical
 distribution functions of each random sample and replaces empirical
 distribution functions in the two-sample Kolmogorov--Smirnov method. The
 smoothed Kolmogorov--Smirnov is minimized with respect to an arbitrary
 shift variable in order to find an estimate of the shift parameter. The
 proposed procedure can be considered the smoothed version of a very little
 known method of shift parameter estimation from Rao-Schuster-Littell (RSL)
 [Rao <italic>et al.</italic>, <italic>Estimation of shift and center of
 symmetry based on Kolmogorov--Smirnov statistics</italic>, Ann. Stat. 3(4)
 (1975), pp. 862--873]. Their estimator will be discussed and compared with
 the proposed estimator in this paper. An example and simulation studies
 have been performed to compare the proposed procedure with existing shift
 parameter estimators such as Hodges--Lehmann (H--L) and least squares in
 addition to RSL's estimator. The results show that the proposed estimator
 has lower mean-squared error as well as higher relative efficiency against
 RSL's estimator under normal or contaminated normal model assumptions.
 Moreover, the proposed estimator performs competitively against H--L and
 least-squares shift estimators. Smoother function and bandwidth selections
 are also discussed and several alternatives are proposed in the study.     
Journal: Journal of Applied Statistics 
Pages: 1147-1159 
Issue: 5 
Volume: 41 
Year: 2014 
Month: 5 
X-DOI: 10.1080/02664763.2013.862225 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.862225 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:5:p:1147-1159




Template-Type: ReDIF-Article 1.0
Author-Name: Jie Shen 
Author-X-Name-First: Jie 
Author-X-Name-Last: Shen 
Author-Name: Colin M. Gallagher 
Author-X-Name-First: Colin M. 
Author-X-Name-Last: Gallagher 
Author-Name: QiQi Lu 
Author-X-Name-First: QiQi 
Author-X-Name-Last: Lu 
Title: Detection of multiple undocumented change-points using adaptive Lasso 
Abstract:
 The problem of detecting multiple undocumented change-points in a
 historical temperature sequence with simple linear trend is formulated by
 a linear model. We apply adaptive least absolute shrinkage and selection
 operator (Lasso) to estimate the number and locations of change-points.
 Model selection criteria are used to choose the Lasso smoothing parameter.
 As adaptive Lasso may overestimate the number of change-points, we perform
 post-selection on change-points detected by adaptive Lasso using
 multivariate <italic>t</italic> simultaneous confidence intervals. Our
 method is demonstrated on the annual temperature data (year: 1902-2000)
 from Tuscaloosa, Alabama.
Journal: Journal of Applied Statistics 
Pages: 1161-1173 
Issue: 6 
Volume: 41 
Year: 2014 
Month: 6 
X-DOI: 10.1080/02664763.2013.862220 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.862220 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:6:p:1161-1173




Template-Type: ReDIF-Article 1.0
Author-Name: Marek Brabec 
Author-X-Name-First: Marek 
Author-X-Name-Last: Brabec 
Author-Name: Viorel Badescu 
Author-X-Name-First: Viorel 
Author-X-Name-Last: Badescu 
Author-Name: Marius Paulescu 
Author-X-Name-First: Marius 
Author-X-Name-Last: Paulescu 
Title: Cloud shade by dynamic logistic modeling 
Abstract:
 During the daytime, the sun is shining or not at ground level depending on
 clouds motion. Two binary variables may be used to quantify this process:
 the sunshine number (SSN) and the sunshine stability number (SSSN). The
 sequential features of SSN are treated in this paper by using Markovian
 Logistic Regression models, which avoid usual weaknesses of autoregressive
 integrated moving average modeling. The theory is illustrated with results
 obtained by using measurements performed in 2010 at Timisoara (southern
 Europe). Simple modeling taking into account internal dynamics with one
 lag history brings substantial reduction of misclassification compared
 with the persistence approach (to less than 57%). When longer history is
 considered, all the lags up to at least 8 are important. The seasonal
 changes are rather concentrated to low lags. Better performance is
 associated with a more stable radiative regime. More involved models add
 external influences (such as sun elevation angle or astronomic declination
 as well as taking into account morning and afternoon effects separately).
 Models including sun elevation effects are significantly better than those
 ignoring them. Clearly, during the winter months, the effect of
 declination is much more pronounced compared with the rest of the year.
 SSSN is important in long-term considerations and it also plays a role in
 retrospective assessment of the SSN. However, it is not easy to use SSSN
 for predicting future SSN. Using more complicated past beam clearness
 models does not necessarily provide better results than more simple models
 with SSN past.
Journal: Journal of Applied Statistics 
Pages: 1174-1188 
Issue: 6 
Volume: 41 
Year: 2014 
Month: 6 
X-DOI: 10.1080/02664763.2013.862221 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.862221 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:6:p:1174-1188




Template-Type: ReDIF-Article 1.0
Author-Name: Mohammad Reza Meshkani 
Author-X-Name-First: Mohammad Reza 
Author-X-Name-Last: Meshkani 
Author-Name: Afshin Fallah 
Author-X-Name-First: Afshin 
Author-X-Name-Last: Fallah 
Author-Name: Amir Kavousi 
Author-X-Name-First: Amir 
Author-X-Name-Last: Kavousi 
Title: Analysis of covariance under inverse Gaussian model 
Abstract:
 This paper considers the problem of analysis of covariance (ANCOVA) under
 the assumption of inverse Gaussian distribution for response variable. We
 develop the essential methodology for estimating the model parameters via
 maximum likelihood method. The general form of the maximum likelihood
 estimator is obtained in color closed form. Adjusted treatment effects and
 adjusted covariate effects are given, too. We also provide the asymptotic
 distribution of the proposed estimators. A simulation study and a real
 world application are also performed to illustrate and evaluate the
 proposed methodology.
Journal: Journal of Applied Statistics 
Pages: 1189-1202 
Issue: 6 
Volume: 41 
Year: 2014 
Month: 6 
X-DOI: 10.1080/02664763.2013.862222 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.862222 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:6:p:1189-1202




Template-Type: ReDIF-Article 1.0
Author-Name: Ngianga-Bakwin Kandala 
Author-X-Name-First: Ngianga-Bakwin 
Author-X-Name-Last: Kandala 
Author-Name: Samuel O.M. Manda 
Author-X-Name-First: Samuel O.M. 
Author-X-Name-Last: Manda 
Author-Name: William W. Tigbe 
Author-X-Name-First: William W. 
Author-X-Name-Last: Tigbe 
Author-Name: Henry Mwambi 
Author-X-Name-First: Henry 
Author-X-Name-Last: Mwambi 
Author-Name: Saverio Stranges 
Author-X-Name-First: Saverio 
Author-X-Name-Last: Stranges 
Title: Geographic distribution of cardiovascular comorbidities in South Africa: a national cross-sectional analysis 
Abstract:
 <italic>Objectives</italic>: We sought to estimate the spatial coexistence
 of hypertension, coronary heart disease (CHD), stroke and
 hypercholesterolaemia in South Africa. <italic>Design</italic>:
 Cross-sectional. <italic>Setting</italic>: Sub-Saharan Africa and South
 Africa. <italic>Participants</italic>: Data were from 13,827 adults
 (mean&plusmn;SD age 39&plusmn;18 years, 58.4% women) interviewed in the
 1998 South African Health and Demographic Survey.
 <italic>Interventions</italic>: N/A. <italic>Primary and secondary outcome
 measures</italic>: We used multivariate spatial disease models to estimate
 district-level shared and disease-specific spatial risk components,
 controlling for known individual risk factors. <italic>Results</italic>:
 In univariate analysis, observed prevalence of hypertension and CHD is was
 high in the south-western parts, and low in the north east. Stroke and
 high blood cholesterol prevalence appeared to be evenly distributed across
 the country. In multivariate analysis (adjusting for age, gender,
 ethnicity, education, urban-dwelling, smoking, alcohol consumption and
 obesity), hypertension and stroke prevalence were highly concentrated in
 the south-western parts, whilst CHD and hypercholesterolaemia were highly
 prevalent in central and top north-eastern corridor, respectively. The
 shared component, which we took to represent nutrition and other lifestyle
 factors not accounted for in the model, had a larger effect on
 cardiovascular disease prevalence in the south-western areas of the
 country. It appeared to have greater effect on hypertension and CHD.
 <italic>Conclusion</italic>: This study suggests a clear geographic
 distribution of cardiovascular disease in South Africa, driven possibly by
 shared lifestyle behaviours. These findings might be useful for public
 health resource allocation in low-income settings.
Journal: Journal of Applied Statistics 
Pages: 1203-1216 
Issue: 6 
Volume: 41 
Year: 2014 
Month: 6 
X-DOI: 10.1080/02664763.2013.862223 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.862223 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:6:p:1203-1216




Template-Type: ReDIF-Article 1.0
Author-Name: Kadri Ulas Akay 
Author-X-Name-First: Kadri Ulas 
Author-X-Name-Last: Akay 
Title: A graphical evaluation of logistic ridge estimator in mixture experiments 
Abstract:
 In comparison to other experimental studies, multicollinearity appears
 frequently in mixture experiments, a special study area of response
 surface methodology, due to the constraints on the components composing
 the mixture. In the analysis of mixture experiments by using a special
 generalized linear model, logistic regression model, multicollinearity
 causes precision problems in the maximum-likelihood logistic regression
 estimate. Therefore, effects due to multicollinearity can be reduced to a
 certain extent by using alternative approaches. One of these approaches is
 to use biased estimators for the estimation of the coefficients. In this
 paper, we suggest the use of logistic ridge regression (RR) estimator in
 the cases where there is multicollinearity during the analysis of mixture
 experiments using logistic regression. Also, for the selection of the
 biasing parameter, we use fraction of design space plots for evaluating
 the effect of the logistic RR estimator with respect to the scaled mean
 squared error of prediction. The suggested graphical approaches are
 illustrated on the tumor incidence data set.
Journal: Journal of Applied Statistics 
Pages: 1217-1232 
Issue: 6 
Volume: 41 
Year: 2014 
Month: 6 
X-DOI: 10.1080/02664763.2013.864261 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.864261 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:6:p:1217-1232




Template-Type: ReDIF-Article 1.0
Author-Name: D.M. Sakate 
Author-X-Name-First: D.M. 
Author-X-Name-Last: Sakate 
Author-Name: D.N. Kashid 
Author-X-Name-First: D.N. 
Author-X-Name-Last: Kashid 
Title: Variable selection via penalized minimum &phi;-divergence estimation in logistic regression 
Abstract:
 We propose penalized minimum &phi;-divergence estimator for parameter
 estimation and variable selection in logistic regression. Using an
 appropriate penalty function, we show that penalized &phi;-divergence
 estimator has oracle property. With probability tending to 1, penalized
 &phi;-divergence estimator identifies the true model and estimates nonzero
 coefficients as efficiently as if the sparsity of the true model was known
 in advance. The advantage of penalized &phi;-divergence estimator is that
 it produces estimates of nonzero parameters efficiently than penalized
 maximum likelihood estimator when sample size is small and is equivalent
 to it for large one. Numerical simulations confirm our findings.
Journal: Journal of Applied Statistics 
Pages: 1233-1246 
Issue: 6 
Volume: 41 
Year: 2014 
Month: 6 
X-DOI: 10.1080/02664763.2013.864262 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.864262 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:6:p:1233-1246




Template-Type: ReDIF-Article 1.0
Author-Name: Stephanie Sapp 
Author-X-Name-First: Stephanie 
Author-X-Name-Last: Sapp 
Author-Name: Mark J. van der Laan 
Author-X-Name-First: Mark J. 
Author-X-Name-Last: van der Laan 
Author-Name: John Canny 
Author-X-Name-First: John 
Author-X-Name-Last: Canny 
Title: Subsemble: an ensemble method for combining subset-specific algorithm fits 
Abstract:
 Ensemble methods using the same underlying algorithm trained on different
 subsets of observations have recently received increased attention as
 practical prediction tools for massive data sets. We propose Subsemble: a
 general subset ensemble prediction method, which can be used for small,
 moderate, or large data sets. Subsemble partitions the full data set into
 subsets of observations, fits a specified underlying algorithm on each
 subset, and uses a clever form of <italic>V</italic>-fold cross-validation
 to output a prediction function that combines the subset-specific fits. We
 give an oracle result that provides a theoretical performance guarantee
 for Subsemble. Through simulations, we demonstrate that Subsemble can be a
 beneficial tool for small- to moderate-sized data sets, and often has
 better prediction performance than the underlying algorithm fit just once
 on the full data set. We also describe how to include Subsemble as a
 candidate in a SuperLearner library, providing a practical way to evaluate
 the performance of Subsemble relative to the underlying algorithm fit just
 once on the full data set.
Journal: Journal of Applied Statistics 
Pages: 1247-1259 
Issue: 6 
Volume: 41 
Year: 2014 
Month: 6 
X-DOI: 10.1080/02664763.2013.864263 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.864263 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:6:p:1247-1259




Template-Type: ReDIF-Article 1.0
Author-Name: Guoyi Zhang 
Author-X-Name-First: Guoyi 
Author-X-Name-Last: Zhang 
Title: Improved <italic>R</italic> and <italic>s</italic> control charts for monitoring the process variance 
Abstract:
 The Shewhart <italic>R</italic> control chart and <italic>s</italic>
 control chart are widely used to monitor shifts in the process spread. One
 fact is that the distributions of the range and sample standard deviation
 are highly skewed. Therefore, the <italic>R</italic> chart and
 <italic>s</italic> chart neither provide an in-control average run length
 (ARL) of approximately 370 nor guarantee the desired type I error of
 0.0027. Another disadvantage of these two charts is their failure in
 detecting an improvement in the process variability. In order to overcome
 these shortcomings, we propose the improved <italic>R</italic> chart (IRC)
 and <italic>s</italic> chart (ISC) with accurate approximation of the
 control limits by using cumulative distribution functions of the sample
 range and standard deviation. Simulation studies show that the IRC and ISC
 perform very well. We also compare the type II error risks and ARLs of the
 IRC and ISC and found that the <italic>s</italic> chart is generally more
 efficient than the <italic>R</italic> chart. Examples are given to
 illustrate the use of the developed charts.
Journal: Journal of Applied Statistics 
Pages: 1260-1273 
Issue: 6 
Volume: 41 
Year: 2014 
Month: 6 
X-DOI: 10.1080/02664763.2013.864264 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.864264 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:6:p:1260-1273




Template-Type: ReDIF-Article 1.0
Author-Name: Ying Dong 
Author-X-Name-First: Ying 
Author-X-Name-Last: Dong 
Author-Name: Lixin Song 
Author-X-Name-First: Lixin 
Author-X-Name-Last: Song 
Author-Name: Mingqiu Wang 
Author-X-Name-First: Mingqiu 
Author-X-Name-Last: Wang 
Author-Name: Ying Xu 
Author-X-Name-First: Ying 
Author-X-Name-Last: Xu 
Title: Combined-penalized likelihood estimations with a diverging number of parameters 
Abstract:
 In the economics and biological gene expression study area where a large
 number of variables will be involved, even when the predictors are
 independent, as long as the dimension is high, the maximum sample
 correlation can be large. Variable selection is a fundamental method to
 deal with such models. The ridge regression performs well when the
 predictors are highly correlated and some nonconcave penalized
 thresholding estimators enjoy the nice oracle property. In order to
 provide a satisfactory solution to the collinearity problem, in this paper
 we report the combined-penalization (CP) mixed by the nonconcave penalty
 and ridge, with a diverging number of parameters. It is observed that the
 CP estimator with a diverging number of parameters can correctly select
 covariates with nonzero coefficients and can estimate parameters
 simultaneously in the presence of multicollinearity. Simulation studies
 and a real data example demonstrate the well performance of the proposed
 method.
Journal: Journal of Applied Statistics 
Pages: 1274-1285 
Issue: 6 
Volume: 41 
Year: 2014 
Month: 6 
X-DOI: 10.1080/02664763.2013.868415 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.868415 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:6:p:1274-1285




Template-Type: ReDIF-Article 1.0
Author-Name: Rafael Pimentel Maia 
Author-X-Name-First: Rafael Pimentel 
Author-X-Name-Last: Maia 
Author-Name: Per Madsen 
Author-X-Name-First: Per 
Author-X-Name-Last: Madsen 
Author-Name: Rodrigo Labouriau 
Author-X-Name-First: Rodrigo 
Author-X-Name-Last: Labouriau 
Title: Multivariate survival mixed models for genetic analysis of longevity traits 
Abstract:
 A class of multivariate mixed survival models for continuous and discrete
 time with a complex covariance structure is introduced in a context of
 quantitative genetic applications. The methods introduced can be used in
 many applications in quantitative genetics although the discussion
 presented concentrates on longevity studies. The framework presented
 allows to combine models based on continuous time with models based on
 discrete time in a joint analysis. The continuous time models are
 approximations of the frailty model in which the baseline hazard function
 will be assumed to be piece-wise constant. The discrete time models used
 are multivariate variants of the discrete relative risk models. These
 models allow for regular parametric likelihood-based inference by
 exploring a coincidence of their likelihood functions and the likelihood
 functions of suitably defined multivariate generalized linear mixed
 models. The models include a dispersion parameter, which is essential for
 obtaining a decomposition of the variance of the trait of interest as a
 sum of parcels representing the additive genetic effects, environmental
 effects and unspecified sources of variability; as required in
 quantitative genetic applications. The methods presented are implemented
 in such a way that large and complex quantitative genetic data can be
 analyzed. Some key model control techniques are discussed in a
 supplementary online material.
Journal: Journal of Applied Statistics 
Pages: 1286-1306 
Issue: 6 
Volume: 41 
Year: 2014 
Month: 6 
X-DOI: 10.1080/02664763.2013.868416 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.868416 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:6:p:1286-1306




Template-Type: ReDIF-Article 1.0
Author-Name: Peter Dawson 
Author-X-Name-First: Peter 
Author-X-Name-Last: Dawson 
Author-Name: Paul Downward 
Author-X-Name-First: Paul 
Author-X-Name-Last: Downward 
Author-Name: Terence C. Mills 
Author-X-Name-First: Terence C. 
Author-X-Name-Last: Mills 
Title: Olympic news and attitudes towards the Olympics: a compositional time-series analysis of how sentiment is affected by events 
Abstract:
 Sentiment affects the evolving economic valuation of companies through the
 stock market. It is unclear how 'news' affects the sentiment towards major
 public investments like the Olympics. In this paper we consider, from the
 context of the pre-event stage of the 30th Olympiad, the relationship
 between attitudes towards the Olympics and Olympic-related news;
 specifically the bad news associated with an increase in the cost of
 provision, and the good news associated with Team Great Britain's medal
 success in 2008. Using a unique data set and an event-study approach that
 involves compositional time-series analysis, it is found that 'good' news
 affects sentiments much more than 'bad', but that the distribution of such
 sentiment varies widely. For example, a much more pronounced effect of
 good news is identified for females than males, but 'bad' news has less of
 an impact on the young and older age groups.
Journal: Journal of Applied Statistics 
Pages: 1307-1314 
Issue: 6 
Volume: 41 
Year: 2014 
Month: 6 
X-DOI: 10.1080/02664763.2013.868417 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.868417 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:6:p:1307-1314




Template-Type: ReDIF-Article 1.0
Author-Name: A.A.M. Nurunnabi 
Author-X-Name-First: A.A.M. 
Author-X-Name-Last: Nurunnabi 
Author-Name: Ali S. Hadi 
Author-X-Name-First: Ali S. 
Author-X-Name-Last: Hadi 
Author-Name: A.H.M.R. Imon 
Author-X-Name-First: A.H.M.R. 
Author-X-Name-Last: Imon 
Title: Procedures for the identification of multiple influential observations in linear regression 
Abstract:
 Since the seminal paper by Cook (1977) in which he introduced Cook's
 distance, the identification of influential observations has received a
 great deal of interest and extensive investigation in linear regression.
 It is well documented that most of the popular diagnostic measures that
 are based on single-case deletion can mislead the analysis in the presence
 of multiple influential observations because of the well-known masking
 and/or swamping phenomena. Atkinson (1981) proposed a modification of
 Cook's distance. In this paper we propose a further modification of the
 Cook's distance for the identification of a single influential
 observation. We then propose new measures for the identification of
 multiple influential observations, which are not affected by the masking
 and swamping problems. The efficiency of the new statistics is presented
 through several well-known data sets and a simulation study.
Journal: Journal of Applied Statistics 
Pages: 1315-1331 
Issue: 6 
Volume: 41 
Year: 2014 
Month: 6 
X-DOI: 10.1080/02664763.2013.868418 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.868418 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:6:p:1315-1331




Template-Type: ReDIF-Article 1.0
Author-Name: Laurens Beran 
Author-X-Name-First: Laurens 
Author-X-Name-Last: Beran 
Title: Hypothesis tests to determine if all true positives have been identified on a receiver operating characteristic curve 
Abstract:
 For classification problems where the test data are labeled sequentially,
 the point at which all true positives are first identified is often of
 critical importance. This article develops hypothesis tests to assess
 whether all true positives have been labeled in the test data. The tests
 use a partial receiver operating characteristic (ROC) that is generated
 from a labeled subset of the test data. These methods are developed in the
 context of unexploded ordnance (UXO) classification, but are applicable to
 any binary classification problem. First, the likelihood of the observed
 ROC given binormal model parameters is derived using order statistics,
 leading to a nonlinear parameter estimation problem. I then derive the
 approximate distribution of the point on the ROC at which all true
 instances are found. Using estimated binormal parameters, this
 distribution can be integrated up to a desired confidence level to define
 a critical false alarm rate (FAR). If the selected operating point is
 before this critical point, then additional labels out to the critical
 point are required. A second test uses the uncertainty in binormal
 parameters to determine the critical FAR. These tests are demonstrated
 with UXO classification examples and both approaches are recommended for
 testing operating points.
Journal: Journal of Applied Statistics 
Pages: 1332-1341 
Issue: 6 
Volume: 41 
Year: 2014 
Month: 6 
X-DOI: 10.1080/02664763.2013.868598 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.868598 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:6:p:1332-1341




Template-Type: ReDIF-Article 1.0
Author-Name: Xiao-Feng Wang 
Author-X-Name-First: Xiao-Feng 
Author-X-Name-Last: Wang 
Author-Name: Bo Hu 
Author-X-Name-First: Bo 
Author-X-Name-Last: Hu 
Author-Name: Bin Wang 
Author-X-Name-First: Bin 
Author-X-Name-Last: Wang 
Author-Name: Kuangnan Fang 
Author-X-Name-First: Kuangnan 
Author-X-Name-Last: Fang 
Title: Bayesian generalized varying coefficient models for longitudinal proportional data with errors-in-covariates 
Abstract:
 This paper is motivated from a neurophysiological study of muscle fatigue,
 in which biomedical researchers are interested in understanding the
 time-dependent relationships of handgrip force and electromyography
 measures. A varying coefficient model is appealing here to investigate the
 dynamic pattern in the longitudinal data. The response variable in the
 study is continuous but bounded on the standard unit interval (0, 1) over
 time, while the longitudinal covariates are contaminated with measurement
 errors. We propose a generalization of varying coefficient models for the
 longitudinal proportional data with errors-in-covariates. We describe two
 estimation methods with penalized splines, which are formalized under a
 Bayesian inferential perspective. The first method is an adaptation of the
 popular regression calibration approach. The second method is based on a
 joint likelihood under the hierarchical Bayesian model. A simulation study
 is conducted to evaluate the efficacy of the proposed methods under
 different scenarios. The analysis of the neurophysiological data is
 presented to demonstrate the use of the methods.
Journal: Journal of Applied Statistics 
Pages: 1342-1357 
Issue: 6 
Volume: 41 
Year: 2014 
Month: 6 
X-DOI: 10.1080/02664763.2013.868870 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.868870 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:6:p:1342-1357




Template-Type: ReDIF-Article 1.0
Author-Name: Zhengming Xing 
Author-X-Name-First: Zhengming 
Author-X-Name-Last: Xing 
Author-Name: Bradley Nicholson 
Author-X-Name-First: Bradley 
Author-X-Name-Last: Nicholson 
Author-Name: Monica Jimenez 
Author-X-Name-First: Monica 
Author-X-Name-Last: Jimenez 
Author-Name: Timothy Veldman 
Author-X-Name-First: Timothy 
Author-X-Name-Last: Veldman 
Author-Name: Lori Hudson 
Author-X-Name-First: Lori 
Author-X-Name-Last: Hudson 
Author-Name: Joseph Lucas 
Author-X-Name-First: Joseph 
Author-X-Name-Last: Lucas 
Author-Name: David Dunson 
Author-X-Name-First: David 
Author-X-Name-Last: Dunson 
Author-Name: Aimee K. Zaas 
Author-X-Name-First: Aimee K. 
Author-X-Name-Last: Zaas 
Author-Name: Christopher W. Woods 
Author-X-Name-First: Christopher W. 
Author-X-Name-Last: Woods 
Author-Name: Geoffrey S. Ginsburg 
Author-X-Name-First: Geoffrey S. 
Author-X-Name-Last: Ginsburg 
Author-Name: Lawrence Carin 
Author-X-Name-First: Lawrence 
Author-X-Name-Last: Carin 
Title: Bayesian modeling of temporal properties of infectious disease in a college student population 
Abstract:
 A Bayesian statistical model is developed for analysis of the
 time-evolving properties of infectious disease, with a particular focus on
 viruses. The model employs a latent semi-Markovian state process, and the
 state-transition statistics are driven by three terms: (i) a general
 time-evolving trend of the overall population, (ii) a semi-periodic term
 that accounts for effects caused by the days of the week, and (iii) a
 regression term that relates the probability of infection to covariates
 (here, specifically, to the Google Flu Trends data). Computations are
 performed using Markov Chain Monte Carlo sampling. Results are presented
 using a novel data set: daily self-reported symptom scores from hundreds
 of Duke University undergraduate students, collected over three academic
 years. The illnesses associated with these students are (imperfectly)
 labeled using real-time (RT) polymerase chain reaction (PCR) testing for
 several viruses, and gene-expression data were also analyzed. The
 statistical analysis is performed on the daily, self-reported symptom
 scores, and the RT PCR and gene-expression data are employed for analysis
 and interpretation of the model results.
Journal: Journal of Applied Statistics 
Pages: 1358-1382 
Issue: 6 
Volume: 41 
Year: 2014 
Month: 6 
X-DOI: 10.1080/02664763.2013.870138 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.870138 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:6:p:1358-1382




Template-Type: ReDIF-Article 1.0
Author-Name: Feng-Chang Xie 
Author-X-Name-First: Feng-Chang 
Author-X-Name-Last: Xie 
Author-Name: Jin-Guan Lin 
Author-X-Name-First: Jin-Guan 
Author-X-Name-Last: Lin 
Author-Name: Bo-Cheng Wei 
Author-X-Name-First: Bo-Cheng 
Author-X-Name-Last: Wei 
Title: Bayesian zero-inflated generalized Poisson regression model: estimation and case influence diagnostics 
Abstract:
 Count data with excess zeros arises in many contexts. Here our concern is
 to develop a Bayesian analysis for the zero-inflated generalized Poisson
 (ZIGP) regression model to address this problem. This model provides a
 useful generalization of zero-inflated Poisson model since the generalized
 Poisson distribution is overdispersed/underdispersed relative to Poisson.
 Due to the complexity of the ZIGP model, Markov chain Monte Carlo methods
 are used to develop a Bayesian procedure for the considered model.
 Additionally, some discussions on the model selection criteria are
 presented and a Bayesian case deletion influence diagnostics is
 investigated for the joint posterior distribution based on the
 Kullback-Leibler divergence. Finally, a simulation study and a
 psychological example are given to illustrate our methodology.
Journal: Journal of Applied Statistics 
Pages: 1383-1392 
Issue: 6 
Volume: 41 
Year: 2014 
Month: 6  
X-DOI: 10.1080/02664763.2013.871508 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.871508 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:6:p:1383-1392




Template-Type: ReDIF-Article 1.0
Author-Name: Chih-Yueh Wang 
Author-X-Name-First: Chih-Yueh 
Author-X-Name-Last: Wang 
Title: Partial differential equations for probabilists 
Journal: Journal of Applied Statistics 
Pages: 1393-1394 
Issue: 6 
Volume: 41 
Year: 2014 
Month: 6 
X-DOI: 10.1080/02664763.2013.859806 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.859806 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:6:p:1393-1394




Template-Type: ReDIF-Article 1.0
Author-Name: Chris Beeley 
Author-X-Name-First: Chris 
Author-X-Name-Last: Beeley 
Title: Statistics 
Journal: Journal of Applied Statistics 
Pages: 1394-1394 
Issue: 6 
Volume: 41 
Year: 2014 
Month: 6 
X-DOI: 10.1080/02664763.2013.868638 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.868638 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:6:p:1394-1394




Template-Type: ReDIF-Article 1.0
Author-Name: J.F. Mu�oz 
Author-X-Name-First: J.F. 
Author-X-Name-Last: Mu�oz 
Author-Name: E. �lvarez 
Author-X-Name-First: E. 
Author-X-Name-Last: �lvarez 
Author-Name: M. Rueda 
Author-X-Name-First: M. 
Author-X-Name-Last: Rueda 
Title: Optimum design-based ratio estimators of the distribution function 
Abstract:
 The ratio method is commonly used to the estimation of means and totals.
 This method was extended to the problem of estimating the distribution
 function. An alternative ratio estimator of the distribution function is
 defined. A result that compares the variances of the aforementioned ratio
 estimators is used to define optimum design-based ratio estimators of the
 distribution function. Different empirical results indicate that the
 optimum ratio estimators can be more efficient than alternative ratio
 estimators. In addition, we show by simulations that alternative ratio
 estimators can have large biases, whereas biases of the optimum ratio
 estimators are negligible in this situation.
Journal: Journal of Applied Statistics 
Pages: 1395-1407 
Issue: 7 
Volume: 41 
Year: 2014 
Month: 7 
X-DOI: 10.1080/02664763.2013.870983 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.870983 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:7:p:1395-1407




Template-Type: ReDIF-Article 1.0
Author-Name: Bruno Chaves Franco 
Author-X-Name-First: Bruno Chaves 
Author-X-Name-Last: Franco 
Author-Name: Philippe Castagliola 
Author-X-Name-First: Philippe 
Author-X-Name-Last: Castagliola 
Author-Name: Giovanni Celano 
Author-X-Name-First: Giovanni 
Author-X-Name-Last: Celano 
Author-Name: Antonio Fernando Branco Costa 
Author-X-Name-First: Antonio Fernando Branco 
Author-X-Name-Last: Costa 
Title: A new sampling strategy to reduce the effect of autocorrelation on a control chart 
Abstract:
 On-line monitoring of quality characteristics is essential to limit scrap
 and rework costs due to bad quality in a manufacturing process. In several
 manufacturing environments, during production process data can be
 massively collected with high sampling rates and tight sampling
 frequencies. As a consequence, natural autocorrelation may arise among
 consecutive measures <italic>within</italic> a sample. Autocorrelation
 significantly inflates the average run length of a control chart and
 deteriorates its sensitivity to the occurrence of assignable causes. In
 this paper, we propose a new <italic>mixed</italic> sampling strategy for
 the Shewhart <inline-formula><inline-graphic
 xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_871507_ilm0001.gif"/></inline-formula> chart monitoring
 the sample mean in a process where temporal autocorrelation between two
 consecutive observations can be represented by means of a first order
 autoregressive model AR(1). With this strategy, the sample mean at each
 inspection time is computed by merging measures of a generic quality
 characteristic from two consecutive samples taken <italic>h</italic> hours
 apart. The statistical properties of a Shewhart
 <inline-formula><inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_871507_ilm0002.gif"/></inline-formula> control chart
 implementing the proposed strategy are compared to those implementing a
 <italic>skipping</italic> strategy recently proposed in literature. A
 numerical analysis shows that the <italic>mixed</italic> sampling
 outperforms the <italic>skipping</italic> sampling strategy for high
 levels of autocorrelation.
Journal: Journal of Applied Statistics 
Pages: 1408-1421 
Issue: 7 
Volume: 41 
Year: 2014 
Month: 7 
X-DOI: 10.1080/02664763.2013.871507 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.871507 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:7:p:1408-1421




Template-Type: ReDIF-Article 1.0
Author-Name: Tapio Nummi 
Author-X-Name-First: Tapio 
Author-X-Name-Last: Nummi 
Author-Name: Tiina Hakanen 
Author-X-Name-First: Tiina 
Author-X-Name-Last: Hakanen 
Author-Name: Liudmila Lipi�inen 
Author-X-Name-First: Liudmila 
Author-X-Name-Last: Lipi�inen 
Author-Name: Ulla Harjunmaa 
Author-X-Name-First: Ulla 
Author-X-Name-Last: Harjunmaa 
Author-Name: Matti K. Salo 
Author-X-Name-First: Matti K. 
Author-X-Name-Last: Salo 
Author-Name: Marja-Terttu Saha 
Author-X-Name-First: Marja-Terttu 
Author-X-Name-Last: Saha 
Author-Name: Nina Vuorela 
Author-X-Name-First: Nina 
Author-X-Name-Last: Vuorela 
Title: A trajectory analysis of body mass index for Finnish children 
Abstract:
 The aim of this study is to investigate the early development of body mass
 index (BMI), a standard tool for assessing the body shape and average
 level of adiposity for children and adults. The main aim of the study is
 to identify the primary trajectories of BMI development and to investigate
 the changes of certain growth characteristics over time. Based on our
 longitudinal data of 4223 Finnish children, we took anthropometric
 measurements from birth up to 15 years of age for birth years 1974, 1981,
 1991 and 1995, but only up to 11 years of age for the birth year 2001. As
 a statistical method, we utilized trajectory analysis with the methods of
 nonparametric regression. We identified four main trajectories of BMI
 growth. Two of these trajectories do not seem to follow the normal growth
 pattern. The highest growth track appears to yield to a track that may
 yield to overweight and the low birth BMI track shows that the girls'
 track differs that of boys on the same track, and on the normal tracks.
 The so-called adiposity rebound time decreased over time and started
 earlier for those on the overweight track. According to our study, this
 kind of acceleration of growth might be more of a general phenomenon that
 also relates to the other phases of BMI development. The major change
 seems to occur especially for those children on high growth tracks.
Journal: Journal of Applied Statistics 
Pages: 1422-1435 
Issue: 7 
Volume: 41 
Year: 2014 
Month: 7 
X-DOI: 10.1080/02664763.2013.872232 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.872232 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:7:p:1422-1435




Template-Type: ReDIF-Article 1.0
Author-Name: Zhi-Sheng Ye 
Author-X-Name-First: Zhi-Sheng 
Author-X-Name-Last: Ye 
Author-Name: Jian-Guo Li 
Author-X-Name-First: Jian-Guo 
Author-X-Name-Last: Li 
Author-Name: Mengru Zhang 
Author-X-Name-First: Mengru 
Author-X-Name-Last: Zhang 
Title: Application of ridge regression and factor analysis in design and production of alloy wheels 
Abstract:
 This study proposes using statistical approaches to help with both the
 design and manufacture of wheels. The quality of a wheel is represented by
 the mechanical properties of spokes. Variation in the mechanical
 properties of different wheels is attributed to two sources, i.e.
 between-model variation and within-model variation. The between-model
 variation is due to different shapes of different wheel models. To model
 the effect of shapes on the mechanical properties, we first specify eight
 shape variables potentially critical to the mechanical properties, and
 then we collect relevant data on 18-wheel models and perform ridge
 regression to find the effects of these variables on the mechanical
 properties. These results are linked to the solidification theory of the
 A356 alloy. The within-model variation is due to natural variability and
 process abnormality. We extract mechanical data of a particular wheel
 model from the database. Factor analysis is employed to analyze the data
 with a view to identifying the latent factors behind the mechanical
 properties. We then look into the microstructure of the alloy to
 corroborate the fact that these two latent factors are essentially the Si
 phase and the Mg<sub>2</sub>Si phase, respectively. These results can be
 used to efficiently identify the root cause when the manufacturing process
 goes wrong.
Journal: Journal of Applied Statistics 
Pages: 1436-1452 
Issue: 7 
Volume: 41 
Year: 2014 
Month: 7 
X-DOI: 10.1080/02664763.2013.872233 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.872233 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:7:p:1436-1452




Template-Type: ReDIF-Article 1.0
Author-Name: Saeed Heravi 
Author-X-Name-First: Saeed 
Author-X-Name-Last: Heravi 
Author-Name: Peter Morgan 
Author-X-Name-First: Peter 
Author-X-Name-Last: Morgan 
Title: Sampling schemes for price index construction: a performance comparison across the classification of individual consumption by purpose food groups 
Abstract:
 Five sampling schemes (SS) for price index construction - one cut-off
 sampling technique and four probability-proportional-to-size
 (<italic>pps</italic>) methods - are evaluated by comparing their
 performance on a homescan market research data set across 21 months for
 each of the 13 classification of individual consumption by purpose
 (COICOP) food groups. Classifications are derived for each of the food
 groups and the population index value is used as a reference to derive
 performance error measures, such as root mean squared error, bias and
 standard deviation for each food type. Repeated samples are taken for each
 of the <italic>pps</italic> schemes and the resulting performance error
 measures analysed using regression of three of the <italic>pps</italic>
 schemes to assess the overall effect of SS and COICOP group whilst
 controlling for sample size, month and population index value. Cut-off
 sampling appears to perform less well than <italic>pps</italic> methods
 and multistage <italic>pps</italic> seems to have no advantage over its
 single-stage counterpart. The jackknife resampling technique is also
 explored as a means of estimating the standard error of the index and
 compared with the actual results from repeated sampling.
Journal: Journal of Applied Statistics 
Pages: 1453-1470 
Issue: 7 
Volume: 41 
Year: 2014 
Month: 7 
X-DOI: 10.1080/02664763.2014.881466 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.881466 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:7:p:1453-1470




Template-Type: ReDIF-Article 1.0
Author-Name: Wei-Hwa Wu 
Author-X-Name-First: Wei-Hwa 
Author-X-Name-Last: Wu 
Author-Name: Hsin-Neng Hsieh 
Author-X-Name-First: Hsin-Neng 
Author-X-Name-Last: Hsieh 
Title: Generalized confidence interval estimation for the mean of delta-lognormal distribution: an application to New Zealand trawl survey data 
Abstract:
 Highly skewed and non-negative data can often be modeled by the
 delta-lognormal distribution in fisheries research. However, the coverage
 probabilities of extant interval estimation procedures are less
 satisfactory in small sample sizes and highly skewed data. We propose a
 heuristic method of estimating confidence intervals for the mean of the
 delta-lognormal distribution. This heuristic method is an estimation based
 on asymptotic generalized pivotal quantity to construct generalized
 confidence interval for the mean of the delta-lognormal distribution.
 Simulation results show that the proposed interval estimation procedure
 yields satisfactory coverage probabilities, expected interval lengths and
 reasonable relative biases. Finally, the proposed method is employed in
 red cod densities data for a demonstration.
Journal: Journal of Applied Statistics 
Pages: 1471-1485 
Issue: 7 
Volume: 41 
Year: 2014 
Month: 7 
X-DOI: 10.1080/02664763.2014.881780 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.881780 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:7:p:1471-1485




Template-Type: ReDIF-Article 1.0
Author-Name: J.S.K. Chan 
Author-X-Name-First: J.S.K. 
Author-X-Name-Last: Chan 
Author-Name: W.Y. Wan 
Author-X-Name-First: W.Y. 
Author-X-Name-Last: Wan 
Author-Name: P.L.H. Yu 
Author-X-Name-First: P.L.H. 
Author-X-Name-Last: Yu 
Title: A Poisson geometric process approach for predicting drop-out and committed first-time blood donors 
Abstract:
 A Poisson geometric process (PGP) model is proposed to study individual
 blood donation patterns for a blood donor retention program. Extended from
 the geometric process (GP) model of Lam [16], the PGP model captures the
 rather pronounced trend patterns across clusters of donors via the ratio
 parameters in a mixture setting. Within the state-space modeling
 framework, it allows for overdispersion by equating the mean of the
 Poisson data distribution to a latent GP. Alternatively, by simply
 setting, the mean of the Poisson distribution to be the mean of a GP, it
 has equidispersion. With the group-specific mean and ratio functions, the
 mixture PGP model facilitates classification of donors into committed,
 drop-out and one-time groups. Based on only two years of observations, the
 PGP model nicely predicts donors' future donations to foster timely
 recruitment decision. The model is implemented using a Bayesian approach
 via the user-friendly software WinBUGS.
Journal: Journal of Applied Statistics 
Pages: 1486-1503 
Issue: 7 
Volume: 41 
Year: 2014 
Month: 7 
X-DOI: 10.1080/02664763.2014.881781 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.881781 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:7:p:1486-1503




Template-Type: ReDIF-Article 1.0
Author-Name: D. Senthilkumar 
Author-X-Name-First: D. 
Author-X-Name-Last: Senthilkumar 
Author-Name: B. Esha Raffie 
Author-X-Name-First: B. Esha 
Author-X-Name-Last: Raffie 
Title: Designing and selection of two-plan variables scheme indexed by crossover point 
Abstract:
 In this paper, the scheme of the inspection plan, namely the tightened
 normal tightened (<italic>n</italic><sub>T</sub>,
 <italic>n</italic><sub>N</sub>; <italic>k</italic>) is considered and
 procedures and necessary tables are developed for the selection of the
 variables sampling scheme, indexed through crossover point (COP). The
 importance of COP, the properties and advantages of the operating
 characteristic curve with respect to COP are studied.
Journal: Journal of Applied Statistics 
Pages: 1504-1515 
Issue: 7 
Volume: 41 
Year: 2014 
Month: 7 
X-DOI: 10.1080/02664763.2014.881782 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.881782 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:7:p:1504-1515




Template-Type: ReDIF-Article 1.0
Author-Name: Antonio Mart�n Andr�s 
Author-X-Name-First: Antonio Mart�n 
Author-X-Name-Last: Andr�s 
Author-Name: Mar�a �lvarez Hern�ndez 
Author-X-Name-First: Mar�a 
Author-X-Name-Last: �lvarez Hern�ndez 
Title: Two-tailed asymptotic inferences for a proportion 
Abstract:
 This paper evaluates 29 methods for obtaining a two-sided confidence
 interval for a binomial proportion (16 of which are new proposals) and
 comes to the conclusion that: Wilson's classic method is only optimal for
 a confidence of 99%, although generally it can be applied when
 <italic>n</italic>&#x2265;50; for a confidence of 95% or 90%, the optimal
 method is the one based on the arcsine transformation (when this is
 applied to the data incremented by 0.5), which behaves in a very similar
 manner to Jeffreys' Bayesian method. A simpler option, though not so good
 as those just mentioned, is the classic-adjusted Wald method of Agresti
 and Coull.
Journal: Journal of Applied Statistics 
Pages: 1516-1529 
Issue: 7 
Volume: 41 
Year: 2014 
Month: 7 
X-DOI: 10.1080/02664763.2014.881783 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.881783 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:7:p:1516-1529




Template-Type: ReDIF-Article 1.0
Author-Name: Jalmar M.F. Carrasco 
Author-X-Name-First: Jalmar M.F. 
Author-X-Name-Last: Carrasco 
Author-Name: Silvia L.P. Ferrari 
Author-X-Name-First: Silvia L.P. 
Author-X-Name-Last: Ferrari 
Author-Name: Reinaldo B. Arellano-Valle 
Author-X-Name-First: Reinaldo B. 
Author-X-Name-Last: Arellano-Valle 
Title: Errors-in-variables beta regression models 
Abstract:
 Beta regression models provide an adequate approach for modeling
 continuous outcomes limited to the interval (0, 1). This paper deals with
 an extension of beta regression models that allow for explanatory
 variables to be measured with error. The structural approach, in which the
 covariates measured with error are assumed to be random variables, is
 employed. Three estimation methods are presented, namely maximum
 likelihood, maximum pseudo-likelihood and regression calibration. Monte
 Carlo simulations are used to evaluate the performance of the proposed
 estimators and the na�ve estimator. Also, a residual analysis for beta
 regression models with measurement errors is proposed. The results are
 illustrated in a real data set.
Journal: Journal of Applied Statistics 
Pages: 1530-1547 
Issue: 7 
Volume: 41 
Year: 2014 
Month: 7 
X-DOI: 10.1080/02664763.2014.881784 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.881784 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:7:p:1530-1547




Template-Type: ReDIF-Article 1.0
Author-Name: Ramesh C. Gupta 
Author-X-Name-First: Ramesh C. 
Author-X-Name-Last: Gupta 
Author-Name: Jie Huang 
Author-X-Name-First: Jie 
Author-X-Name-Last: Huang 
Title: Analysis of survival data by a Weibull-generalized Poisson distribution 
Abstract:
 In life-testing and survival analysis, sometimes the components are
 arranged in series or parallel system and the number of components is
 initially unknown. Thus, the number of components, say <italic>Z</italic>,
 is considered as random with an appropriate probability mass function. In
 this paper, we model the survival data with baseline distribution as
 Weibull and the distribution of <italic>Z</italic> as generalized Poisson,
 giving rise to four parameters in the model: increasing, decreasing,
 bathtub and upside bathtub failure rates. Two examples are provided and
 the maximum-likelihood estimation of the parameters is studied. Rao's
 score test is developed to compare the results with the exponential
 Poisson model studied by Kus [17] and the exponential-generalized Poisson
 distribution with baseline distribution as exponential and the
 distribution of <italic>Z</italic> as generalized Poisson. Simulation
 studies are carried out to examine the performance of the estimates.
Journal: Journal of Applied Statistics 
Pages: 1548-1564 
Issue: 7 
Volume: 41 
Year: 2014 
Month: 7 
X-DOI: 10.1080/02664763.2014.881785 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.881785 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:7:p:1548-1564




Template-Type: ReDIF-Article 1.0
Author-Name: Yazhao Lv 
Author-X-Name-First: Yazhao 
Author-X-Name-Last: Lv 
Author-Name: Riquan Zhang 
Author-X-Name-First: Riquan 
Author-X-Name-Last: Zhang 
Author-Name: Weihua Zhao 
Author-X-Name-First: Weihua 
Author-X-Name-Last: Zhao 
Author-Name: Jicai Liu 
Author-X-Name-First: Jicai 
Author-X-Name-Last: Liu 
Title: Quantile regression and variable selection for the single-index model 
Abstract:
 In this paper, we propose a new full iteration estimation method for
 quantile regression (QR) of the single-index model (SIM). The asymptotic
 properties of the proposed estimator are derived. Furthermore, we propose
 a variable selection procedure for the QR of SIM by combining the
 estimation method with the adaptive LASSO penalized method to get sparse
 estimation of the index parameter. The oracle properties of the variable
 selection method are established. Simulations with various non-normal
 errors are conducted to demonstrate the finite sample performance of the
 estimation method and the variable selection procedure. Furthermore, we
 illustrate the proposed method by analyzing a real data set.
Journal: Journal of Applied Statistics 
Pages: 1565-1577 
Issue: 7 
Volume: 41 
Year: 2014 
Month: 7 
X-DOI: 10.1080/02664763.2014.881786 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.881786 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:7:p:1565-1577




Template-Type: ReDIF-Article 1.0
Author-Name: Rajat Malik 
Author-X-Name-First: Rajat 
Author-X-Name-Last: Malik 
Author-Name: Rob Deardon 
Author-X-Name-First: Rob 
Author-X-Name-Last: Deardon 
Author-Name: Grace P.S. Kwong 
Author-X-Name-First: Grace P.S. 
Author-X-Name-Last: Kwong 
Author-Name: Benjamin J. Cowling 
Author-X-Name-First: Benjamin J. 
Author-X-Name-Last: Cowling 
Title: Individual-level modeling of the spread of influenza within households 
Abstract:
 A class of individual-level models (ILMs) outlined by R. Deardon
 <italic>et al.</italic>, [<italic>Inference for individual level models of
 infectious diseases in large populations</italic>, Statist. Sin. 20
 (2010), pp. 239-261] can be used to model the spread of infectious
 diseases in discrete time. The key feature of these ILMs is that they take
 into account covariate information on susceptible and infectious
 individuals as well as shared covariate information such as geography or
 contact measures. Here, such ILMs are fitted in a Bayesian framework using
 Markov chain Monte Carlo techniques to data sets from two studies on
 influenza transmission within households in Hong Kong during 2008 to 2009
 and 2009 to 2010. The focus of this paper is to estimate the effect of
 vaccination on infection risk and choose a model that best fits the
 infection data.
Journal: Journal of Applied Statistics 
Pages: 1578-1592 
Issue: 7 
Volume: 41 
Year: 2014 
Month: 7 
X-DOI: 10.1080/02664763.2014.881787 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.881787 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:7:p:1578-1592




Template-Type: ReDIF-Article 1.0
Author-Name: Ufuk Beyaztas 
Author-X-Name-First: Ufuk 
Author-X-Name-Last: Beyaztas 
Author-Name: Aylin Alin 
Author-X-Name-First: Aylin 
Author-X-Name-Last: Alin 
Author-Name: Michael A. Martin 
Author-X-Name-First: Michael A. 
Author-X-Name-Last: Martin 
Title: Robust BCa-JaB method as a diagnostic tool for linear regression models 
Abstract:
 The Jackknife-after-bootstrap (JaB) technique originally developed by
 Efron [8] has been proposed as an approach to improve the detection of
 influential observations in linear regression models by Martin and Roberts
 [12] and Beyaztas and Alin [2]. The method is based on the use of
 percentile-method confidence intervals to provide improved cut-off values
 for several single case-deletion influence measures. In order to improve
 JaB, we propose using robust versions of Efron [7]'s bias-corrected and
 accelerated (BCa) bootstrap confidence intervals. In this study, the
 performances of robust BCa-JaB and conventional JaB methods are compared
 in the cases of DFFITS, Welsch's distance and modified Cook's distance
 influence diagnostics. Comparisons are based on both real data examples
 and through a simulation study. Our results reveal that under a variety of
 scenarios, our proposed method provides more accurate and reliable
 results, and it is more robust to masking effects.
Journal: Journal of Applied Statistics 
Pages: 1593-1610 
Issue: 7 
Volume: 41 
Year: 2014 
Month: 7 
X-DOI: 10.1080/02664763.2014.881788 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.881788 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:7:p:1593-1610




Template-Type: ReDIF-Article 1.0
Author-Name: P. Angelopoulos 
Author-X-Name-First: P. 
Author-X-Name-Last: Angelopoulos 
Author-Name: C. Koukouvinos 
Author-X-Name-First: C. 
Author-X-Name-Last: Koukouvinos 
Author-Name: A. Skountzou 
Author-X-Name-First: A. 
Author-X-Name-Last: Skountzou 
Title: A cusum control chart approach for screening active effects in orthogonal-saturated experiments 
Abstract:
 The analysis of designs based on saturated orthogonal arrays poses a very
 difficult challenge since there are no degrees of freedom left to estimate
 the error variance. In this paper we propose a heuristic approach for the
 use of cumulative sum control chart for screening active effects in
 orthogonal-saturated experiments. A comparative simulation study
 establishes the powerfulness of the proposed method.
Journal: Journal of Applied Statistics 
Pages: 1611-1618 
Issue: 7 
Volume: 41 
Year: 2014 
Month: 7 
X-DOI: 10.1080/02664763.2014.881982 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.881982 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:7:p:1611-1618




Template-Type: ReDIF-Article 1.0
Author-Name: Yang-Jin Kim 
Author-X-Name-First: Yang-Jin 
Author-X-Name-Last: Kim 
Title: Regression analysis of recurrent events data with incomplete observation gaps 
Abstract:
 For analyzing recurrent event data, either total time scale or gap time
 scale is adopted according to research interest. In particular, gap time
 scale is known to be more appropriate for modeling a renewal process. In
 this paper, we adopt gap time scale to analyze recurrent event data with
 repeated observation gaps which cannot be observed completely because of
 unknown termination times of observation gaps. In order to estimate
 termination times, interval-censored mechanism is applied. Simulation
 studies are done to compare the suggested methods with the unadjusted
 method ignoring incomplete observation gaps. As a real example, conviction
 data set with suspensions is analyzed with suggested methods.
Journal: Journal of Applied Statistics 
Pages: 1619-1626 
Issue: 7 
Volume: 41 
Year: 2014 
Month: 7 
X-DOI: 10.1080/02664763.2014.885002 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.885002 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:7:p:1619-1626




Template-Type: ReDIF-Article 1.0
Author-Name: Siti Haslinda Mohd Din 
Author-X-Name-First: Siti Haslinda 
Author-X-Name-Last: Mohd Din 
Author-Name: Marek Molas 
Author-X-Name-First: Marek 
Author-X-Name-Last: Molas 
Author-Name: Jolanda Luime 
Author-X-Name-First: Jolanda 
Author-X-Name-Last: Luime 
Author-Name: Emmanuel Lesaffre 
Author-X-Name-First: Emmanuel 
Author-X-Name-Last: Lesaffre 
Title: Longitudinal profiles of bounded outcome scores as predictors for disease activity in rheumatoid arthritis patients: a joint modeling approach 
Abstract:
 A variety of statistical approaches have been suggested in the literature
 for the analysis of bounded outcome scores (BOS). In this paper, we
 suggest a statistical approach when BOSs are repeatedly measured over time
 and used as predictors in a regression model. Instead of directly using
 the BOS as a predictor, we propose to extend the approaches suggested in
 [16,21,28] to a joint modeling setting. Our approach is illustrated on
 longitudinal profiles of multiple patients' reported outcomes to predict
 the current clinical status of rheumatoid arthritis patients by a disease
 activities score of 28 joints (DAS28). Both a maximum likelihood as well
 as a Bayesian approach is developed.
Journal: Journal of Applied Statistics 
Pages: 1627-1644 
Issue: 8 
Volume: 41 
Year: 2014 
Month: 8 
X-DOI: 10.1080/02664763.2014.882499 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.882499 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:8:p:1627-1644




Template-Type: ReDIF-Article 1.0
Author-Name: Tao Lu 
Author-X-Name-First: Tao 
Author-X-Name-Last: Lu 
Author-Name: Yangxin Huang 
Author-X-Name-First: Yangxin 
Author-X-Name-Last: Huang 
Author-Name: Min Wang 
Author-X-Name-First: Min 
Author-X-Name-Last: Wang 
Author-Name: Feng Qian 
Author-X-Name-First: Feng 
Author-X-Name-Last: Qian 
Title: A refined parameter estimating approach for HIV dynamic model 
Abstract:
 HIV dynamic models, a set of ordinary differential equations (ODEs), have
 provided new understanding of the pathogenesis of HIV infection and the
 treatment effects of antiviral therapies. However, to estimate parameters
 for ODEs is very challenging due to the complexity of this nonlinear
 system. In this article, we propose a comprehensive procedure to deal with
 this issue. In the proposed procedure, a series of cutting-edge
 statistical methods and techniques are employed, including nonparametric
 mixed-effects smoothing-based methods for ODE models and stochastic
 approximation expectation-maximization (EM) approach for mixed-effects ODE
 models. A simulation study is performed to validate the proposed approach.
 An application example from a real HIV clinical trial study is used to
 illustrate the usefulness of the proposed method.
Journal: Journal of Applied Statistics 
Pages: 1645-1657 
Issue: 8 
Volume: 41 
Year: 2014 
Month: 8 
X-DOI: 10.1080/02664763.2014.885001 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.885001 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:8:p:1645-1657




Template-Type: ReDIF-Article 1.0
Author-Name: Weihua Zhao 
Author-X-Name-First: Weihua 
Author-X-Name-Last: Zhao 
Author-Name: Riquan Zhang 
Author-X-Name-First: Riquan 
Author-X-Name-Last: Zhang 
Author-Name: Jicai Liu 
Author-X-Name-First: Jicai 
Author-X-Name-Last: Liu 
Title: Sparse group variable selection based on quantile hierarchical Lasso 
Abstract:
 The group Lasso is a penalized regression method, used in regression
 problems where the covariates are partitioned into groups to promote
 sparsity at the group level [27]. Quantile group Lasso, a natural
 extension of quantile Lasso [25], is a good alternative when the data has
 group information and has many outliers and/or heavy tails. How to
 discover important features that are correlated with interest of outcomes
 and immune to outliers has been paid much attention. In many applications,
 however, we may also want to keep the flexibility of selecting variables
 within a group. In this paper, we develop a sparse group variable
 selection based on quantile methods which select important covariates at
 both the group level and within the group level, which penalizes the
 empirical check loss function by the sum of square root group-wise
 <italic>L</italic><sub>1</sub>-norm penalties. The oracle properties are
 established where the number of parameters diverges. We also apply our new
 method to varying coefficient model with categorial effect modifiers.
 Simulations and real data example show that the newly proposed method has
 robust and superior performance.
Journal: Journal of Applied Statistics 
Pages: 1658-1677 
Issue: 8 
Volume: 41 
Year: 2014 
Month: 8 
X-DOI: 10.1080/02664763.2014.888541 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.888541 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:8:p:1658-1677




Template-Type: ReDIF-Article 1.0
Author-Name: Raffaella Calabrese 
Author-X-Name-First: Raffaella 
Author-X-Name-Last: Calabrese 
Title: Optimal cut-off for rare events and unbalanced misclassification costs 
Abstract:
 This paper develops a method for handling two-class classification
 problems with highly unbalanced class sizes and misclassification costs.
 When the class sizes are highly unbalanced and the minority class
 represents a rare event, conventional classification methods tend to
 strongly favour the majority class, resulting in very low detection of the
 minority class. A method is proposed to determine the optimal cut-off for
 asymmetric misclassification costs and for unbalanced class sizes. Monte
 Carlo simulations show that this proposal performs better than the method
 based on the notion of classification accuracy. Finally, the proposed
 method is applied to empirical data on Italian small and medium
 enterprises to classify them into default and non-default groups.
Journal: Journal of Applied Statistics 
Pages: 1678-1693 
Issue: 8 
Volume: 41 
Year: 2014 
Month: 8 
X-DOI: 10.1080/02664763.2014.888542 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.888542 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:8:p:1678-1693




Template-Type: ReDIF-Article 1.0
Author-Name: Alain Bensoussan 
Author-X-Name-First: Alain 
Author-X-Name-Last: Bensoussan 
Author-Name: Pierre Bertrand 
Author-X-Name-First: Pierre 
Author-X-Name-Last: Bertrand 
Author-Name: Alexandre Brouste 
Author-X-Name-First: Alexandre 
Author-X-Name-Last: Brouste 
Title: A generalized linear model approach to seasonal aspects of wind speed modeling 
Abstract:
 The aim of the article is to identify the intraday seasonality in a wind
 speed time series. Following the traditional approach, the marginal
 probability law is Weibull and, consequently, we consider seasonal Weibull
 law. A new estimation and decision procedure to estimate the seasonal
 Weibull law intraday scale parameter is presented. We will also give
 statistical decision-making tools to discard or not the trend parameter
 and to validate the seasonal model.
Journal: Journal of Applied Statistics 
Pages: 1694-1707 
Issue: 8 
Volume: 41 
Year: 2014 
Month: 8 
X-DOI: 10.1080/02664763.2014.888543 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.888543 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:8:p:1694-1707




Template-Type: ReDIF-Article 1.0
Author-Name: Amjad D. Al-Nasser 
Author-X-Name-First: Amjad D. 
Author-X-Name-Last: Al-Nasser 
Title: Two steps generalized maximum entropy estimation procedure for fitting linear regression when both covariates are subject to error 
Abstract:
 This paper presents a procedure utilizing the generalized maximum entropy
 (GME) estimation method in two steps to quantify the uncertainty of the
 simple linear structural measurement error model parameters exactly. The
 first step estimates the unknowns from the horizontal line, and then the
 estimates were used in a second step to estimate the unknowns from the
 vertical line. The proposed estimation procedure has the ability to
 minimize the number of unknown parameters in formulating the GME system
 within each step, and hence reduce variability of the estimates.
 Analytical and illustrative Monte Carlo simulation comparison experiments
 with the maximum likelihood estimators and a one-step GME estimation
 procedure were presented. Simulation experiments demonstrated that the two
 steps estimation procedure produced parameter estimates that are more
 accurate and more efficient than the classical estimation methods. An
 application of the proposed method is illustrated using a data set
 gathered from the Centre for Integrated Government Services in Delma
 Island - UAE to predict the association between perceived quality and the
 customer satisfaction.
Journal: Journal of Applied Statistics 
Pages: 1708-1720 
Issue: 8 
Volume: 41 
Year: 2014 
Month: 8 
X-DOI: 10.1080/02664763.2014.888544 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.888544 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:8:p:1708-1720




Template-Type: ReDIF-Article 1.0
Author-Name: Tahani Coolen-Maturi 
Author-X-Name-First: Tahani 
Author-X-Name-Last: Coolen-Maturi 
Title: A new weighted rank coefficient of concordance 
Abstract:
 There are many situations where <italic>n</italic> objects are ranked by
 <italic>b</italic>>2 independent sources or observers and in which the
 interest is focused on agreement on the top rankings. Kendall's
 coefficient of concordance [10] assigns equal weights to all rankings. In
 this paper, a new coefficient of concordance is introduced which is more
 sensitive to agreement on the top rankings. The limiting distribution of
 the new concordance coefficient under the null hypothesis of no
 association among the rankings is presented, and a summary of the exact
 and approximate quantiles for this coefficient is provided. A simulation
 study is carried out to compare the performance of Kendall's, the top-down
 and the new concordance coefficients in detecting the agreement on the top
 rankings. Finally, examples are given for illustration purposes, including
 a real data set from financial market indices.
Journal: Journal of Applied Statistics 
Pages: 1721-1745 
Issue: 8 
Volume: 41 
Year: 2014 
Month: 8 
X-DOI: 10.1080/02664763.2014.889664 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.889664 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:8:p:1721-1745




Template-Type: ReDIF-Article 1.0
Author-Name: M. Qamarul Islam 
Author-X-Name-First: M. Qamarul 
Author-X-Name-Last: Islam 
Author-Name: Fetih Yildirim 
Author-X-Name-First: Fetih 
Author-X-Name-Last: Yildirim 
Author-Name: Mehmet Yazici 
Author-X-Name-First: Mehmet 
Author-X-Name-Last: Yazici 
Title: Inference in multivariate linear regression models with elliptically distributed errors 
Abstract:
 In this study we investigate the problem of estimation and testing of
 hypotheses in multivariate linear regression models when the errors
 involved are assumed to be non-normally distributed. We consider the class
 of heavy-tailed distributions for this purpose. Although our method is
 applicable for any distribution in this class, we take the multivariate
 <italic>t</italic>-distribution for illustration. This distribution has
 applications in many fields of applied research such as Economics,
 Business, and Finance. For estimation purpose, we use the modified maximum
 likelihood method in order to get the so-called modified maximum
 likelihood estimates that are obtained in a closed form. We show that
 these estimates are substantially more efficient than least-square
 estimates. They are also found to be robust to reasonable deviations from
 the assumed distribution and also many data anomalies such as the presence
 of outliers in the sample, etc. We further provide test statistics for
 testing the relevant hypothesis regarding the regression coefficients.
Journal: Journal of Applied Statistics 
Pages: 1746-1766 
Issue: 8 
Volume: 41 
Year: 2014 
Month: 8 
X-DOI: 10.1080/02664763.2014.890177 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.890177 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:8:p:1746-1766




Template-Type: ReDIF-Article 1.0
Author-Name: Xavier de Luna 
Author-X-Name-First: Xavier 
Author-X-Name-Last: de Luna 
Author-Name: Mathias Lundin 
Author-X-Name-First: Mathias 
Author-X-Name-Last: Lundin 
Title: Sensitivity analysis of the unconfoundedness assumption with an application to an evaluation of college choice effects on earnings 
Abstract:
 We evaluate the effects of college choice on earnings using Swedish
 register databases. This case study is used to motivate the introduction
 of a novel procedure to analyse the sensitivity of such an observational
 study to the assumption made that there are no unobserved confounders -
 variables affecting both college choice and earnings. This assumption is
 not testable without further information, and should be considered an
 approximation of reality. To perform a sensitivity analysis, we measure
 the departure from the unconfoundedness assumption with the correlation
 between college choice and earnings when conditioning on observed
 covariates. The use of a correlation as a measure of dependence allows us
 to propose a standardised procedure by advocating the use of a fixed value
 for the correlation, typically 1% or 5%, when checking the sensitivity of
 an evaluation study. A correlation coefficient is, moreover, intuitive to
 most empirical scientists, which makes the results of our sensitivity
 analysis easier to communicate than those of previously proposed methods.
 In our evaluation of the effects of college choice on earnings, the
 significantly positive effect obtained could not be questioned by a
 sensitivity analysis allowing for unobserved confounders inducing at most
 5% correlation between college choice and earnings.
Journal: Journal of Applied Statistics 
Pages: 1767-1784 
Issue: 8 
Volume: 41 
Year: 2014 
Month: 8 
X-DOI: 10.1080/02664763.2014.890178 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.890178 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:8:p:1767-1784




Template-Type: ReDIF-Article 1.0
Author-Name: Francisco Louzada 
Author-X-Name-First: Francisco 
Author-X-Name-Last: Louzada 
Author-Name: Paulo H. Ferreira 
Author-X-Name-First: Paulo H. 
Author-X-Name-Last: Ferreira 
Author-Name: Carlos A.R. Diniz 
Author-X-Name-First: Carlos A.R. 
Author-X-Name-Last: Diniz 
Title: Skew-normal distribution for growth curve models in presence of a heteroscedasticity structure 
Abstract:
 In general, growth models are adjusted under the assumptions that the
 error terms are homoscedastic and normally distributed. However, these
 assumptions are often not verified in practice. In this work we propose
 four growth models (Morgan-Mercer-Flodin, von Bertalanffy, Gompertz, and
 Richards) considering different distributions (normal, skew-normal) for
 the error terms and three different covariance structures. Maximum
 likelihood estimation procedure is addressed. A simulation study is
 performed in order to verify the appropriateness of the proposed growth
 curve models. The methodology is also illustrated on a real dataset.
Journal: Journal of Applied Statistics 
Pages: 1785-1798 
Issue: 8 
Volume: 41 
Year: 2014 
Month: 8 
X-DOI: 10.1080/02664763.2014.891005 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.891005 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:8:p:1785-1798




Template-Type: ReDIF-Article 1.0
Author-Name: Abdelmalek Kouadri 
Author-X-Name-First: Abdelmalek 
Author-X-Name-Last: Kouadri 
Author-Name: Karim Baiche 
Author-X-Name-First: Karim 
Author-X-Name-Last: Baiche 
Author-Name: Mimoun Zelmat 
Author-X-Name-First: Mimoun 
Author-X-Name-Last: Zelmat 
Title: Blind source separation filters-based-fault detection and isolation in a three tank system 
Abstract:
 Fault detection and Isolation takes a strategic position in modern
 industrial processes for which various approaches are proposed. These
 approaches are usually developed and based on a consistency test between
 an observed state of the process provided by sensors and an expected
 behaviour provided by a mathematical model of the system. These methods
 require a reliable model of the system to be monitored which is a complex
 task. Alternatively, we propose in this paper to use blind source
 separation filters (BSSFs) in order to detect and isolate faults in a
 three tank pilot plant. This technique is very beneficial as it uses blind
 identification without an explicit mathematical model of the system. The
 independent component analysis (ICA), relying on the assumption of the
 statistical independence of the extracted sources, is used as a tool for
 each BSSF to extract signals of the process under consideration. The
 experimental results show the effectiveness and robustness of this
 approach in detecting and isolating faults that are on sensors in the
 system.
Journal: Journal of Applied Statistics 
Pages: 1799-1813 
Issue: 8 
Volume: 41 
Year: 2014 
Month: 8 
X-DOI: 10.1080/02664763.2014.891570 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.891570 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:8:p:1799-1813




Template-Type: ReDIF-Article 1.0
Author-Name: Tsai-Hung Fan 
Author-X-Name-First: Tsai-Hung 
Author-X-Name-Last: Fan 
Author-Name: Yi-Fu Wang 
Author-X-Name-First: Yi-Fu 
Author-X-Name-Last: Wang 
Author-Name: Yi-Chen Zhang 
Author-X-Name-First: Yi-Chen 
Author-X-Name-Last: Zhang 
Title: Bayesian model selection in linear mixed effects models with autoregressive(p) errors using mixture priors 
Abstract:
 In this article, we apply the Bayesian approach to the linear mixed effect
 models with autoregressive(p) random errors under mixture priors obtained
 with the Markov chain Monte Carlo (MCMC) method. The mixture structure of
 a point mass and continuous distribution can help to select the variables
 in fixed and random effects models from the posterior sample generated
 using the MCMC method. Bayesian prediction of future observations is also
 one of the major concerns. To get the best model, we consider the commonly
 used highest posterior probability model and the median posterior
 probability model. As a result, both criteria tend to be needed to choose
 the best model from the entire simulation study. In terms of predictive
 accuracy, a real example confirms that the proposed method provides
 accurate results.
Journal: Journal of Applied Statistics 
Pages: 1814-1829 
Issue: 8 
Volume: 41 
Year: 2014 
Month: 8 
X-DOI: 10.1080/02664763.2014.894001 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.894001 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:8:p:1814-1829




Template-Type: ReDIF-Article 1.0
Author-Name: Luigi Ippoliti 
Author-X-Name-First: Luigi 
Author-X-Name-Last: Ippoliti 
Author-Name: Simone Di Zio 
Author-X-Name-First: Simone 
Author-X-Name-Last: Di Zio 
Author-Name: Arcangelo Merla 
Author-X-Name-First: Arcangelo 
Author-X-Name-Last: Merla 
Title: Classification of biomedical signals for differential diagnosis of Raynaud's phenomenon 
Abstract:
 This paper discusses a supervised classification approach for the
 differential diagnosis of Raynaud's phenomenon (RP). The classification of
 data from healthy subjects and from patients suffering for primary and
 secondary RP is obtained by means of a set of classifiers derived within
 the framework of linear discriminant analysis. A set of functional
 variables and shape measures extracted from rewarming/reperfusion curves
 are proposed as discriminant features. Since the prediction of group
 membership is based on a large number of these features, the high
 dimension/small sample size problem is considered to overcome the
 singularity problem of the within-group covariance matrix. Results on a
 data set of 72 subjects demonstrate that a satisfactory classification of
 the subjects can be achieved through the proposed methodology.
Journal: Journal of Applied Statistics 
Pages: 1830-1847 
Issue: 8 
Volume: 41 
Year: 2014 
Month: 8 
X-DOI: 10.1080/02664763.2014.894002 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.894002 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:8:p:1830-1847




Template-Type: ReDIF-Article 1.0
Author-Name: Pablo Mart�nez-Camblor 
Author-X-Name-First: Pablo 
Author-X-Name-Last: Mart�nez-Camblor 
Author-Name: Carlos Carleos 
Author-X-Name-First: Carlos 
Author-X-Name-Last: Carleos 
Author-Name: Jesus � Baro 
Author-X-Name-First: Jesus � 
Author-X-Name-Last: Baro 
Author-Name: Javier Ca��n 
Author-X-Name-First: Javier 
Author-X-Name-Last: Ca��n 
Title: Standard statistical tools for the breed allocation problem 
Abstract:
 Modern technologies are frequently used in order to deal with new genomic
 problems. For instance, the <monospace>STRUCTURE</monospace> software is
 usually employed for breed assignment based on genetic information.
 However, standard statistical techniques offer a number of valuable tools
 which can be successfully used for dealing with most problems. In this
 paper, we investigated the capability of microsatellite markers for
 individual identification and their potential use for breed assignment of
 individuals in seventy Lidia breed lines and breeders. Traditional
 binomial logistic regression is applied to each line and used to assign
 one individual to a particular line. In addition, the area under receiver
 operating curve (AUC) criterion is used to measure the capability of the
 microsatellite-based models to separate the groups. This method allows us
 to identify which microsatellite loci are related to each line. Overall,
 only one subject was misclassified or a 99.94% correct allocation. The
 minimum observed AUC was 0.986 with an average of 0.997. These results
 suggest that our method is competitive for animal allocation and has some
 interpretative advantages and a strong relationship with methods based on
 SNPs and related techniques.
Journal: Journal of Applied Statistics 
Pages: 1848-1856 
Issue: 8 
Volume: 41 
Year: 2014 
Month: 8 
X-DOI: 10.1080/02664763.2014.898136 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.898136 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:8:p:1848-1856




Template-Type: ReDIF-Article 1.0
Author-Name: Konstantinos C. Fragkos 
Author-X-Name-First: Konstantinos C. 
Author-X-Name-Last: Fragkos 
Title: Applied medical statistics using SAS 
Journal: Journal of Applied Statistics 
Pages: 1857-1858 
Issue: 8 
Volume: 41 
Year: 2014 
Month: 8 
X-DOI: 10.1080/02664763.2013.877650 
File-URL: http://hdl.handle.net/10.1080/02664763.2013.877650 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:8:p:1857-1858




Template-Type: ReDIF-Article 1.0
Author-Name: Bo&zcaron;idar V. Popovi&cacute; 
Author-X-Name-First: Bo&zcaron;idar V. 
Author-X-Name-Last: Popovi&cacute; 
Title: Exercises and solutions in statistical theory 
Journal: Journal of Applied Statistics 
Pages: 1858-1858 
Issue: 8 
Volume: 41 
Year: 2014 
Month: 8 
X-DOI: 10.1080/02664763.2014.883685 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.883685 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:8:p:1858-1858




Template-Type: ReDIF-Article 1.0
Author-Name: Edwin M.M. Ortega 
Author-X-Name-First: Edwin M.M. 
Author-X-Name-Last: Ortega 
Author-Name: Gauss M. Cordeiro 
Author-X-Name-First: Gauss M. 
Author-X-Name-Last: Cordeiro 
Author-Name: Elizabeth M. Hashimoto 
Author-X-Name-First: Elizabeth M. 
Author-X-Name-Last: Hashimoto 
Author-Name: Kahadawala Cooray 
Author-X-Name-First: Kahadawala 
Author-X-Name-Last: Cooray 
Title: A log-linear regression model for the odd Weibull distribution with censored data 
Abstract:
 We introduce the log-odd Weibull regression model based on the odd Weibull
 distribution (Cooray, 2006). We derive some mathematical properties of the
 log-transformed distribution. The new regression model represents a
 parametric family of models that includes as sub-models some widely known
 regression models that can be applied to censored survival data. We employ
 a frequentist analysis and a parametric bootstrap for the parameters of
 the proposed model. We derive the appropriate matrices for assessing local
 influence on the parameter estimates under different perturbation schemes
 and present some ways to assess global influence. Further, for different
 parameter settings, sample sizes and censoring percentages, some
 simulations are performed. In addition, the empirical distribution of some
 modified residuals are given and compared with the standard normal
 distribution. These studies suggest that the residual analysis usually
 performed in normal linear regression models can be extended to a modified
 deviance residual in the proposed regression model applied to censored
 data. We define martingale and deviance residuals to check the model
 assumptions. The extended regression model is very useful for the analysis
 of real data.
Journal: Journal of Applied Statistics 
Pages: 1859-1880 
Issue: 9 
Volume: 41 
Year: 2014 
Month: 9 
X-DOI: 10.1080/02664763.2014.897689 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.897689 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:9:p:1859-1880




Template-Type: ReDIF-Article 1.0
Author-Name: V�ctor Leiva 
Author-X-Name-First: V�ctor 
Author-X-Name-Last: Leiva 
Author-Name: Carolina Marchant 
Author-X-Name-First: Carolina 
Author-X-Name-Last: Marchant 
Author-Name: Helton Saulo 
Author-X-Name-First: Helton 
Author-X-Name-Last: Saulo 
Author-Name: Muhammad Aslam 
Author-X-Name-First: Muhammad 
Author-X-Name-Last: Aslam 
Author-Name: Fernando Rojas 
Author-X-Name-First: Fernando 
Author-X-Name-Last: Rojas 
Title: Capability indices for Birnbaum-Saunders processes applied to electronic and food industries 
Abstract:
 Process capability indices (PCIs) are tools widely used by the industries
 to determine the quality of their products and the performance of their
 manufacturing processes. Classic versions of these indices were
 constructed for processes whose quality characteristics have a normal
 distribution. In practice, many of these characteristics do not follow
 this distribution. In such a case, the classic PCIs must be modified to
 take into account the non-normality. Ignoring the effect of this
 non-normality can lead to misinterpretation of the process capability and
 ill-advised business decisions. An asymmetric non-normal model that is
 receiving considerable attention due to its good properties is the
 Birnbaum-Saunders (BS) distribution. We propose, develop, implement and
 apply a methodology based on PCIs for BS processes considering estimation,
 parametric inference, bootstrap and optimization tools. This methodology
 is implemented in the statistical software {\tt R}. A simulation study is
 conducted to evaluate its performance. Real-world case studies with
 applications for three data sets are carried out to illustrate its
 potentiality. One of these data sets was already published and is
 associated with the electronic industry, whereas the other two are
 unpublished and associated with the food industry.
Journal: Journal of Applied Statistics 
Pages: 1881-1902 
Issue: 9 
Volume: 41 
Year: 2014 
Month: 9 
X-DOI: 10.1080/02664763.2014.897690 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.897690 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:9:p:1881-1902




Template-Type: ReDIF-Article 1.0
Author-Name: Shun Matsuura 
Author-X-Name-First: Shun 
Author-X-Name-Last: Matsuura 
Title: Effectiveness of a random compound noise strategy for robust parameter design 
Abstract:
 Robust parameter design has been widely used to improve the quality of
 products and processes. Although a product array, in which an orthogonal
 array for control factors is crossed with an orthogonal array for noise
 factors, is commonly used for parameter design experiments, this may lead
 to an unacceptably large number of experimental runs. The compound noise
 strategy proposed by Taguchi [30] can be used to reduce the number of
 experimental runs. In this strategy, a compound noise factor is formed
 based on the directionality of the effects of noise factors. However, the
 directionality is usually unknown in practice. Recently, Singh <italic>et
 al.</italic> [28] proposed a random compound noise strategy, in which a
 compound noise factor is formed by randomly selecting a setting of the
 levels of noise factors. The present paper evaluates the random compound
 noise strategy in terms of the precision of the estimators of the response
 mean and the response variance. In addition, the variances of the
 estimators in the random compound noise strategy are compared with those
 in the <italic>n</italic>-replication design. The random compound noise
 strategy is shown to have smaller variances of the estimators than the
 2-replication design, especially when the control-by-noise-interactions
 are strong.
Journal: Journal of Applied Statistics 
Pages: 1903-1918 
Issue: 9 
Volume: 41 
Year: 2014 
Month: 9 
X-DOI: 10.1080/02664763.2014.898130 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.898130 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:9:p:1903-1918




Template-Type: ReDIF-Article 1.0
Author-Name: Rita Esther Zapata-V�zquez 
Author-X-Name-First: Rita Esther 
Author-X-Name-Last: Zapata-V�zquez 
Author-Name: Anthony O'Hagan 
Author-X-Name-First: Anthony 
Author-X-Name-Last: O'Hagan 
Author-Name: Leonardo Soares Bastos 
Author-X-Name-First: Leonardo 
Author-X-Name-Last: Soares Bastos 
Title: Eliciting expert judgements about a set of proportions 
Abstract:
 Eliciting expert knowledge about several uncertain quantities is a complex
 task when those quantities exhibit associations. A well-known example of
 such a problem is eliciting knowledge about a set of uncertain proportions
 which must sum to 1. The usual approach is to assume that the expert's
 knowledge can be adequately represented by a Dirichlet distribution, since
 this is by far the simplest multivariate distribution that is appropriate
 for such a set of proportions. It is also the most convenient,
 particularly when the expert's prior knowledge is to be combined with a
 multinomial sample since then the Dirichlet is the conjugate prior family.
 Several methods have been described in the literature for eliciting
 beliefs in the form of a Dirichlet distribution, which typically involve
 eliciting from the expert enough judgements to identify uniquely the
 Dirichlet hyperparameters. We describe here a new method which employs the
 device of over-fitting, i.e. eliciting more than the minimal number of
 judgements, in order to (a) produce a more carefully considered Dirichlet
 distribution and (b) ensure that the Dirichlet distribution is indeed a
 reasonable fit to the expert's knowledge. The method has been implemented
 in a software extension of the Sheffield elicitation framework (SHELF) to
 facilitate the multivariate elicitation process.
Journal: Journal of Applied Statistics 
Pages: 1919-1933 
Issue: 9 
Volume: 41 
Year: 2014 
Month: 9 
X-DOI: 10.1080/02664763.2014.898131 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.898131 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:9:p:1919-1933




Template-Type: ReDIF-Article 1.0
Author-Name: T. Baghfalaki 
Author-X-Name-First: T. 
Author-X-Name-Last: Baghfalaki 
Author-Name: M. Ganjali 
Author-X-Name-First: M. 
Author-X-Name-Last: Ganjali 
Author-Name: D. Berridge 
Author-X-Name-First: D. 
Author-X-Name-Last: Berridge 
Title: Joint modeling of multivariate longitudinal mixed measurements and time to event data using a Bayesian approach 
Abstract:
 In many longitudinal studies multiple characteristics of each individual,
 along with time to occurrence of an event of interest, are often
 collected. In such data set, some of the correlated characteristics may be
 discrete and some of them may be continuous. In this paper, a joint model
 for analysing multivariate longitudinal data comprising mixed continuous
 and ordinal responses and a time to event variable is proposed. We model
 the association structure between longitudinal mixed data and time to
 event data using a multivariate zero-mean Gaussian process. For modeling
 discrete ordinal data we assume a continuous latent variable follows the
 logistic distribution and for continuous data a Gaussian mixed effects
 model is used. For the event time variable, an accelerated failure time
 model is considered under different distributional assumptions. For
 parameter estimation, a Bayesian approach using Markov Chain Monte Carlo
 is adopted. The performance of the proposed methods is illustrated using
 some simulation studies. A real data set is also analyzed, where different
 model structures are used. Model comparison is performed using a variety
 of statistical criteria.
Journal: Journal of Applied Statistics 
Pages: 1934-1955 
Issue: 9 
Volume: 41 
Year: 2014 
Month: 9 
X-DOI: 10.1080/02664763.2014.898132 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.898132 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:9:p:1934-1955




Template-Type: ReDIF-Article 1.0
Author-Name: Yun Lu 
Author-X-Name-First: Yun 
Author-X-Name-Last: Lu 
Author-Name: Sridhar Hannenhalli 
Author-X-Name-First: Sridhar 
Author-X-Name-Last: Hannenhalli 
Author-Name: Tom Cappola 
Author-X-Name-First: Tom 
Author-X-Name-Last: Cappola 
Author-Name: Mary Putt 
Author-X-Name-First: Mary 
Author-X-Name-Last: Putt 
Title: An evaluation of Monte-Carlo logic and logicFS motivated by a study of the regulation of gene expression in heart failure 
Abstract:
 Monte-Carlo (MC) Logic and Logic Feature Selection (logicFS) identify
 binary predictors of outcome using repeated iterations of logic
 regression, a variable selection method that identifies Boolean
 combinations of predictors. Both methods compute the frequency with which
 predictors appear in the model with the output of the logicFS program
 providing specific summaries of predictor form. We sought to identify
 variables related to transcription factor-related regulation of gene
 expression differences in a study of failing and non-failing hearts.
 Results based broadly on the frequency of occurrence of predictors into
 the MC Logic or logicFS models were similar. However key to logicFS are
 variable importance measures (VIMs), which augment the frequency metrics
 and seek to evaluate a predictor's contribution to classification.
 Analytic work and simulation studies indicate that the VIM vary as a
 function of the joint prevalence of outcome and predictor. Thus, findings
 from logicFS have limited generalizability, particularly with respect to
 case-control studies where the prevalence of outcome is determined by
 study design. Interpretation of VIM for those variables with near-zero or
 negative values is particularly ambiguous. Additional issues with
 interpretability arise because the VIM are strongly affected by other
 variables selected into the model but logicFS does not explicitly identify
 these variables in its output.
Journal: Journal of Applied Statistics 
Pages: 1956-1975 
Issue: 9 
Volume: 41 
Year: 2014 
Month: 9 
X-DOI: 10.1080/02664763.2014.898133 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.898133 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:9:p:1956-1975




Template-Type: ReDIF-Article 1.0
Author-Name: Chwu-Shiun Tarng 
Author-X-Name-First: Chwu-Shiun 
Author-X-Name-Last: Tarng 
Title: Third-order likelihood-based inference for the log-normal regression model 
Abstract:
 This paper examines the general third-order theory to the log-normal
 regression model. The interest parameter is its conditional mean. For
 inference, traditional first-order approximations need large sample sizes
 and normal-like distributions. Some specific third-order methods need the
 explicit forms of the nuisance parameter and ancillary statistic, which
 are quite complicated. Note that this general third-order theory can be
 applied to any continuous models with standard asymptotic properties. It
 only needs the log-likelihood function. With small sample settings, the
 simulation studies for confidence intervals of the conditional mean
 illustrate that the general third-order theory is much superior to the
 traditional first-order methods.
Journal: Journal of Applied Statistics 
Pages: 1976-1988 
Issue: 9 
Volume: 41 
Year: 2014 
Month: 9 
X-DOI: 10.1080/02664763.2014.898134 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.898134 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:9:p:1976-1988




Template-Type: ReDIF-Article 1.0
Author-Name: W. Liu 
Author-X-Name-First: W. 
Author-X-Name-Last: Liu 
Author-Name: J.C. Hsu 
Author-X-Name-First: J.C. 
Author-X-Name-Last: Hsu 
Author-Name: F. Bretz 
Author-X-Name-First: F. 
Author-X-Name-Last: Bretz 
Author-Name: A.J. Hayter 
Author-X-Name-First: A.J. 
Author-X-Name-Last: Hayter 
Author-Name: Y. Han 
Author-X-Name-First: Y. 
Author-X-Name-Last: Han 
Title: Shelf-life and its estimation in drug stability studies 
Abstract:
 One important property of any drug product is its stability over time.
 Drug stability studies are routinely carried out in the pharmaceutical
 industry in order to measure the degradation of an active pharmaceutical
 ingredient of a drug product. One important study objective is to estimate
 the shelf-life of the drug; the estimated shelf-life is required by the US
 Food and Drug Administration to be printed on the package label of the
 drug. This involves a suitable definition of the true shelf-life and the
 construction of an appropriate estimate of the true shelf-life. In this
 paper, the true shelf-life <italic>T</italic><sub>&beta;</sub> is defined
 as the time point at which 100&beta;% of all the individual dosage units
 (e.g. tablets) of the drug have the active ingredient content no less than
 the lowest acceptable limit <italic>L</italic>, where &beta; and
 <italic>L</italic> are prespecified constants. The value of
 <italic>T</italic><sub>&beta;</sub> depends on the parameters of the
 assumed degradation model of the active ingredient content and so is
 unknown. A lower confidence bound
 <italic>T&#x2c6;</italic><sub>&beta;</sub> for
 <italic>T</italic><sub>&beta;</sub> is then provided and used as the
 estimated shelf-life of the drug.
Journal: Journal of Applied Statistics 
Pages: 1989-2000 
Issue: 9 
Volume: 41 
Year: 2014 
Month: 9 
X-DOI: 10.1080/02664763.2014.898135 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.898135 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:9:p:1989-2000




Template-Type: ReDIF-Article 1.0
Author-Name: Le Chen 
Author-X-Name-First: Le 
Author-X-Name-Last: Chen 
Author-Name: Ao Yuan 
Author-X-Name-First: Ao 
Author-X-Name-Last: Yuan 
Author-Name: Aiyi Liu 
Author-X-Name-First: Aiyi 
Author-X-Name-Last: Liu 
Author-Name: Guanjie Chen 
Author-X-Name-First: Guanjie 
Author-X-Name-Last: Chen 
Title: Longitudinal data analysis using Bayesian-frequentist hybrid random effects model 
Abstract:
 The mixed random effect model is commonly used in longitudinal data
 analysis within either frequentist or Bayesian framework. Here we consider
 a case, in which we have prior knowledge on partial parameters, while no
 such information on the rest of the parameters. Thus, we use the hybrid
 approach on the random-effects model with partial parameters. The
 parameters are estimated via Bayesian procedure, and the rest of
 parameters by the frequentist maximum likelihood estimation (MLE),
 simultaneously on the same model. In practice, we often know partial prior
 information such as, covariates of age, gender, etc. These information can
 be used, and accurate estimations in mixed random-effects model can be
 obtained. A series of simulation studies were performed to compare the
 results with the commonly used random-effects model with and without
 partial prior information. The results in hybrid estimation (HYB) and MLE
 were very close to each other. The estimated &#x3b8; values in with
 partial prior information model (HYB) were more closer to true &#x3b8;
 values, and showed less variances than without partial prior information
 in MLE. To compare with true &#x3b8; values, the mean square of errors are
 much less in HYB than in MLE. This advantage of HYB is very obvious in
 longitudinal data with a small sample size. The methods of HYB and MLE are
 applied to a real longitudinal data for illustration purposes.
Journal: Journal of Applied Statistics 
Pages: 2001-2010 
Issue: 9 
Volume: 41 
Year: 2014 
Month: 9 
X-DOI: 10.1080/02664763.2014.898137 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.898137 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:9:p:2001-2010




Template-Type: ReDIF-Article 1.0
Author-Name: Robert Drake 
Author-X-Name-First: Robert 
Author-X-Name-Last: Drake 
Author-Name: Apratim Guha 
Author-X-Name-First: Apratim 
Author-X-Name-Last: Guha 
Title: A mutual information-based <italic>k</italic>-sample test for discrete distributions 
Abstract:
 The two-sample problem and its extension to the <italic>k</italic>-sample
 problem are well known in the statistical literature. However, the
 discrete version of the <italic>k</italic>-sample problem is relatively
 less explored. Here in this work we suggest a <italic>k</italic>-sample
 non-parametric test procedure for discrete distributions based on mutual
 information. A detailed power study with comparison with other
 alternatives is provided. Finally, a comparison of some English soccer
 league teams based on their goal-scoring pattern is discussed.
Journal: Journal of Applied Statistics 
Pages: 2011-2027 
Issue: 9 
Volume: 41 
Year: 2014 
Month: 9 
X-DOI: 10.1080/02664763.2014.899325 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.899325 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:9:p:2011-2027




Template-Type: ReDIF-Article 1.0
Author-Name: Jelani Wiltshire 
Author-X-Name-First: Jelani 
Author-X-Name-Last: Wiltshire 
Author-Name: Fred W. Huffer 
Author-X-Name-First: Fred W. 
Author-X-Name-Last: Huffer 
Author-Name: William C. Parker 
Author-X-Name-First: William C. 
Author-X-Name-Last: Parker 
Title: A general class of test statistics for Van Valen's Red Queen hypothesis 
Abstract:
 Van Valen's Red Queen hypothesis states that within a homogeneous
 taxonomic group the age is statistically independent of the rate of
 extinction. The case of the Red Queen hypothesis being addressed here is
 when the homogeneous taxonomic group is a group of similar species. Since
 Van Valen's work, various statistical approaches have been used to address
 the relationship between taxon age and the rate of extinction. We propose
 a general class of test statistics that can be used to test for the effect
 of age on the rate of extinction. These test statistics allow for a
 varying background rate of extinction and attempt to remove the effects of
 other covariates when assessing the effect of age on extinction. No model
 is assumed for the covariate effects. Instead we control for covariate
 effects by pairing or grouping together similar species. Simulations are
 used to compare the power of the statistics. We apply the test statistics
 to data on Foram extinctions and find that age has a positive effect on
 the rate of extinction. A derivation of the null distribution of one of
 the test statistics is provided in the supplementary material.
Journal: Journal of Applied Statistics 
Pages: 2028-2043 
Issue: 9 
Volume: 41 
Year: 2014 
Month: 9 
X-DOI: 10.1080/02664763.2014.907394 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.907394 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:9:p:2028-2043




Template-Type: ReDIF-Article 1.0
Author-Name: Federico Andreis 
Author-X-Name-First: Federico 
Author-X-Name-Last: Andreis 
Author-Name: Pier Alda Ferrari 
Author-X-Name-First: Pier Alda 
Author-X-Name-Last: Ferrari 
Title: Multidimensional item response theory models for dichotomous data in customer satisfaction evaluation 
Abstract:
 In this paper, multidimensional item response theory models for
 dichotomous data, developed in the fields of psychometrics and ability
 assessment, are discussed in connection with the problem of evaluating
 customer satisfaction. These models allow us to take into account latent
 constructs at various degrees of complexity and provide interesting new
 perspectives for services quality assessment. Markov chain Monte Carlo
 techniques are considered for estimation. An application to a real data
 set is also presented.
Journal: Journal of Applied Statistics 
Pages: 2044-2055 
Issue: 9 
Volume: 41 
Year: 2014 
Month: 9 
X-DOI: 10.1080/02664763.2014.907395 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.907395 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:9:p:2044-2055




Template-Type: ReDIF-Article 1.0
Author-Name: Josemar Rodrigues 
Author-X-Name-First: Josemar 
Author-X-Name-Last: Rodrigues 
Author-Name: Gauss M. Cordeiro 
Author-X-Name-First: Gauss M. 
Author-X-Name-Last: Cordeiro 
Author-Name: Jorge Bazan 
Author-X-Name-First: Jorge 
Author-X-Name-Last: Bazan 
Title: An extended exponentiated-G-negative binomial family with threshold effect 
Abstract:
 In this paper, we formulate a very flexible family of models which unifies
 most recent lifetime distributions. The main idea is to obtain a
 cumulative distribution function to transform the baseline distribution
 with an activation mechanism characterized by a latent threshold variable.
 The new family has a strong biological interpretation from the competitive
 risks point of view and the Box-Cox transformation provides an elegant
 manner to interpret the effect on the baseline distribution to obtain this
 alternative model. Several structural properties of the new model are
 investigated. A Bayesian analysis using Markov Chain Monte Carlo procedure
 is developed to illustrate with a real data the usefulness of the proposed
 family.
Journal: Journal of Applied Statistics 
Pages: 2056-2074 
Issue: 9 
Volume: 41 
Year: 2014 
Month: 9 
X-DOI: 10.1080/02664763.2014.907396 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.907396 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:9:p:2056-2074




Template-Type: ReDIF-Article 1.0
Author-Name: Daniel Thorburn 
Author-X-Name-First: Daniel 
Author-X-Name-Last: Thorburn 
Author-Name: Can Tongur 
Author-X-Name-First: Can 
Author-X-Name-Last: Tongur 
Title: Assessing direct and indirect seasonal decomposition in state space 
Abstract:
 The problem of whether seasonal decomposition should be used prior to or
 after aggregation of time series is quite old. We tackle the problem by
 using a state-space representation and the variance/covariance structure
 of a simplified one-component model. The variances of the estimated
 components in a two-series system are compared for direct and indirect
 approaches and also to a multivariate method. The covariance structure
 between the two time series is important for the relative efficiency.
 Indirect estimation is always best when the series are independent, but
 when the series or the measurement errors are negatively correlated,
 direct estimation may be much better in the above sense. Some covariance
 structures indicate that direct estimation should be used while others
 indicate that an indirect approach is more efficient. Signal-to-noise
 ratios and relative variances are used for inference.
Journal: Journal of Applied Statistics 
Pages: 2075-2091 
Issue: 9 
Volume: 41 
Year: 2014 
Month: 9 
X-DOI: 10.1080/02664763.2014.909779 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.909779 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:9:p:2075-2091




Template-Type: ReDIF-Article 1.0
Author-Name: Weiyan Mu 
Author-X-Name-First: Weiyan 
Author-X-Name-Last: Mu 
Author-Name: Shifeng Xiong 
Author-X-Name-First: Shifeng 
Author-X-Name-Last: Xiong 
Title: Some notes on robust sure independence screening 
Abstract:
 Sure independence screening (SIS) proposed by Fan and Lv [4] uses marginal
 correlations to select important variables, and has proven to be an
 efficient method for ultrahigh-dimensional linear models. This paper
 provides two robust versions of SIS against outliers. The two methods,
 respectively, replace the sample correlation in SIS with two robust
 measures, and screen variables by ranking them. Like SIS, the proposed
 methods are simple and fast. In addition, they are highly robust against a
 substantial fraction of outliers in the data. These features make them
 applicable to large datasets which may contain outliers. Simulation
 results are presented to show their effectiveness.
Journal: Journal of Applied Statistics 
Pages: 2092-2102 
Issue: 10 
Volume: 41 
Year: 2014 
Month: 10 
X-DOI: 10.1080/02664763.2014.909777 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.909777 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:10:p:2092-2102




Template-Type: ReDIF-Article 1.0
Author-Name: Joseph W. Sakshaug 
Author-X-Name-First: Joseph W. 
Author-X-Name-Last: Sakshaug 
Author-Name: Trivellore E. Raghunathan 
Author-X-Name-First: Trivellore E. 
Author-X-Name-Last: Raghunathan 
Title: Generating synthetic data to produce public-use microdata for small geographic areas based on complex sample survey data with application to the National Health Interview Survey 
Abstract:
 Small area statistics obtained from sample survey data provide a critical
 source of information used to study health, economic, and sociological
 trends. However, most large-scale sample surveys are not designed for the
 purpose of producing small area statistics. Moreover, data disseminators
 are prevented from releasing public-use microdata for small geographic
 areas for disclosure reasons; thus, limiting the utility of the data they
 collect. This research evaluates a synthetic data method, intended for
 data disseminators, for releasing public-use microdata for small
 geographic areas based on complex sample survey data. The method replaces
 all observed survey values with synthetic (or imputed) values generated
 from a hierarchical Bayesian model that explicitly accounts for complex
 sample design features, including stratification, clustering, and sampling
 weights. The method is applied to restricted microdata from the National
 Health Interview Survey and synthetic data are generated for both sampled
 and non-sampled small areas. The analytic validity of the resulting small
 area inferences is assessed by direct comparison with the actual data, a
 simulation study, and a cross-validation study.
Journal: Journal of Applied Statistics 
Pages: 2103-2122 
Issue: 10 
Volume: 41 
Year: 2014 
Month: 10 
X-DOI: 10.1080/02664763.2014.909778 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.909778 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:10:p:2103-2122




Template-Type: ReDIF-Article 1.0
Author-Name: A. Rouigueb 
Author-X-Name-First: A. 
Author-X-Name-Last: Rouigueb 
Author-Name: S. Chitroub 
Author-X-Name-First: S. 
Author-X-Name-Last: Chitroub 
Author-Name: A. Bouridane 
Author-X-Name-First: A. 
Author-X-Name-Last: Bouridane 
Title: Bayesian inference over ICA models: application to multibiometric score fusion with quality estimates 
Abstract:
 Bayesian networks are not well-formulated for continuous variables. The
 majority of recent works dealing with Bayesian inference are restricted
 only to special types of continuous variables such as the conditional
 linear Gaussian model for Gaussian variables. In this context, an exact
 Bayesian inference algorithm for clusters of continuous variables which
 may be approximated by independent component analysis models is proposed.
 The complexity in memory space is linear and the overfitting problem is
 attenuated, while the inference time is still exponential. Experiments for
 multibiometric score fusion with quality estimates are conducted, and it
 is observed that the performances are satisfactory compared to some known
 fusion techniques.
Journal: Journal of Applied Statistics 
Pages: 2123-2140 
Issue: 10 
Volume: 41 
Year: 2014 
Month: 10 
X-DOI: 10.1080/02664763.2014.909780 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.909780 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:10:p:2123-2140




Template-Type: ReDIF-Article 1.0
Author-Name: Abdul Haq 
Author-X-Name-First: Abdul 
Author-X-Name-Last: Haq 
Author-Name: Jennifer Brown 
Author-X-Name-First: Jennifer 
Author-X-Name-Last: Brown 
Author-Name: Elena Moltchanova 
Author-X-Name-First: Elena 
Author-X-Name-Last: Moltchanova 
Author-Name: Amer Ibrahim Al-Omari 
Author-X-Name-First: Amer Ibrahim 
Author-X-Name-Last: Al-Omari 
Title: Mixed ranked set sampling design 
Abstract:
 The main focus of agricultural, ecological and environmental studies is to
 develop well designed, cost-effective and efficient sampling designs.
 Ranked set sampling (RSS) is one method that leads to accomplish such
 objectives by incorporating expert knowledge to its advantage. In this
 paper, we propose an efficient sampling scheme, named mixed RSS (MxRSS),
 for estimation of the population mean and median. The MxRSS scheme is a
 suitable mixture of both simple random sampling (SRS) and RSS schemes. The
 MxRSS scheme provides an unbiased estimator of the population mean, and
 its variance is always less than the variance of sample mean based on SRS.
 For both symmetric and asymmetric populations, the mean and median
 estimators based on SRS, partial RSS (PRSS) and MxRSS schemes are
 compared. It turns out that the mean and median estimates under MxRSS
 scheme are more precise than those based on SRS scheme. Moreover, when
 estimating the mean of symmetric and some asymmetric populations, the mean
 estimates under MxRSS scheme are found to be more efficient than the mean
 estimates with PRSS scheme. An application to real data is also provided
 to illustrate the implementation of the proposed sampling scheme.
Journal: Journal of Applied Statistics 
Pages: 2141-2156 
Issue: 10 
Volume: 41 
Year: 2014 
Month: 10 
X-DOI: 10.1080/02664763.2014.909781 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.909781 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:10:p:2141-2156




Template-Type: ReDIF-Article 1.0
Author-Name: Jaehee Kim 
Author-X-Name-First: Jaehee 
Author-X-Name-Last: Kim 
Author-Name: Sooyoung Cheon 
Author-X-Name-First: Sooyoung 
Author-X-Name-Last: Cheon 
Title: Stochastic approximation Monte Carlo Gibbs sampling for structural change inference in a Bayesian heteroscedastic time series model 
Abstract:
 We consider a Bayesian deterministically trending dynamic time series
 model with heteroscedastic error variance, in which there exist multiple
 structural changes in level, trend and error variance, but the number of
 change-points and the timings are unknown. For a Bayesian analysis, a
 truncated Poisson prior and conjugate priors are used for the number of
 change-points and the distributional parameters, respectively. To identify
 the best model and estimate the model parameters simultaneously, we
 propose a new method by sequentially making use of the Gibbs sampler in
 conjunction with stochastic approximation Monte Carlo simulations, as an
 adaptive Monte Carlo algorithm. The numerical results are in favor of our
 method in terms of the quality of estimates.
Journal: Journal of Applied Statistics 
Pages: 2157-2177 
Issue: 10 
Volume: 41 
Year: 2014 
Month: 10 
X-DOI: 10.1080/02664763.2014.909782 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.909782 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:10:p:2157-2177




Template-Type: ReDIF-Article 1.0
Author-Name: Francis Pike 
Author-X-Name-First: Francis 
Author-X-Name-Last: Pike 
Author-Name: Lisa A. Weissfeld 
Author-X-Name-First: Lisa A. 
Author-X-Name-Last: Weissfeld 
Author-Name: Chung-Chou H. Chang 
Author-X-Name-First: Chung-Chou H. 
Author-X-Name-Last: Chang 
Title: Joint modeling of multivariate censored longitudinal and event time data with application to the Genetic Markers of Inflammation Study 
Abstract:
 The Genetic Markers of Inflammation Study (GenIMS) was conceived to
 investigate the role of severe sepsis, which is typically defined as
 system-wide multi-organ failure, on survival. One major hypothesis for
 this systemic collapse, and reduction in survival, is a cascade of
 pro-inflammatory and anti-inflammatory cytokines. In this paper, we
 devised a novel joint modeling strategy to evaluate the joint effect of
 longitudinal anti-inflammatory marker IL-6 and pro-inflammatory marker
 IL-10 on 90-day survival. We found that, on average, patients with high
 initial values of both IL-6 and IL-10, that tend to increase over time,
 are associated with a reduction in survival expectancy and that accounting
 for their assumed correlation was justified.
Journal: Journal of Applied Statistics 
Pages: 2178-2191 
Issue: 10 
Volume: 41 
Year: 2014 
Month: 10 
X-DOI: 10.1080/02664763.2014.909783 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.909783 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:10:p:2178-2191




Template-Type: ReDIF-Article 1.0
Author-Name: Jiin Choi 
Author-X-Name-First: Jiin 
Author-X-Name-Last: Choi 
Author-Name: Stewart J. Anderson 
Author-X-Name-First: Stewart J. 
Author-X-Name-Last: Anderson 
Author-Name: Thomas J. Richards 
Author-X-Name-First: Thomas J. 
Author-X-Name-Last: Richards 
Author-Name: Wesley K. Thompson 
Author-X-Name-First: Wesley K. 
Author-X-Name-Last: Thompson 
Title: Prediction of transplant-free survival in idiopathic pulmonary fibrosis patients using joint models for event times and mixed multivariate longitudinal data 
Abstract:
 We implement a joint model for mixed multivariate longitudinal
 measurements, applied to the prediction of time until lung transplant or
 death in idiopathic pulmonary fibrosis. Specifically, we formulate a
 unified Bayesian joint model for the mixed longitudinal responses and
 time-to-event outcomes. For the longitudinal model of continuous and
 binary responses, we investigate multivariate generalized linear mixed
 models using shared random effects. Longitudinal and time-to-event data
 are assumed to be independent conditional on available covariates and
 shared parameters. A Markov chain Monte Carlo algorithm, implemented in
 OpenBUGS, is used for parameter estimation. To illustrate practical
 considerations in choosing a final model, we fit 37 different candidate
 models using all possible combinations of random effects and employ a
 deviance information criterion to select a best-fitting model. We
 demonstrate the prediction of future event probabilities within a fixed
 time interval for patients utilizing baseline data, post-baseline
 longitudinal responses, and the time-to-event outcome. The performance of
 our joint model is also evaluated in simulation studies.
Journal: Journal of Applied Statistics 
Pages: 2192-2205 
Issue: 10 
Volume: 41 
Year: 2014 
Month: 10 
X-DOI: 10.1080/02664763.2014.909784 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.909784 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:10:p:2192-2205




Template-Type: ReDIF-Article 1.0
Author-Name: Jason S. Bergtold 
Author-X-Name-First: Jason S. 
Author-X-Name-Last: Bergtold 
Author-Name: Eberechukwu Onukwugha 
Author-X-Name-First: Eberechukwu 
Author-X-Name-Last: Onukwugha 
Title: The probabilistic reduction approach to specifying multinomial logistic regression models in health outcomes research 
Abstract:
 The paper provides a novel application of the probabilistic reduction (PR)
 approach to the analysis of multi-categorical outcomes. The PR approach,
 which systematically takes account of heterogeneity and functional form
 concerns, can improve the specification of binary regression models.
 However, its utility for systematically enriching the specification of and
 inference from models of multi-categorical outcomes has not been examined,
 while multinomial logistic regression models are commonly used for
 inference and, increasingly, prediction. Following a theoretical
 derivation of the PR-based multinomial logistic model (MLM), we compare
 functional specification and marginal effects from a traditional
 specification and a PR-based specification in a model of post-stroke
 hospital discharge disposition and find that the traditional MLM is
 misspecified. Results suggest that the impact on the reliability of
 substantive inferences from a misspecified model may be significant, even
 when model fit statistics do not suggest a strong lack of fit compared
 with a properly specified model using the PR approach. We identify
 situations under which a PR-based MLM specification can be advantageous to
 the applied researcher.
Journal: Journal of Applied Statistics 
Pages: 2206-2221 
Issue: 10 
Volume: 41 
Year: 2014 
Month: 10 
X-DOI: 10.1080/02664763.2014.909785 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.909785 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:10:p:2206-2221




Template-Type: ReDIF-Article 1.0
Author-Name: M. Nitti 
Author-X-Name-First: M. 
Author-X-Name-Last: Nitti 
Author-Name: E. Ciavolino 
Author-X-Name-First: E. 
Author-X-Name-Last: Ciavolino 
Title: A deflated indicators approach for estimating second-order reflective models through PLS-PM: an empirical illustration 
Abstract:
 The paper provides a procedure aimed at obtaining more interpretable
 second-order models estimated with the partial least squares-path
 modeling. Advantages in interpretation stem from the separation of the two
 sources of influence on the data. As a matter of fact, in hierarchical
 models effects on manifest variables (MVs) are assigned to both
 first-order (specific) factors and second-order (general) factors. In
 order to separate these overlapping contributions, MVs are deflated from
 the effect of the specific latent variables (LVs) and used as indicators
 of the second-order LV. A case study is presented in order to illustrate
 the application of the proposed method.
Journal: Journal of Applied Statistics 
Pages: 2222-2239 
Issue: 10 
Volume: 41 
Year: 2014 
Month: 10 
X-DOI: 10.1080/02664763.2014.909786 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.909786 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:10:p:2222-2239




Template-Type: ReDIF-Article 1.0
Author-Name: Wei Xiong 
Author-X-Name-First: Wei 
Author-X-Name-Last: Xiong 
Author-Name: Maozai Tian 
Author-X-Name-First: Maozai 
Author-X-Name-Last: Tian 
Title: A new model selection procedure based on dynamic quantile regression 
Abstract:
 In this article, we propose a novel robust data-analytic procedure,
 dynamic quantile regression (DQR), for model selection. It is robust in
 the sense that it can simultaneously estimate the coefficients and the
 distribution of errors over a large collection of error distributions even
 those that are heavy-tailed and may not even possess variances or means;
 and DQR is easy to implement in the sense that it does not need to decide
 in advance which quantile(s) should be gathered. Asymptotic properties of
 related estimators are derived. Simulations and illustrative real examples
 are also given.
Journal: Journal of Applied Statistics 
Pages: 2240-2256 
Issue: 10 
Volume: 41 
Year: 2014 
Month: 10 
X-DOI: 10.1080/02664763.2014.909787 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.909787 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:10:p:2240-2256




Template-Type: ReDIF-Article 1.0
Author-Name: Lin Liu 
Author-X-Name-First: Lin 
Author-X-Name-Last: Liu 
Author-Name: Jianbo Li 
Author-X-Name-First: Jianbo 
Author-X-Name-Last: Li 
Author-Name: Riquan Zhang 
Author-X-Name-First: Riquan 
Author-X-Name-Last: Zhang 
Title: General partially linear additive transformation model with right-censored data 
Abstract:
 We propose a class of general partially linear additive transformation
 models (GPLATM) with right-censored survival data in this work. The class
 of models are flexible enough to cover many commonly used parametric and
 nonparametric survival analysis models as its special cases. Based on the
 B spline interpolation technique, we estimate the unknown regression
 parameters and functions by the maximum marginal likelihood estimation
 method. One important feature of the estimation procedure is that it does
 not need the baseline and censoring cumulative density distributions. Some
 numerical studies illustrate that this procedure can work very well for
 the moderate sample size.
Journal: Journal of Applied Statistics 
Pages: 2257-2269 
Issue: 10 
Volume: 41 
Year: 2014 
Month: 10 
X-DOI: 10.1080/02664763.2014.909788 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.909788 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:10:p:2257-2269




Template-Type: ReDIF-Article 1.0
Author-Name: Feng-Shou Ko 
Author-X-Name-First: Feng-Shou 
Author-X-Name-Last: Ko 
Title: Identification of longitudinal biomarkers for survival by a score test derived from a joint model of longitudinal and competing risks data 
Abstract:
 In this paper, we consider joint modelling of repeated measurements and
 competing risks failure time data. For competing risks time data, a
 semiparametric mixture model in which proportional hazards model are
 specified for failure time models conditional on cause and a multinomial
 model for the marginal distribution of cause conditional on covariates. We
 also derive a score test based on joint modelling of repeated measurements
 and competing risks failure time data to identify longitudinal biomarkers
 or surrogates for a time to event outcome in competing risks data.
Journal: Journal of Applied Statistics 
Pages: 2270-2281 
Issue: 10 
Volume: 41 
Year: 2014 
Month: 10 
X-DOI: 10.1080/02664763.2014.909789 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.909789 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:10:p:2270-2281




Template-Type: ReDIF-Article 1.0
Author-Name: N. Withanage 
Author-X-Name-First: N. 
Author-X-Name-Last: Withanage 
Author-Name: A.R. de Leon 
Author-X-Name-First: A.R. 
Author-X-Name-Last: de Leon 
Author-Name: C.J. Rudnisky 
Author-X-Name-First: C.J. 
Author-X-Name-Last: Rudnisky 
Title: Joint estimation of disease-specific sensitivities and specificities in reader-based multi-disease diagnostic studies of paired organs 
Abstract:
 Binocular data typically arise in ophthalmology where pairs of eyes are
 evaluated, through some diagnostic procedure, for the presence of certain
 diseases or pathologies. Treating eyes as independent and adopting the
 usual approach in estimating the sensitivity and specificity of a
 diagnostic test ignores the correlation between fellow eyes. This may
 consequently yield incorrect estimates, especially of the standard errors.
 The paper is concerned with diagnostic studies wherein several diagnostic
 tests, or the same test read by several readers, are administered to
 identify one or more diseases. A likelihood-based method of estimating
 disease-specific sensitivities and specificities via hierarchical
 generalized linear mixed models is proposed to meaningfully delineate the
 various correlations in the data. The efficiency of the estimates is
 assessed in a simulation study. Data from a study on diabetic retinopathy
 are analyzed to illustrate the methodology.
Journal: Journal of Applied Statistics 
Pages: 2282-2297 
Issue: 10 
Volume: 41 
Year: 2014 
Month: 10 
X-DOI: 10.1080/02664763.2014.909790 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.909790 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:10:p:2282-2297




Template-Type: ReDIF-Article 1.0
Author-Name: Octavio Ramirez 
Author-X-Name-First: Octavio 
Author-X-Name-Last: Ramirez 
Author-Name: Jeff Mullen 
Author-X-Name-First: Jeff 
Author-X-Name-Last: Mullen 
Author-Name: Alba J. Collart 
Author-X-Name-First: Alba J. 
Author-X-Name-Last: Collart 
Title: Insights into the appropriate level of disaggregation for efficient time series model forecasting 
Abstract:
 This paper provides a potentially valuable insight on how to assess if the
 forecasts from an autoregressive moving average model based on aggregated
 data could be substantially improved through disaggregation. It is argued
 that, theoretically, the absence of moving average (MA) terms indicates
 that no forecasting efficiency improvements can be achieved through
 disaggregation. In practice, it is found that there is a strong
 correlation between the statistical significance of the MA component in
 the aggregate model and the magnitude of the forecast mean square error
 (MSE) decreases that can be achieved through disaggregation. That is, if a
 model includes significant MA terms, the forecast MSE improvements that
 may be gained from disaggregation could be substantial. Otherwise, they
 are more likely to be relatively small or non-existent.
Journal: Journal of Applied Statistics 
Pages: 2298-2311 
Issue: 10 
Volume: 41 
Year: 2014 
Month: 10 
X-DOI: 10.1080/02664763.2014.909791 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.909791 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:10:p:2298-2311




Template-Type: ReDIF-Article 1.0
Author-Name: Marta Blangiardo 
Author-X-Name-First: Marta 
Author-X-Name-Last: Blangiardo 
Author-Name: Gianluca Baio 
Author-X-Name-First: Gianluca 
Author-X-Name-Last: Baio 
Title: Evidence of bias in the Eurovision song contest: modelling the votes using Bayesian hierarchical models 
Abstract:
 The Eurovision Song Contest is an annual musical competition held among
 active members of the European Broadcasting Union since 1956. The event is
 televised live across Europe. Each participating country presents a song
 and receive a vote based on a combination of tele-voting and jury. Over
 the years, this has led to speculations of tactical voting, discriminating
 against some participants and thus inducing bias in the final results. In
 this paper we investigate the presence of positive or negative bias (which
 may roughly indicate favouritisms or discrimination) in the votes based on
 geographical proximity, migration and cultural characteristics of the
 participating countries through a Bayesian hierarchical model. Our
 analysis found no evidence of negative bias, although mild positive bias
 does seem to emerge systematically, linking voters to performers.
Journal: Journal of Applied Statistics 
Pages: 2312-2322 
Issue: 10 
Volume: 41 
Year: 2014 
Month: 10 
X-DOI: 10.1080/02664763.2014.909792 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.909792 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:10:p:2312-2322




Template-Type: ReDIF-Article 1.0
Author-Name: R.A.B. Assump&#xe7;&#xe3;o 
Author-X-Name-First: R.A.B. 
Author-X-Name-Last: Assump&#xe7;&#xe3;o 
Author-Name: M.A. Uribe-Opazo 
Author-X-Name-First: M.A. 
Author-X-Name-Last: Uribe-Opazo 
Author-Name: M. Galea 
Author-X-Name-First: M. 
Author-X-Name-Last: Galea 
Title: Analysis of local influence in geostatistics using Student's <italic>t</italic>-distribution 
Abstract:
 This article aims to estimate parameters of spatial variability with
 Student's <italic>t</italic>-distribution by the EM algorithm and present
 the study of local influence by means of two methods known as likelihood
 displacement and <italic>Q</italic>-displacement of likelihood, both using
 Student's <italic>t</italic>-distribution with fixed degrees of freedom
 (&#x3bd;). The results showed that both methods are effective in the
 identification of influential points.
Journal: Journal of Applied Statistics 
Pages: 2323-2341 
Issue: 11 
Volume: 41 
Year: 2014 
Month: 11 
X-DOI: 10.1080/02664763.2014.909793 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.909793 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:11:p:2323-2341




Template-Type: ReDIF-Article 1.0
Author-Name: Julian J. Faraway 
Author-X-Name-First: Julian J. 
Author-X-Name-Last: Faraway 
Title: Regression for non-Euclidean data using distance matrices 
Abstract:
 Regression methods for common data types such as measured, count and
 categorical variables are well understood but increasingly statisticians
 need ways to model relationships between variable types such as shapes,
 curves, trees, correlation matrices and images that do not fit into the
 standard framework. Data types that lie in metric spaces but not in vector
 spaces are difficult to use within the usual regression setting, either as
 the response and/or a predictor. We represent the information in these
 variables using distance matrices which requires only the specification of
 a distance function. A low-dimensional representation of such distance
 matrices can be obtained using methods such as multidimensional scaling.
 Once these variables have been represented as scores, an internal model
 linking the predictors and the responses can be developed using standard
 methods. We call scoring as the transformation from a new observation to a
 score, whereas backscoring is a method to represent a score as an
 observation in the data space. Both methods are essential for prediction
 and explanation. We illustrate the methodology for shape data,
 unregistered curve data and correlation matrices using motion capture data
 from an experiment to study the motion of children with cleft lip.
Journal: Journal of Applied Statistics 
Pages: 2342-2357 
Issue: 11 
Volume: 41 
Year: 2014 
Month: 11 
X-DOI: 10.1080/02664763.2014.909794 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.909794 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:11:p:2342-2357




Template-Type: ReDIF-Article 1.0
Author-Name: Maximilian Riedl 
Author-X-Name-First: Maximilian 
Author-X-Name-Last: Riedl 
Author-Name: Ingo Geishecker 
Author-X-Name-First: Ingo 
Author-X-Name-Last: Geishecker 
Title: Keep it simple: estimation strategies for ordered response models with fixed effects 
Abstract:
 By running Monte Carlo simulations, we compare different estimation
 strategies of ordered response models in the presence of non-random
 unobserved heterogeneity. We find that very simple binary recoding schemes
 deliver parameter estimates with very low bias and high efficiency.
 Furthermore, if the researcher is interested in the relative size of
 parameters the simple linear fixed effects model is the method of choice.
Journal: Journal of Applied Statistics 
Pages: 2358-2374 
Issue: 11 
Volume: 41 
Year: 2014 
Month: 11 
X-DOI: 10.1080/02664763.2014.909969 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.909969 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:11:p:2358-2374




Template-Type: ReDIF-Article 1.0
Author-Name: Manoj Kumar Rastogi 
Author-X-Name-First: Manoj Kumar 
Author-X-Name-Last: Rastogi 
Author-Name: Yogesh Mani Tripathi 
Author-X-Name-First: Yogesh Mani 
Author-X-Name-Last: Tripathi 
Title: Estimation for an inverted exponentiated Rayleigh distribution under type II progressive censoring 
Abstract:
 In this paper, we consider estimation of unknown parameters of an inverted
 exponentiated Rayleigh distribution under type II progressive censored
 samples. Estimation of reliability and hazard functions is also
 considered. Maximum likelihood estimators are obtained using the
 Expectation--Maximization (EM) algorithm. Further, we obtain expected
 Fisher information matrix using the missing value principle. Bayes
 estimators are derived under squared error and linex loss functions. We
 have used Lindley, and Tiernery and Kadane methods to compute these
 estimates. In addition, Bayes estimators are computed using importance
 sampling scheme as well. Samples generated from this scheme are further
 utilized for constructing highest posterior density intervals for unknown
 parameters. For comparison purposes asymptotic intervals are also
 obtained. A numerical comparison is made between proposed estimators using
 simulations and observations are given. A real-life data set is analyzed
 for illustrative purposes.
Journal: Journal of Applied Statistics 
Pages: 2375-2405 
Issue: 11 
Volume: 41 
Year: 2014 
Month: 11 
X-DOI: 10.1080/02664763.2014.910500 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.910500 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:11:p:2375-2405




Template-Type: ReDIF-Article 1.0
Author-Name: Eduardo F&#xe9; 
Author-X-Name-First: Eduardo 
Author-X-Name-Last: F&#xe9; 
Title: Estimation and inference in regression discontinuity designs with asymmetric kernels 
Abstract:
 We study the behaviour of the Wald estimator of causal effects in
 regression discontinuity design when local linear regression (LLR) methods
 are combined with an asymmetric gamma kernel. We show that the resulting
 statistic is no more complex to implement than existing methods, remains
 consistent at the usual non-parametric rate, and maintains an asymptotic
 normal distribution but, crucially, has bias and variance that do not
 depend on kernel-related constants. As a result, the new estimator is more
 efficient and yields more reliable inference. A limited Monte Carlo
 experiment is used to illustrate the efficiency gains. As a by product of
 the main discussion, we extend previous published work by establishing the
 asymptotic normality of the LLR estimator with a gamma kernel. Finally,
 the new method is used in a substantive application.
Journal: Journal of Applied Statistics 
Pages: 2406-2417 
Issue: 11 
Volume: 41 
Year: 2014 
Month: 11 
X-DOI: 10.1080/02664763.2014.910638 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.910638 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:11:p:2406-2417




Template-Type: ReDIF-Article 1.0
Author-Name: Kalyan Das 
Author-X-Name-First: Kalyan 
Author-X-Name-Last: Das 
Author-Name: Angshuman Sarkar 
Author-X-Name-First: Angshuman 
Author-X-Name-Last: Sarkar 
Title: Robust inference for generalized partially linear mixed models that account for censored responses and missing covariates -- an application to Arctic data analysis 
Abstract:
 In this article, we propose a family of bounded influence robust estimates
 for the parametric and non-parametric components of a generalized
 partially linear mixed model that are subject to censored responses and
 missing covariates. The asymptotic properties of the proposed estimates
 have been looked into. The estimates are obtained by using Monte Carlo
 expectation--maximization algorithm. An approximate method which reduces
 the computational time to a great extent is also proposed. A simulation
 study shows that performances of the two approaches are similar in terms
 of bias and mean square error. The analysis is illustrated through a study
 on the effect of environmental factors on the phytoplankton cell count.
Journal: Journal of Applied Statistics 
Pages: 2418-2436 
Issue: 11 
Volume: 41 
Year: 2014 
Month: 11 
X-DOI: 10.1080/02664763.2014.910886 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.910886 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:11:p:2418-2436




Template-Type: ReDIF-Article 1.0
Author-Name: Ji-Xia Wang 
Author-X-Name-First: Ji-Xia 
Author-X-Name-Last: Wang 
Author-Name: Qing-Xian Xiao 
Author-X-Name-First: Qing-Xian 
Author-X-Name-Last: Xiao 
Title: Local composite quantile regression estimation of time-varying parameter vector for multidimensional time-inhomogeneous diffusion models 
Abstract:
 This paper is dedicated to the study of the composite quantile regression
 (CQR) estimations of time-varying parameter vectors for multidimensional
 diffusion models. Based on the local linear fitting for parameter vectors,
 we propose the local linear CQR estimations of the drift parameter
 vectors, and verify their asymptotic biases, asymptotic variances and
 asymptotic normality. Moreover, we discuss the asymptotic relative
 efficiency (ARE) of the local linear CQR estimations with respect to the
 local linear least-squares estimations. We obtain that the local
 estimations that we proposed are much more efficient than the local linear
 least-squares estimations. Simulation studies are constructed to show the
 performance of the estimations proposed.
Journal: Journal of Applied Statistics 
Pages: 2437-2449 
Issue: 11 
Volume: 41 
Year: 2014 
Month: 11 
X-DOI: 10.1080/02664763.2014.911824 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.911824 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:11:p:2437-2449




Template-Type: ReDIF-Article 1.0
Author-Name: Sean L. Simpson 
Author-X-Name-First: Sean L. 
Author-X-Name-Last: Simpson 
Author-Name: Lloyd J. Edwards 
Author-X-Name-First: Lloyd J. 
Author-X-Name-Last: Edwards 
Author-Name: Martin A. Styner 
Author-X-Name-First: Martin A. 
Author-X-Name-Last: Styner 
Author-Name: Keith E. Muller 
Author-X-Name-First: Keith E. 
Author-X-Name-Last: Muller 
Title: Separability tests for high-dimensional, low-sample size multivariate repeated measures data 
Abstract:
 Longitudinal imaging studies have moved to the forefront of medical
 research due to their ability to characterize spatio-temporal features of
 biological structures across the lifespan. Valid inference in longitudinal
 imaging requires enough flexibility of the covariance model to allow
 reasonable fidelity to the true pattern. On the other hand, the existence
 of computable estimates demands a parsimonious parameterization of the
 covariance structure. Separable (Kronecker product) covariance models
 provide one such parameterization in which the spatial and temporal
 covariances are modeled separately. However, evaluating the validity of
 this parameterization in high dimensions remains a challenge. Here we
 provide a scientifically informed approach to assessing the adequacy of
 separable (Kronecker product) covariance models when the number of
 observations is large relative to the number of independent sampling units
 (sample size). We address both the general case, in which unstructured
 matrices are considered for each covariance model, and the structured
 case, which assumes a particular structure for each model. For the
 structured case, we focus on the situation where the within-subject
 correlation is believed to decrease exponentially in time and space as is
 common in longitudinal imaging studies. However, the provided framework
 equally applies to all covariance patterns used within the more general
 multivariate repeated measures context. Our approach provides useful
 guidance for high dimension, low-sample size data that preclude using
 standard likelihood-based tests. Longitudinal medical imaging data of
 caudate morphology in schizophrenia illustrate the approaches appeal.
Journal: Journal of Applied Statistics 
Pages: 2450-2461 
Issue: 11 
Volume: 41 
Year: 2014 
Month: 11 
X-DOI: 10.1080/02664763.2014.919251 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.919251 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:11:p:2450-2461




Template-Type: ReDIF-Article 1.0
Author-Name: Amadou Sawadogo 
Author-X-Name-First: Amadou 
Author-X-Name-Last: Sawadogo 
Author-Name: Dominique Lafon 
Author-X-Name-First: Dominique 
Author-X-Name-Last: Lafon 
Author-Name: Simplice Dossou Gb&#xe9;t&#xe9; 
Author-X-Name-First: Simplice Dossou 
Author-X-Name-Last: Gb&#xe9;t&#xe9; 
Title: Statistical analysis of rank data from a visual matching of colored textures 
Abstract:
 Nowadays, sensory properties of materials are subject to growing attention
 both in an hedonic point of view and in an utilitarian one. Hence, the
 formulation of the foundations of an instrumental metrological approach
 that will allow for the characterization of visual similarities between
 textures belonging to the same type becomes a challenge of the research
 activities in the domain of perception. In this paper, our specific
 objective is to link an instrumental approach of metrology of the
 assessment of visual textures with a metrology approach based on a
 softcopy experiment performed by human judges. The experiment consisted in
 ranking of isochromatic colored textures according to the visual contrast.
 A fixed effects additive model is considered for the analysis of the rank
 data collected from the softcopy experiment. The model is fitted to the
 data using a least-squares criterion. The resulting data analysis gives
 rise to a sensory scale that shows a non-linear correlation and a
 monotonic functional relationship with the physical attribute on which the
 ranking experiment is based. Furthermore, the capacity of the judges to
 discriminate the textures according to the visual contrast varies
 according to the color ranges and the textures types.
Journal: Journal of Applied Statistics 
Pages: 2462-2482 
Issue: 11 
Volume: 41 
Year: 2014 
Month: 11 
X-DOI: 10.1080/02664763.2014.920775 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.920775 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:11:p:2462-2482




Template-Type: ReDIF-Article 1.0
Author-Name: Riten Mitra 
Author-X-Name-First: Riten 
Author-X-Name-Last: Mitra 
Author-Name: Peter M&#xfc;ller 
Author-X-Name-First: Peter 
Author-X-Name-Last: M&#xfc;ller 
Author-Name: Yuan Ji 
Author-X-Name-First: Yuan 
Author-X-Name-Last: Ji 
Author-Name: Yitan Zhu 
Author-X-Name-First: Yitan 
Author-X-Name-Last: Zhu 
Author-Name: Gordon Mills 
Author-X-Name-First: Gordon 
Author-X-Name-Last: Mills 
Author-Name: Yiling Lu 
Author-X-Name-First: Yiling 
Author-X-Name-Last: Lu 
Title: A Bayesian hierarchical model for inference across related reverse phase protein arrays experiments 
Abstract:
 We consider inference for functional proteomics experiments that record
 protein activation over time following perturbation under different dose
 levels of several drugs. The main inference goal is the dependence
 structure of the selected proteins. A critical challenge is the lack of
 sufficient data under any one drug and dose level to allow meaningful
 inference on dependence structure. We propose a hierarchical model to
 implement the desired inference. The key element of the model is a shared
 dependence structure on (latent) binary indicators of protein activation.
Journal: Journal of Applied Statistics 
Pages: 2483-2492 
Issue: 11 
Volume: 41 
Year: 2014 
Month: 11 
X-DOI: 10.1080/02664763.2014.920776 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.920776 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:11:p:2483-2492




Template-Type: ReDIF-Article 1.0
Author-Name: Maryam Karimi 
Author-X-Name-First: Maryam 
Author-X-Name-Last: Karimi 
Author-Name: Sayed Mohammad Reza Alavi 
Author-X-Name-First: Sayed Mohammad Reza 
Author-X-Name-Last: Alavi 
Title: The effect of weight function on hypothesis testing in weighted sampling 
Abstract:
 In this paper the problem of statistical hypothesis testing under weighted
 sampling is considered for obtaining the most powerful test. Some
 simulated powers of tests, using the Monte Carlo method, are performed.
 Using a convenient sample of the specialist physicians of Social Security
 Organization of Ahvaz in Iran, two weighted samplings versus random
 sampling are tested. Among the three mentioned sampling, the size-biased
 sampling order 0.2 is more appropriate for the mechanism of data
 collection.
Journal: Journal of Applied Statistics 
Pages: 2493-2503 
Issue: 11 
Volume: 41 
Year: 2014 
Month: 11 
X-DOI: 10.1080/02664763.2014.920777 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.920777 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:11:p:2493-2503




Template-Type: ReDIF-Article 1.0
Author-Name: Dengke Xu 
Author-X-Name-First: Dengke 
Author-X-Name-Last: Xu 
Author-Name: Zhongzhan Zhang 
Author-X-Name-First: Zhongzhan 
Author-X-Name-Last: Zhang 
Author-Name: Liucang Wu 
Author-X-Name-First: Liucang 
Author-X-Name-Last: Wu 
Title: Bayesian analysis of joint mean and covariance models for longitudinal data 
Abstract:
 Efficient estimation of the regression coefficients in longitudinal data
 analysis requires a correct specification of the covariance structure. If
 misspecification occurs, it may lead to inefficient or biased estimators
 of parameters in the mean. One of the most commonly used methods for
 handling the covariance matrix is based on simultaneous modeling of the
 Cholesky decomposition. Therefore, in this paper, we reparameterize
 covariance structures in longitudinal data analysis through the modified
 Cholesky decomposition of itself. Based on this modified Cholesky
 decomposition, the within-subject covariance matrix is decomposed into a
 unit lower triangular matrix involving moving average coefficients and a
 diagonal matrix involving innovation variances, which are modeled as
 linear functions of covariates. Then, we propose a fully Bayesian
 inference for joint mean and covariance models based on this
 decomposition. A computational efficient Markov chain Monte Carlo method
 which combines the Gibbs sampler and Metropolis--Hastings algorithm is
 implemented to simultaneously obtain the Bayesian estimates of unknown
 parameters, as well as their standard deviation estimates. Finally,
 several simulation studies and a real example are presented to illustrate
 the proposed methodology.
Journal: Journal of Applied Statistics 
Pages: 2504-2514 
Issue: 11 
Volume: 41 
Year: 2014 
Month: 11 
X-DOI: 10.1080/02664763.2014.920778 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.920778 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:11:p:2504-2514




Template-Type: ReDIF-Article 1.0
Author-Name: Su Yun Kang 
Author-X-Name-First: Su Yun 
Author-X-Name-Last: Kang 
Author-Name: James McGree 
Author-X-Name-First: James 
Author-X-Name-Last: McGree 
Author-Name: Peter Baade 
Author-X-Name-First: Peter 
Author-X-Name-Last: Baade 
Author-Name: Kerrie Mengersen 
Author-X-Name-First: Kerrie 
Author-X-Name-Last: Mengersen 
Title: An investigation of the impact of various geographical scales for the specification of spatial dependence 
Abstract:
 Ecological studies are based on characteristics of groups of individuals,
 which are common in various disciplines including epidemiology. It is of
 great interest for epidemiologists to study the geographical variation of
 a disease by accounting for the positive spatial dependence between
 neighbouring areas. However, the choice of scale of the spatial
 correlation requires much attention. In view of a lack of studies in this
 area, this study aims to investigate the impact of differing definitions
 of geographical scales using a multilevel model. We propose a new approach
 -- the grid-based partitions and compare it with the popular census region
 approach. Unexplained geographical variation is accounted for via
 area-specific unstructured random effects and spatially structured random
 effects specified as an intrinsic conditional autoregressive process.
 Using grid-based modelling of random effects in contrast to the census
 region approach, we illustrate conditions where improvements are observed
 in the estimation of the linear predictor, random effects, parameters, and
 the identification of the distribution of residual risk and the aggregate
 risk in a study region. The study has found that grid-based modelling is a
 valuable approach for spatially sparse data while the statistical local
 area-based and grid-based approaches perform equally well for spatially
 dense data.
Journal: Journal of Applied Statistics 
Pages: 2515-2538 
Issue: 11 
Volume: 41 
Year: 2014 
Month: 11 
X-DOI: 10.1080/02664763.2014.920779 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.920779 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:11:p:2515-2538




Template-Type: ReDIF-Article 1.0
Author-Name: N. Lu 
Author-X-Name-First: N. 
Author-X-Name-Last: Lu 
Author-Name: T. Chen 
Author-X-Name-First: T. 
Author-X-Name-Last: Chen 
Author-Name: P. Wu 
Author-X-Name-First: P. 
Author-X-Name-Last: Wu 
Author-Name: D. Gunzler 
Author-X-Name-First: D. 
Author-X-Name-Last: Gunzler 
Author-Name: H. Zhang 
Author-X-Name-First: H. 
Author-X-Name-Last: Zhang 
Author-Name: H. He 
Author-X-Name-First: H. 
Author-X-Name-Last: He 
Author-Name: X.M. Tu 
Author-X-Name-First: X.M. 
Author-X-Name-Last: Tu 
Title: Functional response models for intraclass correlation coefficients 
Abstract:
 Intraclass correlation coefficients (ICC) are employed in a wide range of
 behavioral, biomedical, psychosocial, and health care related research for
 assessing reliability of continuous outcomes. The linear mixed-effects
 model (LMM) is the most popular approach for inference about the ICC.
 However, since LMM is a normal distribution-based model and non-normal
 data are the norm rather than the exception in most studies, its
 applications to real study data always beg the question of inference
 validity. In this paper, we propose a distribution-free alternative to
 provide robust inference based on the functional response models. We
 illustrate the performance of the new approach using both real and
 simulated data.
Journal: Journal of Applied Statistics 
Pages: 2539-2556 
Issue: 11 
Volume: 41 
Year: 2014 
Month: 11 
X-DOI: 10.1080/02664763.2014.920780 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.920780 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:11:p:2539-2556




Template-Type: ReDIF-Article 1.0
Author-Name: Sanku Dey 
Author-X-Name-First: Sanku 
Author-X-Name-Last: Dey 
Author-Name: Tanujit Dey 
Author-X-Name-First: Tanujit 
Author-X-Name-Last: Dey 
Title: On progressively censored generalized inverted exponential distribution 
Abstract:
 A generalized version of inverted exponential distribution (IED) is
 considered in this paper. This lifetime distribution is capable of
 modeling various shapes of failure rates, and hence various shapes of
 aging criteria. The model can be considered as another useful
 two-parameter generalization of the IED. Maximum likelihood and Bayes
 estimates for two parameters of the generalized inverted exponential
 distribution (GIED) are obtained on the basis of a progressively type-II
 censored sample. We also showed the existence, uniqueness and finiteness
 of the maximum likelihood estimates of the parameters of GIED based on
 progressively type-II censored data. Bayesian estimates are obtained using
 squared error loss function. These Bayesian estimates are evaluated by
 applying the Lindley's approximation method and via importance sampling
 technique. The importance sampling technique is used to compute the Bayes
 estimates and the associated credible intervals. We further consider the
 Bayes prediction problem based on the observed samples, and provide the
 appropriate predictive intervals. Monte Carlo simulations are performed to
 compare the performances of the proposed methods and a data set has been
 analyzed for illustrative purposes.
Journal: Journal of Applied Statistics 
Pages: 2557-2576 
Issue: 12 
Volume: 41 
Year: 2014 
Month: 12 
X-DOI: 10.1080/02664763.2014.922165 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.922165 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:12:p:2557-2576




Template-Type: ReDIF-Article 1.0
Author-Name: Jiaqing Xu 
Author-X-Name-First: Jiaqing 
Author-X-Name-Last: Xu 
Author-Name: Cheng Peng 
Author-X-Name-First: Cheng 
Author-X-Name-Last: Peng 
Title: Fitting and testing the Marshall-Olkin extended Weibull model with randomly censored data 
Abstract:
 The random censorship model (RCM) is commonly used in biomedical science
 for modeling life distributions. The popular non-parametric Kaplan-Meier
 estimator and some semiparametric models such as Cox proportional hazard
 models are extensively discussed in the literature. In this paper, we
 propose to fit the RCM with the assumption that the actual life
 distribution and the censoring distribution have a proportional odds
 relationship. The parametric model is defined using Marshall-Olkin's
 extended Weibull distribution. We utilize the maximum-likelihood procedure
 to estimate model parameters, the survival distribution, the mean residual
 life function, and the hazard rate as well. The proportional odds
 assumption is also justified by the newly proposed bootstrap
 Komogorov-Smirnov type goodness-of-fit test. A simulation study on the MLE
 of model parameters and the median survival time is carried out to assess
 the finite sample performance of the model. Finally, we implement the
 proposed model on two real-life data sets.
Journal: Journal of Applied Statistics 
Pages: 2577-2595 
Issue: 12 
Volume: 41 
Year: 2014 
Month: 12 
X-DOI: 10.1080/02664763.2014.922166 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.922166 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:12:p:2577-2595




Template-Type: ReDIF-Article 1.0
Author-Name: Athanasios Christou Micheas 
Author-X-Name-First: Athanasios 
Author-X-Name-Last: Christou Micheas 
Title: Hierarchical Bayesian modeling of marked non-homogeneous Poisson processes with finite mixtures and inclusion of covariate information 
Abstract:
 We investigate marked non-homogeneous Poisson processes using finite
 mixtures of bivariate normal components to model the spatial intensity
 function. We employ a Bayesian hierarchical framework for estimation of
 the parameters in the model, and propose an approach for including
 covariate information in this context. The methodology is exemplified
 through an application involving modeling of and inference for tornado
 occurrences.
Journal: Journal of Applied Statistics 
Pages: 2596-2615 
Issue: 12 
Volume: 41 
Year: 2014 
Month: 12 
X-DOI: 10.1080/02664763.2014.922167 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.922167 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:12:p:2596-2615




Template-Type: ReDIF-Article 1.0
Author-Name: Walmes Marques Zeviani 
Author-X-Name-First: Walmes Marques 
Author-X-Name-Last: Zeviani 
Author-Name: Paulo Justiniano Ribeiro 
Author-X-Name-First: Paulo Justiniano 
Author-X-Name-Last: Ribeiro 
Author-Name: Wagner Hugo Bonat 
Author-X-Name-First: Wagner Hugo 
Author-X-Name-Last: Bonat 
Author-Name: Silvia Emiko Shimakura 
Author-X-Name-First: Silvia Emiko 
Author-X-Name-Last: Shimakura 
Author-Name: Joel Augusto Muniz 
Author-X-Name-First: Joel Augusto 
Author-X-Name-Last: Muniz 
Title: The Gamma-count distribution in the analysis of experimental underdispersed data 
Abstract:
 Event counts are response variables with non-negative integer values
 representing the number of times that an event occurs within a fixed
 domain such as a time interval, a geographical area or a cell of a
 contingency table. Analysis of counts by Gaussian regression models
 ignores the discreteness, asymmetry and heteroscedasticity and is
 inefficient, providing unrealistic standard errors or possibly negative
 predictions of the expected number of events. The Poisson regression is
 the standard model for count data with underlying assumptions on the
 generating process which may be implausible in many applications.
 Statisticians have long recognized the limitation of imposing
 equidispersion under the Poisson regression model. A typical situation is
 when the conditional variance exceeds the conditional mean, in which case
 models allowing for overdispersion are routinely used. Less reported is
 the case of underdispersion with fewer modeling alternatives and
 assessments available in the literature. One of such alternatives, the
 Gamma-count model, is adopted here in the analysis of an agronomic
 experiment designed to investigate the effect of levels of defoliation on
 different phenological states upon the number of cotton bolls. Data set
 and code for analysis are available as online supplements. Results show
 improvements over the Poisson model and the semi-parametric quasi-Poisson
 model in capturing the observed variability in the data. Estimating rather
 than assuming the underlying variance process leads to important insights
 into the process.
Journal: Journal of Applied Statistics 
Pages: 2616-2626 
Issue: 12 
Volume: 41 
Year: 2014 
Month: 12 
X-DOI: 10.1080/02664763.2014.922168 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.922168 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:12:p:2616-2626




Template-Type: ReDIF-Article 1.0
Author-Name: Shin-Fu Tsai 
Author-X-Name-First: Shin-Fu 
Author-X-Name-Last: Tsai 
Title: A generalized test variable approach for grain yield comparisons of rice 
Abstract:
 Traditionally, an assessment for grain yield of rice is to split it into
 the yield components, including the number of panicles per plant, the
 number of spikelets per panicle, the 1000-grain weight and the
 filled-spikelet percentage, such that the yield performance can be
 individually evaluated through each component, and the products of yield
 components are employed for grain yield comparisons. However, when using
 the standard statistical methods, such as the two-sample
 <italic>t</italic>-test and analysis of variance, the assumptions of
 normality and variance homogeneity cannot be fully justified for comparing
 the grain yields, leading to that the empirical sizes cannot be adequately
 controlled. In this study, based on the concepts of generalized test
 variables and generalized <italic>p</italic>-values, a novel statistical
 testing procedure is developed for grain yield comparisons of rice. The
 proposed method is assessed by a series of numerical simulations.
 According to the simulation results, the proposed method performs
 reasonably well in Type I error control and empirical power. In addition,
 a real-life field experiment is analyzed by the proposed method, some
 productive rice varieties are screened out and suggested for a follow-up
 investigation.
Journal: Journal of Applied Statistics 
Pages: 2627-2638 
Issue: 12 
Volume: 41 
Year: 2014 
Month: 12 
X-DOI: 10.1080/02664763.2014.922169 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.922169 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:12:p:2627-2638




Template-Type: ReDIF-Article 1.0
Author-Name: Yangyi Xu 
Author-X-Name-First: Yangyi 
Author-X-Name-Last: Xu 
Author-Name: Inyoung Kim 
Author-X-Name-First: Inyoung 
Author-X-Name-Last: Kim 
Author-Name: Patrick Schaumont 
Author-X-Name-First: Patrick 
Author-X-Name-Last: Schaumont 
Title: Adaptive Bayes sum test for the equality of two nonparametric functions 
Abstract:
 The statistical difference among massive data sets or signals is of
 interest to many diverse fields including neurophysiology, imaging,
 engineering, and other related fields. However, such data often have
 nonlinear curves, depending on spatial patterns, and have non-white noise
 that leads to difficulties in testing the significant differences between
 them. In this paper, we propose an adaptive Bayes sum test that can test
 the significance between two nonlinear curves by taking into account
 spatial dependence and by reducing the effect of non-white noise. Our
 approach is developed by adapting the Bayes sum test statistic by Hart
 [13]. The spatial pattern is treated through Fourier transformation.
 Resampling techniques are employed to construct the empirical distribution
 of test statistic to reduce the effect of non-white noise. A simulation
 study suggests that our approach performs better than the alternative
 method, the adaptive Neyman test by Fan and Lin [9]. The usefulness of our
 approach is demonstrated with an application in the identification of
 electronic chips as well as an application to test the change of pattern
 of precipitations.
Journal: Journal of Applied Statistics 
Pages: 2639-2657 
Issue: 12 
Volume: 41 
Year: 2014 
Month: 12 
X-DOI: 10.1080/02664763.2014.925100 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.925100 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:12:p:2639-2657




Template-Type: ReDIF-Article 1.0
Author-Name: R. Chen 
Author-X-Name-First: R. 
Author-X-Name-Last: Chen 
Author-Name: T. Chen 
Author-X-Name-First: T. 
Author-X-Name-Last: Chen 
Author-Name: N. Lu 
Author-X-Name-First: N. 
Author-X-Name-Last: Lu 
Author-Name: H. Zhang 
Author-X-Name-First: H. 
Author-X-Name-Last: Zhang 
Author-Name: P. Wu 
Author-X-Name-First: P. 
Author-X-Name-Last: Wu 
Author-Name: C. Feng 
Author-X-Name-First: C. 
Author-X-Name-Last: Feng 
Author-Name: X.M. Tu 
Author-X-Name-First: X.M. 
Author-X-Name-Last: Tu 
Title: Extending the Mann-Whitney-Wilcoxon rank sum test to longitudinal regression analysis 
Abstract:
 Outliers are commonly observed in psychosocial research, generally
 resulting in biased estimates when comparing group differences using
 popular mean-based models such as the analysis of variance model.
 Rank-based methods such as the popular Mann-Whitney-Wilcoxon (MWW) rank
 sum test are more effective to address such outliers. However, available
 methods for inference are limited to cross-sectional data and cannot be
 applied to longitudinal studies under missing data. In this paper, we
 propose a generalized MWW test for comparing multiple groups with
 covariates within a longitudinal data setting, by utilizing the functional
 response models. Inference is based on a class of U-statistics-based
 weighted generalized estimating equations, providing consistent and
 asymptotically normal estimates not only under complete but missing data
 as well. The proposed approach is illustrated with both real and simulated
 study data.
Journal: Journal of Applied Statistics 
Pages: 2658-2675 
Issue: 12 
Volume: 41 
Year: 2014 
Month: 12 
X-DOI: 10.1080/02664763.2014.925101 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.925101 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:12:p:2658-2675




Template-Type: ReDIF-Article 1.0
Author-Name: Michael R. Crager 
Author-X-Name-First: Michael R. 
Author-X-Name-Last: Crager 
Author-Name: Gong Tang 
Author-X-Name-First: Gong 
Author-X-Name-Last: Tang 
Title: Patient-specific meta-analysis for risk assessment using multivariate proportional hazards regression 
Abstract:
 We propose a method for assessing an individual patient's risk of a future
 clinical event using clinical trial or cohort data and Cox proportional
 hazards regression, combining the information from several studies using
 meta-analysis techniques. The method combines patient-specific estimates
 of the log cumulative hazard across studies, weighting by the relative
 precision of the estimates, using either fixed- or random-effects
 meta-analysis calculations. Risk assessment can be done for any future
 patient using a few key summary statistics determined once and for all
 from each study. Generalizations of the method to logistic regression and
 linear models are immediate. We evaluate the methods using simulation
 studies and illustrate their application using real data.
Journal: Journal of Applied Statistics 
Pages: 2676-2695 
Issue: 12 
Volume: 41 
Year: 2014 
Month: 12 
X-DOI: 10.1080/02664763.2014.925102 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.925102 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:12:p:2676-2695




Template-Type: ReDIF-Article 1.0
Author-Name: Delphine Maucort-Boulch 
Author-X-Name-First: Delphine 
Author-X-Name-Last: Maucort-Boulch 
Author-Name: Pascal Roy 
Author-X-Name-First: Pascal 
Author-X-Name-Last: Roy 
Author-Name: Janez Stare 
Author-X-Name-First: Janez 
Author-X-Name-Last: Stare 
Title: On a measure of information gain for regression models in survival analysis 
Abstract:
 Papers dealing with measures of predictive power in survival analysis have
 seen their independence of censoring, or their estimates being unbiased
 under censoring, as the most important property. We argue that this
 property has been wrongly understood. Discussing the so-called measure of
 information gain, we point out that we cannot have unbiased estimates if
 all values, greater than a given time &tau;, are censored. This is due to
 the fact that censoring before &tau; has a different effect than censoring
 after &tau;. Such &tau; is often introduced by design of a study.
 Independence can only be achieved under the assumption of the model being
 valid after &tau;, which is impossible to verify. But if one is willing to
 make such an assumption, we suggest using multiple imputation to obtain a
 consistent estimate. We further show that censoring has different effects
 on the estimation of the measure for the Cox model than for parametric
 models, and we discuss them separately. We also give some warnings about
 the usage of the measure, especially when it comes to comparing
 essentially different models.
Journal: Journal of Applied Statistics 
Pages: 2696-2708 
Issue: 12 
Volume: 41 
Year: 2014 
Month: 12 
X-DOI: 10.1080/02664763.2014.926596 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.926596 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:12:p:2696-2708




Template-Type: ReDIF-Article 1.0
Author-Name: Jonathan Jaeger 
Author-X-Name-First: Jonathan 
Author-X-Name-Last: Jaeger 
Author-Name: Philippe Lambert 
Author-X-Name-First: Philippe 
Author-X-Name-Last: Lambert 
Title: Bayesian penalized smoothing approaches in models specified using differential equations with unknown error distributions 
Abstract:
 A full Bayesian approach based on ordinary differential equation
 (ODE)-penalized B-splines and penalized Gaussian mixture is proposed to
 jointly estimate ODE-parameters, state function and error distribution
 from the observation of some state functions involved in systems of affine
 differential equations. Simulations inspired by pharmacokinetic (PK)
 studies show that the proposed method provides comparable results to the
 method based on the standard ODE-penalized B-spline approach (i.e. with
 the Gaussian error distribution assumption) and outperforms the standard
 ODE-penalized B-splines when the distribution is not Gaussian. This
 methodology is illustrated on a PK data set.
Journal: Journal of Applied Statistics 
Pages: 2709-2726 
Issue: 12 
Volume: 41 
Year: 2014 
Month: 12 
X-DOI: 10.1080/02664763.2014.927839 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.927839 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:12:p:2709-2726




Template-Type: ReDIF-Article 1.0
Author-Name: Ram C. Kafle 
Author-X-Name-First: Ram C. 
Author-X-Name-Last: Kafle 
Author-Name: Netra Khanal 
Author-X-Name-First: Netra 
Author-X-Name-Last: Khanal 
Author-Name: Chris P. Tsokos 
Author-X-Name-First: Chris P. 
Author-X-Name-Last: Tsokos 
Title: Bayesian age-stratified joinpoint regression model: an application to lung and brain cancer mortality 
Abstract:
 Joinpoint regression model identifies significant changes in the trends of
 the incidence, mortality, and survival of a specific disease in a given
 population. The purpose of the present study is to develop an
 age-stratified Bayesian joinpoint regression model to describe mortality
 trend assuming that the observed counts are probabilistically
 characterized by the Poisson distribution. The proposed model is based on
 Bayesian model selection criteria with the smallest number of joinpoints
 that are sufficient to explain the Annual Percentage Change. The prior
 probability distributions are chosen in such a way that they are
 automatically derived from the model index contained in the model space.
 The proposed model and methodology estimates the age-adjusted mortality
 rates in different epidemiological studies to compare the trends by
 accounting the confounding effects of age. In developing the subject
 methods, we use the cancer mortality counts of adult lung and bronchus
 cancer, and brain and other Central Nervous System cancer patients
 obtained from the Surveillance Epidemiology and End Results data base of
 the National Cancer Institute.
Journal: Journal of Applied Statistics 
Pages: 2727-2742 
Issue: 12 
Volume: 41 
Year: 2014 
Month: 12 
X-DOI: 10.1080/02664763.2014.927840 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.927840 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:12:p:2727-2742




Template-Type: ReDIF-Article 1.0
Author-Name: Huaiye Zhang 
Author-X-Name-First: Huaiye 
Author-X-Name-Last: Zhang 
Author-Name: Inyoung Kim 
Author-X-Name-First: Inyoung 
Author-X-Name-Last: Kim 
Author-Name: Chun Gun Park 
Author-X-Name-First: Chun Gun 
Author-X-Name-Last: Park 
Title: Semiparametric Bayesian hierarchical models for heterogeneous population in nonlinear mixed effect model: application to gastric emptying studies 
Abstract:
 Gastric emptying studies are frequently used in medical research, both
 human and animal, when evaluating the effectiveness and determining the
 unintended side-effects of new and existing medications, diets, and
 procedures or interventions. It is essential that gastric emptying data be
 appropriately summarized before making comparisons between study groups of
 interest and to allow study the comparisons. Since gastric emptying data
 have a nonlinear emptying curve and are longitudinal data, nonlinear mixed
 effect (NLME) models can accommodate both the variation among measurements
 within individuals and the individual-to-individual variation. However,
 the NLME model requires strong assumptions that are often not satisfied in
 real applications that involve a relatively small number of subjects, have
 heterogeneous measurement errors, or have large variation among subjects.
 Therefore, we propose three semiparametric Bayesian NLMEs constructed with
 Dirichlet process priors, which automatically cluster sub-populations and
 estimate heterogeneous measurement errors. To compare three semiparametric
 models with the parametric model we propose a penalized posterior Bayes
 factor. We compare the performance of our semiparametric hierarchical
 Bayesian approaches with that of the parametric Bayesian hierarchical
 approach. Simulation results suggest that our semiparametric approaches
 are more robust and flexible. Our gastric emptying studies from equine
 medicine are used to demonstrate the advantage of our approaches.
Journal: Journal of Applied Statistics 
Pages: 2743-2760 
Issue: 12 
Volume: 41 
Year: 2014 
Month: 12 
X-DOI: 10.1080/02664763.2014.928848 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.928848 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:12:p:2743-2760




Template-Type: ReDIF-Article 1.0
Author-Name: E. Bahrami Samani 
Author-X-Name-First: E. Bahrami 
Author-X-Name-Last: Samani 
Title: Sensitivity analysis for the identifiability with application to latent random effect model for the mixed data 
Abstract:
 In this paper, we study the indentifiability of a latent random effect
 model for the mixed correlated continuous and ordinal longitudinal
 responses. We derive conditions for the identifiability of the covariance
 parameters of the responses. Also, we proposed sensitivity analysis to
 investigate the perturbation from the non-identifiability of the
 covariance parameters, it is shown how one can use some elements of
 covariance structure. These elements associate conditions for
 identifiability of the covariance parameters of the responses. Influence
 of small perturbation of these elements on maximal normal curvature is
 also studied. The model is illustrated using medical data.
Journal: Journal of Applied Statistics 
Pages: 2761-2776 
Issue: 12 
Volume: 41 
Year: 2014 
Month: 12 
X-DOI: 10.1080/02664763.2014.929641 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.929641 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:12:p:2761-2776




Template-Type: ReDIF-Article 1.0
Author-Name: Bo&zcaron;idar V. Popovi&cacute; 
Author-X-Name-First: Bo&zcaron;idar V. 
Author-X-Name-Last: Popovi&cacute; 
Title: Understanding advanced statistical methods 
Journal: Journal of Applied Statistics 
Pages: 2777-2777 
Issue: 12 
Volume: 41 
Year: 2014 
Month: 12 
X-DOI: 10.1080/02664763.2014.913838 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.913838 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:12:p:2777-2777




Template-Type: ReDIF-Article 1.0
Author-Name: S�ren Feodor Nielsen 
Author-X-Name-First: S�ren Feodor 
Author-X-Name-Last: Nielsen 
Title: An introduction to analysis of financial data with R 
Journal: Journal of Applied Statistics 
Pages: 2777-2778 
Issue: 12 
Volume: 41 
Year: 2014 
Month: 12 
X-DOI: 10.1080/02664763.2014.913839 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.913839 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:12:p:2777-2778




Template-Type: ReDIF-Article 1.0
Author-Name: Yves Laberge 
Author-X-Name-First: Yves 
Author-X-Name-Last: Laberge 
Title: Self-organised criticality: theory, models and characterisation 
Journal: Journal of Applied Statistics 
Pages: 2778-2779 
Issue: 12 
Volume: 41 
Year: 2014 
Month: 12 
X-DOI: 10.1080/02664763.2014.913844 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.913844 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:12:p:2778-2779




Template-Type: ReDIF-Article 1.0
Author-Name: Mariano Ruiz Espejo 
Author-X-Name-First: Mariano Ruiz 
Author-X-Name-Last: Espejo 
Title: Statistical methods for handling incomplete data 
Journal: Journal of Applied Statistics 
Pages: 2779-2780 
Issue: 12 
Volume: 41 
Year: 2014 
Month: 12 
X-DOI: 10.1080/02664763.2014.913845 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.913845 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:12:p:2779-2780




Template-Type: ReDIF-Article 1.0
Author-Name: Michail Tsagris 
Author-X-Name-First: Michail 
Author-X-Name-Last: Tsagris 
Title: Statistics through resampling methods and R, second edition 
Journal: Journal of Applied Statistics 
Pages: 2780-2781 
Issue: 12 
Volume: 41 
Year: 2014 
Month: 12 
X-DOI: 10.1080/02664763.2014.914130 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.914130 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:12:p:2780-2781




Template-Type: ReDIF-Article 1.0
Author-Name: Yves Laberge 
Author-X-Name-First: Yves 
Author-X-Name-Last: Laberge 
Title: Risk modelling in general insurance from principles to practice 
Journal: Journal of Applied Statistics 
Pages: 2781-2782 
Issue: 12 
Volume: 41 
Year: 2014 
Month: 12 
X-DOI: 10.1080/02664763.2014.913849 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.913849 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:41:y:2014:i:12:p:2781-2782




Template-Type: ReDIF-Article 1.0
Author-Name: Nilesh H. Shah 
Author-X-Name-First: Nilesh H. 
Author-X-Name-Last: Shah 
Author-Name: Alison E. Hipwell 
Author-X-Name-First: Alison E. 
Author-X-Name-Last: Hipwell 
Author-Name: Stephanie D. Stepp 
Author-X-Name-First: Stephanie D. 
Author-X-Name-Last: Stepp 
Author-Name: Chung-Chou H. Chang 
Author-X-Name-First: Chung-Chou H. 
Author-X-Name-Last: Chang 
Title: Measures of discrimination for latent group-based trajectory models 
Abstract:
 In clinical research, patient care decisions are often easier to make if
 patients are classified into a manageable number of groups based on
 homogeneous risk patterns. Investigators can use latent group-based
 trajectory modeling to estimate the posterior probabilities that an
 individual will be classified into a particular group of risk patterns.
 Although this method is increasingly used in clinical research, there is
 currently no measure that can be used to determine whether an individual's
 group assignment has a high level of discrimination. In this study, we
 propose a discrimination index and provide confidence intervals of the
 probability of the assigned group for each individual. We also propose a
 modified form of entropy to measure discrimination. The two proposed
 measures were applied to assess the group assignments of the longitudinal
 patterns of conduct disorders among young adolescent girls.
Journal: Journal of Applied Statistics 
Pages: 1-11 
Issue: 1 
Volume: 42 
Year: 2015 
Month: 1 
X-DOI: 10.1080/02664763.2014.928849 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.928849 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:1:p:1-11




Template-Type: ReDIF-Article 1.0
Author-Name: Arief Gusnanto 
Author-X-Name-First: Arief 
Author-X-Name-Last: Gusnanto 
Author-Name: Yudi Pawitan 
Author-X-Name-First: Yudi 
Author-X-Name-Last: Pawitan 
Title: Sparse alternatives to ridge regression: a random effects approach 
Abstract:
 In a calibration of near-infrared (NIR) instrument, we regress some
 chemical compositions of interest as a function of their NIR spectra. In
 this process, we have two immediate challenges: first, the number of
 variables exceeds the number of observations and, second, the
 multicollinearity between variables are extremely high. To deal with the
 challenges, prediction models that produce sparse solutions have recently
 been proposed. The term 'sparse' means that some model parameters are zero
 estimated and the other parameters are estimated naturally away from zero.
 In effect, a variable selection is embedded in the model to potentially
 achieve a better prediction. Many studies have investigated sparse
 solutions for latent variable models, such as partial least squares and
 principal component regression, and for direct regression models such as
 ridge regression (RR). However, in the latter, it mainly involves an
 <italic>L</italic><sub>1</sub> norm penalty to the objective function such
 as lasso regression. In this study, we investigate new sparse alternative
 models for RR within a random effects model framework, where we consider
 Cauchy and mixture-of-normals distributions on the random effects. The
 results indicate that the mixture-of-normals model produces a sparse
 solution with good prediction and better interpretation. We illustrate the
 methods using NIR spectra datasets from milk and corn specimens.
Journal: Journal of Applied Statistics 
Pages: 12-26 
Issue: 1 
Volume: 42 
Year: 2015 
Month: 1 
X-DOI: 10.1080/02664763.2014.929640 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.929640 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:1:p:12-26




Template-Type: ReDIF-Article 1.0
Author-Name: P. Elliott 
Author-X-Name-First: P. 
Author-X-Name-Last: Elliott 
Author-Name: K. Riggs 
Author-X-Name-First: K. 
Author-X-Name-Last: Riggs 
Title: Confidence regions for two proportions from independent negative binomial distributions 
Abstract:
 The negative binomial distribution offers an alternative view to the
 binomial distribution for modeling count data. This alternative view is
 particularly useful when the probability of success is very small,
 because, unlike the fixed sampling scheme of the binomial distribution,
 the inverse sampling approach allows one to collect enough data in order
 to adequately estimate the proportion of success. However, despite work
 that has been done on the joint estimation of two binomial proportions
 from independent samples, there is little, if any, similar work for
 negative binomial proportions. In this paper, we construct and investigate
 three confidence regions for two negative binomial proportions based on
 three statistics: the Wald (W), score (S) and likelihood ratio (LR)
 statistics. For large-to-moderate sample sizes, this paper finds that all
 three regions have good coverage properties, with comparable average areas
 for large sample sizes but with the S method producing the smaller regions
 for moderate sample sizes. In the small sample case, the LR method has
 good coverage properties, but often at the expense of comparatively larger
 areas. Finally, we apply these three regions to some real data for the
 joint estimation of liver damage rates in patients taking one of two
 drugs.
Journal: Journal of Applied Statistics 
Pages: 27-36 
Issue: 1 
Volume: 42 
Year: 2015 
Month: 1 
X-DOI: 10.1080/02664763.2014.929642 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.929642 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:1:p:27-36




Template-Type: ReDIF-Article 1.0
Author-Name: Stan Lipovetsky 
Author-X-Name-First: Stan 
Author-X-Name-Last: Lipovetsky 
Title: Analytical closed-form solution for binary logit regression by categorical predictors 
Abstract:
 In contrast to the common belief that the logit model has no analytical
 presentation, it is possible to find such a solution in the case of
 categorical predictors. This paper shows that a binary logistic regression
 by categorical explanatory variables can be constructed in a closed-form
 solution. No special software and no iterative procedures of nonlinear
 estimation are needed to obtain a model with all its parameters and
 characteristics, including coefficients of regression, their standard
 errors and <italic>t</italic>-statistics, as well as the residual and null
 deviances. The derivation is performed for logistic models with one binary
 or categorical predictor, and several binary or categorical predictors.
 The analytical formulae can be used for arithmetical calculation of all
 the parameters of the logit regression. The explicit expressions for the
 characteristics of logit regression are convenient for the analysis and
 interpretation of the results of logistic modeling.
Journal: Journal of Applied Statistics 
Pages: 37-49 
Issue: 1 
Volume: 42 
Year: 2015 
Month: 1 
X-DOI: 10.1080/02664763.2014.932760 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.932760 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:1:p:37-49




Template-Type: ReDIF-Article 1.0
Author-Name: Leena Pasanen 
Author-X-Name-First: Leena 
Author-X-Name-Last: Pasanen 
Author-Name: Lasse Holmstr�m 
Author-X-Name-First: Lasse 
Author-X-Name-Last: Holmstr�m 
Title: Bayesian scale space analysis of temporal changes in satellite images 
Abstract:
 We consider the detection of land cover changes using pairs of Landsat
 ETM+ satellite images. The images consist of eight spectral bands and to
 simplify the multidimensional change detection task, the image pair is
 first transformed to a one-dimensional image. When the transformation is
 non-linear, the true change in the images may be masked by complex noise.
 For example, when changes in the Normalized Difference Vegetation Index is
 considered, the variance of noise may not be constant over the image and
 methods based on image thresholding can be ineffective. To facilitate
 detection of change in such situations, we propose an approach that uses
 Bayesian statistical modeling and simulation-based inference. In order to
 detect both large and small scale changes, our method uses a scale space
 approach that employs multi-level smoothing. We demonstrate the technique
 using artificial test images and two pairs of real Landsat ETM+satellite
 images.
Journal: Journal of Applied Statistics 
Pages: 50-70 
Issue: 1 
Volume: 42 
Year: 2015 
Month: 1 
X-DOI: 10.1080/02664763.2014.932761 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.932761 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:1:p:50-70




Template-Type: ReDIF-Article 1.0
Author-Name: Evgeny D. Maslennikov 
Author-X-Name-First: Evgeny D. 
Author-X-Name-Last: Maslennikov 
Author-Name: Alexey V. Sulimov 
Author-X-Name-First: Alexey V. 
Author-X-Name-Last: Sulimov 
Author-Name: Igor A. Savkin 
Author-X-Name-First: Igor A. 
Author-X-Name-Last: Savkin 
Author-Name: Marina A. Evdokimova 
Author-X-Name-First: Marina A. 
Author-X-Name-Last: Evdokimova 
Author-Name: Dmitry A. Zateyshchikov 
Author-X-Name-First: Dmitry A. 
Author-X-Name-Last: Zateyshchikov 
Author-Name: Valery V. Nosikov 
Author-X-Name-First: Valery V. 
Author-X-Name-Last: Nosikov 
Author-Name: Vladimir B. Sulimov 
Author-X-Name-First: Vladimir B. 
Author-X-Name-Last: Sulimov 
Title: An intuitive risk factors search algorithm: usage of the Bayesian network technique in personalized medicine 
Abstract:
 The article focuses on the application of the Bayesian networks (BN)
 technique to problems of personalized medicine. The simple (intuitive)
 algorithm of BN optimization with respect to the number of nodes using
 naive network topology is developed. This algorithm allows to increase the
 BN prediction quality and to identify the most important variables of the
 network. The parallel program implementing the algorithm has demonstrated
 good scalability with an increase in the computational cores number, and
 it can be applied to the large patients database containing thousands of
 variables. This program is applied for the prediction for the unfavorable
 outcome of coronary artery disease (CAD) for patients who survived the
 acute coronary syndrome (ACS). As a result, the quality of the predictions
 of the investigated networks was significantly improved and the most
 important risk factors were detected. The significance of the tumor
 necrosis factor-alpha gene polymorphism for the prediction of the
 unfavorable outcome of CAD for patients survived after ACS was revealed
 for the first time.
Journal: Journal of Applied Statistics 
Pages: 71-87 
Issue: 1 
Volume: 42 
Year: 2015 
Month: 1 
X-DOI: 10.1080/02664763.2014.934664 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.934664 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:1:p:71-87




Template-Type: ReDIF-Article 1.0
Author-Name: Douglas M. Hawkins 
Author-X-Name-First: Douglas M. 
Author-X-Name-Last: Hawkins 
Author-Name: F. Lombard 
Author-X-Name-First: F. 
Author-X-Name-Last: Lombard 
Title: Segmentation of circular data 
Abstract:
 Circular data - data whose values lie in the interval [0,2&pi;) - are
 important in a number of application areas. In some, there is a suspicion
 that a sequence of circular readings may contain two or more segments
 following different models. An analysis may then seek to decide whether
 there are multiple segments, and if so, to estimate the changepoints
 separating them. This paper presents an optimal method for segmenting
 sequences of data following the von Mises distribution. It is shown by
 example that the method is also successful in data following a
 distribution with much heavier tails.
Journal: Journal of Applied Statistics 
Pages: 88-97 
Issue: 1 
Volume: 42 
Year: 2015 
Month: 1 
X-DOI: 10.1080/02664763.2014.934665 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.934665 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:1:p:88-97




Template-Type: ReDIF-Article 1.0
Author-Name: Peter Malave 
Author-X-Name-First: Peter 
Author-X-Name-Last: Malave 
Author-Name: Arkadiusz Sitek 
Author-X-Name-First: Arkadiusz 
Author-X-Name-Last: Sitek 
Title: Bayesian analysis of a one-compartment kinetic model used in medical imaging 
Abstract:
 Kinetic models are used extensively in science, engineering, and medicine.
 Mathematically, they are a set of coupled differential equations including
 a source function, otherwise known as an input function. We investigate
 whether parametric modeling of a noisy input function offers any benefit
 over the non-parametric input function in estimating kinetic parameters.
 Our analysis includes four formulations of Bayesian posteriors of model
 parameters where noise is taken into account in the likelihood functions.
 Posteriors are determined numerically with a Markov chain Monte Carlo
 simulation. We compare point estimates derived from the posteriors to a
 weighted non-linear least squares estimate. Results imply that parametric
 modeling of the input function does not improve the accuracy of model
 parameters, even with perfect knowledge of the functional form. Posteriors
 are validated using an unconventional utilization of the
 &chi;-super-2-test. We demonstrate that if the noise in the input function
 is not taken into account, the resulting posteriors are incorrect.
Journal: Journal of Applied Statistics 
Pages: 98-113 
Issue: 1 
Volume: 42 
Year: 2015 
Month: 1 
X-DOI: 10.1080/02664763.2014.934666 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.934666 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:1:p:98-113




Template-Type: ReDIF-Article 1.0
Author-Name: Afsane Rastegaran 
Author-X-Name-First: Afsane 
Author-X-Name-Last: Rastegaran 
Author-Name: Mohammad Reza Zadkarami 
Author-X-Name-First: Mohammad Reza 
Author-X-Name-Last: Zadkarami 
Title: A skew-normal random effects model for longitudinal ordinal categorical responses with missing data 
Abstract:
 Missing values are common in longitudinal data studies. The missing data
 mechanism is termed non-ignorable (NI) if the probability of missingness
 depends on the non-response (missing) observations. This paper presents a
 model for the ordinal categorical longitudinal data with NI non-monotone
 missing values. We assumed two separate models for the response and
 missing procedure. The response is modeled as ordinal logistic, whereas
 the logistic binary model is considered for the missing process. We employ
 these models in the context of so-called shared-parameter models, where
 the outcome and missing data models are connected by a common set of
 random effects. It is commonly assumed that the random effect follows the
 normal distribution in longitudinal data with or without missing data.
 This can be extremely restrictive in practice, and it may result in
 misleading statistical inferences. In this paper, we instead adopt a more
 flexible alternative distribution which is called the skew-normal
 distribution. The methodology is illustrated through an application to
 Schizophrenia Collaborative Study data [19] and a simulation.
Journal: Journal of Applied Statistics 
Pages: 114-126 
Issue: 1 
Volume: 42 
Year: 2015 
Month: 1 
X-DOI: 10.1080/02664763.2014.938223 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.938223 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:1:p:114-126




Template-Type: ReDIF-Article 1.0
Author-Name: Ronaldo Dias 
Author-X-Name-First: Ronaldo 
Author-X-Name-Last: Dias 
Author-Name: Nancy L. Garcia 
Author-X-Name-First: Nancy L. 
Author-X-Name-Last: Garcia 
Author-Name: Guilherme Ludwig 
Author-X-Name-First: Guilherme 
Author-X-Name-Last: Ludwig 
Author-Name: Marley A. Saraiva 
Author-X-Name-First: Marley A. 
Author-X-Name-Last: Saraiva 
Title: Aggregated functional data model for near-infrared spectroscopy calibration and prediction 
Abstract:
 Calibration and prediction for NIR spectroscopy data are performed based
 on a functional interpretation of the Beer-Lambert formula. Considering
 that, for each chemical sample, the resulting spectrum is a continuous
 curve obtained as the summation of overlapped absorption spectra from each
 analyte plus a Gaussian error, we assume that each individual spectrum can
 be expanded as a linear combination of B-splines basis. Calibration is
 then performed using two procedures for estimating the individual
 analytes' curves: basis smoothing and smoothing splines. Prediction is
 done by minimizing the square error of prediction. To assess the variance
 of the predicted values, we use a leave-one-out jackknife technique.
 Departures from the standard error models are discussed through a
 simulation study, in particular, how correlated errors impact on the
 calibration step and consequently on the analytes' concentration
 prediction. Finally, the performance of our methodology is demonstrated
 through the analysis of two publicly available datasets.
Journal: Journal of Applied Statistics 
Pages: 127-143 
Issue: 1 
Volume: 42 
Year: 2015 
Month: 1 
X-DOI: 10.1080/02664763.2014.938224 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.938224 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:1:p:127-143




Template-Type: ReDIF-Article 1.0
Author-Name: Ying-Ju Chen 
Author-X-Name-First: Ying-Ju 
Author-X-Name-Last: Chen 
Author-Name: Wei Ning 
Author-X-Name-First: Wei 
Author-X-Name-Last: Ning 
Author-Name: Arjun K. Gupta 
Author-X-Name-First: Arjun K. 
Author-X-Name-Last: Gupta 
Title: Jackknife empirical likelihood method for testing the equality of two variances 
Abstract:
 In this paper, we propose a nonparametric method based on jackknife
 empirical likelihood ratio to test the equality of two variances. The
 asymptotic distribution of the test statistic has been shown to follow
 &chi;-super-2 distribution with the degree of freedom 1. Simulations have
 been conducted to show the type I error and the power compared to Levene's
 test and <italic>F</italic> test under different distribution settings.
 The proposed method has been applied to a real data set to illustrate the
 testing procedure.
Journal: Journal of Applied Statistics 
Pages: 144-160 
Issue: 1 
Volume: 42 
Year: 2015 
Month: 1 
X-DOI: 10.1080/02664763.2014.938225 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.938225 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:1:p:144-160




Template-Type: ReDIF-Article 1.0
Author-Name: Enrico Ciavolino 
Author-X-Name-First: Enrico 
Author-X-Name-Last: Ciavolino 
Author-Name: Maurizio Carpita 
Author-X-Name-First: Maurizio 
Author-X-Name-Last: Carpita 
Author-Name: Amjad Al-Nasser 
Author-X-Name-First: Amjad 
Author-X-Name-Last: Al-Nasser 
Title: Modelling the quality of work in the Italian social co-operatives combining NPCA-RSM and SEM-GME approaches 
Abstract:
 The objective of this paper is to describe and analyse with appropriate
 statistical models the links between work quality latent factors. Due to
 the complexity of the task, the analysis is carried out through a two-step
 approach: <list list-type="bullet"><list-item>In the first step, we
 construct some multidimensional measures of the subjective quality of
 work, using <italic>nonlinear principal component analysis</italic> (NPCA)
 and <italic>Rasch analysis</italic> with the <italic>Rating Scale
 Model</italic> (NPCA-RSM);</list-item><list-item>In the second step, we
 adopt a <italic>Structural Equation Model</italic> based on
 <italic>generalized maximum entropy</italic> (SEM-GME) to integrate the
 measures achieved with the previous step and to evaluate the relationships
 between the subjective work quality latent factors.</list-item></list>
 Therefore, the novel aspects of this paper are the following: (i) The
 integration between the NPCA-RSM and SEM-GME, which allows reduction of
 the variables analysed and evaluation of the measurement errors; (ii) The
 formalization of a Job Satisfaction Model for the study of the
 relationships between the subjective work quality latent factors in the
 Italian social services sector.
Journal: Journal of Applied Statistics 
Pages: 161-179 
Issue: 1 
Volume: 42 
Year: 2015 
Month: 1 
X-DOI: 10.1080/02664763.2014.938226 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.938226 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:1:p:161-179




Template-Type: ReDIF-Article 1.0
Author-Name: Krystallenia Drosou 
Author-X-Name-First: Krystallenia 
Author-X-Name-Last: Drosou 
Author-Name: Andreas Artemiou 
Author-X-Name-First: Andreas 
Author-X-Name-Last: Artemiou 
Author-Name: Christos Koukouvinos 
Author-X-Name-First: Christos 
Author-X-Name-Last: Koukouvinos 
Title: A comparative study of the use of large margin classifiers on seismic data 
Abstract:
 In this work we present a study on the analysis of a large data set from
 seismology. A set of different large margin classifiers based on the
 well-known support vector machine (SVM) algorithm is used to classify the
 data into two classes based on their magnitude on the Richter scale. Due
 to the imbalance of nature between the two classes reweighing techniques
 are used to show the importance of reweighing algorithms. Moreover, we
 present an incremental algorithm to explore the possibility of predicting
 the strength of an earthquake with incremental techniques.
Journal: Journal of Applied Statistics 
Pages: 180-201 
Issue: 1 
Volume: 42 
Year: 2015 
Month: 1 
X-DOI: 10.1080/02664763.2014.938619 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.938619 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:1:p:180-201




Template-Type: ReDIF-Article 1.0
Author-Name: Nicholas Apergis 
Author-X-Name-First: Nicholas 
Author-X-Name-Last: Apergis 
Author-Name: Christina Christou 
Author-X-Name-First: Christina 
Author-X-Name-Last: Christou 
Author-Name: James E. Payne 
Author-X-Name-First: James E. 
Author-X-Name-Last: Payne 
Author-Name: James W. Saunoris 
Author-X-Name-First: James W. 
Author-X-Name-Last: Saunoris 
Title: The change in real interest rate persistence in OECD countries: evidence from modified panel ratio tests 
Abstract:
 This study examines whether real interest rates exhibit changes in
 persistence for a panel of Organization of Economic Cooperation and
 Development countries. The findings show that for long-term real interest
 rates there are changes in persistence from I(0) to I(1). For short-term
 real interest rates, the results display the absence of changes in
 persistence, while under cross-sectional dependence there is only weak
 evidence of changes in persistence from I(1) to I(0). The evidence of
 changes in persistence when the direction is considered unknown is even
 weaker.
Journal: Journal of Applied Statistics 
Pages: 202-213 
Issue: 1 
Volume: 42 
Year: 2015 
Month: 1 
X-DOI: 10.1080/02664763.2014.938620 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.938620 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:1:p:202-213




Template-Type: ReDIF-Article 1.0
Author-Name: Juan Martin Barrios 
Author-X-Name-First: Juan Martin 
Author-X-Name-Last: Barrios 
Author-Name: Eliane R. Rodrigues 
Author-X-Name-First: Eliane R. 
Author-X-Name-Last: Rodrigues 
Title: A queueing model to study the occurrence and duration of ozone exceedances in Mexico City 
Abstract:
 It is well known that long-term exposure to high levels of pollution is
 hazardous to human health. Therefore, it is important to study and
 understand the behavior of pollutants in general. In this work, we study
 the occurrence of a pollutant concentration's surpassing a given threshold
 (an exceedance) as well as the length of time that the concentration stays
 above it. A general
 <italic>N</italic>(<italic>t</italic>)/<italic>D</italic>/1 queueing model
 is considered to jointly analyze those problems. A non-homogeneous Poisson
 process is used to model the arrivals of clusters of exceedances.
 Geometric and generalized negative binomial distributions are used to
 model the amount of time (cluster size) that the pollutant concentration
 stays above the threshold. A mixture model is also used for the cluster
 size distribution. The rate function of the non-homogeneous Poisson
 process is assumed to be of either the Weibull or the Musa-Okumoto type.
 The selection of the model that best fits the data is performed using the
 Bayes discrimination method and the sum of absolute differences as well as
 using a graphical criterion. Results are applied to the daily maximum
 ozone measurements provided by the monitoring network of the Metropolitan
 Area of Mexico City.
Journal: Journal of Applied Statistics 
Pages: 214-230 
Issue: 1 
Volume: 42 
Year: 2015 
Month: 1 
X-DOI: 10.1080/02664763.2014.939613 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.939613 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:1:p:214-230




Template-Type: ReDIF-Article 1.0
Author-Name: Zeinab Amin 
Author-X-Name-First: Zeinab 
Author-X-Name-Last: Amin 
Author-Name: Maram Salem 
Author-X-Name-First: Maram 
Author-X-Name-Last: Salem 
Title: Bayesian modelling of health insurance losses 
Abstract:
 The purpose of this paper is to build a model for aggregate losses which
 constitutes a crucial step in evaluating premiums for health insurance
 systems. It aims at obtaining the predictive distribution of the aggregate
 loss within each age class of insured persons over the time horizon
 involved in planning employing the Bayesian methodology. The model
 proposed using the Bayesian approach is a generalization of the collective
 risk model, a commonly used model for analysing risk of an insurance
 system. Aggregate loss prediction is based on past information on size of
 loss, number of losses and size of population at risk. In modelling the
 frequency and severity of losses, the number of losses is assumed to
 follow a negative binomial distribution, individual loss sizes are
 independent and identically distributed exponential random variables,
 while the number of insured persons in a finite number of possible age
 groups is assumed to follow the multinomial distribution. Prediction of
 aggregate losses is based on the Gibbs sampling algorithm which
 incorporates the missing data approach.
Journal: Journal of Applied Statistics 
Pages: 231-251 
Issue: 2 
Volume: 42 
Year: 2015 
Month: 2 
X-DOI: 10.1080/02664763.2014.947247 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.947247 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:2:p:231-251




Template-Type: ReDIF-Article 1.0
Author-Name: Wagner Hugo Bonat 
Author-X-Name-First: Wagner Hugo 
Author-X-Name-Last: Bonat 
Author-Name: Paulo Justiniano Ribeiro 
Author-X-Name-First: Paulo Justiniano 
Author-X-Name-Last: Ribeiro 
Author-Name: Walmes Marques Zeviani 
Author-X-Name-First: Walmes Marques 
Author-X-Name-Last: Zeviani 
Title: Likelihood analysis for a class of beta mixed models 
Abstract:
 Beta regression is a suitable choice for modelling continuous response
 variables taking values on the unit interval. Data structures such as
 hierarchical, repeated measures and longitudinal typically induce extra
 variability and/or dependence and can be accounted for by the inclusion of
 random effects. In this sense, Statistical inference typically requires
 numerical methods, possibly combined with sampling algorithms. A class of
 Beta mixed models is adopted for the analysis of two real problems with
 grouped data structures. We focus on likelihood inference and describe the
 implemented algorithms. The first is a study on the life quality index of
 industry workers with data collected according to an hierarchical sampling
 scheme. The second is a study assessing the impact of hydroelectric power
 plants upon measures of water quality indexes up, downstream and at the
 reservoirs of the dammed rivers, with a nested and longitudinal data
 structure. Results from different algorithms are reported for comparison
 including from data-cloning, an alternative to numerical approximations
 which also allows assessing identifiability. Confidence intervals based on
 profiled likelihoods are compared with those obtained by asymptotic
 quadratic approximations, showing relevant differences for parameters
 related to the random effects. In both cases, the scientific hypothesis of
 interest was investigated by comparing alternative models, leading to
 relevant interpretations of the results within each context.
Journal: Journal of Applied Statistics 
Pages: 252-266 
Issue: 2 
Volume: 42 
Year: 2015 
Month: 2 
X-DOI: 10.1080/02664763.2014.947248 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.947248 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:2:p:252-266




Template-Type: ReDIF-Article 1.0
Author-Name: Jairo Alberto F&uacute;quene Pati�o 
Author-X-Name-First: Jairo Alberto 
Author-X-Name-Last: F&uacute;quene Pati�o 
Title: A semi-parametric Bayesian extreme value model using a Dirichlet process mixture of gamma densities 
Abstract:
 In this paper, we propose a model with a Dirichlet process mixture of
 gamma densities in the bulk part below threshold and a generalized Pareto
 density in the tail for extreme value estimation. The proposed model is
 simple and flexible for posterior density estimation and posterior
 inference for high quantiles. The model works well even for small sample
 sizes and in the absence of prior information. We evaluate the performance
 of the proposed model through a simulation study. Finally, the proposed
 model is applied to a real environmental data.
Journal: Journal of Applied Statistics 
Pages: 267-280 
Issue: 2 
Volume: 42 
Year: 2015 
Month: 2 
X-DOI: 10.1080/02664763.2014.947357 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.947357 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:2:p:267-280




Template-Type: ReDIF-Article 1.0
Author-Name: Young-Ju Kim 
Author-X-Name-First: Young-Ju 
Author-X-Name-Last: Kim 
Title: Nonparametric estimation of varying-coefficient single-index models 
Abstract:
 The varying-coefficient single-index model has two distinguishing
 features: partially linear varying-coefficient functions and a
 single-index structure. This paper proposes a nonparametric method based
 on smoothing splines for estimating varying-coefficient functions and an
 unknown link function. Moreover, the average derivative estimation method
 is applied to obtain the single-index parameter estimates. For interval
 inference, Bayesian confidence intervals were obtained based on Bayes
 models for varying-coefficient functions and the link function. The
 performance of the proposed method is examined both through simulations
 and by applying it to Boston housing data.
Journal: Journal of Applied Statistics 
Pages: 281-291 
Issue: 2 
Volume: 42 
Year: 2015 
Month: 2 
X-DOI: 10.1080/02664763.2014.947358 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.947358 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:2:p:281-291




Template-Type: ReDIF-Article 1.0
Author-Name: Xinzhong Bao 
Author-X-Name-First: Xinzhong 
Author-X-Name-Last: Bao 
Author-Name: Qiuyan Tao 
Author-X-Name-First: Qiuyan 
Author-X-Name-Last: Tao 
Author-Name: Hongyu Fu 
Author-X-Name-First: Hongyu 
Author-X-Name-Last: Fu 
Title: Dynamic financial distress prediction based on Kalman filtering 
Abstract:
 In models for predicting financial distress, ranging from traditional
 statistical models to artificial intelligence models, scholars have
 primarily paid attention to improving predictive accuracy as well as the
 progressivism and intellectualization of the prognostic methods. However,
 the extant models use static or short-term data rather than time-series
 data to draw inferences on future financial distress. If financial
 distress occurs at the end of a progressive process, then omitting time
 series of historical financial ratios from the analysis ignores the
 cumulative effect of previous financial ratios on the current
 consequences. This study incorporated the cumulative characteristics of
 financial distress by using the characteristics of a state space model
 that is able to perform long-term forecasts to dynamically predict an
 enterprise's financial distress. Kalman filtering is used to estimate the
 model parameters. Thus, the model constructed in this paper is a dynamic
 financial prediction model that has the benefit of forecasting over the
 long term. Additionally, current data are used to forecast the future
 annual financial position and to judge whether the establishment will be
 in financial distress.
Journal: Journal of Applied Statistics 
Pages: 292-308 
Issue: 2 
Volume: 42 
Year: 2015 
Month: 2 
X-DOI: 10.1080/02664763.2014.947359 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.947359 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:2:p:292-308




Template-Type: ReDIF-Article 1.0
Author-Name: Haiyan Zhao 
Author-X-Name-First: Haiyan 
Author-X-Name-Last: Zhao 
Author-Name: Fred Huffer 
Author-X-Name-First: Fred 
Author-X-Name-Last: Huffer 
Author-Name: Xu-Feng Niu 
Author-X-Name-First: Xu-Feng 
Author-X-Name-Last: Niu 
Title: Time-varying coefficient models with ARMA-GARCH structures for longitudinal data analysis 
Abstract:
 Time-varying coefficient models with autoregressive and
 moving-average-generalized autoregressive conditional heteroscedasticity
 structure are proposed for examining the time-varying effects of risk
 factors in longitudinal studies. Compared with existing models in the
 literature, the proposed models give explicit patterns for the
 time-varying coefficients. Maximum likelihood and marginal likelihood
 (based on a Laplace approximation) are used to estimate the parameters in
 the proposed models. Simulation studies are conducted to evaluate the
 performance of these two estimation methods, which is measured in terms of
 the Kullback-Leibler divergence and the root mean square error. The
 marginal likelihood approach leads to the more accurate parameter
 estimates, although it is more computationally intensive. The proposed
 models are applied to the Framingham Heart Study to investigate the
 time-varying effects of covariates on coronary heart disease incidence.
 The Bayesian information criterion is used for specifying the time series
 structures of the coefficients of the risk factors.
Journal: Journal of Applied Statistics 
Pages: 309-326 
Issue: 2 
Volume: 42 
Year: 2015 
Month: 2 
X-DOI: 10.1080/02664763.2014.949638 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.949638 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:2:p:309-326




Template-Type: ReDIF-Article 1.0
Author-Name: Yan Fang 
Author-X-Name-First: Yan 
Author-X-Name-Last: Fang 
Author-Name: Ling Liu 
Author-X-Name-First: Ling 
Author-X-Name-Last: Liu 
Author-Name: JinZhi Liu 
Author-X-Name-First: JinZhi 
Author-X-Name-Last: Liu 
Title: A dynamic double asymmetric copula generalized autoregressive conditional heteroskedasticity model: application to China's and US stock market 
Abstract:
 Modeling the relationship between multiple financial markets has had a
 great deal of attention in both literature and real-life applications. One
 state-of-the-art technique is that the individual financial market is
 modeled by generalized autoregressive conditional heteroskedasticity
 (GARCH) process, while market dependence is modeled by copula, e.g.
 dynamic asymmetric copula-GARCH. As an extension, we propose a dynamic
 double asymmetric copula (DDAC)-GARCH model to allow for the joint
 asymmetry caused by the negative shocks as well as by the copula model.
 Furthermore, our model adopts a more intuitive way of constructing the
 sample correlation matrix. Our new model yet satisfies the
 positive-definite condition as found in dynamic conditional
 correlation-GARCH and constant conditional correlation-GARCH models. The
 simulation study shows the performance of the maximum likelihood estimate
 for DDAC-GARCH model. As a case study, we apply this model to examine the
 dependence between China and US stock markets since 1990s. We conduct a
 series of likelihood ratio test tests that demonstrate our extension
 (dynamic double joint asymmetry) is adequate in dynamic dependence
 modeling. Also, we propose a simulation method involving the DDAC-GARCH
 model to estimate value at risk (VaR) of a portfolio. Our study shows that
 the proposed method depicts VaR much better than well-established
 variance-covariance method.
Journal: Journal of Applied Statistics 
Pages: 327-346 
Issue: 2 
Volume: 42 
Year: 2015 
Month: 2 
X-DOI: 10.1080/02664763.2014.949639 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.949639 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:2:p:327-346




Template-Type: ReDIF-Article 1.0
Author-Name: Kofi Placid Adragni 
Author-X-Name-First: Kofi Placid 
Author-X-Name-Last: Adragni 
Title: Independent screening in high-dimensional exponential family predictors' space 
Abstract:
 We present a methodology for screening predictors that, given the
 response, follow a one-parameter exponential family distributions.
 Screening predictors can be an important step in regressions when the
 number of predictors <italic>p</italic> is excessively large or larger
 than <italic>n</italic> the number of observations. We consider instances
 where a large number of predictors are suspected irrelevant for having no
 information about the response. The proposed methodology helps remove
 these irrelevant predictors while capturing those linearly or nonlinearly
 related to the response.
Journal: Journal of Applied Statistics 
Pages: 347-359 
Issue: 2 
Volume: 42 
Year: 2015 
Month: 2 
X-DOI: 10.1080/02664763.2014.949640 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.949640 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:2:p:347-359




Template-Type: ReDIF-Article 1.0
Author-Name: Pao-Sheng Shen 
Author-X-Name-First: Pao-Sheng 
Author-X-Name-Last: Shen 
Title: Median regression model with doubly truncated data 
Abstract:
 We study the problem of fitting a heteroscedastic median regression model
 with doubly truncated data. A self-consistency equation is proposed to
 obtain an estimator. We set up a least absolute deviation estimating
 function. We establish the consistency and asymptotic normality for the
 case when covariates are discrete. The finite sample performance of the
 proposed estimators are investigated through simulation studies. The
 proposed method is illustrated using the AIDS Blood Transfusion Data.
Journal: Journal of Applied Statistics 
Pages: 360-370 
Issue: 2 
Volume: 42 
Year: 2015 
Month: 2 
X-DOI: 10.1080/02664763.2014.951602 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.951602 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:2:p:360-370




Template-Type: ReDIF-Article 1.0
Author-Name: Kouji Tahata 
Author-X-Name-First: Kouji 
Author-X-Name-Last: Tahata 
Author-Name: Takuya Yoshimoto 
Author-X-Name-First: Takuya 
Author-X-Name-Last: Yoshimoto 
Title: Marginal asymmetry model for square contingency tables with ordered categories 
Abstract:
 For the analysis of square contingency tables with ordered categories,
 this paper proposes a model which indicates the structure of marginal
 asymmetry. The model states that the absolute values of logarithm of ratio
 of the cumulative probability that an observation will fall in row
 category <italic>i</italic> or below and column category
 <italic>i</italic>+1 or above to the corresponding cumulative probability
 that the observation falls in column category <italic>i</italic> or below
 and row category <italic>i</italic>+1 or above are constant for every
 <italic>i</italic>. We deal with the estimation problem for the model
 parameter and goodness-of-fit tests. Also we discuss the relationships
 between the model and a measure which represents the degree of departure
 from marginal homogeneity. Examples are given.
Journal: Journal of Applied Statistics 
Pages: 371-379 
Issue: 2 
Volume: 42 
Year: 2015 
Month: 2 
X-DOI: 10.1080/02664763.2014.951603 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.951603 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:2:p:371-379




Template-Type: ReDIF-Article 1.0
Author-Name: Bertil Wegmann 
Author-X-Name-First: Bertil 
Author-X-Name-Last: Wegmann 
Title: Bayesian comparison of private and common values in structural second-price auctions 
Abstract:
 Private and common values (CVs) are the two main competing valuation
 models in auction theory and empirical work. In the framework of
 second-price auctions, we compare the empirical performance of the
 independent private value (IPV) model to the CV model on a number of
 different dimensions, both on real data from eBay coin auctions and on
 simulated data. Both models fit the eBay data well with a slight edge for
 the CV model. However, the differences between the fit of the models seem
 to depend to some extent on the complexity of the models. According to log
 predictive score the IPV model predicts auction prices slightly better in
 most auctions, while the more robust CV model is much better at predicting
 auction prices in more unusual auctions. In terms of posterior odds, the
 CV model is clearly more supported by the eBay data.
Journal: Journal of Applied Statistics 
Pages: 380-397 
Issue: 2 
Volume: 42 
Year: 2015 
Month: 2 
X-DOI: 10.1080/02664763.2014.951604 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.951604 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:2:p:380-397




Template-Type: ReDIF-Article 1.0
Author-Name: T. G�recki 
Author-X-Name-First: T. 
Author-X-Name-Last: G�recki 
Title: Sequential combining in discriminant analysis 
Abstract:
 In practice, it often happens that we have a number of base methods of
 classification. We are not able to clearly determine which method is
 optimal in the sense of the smallest error rate. Then we have a combined
 method that allows us to consolidate information from multiple sources in
 a better classifier. I propose a different approach, a sequential
 approach. Sequentiality is understood here in the sense of adding
 posterior probabilities to the original data set and so created data are
 used during classification process. We combine posterior probabilities
 obtained from base classifiers using all combining methods. Finally, we
 combine these probabilities using a mean combining method. To the original
 data set we add obtained posterior probabilities as additional features.
 In each step we change our additional probabilities to achieve the minimum
 error rate for base methods. Experimental results on different data sets
 demonstrate that the method is efficient and that this approach
 outperforms base methods providing a reduction in the mean classification
 error rate.
Journal: Journal of Applied Statistics 
Pages: 398-408 
Issue: 2 
Volume: 42 
Year: 2015 
Month: 2 
X-DOI: 10.1080/02664763.2014.951605 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.951605 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:2:p:398-408




Template-Type: ReDIF-Article 1.0
Author-Name: Sterling McPherson 
Author-X-Name-First: Sterling 
Author-X-Name-Last: McPherson 
Author-Name: Celestina Barbosa-Leiker 
Author-X-Name-First: Celestina 
Author-X-Name-Last: Barbosa-Leiker 
Title: Biomarker classification derived from finite growth mixture modeling with a time-varying covariate: an example with phosphorus and glomerular filtration rate 
Abstract:
 Finite growth mixture modeling may prove extremely useful for identifying
 initial pharmacotherapeutic targets for clinical intervention purposes in
 chronic kidney disease. The primary goal of this research is to
 demonstrate and describe the process of identifying a longitudinal
 classification scheme to guide timing and dose of treatment in future
 randomized clinical trials. After discussing the statistical architecture,
 we describe the model selection and fit criteria in detail before choosing
 and selecting our final 4-class solution (BIC = 1612.577, BLRT of
 <italic>p ></italic> .001). The first class (highly elevated group) had an
 average starting point of 3.969 mg/dl of phosphorus at Visit 1, and
 increased 0.143 every two years until Visit 4. The second, elevated class
 had an average starting point of 3.460 mg/dl of phosphorus at Visit 1, and
 increased 0.101 every two years until Visit 4. The normative class had an
 average starting point of 3.019 mg/dl of phosphorus at Visit 1, and
 increased 0.099 every two years until Visit 4. Lastly, the low class had
 an average starting point of 2.525 mg/dl of phosphorus at Visit 1, and
 increased 0.158 every two years until Visit 4. We hope that this example
 will spur future applications in biomedical sciences in order to refine
 therapeutic targets and/or construct long-term risk categories.
Journal: Journal of Applied Statistics 
Pages: 409-427 
Issue: 2 
Volume: 42 
Year: 2015 
Month: 2 
X-DOI: 10.1080/02664763.2014.957263 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.957263 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:2:p:409-427




Template-Type: ReDIF-Article 1.0
Author-Name: Lei Shi 
Author-X-Name-First: Lei 
Author-X-Name-Last: Shi 
Author-Name: Md. Mostafizur Rahman 
Author-X-Name-First: Md. Mostafizur 
Author-X-Name-Last: Rahman 
Author-Name: Wen Gan 
Author-X-Name-First: Wen 
Author-X-Name-Last: Gan 
Author-Name: Jianhua Zhao 
Author-X-Name-First: Jianhua 
Author-X-Name-Last: Zhao 
Title: Stepwise local influence in generalized autoregressive conditional heteroskedasticity models 
Abstract:
 Detection of outliers or influential observations is an important work in
 statistical modeling, especially for the correlated time series data. In
 this paper we propose a new procedure to detect patch of influential
 observations in the generalized autoregressive conditional
 heteroskedasticity (GARCH) model. Firstly we compare the performance of
 innovative perturbation scheme, additive perturbation scheme and data
 perturbation scheme in local influence analysis. We find that the
 innovative perturbation scheme give better result than other two schemes
 although this perturbation scheme may suffer from masking effects. Then we
 use the stepwise local influence method under innovative perturbation
 scheme to detect patch of influential observations and uncover the masking
 effects. The simulated studies show that the new technique can
 successfully detect a patch of influential observations or outliers under
 innovative perturbation scheme. The analysis based on simulation studies
 and two real data sets show that the stepwise local influence method under
 innovative perturbation scheme is efficient for detecting multiple
 influential observations and dealing with masking effects in the GARCH
 model.
Journal: Journal of Applied Statistics 
Pages: 428-444 
Issue: 2 
Volume: 42 
Year: 2015 
Month: 2 
X-DOI: 10.1080/02664763.2014.957661 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.957661 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:2:p:428-444




Template-Type: ReDIF-Article 1.0
Author-Name: Rahim Alhamzawi 
Author-X-Name-First: Rahim 
Author-X-Name-Last: Alhamzawi 
Title: Model selection in quantile regression models 
Abstract:
 Lasso methods are regularisation and shrinkage methods widely used for
 subset selection and estimation in regression problems. From a Bayesian
 perspective, the Lasso-type estimate can be viewed as a Bayesian posterior
 mode when specifying independent Laplace prior distributions for the
 coefficients of independent variables [32]. A scale mixture of normal
 priors can also provide an adaptive regularisation method and represents
 an alternative model to the Bayesian Lasso-type model. In this paper, we
 assign a normal prior with mean zero and unknown variance for each
 quantile coefficient of independent variable. Then, a simple Markov Chain
 Monte Carlo-based computation technique is developed for quantile
 regression (QReg) models, including continuous, binary and left-censored
 outcomes. Based on the proposed prior, we propose a criterion for model
 selection in QReg models. The proposed criterion can be applied to
 classical least-squares, classical QReg, classical Tobit QReg and many
 others. For example, the proposed criterion can be applied to
 <monospace>rq()</monospace>, <monospace>lm()</monospace> and
 <monospace>crq()</monospace> which is available in an R package called
 Brq. Through simulation studies and analysis of a prostate cancer data
 set, we assess the performance of the proposed methods. The simulation
 studies and the prostate cancer data set analysis confirm that our methods
 perform well, compared with other approaches.
Journal: Journal of Applied Statistics 
Pages: 445-458 
Issue: 2 
Volume: 42 
Year: 2015 
Month: 2 
X-DOI: 10.1080/02664763.2014.959905 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.959905 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:2:p:445-458




Template-Type: ReDIF-Article 1.0
Author-Name: William Hughes 
Author-X-Name-First: William 
Author-X-Name-Last: Hughes 
Title: Paradoxes in scientific inference 
Journal: Journal of Applied Statistics 
Pages: 459-460 
Issue: 2 
Volume: 42 
Year: 2015 
Month: 2 
X-DOI: 10.1080/02664763.2014.942770 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.942770 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:2:p:459-460




Template-Type: ReDIF-Article 1.0
Author-Name: Abhay Kumar Tiwari 
Author-X-Name-First: Abhay Kumar 
Author-X-Name-Last: Tiwari 
Title: Getting started with business analytics 
Journal: Journal of Applied Statistics 
Pages: 460-461 
Issue: 2 
Volume: 42 
Year: 2015 
Month: 2 
X-DOI: 10.1080/02664763.2014.942771 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.942771 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:2:p:460-461




Template-Type: ReDIF-Article 1.0
Author-Name: Prabhanjan Tattar 
Author-X-Name-First: Prabhanjan 
Author-X-Name-Last: Tattar 
Title: Statistical methods with applications to demography and life insurance 
Journal: Journal of Applied Statistics 
Pages: 461-462 
Issue: 2 
Volume: 42 
Year: 2015 
Month: 2 
X-DOI: 10.1080/02664763.2014.942772 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.942772 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:2:p:461-462




Template-Type: ReDIF-Article 1.0
Author-Name: Hassan S. Bakouch 
Author-X-Name-First: Hassan S. 
Author-X-Name-Last: Bakouch 
Title: Generalized linear models for categorical and continuous limited dependent variables 
Journal: Journal of Applied Statistics 
Pages: 462-462 
Issue: 2 
Volume: 42 
Year: 2015 
Month: 2 
X-DOI: 10.1080/02664763.2014.942773 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.942773 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:2:p:462-462




Template-Type: ReDIF-Article 1.0
Author-Name: Wei Liu 
Author-X-Name-First: Wei 
Author-X-Name-Last: Liu 
Author-Name: Shuyou Li 
Author-X-Name-First: Shuyou 
Author-X-Name-Last: Li 
Title: A multiple imputation approach to nonlinear mixed-effects models with covariate measurement errors and missing values 
Abstract:
 In longitudinal studies, nonlinear mixed-effects models have been widely
 applied to describe the intra- and the inter-subject variations in data.
 The inter-subject variation usually receives great attention and it may be
 partially explained by time-dependent covariates. However, some covariates
 may be measured with substantial errors and may contain missing values. We
 proposed a multiple imputation method, implemented by a Markov Chain
 Monte-Carlo method along with Gibbs sampler, to address the covariate
 measurement errors and missing data in nonlinear mixed-effects models. The
 multiple imputation method is illustrated in a real data example.
 Simulation studies show that the multiple imputation method outperforms
 the commonly used naive methods.
Journal: Journal of Applied Statistics 
Pages: 463-476 
Issue: 3 
Volume: 42 
Year: 2015 
Month: 3 
X-DOI: 10.1080/02664763.2014.960372 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.960372 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:3:p:463-476




Template-Type: ReDIF-Article 1.0
Author-Name: Ahmed Hossain 
Author-X-Name-First: Ahmed 
Author-X-Name-Last: Hossain 
Author-Name: Joseph Beyene 
Author-X-Name-First: Joseph 
Author-X-Name-Last: Beyene 
Title: Application of skew-normal distribution for detecting differential expression to microRNA data 
Abstract:
 Traditional statistical modeling of continuous outcome variables relies
 heavily on the assumption of a normal distribution. However, in some
 applications, such as analysis of microRNA (miRNA) data, normality may not
 hold. Skewed distributions play an important role in such studies and
 might lead to robust results in the presence of extreme outliers. We apply
 a skew-normal (SN) distribution, which is indexed by three parameters
 (location, scale and shape), in the context of miRNA studies. We developed
 a test statistic for comparing means of two conditions replacing the
 normal assumption with SN distribution. We compared the performance of the
 statistic with other Wald-type statistics through simulations. Two real
 miRNA datasets are analyzed to illustrate the methods. Our simulation
 findings showed that the use of a SN distribution can result in improved
 identification of differentially expressed miRNAs, especially with
 markedly skewed data and when the two groups have different variances. It
 also appeared that the statistic with SN assumption performs comparably
 with other Wald-type statistics irrespective of the sample size or
 distribution. Moreover, the real dataset analyses suggest that the
 statistic with SN assumption can be used effectively for identification of
 important miRNAs. Overall, the statistic with SN distribution is useful
 when data are asymmetric and when the samples have different variances for
 the two groups.
Journal: Journal of Applied Statistics 
Pages: 477-491 
Issue: 3 
Volume: 42 
Year: 2015 
Month: 3 
X-DOI: 10.1080/02664763.2014.962490 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.962490 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:3:p:477-491




Template-Type: ReDIF-Article 1.0
Author-Name: Himadri Ghosh 
Author-X-Name-First: Himadri 
Author-X-Name-Last: Ghosh 
Author-Name: Bishal Gurung 
Author-X-Name-First: Bishal 
Author-X-Name-Last: Gurung 
Author-Name:  Prajneshu 
Author-X-Name-First:  
Author-X-Name-Last: Prajneshu 
Title: Kalman filter-based modelling and forecasting of stochastic volatility with threshold 
Abstract:
 We propose a parametric nonlinear time-series model, namely the
 Autoregressive-Stochastic volatility with threshold (AR-SVT) model with
 mean equation for forecasting level and volatility. Methodology for
 estimation of parameters of this model is developed by first obtaining
 recursive Kalman filter time-update equation and then employing the
 unrestricted quasi-maximum likelihood method. Furthermore, optimal
 one-step and two-step-ahead out-of-sample forecasts formulae along with
 forecast error variances are derived analytically by recursive use of
 conditional expectation and variance. As an illustration, volatile
 all-India monthly spices export during the period January 2006 to January
 2012 is considered. Entire data analysis is carried out using EViews and
 matrix laboratory (MATLAB) software packages. The AR-SVT model is fitted
 and interval forecasts for 10 hold-out data points are obtained.
 Superiority of this model for describing and forecasting over other
 competing models for volatility, namely AR-Generalized autoregressive
 conditional heteroscedastic, AR-Exponential GARCH, AR-Threshold GARCH, and
 AR-Stochastic volatility models is shown for the data under consideration.
 Finally, for the AR-SVT model, optimal out-of-sample forecasts along with
 forecasts of one-step-ahead variances are obtained.
Journal: Journal of Applied Statistics 
Pages: 492-507 
Issue: 3 
Volume: 42 
Year: 2015 
Month: 3 
X-DOI: 10.1080/02664763.2014.963524 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.963524 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:3:p:492-507




Template-Type: ReDIF-Article 1.0
Author-Name: Babulal Seal 
Author-X-Name-First: Babulal 
Author-X-Name-Last: Seal 
Author-Name: Sk Jakir Hossain 
Author-X-Name-First: Sk Jakir 
Author-X-Name-Last: Hossain 
Title: Empirical Bayes estimation of parameters in Markov transition probability matrix with computational methods 
Abstract:
 Empirical Bayes estimator for the transition probability matrix is worked
 out in the cases where we have belief regarding the parameters, For
 example, where the states seem to be equal or not. In both cases, priors
 are in accordance with our beliefs. Using EM algorithm, computational
 methods for different hyperparameters of the empirical Bayes are
 described. Also, robustness of empirical Bayes procedure is investigated.
Journal: Journal of Applied Statistics 
Pages: 508-519 
Issue: 3 
Volume: 42 
Year: 2015 
Month: 3 
X-DOI: 10.1080/02664763.2014.963525 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.963525 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:3:p:508-519




Template-Type: ReDIF-Article 1.0
Author-Name: E. Ciavolino 
Author-X-Name-First: E. 
Author-X-Name-Last: Ciavolino 
Author-Name: A. Calcagn&igrave; 
Author-X-Name-First: A. 
Author-X-Name-Last: Calcagn&igrave; 
Title: Generalized cross entropy method for analysing the SERVQUAL model 
Abstract:
 The aim of this paper is to define a new approach for the analysis of data
 collected by means of SERVQUAL questionnaires which is based on the
 generalized cross entropy (GCE) approach. In this respect, we firstly give
 a short review about the important role that SERVQUAL plays in the
 analysis of service quality as well as in the assessment of the
 competitiveness of public and private organizations. Secondly, we provide
 a formal definition of GCE approach together with a brief discussion about
 its features and usefulness. Finally, we show the application of GCE for a
 SERVQUAL model, based on a patients' satisfaction case study and we
 discuss the results obtained by using the proposed GCE-SERVQUAL
 methodology.
Journal: Journal of Applied Statistics 
Pages: 520-534 
Issue: 3 
Volume: 42 
Year: 2015 
Month: 3 
X-DOI: 10.1080/02664763.2014.963526 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.963526 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:3:p:520-534




Template-Type: ReDIF-Article 1.0
Author-Name: Paola Annoni 
Author-X-Name-First: Paola 
Author-X-Name-Last: Annoni 
Author-Name: Rainer Bruggemann 
Author-X-Name-First: Rainer 
Author-X-Name-Last: Bruggemann 
Author-Name: Lars Carlsen 
Author-X-Name-First: Lars 
Author-X-Name-Last: Carlsen 
Title: A multidimensional view on poverty in the European Union by partial order theory 
Abstract:
 Poverty can be seen as a multidimensional phenomenon described by a set of
 indicators, the poverty components. A one-dimensional measure of poverty
 serving as a ranking index can be obtained by combining the component
 indicators via aggregation techniques. Ranking indices are thought of as
 supporting political decisions. This paper proposes an alternative to
 aggregation based on simple concepts of partial order theory and
 illustrates the pros and cons of this approach taking as case study a
 multidimensional measure of poverty comprising three components - absolute
 poverty, relative poverty and income - computed for the European Union
 regions. The analysis enables one to highlight conflicts across the
 components with some regions detected as controversial, with, for example,
 low levels of relative poverty and high levels of monetary poverty. The
 partial order approach enables one to point to the regions with the most
 severe data conflicts and to the component indicators that cause these
 conflicts.
Journal: Journal of Applied Statistics 
Pages: 535-554 
Issue: 3 
Volume: 42 
Year: 2015 
Month: 3 
X-DOI: 10.1080/02664763.2014.978269 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.978269 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:3:p:535-554




Template-Type: ReDIF-Article 1.0
Author-Name: Nicholas Beyler 
Author-X-Name-First: Nicholas 
Author-X-Name-Last: Beyler 
Author-Name: Wayne Fuller 
Author-X-Name-First: Wayne 
Author-X-Name-Last: Fuller 
Author-Name: Sarah Nusser 
Author-X-Name-First: Sarah 
Author-X-Name-Last: Nusser 
Author-Name: Gregory Welk 
Author-X-Name-First: Gregory 
Author-X-Name-Last: Welk 
Title: Predicting objective physical activity from self-report surveys: a model validation study using estimated generalized least-squares regression 
Abstract:
 Physical activity measurements derived from self-report surveys are prone
 to measurement errors. Monitoring devices like accelerometers offer more
 objective measurements of physical activity, but are impractical for use
 in large-scale surveys. A model capable of predicting objective
 measurements of physical activity from self-reports would offer a
 practical alternative to obtaining measurements directly from monitoring
 devices. Using data from National Health and Nutrition Examination Survey
 2003-2006, we developed and validated models for predicting objective
 physical activity from self-report variables and other demographic
 characteristics. The prediction intervals produced by the models were
 large, suggesting that the ability to predict objective physical activity
 for individuals from self-reports is limited.
Journal: Journal of Applied Statistics 
Pages: 555-565 
Issue: 3 
Volume: 42 
Year: 2015 
Month: 3 
X-DOI: 10.1080/02664763.2014.978271 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.978271 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:3:p:555-565




Template-Type: ReDIF-Article 1.0
Author-Name: Tong Siu Tung Wong 
Author-X-Name-First: Tong Siu Tung 
Author-X-Name-Last: Wong 
Author-Name: Wai Keung Li 
Author-X-Name-First: Wai Keung 
Author-X-Name-Last: Li 
Title: Extreme values identification in regression using a peaks-over-threshold approach 
Abstract:
 The problem of heavy tail in regression models is studied. It is proposed
 that regression models are estimated by a standard procedure and a
 statistical check for heavy tail using residuals is conducted as a tool
 for regression diagnostic. Using the peaks-over-threshold approach, the
 generalized Pareto distribution quantifies the degree of heavy tail by the
 extreme value index. The number of excesses is determined by means of an
 innovative threshold model which partitions the random sample into extreme
 values and ordinary values. The overall decision on a significant heavy
 tail is justified by both a statistical test and a quantile-quantile plot.
 The usefulness of the approach includes justification of goodness of fit
 of the estimated regression model and quantification of the occurrence of
 extremal events. The proposed methodology is supplemented by surface ozone
 level in the city center of Leeds.
Journal: Journal of Applied Statistics 
Pages: 566-576 
Issue: 3 
Volume: 42 
Year: 2015 
Month: 3 
X-DOI: 10.1080/02664763.2014.978843 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.978843 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:3:p:566-576




Template-Type: ReDIF-Article 1.0
Author-Name: Luo Yong 
Author-X-Name-First: Luo 
Author-X-Name-Last: Yong 
Author-Name: Zhu Bo 
Author-X-Name-First: Zhu 
Author-X-Name-Last: Bo 
Author-Name: Tang Yong 
Author-X-Name-First: Tang 
Author-X-Name-Last: Yong 
Title: Dynamic optimal capital growth of diversified investment 
Abstract:
 We investigate the problem of dynamic optimal capital growth of
 diversified investment. A general framework that the trader maximize the
 expected log utility of long-term growth rate of initial wealth was
 developed. We show that the trader's fortune will exceed any fixed bound
 when the fraction is chosen less than critical value. But, if the fraction
 is larger than that value, ruin is almost sure. In order to maximize
 wealth, we should choose the optimal fraction at each trade. Empirical
 results with real financial data show the feasible allocation. The larger
 the fraction and hence the larger the chance of falling below the desired
 wealth growth path.
Journal: Journal of Applied Statistics 
Pages: 577-588 
Issue: 3 
Volume: 42 
Year: 2015 
Month: 3 
X-DOI: 10.1080/02664763.2014.980783 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.980783 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:3:p:577-588




Template-Type: ReDIF-Article 1.0
Author-Name: Guglielmo Maria Caporale 
Author-X-Name-First: Guglielmo Maria 
Author-X-Name-Last: Caporale 
Author-Name: Luis A. Gil-Alana 
Author-X-Name-First: Luis A. 
Author-X-Name-Last: Gil-Alana 
Title: Infant mortality rates: time trends and fractional integration 
Abstract:
 This paper examines the existence of time trends in the infant mortality
 rates in a number of countries in the twentieth century. We test for the
 presence of deterministic trends by adopting a linear model for the
 log-transformed data. Instead of assuming that the error term is a
 stationary <italic>I</italic>(0), or alternatively, a non-stationary
 <italic>I</italic>(1) process, we allow for the possibility of fractional
 integration and hence for a much greater degree of flexibility in the
 dynamic specification of the series. Indeed, once the linear trend is
 removed, all series appear to be <italic>I</italic>(<italic>d</italic>)
 with 0><italic>d</italic>>1, implying long-range dependence. As expected,
 the time trend coefficients are significantly negative, although of a
 different magnitude from those obtained assuming integer orders of
 differentiation.
Journal: Journal of Applied Statistics 
Pages: 589-602 
Issue: 3 
Volume: 42 
Year: 2015 
Month: 3 
X-DOI: 10.1080/02664763.2014.980785 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.980785 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:3:p:589-602




Template-Type: ReDIF-Article 1.0
Author-Name: Guoyi Zhang 
Author-X-Name-First: Guoyi 
Author-X-Name-Last: Zhang 
Author-Name: Zhongxue Chen 
Author-X-Name-First: Zhongxue 
Author-X-Name-Last: Chen 
Title: Inferences on correlation coefficients of bivariate log-normal distributions 
Abstract:
 This article considers inference on correlation coefficients of bivariate
 log-normal distributions. We developed generalized confidence intervals
 and hypothesis tests for the correlation coefficients, and extended the
 results to compare two independent correlations. Simulation studies show
 that the suggested methods work well. Two practical examples are used to
 illustrate the application of the proposed methods.
Journal: Journal of Applied Statistics 
Pages: 603-613 
Issue: 3 
Volume: 42 
Year: 2015 
Month: 3 
X-DOI: 10.1080/02664763.2014.980786 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.980786 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:3:p:603-613




Template-Type: ReDIF-Article 1.0
Author-Name: Pavel Krupskii 
Author-X-Name-First: Pavel 
Author-X-Name-Last: Krupskii 
Author-Name: Harry Joe 
Author-X-Name-First: Harry 
Author-X-Name-Last: Joe 
Title: Tail-weighted measures of dependence 
Abstract:
 Multivariate copula models are commonly used in place of Gaussian
 dependence models when plots of the data suggest tail dependence and tail
 asymmetry. In these cases, it is useful to have simple statistics to
 summarize the strength of dependence in different joint tails. Measures of
 monotone association such as Kendall's tau and Spearman's rho are
 insufficient to distinguish commonly used parametric bivariate families
 with different tail properties. We propose lower and upper tail-weighted
 bivariate measures of dependence as additional scalar measures to
 distinguish bivariate copulas with roughly the same overall monotone
 dependence. These measures allow the efficient estimation of strength of
 dependence in the joint tails and can be used as a guide for selection of
 bivariate linking copulas in vine and factor models as well as for
 assessing the adequacy of fit of multivariate copula models. We apply the
 tail-weighted measures of dependence to a financial data set and show that
 the measures better discriminate models with different tail properties
 compared to commonly used risk measures - the portfolio value-at-risk and
 conditional tail expectation.
Journal: Journal of Applied Statistics 
Pages: 614-629 
Issue: 3 
Volume: 42 
Year: 2015 
Month: 3 
X-DOI: 10.1080/02664763.2014.980787 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.980787 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:3:p:614-629




Template-Type: ReDIF-Article 1.0
Author-Name: Chun-Xia Zhang 
Author-X-Name-First: Chun-Xia 
Author-X-Name-Last: Zhang 
Author-Name: Guan-Wei Wang 
Author-X-Name-First: Guan-Wei 
Author-X-Name-Last: Wang 
Author-Name: Jun-Min Liu 
Author-X-Name-First: Jun-Min 
Author-X-Name-Last: Liu 
Title: RandGA: injecting randomness into parallel genetic algorithm for variable selection 
Abstract:
 Recently, the ensemble learning approaches have been proven to be quite
 effective for variable selection in linear regression models. In general,
 a good variable selection ensemble should consist of a diverse collection
 of strong members. Based on the parallel genetic algorithm (PGA) proposed
 in [41], in this paper, we propose a novel method RandGA through injecting
 randomness into PGA with the aim to increase the diversity among ensemble
 members. Using a number of simulated data sets, we show that the newly
 proposed method RandGA compares favorably with other variable selection
 techniques. As a real example, the new method is applied to the diabetes
 data.
Journal: Journal of Applied Statistics 
Pages: 630-647 
Issue: 3 
Volume: 42 
Year: 2015 
Month: 3 
X-DOI: 10.1080/02664763.2014.980788 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.980788 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:3:p:630-647




Template-Type: ReDIF-Article 1.0
Author-Name: C.B. Garc�a 
Author-X-Name-First: C.B. 
Author-X-Name-Last: Garc�a 
Author-Name: J. Garc�a 
Author-X-Name-First: J. 
Author-X-Name-Last: Garc�a 
Author-Name: M.M. L�pez Mart�n 
Author-X-Name-First: M.M. 
Author-X-Name-Last: L�pez Mart�n 
Author-Name: R. Salmer�n 
Author-X-Name-First: R. 
Author-X-Name-Last: Salmer�n 
Title: Collinearity: revisiting the variance inflation factor in ridge regression 
Abstract:
 Ridge regression has been widely applied to estimate under collinearity by
 defining a class of estimators that are dependent on the parameter
 <italic>k</italic>. The variance inflation factor (VIF) is applied to
 detect the presence of collinearity and also as an objective method to
 obtain the value of <italic>k</italic> in ridge regression. Contrarily to
 the definition of the VIF, the expressions traditionally applied in ridge
 regression do not necessarily lead to values of VIFs equal to or greater
 than 1. This work presents an alternative expression to calculate the VIF
 in ridge regression that satisfies the aforementioned condition and also
 presents other interesting properties.
Journal: Journal of Applied Statistics 
Pages: 648-661 
Issue: 3 
Volume: 42 
Year: 2015 
Month: 3 
X-DOI: 10.1080/02664763.2014.980789 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.980789 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:3:p:648-661




Template-Type: ReDIF-Article 1.0
Author-Name: Aviral Kumar Tiwari 
Author-X-Name-First: Aviral Kumar 
Author-X-Name-Last: Tiwari 
Author-Name: Alexander Ludwig 
Author-X-Name-First: Alexander 
Author-X-Name-Last: Ludwig 
Title: Short- and long-run rolling causality techniques and optimal window-wise lag selection: an application to the export-led growth hypothesis 
Abstract:
 The literature devoted to the export-led growth (ELG) hypothesis, which is
 of utmost importance for policymaking in emerging countries, provides
 mixed evidence for the validity of the hypothesis. Recent contributions
 focus on the time-dependence of the relationship between export and output
 growth using rolling causality techniques based on vector autoregressive
 models. These models focus on a short-term view which captures single
 policy-induced developments. However, long-term structural changes cannot
 be covered by examinations related to the short-term. This paper hence
 examines the time-varying validity of the ELG hypothesis for India for the
 period 1960-2011 using rolling causality techniques for both the short-run
 and long-run horizon. For the first time, window-wise optimal
 lag-selection procedures are applied in connection with these techniques.
 We find that exports long-run caused output growth from 1997 until 2009
 which can be seen as a consequence of political reforms of the 1990s that
 boosted economic growth by generating foreign direct investment
 opportunities and higher exports. For the short-run, export significantly
 caused output in the period 1998-2003 which followed a concentration of
 liberalization measures in 1997. Causality in the reversed direction, from
 output to exports, only seems to be relevant in the short-run.
Journal: Journal of Applied Statistics 
Pages: 662-675 
Issue: 3 
Volume: 42 
Year: 2015 
Month: 3 
X-DOI: 10.1080/02664763.2014.980790 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.980790 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:3:p:662-675




Template-Type: ReDIF-Article 1.0
Author-Name: J. Lee 
Author-X-Name-First: J. 
Author-X-Name-Last: Lee 
Author-Name: Y. Wu 
Author-X-Name-First: Y. 
Author-X-Name-Last: Wu 
Author-Name: H. Kim 
Author-X-Name-First: H. 
Author-X-Name-Last: Kim 
Title: Unbalanced data classification using support vector machines with active learning on scleroderma lung disease patterns 
Abstract:
 Unbalanced data classification has been a long-standing issue in the field
 of medical vision science. We introduced the methods of support vector
 machines (SVM) with active learning (AL) to improve prediction of
 unbalanced classes in the medical imaging field. A standard SVM algorithm
 with four different AL approaches are proposed: (1) The first one uses
 random sampling to select the initial pool with AL algorithm; (2) the
 second doubles the training instances of the rare category to reduce the
 unbalanced ratio before the AL algorithm; (3) the third uses a balanced
 pool with equal number from each category; and (4) the fourth uses a
 balanced pool and implements balanced sampling throughout the AL
 algorithm. Grid pixel data of two scleroderma lung disease patterns, lung
 fibrosis (LF), and honeycomb (HC) were extracted from computed tomography
 images of 71 patients to produce a training set of 348 HC and 3009 LF
 instances and a test set of 291 HC and 2665 LF. From our research, SVM
 with AL using balanced sampling compared to random sampling increased the
 test sensitivity of HC by 56% (17.5% vs. 73.5%) and 47% (23% vs. 70%) for
 the original and denoised dataset, respectively. SVM with AL with balanced
 sampling can improve the classification performances of unbalanced data.
Journal: Journal of Applied Statistics 
Pages: 676-689 
Issue: 3 
Volume: 42 
Year: 2015 
Month: 3 
X-DOI: 10.1080/02664763.2014.978270 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.978270 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:3:p:676-689




Template-Type: ReDIF-Article 1.0
Author-Name: Muhammad Shahbaz 
Author-X-Name-First: Muhammad 
Author-X-Name-Last: Shahbaz 
Author-Name: Aviral Kumar Tiwari 
Author-X-Name-First: Aviral Kumar 
Author-X-Name-Last: Tiwari 
Author-Name: Mohammad Iqbal Tahir 
Author-X-Name-First: Mohammad Iqbal 
Author-X-Name-Last: Tahir 
Title: Analyzing time-frequency relationship between oil price and exchange rate in Pakistan through wavelets 
Abstract:
 This study analyzed the time-frequency relationship between oil price and
 exchange rate for Pakistan by using measures of continuous wavelet such as
 wavelet power, cross-wavelet power, and cross-wavelet coherency (WTC). The
 results of cross-wavelet analysis indicated that covariance between oil
 price and exchange rate is unable to give clear-cut results, but both
 variables have been in phase and out phase (i.e. they are anti-cyclical
 and cyclical in nature) in some or other durations. However, results of
 squared wavelet coherence disclose that both variables are out of phase
 and real exchange rate was leading during the entire period studied,
 corresponding to the 10-15 months' scale<italic>.</italic> These results
 are the unique contribution of the present study, which would have not
 been drawn if one would have utilized any other time series or frequency
 domain-based approach. This finding provides evidence of anti-cyclical
 relationship between oil price and real effective exchange rate; however,
 in most of the period studied, real exchange rate was leading and passing
 anti-cycle effects on oil price shocks which is the major contribution of
 the study.
Journal: Journal of Applied Statistics 
Pages: 690-704 
Issue: 4 
Volume: 42 
Year: 2015 
Month: 4 
X-DOI: 10.1080/02664763.2014.980784 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.980784 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:4:p:690-704




Template-Type: ReDIF-Article 1.0
Author-Name: Resit &Ccedil;elik 
Author-X-Name-First: Resit 
Author-X-Name-Last: &Ccedil;elik 
Title: Stabilizing heteroscedasticity for butterfly-distributed residuals by the weighting absolute centered external variable 
Abstract:
 In the current study, a new method by the weighting absolute centered
 external variable (WCEV) was proposed to stabilize heteroscedasticity for
 butterfly-distributed residuals (BDRs). After giving brief information
 about heteroscedasticity and BDRs, WCEV was introduced. The WCEV and
 commonly used variance stabilizing methods are compared on a simple and a
 multiple regression model. The WCEV was also tested for other type of
 heteroscedasticity patterns. In addition to heteroscedasticity, other
 regression assumptions were checked for the WCEV.
Journal: Journal of Applied Statistics 
Pages: 705-721 
Issue: 4 
Volume: 42 
Year: 2015 
Month: 4 
X-DOI: 10.1080/02664763.2014.980791 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.980791 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:4:p:705-721




Template-Type: ReDIF-Article 1.0
Author-Name: L. Alam� 
Author-X-Name-First: L. 
Author-X-Name-Last: Alam� 
Author-Name: D. Conesa 
Author-X-Name-First: D. 
Author-X-Name-Last: Conesa 
Author-Name: A. Forte 
Author-X-Name-First: A. 
Author-X-Name-Last: Forte 
Author-Name: E. Tortosa-Ausina 
Author-X-Name-First: E. 
Author-X-Name-Last: Tortosa-Ausina 
Title: The geography of Spanish bank branches 
Abstract:
 This article analyzes the determinants of bank branch location in Spain
 taking the role of geography explicitly into account. After a long period
 of intense territorial expansion, especially by savings banks, many of
 these firms are now involved in merger processes triggered off by the
 financial crisis, most of which entail the closing of many branches.
 However, given the contributions of this type of banks to limit financial
 exclusion, this process might exacerbate the consequences of the crisis
 for some disadvantaged social groups. Related problems such as new banking
 regulation initiatives (Basel III), or the current excess capacity in the
 sector add further relevance to this problem. We address this issue from a
 Bayesian perspective, using a Poisson regression model within the
 framework of generalized linear mixed models. This proposal allows us to
 assess whether over-branching or under-branching has taken place. Our
 results suggest, among other findings, that both phenomena are present in
 the Spanish banking sector, although the implications for the three types
 of banks in the industry, namely commercial banks, savings banks or credit
 unions, vary a great deal.
Journal: Journal of Applied Statistics 
Pages: 722-744 
Issue: 4 
Volume: 42 
Year: 2015 
Month: 4 
X-DOI: 10.1080/02664763.2014.980792 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.980792 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:4:p:722-744




Template-Type: ReDIF-Article 1.0
Author-Name: Ying-zi Fu 
Author-X-Name-First: Ying-zi 
Author-X-Name-Last: Fu 
Author-Name: Pei-xiao Chu 
Author-X-Name-First: Pei-xiao 
Author-X-Name-Last: Chu 
Author-Name: Li-ying Lu 
Author-X-Name-First: Li-ying 
Author-X-Name-Last: Lu 
Title: A Bayesian approach of joint models for clustered zero-inflated count data with skewness and measurement errors 
Abstract:
 Count data with excess zeros are widely encountered in the fields of
 biomedical, medical, public health and social survey, etc. Zero-inflated
 Poisson (ZIP) regression models with mixed effects are useful tools for
 analyzing such data, in which covariates are usually incorporated in the
 model to explain inter-subject variation and normal distribution is
 assumed for both random effects and random errors. However, in many
 practical applications, such assumptions may be violated as the data often
 exhibit skewness and some covariates may be measured with measurement
 errors. In this paper, we deal with these issues simultaneously by
 developing a Bayesian joint hierarchical modeling approach. Specifically,
 by treating intercepts and slopes in logistic and Poisson regression as
 random, a flexible two-level ZIP regression model is proposed, where a
 covariate process with measurement errors is established and a
 skew-t-distribution is considered for both random errors and random
 effects. Under the Bayesian framework, model selection is carried out
 using deviance information criterion (DIC) and a goodness-of-fit
 statistics is also developed for assessing the plausibility of the posited
 model. The main advantage of our method is that it allows for more
 robustness and correctness for investigating heterogeneity from different
 levels, while accommodating the skewness and measurement errors
 simultaneously. An application to Shanghai Youth Fitness Survey is used as
 an illustrate example. Through this real example, it is showed that our
 approach is of interest and usefulness for applications.
Journal: Journal of Applied Statistics 
Pages: 745-761 
Issue: 4 
Volume: 42 
Year: 2015 
Month: 4 
X-DOI: 10.1080/02664763.2014.980941 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.980941 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:4:p:745-761




Template-Type: ReDIF-Article 1.0
Author-Name: Jing Chang 
Author-X-Name-First: Jing 
Author-X-Name-Last: Chang 
Author-Name: Herbert K.H. Lee 
Author-X-Name-First: Herbert K.H. 
Author-X-Name-Last: Lee 
Title: Variable selection via a multi-stage strategy 
Abstract:
 Variable selection for nonlinear regression is a complex problem, made
 even more difficult when there are a large number of potential covariates
 and a limited number of datapoints. We propose herein a multi-stage method
 that combines state-of-the-art techniques at each stage to best discover
 the relevant variables. At the first stage, an extension of the Bayesian
 Additive Regression tree is adopted to reduce the total number of
 variables to around 30. At the second stage, sensitivity analysis in the
 treed Gaussian process is adopted to further reduce the total number of
 variables. Two stopping rules are designed and sequential design is
 adopted to make best use of previous information. We demonstrate our
 approach on two simulated examples and one real data set.
Journal: Journal of Applied Statistics 
Pages: 762-774 
Issue: 4 
Volume: 42 
Year: 2015 
Month: 4 
X-DOI: 10.1080/02664763.2014.985640 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.985640 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:4:p:762-774




Template-Type: ReDIF-Article 1.0
Author-Name: Mohamed El Ghourabi 
Author-X-Name-First: Mohamed 
Author-X-Name-Last: El Ghourabi 
Author-Name: Amira Dridi 
Author-X-Name-First: Amira 
Author-X-Name-Last: Dridi 
Author-Name: Mohamed Limam 
Author-X-Name-First: Mohamed 
Author-X-Name-Last: Limam 
Title: A new financial stress index model based on support vector regression and control chart 
Abstract:
 Financial stress index (FSI) is considered to be an important risk
 management tool to quantify financial vulnerabilities. This paper proposes
 a new framework based on a hybrid classifier model that integrates rough
 set theory (RST), FSI, support vector regression (SVR) and a control chart
 to identify stressed periods. First, the RST method is applied to select
 variables. The outputs are used as input data for FSI-SVR computation.
 Empirical analysis is conducted based on monthly FSI of the Federal
 Reserve Bank of Saint Louis from January 1992 to June 2011. A comparison
 study is performed between FSI based on the principal component analysis
 and FSI-SVR. A control chart based on FSI-SVR and extreme value theory is
 proposed to identify the extremely stressed periods. Our approach
 identified different stressed periods including internet bubble, subprime
 crisis and actual financial stress episodes, along with the calmest
 periods, agreeing with those given by Federal Reserve System reports.
Journal: Journal of Applied Statistics 
Pages: 775-788 
Issue: 4 
Volume: 42 
Year: 2015 
Month: 4 
X-DOI: 10.1080/02664763.2014.986076 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.986076 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:4:p:775-788




Template-Type: ReDIF-Article 1.0
Author-Name: M. Liu 
Author-X-Name-First: M. 
Author-X-Name-Last: Liu 
Author-Name: T.I. Lin 
Author-X-Name-First: T.I. 
Author-X-Name-Last: Lin 
Title: Skew-normal factor analysis models with incomplete data 
Abstract:
 Traditional factor analysis (FA) rests on the assumption of multivariate
 normality. However, in some practical situations, the data do not meet
 this assumption; thus, the statistical inference made from such data may
 be misleading. This paper aims at providing some new tools for the
 skew-normal (SN) FA model when missing values occur in the data. In such a
 model, the latent factors are assumed to follow a restricted version of
 multivariate SN distribution with additional shape parameters for
 accommodating skewness. We develop an analytically feasible expectation
 conditional maximization algorithm for carrying out parameter estimation
 and imputation of missing values under missing at random mechanisms. The
 practical utility of the proposed methodology is illustrated with two real
 data examples and the results are compared with those obtained from the
 traditional FA counterparts.
Journal: Journal of Applied Statistics 
Pages: 789-805 
Issue: 4 
Volume: 42 
Year: 2015 
Month: 4 
X-DOI: 10.1080/02664763.2014.986437 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.986437 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:4:p:789-805




Template-Type: ReDIF-Article 1.0
Author-Name: Fei Yang 
Author-X-Name-First: Fei 
Author-X-Name-Last: Yang 
Author-Name: Lin Chen 
Author-X-Name-First: Lin 
Author-X-Name-Last: Chen 
Author-Name: Yang Cheng 
Author-X-Name-First: Yang 
Author-X-Name-Last: Cheng 
Author-Name: Zhenxing Yao 
Author-X-Name-First: Zhenxing 
Author-X-Name-Last: Yao 
Author-Name: Xu Zhang 
Author-X-Name-First: Xu 
Author-X-Name-Last: Zhang 
Title: Urban public transport choice behavior analysis and service improvement policy-making: a case study from the metropolitan city, Chengdu, China 
Abstract:
 As the metropolitan city in Western China, Chengdu has been suffered from
 serious traffic congestion. The strategy of urban public transport
 priority was put into agenda to relieve traffic congestion. But the public
 transport sharing rate is only 27% in Chengdu which is much lower than the
 developed country. Consequently, it is of great importance to study the
 measures to improve the service, and provide technical support to the
 policy-makers. This paper selected the traffic corridor between Southwest
 Jiaotong University district and downtown as the experiment subject. The
 orthogonal design was used to generate stated preference questionnaires in
 order to achieve the reliable parameter estimates. Some variables were
 used to define the utility of the three alternatives and construct the
 Logit model. Then, the relationships between the cost, time variable and
 the choice probability of the public transport were analyzed. According to
 the results, we found that the orthogonal design does improve the
 goodness-of-fit. The workability of Multinomial Logit Model was better
 than Nest Logit model. We also put forward some effective measures to
 improve the service level of public transit, including reducing the access
 time to Metro, limiting parking supply to control the car use.
Journal: Journal of Applied Statistics 
Pages: 806-816 
Issue: 4 
Volume: 42 
Year: 2015 
Month: 4 
X-DOI: 10.1080/02664763.2014.986438 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.986438 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:4:p:806-816




Template-Type: ReDIF-Article 1.0
Author-Name: Gurprit Grover 
Author-X-Name-First: Gurprit 
Author-X-Name-Last: Grover 
Author-Name: Vinay K. Gupta 
Author-X-Name-First: Vinay K. 
Author-X-Name-Last: Gupta 
Title: Multiple imputation of censored survival data in the presence of missing covariates using restricted mean survival time 
Abstract:
 Missing covariates data with censored outcomes put a challenge in the
 analysis of clinical data especially in small sample settings. Multiple
 imputation (MI) techniques are popularly used to impute missing covariates
 and the data are then analyzed through methods that can handle censoring.
 However, techniques based on MI are available to impute censored data also
 but they are not much in practice. In the present study, we applied a
 method based on multiple imputation by chained equations to impute missing
 values of covariates and also to impute censored outcomes using restricted
 survival time in small sample settings. The complete data were then
 analyzed using linear regression models. Simulation studies and a real
 example of CHD data show that the present method produced better estimates
 and lower standard errors when applied on the data having missing
 covariate values and censored outcomes than the analysis of the data
 having censored outcome but excluding cases with missing covariates or the
 analysis when cases with missing covariate values and censored outcomes
 were excluded from the data (complete case analysis).
Journal: Journal of Applied Statistics 
Pages: 817-827 
Issue: 4 
Volume: 42 
Year: 2015 
Month: 4  
X-DOI: 10.1080/02664763.2014.986439 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.986439 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:4:p:817-827




Template-Type: ReDIF-Article 1.0
Author-Name: M.S. Hamada 
Author-X-Name-First: M.S. 
Author-X-Name-Last: Hamada 
Author-Name: B.L. Mitchell 
Author-X-Name-First: B.L. 
Author-X-Name-Last: Mitchell 
Author-Name: C.T. Necker 
Author-X-Name-First: C.T. 
Author-X-Name-Last: Necker 
Title: On uncertainty of a proportion from a stratified random sample of a small population 
Abstract:
 This article considers the uncertainty of a proportion based on a
 stratified random sample of a small population. Using the hypergeometric
 distribution, a Clopper-Pearson type upper confidence bound is presented.
 Another frequentist approach that uses the estimated variance of the
 proportion estimator is also considered as well as a Bayesian alternative.
 These methods are demonstrated with an illustrative example. Some aspects
 of planning, that is, the impact of specified strata sample sizes, on
 uncertainty are studied through a simulation study.
Journal: Journal of Applied Statistics 
Pages: 828-833 
Issue: 4 
Volume: 42 
Year: 2015 
Month: 4 
X-DOI: 10.1080/02664763.2014.987651 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.987651 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:4:p:828-833




Template-Type: ReDIF-Article 1.0
Author-Name: Andrew Hoegh 
Author-X-Name-First: Andrew 
Author-X-Name-Last: Hoegh 
Author-Name: Scotland Leman 
Author-X-Name-First: Scotland 
Author-X-Name-Last: Leman 
Title: A spatio-temporal model for assessing winter damage risk to east coast vineyards 
Abstract:
 Climate is an essential component in site suitability for agriculture in
 general, and specifically in viticulture. With the recent increase in
 vineyards on the East Coast, an important climactic consideration in site
 suitability is extreme winter temperature. Often, maps of annual minimum
 temperatures are used to determine cold hardiness. However, cold hardiness
 of grapes is a more complicated process, since the temperature that grapes
 are able to withstand without damage is not constant. Rather, recent
 temperature cause acclimation or deacclimation and hence, have a large
 influence on cold hardiness. By combining National Oceanic and Atmospheric
 Administration (NOAA) weather station data and leveraging recently created
 cold hardiness models for grapes, we develop a dynamic spatio-temporal
 model to determine the risk of winter damage due to extreme cold for
 several grape varieties commonly grown in the eastern United States. This
 analysis provides maps of winter damage risk to three grape varieties,
 Chardonnay, Cabernet Sauvignon, and Concord.
Journal: Journal of Applied Statistics 
Pages: 834-845 
Issue: 4 
Volume: 42 
Year: 2015 
Month: 4 
X-DOI: 10.1080/02664763.2014.987652 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.987652 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:4:p:834-845




Template-Type: ReDIF-Article 1.0
Author-Name: Costas Panagiotakis 
Author-X-Name-First: Costas 
Author-X-Name-Last: Panagiotakis 
Author-Name: Georgios Tziritas 
Author-X-Name-First: Georgios 
Author-X-Name-Last: Tziritas 
Title: A minimum spanning tree equipartition algorithm for microaggregation 
Abstract:
 In this paper, we propose a solution on microaggregation problem based on
 the hierarchical tree equi-partition (HTEP) algorithm. Microaggregation is
 a family of methods for statistical disclosure control of microdata, that
 is, for masking microdata, so that they can be released without disclose
 private information on the underlying individuals. Knowing that the
 microaggregation problem is non-deterministic polynomial-time-hard, the
 goal is to partition <italic>N</italic> given data into groups of at least
 <italic>K</italic> items, so that the sum of the within-partition squared
 error is minimized. The proposed method is general and it can be applied
 to any tree partition problem aiming at the minimization of a total score.
 The method is divisive, so that the tree with the highest 'score' is split
 into two trees, resulting in a hierarchical forest of trees with almost
 equal 'score' (equipartition). We propose a version of HTEP for
 microaggregation (HTEPM), that is applied on the minimum spanning tree
 (MST) of the graph defined by the data. The merit of the HTEPM algorithm
 is that it solves optimally some instances of the multivariate
 microaggregation problem on MST search space in
 <inline-formula><inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_993361_ilm0001.gif"/></inline-formula>. Experimental
 results and comparisons with existing methods from literature prove the
 high performance and robustness of HTEPM.
Journal: Journal of Applied Statistics 
Pages: 846-865 
Issue: 4 
Volume: 42 
Year: 2015 
Month: 4 
X-DOI: 10.1080/02664763.2014.993361 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.993361 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:4:p:846-865




Template-Type: ReDIF-Article 1.0
Author-Name: Kouji Yamamoto 
Author-X-Name-First: Kouji 
Author-X-Name-Last: Yamamoto 
Author-Name: Fumika Shimada 
Author-X-Name-First: Fumika 
Author-X-Name-Last: Shimada 
Author-Name: Sadao Tomizawa 
Author-X-Name-First: Sadao 
Author-X-Name-Last: Tomizawa 
Title: Measure of departure from symmetry for the analysis of collapsed square contingency tables with ordered categories 
Abstract:
 For square contingency tables with ordered categories, there may be some
 cases that one wants to analyze them by considering collapsed tables with
 some adjacent categories combined in the original table. This paper
 considers the symmetry model for collapsed square contingency tables and
 proposes a measure to represent the degree of departure from symmetry. The
 proposed measure is defined as the arithmetic mean of submeasures each of
 which represents the degree of departure from symmetry for each collapsed
 3&times;3 table. Each submeasure also represents the mean of
 power-divergence or diversity index for each collapsed table. Examples are
 given.
Journal: Journal of Applied Statistics 
Pages: 866-875 
Issue: 4 
Volume: 42 
Year: 2015 
Month: 4 
X-DOI: 10.1080/02664763.2014.993362 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.993362 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:4:p:866-875




Template-Type: ReDIF-Article 1.0
Author-Name: Wali Ullah 
Author-X-Name-First: Wali 
Author-X-Name-Last: Ullah 
Author-Name: Yasumasa Matsuda 
Author-X-Name-First: Yasumasa 
Author-X-Name-Last: Matsuda 
Author-Name: Yoshihiko Tsukuda 
Author-X-Name-First: Yoshihiko 
Author-X-Name-Last: Tsukuda 
Title: Generalized Nelson-Siegel term structure model: do the second slope and curvature factors improve the in-sample fit and out-of-sample forecasts? 
Abstract:
 The dynamic Nelson-Siegel (DNS) model and even the Svensson generalization
 of the model have trouble in fitting the short maturity yields and fail to
 grasp the characteristics of the Japanese government bonds yield curve,
 which is flat at the short end and has multiple inflection points.
 Therefore, a closely related generalized dynamic Nelson-Siegel (GDNS)
 model that has two slopes and curvatures is considered and compared
 empirically to the traditional DNS in terms of in-sample fit as well as
 out-of-sample forecasts. Furthermore, the GDNS with time-varying
 volatility component, modeled as standard EGARCH process, is also
 considered to evaluate its performance in relation to the GDNS. The GDNS
 model unanimously outperforms the DNS in terms of in-sample fit as well as
 out-of-sample forecasts. Moreover, the extended model that accounts for
 time-varying volatility outpace the other models for fitting the yield
 curve and produce relatively more accurate 6- and 12-month ahead
 forecasts, while the GDNS model comes with more precise forecasts for very
 short forecast horizons.
Journal: Journal of Applied Statistics 
Pages: 876-904 
Issue: 4 
Volume: 42 
Year: 2015 
Month: 4 
X-DOI: 10.1080/02664763.2014.993363 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.993363 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:4:p:876-904




Template-Type: ReDIF-Article 1.0
Author-Name: S. Rao Jammalamadaka 
Author-X-Name-First: S. Rao 
Author-X-Name-Last: Jammalamadaka 
Author-Name: Elvynna Leong 
Author-X-Name-First: Elvynna 
Author-X-Name-Last: Leong 
Title: Analysis of discrete lifetime data under middle-censoring and in the presence of covariates 
Abstract:
 'Middle censoring' is a very general censoring scheme where the actual
 value of an observation in the data becomes unobservable if it falls
 inside a random interval (<italic>L, R</italic>) and includes both left
 and right censoring. In this paper, we consider discrete lifetime data
 that follow a geometric distribution that is subject to middle censoring.
 Two major innovations in this paper, compared to the earlier work of
 Davarzani and Parsian [3], include (i) an extension and generalization to
 the case where covariates are present along with the data and (ii) an
 alternate approach and proofs which exploit the simple relationship
 between the geometric and the exponential distributions, so that the
 theory is more in line with the work of Iyer <italic>et al.</italic> [6].
 It is also demonstrated that this kind of discretization of life times
 gives results that are close to the original data involving exponential
 life times. Maximum likelihood estimation of the parameters is studied for
 this middle-censoring scheme with covariates and their large sample
 distributions discussed. Simulation results indicate how well the proposed
 estimation methods work and an illustrative example using
 time-to-pregnancy data from Baird and Wilcox [1] is included.
Journal: Journal of Applied Statistics 
Pages: 905-913 
Issue: 4 
Volume: 42 
Year: 2015 
Month: 4 
X-DOI: 10.1080/02664763.2014.993364 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.993364 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:4:p:905-913




Template-Type: ReDIF-Article 1.0
Author-Name: Philip Pallmann 
Author-X-Name-First: Philip 
Author-X-Name-Last: Pallmann 
Title: Applied meta-analysis with R 
Journal: Journal of Applied Statistics 
Pages: 914-915 
Issue: 4 
Volume: 42 
Year: 2015 
Month: 4 
X-DOI: 10.1080/02664763.2014.989464 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.989464 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:4:p:914-915




Template-Type: ReDIF-Article 1.0
Author-Name: Marina A.P. Andrade 
Author-X-Name-First: Marina A.P. 
Author-X-Name-Last: Andrade 
Title: Statistical analysis of human growth and development 
Journal: Journal of Applied Statistics 
Pages: 915-915 
Issue: 4 
Volume: 42 
Year: 2015 
Month: 4 
X-DOI: 10.1080/02664763.2014.989465 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.989465 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:4:p:915-915




Template-Type: ReDIF-Article 1.0
Author-Name: Jonathan Gillard 
Author-X-Name-First: Jonathan 
Author-X-Name-Last: Gillard 
Title: Constrained principal component analysis and related techniques 
Journal: Journal of Applied Statistics 
Pages: 916-916 
Issue: 4 
Volume: 42 
Year: 2015 
Month: 4 
X-DOI: 10.1080/02664763.2014.989466 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.989466 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:4:p:916-916




Template-Type: ReDIF-Article 1.0
Author-Name: Paul M. Ramsay 
Author-X-Name-First: Paul M. 
Author-X-Name-Last: Ramsay 
Title: Handbook of spatial point-pattern analysis in ecology 
Journal: Journal of Applied Statistics 
Pages: 916-917 
Issue: 4 
Volume: 42 
Year: 2015 
Month: 4 
X-DOI: 10.1080/02664763.2014.989467 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.989467 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:4:p:916-917




Template-Type: ReDIF-Article 1.0
Author-Name: Mariano Ruiz Espejo 
Author-X-Name-First: Mariano Ruiz 
Author-X-Name-Last: Espejo 
Title: Modern survey sampling 
Journal: Journal of Applied Statistics 
Pages: 917-918 
Issue: 4 
Volume: 42 
Year: 2015 
Month: 4 
X-DOI: 10.1080/02664763.2014.991071 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.991071 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:4:p:917-918




Template-Type: ReDIF-Article 1.0
Author-Name: Giuseppe Pandolfo 
Author-X-Name-First: Giuseppe 
Author-X-Name-Last: Pandolfo 
Title: Circular statistics in R 
Journal: Journal of Applied Statistics 
Pages: 918-919 
Issue: 4 
Volume: 42 
Year: 2015 
Month: 4 
X-DOI: 10.1080/02664763.2014.991072 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.991072 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:4:p:918-919




Template-Type: ReDIF-Article 1.0
Author-Name: M. Brabec 
Author-X-Name-First: M. 
Author-X-Name-Last: Brabec 
Author-Name: O. Kon�r 
Author-X-Name-First: O. 
Author-X-Name-Last: Kon�r 
Author-Name: M. Mal&yacute; 
Author-X-Name-First: M. 
Author-X-Name-Last: Mal&yacute; 
Author-Name: I. Kasanick&yacute; 
Author-X-Name-First: I. 
Author-X-Name-Last: Kasanick&yacute; 
Author-Name: E. Pelik�n 
Author-X-Name-First: E. 
Author-X-Name-Last: Pelik�n 
Title: Statistical models for disaggregation and reaggregation of natural gas consumption data 
Abstract:
 In this paper, we present a unified framework for natural gas consumption
 modeling and forecasting. This consists of models of GAM class and their
 nonlinear extension, tailored for easy estimation, aggregation and
 treatment of the delayed relationship between temperature and consumption.
 Since the consumption data for households and small commercial customers
 are routinely available in many countries only as long-term sum meter
 readings, their disaggregation and possibly reaggregation to different
 time intervals is necessary for a variety of purposes. We show some
 examples of specific models based on the presented framework and then we
 demonstrate their use in practice, especially for the disaggregation and
 reaggregation tasks.
Journal: Journal of Applied Statistics 
Pages: 921-937 
Issue: 5 
Volume: 42 
Year: 2015 
Month: 5 
X-DOI: 10.1080/02664763.2014.993365 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.993365 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:5:p:921-937




Template-Type: ReDIF-Article 1.0
Author-Name: Ekele Alih 
Author-X-Name-First: Ekele 
Author-X-Name-Last: Alih 
Author-Name: Hong Choon Ong 
Author-X-Name-First: Hong Choon 
Author-X-Name-Last: Ong 
Title: Cluster-based multivariate outlier identification and re-weighted regression in linear models 
Abstract:
 A cluster methodology, motivated by a robust similarity matrix is proposed
 for identifying likely multivariate outlier structure and to estimate
 weighted least-square (<italic>WLS</italic>) regression parameters in
 linear models. The proposed method is an agglomeration of procedures that
 begins from clustering the <italic>n</italic>-observations through a test
 of 'no-outlier hypothesis' (<italic>TONH</italic>) to a weighted
 least-square regression estimation. The cluster phase partition the
 <italic>n</italic>-observations into <italic>h</italic>-set called main
 cluster and a minor cluster of size <italic>n</italic> -
 <italic>h</italic>. A robust distance emerge from the main cluster upon
 which a test of no outlier hypothesis' is conducted. An initial
 <italic>WLS</italic> regression estimation is computed from the robust
 distance obtained from the main cluster. Until convergence, a re-weighted
 least-squares (<italic>RLS</italic>) regression estimate is updated with
 weights based on the normalized residuals. The proposed procedure blends
 an agglomerative hierarchical cluster analysis of a complete linkage
 through the <italic>TONH</italic> to the Re-weighted regression estimation
 phase. Hence, we propose to call it cluster-based re-weighted regression
 (<italic>CBRR</italic>). The <italic>CBRR</italic> is compared with three
 existing procedures using two data sets known to exhibit masking and
 swamping. The performance of <italic>CBRR</italic> is further examined
 through simulation experiment. The results obtained from the data set
 illustration and the Monte Carlo study shows that the
 <italic>CBRR</italic> is effective in detecting multivariate outliers
 where other methods are susceptible to it. The <italic>CBRR</italic> does
 not require enormous computation and is substantially not susceptible to
 masking and swamping.
Journal: Journal of Applied Statistics 
Pages: 938-955 
Issue: 5 
Volume: 42 
Year: 2015 
Month: 5 
X-DOI: 10.1080/02664763.2014.993366 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.993366 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:5:p:938-955




Template-Type: ReDIF-Article 1.0
Author-Name: Monica Billio 
Author-X-Name-First: Monica 
Author-X-Name-Last: Billio 
Author-Name: Silvio Di Sanzo 
Author-X-Name-First: Silvio 
Author-X-Name-Last: Di Sanzo 
Title: Granger-causality in Markov switching models 
Abstract:
 In this paper, we propose a new approach for characterizing and testing
 Granger-causality, which is well equipped to handle models where the
 change in regime evolves according to multiple Markov chains. Differently
 from the existing literature, we propose a method for analysing causal
 links that specifically takes into account Markov chains. Tests for
 independence are also provided. We illustrate the methodology with an
 empirical application, and in particular, we investigate the causality and
 interdependence between financial and economic cycles in USA using the
 bivariate Markov switching model proposed by Hamilton and Lin [13]. We
 find that financial variables are useful in forecasting the aggregate
 economic activity, and vice versa.
Journal: Journal of Applied Statistics 
Pages: 956-966 
Issue: 5 
Volume: 42 
Year: 2015 
Month: 5 
X-DOI: 10.1080/02664763.2014.993367 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.993367 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:5:p:956-966




Template-Type: ReDIF-Article 1.0
Author-Name: Francisco Cribari-Neto 
Author-X-Name-First: Francisco 
Author-X-Name-Last: Cribari-Neto 
Author-Name: Sadraque E.F. Lucena 
Author-X-Name-First: Sadraque E.F. 
Author-X-Name-Last: Lucena 
Title: Nonnested hypothesis testing in the class of varying dispersion beta regressions 
Abstract:
 Oftentimes practitioners have at their disposal two or more competing
 models with different parametric structures. Whenever each model cannot be
 obtained as a particular case of the remaining models through a set of
 parametric restrictions the models are said to be nonnested. Tests that
 can be used to select a model from a set of nonnested linear regression
 models are available in the literature. Particularly, useful tests are the
 <italic>J</italic> and <italic>MJ</italic> tests. In this paper, we extend
 these two tests to the class of beta regression models, which is useful
 for modeling responses that assume values in the standard unit interval,
 <inline-formula><inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_993368_ilm0001.gif"/></inline-formula>. We report Monte
 Carlo evidence on the finite sample behavior of the tests. Bootstrap-based
 testing inference is also considered. Overall, the best performing test is
 the bootstrap <italic>MJ</italic> test. Two empirical applications are
 presented and discussed.
Journal: Journal of Applied Statistics 
Pages: 967-985 
Issue: 5 
Volume: 42 
Year: 2015 
Month: 5 
X-DOI: 10.1080/02664763.2014.993368 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.993368 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:5:p:967-985




Template-Type: ReDIF-Article 1.0
Author-Name: Alper Sinan 
Author-X-Name-First: Alper 
Author-X-Name-Last: Sinan 
Author-Name: B. Bar&#x131;s  Alkan 
Author-X-Name-First: B. Bar&#x131;s  
Author-X-Name-Last: Alkan 
Title: A useful approach to identify the multicollinearity in the presence of outliers 
Abstract:
 The presence of outliers in the data sets affects the structure of
 multicollinearity which arises from a high degree of correlation between
 explanatory variables in a linear regression analysis. This affect could
 be seen as an increase or decrease in the diagnostics used to determine
 multicollinearity. Thus, the cases of outliers reduce the reliability of
 diagnostics such as variance inflation factors, condition numbers and
 variance decomposition proportions. In this study, we propose to use a
 robust estimation of the correlation matrix obtained by the minimum
 covariance determinant method to determine the diagnostics of
 multicollinearity in the presence of outliers. As a result, the present
 paper demonstrates that the diagnostics of multicollinearity obtained by
 the robust estimation of the correlation matrix are more reliable in the
 presence of outliers.
Journal: Journal of Applied Statistics 
Pages: 986-993 
Issue: 5 
Volume: 42 
Year: 2015 
Month: 5 
X-DOI: 10.1080/02664763.2014.993369 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.993369 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:5:p:986-993




Template-Type: ReDIF-Article 1.0
Author-Name: Fernando A. Otero 
Author-X-Name-First: Fernando A. 
Author-X-Name-Last: Otero 
Author-Name: Helcio R. Barreto Orlande 
Author-X-Name-First: Helcio R. 
Author-X-Name-Last: Barreto Orlande 
Author-Name: Gloria L. Frontini 
Author-X-Name-First: Gloria L. 
Author-X-Name-Last: Frontini 
Author-Name: Guillermo E. Eli�abe 
Author-X-Name-First: Guillermo E. 
Author-X-Name-Last: Eli�abe 
Title: Bayesian approach to the inverse problem in a light scattering application 
Abstract:
 In this article, static light scattering (SLS) measurements are processed
 to estimate the particle size distribution of particle systems
 incorporating prior information obtained from an alternative experimental
 technique: scanning electron microscopy (SEM). For this purpose we propose
 two Bayesian schemes (one parametric and another non-parametric) to solve
 the stated light scattering problem and take advantage of the obtained
 results to summarize some features of the Bayesian approach within the
 context of inverse problems. The features presented in this article
 include the improvement of the results when some useful prior information
 from an alternative experiment is considered instead of a non-informative
 prior as it occurs in a deterministic maximum likelihood estimation. This
 improvement will be shown in terms of accuracy and precision in the
 corresponding results and also in terms of minimizing the effect of
 multiple minima by including significant information in the optimization.
 Both Bayesian schemes are implemented using Markov Chain Monte Carlo
 methods. They have been developed on the basis of the Metropolis-Hastings
 (MH) algorithm using Matlab-super-&reg; and are tested with the analysis
 of simulated and experimental examples of concentrated and
 semi-concentrated particles. In the simulated examples, SLS measurements
 were generated using a rigorous model, while the inversion stage was
 solved using an approximate model in both schemes and also using the
 rigorous model in the parametric scheme. Priors from SEM micrographs were
 also simulated and experimented, where the simulated ones were obtained
 using a Monte Carlo routine. In addition to the presentation of these
 features of the Bayesian approach, some other topics will be discussed,
 such as regularization and some implementation issues of the proposed
 schemes, among which we remark the selection of the parameters used in the
 MH algorithm.
Journal: Journal of Applied Statistics 
Pages: 994-1016 
Issue: 5 
Volume: 42 
Year: 2015 
Month: 5 
X-DOI: 10.1080/02664763.2014.993370 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.993370 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:5:p:994-1016




Template-Type: ReDIF-Article 1.0
Author-Name: Stan Lipovetsky 
Author-X-Name-First: Stan 
Author-X-Name-Last: Lipovetsky 
Author-Name: W. Michael Conklin 
Author-X-Name-First: W. Michael 
Author-X-Name-Last: Conklin 
Title: Predictor relative importance and matching regression parameters 
Abstract:
 Predictor importance in applied regression modeling gives the main
 operational tools for managers and decision-makers. The paper considers
 estimation of predictors' importance in regression using measures
 introduced in works by Gibson and R. Johnson (GJ), then modified by Green,
 Carroll, and DeSarbo, and developed further by J. Johnson (JJ). These
 indices of importance are based on the orthonormal decomposition of the
 data matrix, and the work shows how to improve this approximation. Using
 predictor importance, the regression coefficients can also be adjusted to
 reach the best data fit and to be meaningful and interpretable. The
 results are compared with the robust to multicollinearity, but
 computationally difficult, Shapley value regression (SVR). They show that
 the JJ index is good for importance estimation, but the GJ index
 outperforms it if both predictor importance and coefficients of regression
 are needed; hence, this index (GJ) can be used in place of the more
 computationally intensive estimation by SVR. The results can be easily
 estimated by the considered approach that is very useful in practical
 regression modeling and analysis, especially for big data.
Journal: Journal of Applied Statistics 
Pages: 1017-1031 
Issue: 5 
Volume: 42 
Year: 2015 
Month: 5 
X-DOI: 10.1080/02664763.2014.994480 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.994480 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:5:p:1017-1031




Template-Type: ReDIF-Article 1.0
Author-Name: Shazia Ghufran 
Author-X-Name-First: Shazia 
Author-X-Name-Last: Ghufran 
Author-Name: Saman Khowaja 
Author-X-Name-First: Saman 
Author-X-Name-Last: Khowaja 
Author-Name: M.J. Ahsan 
Author-X-Name-First: M.J. 
Author-X-Name-Last: Ahsan 
Title: Optimum multivariate stratified double sampling design: Chebyshev's Goal Programming approach 
Abstract:
 In stratified sampling when strata weights are unknown a double sampling
 technique may be used to estimate them. A large simple random sample from
 the unstratified population is drawn and units falling in each stratum are
 recorded. A stratified random sample is then selected and simple random
 subsamples are obtained out of the previously selected units of the
 strata. This procedure is called double sampling for stratification. If
 the problem of non-response is there, then subsamples are divided into
 classes of respondents and non-respondents. A second subsample is then
 obtained out of the non-respondents and an attempt is made to obtain the
 information by increasing efforts, persuasion and call backs. In this
 paper, the problem of obtaining a compromise allocation in multivariate
 stratified random sampling is discussed when strata weights are unknown
 and non-response is present. The problem turns out to be a multiobjective
 non-linear integer programming problem. An approximation of the problem to
 an integer linear programming problem by linearizing the non-linear
 objective functions at their individual optima is worked out. Chebyshev's
 goal programming technique is then used to solve the approximated problem.
 A numerical example is also presented to exhibit the practical application
 of the developed procedure.
Journal: Journal of Applied Statistics 
Pages: 1032-1042 
Issue: 5 
Volume: 42 
Year: 2015 
Month: 5 
X-DOI: 10.1080/02664763.2014.995603 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.995603 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:5:p:1032-1042




Template-Type: ReDIF-Article 1.0
Author-Name: M. Pilar Alonso 
Author-X-Name-First: M. Pilar 
Author-X-Name-Last: Alonso 
Author-Name: Asunci�n Beamonte 
Author-X-Name-First: Asunci�n 
Author-X-Name-Last: Beamonte 
Author-Name: Pilar Gargallo 
Author-X-Name-First: Pilar 
Author-X-Name-Last: Gargallo 
Author-Name: Manuel Salvador 
Author-X-Name-First: Manuel 
Author-X-Name-Last: Salvador 
Title: Local labour markets delineation: an approach based on evolutionary algorithms and classification methods 
Abstract:
 In this paper a methodology for the delineation of local labour markets
 (LLMs) using evolutionary algorithms is proposed. This procedure, based on
 that in Fl�rez-Revuelta <italic>et al.</italic> [13,14], introduces three
 modifications. First, initial groups of municipalities with a minimum size
 requirement are built using the travel time between them. Second, a not
 fully random initiation algorithm is proposed. And third, as a final stage
 of the procedure, a contiguity step is implemented. These modifications
 significantly decrease the computational times of the algorithm (up to a
 99%) without any deterioration of the quality of the solutions. The
 optimization algorithm may give a set of potential solutions with very
 similar values with respect to the objective function what would lead to
 different partitions, both in terms of number of markets and their
 composition. In order to capture their common aspects an algorithm based
 on a cluster partitioning of <italic>k</italic>-means type is presented.
 This stage of the procedure also provides a ranking of LLMs foci useful
 for planners and administrations in decision-making processes on issues
 related to labour activities. Finally, to evaluate the performance of the
 algorithm a toy example with artificial data is analysed. The full
 methodology is illustrated through a real commuting data set of the region
 of Arag�n (Spain).
Journal: Journal of Applied Statistics 
Pages: 1043-1063 
Issue: 5 
Volume: 42 
Year: 2015 
Month: 5 
X-DOI: 10.1080/02664763.2014.995604 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.995604 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:5:p:1043-1063




Template-Type: ReDIF-Article 1.0
Author-Name: S.M. Najibi 
Author-X-Name-First: S.M. 
Author-X-Name-Last: Najibi 
Author-Name: M.R. Faghihi 
Author-X-Name-First: M.R. 
Author-X-Name-Last: Faghihi 
Author-Name: M. Golalizadeh 
Author-X-Name-First: M. 
Author-X-Name-Last: Golalizadeh 
Author-Name: S.S. Arab 
Author-X-Name-First: S.S. 
Author-X-Name-Last: Arab 
Title: Bayesian alignment of proteins via Delaunay tetrahedralization 
Abstract:
 An active area of research in bioinformatics is finding structural
 similarity of proteins by alignment. Among many methods, the popular one
 is to find the similarity based on statistical features. This method
 involves gathering information from the complex biomolecule structure and
 obtaining the best alignment by maximizing the number of matched features.
 In this paper, after reviewing statistical models for matching the
 structural biomolecule, it is shown that local alignment based on the
 Delaunay tetrahedralization (DT) can be used for Bayesian alignment of
 proteins. In this method, we use DT to add a priori structural information
 of protein in the Bayesian methodology. We demonstrate that this method
 shows advantages over competing methods in achieving a global alignment of
 proteins, accelerating the convergence rate and improving the parameter
 estimates.
Journal: Journal of Applied Statistics 
Pages: 1064-1079 
Issue: 5 
Volume: 42 
Year: 2015 
Month: 5 
X-DOI: 10.1080/02664763.2014.995605 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.995605 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:5:p:1064-1079




Template-Type: ReDIF-Article 1.0
Author-Name: Sheng Luo 
Author-X-Name-First: Sheng 
Author-X-Name-Last: Luo 
Author-Name: Xiao Su 
Author-X-Name-First: Xiao 
Author-X-Name-Last: Su 
Author-Name: Min Yi 
Author-X-Name-First: Min 
Author-X-Name-Last: Yi 
Author-Name: Kelly K. Hunt 
Author-X-Name-First: Kelly K. 
Author-X-Name-Last: Hunt 
Title: Simultaneous inference of a misclassified outcome and competing risks failure time data 
Abstract:
 Ipsilateral breast tumor relapse (IBTR) often occurs in breast cancer
 patients after their breast conservation therapy. The IBTR status'
 classification (true local recurrence versus new ipsilateral primary
 tumor) is subject to error and there is no widely accepted gold standard.
 Time to IBTR is likely informative for IBTR classification because new
 primary tumor tends to have a longer mean time to IBTR and is associated
 with improved survival as compared with the true local recurrence tumor.
 Moreover, some patients may die from breast cancer or other causes in a
 competing risk scenario during the follow-up period. Because the time to
 death can be correlated to the unobserved true IBTR status and time to
 IBTR (if relapse occurs), this terminal mechanism is non-ignorable. In
 this paper, we propose a unified framework that addresses these issues
 simultaneously by modeling the misclassified binary outcome without a gold
 standard and the correlated time to IBTR, subject to dependent competing
 terminal events. We evaluate the proposed framework by a simulation study
 and apply it to a real data set consisting of 4477 breast cancer patients.
 The adaptive Gaussian quadrature tools in <monospace>SAS</monospace>
 procedure <monospace>NLMIXED</monospace> can be conveniently used to fit
 the proposed model. We expect to see broad applications of our model in
 other studies with a similar data structure.
Journal: Journal of Applied Statistics 
Pages: 1080-1090 
Issue: 5 
Volume: 42 
Year: 2015 
Month: 5 
X-DOI: 10.1080/02664763.2014.995606 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.995606 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:5:p:1080-1090




Template-Type: ReDIF-Article 1.0
Author-Name: B. Ganguli 
Author-X-Name-First: B. 
Author-X-Name-Last: Ganguli 
Author-Name: M. Naskar 
Author-X-Name-First: M. 
Author-X-Name-Last: Naskar 
Author-Name: E.J. Malloy 
Author-X-Name-First: E.J. 
Author-X-Name-Last: Malloy 
Author-Name: E.A. Eisen 
Author-X-Name-First: E.A. 
Author-X-Name-Last: Eisen 
Title: Determination of the functional form of the relationship of covariates to the log hazard ratio in a Cox model 
Abstract:
 In this paper, we review available methods for determination of the
 functional form of the relation between a covariate and the log hazard
 ratio for a Cox model. We pay special attention to the detection of
 influential observations to the extent that they influence the estimated
 functional form of the relation between a covariate and the log hazard
 ratio. Our paper is motivated by a data set from a cohort study of lung
 cancer and silica exposure, where the nonlinear shape of the estimated log
 hazard ratio for silica exposure plotted against cumulative exposure and
 hereafter referred to as the exposure-response curve was greatly affected
 by whether or not two individuals with the highest exposures were included
 in the analysis. Formal influence diagnostics did not identify these two
 individuals but did identify the three highest exposed cases. Removal of
 these three cases resulted in a biologically plausible exposure-response
 curve.
Journal: Journal of Applied Statistics 
Pages: 1091-1105 
Issue: 5 
Volume: 42 
Year: 2015 
Month: 5 
X-DOI: 10.1080/02664763.2014.995607 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.995607 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:5:p:1091-1105




Template-Type: ReDIF-Article 1.0
Author-Name: Adam J. Branscum 
Author-X-Name-First: Adam J. 
Author-X-Name-Last: Branscum 
Author-Name: Dunlei Cheng 
Author-X-Name-First: Dunlei 
Author-X-Name-Last: Cheng 
Author-Name: J. Jack Lee 
Author-X-Name-First: J. Jack 
Author-X-Name-Last: Lee 
Title: Testing hypotheses about medical test accuracy: considerations for design and inference 
Abstract:
 Developing new medical tests and identifying single biomarkers or panels
 of biomarkers with superior accuracy over existing classifiers promotes
 lifelong health of individuals and populations. Before a medical test can
 be routinely used in clinical practice, its accuracy within diseased and
 non-diseased populations must be rigorously evaluated. We introduce a
 method for sample size determination for studies designed to test
 hypotheses about medical test or biomarker sensitivity and specificity. We
 show how a sample size can be determined to guard against making type I
 and/or type II errors by calculating Bayes factors from multiple data sets
 simulated under null and/or alternative models. The approach can be
 implemented across a variety of study designs, including investigations
 into one test or two conditionally independent or dependent tests. We
 focus on a general setting that involves non-identifiable models for data
 when true disease status is unavailable due to the nonexistence of or
 undesirable side effects from a perfectly accurate (i.e. 'gold standard')
 test; special cases of the general method apply to identifiable models
 with or without gold-standard data. Calculation of Bayes factors is
 performed by incorporating prior information for model parameters (e.g.
 sensitivity, specificity, and disease prevalence) and augmenting the
 observed test-outcome data with unobserved latent data on disease status
 to facilitate Gibbs sampling from posterior distributions. We illustrate
 our methods using a thorough simulation study and an application to
 toxoplasmosis.
Journal: Journal of Applied Statistics 
Pages: 1106-1119 
Issue: 5 
Volume: 42 
Year: 2015 
Month: 5 
X-DOI: 10.1080/02664763.2014.995608 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.995608 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:5:p:1106-1119




Template-Type: ReDIF-Article 1.0
Author-Name: Huei-Wen Teng 
Author-X-Name-First: Huei-Wen 
Author-X-Name-Last: Teng 
Author-Name: Wen-Liang Hung 
Author-X-Name-First: Wen-Liang 
Author-X-Name-Last: Hung 
Author-Name: Yen-Ju Chao 
Author-X-Name-First: Yen-Ju 
Author-X-Name-Last: Chao 
Title: Bayesian Markov chain Monte Carlo imputation for the transiting exoplanets with an application in clustering analysis 
Abstract:
 To impute the missing values of mass in the transiting exoplanet data,
 this paper uses the Frank copula to combine two Pareto marginal
 distributions. Next, a Bayesian Markov chain Monte Carlo (MCMC) imputation
 method is proposed. The proposed Bayesian MCMC imputation method is found
 to outperform the mean imputation method. Clustering analysis can shed
 light on the formation and evolution of exoplanets. After imputing the
 missing values of mass in the transiting exoplanet data using the proposed
 approach, the similarity-based clustering method (SCM) clustering
 algorithm is applied to the logarithm of mass and period for this complete
 data set. The SCM clustering result indicates two clusters. Furthermore,
 the intracluster Spearman rank-order correlation coefficients
 <inline-formula><inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_995609_ilm0001.gif"/></inline-formula> for mass and
 period in these two clusters are 0.401 and <inline-formula><inline-graphic
 xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_995609_ilm0002.gif"/></inline-formula>, respectively,
 at a significance level of 0.01. This result illustrates that the mass and
 period correlate in an opposite way between the two different clusters. It
 implies that the formation and evolution processes of these two clusters
 are different.
Journal: Journal of Applied Statistics 
Pages: 1120-1132 
Issue: 5 
Volume: 42 
Year: 2015 
Month: 5 
X-DOI: 10.1080/02664763.2014.995609 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.995609 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:5:p:1120-1132




Template-Type: ReDIF-Article 1.0
Author-Name: Hongxia Yang 
Author-X-Name-First: Hongxia 
Author-X-Name-Last: Yang 
Author-Name: Aurelie Lozano 
Author-X-Name-First: Aurelie 
Author-X-Name-Last: Lozano 
Title: Multi-relational learning via hierarchical nonparametric Bayesian collective matrix factorization 
Abstract:
 Relational learning addresses problems where the data come from multiple
 sources and are linked together through complex relational networks. Two
 important goals are pattern discovery (e.g. by (co)-clustering) and
 predicting unknown values of a relation, given a set of entities and
 observed relations among entities. In the presence of multiple relations,
 combining information from different but related relations can lead to
 better insights and improved prediction. For this purpose, we propose a
 nonparametric hierarchical Bayesian model that improves on existing
 collaborative factorization models and frames a large number of relational
 learning problems. The proposed model naturally incorporates
 (co)-clustering and prediction analysis in a single unified framework, and
 allows for the estimation of entire missing row or column vectors. We
 develop an efficient Gibbs algorithm and a hybrid Gibbs using Newton's
 method to enable fast computation in high dimensions. We demonstrate the
 value of our framework on simulated experiments and on two real-world
 problems: discovering kinship systems and predicting the authors of
 certain articles based on article-word co-occurrence features.
Journal: Journal of Applied Statistics 
Pages: 1133-1147 
Issue: 5 
Volume: 42 
Year: 2015 
Month: 5 
X-DOI: 10.1080/02664763.2014.999028 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.999028 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:5:p:1133-1147




Template-Type: ReDIF-Article 1.0
Author-Name: Aldo M. Garay 
Author-X-Name-First: Aldo M. 
Author-X-Name-Last: Garay 
Author-Name: Victor H. Lachos 
Author-X-Name-First: Victor H. 
Author-X-Name-Last: Lachos 
Author-Name: Heleno Bolfarine 
Author-X-Name-First: Heleno 
Author-X-Name-Last: Bolfarine 
Title: Bayesian estimation and case influence diagnostics for the zero-inflated negative binomial regression model 
Abstract:
 In recent years, there has been considerable interest in regression models
 based on zero-inflated distributions. These models are commonly
 encountered in many disciplines, such as medicine, public health, and
 environmental sciences, among others. The zero-inflated Poisson (ZIP)
 model has been typically considered for these types of problems. However,
 the ZIP model can fail if the non-zero counts are overdispersed in
 relation to the Poisson distribution, hence the zero-inflated negative
 binomial (ZINB) model may be more appropriate. In this paper, we present a
 Bayesian approach for fitting the ZINB regression model. This model
 considers that an observed zero may come from a point mass distribution at
 zero or from the negative binomial model. The likelihood function is
 utilized to compute not only some Bayesian model selection measures, but
 also to develop Bayesian case-deletion influence diagnostics based on
 <italic>q</italic>-divergence measures. The approach can be easily
 implemented using standard Bayesian software, such as WinBUGS. The
 performance of the proposed method is evaluated with a simulation study.
 Further, a real data set is analyzed, where we show that ZINB regression
 models seems to fit the data better than the Poisson counterpart.
Journal: Journal of Applied Statistics 
Pages: 1148-1165 
Issue: 6 
Volume: 42 
Year: 2015 
Month: 6 
X-DOI: 10.1080/02664763.2014.995610 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.995610 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:6:p:1148-1165




Template-Type: ReDIF-Article 1.0
Author-Name: Tatjana Miljkovic 
Author-X-Name-First: Tatjana 
Author-X-Name-Last: Miljkovic 
Author-Name: Nikita Barabanov 
Author-X-Name-First: Nikita 
Author-X-Name-Last: Barabanov 
Title: Modeling veterans' health benefit grants using the expectation maximization algorithm 
Abstract:
 A novel application of the expectation maximization (EM) algorithm is
 proposed for modeling right-censored multiple regression. Parameter
 estimates, variability assessment, and model selection are summarized in a
 multiple regression settings assuming a normal model. The performance of
 this method is assessed through a simulation study. New formulas for
 measuring model utility and diagnostics are derived based on the EM
 algorithm. They include reconstructed coefficient of determination and
 influence diagnostics based on a one-step deletion method. A real data
 set, provided by North Dakota Department of Veterans Affairs, is modeled
 using the proposed methodology. Empirical findings should be of benefit to
 government policy-makers.
Journal: Journal of Applied Statistics 
Pages: 1166-1182 
Issue: 6 
Volume: 42 
Year: 2015 
Month: 6 
X-DOI: 10.1080/02664763.2014.999029 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.999029 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:6:p:1166-1182




Template-Type: ReDIF-Article 1.0
Author-Name: Hamid Shahriari 
Author-X-Name-First: Hamid 
Author-X-Name-Last: Shahriari 
Author-Name: Orod Ahmadi 
Author-X-Name-First: Orod 
Author-X-Name-Last: Ahmadi 
Title: Robust estimation of the mean vector for high-dimensional data set using robust clustering 
Abstract:
 The first step in statistical analysis is the parameter estimation. In
 multivariate analysis, one of the parameters of interest to be estimated
 is the mean vector. In multivariate statistical analysis, it is usually
 assumed that the data come from a multivariate normal distribution. In
 this situation, the maximum likelihood estimator (MLE), that is, the
 sample mean vector, is the best estimator. However, when outliers exist in
 the data, the use of sample mean vector will result in poor estimation.
 So, other estimators which are robust to the existence of outliers should
 be used. The most popular robust multivariate estimator for estimating the
 mean vector is S-estimator with desirable properties. However, computing
 this estimator requires the use of a robust estimate of mean vector as a
 starting point. Usually minimum volume ellipsoid (MVE) is used as a
 starting point in computing S-estimator. For high-dimensional data
 computing, the MVE takes too much time. In some cases, this time is so
 large that the existing computers cannot perform the computation. In
 addition to the computation time, for high-dimensional data set the MVE
 method is not precise. In this paper, a robust starting point for
 S-estimator based on robust clustering is proposed which could be used for
 estimating the mean vector of the high-dimensional data. The performance
 of the proposed estimator in the presence of outliers is studied and the
 results indicate that the proposed estimator performs precisely and much
 better than some of the existing robust estimators for high-dimensional
 data.
Journal: Journal of Applied Statistics 
Pages: 1183-1205 
Issue: 6 
Volume: 42 
Year: 2015 
Month: 6 
X-DOI: 10.1080/02664763.2014.999030 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.999030 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:6:p:1183-1205




Template-Type: ReDIF-Article 1.0
Author-Name: Cindy Xin Feng 
Author-X-Name-First: Cindy Xin 
Author-X-Name-Last: Feng 
Title: Bayesian joint modeling of correlated counts data with application to adverse birth outcomes 
Abstract:
 In disease mapping, health outcomes measured at the same spatial locations
 may be correlated, so one can consider joint modeling the multivariate
 health outcomes accounting for their dependence. The general approaches
 often used for joint modeling include shared component models and
 multivariate models. An alternative way to model the association between
 two health outcomes, when one outcome can naturally serve as a covariate
 of the other, is to use ecological regression model. For example, in our
 application, preterm birth (PTB) can be treated as a predictor for low
 birth weight (LBW) and vice versa. Therefore, we proposed to blend the
 ideas from joint modeling and ecological regression methods to jointly
 model the relative risks for LBW and PTBs over the health districts in
 Saskatchewan, Canada, in 2000-2010. This approach is helpful when proxy of
 areal-level contextual factors can be derived based on the outcomes
 themselves when direct information on risk factors are not readily
 available. Our results indicate that the proposed approach improves the
 model fit when compared with the conventional joint modeling methods.
 Further, we showed that when no strong spatial autocorrelation is present,
 joint outcome modeling using only independent error terms can still
 provide a better model fit when compared with the separate modeling.
Journal: Journal of Applied Statistics 
Pages: 1206-1222 
Issue: 6 
Volume: 42 
Year: 2015 
Month: 6 
X-DOI: 10.1080/02664763.2014.999031 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.999031 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:6:p:1206-1222




Template-Type: ReDIF-Article 1.0
Author-Name: Carles Serrat 
Author-X-Name-First: Carles 
Author-X-Name-Last: Serrat 
Author-Name: Montserrat Ru� 
Author-X-Name-First: Montserrat 
Author-X-Name-Last: Ru� 
Author-Name: Carmen Armero 
Author-X-Name-First: Carmen 
Author-X-Name-Last: Armero 
Author-Name: Xavier Piulachs 
Author-X-Name-First: Xavier 
Author-X-Name-Last: Piulachs 
Author-Name: H�ctor Perpi��n 
Author-X-Name-First: H�ctor 
Author-X-Name-Last: Perpi��n 
Author-Name: Anabel Forte 
Author-X-Name-First: Anabel 
Author-X-Name-Last: Forte 
Author-Name: �lvaro P�ez 
Author-X-Name-First: �lvaro 
Author-X-Name-Last: P�ez 
Author-Name: Guadalupe G�mez 
Author-X-Name-First: Guadalupe 
Author-X-Name-Last: G�mez 
Title: Frequentist and Bayesian approaches for a joint model for prostate cancer risk and longitudinal prostate-specific antigen data 
Abstract:
 The paper describes the use of frequentist and Bayesian shared-parameter
 joint models of longitudinal measurements of prostate-specific antigen
 (PSA) and the risk of prostate cancer (PCa). The motivating dataset
 corresponds to the screening arm of the Spanish branch of the European
 Randomized Screening for Prostate Cancer study. The results show that PSA
 is highly associated with the risk of being diagnosed with PCa and that
 there is an age-varying effect of PSA on PCa risk. Both the frequentist
 and Bayesian paradigms produced very close parameter estimates and
 subsequent 95% confidence and credibility intervals. Dynamic estimations
 of disease-free probabilities obtained using Bayesian inference highlight
 the potential of joint models to guide personalized risk-based screening
 strategies.
Journal: Journal of Applied Statistics 
Pages: 1223-1239 
Issue: 6 
Volume: 42 
Year: 2015 
Month: 6 
X-DOI: 10.1080/02664763.2014.999032 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.999032 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:6:p:1223-1239




Template-Type: ReDIF-Article 1.0
Author-Name: Guoyou Qin 
Author-X-Name-First: Guoyou 
Author-X-Name-Last: Qin 
Author-Name: Zhongyi Zhu 
Author-X-Name-First: Zhongyi 
Author-X-Name-Last: Zhu 
Title: Robust estimation of mean and covariance for longitudinal data with dropouts 
Abstract:
 In this paper, we study estimation of linear models in the framework of
 longitudinal data with dropouts. Under the assumptions that random errors
 follow an elliptical distribution and all the subjects share the same
 within-subject covariance matrix which does not depend on covariates, we
 develop a robust method for simultaneous estimation of mean and
 covariance. The proposed method is robust against outliers, and does not
 require to model the covariance and missing data process. Theoretical
 properties of the proposed estimator are established and simulation
 studies show its good performance. In the end, the proposed method is
 applied to a real data analysis for illustration.
Journal: Journal of Applied Statistics 
Pages: 1240-1254 
Issue: 6 
Volume: 42 
Year: 2015 
Month: 6 
X-DOI: 10.1080/02664763.2014.999033 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.999033 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:6:p:1240-1254




Template-Type: ReDIF-Article 1.0
Author-Name: Thierry Chekouo 
Author-X-Name-First: Thierry 
Author-X-Name-Last: Chekouo 
Author-Name: Alejandro Murua 
Author-X-Name-First: Alejandro 
Author-X-Name-Last: Murua 
Title: The penalized biclustering model and related algorithms 
Abstract:
 Biclustering is the simultaneous clustering of two related dimensions, for
 example, of individuals and features, or genes and experimental
 conditions. Very few statistical models for biclustering have been
 proposed in the literature. Instead, most of the research has focused on
 algorithms to find biclusters. The models underlying them have not
 received much attention. Hence, very little is known about the adequacy
 and limitations of the models and the efficiency of the algorithms. In
 this work, we shed light on associated statistical models behind the
 algorithms. This allows us to generalize most of the known popular
 biclustering techniques, and to justify, and many times improve on, the
 algorithms used to find the biclusters. It turns out that most of the
 known techniques have a hidden Bayesian flavor. Therefore, we adopt a
 Bayesian framework to model biclustering. We propose a measure of
 biclustering complexity (number of biclusters and overlapping) through a
 penalized plaid model, and present a suitable version of the deviance
 information criterion to choose the number of biclusters, a problem that
 has not been adequately addressed yet. Our ideas are motivated by the
 analysis of gene expression data.
Journal: Journal of Applied Statistics 
Pages: 1255-1277 
Issue: 6 
Volume: 42 
Year: 2015 
Month: 6 
X-DOI: 10.1080/02664763.2014.999647 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.999647 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:6:p:1255-1277




Template-Type: ReDIF-Article 1.0
Author-Name: Antoine Dany 
Author-X-Name-First: Antoine 
Author-X-Name-Last: Dany 
Author-Name: Emmanuelle Dantony 
Author-X-Name-First: Emmanuelle 
Author-X-Name-Last: Dantony 
Author-Name: Mad-H�l�nie Elsensohn 
Author-X-Name-First: Mad-H�l�nie 
Author-X-Name-Last: Elsensohn 
Author-Name: Emmanuel Villar 
Author-X-Name-First: Emmanuel 
Author-X-Name-Last: Villar 
Author-Name: C�cile Couchoud 
Author-X-Name-First: C�cile 
Author-X-Name-Last: Couchoud 
Author-Name: Ren� Ecochard 
Author-X-Name-First: Ren� 
Author-X-Name-Last: Ecochard 
Title: Using repeated-prevalence data in multi-state modeling of renal replacement therapy 
Abstract:
 Multi-state models help predict future numbers of patients requiring
 specific treatments but these models require exhaustive incidence data.
 Deriving reliable predictions from repeated-prevalence data would be
 helpful. A new method to model the number of patients that switch between
 therapeutic modalities using repeated-prevalence data is presented and
 illustrated. The parameters and goodness of fit obtained with the new
 method and repeated-prevalence data were compared to those obtained with
 the classical method and incidence data. The multi-state model parameters'
 confidence intervals obtained with annually collected repeated-prevalence
 data were wider than those obtained with incidence data and six out of
 nine pairs of confidence intervals did not overlap. However, most
 parameters were of the same order of magnitude and the predicted patient
 distributions among various renal replacement therapies were similar
 regardless of the type of data used. In the absence of incidence data, a
 multi-state model can still be successfully built with annually collected
 repeated-prevalence data to predict the numbers of patients requiring
 specific treatments. This modeling technique can be extended to other
 chronic diseases.
Journal: Journal of Applied Statistics 
Pages: 1278-1290 
Issue: 6 
Volume: 42 
Year: 2015 
Month: 6 
X-DOI: 10.1080/02664763.2014.999648 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.999648 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:6:p:1278-1290




Template-Type: ReDIF-Article 1.0
Author-Name: Gurprit Grover 
Author-X-Name-First: Gurprit 
Author-X-Name-Last: Grover 
Author-Name: Ravi Vajala 
Author-X-Name-First: Ravi 
Author-X-Name-Last: Vajala 
Author-Name: Prafulla Kumar Swain 
Author-X-Name-First: Prafulla Kumar 
Author-X-Name-Last: Swain 
Title: On the assessment of various factors effecting the improvement in CD4 count of aids patients undergoing antiretroviral therapy using generalized Poisson regression 
Abstract:
 An important marker for identifying the progression of human
 immunodeficiency virus (HIV) infection in an individual is the CD4 cell
 count. Antiretroviral therapy (ART) is a treatment for HIV/AIDS (AIDS,
 acquired immune-deficiency syndrome) which prolongs and improves the lives
 of patients by improving the CD4 cell count and strengthen the immune
 system. This strengthening of the immune system in terms of CD4 count, not
 only depends on various biological factors, but also other behavioral
 factors. Previous studies have shown the effect of CD4 count on the
 mortality, but nobody has attempted to study the factors which are likely
 to influence the improvement in CD4 count of patients diagnosed of AIDS
 and undergoing ART. In this paper, we use Poisson regression model (GPR)
 for exploring the effect of various socio-demographic covariates such as
 age, gender, geographical location, and drug usage on the improvement in
 the CD4 count of AIDS patients. However, if the CD4 count data suffers
 from under or overdispersion, we use GPR model and compare it with
 negative binomial distribution. Finally, the model is applied for the
 analysis of data on patients undergoing the ART in the Ram Manohar Lohia
 Hospital, Delhi, India. The data exhibited overdispersion and hence, GPR
 model provided the best fit.
Journal: Journal of Applied Statistics 
Pages: 1291-1305 
Issue: 6 
Volume: 42 
Year: 2015 
Month: 6 
X-DOI: 10.1080/02664763.2014.999649 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.999649 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:6:p:1291-1305




Template-Type: ReDIF-Article 1.0
Author-Name: Peter A. Dowd 
Author-X-Name-First: Peter A. 
Author-X-Name-Last: Dowd 
Author-Name: Eulogio Pardo-Ig&uacute;zquiza 
Author-X-Name-First: Eulogio 
Author-X-Name-Last: Pardo-Ig&uacute;zquiza 
Author-Name: Juan Jos� Egozcue 
Author-X-Name-First: Juan Jos� 
Author-X-Name-Last: Egozcue 
Title: The total bootstrap median: a robust and efficient estimator of location and scale for small samples 
Abstract:
 We propose the total bootstrap median (TBM) as a robust and efficient
 estimator of location and scale for small samples. We demonstrate its
 performance by estimating the mean and variance of a variety of
 distributions. We also show that, if the underlying distribution is
 unknown and there is either no contamination or low to moderate
 contamination, the TBM provides a better estimate of the mean, in mean
 square terms, than the sample mean or the sample median. In addition, the
 TBM is a better estimator of the variance of the underlying distribution
 than the sample variance or the square of the bias-corrected median
 absolute deviation from the median estimator. We also show that the TBM is
 an explicit L-estimator, which allows a direct study of its properties.
Journal: Journal of Applied Statistics 
Pages: 1306-1321 
Issue: 6 
Volume: 42 
Year: 2015 
Month: 6 
X-DOI: 10.1080/02664763.2014.999650 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.999650 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:6:p:1306-1321




Template-Type: ReDIF-Article 1.0
Author-Name: Thiago Rezende dos Santos 
Author-X-Name-First: Thiago Rezende 
Author-X-Name-Last: dos Santos 
Author-Name: Enrico A. Colosimo 
Author-X-Name-First: Enrico A. 
Author-X-Name-Last: Colosimo 
Title: A modified approximate method for analysis of degradation data 
Abstract:
 Estimation of the lifetime distribution of industrial components and
 systems yields very important information for manufacturers and consumers.
 However, obtaining reliability data is time consuming and costly. In this
 context, degradation tests are a useful alternative approach to lifetime
 and accelerated life tests in reliability studies. The approximate method
 is one of the most used techniques for degradation data analysis. It is
 very simple to understand and easy to implement numerically in any
 statistical software package. This paper uses time series techniques in
 order to propose a modified approximate method (MAM). The MAM improves the
 standard one in two aspects: (1) it uses previous observations in the
 degradation path as a Markov process for future prediction and (2) it is
 not necessary to specify a parametric form for the degradation path.
 Characteristics of interest such as mean or median time to failure and
 percentiles, among others, are obtained by using the modified method. A
 simulation study is performed in order to show the improved properties of
 the modified method over the standard one. Both methods are also used to
 estimate the failure time distribution of the fatigue-crack-growth data
 set.
Journal: Journal of Applied Statistics 
Pages: 1322-1331 
Issue: 6 
Volume: 42 
Year: 2015 
Month: 6 
X-DOI: 10.1080/02664763.2014.999651 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.999651 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:6:p:1322-1331




Template-Type: ReDIF-Article 1.0
Author-Name: Xiaorong Yang 
Author-X-Name-First: Xiaorong 
Author-X-Name-Last: Yang 
Title: Bootstrap unit root test based on least absolute deviation estimation under dependence assumptions 
Abstract:
 In this paper, a bootstrap test based on the least absolute deviation
 (LAD) estimation for the unit root test in first-order autoregressive
 models with dependent residuals is considered. The convergence in
 probability of the bootstrap distribution function is established. Under
 the frame of dependence assumptions, the asymptotic behavior of the
 bootstrap LAD estimator is independent of the covariance matrix of the
 residuals, which automatically approximates the target distribution.
Journal: Journal of Applied Statistics 
Pages: 1332-1347 
Issue: 6 
Volume: 42 
Year: 2015 
Month: 6 
X-DOI: 10.1080/02664763.2014.999652 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.999652 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:6:p:1332-1347




Template-Type: ReDIF-Article 1.0
Author-Name: Xu Tang 
Author-X-Name-First: Xu 
Author-X-Name-Last: Tang 
Author-Name: Fah Fatt Gan 
Author-X-Name-First: Fah Fatt 
Author-X-Name-Last: Gan 
Author-Name: Lingyun Zhang 
Author-X-Name-First: Lingyun 
Author-X-Name-Last: Zhang 
Title: Standardized mortality ratio for an estimated number of deaths 
Abstract:
 The traditional standardized mortality ratio (SMR) compares the mortality
 rate of a study population with that of a reference population. In order
 to measure the performance of a surgeon or a group of surgeons in a
 hospital performing a particular type of surgical operation, a different
 SMR is used. This SMR compares the observed number of deaths in a sample
 with an estimated number of deaths usually calculated based on the average
 performance of a group of surgeons. The estimated number of deaths
 involved in the new SMR is not a constant but a random variable. This
 means that all existing results for the traditional SMR may no longer be
 valid for the new SMR. In this paper, the asymptotic distribution of the
 SMR based on an estimated number of deaths is derived. We also use the
 bootstrap procedure to estimate the finite-sample distribution. A
 simulation study is used to compare both probabilities of type I error and
 powers of existing confidence intervals and confidence intervals
 constructed using the asymptotic and bootstrap distributions of SMR. Our
 study reveals that, in general, existing confidence intervals are
 conservative in terms of probability of type I error, and the two new
 confidence intervals are more accurate. To perform a fair power
 comparison, the coverage probabilities of existing confidence intervals
 are recalibrated to match that based on the asymptotic distribution of
 SMR, and then our study shows that the powers of the asymptotic and
 bootstrap approaches are lower than existing approaches when the odds
 ratio of death <italic>Q</italic> is greater than the odds ratio of death
 under the null hypothesis, <inline-formula><inline-graphic
 xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_999653_ilm0001.gif"/></inline-formula>, but higher when
 <italic>Q</italic> is smaller than <inline-formula><inline-graphic
 xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_999653_ilm0002.gif"/></inline-formula>. The effect of
 patients' risk distribution on the SMR is also investigated.
Journal: Journal of Applied Statistics 
Pages: 1348-1366 
Issue: 6 
Volume: 42 
Year: 2015 
Month: 6 
X-DOI: 10.1080/02664763.2014.999653 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.999653 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:6:p:1348-1366




Template-Type: ReDIF-Article 1.0
Author-Name: Shuling Wang 
Author-X-Name-First: Shuling 
Author-X-Name-Last: Wang 
Author-Name: Xiaoyan Wang 
Author-X-Name-First: Xiaoyan 
Author-X-Name-Last: Wang 
Author-Name: Jiangtao Dai 
Author-X-Name-First: Jiangtao 
Author-X-Name-Last: Dai 
Title: Statistical diagnosis for non-parametric regression models with random right censorship based on the empirical likelihood method 
Abstract:
 In this paper, we consider statistical diagnostic for non-parametric
 regression models with right-censored data based on empirical likelihood.
 First, the primary model is transformed to the non-parametric regression
 model. Then, based on empirical likelihood methodology, we define some
 diagnostic statistics. At last, some simulation studies show that our
 proposed procedure can work fairly well.
Journal: Journal of Applied Statistics 
Pages: 1367-1373 
Issue: 6 
Volume: 42 
Year: 2015 
Month: 6 
X-DOI: 10.1080/02664763.2014.999656 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.999656 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:6:p:1367-1373




Template-Type: ReDIF-Article 1.0
Author-Name: Kung-Jong Lui 
Author-X-Name-First: Kung-Jong 
Author-X-Name-Last: Lui 
Title: Notes on estimation of the intraclass correlation under the AB/BA crossover trial 
Abstract:
 Under the AB/BA crossover trial, we focus our attention on estimation of
 the intraclass correlation in normal data. We develop both point and
 interval estimators in closed form for the intraclass correlation. We
 employ Monte Carlo simulation to study the performance of these estimators
 in a variety of situations. We note that the estimators developed here for
 the intraclass correlation remain valid even when there are possibly
 unexpected carry-over effects.
Journal: Journal of Applied Statistics 
Pages: 1374-1381 
Issue: 6 
Volume: 42 
Year: 2015 
Month: 6 
X-DOI: 10.1080/02664763.2014.999657 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.999657 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:6:p:1374-1381




Template-Type: ReDIF-Article 1.0
Author-Name: Jung-Yu Cheng 
Author-X-Name-First: Jung-Yu 
Author-X-Name-Last: Cheng 
Author-Name: Shinn-Jia Tzeng 
Author-X-Name-First: Shinn-Jia 
Author-X-Name-Last: Tzeng 
Title: Buckley-James-type estimation of quantile regression with recurrent gap time data 
Abstract:
 In longitudinal studies, an individual may potentially undergo a series of
 repeated recurrence events. The gap times, which are referred to as the
 times between successive recurrent events, are typically the outcome
 variables of interest. Various regression models have been developed in
 order to evaluate covariate effects on gap times based on recurrence event
 data. The proportional hazards model, additive hazards model, and the
 accelerated failure time model are all notable examples. Quantile
 regression is a useful alternative to the aforementioned models for
 survival analysis since it can provide great flexibility to assess
 covariate effects on the entire distribution of the gap time. In order to
 analyze recurrence gap time data, we must overcome the problem of the last
 gap time subjected to induced dependent censoring, when numbers of
 recurrent events exceed one time. In this paper, we adopt the
 Buckley-James-type estimation method in order to construct a weighted
 estimation equation for regression coefficients under the quantile model,
 and develop an iterative procedure to obtain the estimates. We use
 extensive simulation studies to evaluate the finite-sample performance of
 the proposed estimator. Finally, analysis of bladder cancer data is
 presented as an illustration of our proposed methodology.
Journal: Journal of Applied Statistics 
Pages: 1383-1401 
Issue: 7 
Volume: 42 
Year: 2015 
Month: 7 
X-DOI: 10.1080/02664763.2014.999654 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.999654 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:7:p:1383-1401




Template-Type: ReDIF-Article 1.0
Author-Name: Cuizhen Niu 
Author-X-Name-First: Cuizhen 
Author-X-Name-Last: Niu 
Author-Name: Qiang Xia 
Author-X-Name-First: Qiang 
Author-X-Name-Last: Xia 
Title: Testing the rate ratio under inverse sampling based on gradient statistic 
Abstract:
 Inverse sampling is widely applied in studies with dichotomous outcomes,
 especially when the subjects arrive sequentially or the response of
 interest is difficult to obtain. In this paper, we investigate the rate
 ratio test problem under inverse sampling based on gradient statistic with
 the asymptotic method and parametric bootstrap technique. The gradient
 statistic has many advantages, for example, it is simple to calculate and
 competitive with Wald-type, score and likelihood ratio tests in terms of
 local power. Numerical studies are carried out to evaluate the performance
 of our gradient test and the existing tests, namely Wald-type, score and
 likelihood ratio tests. The simulation results suggest that the gradient
 test based on the parametric bootstrap method has excellent type I error
 control and large powers even in small sample design. Two real examples,
 from a heart disease study and a drug comparison study, are applied to
 illustrate our methods.
Journal: Journal of Applied Statistics 
Pages: 1402-1420 
Issue: 7 
Volume: 42 
Year: 2015 
Month: 7 
X-DOI: 10.1080/02664763.2014.999655 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.999655 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:7:p:1402-1420




Template-Type: ReDIF-Article 1.0
Author-Name: Derek S. Young 
Author-X-Name-First: Derek S. 
Author-X-Name-Last: Young 
Author-Name: David R. Hunter 
Author-X-Name-First: David R. 
Author-X-Name-Last: Hunter 
Title: Random effects regression mixtures for analyzing infant habituation 
Abstract:
 Random effects regression mixture models are a way to classify
 longitudinal data (or trajectories) having possibly varying lengths. The
 mixture structure of the traditional random effects regression mixture
 model arises through the distribution of the random regression
 coefficients, which is assumed to be a mixture of multivariate normals. An
 extension of this standard model is presented that accounts for various
 levels of heterogeneity among the trajectories, depending on their assumed
 error structure. A standard likelihood ratio test is presented for testing
 this error structure assumption. Full details of an
 expectation-conditional maximization algorithm for maximum likelihood
 estimation are also presented. This model is used to analyze data from an
 infant habituation experiment, where it is desirable to assess whether
 infants comprise different populations in terms of their habituation time.
Journal: Journal of Applied Statistics 
Pages: 1421-1441 
Issue: 7 
Volume: 42 
Year: 2015 
Month: 7 
X-DOI: 10.1080/02664763.2014.1000272 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.1000272 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:7:p:1421-1441




Template-Type: ReDIF-Article 1.0
Author-Name: Bijamma Thomas 
Author-X-Name-First: Bijamma 
Author-X-Name-Last: Thomas 
Author-Name: N.N. Midhu 
Author-X-Name-First: N.N. 
Author-X-Name-Last: Midhu 
Author-Name: P.G. Sankaran 
Author-X-Name-First: P.G. 
Author-X-Name-Last: Sankaran 
Title: A software reliability model using mean residual quantile function 
Abstract:
 In this paper, we propose a class of distributions with the inverse linear
 mean residual quantile function. The distributional properties of the
 family of distributions are studied. We then discuss the reliability
 characteristics of the family of distributions. Some characterizations of
 the class of distributions are also discussed. The parameters of the class
 of distributions are estimated using the method of L-moments. The proposed
 class of distributions is applied to a real data set.
Journal: Journal of Applied Statistics 
Pages: 1442-1457 
Issue: 7 
Volume: 42 
Year: 2015 
Month: 7 
X-DOI: 10.1080/02664763.2014.1000273 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.1000273 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:7:p:1442-1457




Template-Type: ReDIF-Article 1.0
Author-Name: Meihui Guo 
Author-X-Name-First: Meihui 
Author-X-Name-Last: Guo 
Author-Name: Yi-Ting Guo 
Author-X-Name-First: Yi-Ting 
Author-X-Name-Last: Guo 
Author-Name: Chi-Jeng Wang 
Author-X-Name-First: Chi-Jeng 
Author-X-Name-Last: Wang 
Author-Name: Liang-Ching Lin 
Author-X-Name-First: Liang-Ching 
Author-X-Name-Last: Lin 
Title: Assessing influential trade effects via high-frequency market reactions 
Abstract:
 In the literature, traders are often classified into informed and
 uninformed and the trades from informed traders have market impacts. We
 investigate these trades by first establishing a scheme to identify the
 influential trades from the ordinary trades under certain criteria. The
 differential properties between these two types of trades are examined via
 the four transaction states classified by the trade price, trade volume,
 quotes, and quoted depth. Marginal distribution of the four states and the
 transition probability between different states are shown to be distinct
 for informed trades and ordinary liquidity trades. Furthermore, four
 market reaction factors are introduced and logistic regression models of
 the influential trades are established based on these four factors.
 Empirical study on the high-frequency transaction data from the NYSE TAQ
 database show supportive evidence for high correct classification rates of
 the logistic regression models.
Journal: Journal of Applied Statistics 
Pages: 1458-1471 
Issue: 7 
Volume: 42 
Year: 2015 
Month: 7 
X-DOI: 10.1080/02664763.2014.1000274 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.1000274 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:7:p:1458-1471




Template-Type: ReDIF-Article 1.0
Author-Name: Aydin Karakoca 
Author-X-Name-First: Aydin 
Author-X-Name-Last: Karakoca 
Author-Name: Ulku Erisoglu 
Author-X-Name-First: Ulku 
Author-X-Name-Last: Erisoglu 
Author-Name: Murat Erisoglu 
Author-X-Name-First: Murat 
Author-X-Name-Last: Erisoglu 
Title: A comparison of the parameter estimation methods for bimodal mixture Weibull distribution with complete data 
Abstract:
 Bimodal mixture Weibull distribution being a special case of mixture
 Weibull distribution has been used recently as a suitable model for
 heterogeneous data sets in many practical applications. The bimodal
 mixture Weibull term represents a mixture of two Weibull distributions.
 Although many estimation methods have been proposed for the bimodal
 mixture Weibull distribution, there is not a comprehensive comparison.
 This paper presents a detailed comparison of five kinds of numerical
 methods, such as maximum likelihood estimation, least-squares method,
 method of moments, method of logarithmic moments and percentile method
 (PM) in terms of several criteria by simulation study. Also parameter
 estimation methods are applied to real data.
Journal: Journal of Applied Statistics 
Pages: 1472-1489 
Issue: 7 
Volume: 42 
Year: 2015 
Month: 7 
X-DOI: 10.1080/02664763.2014.1000275 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.1000275 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:7:p:1472-1489




Template-Type: ReDIF-Article 1.0
Author-Name: Hasan Ertas 
Author-X-Name-First: Hasan 
Author-X-Name-Last: Ertas 
Author-Name: Selma Toker 
Author-X-Name-First: Selma 
Author-X-Name-Last: Toker 
Author-Name: Selahattin Ka�&#x131;ranlar 
Author-X-Name-First: Selahattin 
Author-X-Name-Last: Ka�&#x131;ranlar 
Title: Robust two parameter ridge M-estimator for linear regression 
Abstract:
 The problem of multicollinearity and outliers in the data set produce
 undesirable effects on the ordinary least squares estimator. Therefore,
 robust two parameter ridge estimation based on M-estimator (ME) is
 introduced to deal with multicollinearity and outliers in the
 <italic>y</italic>-direction. The proposed estimator outperforms ME, two
 parameter ridge estimator and robust ridge M-estimator according to mean
 square error criterion. Moreover, a numerical example and a Monte Carlo
 simulation experiment are presented.
Journal: Journal of Applied Statistics 
Pages: 1490-1502 
Issue: 7 
Volume: 42 
Year: 2015 
Month: 7 
X-DOI: 10.1080/02664763.2014.1000577 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.1000577 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:7:p:1490-1502




Template-Type: ReDIF-Article 1.0
Author-Name: Wen-Liang Hung 
Author-X-Name-First: Wen-Liang 
Author-X-Name-Last: Hung 
Author-Name: Jenn-Hwai Yang 
Author-X-Name-First: Jenn-Hwai 
Author-X-Name-Last: Yang 
Title: Automatic clustering algorithm for fuzzy data 
Abstract:
 Coppi <italic>et al.</italic> [7] applied Yang and Wu's [20] idea to
 propose a possibilistic <italic>k</italic>-means (P<italic>k</italic>M)
 clustering algorithm for <italic>LR</italic>-type fuzzy numbers. The
 memberships in the objective function of P<italic>k</italic>M no longer
 need to satisfy the constraint in fuzzy <italic>k</italic>-means that of a
 data point across classes sum to one. However, the clustering performance
 of P<italic>k</italic>M depends on the initializations and weighting
 exponent. In this paper, we propose a robust clustering method based on a
 self-updating procedure. The proposed algorithm not only solves the
 initialization problems but also obtains a good clustering result. Several
 numerical examples also demonstrate the effectiveness and accuracy of the
 proposed clustering method, especially the robustness to initial values
 and noise. Finally, three real fuzzy data sets are used to illustrate the
 superiority of this proposed algorithm.
Journal: Journal of Applied Statistics 
Pages: 1503-1518 
Issue: 7 
Volume: 42 
Year: 2015 
Month: 7 
X-DOI: 10.1080/02664763.2014.1001326 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.1001326 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:7:p:1503-1518




Template-Type: ReDIF-Article 1.0
Author-Name: Jahida Gulshan 
Author-X-Name-First: Jahida 
Author-X-Name-Last: Gulshan 
Author-Name: Md. Mejbahuddin Mina 
Author-X-Name-First: Md. Mejbahuddin 
Author-X-Name-Last: Mina 
Author-Name: Syed Shahadat Hossain 
Author-X-Name-First: Syed Shahadat 
Author-X-Name-Last: Hossain 
Title: Migration pattern in Bangladesh: a covariate-dependent Markov model 
Abstract:
 Internal migration is one of the major components of rapid and unplanned
 growth of towns and cities especially in the developing countries. This
 paper describes the transition pattern of internal out migration in
 Bangladesh and some sociodemographic factors influencing such migration in
 the country using a covariate-dependent Markov model. Four types of
 migration behavior namely, rural to rural, rural to urban, urban to rural
 and urban to urban are under consideration of this paper. Defining two
 discrete states, urban and rural, each of such transition can be
 characterized by a stochastic process; hence we use a two-state Markov
 chain for this purpose. We find that age, sex, division and reason of
 migration are significantly associated with internal migration in
 Bangladesh. The major findings include that any type of migration, rural
 to rural, rural to urban, urban to rural and urban to urban, mostly take
 place at the ages of 15-30 as well as at the ages of 0-15; females have
 higher odds than males to make a migration; Dhaka, Rajshahi and Chittagong
 divisions have remarkably higher migration rate as compared to Barisal and
 Sylhet division; and the professional reason is the main reason for rural
 to urban migration.
Journal: Journal of Applied Statistics 
Pages: 1519-1530 
Issue: 7 
Volume: 42 
Year: 2015 
Month: 7 
X-DOI: 10.1080/02664763.2014.1001327 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.1001327 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:7:p:1519-1530




Template-Type: ReDIF-Article 1.0
Author-Name: Luis A. Gil-Alana 
Author-X-Name-First: Luis A. 
Author-X-Name-Last: Gil-Alana 
Title: Linear and segmented trends in sea surface temperature data 
Abstract:
 This paper deals with the analysis of the MET Office Hadley Centre's sea
 surface temperature data set (HadSST3) by using long-range dependence
 techniques. We incorporate linear and segmented trends using fractional
 integration, and thus permitting long memory behavior in the detrended
 series. The results indicate the existence of warming trends in the three
 series examined (Northern and Southern Hemispheres along with global
 temperatures), with orders of integration which are in the range (0.5, 1)
 and thus implying nonstationary long memory and mean reverting behavior.
 This is innovative compared with other works that assume short memory
 behavior in the detrended series. Allowing for segmented trends two
 features are observed: increasing values in the degree of dependence of
 the series across time and significant warming trends from 1940 onwards.
Journal: Journal of Applied Statistics 
Pages: 1531-1546 
Issue: 7 
Volume: 42 
Year: 2015 
Month: 7 
X-DOI: 10.1080/02664763.2014.1001328 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.1001328 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:7:p:1531-1546




Template-Type: ReDIF-Article 1.0
Author-Name: Sugata Sen Roy 
Author-X-Name-First: Sugata Sen 
Author-X-Name-Last: Roy 
Author-Name: Moumita Chatterjee 
Author-X-Name-First: Moumita 
Author-X-Name-Last: Chatterjee 
Title: Estimating the hazard functions of two alternately occurring recurrent events 
Abstract:
 Often two recurrent events of equal importance can occur alternately. The
 life-time patterns of the two events can then be of considerable interest.
 In this paper, we consider two such events, the inclusion and exclusion of
 players in a team sport, and study whether there is any inherent pattern
 in the time-lengths between these events. The life-time distributions are
 modelled and methods of estimating the model parameters suggested taking
 into account any relationship in the pattern of recurrence. The results
 are then applied to the inclusion and exclusion of players in the Indian
 national cricket team. As further illustration, a simulation study is
 made. Broad application areas are identified both in the introduction and
 conclusion.
Journal: Journal of Applied Statistics 
Pages: 1547-1555 
Issue: 7 
Volume: 42 
Year: 2015 
Month: 7 
X-DOI: 10.1080/02664763.2014.1001329 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.1001329 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:7:p:1547-1555




Template-Type: ReDIF-Article 1.0
Author-Name: Ayten Yi&#x11f;iter 
Author-X-Name-First: Ayten 
Author-X-Name-Last: Yi&#x11f;iter 
Author-Name: Jie Chen 
Author-X-Name-First: Jie 
Author-X-Name-Last: Chen 
Author-Name: Lingling An 
Author-X-Name-First: Lingling 
Author-X-Name-Last: An 
Author-Name: Nazan Danacio&#x11f;lu 
Author-X-Name-First: Nazan 
Author-X-Name-Last: Danacio&#x11f;lu 
Title: An online copy number variant detection method for short sequencing reads 
Abstract:
 The availability of the next generation sequencing (NGS) technology in
 today's biomedical research has provided new opportunities in scientific
 discovery of genetic information. The high-throughput NGS technology,
 especially DNA-seq, is particularly useful in profiling a genome for the
 analysis of DNA copy number variants (CNVs). The read count (RC) data
 resulting from NGS technology are massive and information rich. How to
 exploit the RC data for accurate CNV detection has become a computational
 and statistical challenge. We provide a statistical online change point
 method to help detect CNVs in the sequencing RC data in this paper. This
 method uses the idea of online searching for change point (or breakpoint)
 with a Markov chain assumption on the breakpoints loci and an iterative
 computing process via a Bayesian framework. We illustrate that an online
 change-point detection method is particularly suitable for identifying
 CNVs in the RC data. The algorithm is applied to the publicly available
 NCI-H2347 lung cancer cell line sequencing reads data for locating the
 breakpoints. Extensive simulation studies have been carried out and
 results show the good behavior of the proposed algorithm. The algorithm is
 implemented in R and the codes are available upon request.
Journal: Journal of Applied Statistics 
Pages: 1556-1571 
Issue: 7 
Volume: 42 
Year: 2015 
Month: 7 
X-DOI: 10.1080/02664763.2014.1001330 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.1001330 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:7:p:1556-1571




Template-Type: ReDIF-Article 1.0
Author-Name: E. Cene 
Author-X-Name-First: E. 
Author-X-Name-Last: Cene 
Author-Name: F. Karaman 
Author-X-Name-First: F. 
Author-X-Name-Last: Karaman 
Title: Analysing organic food buyers' perceptions with Bayesian networks: a case study in Turkey 
Abstract:
 Bayesian network (BN) is an efficient graphical method that uses directed
 acyclic graphs (DAG) to provide information about a set of data. BNs
 consist of nodes and arcs (or edges) where nodes represent variables and
 arcs represent relations and influences between nodes. Interest in organic
 food has been increasing in the world during the last decade. The same
 trend is also valid in Turkey. Although there are numerous studies that
 deal with customer perception of organic food and customer
 characteristics, none of them used BNs. Thus, this study, which shows a
 new application area of BNs, aims to reveal the perception and
 characteristics of organic food buyers. In this work, a survey is designed
 and applied in seven different organic bazaars in Turkey. Afterwards, BNs
 are constructed with the data gathered from 611 organic food consumers.
 The findings match with the previous studies as factors such as health,
 environmental factors, food availability, product price, consumers' income
 and trust to organization are found to influence consumers effectively.
Journal: Journal of Applied Statistics 
Pages: 1572-1590 
Issue: 7 
Volume: 42 
Year: 2015 
Month: 7 
X-DOI: 10.1080/02664763.2014.1001331 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.1001331 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:7:p:1572-1590




Template-Type: ReDIF-Article 1.0
Author-Name: Karan Veer 
Author-X-Name-First: Karan 
Author-X-Name-Last: Veer 
Author-Name: Ravinder Agarwal 
Author-X-Name-First: Ravinder 
Author-X-Name-Last: Agarwal 
Title: Wavelet and short-time Fourier transform comparison-based analysis of myoelectric signals 
Abstract:
 In this investigation, extracted features ofsignals have been analyzed for
 the recognition of arm movements. Short-time Fourier transform and wavelet
 transform based on Euclidian distance were applied to reordered signals.
 Results show that wavelet is a more useful and powerful tool for analyzing
 signals, since it shows multiresolution property with a significant
 reduction in the computation time for eliminating resolution problems.
 Finally, a statistical technique of repeated factorial analysis of
 variance for experimental recorded data was implemented in a way to
 investigate the effect of class separability for multiple motions for
 establishing surface electromyogram-muscular force relationship.
Journal: Journal of Applied Statistics 
Pages: 1591-1601 
Issue: 7 
Volume: 42 
Year: 2015 
Month: 7 
X-DOI: 10.1080/02664763.2014.1001728 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.1001728 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:7:p:1591-1601




Template-Type: ReDIF-Article 1.0
Author-Name: Jin-Jian Hsieh 
Author-X-Name-First: Jin-Jian 
Author-X-Name-Last: Hsieh 
Author-Name: Wei-Cheng Huang 
Author-X-Name-First: Wei-Cheng 
Author-X-Name-Last: Huang 
Title: Nonparametric estimation and test of conditional Kendall's tau under semi-competing risks data and truncated data 
Abstract:
 In this article, we focus on estimation and test of conditional Kendall's
 tau under semi-competing risks data and truncated data. We apply the
 inverse probability censoring weighted technique to construct an estimator
 of conditional Kendall's tau, <inline-formula><inline-graphic
 xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1004624_ilm0001.gif"/></inline-formula>. Then, this
 study provides a test statistic for <inline-formula><inline-graphic
 xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1004624_ilm0002.gif"/></inline-formula>, where
 <inline-formula><inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1004624_ilm0003.gif"/></inline-formula>. When two
 random variables are quasi-independent, it implies
 <inline-formula><inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1004624_ilm0004.gif"/></inline-formula>. Thus,
 <inline-formula><inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1004624_ilm0005.gif"/></inline-formula> is a proxy for
 quasi-independence. Tsai [12], and Martin and Betensky [10] considered the
 testing problem for quasi-independence. Via simulation studies, we compare
 the three test statistics for quasi-independence, and examine the
 finite-sample performance of the proposed estimator and the suggested test
 statistic. Furthermore, we provide the large sample properties for our
 proposed estimator. Finally, we provide two real data examples for
 illustration.
Journal: Journal of Applied Statistics 
Pages: 1602-1616 
Issue: 7 
Volume: 42 
Year: 2015 
Month: 7 
X-DOI: 10.1080/02664763.2015.1004624 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1004624 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:7:p:1602-1616




Template-Type: ReDIF-Article 1.0
Author-Name: Ekele Alih 
Author-X-Name-First: Ekele 
Author-X-Name-Last: Alih 
Author-Name: Hong Choon Ong 
Author-X-Name-First: Hong Choon 
Author-X-Name-Last: Ong 
Title: An outlier-resistant test for heteroscedasticity in linear models 
Abstract:
 The presence of contamination often called outlier is a very common
 attribute in data. Among other causes, outliers in a homoscedastic model
 make the model heteroscedastic. Moreover, outliers distort diagnostic
 tools for heteroscedasticity such that it may not be correctly identified.
 In this article, we show how outliers affect heteroscedasticity
 diagnostics. We then proposed a robust procedure for detecting
 heteroscedasticity in the presence of outliers by robustifying the
 non-robust component of the Goldfeld-Quandt (GQ) test. The performance of
 the proposed procedure is examined using simulation experiment and real
 data sets. The proposed procedure offers great improvement where the
 conventional GQ and other procedures fail.
Journal: Journal of Applied Statistics 
Pages: 1617-1634 
Issue: 8 
Volume: 42 
Year: 2015 
Month: 8 
X-DOI: 10.1080/02664763.2015.1004623 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1004623 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:8:p:1617-1634




Template-Type: ReDIF-Article 1.0
Author-Name: Guanfu Liu 
Author-X-Name-First: Guanfu 
Author-X-Name-Last: Liu 
Author-Name: Xiaolong Pu 
Author-X-Name-First: Xiaolong 
Author-X-Name-Last: Pu 
Author-Name: Lei Wang 
Author-X-Name-First: Lei 
Author-X-Name-Last: Wang 
Author-Name: Dongdong Xiang 
Author-X-Name-First: Dongdong 
Author-X-Name-Last: Xiang 
Title: CUSUM chart for detecting range shifts when monotonicity of likelihood ratio is invalid 
Abstract:
 It is often encountered in the literature that the log-likelihood ratios
 (LLR) of some distributions (e.g. the student <italic>t</italic>
 distribution) are not monotonic. Existing charts for monitoring such
 processes may suffer from the fact that the average run length (ARL) curve
 is a discontinuous function of control limit. It implies that some
 pre-specified in-control (IC) ARLs of these charts may not be reached. To
 guarantee the false alarm rate of a control chart lower than the nominal
 level, a larger IC ARL is usually suggested in the literature. However,
 the large IC ARL may weaken the performance of a control chart when the
 process is out-of-control (OC), compared with a just right IC ARL. To
 overcome it, we adjust the LLR to be a monotonic one in this paper. Based
 on it, a multiple CUSUM chart is developed to detect range shifts in IC
 distribution. Theoretical result in this paper ensures the continuity of
 its ARL curve. Numerical results show our proposed chart performs well
 under the range shifts, especially under the large shifts. In the end, a
 real data example is utilized to illustrate our proposed chart.
Journal: Journal of Applied Statistics 
Pages: 1635-1644 
Issue: 8 
Volume: 42 
Year: 2015 
Month: 8 
X-DOI: 10.1080/02664763.2015.1004625 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1004625 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:8:p:1635-1644




Template-Type: ReDIF-Article 1.0
Author-Name: Y. Ziane 
Author-X-Name-First: Y. 
Author-X-Name-Last: Ziane 
Author-Name: S. Adjabi 
Author-X-Name-First: S. 
Author-X-Name-Last: Adjabi 
Author-Name: N. Zougab 
Author-X-Name-First: N. 
Author-X-Name-Last: Zougab 
Title: Adaptive Bayesian bandwidth selection in asymmetric kernel density estimation for nonnegative heavy-tailed data 
Abstract:
 In this paper, we consider an interesting problem on adaptive
 Birnbaum-Saunders-power-exponential (BS-PE) kernel density estimation for
 nonnegative heavy-tailed (HT) data. Treating the variable bandwidths
 <inline-formula><inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1004626_ilm0001.gif"/></inline-formula>,
 <inline-formula><inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1004626_ilm0002.gif"/></inline-formula> of adaptive
 BS-PE kernel as parameters, we then propose a conjugate prior and estimate
 the <inline-formula><inline-graphic
 xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1004626_ilm0003.gif"/></inline-formula>'s by using the
 popular quadratic and entropy loss functions. Explicit formulas are
 obtained for the posterior and Bayes estimators. Comparison simulations
 with global unbiased cross-validation bandwidth selection technique were
 conducted under four HT distributions. Finally, two applications based on
 HT real data are presented and analyzed.
Journal: Journal of Applied Statistics 
Pages: 1645-1658 
Issue: 8 
Volume: 42 
Year: 2015 
Month: 8 
X-DOI: 10.1080/02664763.2015.1004626 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1004626 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:8:p:1645-1658




Template-Type: ReDIF-Article 1.0
Author-Name: Filidor Vilca 
Author-X-Name-First: Filidor 
Author-X-Name-Last: Vilca 
Author-Name: Camila Borelli Zeller 
Author-X-Name-First: Camila Borelli 
Author-X-Name-Last: Zeller 
Author-Name: Gauss M. Cordeiro 
Author-X-Name-First: Gauss M. 
Author-X-Name-Last: Cordeiro 
Title: The sinh-normal/independent nonlinear regression model 
Abstract:
 The normal/independent family of distributions is an attractive class of
 symmetric heavy-tailed density functions. They have a nice hierarchical
 representation to make inferences easily. We propose the
 Sinh-normal/independent distribution which extends the Sinh-normal (SN)
 distribution [23]. We discuss some of its properties and propose the
 Sinh-normal/independent nonlinear regression model based on a similar
 setup of Lemonte and Cordeiro [18], who applied the Birnbaum-Saunders
 distribution. We develop an EM-algorithm for maximum likelihood estimation
 of the model parameters. In order to examine the robustness of this
 flexible class against outlying observations, we perform a simulation
 study and analyze a real data set to illustrate the usefulness of the new
 model.
Journal: Journal of Applied Statistics 
Pages: 1659-1676 
Issue: 8 
Volume: 42 
Year: 2015 
Month: 8 
X-DOI: 10.1080/02664763.2015.1005059 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1005059 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:8:p:1659-1676




Template-Type: ReDIF-Article 1.0
Author-Name: Meesun Sun 
Author-X-Name-First: Meesun 
Author-X-Name-Last: Sun 
Author-Name: Kwanghyun Choi 
Author-X-Name-First: Kwanghyun 
Author-X-Name-Last: Choi 
Author-Name: Sungzoon Cho 
Author-X-Name-First: Sungzoon 
Author-X-Name-Last: Cho 
Title: Estimating the minority class proportion with the ROC curve using Military Personality Inventory data of the ROK Armed Forces 
Abstract:
 The Republic of Korea Armed Forces includes maladjusted conscripts such as
 the mentally ill, the suicidal, the imprisoned, and those determined by
 the military commander to be maladjusted. To counteract these problems, it
 is necessary to identify the maladjusted conscripts to determine who among
 them would qualify for exemption from active military service or need
 special attention. We use the Military Personality Inventory (MPI) to make
 this prediction. Such a prediction presents a kind of class imbalance and
 class overlap problem, where the majority fulfil active service and the
 minority are maladjusted, the latter being discharged early from active
 service. Therefore, most classification algorithms are likely to show low
 classification performance. As an alternative, this study demonstrates the
 effective utilization of the receiver operating characteristics curve
 using MPI data to estimate the maladjusted proportion of persons sharing
 similar MPI test results. We confirm that the suggested method performs
 well using the real-world MPI data set. The suggested method is very
 useful to estimate the proportion of conscripts maladjusted to military
 life and can help in the management of such persons subject to
 conscription.
Journal: Journal of Applied Statistics 
Pages: 1677-1689 
Issue: 8 
Volume: 42 
Year: 2015 
Month: 8 
X-DOI: 10.1080/02664763.2015.1005060 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1005060 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:8:p:1677-1689




Template-Type: ReDIF-Article 1.0
Author-Name: Deidra A. Coleman 
Author-X-Name-First: Deidra A. 
Author-X-Name-Last: Coleman 
Author-Name: Donald E.K. Martin 
Author-X-Name-First: Donald E.K. 
Author-X-Name-Last: Martin 
Author-Name: Brian J. Reich 
Author-X-Name-First: Brian J. 
Author-X-Name-Last: Reich 
Title: Multiple window discrete scan statistic for higher-order Markovian sequences 
Abstract:
 Accurate and efficient methods to detect unusual clusters of abnormal
 activity are needed in many fields such as medicine and business. Often
 the size of clusters is unknown; hence, multiple (variable) window scan
 statistics are used to identify clusters using a set of different
 potential cluster sizes. We give an efficient method to compute the exact
 distribution of multiple window discrete scan statistics for higher-order,
 multi-state Markovian sequences. We define a Markov chain to efficiently
 keep track of probabilities needed to compute <italic>p</italic>-values
 for the statistic. The state space of the Markov chain is set up by a
 criterion developed to identify strings that are associated with observing
 the specified values of the statistic. Using our algorithm, we identify
 cases where the available approximations do not perform well. We
 demonstrate our methods by detecting unusual clusters of made free throw
 shots by National Basketball Association players during the 2009-2010
 regular season.
Journal: Journal of Applied Statistics 
Pages: 1690-1705 
Issue: 8 
Volume: 42 
Year: 2015 
Month: 8 
X-DOI: 10.1080/02664763.2015.1005061 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1005061 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:8:p:1690-1705




Template-Type: ReDIF-Article 1.0
Author-Name: Tsung-Shan Tsou 
Author-X-Name-First: Tsung-Shan 
Author-X-Name-Last: Tsou 
Author-Name: Hsiao-Yun Liu 
Author-X-Name-First: Hsiao-Yun 
Author-X-Name-Last: Liu 
Title: Testing the homogeneity of proportions for clustered binary data without knowing the correlation structure 
Abstract:
 A robust generalized score test for comparing groups of cluster binary
 data is proposed. This novel test is asymptotically valid for practically
 any underlying correlation configurations including the situation when
 correlation coefficients vary within or between clusters. This structure
 generally undermines the validity of the typical large sample properties
 of the method of maximum likelihood. Simulations and real data analysis
 are used to demonstrate the merit of this parametric robust method.
 Results show that our test is superior to two recently proposed test
 statistics advocated by other researchers.
Journal: Journal of Applied Statistics 
Pages: 1706-1715 
Issue: 8 
Volume: 42 
Year: 2015 
Month: 8 
X-DOI: 10.1080/02664763.2015.1005062 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1005062 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:8:p:1706-1715




Template-Type: ReDIF-Article 1.0
Author-Name: B. Baris Alkan 
Author-X-Name-First: B. Baris 
Author-X-Name-Last: Alkan 
Author-Name: Cemal Atakan 
Author-X-Name-First: Cemal 
Author-X-Name-Last: Atakan 
Author-Name: Nesrin Alkan 
Author-X-Name-First: Nesrin 
Author-X-Name-Last: Alkan 
Title: A comparison of different procedures for principal component analysis in the presence of outliers 
Abstract:
 Principal component analysis (PCA) is a popular technique that is useful
 for dimensionality reduction but it is affected by the presence of
 outliers. The outlier sensitivity of classical PCA (CPCA) has caused the
 development of new approaches. Effects of using estimates obtained by
 expectation-maximization - EM and multiple imputation - MI instead of
 outliers were examined on the artificial and a real data set. Furthermore,
 robust PCA based on minimum covariance determinant (MCD), PCA based on
 estimates obtained by EM instead of outliers and PCA based on estimates
 obtained by MI instead of outliers were compared with the results of CPCA.
 In this study, we tried to show the effects of using estimates obtained by
 MI and EM instead of outliers, depending on the ratio of outliers in data
 set. Finally, when the ratio of outliers exceeds 20%, we suggest the use
 of estimates obtained by MI and EM instead of outliers as an alternative
 approach.
Journal: Journal of Applied Statistics 
Pages: 1716-1722 
Issue: 8 
Volume: 42 
Year: 2015 
Month: 8 
X-DOI: 10.1080/02664763.2015.1005063 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1005063 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:8:p:1716-1722




Template-Type: ReDIF-Article 1.0
Author-Name: Waleed Dhhan 
Author-X-Name-First: Waleed 
Author-X-Name-Last: Dhhan 
Author-Name: Sohel Rana 
Author-X-Name-First: Sohel 
Author-X-Name-Last: Rana 
Author-Name: Habshah Midi 
Author-X-Name-First: Habshah 
Author-X-Name-Last: Midi 
Title: Non-sparse <italic>&#x3f5;</italic>-insensitive support vector regression for outlier detection 
Abstract:
 To estimate the approximate relationship between the dependent variable
 and its independent variables, it is necessary to diagnose outliers
 commonly present in numerous real applications before constructing the
 model. Nevertheless, the techniques of the standard support vector
 regression (<inline-formula><inline-graphic
 xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1005064_ilm0001.gif"/></inline-formula>-SVR) and
 modified support vector regression (<inline-formula><inline-graphic
 xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1005064_ilm0002.gif"/></inline-formula>) achieved good
 performance for outliers' detection for nonlinear functions with
 high-dimensional inputs. However, they still suffer from the costs of time
 and the setting of parameters. In this study, we propose a practical
 method for detecting outliers, using non-sparse
 <inline-formula><inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1005064_ilm0003.gif"/></inline-formula>-SVR, which
 minimizes time cost and introduces fixed parameters. We apply this
 approach for real and simulation data sets to test its effectiveness.
Journal: Journal of Applied Statistics 
Pages: 1723-1739 
Issue: 8 
Volume: 42 
Year: 2015 
Month: 8 
X-DOI: 10.1080/02664763.2015.1005064 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1005064 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:8:p:1723-1739




Template-Type: ReDIF-Article 1.0
Author-Name: Aaron Anderson 
Author-X-Name-First: Aaron 
Author-X-Name-Last: Anderson 
Title: A Monte Carlo comparison of alternative methods of maximum likelihood ranking in racing sports 
Abstract:
 Applications of maximum likelihood techniques to rank competitors in
 sports are commonly based on the assumption that each competitor's
 performance is a function of a deterministic component that represents
 inherent ability and a stochastic component that the competitor has
 limited control over. Perhaps based on an appeal to the central limit
 theorem, the stochastic component of performance has often been assumed to
 be a normal random variable. However, in the context of a racing sport,
 this assumption is problematic because the resulting model is the
 computationally difficult rank-ordered probit. Although a rank-ordered
 logit is a viable alternative, a Thurstonian paired-comparison model could
 also be applied. The purpose of this analysis was to compare the
 performance of the rank-ordered logit and Thurstonian paired-comparison
 models given the objective of ranking competitors based on ability. Monte
 Carlo simulations were used to generate race results based on a known
 ranking of competitors, assign rankings from the results of the two
 models, and judge performance based on Spearman's rank correlation
 coefficient. Results suggest that in many applications, a Thurstonian
 model can outperform a rank-ordered logit if each competitor's performance
 is normally distributed.
Journal: Journal of Applied Statistics 
Pages: 1740-1756 
Issue: 8 
Volume: 42 
Year: 2015 
Month: 8 
X-DOI: 10.1080/02664763.2015.1005065 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1005065 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:8:p:1740-1756




Template-Type: ReDIF-Article 1.0
Author-Name: Mario Hasler 
Author-X-Name-First: Mario 
Author-X-Name-Last: Hasler 
Title: Comment on multiple comparisons with a control under heteroscedasticity 
Journal: Journal of Applied Statistics 
Pages: 1757-1758 
Issue: 8 
Volume: 42 
Year: 2015 
Month: 8 
X-DOI: 10.1080/02664763.2015.1005582 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1005582 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:8:p:1757-1758




Template-Type: ReDIF-Article 1.0
Author-Name: Javier Fern�ndez-Macho 
Author-X-Name-First: Javier 
Author-X-Name-Last: Fern�ndez-Macho 
Title: Comment on testing for spurious and cointegrated regressions: a wavelet approach 
Abstract:
 In a recent paper, Leong and Huang [6] proposed a
 wavelet-correlation-based approach to test for cointegration between two
 time series. However, correlation and cointegration are two different
 concepts even when wavelet analysis is used. It is known that statistics
 based on non-stationary integrated variables have non-standard asymptotic
 distributions. However, wavelet analysis offsets the integrating order of
 non-stationary series so that traditional asymptotics on stationary
 variables suffices to ascertain the statistical properties of
 wavelet-based statistics. Based on this, this note shows that wavelet
 correlations cannot be used as a test of cointegration.
Journal: Journal of Applied Statistics 
Pages: 1759-1769 
Issue: 8 
Volume: 42 
Year: 2015 
Month: 8 
X-DOI: 10.1080/02664763.2015.1005583 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1005583 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:8:p:1759-1769




Template-Type: ReDIF-Article 1.0
Author-Name: Chee Kian Leong 
Author-X-Name-First: Chee Kian 
Author-X-Name-Last: Leong 
Title: Response to the comment on testing for spurious and cointegrated regressions: a wavelet approach 
Journal: Journal of Applied Statistics 
Pages: 1770-1772 
Issue: 8 
Volume: 42 
Year: 2015 
Month: 8 
X-DOI: 10.1080/02664763.2015.1020006 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1020006 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:8:p:1770-1772




Template-Type: ReDIF-Article 1.0
Author-Name: Oguz Akbilgic 
Author-X-Name-First: Oguz 
Author-X-Name-Last: Akbilgic 
Title: Classification trees aided mixed regression model 
Abstract:
 This paper introduces a novel hybrid regression method (MixReg) combining
 two linear regression methods, ordinary least square (OLS) and least
 squares ratio (LSR) regression. LSR regression is a method to find the
 regression coefficients minimizing the sum of squared error rate while OLS
 minimizes the sum of squared error itself. The goal of this study is to
 combine two methods in a way that the proposed method superior both OLS
 and LSR regression methods in terms of <italic>R</italic>-super-2
 statistics and relative error rate. Applications of MixReg, on both
 simulated and real data, show that MixReg method outperforms both OLS and
 LSR regression.
Journal: Journal of Applied Statistics 
Pages: 1773-1781 
Issue: 8 
Volume: 42 
Year: 2015 
Month: 8 
X-DOI: 10.1080/02664763.2015.1006394 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1006394 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:8:p:1773-1781




Template-Type: ReDIF-Article 1.0
Author-Name: Ehab F. Abd-Elfattah 
Author-X-Name-First: Ehab F. 
Author-X-Name-Last: Abd-Elfattah 
Title: Saddlepoint <italic>p</italic>-values for a class of tests for comparing competing risks with censored data 
Abstract:
 One of the general problems in clinical trials and mortality rates is the
 comparison of competing risks. Most of the test statistics used for
 independent and dependent risks with censored data belong to the class of
 weighted linear rank tests in its multivariate version. In this paper, we
 introduce the saddlepoint approximations as accurate and fast
 approximations for the exact <italic>p</italic>-values of this class of
 tests instead of the asymptotic and permutation simulated calculations.
 Real data examples and extensive simulation studies showed the accuracy
 and stability performance of the saddlepoint approximations over different
 scenarios of lifetime distributions, sample sizes and censoring.
Journal: Journal of Applied Statistics 
Pages: 1782-1791 
Issue: 8 
Volume: 42 
Year: 2015 
Month: 8 
X-DOI: 10.1080/02664763.2015.1006590 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1006590 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:8:p:1782-1791




Template-Type: ReDIF-Article 1.0
Author-Name: S.P. Singh 
Author-X-Name-First: S.P. 
Author-X-Name-Last: Singh 
Author-Name: S. Mukhopadhyay 
Author-X-Name-First: S. 
Author-X-Name-Last: Mukhopadhyay 
Author-Name: A. Roy 
Author-X-Name-First: A. 
Author-X-Name-Last: Roy 
Title: Comparison of three-level cluster randomized trials using quantile dispersion graphs 
Abstract:
 The purpose of this article is to evaluate and compare several three-level
 cluster randomized designs on the basis of their power functions. The
 power function of cluster designs depends on the intracluster correlations
 (ICCs), which are generally unknown at the planning stage. Thus, to
 compare these designs a prior knowledge of the ICCs is required. Three
 interval estimation methods are proposed for assigning joint confidence
 intervals to the two ICCs (corresponding to each cluster level). A
 detailed simulation study comparing the confidence intervals attained by
 the different techniques is given. The technique of quantile dispersion
 graphs is used for comparing the three-level cluster designs. For a given
 design, quantiles of the power function, are obtained for various effect
 sizes. These quantiles are functions of the unknown ICC coefficients. To
 address the dependence of the quantiles on the correlations, a
 <inline-formula><inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1010491_ilm0001.gif"/></inline-formula> confidence
 region is computed, and used as a parameter space. A three-level nested
 data set collected by the University of Michigan to study various school
 reforms on the achievements of students is used to illustrate the proposed
 methodology.
Journal: Journal of Applied Statistics 
Pages: 1792-1812 
Issue: 8 
Volume: 42 
Year: 2015 
Month: 8 
X-DOI: 10.1080/02664763.2015.1010491 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1010491 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:8:p:1792-1812




Template-Type: ReDIF-Article 1.0
Author-Name: Sanghoo Yoon 
Author-X-Name-First: Sanghoo 
Author-X-Name-Last: Yoon 
Author-Name: Bungon Kumphon 
Author-X-Name-First: Bungon 
Author-X-Name-Last: Kumphon 
Author-Name: Jeong-Soo Park 
Author-X-Name-First: Jeong-Soo 
Author-X-Name-Last: Park 
Title: Spatial modeling of extreme rainfall in northeast Thailand 
Abstract:
 It is well recognized that the generalized extreme value (GEV)
 distribution is widely used for any extreme events. This notion is based
 on the study of discrete choice behavior; however, there is a limit for
 predicting the distribution at ungauged sites. Hence, there have been
 studies on spatial dependence within extreme events in continuous space
 using recorded observations. We model the annual maximum daily rainfall
 data consisting of 25 locations for the period from 1982 to 2013. The
 spatial GEV model that is established under observations is assumed to be
 mutually independent because there is no spatial dependency between the
 stations. Furthermore, we divide the region into two regions for a better
 model fit and identify the best model for each region. We show that the
 regional spatial GEV model reflects the spatial pattern well compared with
 the spatial GEV model over the entire region as the local GEV
 distribution. The advantage of spatial extreme modeling is that more
 robust return levels and some indices of extreme rainfall can be obtained
 for observed stations as well as for locations without observed data.
 Thus, the model helps to determine the effects and assessment of
 vulnerability due to heavy rainfall in northeast Thailand.
Journal: Journal of Applied Statistics 
Pages: 1813-1828 
Issue: 8 
Volume: 42 
Year: 2015 
Month: 8 
X-DOI: 10.1080/02664763.2015.1010492 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1010492 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:8:p:1813-1828




Template-Type: ReDIF-Article 1.0
Author-Name: Chien-Lang Chen 
Author-X-Name-First: Chien-Lang 
Author-X-Name-Last: Chen 
Author-Name: Shang-Ling Ou 
Author-X-Name-First: Shang-Ling 
Author-X-Name-Last: Ou 
Author-Name: Chen-Tuo Liao 
Author-X-Name-First: Chen-Tuo 
Author-X-Name-Last: Liao 
Title: Interval estimation for conformance proportions of multiple quality characteristics 
Abstract:
 A conformance proportion is an important and useful index to assess
 industrial quality improvement. Statistical confidence limits for a
 conformance proportion are usually required not only to perform
 statistical significance tests, but also to provide useful information for
 determining practical significance. In this article, we propose approaches
 for constructing statistical confidence limits for a conformance
 proportion of multiple quality characteristics. Under the assumption that
 the variables of interest are distributed with a multivariate normal
 distribution, we develop an approach based on the concept of a fiducial
 generalized pivotal quantity (FGPQ). Without any distribution assumption
 on the variables, we apply some confidence interval construction methods
 for the conformance proportion by treating it as the probability of a
 success in a binomial distribution. The performance of the proposed
 methods is evaluated through detailed simulation studies. The results
 reveal that the simulated coverage probability (cp) for the FGPQ-based
 method is generally larger than the claimed value. On the other hand, one
 of the binomial distribution-based methods, that is, the standard method
 suggested in classical textbooks, appears to have smaller simulated cps
 than the nominal level. Two alternatives to the standard method are found
 to maintain their simulated cps sufficiently close to the claimed level,
 and hence their performances are judged to be satisfactory. In addition,
 three examples are given to illustrate the application of the proposed
 methods.
Journal: Journal of Applied Statistics 
Pages: 1829-1841 
Issue: 8 
Volume: 42 
Year: 2015 
Month: 8 
X-DOI: 10.1080/02664763.2015.1010493 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1010493 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:8:p:1829-1841




Template-Type: ReDIF-Article 1.0
Author-Name: Yuan Liu 
Author-X-Name-First: Yuan 
Author-X-Name-Last: Liu 
Author-Name: Hongyun Liu 
Author-X-Name-First: Hongyun 
Author-X-Name-Last: Liu 
Author-Name: Hang Li 
Author-X-Name-First: Hang 
Author-X-Name-Last: Li 
Author-Name: Qian Zhao 
Author-X-Name-First: Qian 
Author-X-Name-Last: Zhao 
Title: The effects of individually varying times of observations on growth parameter estimations in piecewise growth model 
Abstract:
 When using latent growth modeling (LGM), researchers often restrict the
 factor loadings, while the multilevel modeling (MLM) treats time as a
 metric variable. However, when individually varying times of observations
 are concerned in the longitudinal studies, the use of specified loadings
 would lead to inaccurate estimation. Based on piecewise growth modeling
 (PGM), this simulation study showed that (i) individually varying times of
 observations with larger boundaries got worse estimates and model fits
 when LGM was used; (ii) estimating the PGM across all the simulation
 situations was robust within MLM, whereas LGM got identically equal
 estimation with MLM only in the case of time boundaries of &plusmn;1 month
 or shorter; (iii) larger change of slope in piecewise modeling indicated
 better estimation.
Journal: Journal of Applied Statistics 
Pages: 1843-1860 
Issue: 9 
Volume: 42 
Year: 2015 
Month: 9 
X-DOI: 10.1080/02664763.2015.1014884 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1014884 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:9:p:1843-1860




Template-Type: ReDIF-Article 1.0
Author-Name: Y. Xia 
Author-X-Name-First: Y. 
Author-X-Name-Last: Xia 
Author-Name: N. Lu 
Author-X-Name-First: N. 
Author-X-Name-Last: Lu 
Author-Name: I. Katz 
Author-X-Name-First: I. 
Author-X-Name-Last: Katz 
Author-Name: R. Bossarte 
Author-X-Name-First: R. 
Author-X-Name-Last: Bossarte 
Author-Name: J. Arora 
Author-X-Name-First: J. 
Author-X-Name-Last: Arora 
Author-Name: H. He 
Author-X-Name-First: H. 
Author-X-Name-Last: He 
Author-Name: J.X. Tu 
Author-X-Name-First: J.X. 
Author-X-Name-Last: Tu 
Author-Name: B. Stephens 
Author-X-Name-First: B. 
Author-X-Name-Last: Stephens 
Author-Name: A. Watts 
Author-X-Name-First: A. 
Author-X-Name-Last: Watts 
Author-Name: X.M. Tu 
Author-X-Name-First: X.M. 
Author-X-Name-Last: Tu 
Title: Models for surveillance data under reporting delay: applications to US veteran first-time suicide attempters 
Abstract:
 Surveillance data provide a vital source of information for assessing the
 spread of a health problem or disease of interest and for planning for
 future health-care needs. However, the use of surveillance data requires
 proper adjustments of the reported caseload due to underreporting caused
 by reporting delays within a limited observation period. Although methods
 are available to address this classic statistical problem, they are
 largely focused on inference for the reporting delay distribution, with
 inference about caseload of disease incidence based on estimates for the
 delay distribution. This approach limits the complexity of models for
 disease incidence to provide reliable estimates and projections of
 incidence. Also, many of the available methods lack robustness since they
 require parametric distribution assumptions. We propose a new approach to
 overcome such limitations by allowing for separate models for the
 incidence and the reporting delay in a distribution-free fashion, but with
 joint inference for both modeling components, based on functional response
 models. In addition, we discuss inference about projections of future
 disease incidence to help identify significant shifts in temporal trends
 modeled based on the observed data. This latter issue on detecting 'change
 points' is not sufficiently addressed in the literature, despite the fact
 that such warning signs of potential outbreak are critically important for
 prevention purposes. We illustrate the approach with both simulated and
 real data, with the latter involving data for suicide attempts from the
 Veteran Healthcare Administration.
Journal: Journal of Applied Statistics 
Pages: 1861-1876 
Issue: 9 
Volume: 42 
Year: 2015 
Month: 9 
X-DOI: 10.1080/02664763.2015.1014885 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1014885 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:9:p:1861-1876




Template-Type: ReDIF-Article 1.0
Author-Name: Hea-Jung Kim 
Author-X-Name-First: Hea-Jung 
Author-X-Name-Last: Kim 
Title: Segmented classification analysis with a class of rectangle-screened elliptical populations 
Abstract:
 In many practical situations, a statistical practitioner often faces a
 problem of classifying an object from one of the segmented (or screened)
 populations where the segmentation was conducted by a set of screening
 variables. This paper addresses this problem, proposing and studying yet
 another optimal rule for classification with segmented populations. A
 class of <italic>q</italic>-dimensional rectangle-screened elliptically
 contoured (RSEC) distributions is considered for flexibly modeling the
 segmented populations. Based on the properties of the RSEC distributions,
 a parametric procedure for the segmented classification analysis (SCA) is
 proposed. This includes motivation for the SCA as well as some theoretical
 propositions regarding its optimal rule and properties. These properties
 allow us to establish other important results which include an efficient
 estimation of the rule by the Monte Carlo expectation-conditional
 maximization algorithm and an optimal variable selection procedure. Two
 numerical examples making use of utilizing a simulation study and a real
 dataset application and advocating the SCA procedure are also provided.
Journal: Journal of Applied Statistics 
Pages: 1877-1895 
Issue: 9 
Volume: 42 
Year: 2015 
Month: 9 
X-DOI: 10.1080/02664763.2015.1014886 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1014886 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:9:p:1877-1895




Template-Type: ReDIF-Article 1.0
Author-Name: A. Spagnoli 
Author-X-Name-First: A. 
Author-X-Name-Last: Spagnoli 
Author-Name: J.J. Houwing-Duistermaat 
Author-X-Name-First: J.J. 
Author-X-Name-Last: Houwing-Duistermaat 
Author-Name: M. Alf� 
Author-X-Name-First: M. 
Author-X-Name-Last: Alf� 
Title: Mixed-effect models for longitudinal responses with different types of dropout: an application to the Leiden 85-plus study 
Abstract:
 Longitudinal studies on cognitive functioning in geriatric populations
 usually cover short follow-up times and may be influenced by different
 sources of selection: only a portion of the designed sample may agree to
 participate in the study, and only some of the participants may complete
 the study. Motivated by a real-life data example, we discuss a variance
 component model with two peculiar features. First, we account for
 differences in individual status when entering the study by defining a
 flexible association structure between baseline and subsequent responses,
 where individual characteristics influencing entrance and participation in
 the follow-up are jointly modelled. Second, since we may argue that death
 and non-participation could not be treated as equivalent reasons for
 dropout, we introduce a pattern mixture model that takes into account the
 information on the time spent in the study and the reasons for dropout.
 The model is applied to data on cognitive functioning from the Leiden
 <inline-formula><inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1014887_ilm0001.gif"/></inline-formula> study, and its
 performance is analysed through a large-scale simulation study.
Journal: Journal of Applied Statistics 
Pages: 1896-1910 
Issue: 9 
Volume: 42 
Year: 2015 
Month: 9 
X-DOI: 10.1080/02664763.2015.1014887 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1014887 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:9:p:1896-1910




Template-Type: ReDIF-Article 1.0
Author-Name: Rabindra Nath Das 
Author-X-Name-First: Rabindra Nath 
Author-X-Name-Last: Das 
Author-Name: Jinseog Kim 
Author-X-Name-First: Jinseog 
Author-X-Name-Last: Kim 
Author-Name: Youngjo Lee 
Author-X-Name-First: Youngjo 
Author-X-Name-Last: Lee 
Title: Robust first-order rotatable lifetime improvement experimental designs 
Abstract:
 Experimental designs are widely used in predicting the optimal operating
 conditions of the process parameters in lifetime improvement experiments.
 The most commonly observed lifetime distributions are log-normal,
 exponential, gamma and Weibull. In the present article,
 <italic>invariant</italic> robust first-order rotatable designs are
 derived for autocorrelated lifetime responses having log-normal,
 exponential, gamma and Weibull distributions. In the process, robust
 first-order <italic>D</italic>-optimal and rotatable conditions have been
 derived under these situations. For these lifetime distributions with
 correlated errors, it is shown that robust first-order
 <italic>D</italic>-optimal designs are always robust rotatable but the
 converse is <italic>not</italic> true. Moreover, it is observed that
 robust first-order <italic>D</italic>-optimal and rotatable designs depend
 on the respective error variance-covariance structure <italic>but</italic>
 are independent from these considered lifetime response distributions.
Journal: Journal of Applied Statistics 
Pages: 1911-1930 
Issue: 9 
Volume: 42 
Year: 2015 
Month: 9 
X-DOI: 10.1080/02664763.2015.1014888 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1014888 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:9:p:1911-1930




Template-Type: ReDIF-Article 1.0
Author-Name: Amal Saki Malehi 
Author-X-Name-First: Amal 
Author-X-Name-Last: Saki Malehi 
Author-Name: Ebrahim Hajizadeh 
Author-X-Name-First: Ebrahim 
Author-X-Name-Last: Hajizadeh 
Author-Name: Kambiz A. Ahmadi 
Author-X-Name-First: Kambiz A. 
Author-X-Name-Last: Ahmadi 
Author-Name: Parvin Mansouri 
Author-X-Name-First: Parvin 
Author-X-Name-Last: Mansouri 
Title: Joint modelling of longitudinal biomarker and gap time between recurrent events: copula-based dependence 
Abstract:
 In this paper, we will extend the joint model of longitudinal biomarker
 and recurrent event via copula function for accounting the dependence
 between the two processes. The general idea of joining separate processes
 by allowing model-specific random effect may come from different families
 distribution. It is a main advantage of the proposed method that a copula
 construction does not constrain the choice of marginal distributions of
 random effects. A maximum likelihood estimation with importance sampling
 technique as a simple and easy understanding method is employed to model
 inference. To evaluate and verify the validation of the proposed joint
 model, a bootstrapping method as a model-based resampling is developed.
 Our proposed joint model is also applied to pemphigus disease data for
 assessing the effect of biomarker trajectory on risk of recurrence.
Journal: Journal of Applied Statistics 
Pages: 1931-1945 
Issue: 9 
Volume: 42 
Year: 2015 
Month: 9 
X-DOI: 10.1080/02664763.2015.1014889 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1014889 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:9:p:1931-1945




Template-Type: ReDIF-Article 1.0
Author-Name: Seokho Lee 
Author-X-Name-First: Seokho 
Author-X-Name-Last: Lee 
Author-Name: Johan Lim 
Author-X-Name-First: Johan 
Author-X-Name-Last: Lim 
Author-Name: Insuk Sohn 
Author-X-Name-First: Insuk 
Author-X-Name-Last: Sohn 
Author-Name: Sin-Ho Jung 
Author-X-Name-First: Sin-Ho 
Author-X-Name-Last: Jung 
Author-Name: Cheol-Keun Park 
Author-X-Name-First: Cheol-Keun 
Author-X-Name-Last: Park 
Title: Two sample test for high-dimensional partially paired data 
Abstract:
 In this paper, we study two sample test for the equality of mean vectors
 of high-dimensional partially paired data. Extending the results of Lim
 <italic>et al.</italic> [12], we propose a new type of regularized
 statistics, denoted by <inline-formula><inline-graphic
 xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1014890_ilm0001.gif"/></inline-formula>, which is a
 convex combination of the regularized Hotelling's
 <italic>t</italic>-statistic (HT) for two independent multivariate samples
 and that for multivariate paired samples. The proposed
 <inline-formula><inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1014890_ilm0002.gif"/></inline-formula> involves the
 shrinkage estimator of the covariance matrix and, depending on the choice
 of the shrinkage estimator, two versions of the
 <inline-formula><inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1014890_ilm0003.gif"/></inline-formula> are proposed.
 We compute the asymptotic null distribution of one version of the RT for a
 fixed tuning parameter of the covariance matrix estimation. A procedure to
 estimate the tuning parameter is proposed and discussed. The power of the
 proposed test is compared to two existing ad-hoc procedures, the HT based
 on a few principal components (PCs) from the PC analysis and that with the
 generalized inverse of the sample covariance matrix. It is also compared
 to the test with only independent two samples or paired samples. Finally,
 we illustrate the advantage of the <inline-formula><inline-graphic
 xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1014890_ilm0004.gif"/></inline-formula> using the
 microarray experiment of the liver cancer.
Journal: Journal of Applied Statistics 
Pages: 1946-1961 
Issue: 9 
Volume: 42 
Year: 2015 
Month: 9 
X-DOI: 10.1080/02664763.2015.1014890 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1014890 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:9:p:1946-1961




Template-Type: ReDIF-Article 1.0
Author-Name: A. Nodehi 
Author-X-Name-First: A. 
Author-X-Name-Last: Nodehi 
Author-Name: M. Golalizadeh 
Author-X-Name-First: M. 
Author-X-Name-Last: Golalizadeh 
Author-Name: A. Heydari 
Author-X-Name-First: A. 
Author-X-Name-Last: Heydari 
Title: Dihedral angles principal geodesic analysis using nonlinear statistics 
Abstract:
 Statistics, as one of the applied sciences, has great impacts in vast area
 of other sciences. Prediction of protein structures with great emphasize
 on their geometrical features using dihedral angles has invoked the new
 branch of statistics, known as directional statistics. One of the
 available biological techniques to predict is molecular dynamics
 simulations producing high-dimensional molecular structure data. Hence, it
 is expected that the principal component analysis (PCA) can response some
 related statistical problems particulary to reduce dimensions of the
 involved variables. Since the dihedral angles are variables on
 non-Euclidean space (their locus is the torus), it is expected that direct
 implementation of PCA does not provide great information in this case. The
 principal geodesic analysis is one of the recent methods to reduce the
 dimensions in the non-Euclidean case. A procedure to utilize this
 technique for reducing the dimension of a set of dihedral angles is
 highlighted in this paper. We further propose an extension of this tool,
 implemented in such way the torus is approximated by the product of two
 unit circle and evaluate its application in studying a real data set. A
 comparison of this technique with some previous methods is also
 undertaken.
Journal: Journal of Applied Statistics 
Pages: 1962-1972 
Issue: 9 
Volume: 42 
Year: 2015 
Month: 9 
X-DOI: 10.1080/02664763.2015.1014892 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1014892 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:9:p:1962-1972




Template-Type: ReDIF-Article 1.0
Author-Name: Hadi Alizadeh Noughabi 
Author-X-Name-First: Hadi 
Author-X-Name-Last: Alizadeh Noughabi 
Title: Empirical likelihood ratio-based goodness-of-fit test for the logistic distribution 
Abstract:
 The logistic distribution has been used to model growth curves in survival
 analysis and biological studies. In this article, we propose a
 goodness-of-fit test for the logistic distribution based on the empirical
 likelihood ratio. The test is constructed based on the methodology
 introduced by Vexler and Gurevich [17]. In order to compute the test
 statistic, parameters of the distribution are estimated by the method of
 maximum likelihood. Power comparisons of the proposed test with some known
 competing tests are carried out via simulations. Finally, an illustrative
 example is presented and analyzed.
Journal: Journal of Applied Statistics 
Pages: 1973-1983 
Issue: 9 
Volume: 42 
Year: 2015 
Month: 9 
X-DOI: 10.1080/02664763.2015.1014893 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1014893 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:9:p:1973-1983




Template-Type: ReDIF-Article 1.0
Author-Name: A.R. Silva 
Author-X-Name-First: A.R. 
Author-X-Name-Last: Silva 
Author-Name: C.T.S. Dias 
Author-X-Name-First: C.T.S. 
Author-X-Name-Last: Dias 
Author-Name: P.R. Cecon 
Author-X-Name-First: P.R. 
Author-X-Name-Last: Cecon 
Author-Name: E.R. R&ecirc;go 
Author-X-Name-First: E.R. 
Author-X-Name-Last: R&ecirc;go 
Title: An alternative procedure for performing a power analysis of Mantel's test 
Abstract:
 This study proposes a simple way to perform a power analysis of Mantel's
 test applied to squared Euclidean distance matrices. The general
 statistical aspects of the simple Mantel's test are reviewed. The Monte
 Carlo method is used to generate bivariate Gaussian variables in order to
 create squared Euclidean distance matrices. The power of the parametric
 correlation <italic>t</italic>-test applied to raw data is also evaluated
 and compared with that of Mantel's test. The standard procedure for
 calculating punctual power levels is used for validation. The proposed
 procedure allows one to draw the power curve by running the test only
 once, dispensing with the time demanding standard procedure of Monte Carlo
 simulations. Unlike the standard procedure, it does not depend on a
 knowledge of the distribution of the raw data. The <italic>simulated
 power</italic> function has all the properties of the power analysis
 theory and is in agreement with the results of the standard procedure.
Journal: Journal of Applied Statistics 
Pages: 1984-1992 
Issue: 9 
Volume: 42 
Year: 2015 
Month: 9  
X-DOI: 10.1080/02664763.2015.1014894 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1014894 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:9:p:1984-1992




Template-Type: ReDIF-Article 1.0
Author-Name: Firoozeh Rivaz 
Author-X-Name-First: Firoozeh 
Author-X-Name-Last: Rivaz 
Author-Name: Majid Jafari Khaledi 
Author-X-Name-First: Majid Jafari 
Author-X-Name-Last: Khaledi 
Title: Bayesian spatial prediction of skew and censored data via a hybrid algorithm 
Abstract:
 A correct detection of areas with excess of pollution relies first on
 accurate predictions of pollutant concentrations, a task that is usually
 complicated by skewed histograms and the presence of censored data. The
 unified skew-Gaussian (SUG) random field proposed by Zareifard and Jafari
 Khaledi [19] offers a more flexible class of sampling spatial models to
 account for skewness. In this paper, we adopt a Bayesian framework to
 perform prediction for the SUG model in the presence of censored data.
 Owing to the presence of many latent variables with strongly dependent
 components in the model, we encounter convergence issues when using Monte
 Carlo Markov Chain algorithms. To overcome this obstacle, we use a
 computationally efficient inverse Bayes formulas sampling procedure to
 obtain approximately independent samples from the posterior distribution
 of latent variables. Then they are applied to update parameters in a Gibbs
 sampler scheme. This hybrid algorithm provides effective samples,
 resulting in some computational advantages and precise predictions. The
 proposed approach is illustrated with a simulation study and applied to a
 spatial data set which contains right censored data.
Journal: Journal of Applied Statistics 
Pages: 1993-2009 
Issue: 9 
Volume: 42 
Year: 2015 
Month: 9 
X-DOI: 10.1080/02664763.2015.1014895 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1014895 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:9:p:1993-2009




Template-Type: ReDIF-Article 1.0
Author-Name: Eufr�sio de A. Lima Neto 
Author-X-Name-First: Eufr�sio de A. 
Author-X-Name-Last: Lima Neto 
Author-Name: Ulisses U. dos Anjos 
Author-X-Name-First: Ulisses U. 
Author-X-Name-Last: dos Anjos 
Title: Regression model for interval-valued variables based on copulas 
Abstract:
 In real problems, it is usual to have the available data presented as
 intervals. Therefore, different approaches have been proposed to obtain a
 regression model for this new type of data. In this paper, we represent
 the interval-valued response variable <inline-formula><inline-graphic
 xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1015114_ilm0001.gif"/></inline-formula> as a bivariate
 random vector and we consider the copula's theory to propose a general
 bivariate distribution for <italic>Z</italic>, creating a more flexible
 random component to the model. Inference techniques and a residual
 definition based on deviance are considered, as well as applications to
 synthetic and real data sets that demonstrate the usefulness of the
 proposed approach. The new method is also compared with other methods
 reported in the literature.
Journal: Journal of Applied Statistics 
Pages: 2010-2029 
Issue: 9 
Volume: 42 
Year: 2015 
Month: 9 
X-DOI: 10.1080/02664763.2015.1015114 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1015114 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:9:p:2010-2029




Template-Type: ReDIF-Article 1.0
Author-Name: Marcin Kozak 
Author-X-Name-First: Marcin 
Author-X-Name-Last: Kozak 
Author-Name: Wojtek Krzanowski 
Author-X-Name-First: Wojtek 
Author-X-Name-Last: Krzanowski 
Author-Name: Izabela Cichocka 
Author-X-Name-First: Izabela 
Author-X-Name-Last: Cichocka 
Author-Name: James Hartley 
Author-X-Name-First: James 
Author-X-Name-Last: Hartley 
Title: The effects of data input errors on subsequent statistical inference 
Abstract:
 Data input errors can potentially affect statistical inferences, but
 little research has been published to date on this topic. In the present
 paper, we report the effect of data input errors on the statistical
 inferences drawn about population parameters in an empirical study
 involving 280 students from two Polish universities, namely the Warsaw
 University of Life Sciences - SGGW and the University of Information
 Technology and Management in Rzeszow. We found that 28% of the students
 committed at least one data error. While some of these errors were small
 and did not have any real effect, a few of them had substantial effects on
 the statistical inferences drawn about the population parameters.
Journal: Journal of Applied Statistics 
Pages: 2030-2037 
Issue: 9 
Volume: 42 
Year: 2015 
Month: 9 
X-DOI: 10.1080/02664763.2015.1016410 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1016410 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:9:p:2030-2037




Template-Type: ReDIF-Article 1.0
Author-Name: Philip B. Holden 
Author-X-Name-First: Philip B. 
Author-X-Name-Last: Holden 
Author-Name: Neil R. Edwards 
Author-X-Name-First: Neil R. 
Author-X-Name-Last: Edwards 
Author-Name: Paul H. Garthwaite 
Author-X-Name-First: Paul H. 
Author-X-Name-Last: Garthwaite 
Author-Name: Richard D. Wilkinson 
Author-X-Name-First: Richard D. 
Author-X-Name-Last: Wilkinson 
Title: Emulation and interpretation of high-dimensional climate model outputs 
Abstract:
 Running complex computer models can be expensive in computer time, while
 learning about the relationships between input and output variables can be
 difficult. An emulator is a fast approximation to a computationally
 expensive model that can be used as a surrogate for the model, to quantify
 uncertainty or to improve process understanding. Here, we examine
 emulators based on singular value decompositions (SVDs) and use them to
 emulate global climate and vegetation fields, examining how these fields
 are affected by changes in the Earth's orbit. The vegetation field may be
 emulated directly from the orbital variables, but an appealing alternative
 is to relate it to emulations of the climate fields, which involves
 high-dimensional input and output. The SVDs radically reduce the
 dimensionality of the input and output spaces and are shown to clarify the
 relationships between them. The method could potentially be useful for any
 complex process with correlated, high-dimensional inputs and/or outputs.
Journal: Journal of Applied Statistics 
Pages: 2038-2055 
Issue: 9 
Volume: 42 
Year: 2015 
Month: 9 
X-DOI: 10.1080/02664763.2015.1016412 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1016412 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:9:p:2038-2055




Template-Type: ReDIF-Article 1.0
Author-Name: Abhik Ghosh 
Author-X-Name-First: Abhik 
Author-X-Name-Last: Ghosh 
Author-Name: Ayanendranath Basu 
Author-X-Name-First: Ayanendranath 
Author-X-Name-Last: Basu 
Title: Robust estimation for non-homogeneous data and the selection of the optimal tuning parameter: the density power divergence approach 
Abstract:
 The density power divergence (DPD) measure, defined in terms of a single
 parameter <italic>&alpha;</italic>, has proved to be a popular tool in the
 area of robust estimation [1]. Recently, Ghosh and Basu [5] rigorously
 established the asymptotic properties of the MDPDEs in case of independent
 non-homogeneous observations. In this paper, we present an extensive
 numerical study to describe the performance of the method in the case of
 linear regression, the most common setup under the case of non-homogeneous
 data. In addition, we extend the existing methods for the selection of the
 optimal robustness tuning parameter from the case of independent and
 identically distributed (i.i.d.) data to the case of non-homogeneous
 observations. Proper selection of the tuning parameter is critical to the
 appropriateness of the resulting analysis. The selection of the optimal
 robustness tuning parameter is explored in the context of the linear
 regression problem with an extensive numerical study involving real and
 simulated data.
Journal: Journal of Applied Statistics 
Pages: 2056-2072 
Issue: 9 
Volume: 42 
Year: 2015 
Month: 9 
X-DOI: 10.1080/02664763.2015.1016901 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1016901 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:9:p:2056-2072




Template-Type: ReDIF-Article 1.0
Author-Name: Myung Geun Kim 
Author-X-Name-First: Myung Geun 
Author-X-Name-Last: Kim 
Title: Geometric aspects of deletion diagnostics in multivariate regression 
Abstract:
 In multivariate regression, a graphical diagnostic method of detecting
 observations that are influential in estimating regression coefficients is
 introduced. It is based on the principal components and their variances
 obtained from the covariance matrix of the probability distribution for
 the change in the estimator of the matrix of unknown regression
 coefficients due to a single-case deletion. As a result, each deletion
 statistic obtained in a form of matrix is transformed into a
 two-dimensional quantity. Its univariate version is also introduced in a
 little different way. No distributional form is assumed. For illustration,
 we provide a numerical example in which the graphical method introduced
 here is seen to be effective in getting information about influential
 observations.
Journal: Journal of Applied Statistics 
Pages: 2073-2079 
Issue: 10 
Volume: 42 
Year: 2015 
Month: 10 
X-DOI: 10.1080/02664763.2015.1016411 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1016411 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:10:p:2073-2079




Template-Type: ReDIF-Article 1.0
Author-Name: M.G.M. Khan 
Author-X-Name-First: M.G.M. 
Author-X-Name-Last: Khan 
Author-Name: K.G. Reddy 
Author-X-Name-First: K.G. 
Author-X-Name-Last: Reddy 
Author-Name: D.K. Rao 
Author-X-Name-First: D.K. 
Author-X-Name-Last: Rao 
Title: Designing stratified sampling in economic and business surveys 
Abstract:
 In most economic and business surveys, the target variables (e.g. turnover
 of enterprises, income of households, etc.) commonly resemble skewed
 distributions with many small and few large units. In such surveys, if a
 stratified sampling technique is used as a method of sampling and
 estimation, the convenient way of stratification such as the use of
 demographical variables (e.g. gender, socioeconomic class, geographical
 region, religion, ethnicity, etc.) or other natural criteria, which is
 widely practiced in economic surveys, may fail to form homogeneous strata
 and is not much useful in order to increase the precision of the estimates
 of variables of interest. In this paper, a stratified sampling design for
 economic surveys based on auxiliary information has been developed, which
 can be used for constructing optimum stratification and determining
 optimum sample allocation to maximize the precision in estimate.
Journal: Journal of Applied Statistics 
Pages: 2080-2099 
Issue: 10 
Volume: 42 
Year: 2015 
Month: 10 
X-DOI: 10.1080/02664763.2015.1018674 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1018674 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:10:p:2080-2099




Template-Type: ReDIF-Article 1.0
Author-Name: Uttam Bandyopadhyay 
Author-X-Name-First: Uttam 
Author-X-Name-Last: Bandyopadhyay 
Author-Name: Joydeep Basu 
Author-X-Name-First: Joydeep 
Author-X-Name-Last: Basu 
Author-Name: Ganesh Dutta 
Author-X-Name-First: Ganesh 
Author-X-Name-Last: Dutta 
Title: Crossover design in clinical trials for binary response 
Abstract:
 In this paper, we consider a binary response model for the analysis of the
 two-treatment, two-period and four-sequence crossover design. We have
 introduced intra-patient drug dependency parameter in the model and
 provide two tests for the hypothesis of equality of treatment effects. We
 employ Monte Carlo simulation to compare our tests and a test that works
 under parallel design on the basis of type I error rate and power. We find
 that our procedures are dominant over the competitor with respect to
 power. Finally, we use a data set to illustrate the applicability of our
 procedure.
Journal: Journal of Applied Statistics 
Pages: 2100-2114 
Issue: 10 
Volume: 42 
Year: 2015 
Month: 10 
X-DOI: 10.1080/02664763.2015.1018675 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1018675 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:10:p:2100-2114




Template-Type: ReDIF-Article 1.0
Author-Name: Juan Ding 
Author-X-Name-First: Juan 
Author-X-Name-Last: Ding 
Author-Name: Wenjun Xiong 
Author-X-Name-First: Wenjun 
Author-X-Name-Last: Xiong 
Title: Robust group testing for multiple traits with misclassification 
Abstract:
 Determining group size is a crucial stage before conducting experiments
 using group testing methods. Considering misclassification, we propose
 <italic>D</italic>-criterion and <italic>A</italic>-criterion to determine
 a robust group size for screening multiple infections simultaneously.
 Extensive simulation shows the advantage of the proposed method when the
 goal is estimation.
Journal: Journal of Applied Statistics 
Pages: 2115-2125 
Issue: 10 
Volume: 42 
Year: 2015 
Month: 10 
X-DOI: 10.1080/02664763.2015.1019841 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1019841 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:10:p:2115-2125




Template-Type: ReDIF-Article 1.0
Author-Name: Istem Koymen Keser 
Author-X-Name-First: Istem 
Author-X-Name-Last: Koymen Keser 
Author-Name: Ipek Deveci Kocako� 
Author-X-Name-First: Ipek 
Author-X-Name-Last: Deveci Kocako� 
Title: Smoothed functional canonical correlation analysis of humidity and temperature data 
Abstract:
 This paper focuses on smoothed functional canonical correlation analysis
 (SFCCA) to investigate the relationships and changes in large, seasonal
 and long-term data sets. The aim of this study is to introduce a guideline
 for SFCCA for functional data and to give some insights on the fine tuning
 of the methodology for long-term periodical data. The guidelines are
 applied on temperature and humidity data for 11 years between 2000 and
 2010 and the results are interpreted. Seasonal changes or periodical
 shifts are visually studied by yearly comparisons. The effects of the
 'number of basis functions' and the 'selection of smoothing parameter' on
 the general variability structure and on correlations between the curves
 are examined. It is concluded that the number of time points (knots),
 number of basis functions and the time span of evaluation (monthly, daily,
 etc.) should all be chosen harmoniously. It is found that changing the
 smoothing parameter does not have a significant effect on the structure of
 curves and correlations. The number of basis functions is found to be the
 main effector on both individual and correlation weight functions.
Journal: Journal of Applied Statistics 
Pages: 2126-2140 
Issue: 10 
Volume: 42 
Year: 2015 
Month: 10 
X-DOI: 10.1080/02664763.2015.1019842 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1019842 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:10:p:2126-2140




Template-Type: ReDIF-Article 1.0
Author-Name: Lucia Modugno 
Author-X-Name-First: Lucia 
Author-X-Name-Last: Modugno 
Author-Name: Silvia Cagnone 
Author-X-Name-First: Silvia 
Author-X-Name-Last: Cagnone 
Author-Name: Simone Giannerini 
Author-X-Name-First: Simone 
Author-X-Name-Last: Giannerini 
Title: A multilevel model with autoregressive components for the analysis of tribal art prices 
Abstract:
 In this paper, we introduce a multilevel model specification with
 time-series components for the analysis of prices of artworks sold at
 auctions. Since auction data do not constitute a panel or a time series
 but are composed of repeated cross-sections, they require a specification
 with items at the first level nested in time-points. Our approach combines
 the flexibility of mixed effect models together with the predicting
 performance of time series as it allows to model the time dynamics
 directly. Model estimation is obtained by means of maximum likelihood
 through the expectation-maximization algorithm. The model is motivated by
 the analysis of the first database ethnic artworks sold in the most
 important auctions worldwide. The results show that the proposed
 specification improves considerably over classical proposals both in terms
 of fit and prediction.
Journal: Journal of Applied Statistics 
Pages: 2141-2158 
Issue: 10 
Volume: 42 
Year: 2015 
Month: 10 
X-DOI: 10.1080/02664763.2015.1021304 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1021304 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:10:p:2141-2158




Template-Type: ReDIF-Article 1.0
Author-Name: Edwin M.M. Ortega 
Author-X-Name-First: Edwin M.M. 
Author-X-Name-Last: Ortega 
Author-Name: Artur J. Lemonte 
Author-X-Name-First: Artur J. 
Author-X-Name-Last: Lemonte 
Author-Name: Giovana O. Silva 
Author-X-Name-First: Giovana O. 
Author-X-Name-Last: Silva 
Author-Name: Gauss M. Cordeiro 
Author-X-Name-First: Gauss M. 
Author-X-Name-Last: Cordeiro 
Title: New flexible models generated by gamma random variables for lifetime modeling 
Abstract:
 In this paper we introduce a new three-parameter exponential-type
 distribution. The new distribution is quite flexible and can be used
 effectively in modeling survival data and reliability problems. It can
 have constant, decreasing, increasing, upside-down bathtub and
 bathtub-shaped hazard rate functions. It also generalizes some well-known
 distributions. We discuss maximum likelihood estimation of the model
 parameters for complete sample and for censored sample. Additionally, we
 formulate a new cure rate survival model by assuming that the number of
 competing causes of the event of interest has the Poisson distribution and
 the time to this event follows the proposed distribution. Maximum
 likelihood estimation of the model parameters of the new cure rate
 survival model is discussed for complete sample and censored sample. Two
 applications to real data are provided to illustrate the flexibility of
 the new model in practice.
Journal: Journal of Applied Statistics 
Pages: 2159-2179 
Issue: 10 
Volume: 42 
Year: 2015 
Month: 10 
X-DOI: 10.1080/02664763.2015.1021669 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1021669 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:10:p:2159-2179




Template-Type: ReDIF-Article 1.0
Author-Name: Musie Ghebremichael 
Author-X-Name-First: Musie 
Author-X-Name-Last: Ghebremichael 
Title: Joint modeling of correlated binary outcomes: HIV-1 and HSV-2 co-infection 
Abstract:
 Herpes Simplex Virus Type 2 (HSV-2) facilitates the sexual acquisition and
 transmission of HIV-1 infection and is highly prevalent in most regions
 experiencing severe HIV epidemics. In sub-Saharan Africa, where HIV
 infection is a public health burden, the prevalence of HSV-2 is
 substantially high. The high prevalence of HSV-2 and the association
 between HSV-2 infection and HIV-1 acquisition could play a significant
 role in the spread of HIV-1 in the region. The objective of our study was
 to identify risk factors for HSV-2 and HIV-1 infections among men in
 sub-Saharan Africa. We used a joint response model that accommodates the
 interdependence between the two infections in assessing their risk
 factors. Simulation studies show superiority of the joint response model
 compared to the traditional models which ignore the dependence between the
 two infections. We found higher odds of having HSV-2/HIV-1 among older
 men, in men who had multiple sexual partners, abused alcohol, or reported
 symptoms of sexually transmitted infections. These findings suggest that
 interventions that identify and control the risk factors of the two
 infections should be part of HIV-1 prevention programs in sub-Saharan
 Africa where antiretroviral therapy is not readily available.
Journal: Journal of Applied Statistics 
Pages: 2180-2191 
Issue: 10 
Volume: 42 
Year: 2015 
Month: 10 
X-DOI: 10.1080/02664763.2015.1022138 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1022138 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:10:p:2180-2191




Template-Type: ReDIF-Article 1.0
Author-Name: Antonello D'Ambra 
Author-X-Name-First: Antonello 
Author-X-Name-Last: D'Ambra 
Author-Name: Anna Crisci 
Author-X-Name-First: Anna 
Author-X-Name-Last: Crisci 
Author-Name: Pasquale Sarnacchiaro 
Author-X-Name-First: Pasquale 
Author-X-Name-Last: Sarnacchiaro 
Title: A generalized analysis of the dependence structure by means of ANOVA 
Abstract:
 The multiple non-symmetric correspondence analysis (MNSCA) is a useful
 technique for analysing the prediction of a categorical variable through
 two or more predictor variables placed in a contingency table. In MNSCA
 framework, for summarizing the predictability between criterion and
 predictor variables, the Multiple-TAU index has been proposed. But it
 cannot be used to test association, and for overcoming this limitation, a
 relationship with C-Statistic has been recommended. Multiple-TAU index is
 an overall measure of association that contains both main effects and
 interaction terms. The main effects represent the change in the response
 variables due to the change in the level/categories of the predictor
 variables, considering the effects of their addition. On the other hand,
 the interaction effect represents the combined effect of predictor
 variables on the response variable. In this paper, we propose a
 decomposition of the Multiple-TAU index in main effects and interaction
 terms. In order to show this decomposition, we consider an empirical case
 in which the relationship between the demographic characteristics of the
 American people, such as race, gender and location (<italic>column
 variables</italic>), and their propensity to move (<italic>row
 variable</italic>) to a new town to find a job is considered.
Journal: Journal of Applied Statistics 
Pages: 2192-2202 
Issue: 10 
Volume: 42 
Year: 2015 
Month: 10 
X-DOI: 10.1080/02664763.2015.1023269 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1023269 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:10:p:2192-2202




Template-Type: ReDIF-Article 1.0
Author-Name: H. He 
Author-X-Name-First: H. 
Author-X-Name-Last: He 
Author-Name: W. Wang 
Author-X-Name-First: W. 
Author-X-Name-Last: Wang 
Author-Name: J. Hu 
Author-X-Name-First: J. 
Author-X-Name-Last: Hu 
Author-Name: R. Gallop 
Author-X-Name-First: R. 
Author-X-Name-Last: Gallop 
Author-Name: P. Crits-Christoph 
Author-X-Name-First: P. 
Author-X-Name-Last: Crits-Christoph 
Author-Name: Y. Xia 
Author-X-Name-First: Y. 
Author-X-Name-Last: Xia 
Title: Distribution-free inference of zero-inflated binomial data for longitudinal studies 
Abstract:
 Count responses with structural zeros are very common in medical and
 psychosocial research, especially in alcohol and HIV research, and the
 zero-inflated Poisson (ZIP) and zero-inflated negative binomial models are
 widely used for modeling such outcomes. However, as alcohol drinking
 outcomes such as days of drinkings are counts within a given period, their
 distributions are bounded above by an upper limit (total days in the
 period) and thus inherently follow a binomial or zero-inflated binomial
 (ZIB) distribution, rather than a Poisson or ZIP distribution, in the
 presence of structural zeros. In this paper, we develop a new
 semiparametric approach for modeling ZIB-like count responses for
 cross-sectional as well as longitudinal data. We illustrate this approach
 with both simulated and real study data.
Journal: Journal of Applied Statistics 
Pages: 2203-2219 
Issue: 10 
Volume: 42 
Year: 2015 
Month: 10 
X-DOI: 10.1080/02664763.2015.1023270 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1023270 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:10:p:2203-2219




Template-Type: ReDIF-Article 1.0
Author-Name: Wen-Liang Hung 
Author-X-Name-First: Wen-Liang 
Author-X-Name-Last: Hung 
Author-Name: Shou-Jen Chang-Chien 
Author-X-Name-First: Shou-Jen 
Author-X-Name-Last: Chang-Chien 
Author-Name: Miin-Shen Yang 
Author-X-Name-First: Miin-Shen 
Author-X-Name-Last: Yang 
Title: An intuitive clustering algorithm for spherical data with application to extrasolar planets 
Abstract:
 This paper proposes an intuitive clustering algorithm capable of
 automatically self-organizing data groups based on the original data
 structure. Comparisons between the propopsed algorithm and EM [1] and
 spherical <italic>k</italic>-means [7] algorithms are given. These
 numerical results show the effectiveness of the proposed algorithm, using
 the correct classification rate and the adjusted Rand index as evaluation
 criteria [5,6]. In 1995, Mayor and Queloz announced the detection of the
 first extrasolar planet (exoplanet) around a Sun-like star. Since then,
 observational efforts of astronomers have led to the detection of more
 than 1000 exoplanets. These discoveries may provide important information
 for understanding the formation and evolution of planetary systems. The
 proposed clustering algorithm is therefore used to study the data gathered
 on exoplanets. Two main implications are also suggested: (1) there are
 three major clusters, which correspond to the exoplanets in the regimes of
 disc, ongoing tidal and tidal interactions, respectively, and (2) the
 stellar metallicity does not play a key role in exoplanet migration.
Journal: Journal of Applied Statistics 
Pages: 2220-2232 
Issue: 10 
Volume: 42 
Year: 2015 
Month: 10 
X-DOI: 10.1080/02664763.2015.1023271 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1023271 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:10:p:2220-2232




Template-Type: ReDIF-Article 1.0
Author-Name: M. Teimourian 
Author-X-Name-First: M. 
Author-X-Name-Last: Teimourian 
Author-Name: T. Baghfalaki 
Author-X-Name-First: T. 
Author-X-Name-Last: Baghfalaki 
Author-Name: M. Ganjali 
Author-X-Name-First: M. 
Author-X-Name-Last: Ganjali 
Author-Name: D. Berridge 
Author-X-Name-First: D. 
Author-X-Name-Last: Berridge 
Title: Joint modeling of mixed skewed continuous and ordinal longitudinal responses: a Bayesian approach 
Abstract:
 In this paper, a joint model for analyzing multivariate mixed ordinal and
 continuous responses, where continuous outcomes may be skew, is presented.
 For modeling the discrete ordinal responses, a continuous latent variable
 approach is considered and for describing continuous responses, a
 skew-normal mixed effects model is used. A Bayesian approach using Markov
 Chain Monte Carlo (MCMC) is adopted for parameter estimation. Some
 simulation studies are performed for illustration of the proposed
 approach. The results of the simulation studies show that the use of the
 separate models or the normal distributional assumption for shared random
 effects and within-subject errors of continuous and ordinal variables,
 instead of the joint modeling under a skew-normal distribution, leads to
 biased parameter estimates. The approach is used for analyzing a part of
 the British Household Panel Survey (BHPS) data set. Annual income and life
 satisfaction are considered as the continuous and the ordinal longitudinal
 responses, respectively. The annual income variable is severely skewed,
 therefore, the use of the normality assumption for the continuous response
 does not yield acceptable results. The results of data analysis show that
 gender, marital status, educational levels and the amount of money spent
 on leisure have a significant effect on annual income, while marital
 status has the highest impact on life satisfaction.
Journal: Journal of Applied Statistics 
Pages: 2233-2256 
Issue: 10 
Volume: 42 
Year: 2015 
Month: 10 
X-DOI: 10.1080/02664763.2015.1023557 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1023557 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:10:p:2233-2256




Template-Type: ReDIF-Article 1.0
Author-Name: A.F. Donneau 
Author-X-Name-First: A.F. 
Author-X-Name-Last: Donneau 
Author-Name: M. Mauer 
Author-X-Name-First: M. 
Author-X-Name-Last: Mauer 
Author-Name: P. Lambert 
Author-X-Name-First: P. 
Author-X-Name-Last: Lambert 
Author-Name: E. Lesaffre 
Author-X-Name-First: E. 
Author-X-Name-Last: Lesaffre 
Author-Name: A. Albert 
Author-X-Name-First: A. 
Author-X-Name-Last: Albert 
Title: Testing the proportional odds assumption in multiply imputed ordinal longitudinal data 
Abstract:
 A popular choice when analyzing ordinal data is to consider the cumulative
 proportional odds model to relate the marginal probabilities of the
 ordinal outcome to a set of covariates. However, application of this model
 relies on the condition of identical cumulative odds ratios across the
 cut-offs of the ordinal outcome; the well-known proportional odds
 assumption. This paper focuses on the assessment of this assumption while
 accounting for repeated and missing data. In this respect, we develop a
 statistical method built on multiple imputation (MI)&nbsp;based on
 generalized estimating equations that allows to test the proportionality
 assumption under the missing at random setting. The performance of the
 proposed method is evaluated for two MI algorithms for incomplete
 longitudinal ordinal data. The impact of both MI methods is compared with
 respect to the type I error rate and the power for situations covering
 various numbers of categories of the ordinal outcome, sample sizes, rates
 of missingness, well-balanced and skewed data. The comparison of both MI
 methods with the complete-case analysis is also provided. We illustrate
 the use of the proposed methods on a quality of life data from a cancer
 clinical trial.
Journal: Journal of Applied Statistics 
Pages: 2257-2279 
Issue: 10 
Volume: 42 
Year: 2015 
Month: 10 
X-DOI: 10.1080/02664763.2015.1023704 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1023704 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:10:p:2257-2279




Template-Type: ReDIF-Article 1.0
Author-Name: Gulder Kemalbay 
Author-X-Name-First: Gulder 
Author-X-Name-Last: Kemalbay 
Author-Name: Ismihan Bayramoglu (Bairamov) 
Author-X-Name-First: Ismihan 
Author-X-Name-Last: Bayramoglu (Bairamov) 
Title: Joint distribution of new sample rank of bivariate order statistics 
Abstract:
 Let <inline-formula><inline-graphic
 xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1023705_ilm0001.gif"/></inline-formula>, be independent
 copies of bivariate random vector <inline-formula><inline-graphic
 xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1023705_ilm0002.gif"/></inline-formula> with joint
 cumulative distribution function <inline-formula><inline-graphic
 xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1023705_ilm0003.gif"/></inline-formula> and probability
 density function <inline-formula><inline-graphic
 xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1023705_ilm0004.gif"/></inline-formula>. For
 <inline-formula><inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1023705_ilm0005.gif"/></inline-formula>, the vector of
 order statistics of <inline-formula><inline-graphic
 xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1023705_ilm0006.gif"/></inline-formula> and
 <inline-formula><inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1023705_ilm0007.gif"/></inline-formula>, respectively,
 is denoted by <inline-formula><inline-graphic
 xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1023705_ilm0008.gif"/></inline-formula>. Let
 <inline-formula><inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1023705_ilm0009.gif"/></inline-formula>,
 <inline-formula><inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1023705_ilm0010.gif"/></inline-formula>, be a new
 sample from <inline-formula><inline-graphic
 xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1023705_ilm0011.gif"/></inline-formula>, which is
 independent from <inline-formula><inline-graphic
 xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1023705_ilm0012.gif"/></inline-formula>. Let
 <inline-formula><inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1023705_ilm0013.gif"/></inline-formula> be the rank of
 order statistics <inline-formula><inline-graphic
 xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1023705_ilm0014.gif"/></inline-formula> in a new sample
 <inline-formula><inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1023705_ilm0015.gif"/></inline-formula> and
 <inline-formula><inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1023705_ilm0016.gif"/></inline-formula> be the rank of
 order statistics <inline-formula><inline-graphic
 xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1023705_ilm0017.gif"/></inline-formula> in a new sample
 <inline-formula><inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1023705_ilm0018.gif"/></inline-formula>. We derive the
 joint distribution of discrete random vector
 <inline-formula><inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1023705_ilm0019.gif"/></inline-formula> and a general
 scheme wherein the distributions of new and old samples are different is
 considered. Numerical examples for given well-known distribution are also
 provided.
Journal: Journal of Applied Statistics 
Pages: 2280-2289 
Issue: 10 
Volume: 42 
Year: 2015 
Month: 10 
X-DOI: 10.1080/02664763.2015.1023705 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1023705 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:10:p:2280-2289




Template-Type: ReDIF-Article 1.0
Author-Name: Pieter Bastiaan Ober 
Author-X-Name-First: Pieter Bastiaan 
Author-X-Name-Last: Ober 
Title: Sequential analysis: hypothesis testing and changepoint detection 
Journal: Journal of Applied Statistics 
Pages: 2290-2290 
Issue: 10 
Volume: 42 
Year: 2015 
Month: 10 
X-DOI: 10.1080/02664763.2015.1015813 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1015813 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:10:p:2290-2290




Template-Type: ReDIF-Article 1.0
Author-Name: Bo&zcaron;idar V. Popovi&#x107; 
Author-X-Name-First: Bo&zcaron;idar V. 
Author-X-Name-Last: Popovi&#x107; 
Title: Handbook of univariate and multivariate data analysis with IBM SPSS, second edition 
Journal: Journal of Applied Statistics 
Pages: 2291-2291 
Issue: 10 
Volume: 42 
Year: 2015 
Month: 10 
X-DOI: 10.1080/02664763.2015.1015811 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1015811 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:10:p:2291-2291




Template-Type: ReDIF-Article 1.0
Author-Name: Sibel Balci 
Author-X-Name-First: Sibel 
Author-X-Name-Last: Balci 
Author-Name: Aysen Dener Akkaya 
Author-X-Name-First: Aysen Dener 
Author-X-Name-Last: Akkaya 
Title: Robust pairwise multiple comparisons under short-tailed symmetric distributions 
Abstract:
 In one-way ANOVA, most of the pairwise multiple comparison procedures
 depend on normality assumption of errors. In practice, errors have
 non-normal distributions so frequently. Therefore, it is very important to
 develop robust estimators of location and the associated variance under
 non-normality. In this paper, we consider the estimation of one-way ANOVA
 model parameters to make pairwise multiple comparisons under short-tailed
 symmetric (STS) distribution. The classical least squares method is
 neither efficient nor robust and maximum likelihood estimation technique
 is problematic in this situation. Modified maximum likelihood (MML)
 estimation technique gives the opportunity to estimate model parameters in
 closed forms under non-normal distributions. Hence, the use of MML
 estimators in the test statistic is proposed for pairwise multiple
 comparisons under STS distribution. The efficiency and power comparisons
 of the test statistic based on sample mean, trimmed mean, wave and MML
 estimators are given and the robustness of the test obtained using these
 estimators under plausible alternatives and inlier model are examined. It
 is demonstrated that the test statistic based on MML estimators is
 efficient and robust and the corresponding test is more powerful and
 having smallest Type I error.
Journal: Journal of Applied Statistics 
Pages: 2293-2306 
Issue: 11 
Volume: 42 
Year: 2015 
Month: 11 
X-DOI: 10.1080/02664763.2015.1023706 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1023706 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:11:p:2293-2306




Template-Type: ReDIF-Article 1.0
Author-Name: Wei Chen 
Author-X-Name-First: Wei 
Author-X-Name-Last: Chen 
Author-Name: Dehui Wang 
Author-X-Name-First: Dehui 
Author-X-Name-Last: Wang 
Author-Name: Yanfeng Li 
Author-X-Name-First: Yanfeng 
Author-X-Name-Last: Li 
Title: A class of tests of proportional hazards assumption for left-truncated and right-censored data 
Abstract:
 In this paper, we proposed a class of tests of proportional hazards
 assumption for left-truncated and right-censored data based on a pair of
 estimators of the hazard ratio constant. Using counting process and
 martingale theory, the asymptotically normal distribution of the test
 statistic is derived and a family of consistent estimators of variance are
 also provided. Extensive simulation studies were conducted to evaluate the
 performance of the proposed test statistics under finite sample
 situations. Two real data sets are analyzed to illustrate our method.
Journal: Journal of Applied Statistics 
Pages: 2307-2320 
Issue: 11 
Volume: 42 
Year: 2015 
Month: 11 
X-DOI: 10.1080/02664763.2015.1027884 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1027884 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:11:p:2307-2320




Template-Type: ReDIF-Article 1.0
Author-Name: Gift Nyamundanda 
Author-X-Name-First: Gift 
Author-X-Name-Last: Nyamundanda 
Author-Name: Avril Hegarty 
Author-X-Name-First: Avril 
Author-X-Name-Last: Hegarty 
Author-Name: Kevin Hayes 
Author-X-Name-First: Kevin 
Author-X-Name-Last: Hayes 
Title: Product partition latent variable model for multiple change-point detection in multivariate data 
Abstract:
 The product partition model (PPM) is a well-established efficient
 statistical method for detecting multiple change points in time-evolving
 univariate data. In this article, we refine the PPM for the purpose of
 detecting multiple change points in correlated multivariate time-evolving
 data. Our model detects distributional changes in both the mean and
 covariance structures of multivariate Gaussian data by exploiting a
 smaller dimensional representation of correlated multiple time series. The
 utility of the proposed method is demonstrated through experiments on
 simulated and real datasets.
Journal: Journal of Applied Statistics 
Pages: 2321-2334 
Issue: 11 
Volume: 42 
Year: 2015 
Month: 11 
X-DOI: 10.1080/02664763.2015.1029444 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1029444 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:11:p:2321-2334




Template-Type: ReDIF-Article 1.0
Author-Name: Iris Pigeot 
Author-X-Name-First: Iris 
Author-X-Name-Last: Pigeot 
Author-Name: Fabian Sobotka 
Author-X-Name-First: Fabian 
Author-X-Name-Last: Sobotka 
Author-Name: Svend Kreiner 
Author-X-Name-First: Svend 
Author-X-Name-Last: Kreiner 
Author-Name: Ronja Foraita 
Author-X-Name-First: Ronja 
Author-X-Name-Last: Foraita 
Title: The uncertainty of a selected graphical model 
Abstract:
 Graphical models are useful to detect multivariate association structures
 in terms of conditional independencies and to represent these structures
 in a graph. When fitting graphical models to multivariate data, the
 uncertainty of a selected graphical model cannot be directly assessed. In
 this paper, we therefore propose various descriptive measures to assess
 the uncertainty of a graphical model based on the nonparametric bootstrap.
 We also introduce a so-called mean graphical model. Simulations and one
 real data example illustrate the application and interpretation of the
 newly proposed measures and demonstrate that the mean graphical model
 performs better than a single selected graphical model.
Journal: Journal of Applied Statistics 
Pages: 2335-2352 
Issue: 11 
Volume: 42 
Year: 2015 
Month: 11 
X-DOI: 10.1080/02664763.2015.1030368 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1030368 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:11:p:2335-2352




Template-Type: ReDIF-Article 1.0
Author-Name: Francisco Louzada 
Author-X-Name-First: Francisco 
Author-X-Name-Last: Louzada 
Author-Name: M�rcia A.C. Macera 
Author-X-Name-First: M�rcia A.C. 
Author-X-Name-Last: Macera 
Author-Name: Vicente G. Cancho 
Author-X-Name-First: Vicente G. 
Author-X-Name-Last: Cancho 
Title: The Poisson-exponential model for recurrent event data: an application to bowel motility data 
Abstract:
 This paper presents a new parametric model for recurrent events, in which
 the time of each recurrence is associated to one or multiple latent causes
 and no information is provided about the responsible cause for the event.
 This model is characterized by a rate function and it is based on the
 Poisson-exponential distribution, namely the distribution of the maximum
 among a random number (truncated Poisson distributed) of exponential
 times. The time of each recurrence is then given by the maximum lifetime
 value among all latent causes. Inference is based on a maximum likelihood
 approach. A simulation study is performed in order to observe the
 frequentist properties of the estimation procedure for small and moderate
 sample sizes. We also investigated likelihood-based tests procedures. A
 real example from a gastroenterology study concerning small bowel motility
 during fasting state is used to illustrate the methodology. Finally, we
 apply the proposed model to a real data set and compare it with the
 classical Homogeneous Poisson model, which is a particular case.
Journal: Journal of Applied Statistics 
Pages: 2353-2366 
Issue: 11 
Volume: 42 
Year: 2015 
Month: 11 
X-DOI: 10.1080/02664763.2015.1030369 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1030369 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:11:p:2353-2366




Template-Type: ReDIF-Article 1.0
Author-Name: Julie E. Shortridge 
Author-X-Name-First: Julie E. 
Author-X-Name-Last: Shortridge 
Author-Name: Stefanie M. Falconi 
Author-X-Name-First: Stefanie M. 
Author-X-Name-Last: Falconi 
Author-Name: Benjamin F. Zaitchik 
Author-X-Name-First: Benjamin F. 
Author-X-Name-Last: Zaitchik 
Author-Name: Seth D. Guikema 
Author-X-Name-First: Seth D. 
Author-X-Name-Last: Guikema 
Title: Climate, agriculture, and hunger: statistical prediction of undernourishment using nonlinear regression and data-mining techniques 
Abstract:
 An estimated 1 billion people suffer from hunger worldwide, and climate
 change, urbanization, and globalization have the potential to exacerbate
 this situation. Improved models for predicting food security are needed to
 understand these impacts and design interventions. However, food
 insecurity is the result of complex interactions between physical and
 socio-economic factors that can overwhelm linear regression models. More
 sophisticated data-mining approaches could provide an effective way to
 model these relationships and accurately predict food insecure situations.
 In this paper, we compare multiple regression and data-mining methods in
 their ability to predict the percent of a country's population that
 suffers from undernourishment using widely available predictor variables
 related to socio-economic settings, agricultural production and trade, and
 climate conditions. Averaging predictions from multiple models results in
 the lowest predictive error and provides an accurate method to predict
 undernourishment levels. Partial dependence plots are used to evaluate
 covariate influence and demonstrate the relationship between food
 insecurity and climatic and socio-economic variables. By providing
 insights into these relationships and a mechanism for predicting
 undernourishment using readily available data, statistical models like
 those developed here could be a useful tool for those tasked with
 understanding and addressing food insecurity.
Journal: Journal of Applied Statistics 
Pages: 2367-2390 
Issue: 11 
Volume: 42 
Year: 2015 
Month: 11 
X-DOI: 10.1080/02664763.2015.1032216 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1032216 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:11:p:2367-2390




Template-Type: ReDIF-Article 1.0
Author-Name: Tahir Ekin 
Author-X-Name-First: Tahir 
Author-X-Name-Last: Ekin 
Author-Name: R. Muzaffer Musal 
Author-X-Name-First: R. Muzaffer 
Author-X-Name-Last: Musal 
Author-Name: Lawrence V. Fulton 
Author-X-Name-First: Lawrence V. 
Author-X-Name-Last: Fulton 
Title: Overpayment models for medical audits: multiple scenarios 
Abstract:
 Comprehensive auditing in Medicare programs is infeasible due to the large
 number of claims, therefore, the use of statistical sampling and
 estimation methods is crucial. We introduce super-population models to
 understand the overpayment phenomena within the claims population. The
 zero- and one-inflated mixture-based models can capture various
 overpayment patterns including the fully legitimate or fraudulent cases.
 We compare them with the existing models for symmetric and mixed payment
 populations that have different overpayment patterns. The distributional
 fit between the actual and estimated overpayments is assessed. We also
 provide comparisons of models with respect to their conformance with
 Centers for Medicare and Medicaid Services (CMS) guidelines. In addition
 to estimating the dollar amount of recovery, the proposed models can help
 the investigators to detect overpayment patterns.
Journal: Journal of Applied Statistics 
Pages: 2391-2405 
Issue: 11 
Volume: 42 
Year: 2015 
Month: 11 
X-DOI: 10.1080/02664763.2015.1034659 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1034659 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:11:p:2391-2405




Template-Type: ReDIF-Article 1.0
Author-Name: Haiqiang Chen 
Author-X-Name-First: Haiqiang 
Author-X-Name-Last: Chen 
Author-Name: Yanli Zhu 
Author-X-Name-First: Yanli 
Author-X-Name-Last: Zhu 
Title: An empirical study on the threshold cointegration of Chinese A and H cross-listed shares 
Abstract:
 We investigate the dynamic relationship between the prices of Chinese A
 and H market cross-listed shares using the Enders-Siklos threshold
 cointegration approach. Our data are the daily closing prices of the Hang
 Seng China AH (A) index and the Hang Seng China AH (H) index from 4
 January 2006 to 1 November 2013. We find a threshold cointegration between
 these two indices, instead of the linear cointegration well established in
 the literature. The short-term adjustment to the equilibrium shows an
 asymmetric effect according to the price deviation from the equilibrium.
 Moreover, using a Granger causality test, we find a bi-directional
 causality between these two markets, indicating a close relationship
 between them. A pairs trading rule, based on the estimated threshold
 cointegration model, demonstrates the usefulness of our results as it
 generates a significantly higher return than a naive buy-and-hold trading
 rule.
Journal: Journal of Applied Statistics 
Pages: 2406-2419 
Issue: 11 
Volume: 42 
Year: 2015 
Month: 11 
X-DOI: 10.1080/02664763.2015.1034660 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1034660 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:11:p:2406-2419




Template-Type: ReDIF-Article 1.0
Author-Name: Chen-ju Lin 
Author-X-Name-First: Chen-ju 
Author-X-Name-Last: Lin 
Author-Name: Yi-chun Shu 
Author-X-Name-First: Yi-chun 
Author-X-Name-Last: Shu 
Title: Detecting clusters with increased mean using scan windows with variable radius 
Abstract:
 Applying spatiotemporal scan statistics is an effective method to detect
 the clustering of mean shifts in many application fields. Although several
 exponentially weighted moving average (EWMA) based scan statistics have
 been proposed, the existing methods generally require a fixed scan window
 size or apply the weighting technique across the temporal axis only.
 However, the size of shift coverage is often unavailable in practical
 problems. Using a mismatching scan radius may mislead the size of cluster
 coverage in space or delay the time to detection. This research proposed
 an stEWMA method by applying the weighting technique across both temporal
 and spatial axes with variable scan radius. The simulation analysis showed
 that the stEWMA method can have a significantly shorter time to detection
 than the likelihood ratio-based scan statistic using variable scan radius,
 especially when cluster coverage size is small. The application to
 detecting the increase of male thyroid cancer in the New Mexico state also
 showed the effectiveness of the proposed method.
Journal: Journal of Applied Statistics 
Pages: 2420-2431 
Issue: 11 
Volume: 42 
Year: 2015 
Month: 11 
X-DOI: 10.1080/02664763.2015.1041013 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1041013 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:11:p:2420-2431




Template-Type: ReDIF-Article 1.0
Author-Name: Jin-Guan Lin 
Author-X-Name-First: Jin-Guan 
Author-X-Name-Last: Lin 
Author-Name: Yan-Yong Zhao 
Author-X-Name-First: Yan-Yong 
Author-X-Name-Last: Zhao 
Author-Name: Hong-Xia Wang 
Author-X-Name-First: Hong-Xia 
Author-X-Name-Last: Wang 
Title: Heteroscedasticity diagnostics in varying-coefficient partially linear regression models and applications in analyzing Boston housing data 
Abstract:
 It is important to detect the variance heterogeneity in regression model
 because efficient inference requires that heteroscedasticity is taken into
 consideration if it really exists. For the varying-coefficient partially
 linear regression models, however, the problem of detecting
 heteroscedasticity has received very little attention. In this paper, we
 present two classes of tests of heteroscedasticity for varying-coefficient
 partially linear regression models. The first test statistic is
 constructed based on the residuals, in which the error term is from a
 normal distribution. The second one is motivated by the idea that testing
 heteroscedasticity is equivalent to testing pseudo-residuals for a
 constant mean. Asymptotic normality is established with different rates
 corresponding to the null hypothesis of homoscedasticity and the
 alternative. Some Monte Carlo simulations are conducted to investigate the
 finite sample performance of the proposed tests. The test methodologies
 are illustrated with a real data set example.
Journal: Journal of Applied Statistics 
Pages: 2432-2448 
Issue: 11 
Volume: 42 
Year: 2015 
Month: 11 
X-DOI: 10.1080/02664763.2015.1043623 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1043623 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:11:p:2432-2448




Template-Type: ReDIF-Article 1.0
Author-Name: Opeoluwa F. Oyedele 
Author-X-Name-First: Opeoluwa F. 
Author-X-Name-Last: Oyedele 
Author-Name: Sugnet Lubbe 
Author-X-Name-First: Sugnet 
Author-X-Name-Last: Lubbe 
Title: The construction of a partial least-squares biplot 
Abstract:
 Biplots are useful tools to explore the relationship among variables. In
 this paper, the specific regression relationship between a set of
 predictors <bold>X</bold> and set of response variables <bold>Y</bold> by
 means of partial least-squares (PLS) regression is represented. The PLS
 biplot provides a single graphical representation of the samples together
 with the predictor and response variables, as well as their
 interrelationships in terms of the matrix of regression coefficients.
Journal: Journal of Applied Statistics 
Pages: 2449-2460 
Issue: 11 
Volume: 42 
Year: 2015 
Month: 11 
X-DOI: 10.1080/02664763.2015.1043858 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1043858 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:11:p:2449-2460




Template-Type: ReDIF-Article 1.0
Author-Name: Xiaobing Zhao 
Author-X-Name-First: Xiaobing 
Author-X-Name-Last: Zhao 
Author-Name: Xian Zhou 
Author-X-Name-First: Xian 
Author-X-Name-Last: Zhou 
Title: Semiparametric models of longitudinal and time-to-event data with applications to HIV viral dynamics and CD4 counts 
Abstract:
 We propose a semiparametric approach based on proportional hazards and
 copula method to jointly model longitudinal outcomes and the
 time-to-event. The dependence between the longitudinal outcomes on the
 covariates is modeled by a copula-based times series, which allows
 non-Gaussian random effects and overcomes the limitation of the parametric
 assumptions in existing linear and nonlinear random effects models. A
 modified partial likelihood method using estimated covariates at failure
 times is employed to draw statistical inference. The proposed model and
 method are applied to analyze a set of progression to AIDS data in a study
 of the association between the human immunodeficiency virus viral dynamics
 and the time trend in the CD4/CD8 ratio with measurement errors.
 Simulations are also reported to evaluate the proposed model and method.
Journal: Journal of Applied Statistics 
Pages: 2461-2477 
Issue: 11 
Volume: 42 
Year: 2015 
Month: 11 
X-DOI: 10.1080/02664763.2015.1043859 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1043859 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:11:p:2461-2477




Template-Type: ReDIF-Article 1.0
Author-Name: Cini Varghese 
Author-X-Name-First: Cini 
Author-X-Name-Last: Varghese 
Author-Name: Eldho Varghese 
Author-X-Name-First: Eldho 
Author-X-Name-Last: Varghese 
Author-Name: Seema Jaggi 
Author-X-Name-First: Seema 
Author-X-Name-Last: Jaggi 
Author-Name: Arpan Bhowmik 
Author-X-Name-First: Arpan 
Author-X-Name-Last: Bhowmik 
Title: Experimental designs for open pollination in polycross trials 
Abstract:
 A polycross is the pollination by natural hybridization of a group of
 genotypes, generally selected, grown in isolation from other compatible
 genotypes in such a way to promote random open pollination. A particular
 practical application of the polycross method occurs in the production of
 a synthetic variety resulting from cross-pollinated plants. Laying out
 these experiments in appropriate designs, known as polycross designs,
 would not only save experimental resources but also gather more
 information from the experiment. Different situations may arise in
 polycross nurseries where accordingly different polycross designs may be
 used. For situations in which some genotypes interfere in the growth or
 production of other genotypes, but have to be grown together,
 neighbour-restricted design is a better option. Furthermore, when the
 topography of the nursery is such that a known wind system in a certain
 direction may prevail, then designs balanced for neighbour effects of
 genotypes only in the direction of wind are appropriate which may help in
 saving experimental resources to a great extent. Also, when genotypes are
 planted in a small area without leaving much space between rows, designs
 balanced for neighbour effects from all possible eight directions are
 useful to have equal chance of pollinating and being pollinated by every
 other genotype. Here, polycross designs have been obtained to match
 above-mentioned three situations. SAS Macros have also been developed to
 generate these proposed designs.
Journal: Journal of Applied Statistics 
Pages: 2478-2484 
Issue: 11 
Volume: 42 
Year: 2015 
Month: 11 
X-DOI: 10.1080/02664763.2015.1043860 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1043860 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:11:p:2478-2484




Template-Type: ReDIF-Article 1.0
Author-Name: Muhammad Aslam 
Author-X-Name-First: Muhammad 
Author-X-Name-Last: Aslam 
Author-Name: Nasrullah Khan 
Author-X-Name-First: Nasrullah 
Author-X-Name-Last: Khan 
Author-Name: Chi-Hyuck Jun 
Author-X-Name-First: Chi-Hyuck 
Author-X-Name-Last: Jun 
Title: A new <italic>S</italic>-super-2 control chart using repetitive sampling 
Abstract:
 A new <italic>S</italic>-super-2 control chart is presented for monitoring
 the process variance by utilizing a repetitive sampling scheme. The double
 control limits called inner and outer control limits are proposed, whose
 coefficients are determined by considering the average run length (ARL)
 and the average sample number when the process is in control. The proposed
 control chart is compared with the existing Shewhart
 <italic>S</italic>-super-2 control chart in terms of the ARLs. The result
 shows that the proposed control chart is more efficient than the existing
 control chart in detecting the process shift.
Journal: Journal of Applied Statistics 
Pages: 2485-2496 
Issue: 11 
Volume: 42 
Year: 2015 
Month: 11 
X-DOI: 10.1080/02664763.2015.1043861 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1043861 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:11:p:2485-2496




Template-Type: ReDIF-Article 1.0
Author-Name: Yunlu Jiang 
Author-X-Name-First: Yunlu 
Author-X-Name-Last: Jiang 
Title: Robust estimation in partially linear regression models 
Abstract:
 A new class of robust estimators via the exponential squared loss function
 with a tuning parameter are presented for the partially linear regression
 models. Under some conditions, we show that our proposed estimators for
 the regression parameter can achieve the highest asymptotic breakdown
 point of <inline-formula><inline-graphic
 xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1043862_ilm0001.gif"/></inline-formula>. In addition,
 we propose the data-driven procedure to choose the tuning parameter.
 Simulation studies are conducted to compare the performances of the
 proposed method with the existing methods in terms of the bias, standard
 deviation (Sd) as well as the mean-squared errors (MSE). The results show
 that our proposed method has smaller Sd and MSE than the existing methods
 when there are outliers in the dataset. Finally, we apply the proposed
 method to analyze the Ragweed Pollen Level data and the salinity data, and
 the results reveal that our method performs better than the existing
 methods.
Journal: Journal of Applied Statistics 
Pages: 2497-2508 
Issue: 11 
Volume: 42 
Year: 2015 
Month: 11 
X-DOI: 10.1080/02664763.2015.1043862 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1043862 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:11:p:2497-2508




Template-Type: ReDIF-Article 1.0
Author-Name: A. Sani 
Author-X-Name-First: A. 
Author-X-Name-Last: Sani 
Author-Name: B. Abapihi 
Author-X-Name-First: B. 
Author-X-Name-Last: Abapihi 
Author-Name: Mukhsar Mukhsar 
Author-X-Name-First: Mukhsar 
Author-X-Name-Last: Mukhsar 
Author-Name: Kadir Kadir 
Author-X-Name-First: Kadir 
Author-X-Name-Last: Kadir 
Title: Relative risk analysis of dengue cases using convolution extended into spatio-temporal model 
Abstract:
 Dengue Hemmorage Fever (DHF) cases have become a serious problem every
 year in tropical countries such as Indonesia. Understanding the dynamic
 spread of the disease is essential in order to find an effective strategy
 in controlling its spread. In this study, a convolution
 (Poisson-lognormal) model that integrates both uncorrelated and correlated
 random effects was developed. A spatial-temporal convolution model to
 accomodate both spatial and temporal variations of the disease spread
 dynamics was considered. The model was applied to the DHF cases in the
 city of Kendari, Indonesia. DHF data for 10 districts during the period
 2007-2010 were collected from the health services. The data of rainfall
 and population density were obtained from the local offices in Kendari.
 The numerical experiments indicated that both the rainfall and the
 population density played an important role in the increasing DHF cases in
 the city of Kendari. The result suggested that DHF cases mostly occured in
 January, the wet session with high rainfall, and in Kadia, the densest
 district in the city. As people in the city have high mobility while
 dengue mosquitoes tend to stay localized in their area, the best
 intervention is in January and in the district of Kadia.
Journal: Journal of Applied Statistics 
Pages: 2509-2519 
Issue: 11 
Volume: 42 
Year: 2015 
Month: 11 
X-DOI: 10.1080/02664763.2015.1043863 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1043863 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:11:p:2509-2519




Template-Type: ReDIF-Article 1.0
Author-Name: Zahra Hadidoust 
Author-X-Name-First: Zahra 
Author-X-Name-Last: Hadidoust 
Author-Name: Yaser Samimi 
Author-X-Name-First: Yaser 
Author-X-Name-Last: Samimi 
Author-Name: Hamid Shahriari 
Author-X-Name-First: Hamid 
Author-X-Name-Last: Shahriari 
Title: Monitoring and change-point estimation for spline-modeled non-linear profiles in phase II 
Abstract:
 In some applications of statistical quality control, quality of a process
 or a product is best characterized by a functional relationship between a
 response variable and one or more explanatory variables. This relationship
 is referred to as a profile. In certain cases, the quality of a process or
 a product is better described by a non-linear profile which does not
 follow a specific parametric model. In these circumstances, nonparametric
 approaches with greater flexibility in modeling the complicated profiles
 are adopted. In this research, the spline smoothing method is used to
 model a complicated non-linear profile and the Hotelling
 <italic>T</italic>-super-2 control chart based on the spline coefficients
 is used to monitor the process. After receiving an out-of-control signal,
 a maximum likelihood estimator is employed for change point estimation.
 The simulation studies, which include both global and local shifts,
 provide appropriate evaluation of the performance of the proposed
 estimation and monitoring procedure. The results indicate that the
 proposed method detects large global shifts while it is very sensitive in
 detecting local shifts.
Journal: Journal of Applied Statistics 
Pages: 2520-2530 
Issue: 12 
Volume: 42 
Year: 2015 
Month: 12 
X-DOI: 10.1080/02664763.2015.1043864 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1043864 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:12:p:2520-2530




Template-Type: ReDIF-Article 1.0
Author-Name: Julia S. Benoit 
Author-X-Name-First: Julia S. 
Author-X-Name-Last: Benoit 
Author-Name: Wenyaw Chan 
Author-X-Name-First: Wenyaw 
Author-X-Name-Last: Chan 
Author-Name: Rachelle S. Doody 
Author-X-Name-First: Rachelle S. 
Author-X-Name-Last: Doody 
Title: Joint coverage probability in a simulation study on continuous-time Markov chain parameter estimation 
Abstract:
 Parameter dependency within data sets in simulation studies is common,
 especially in models such as continuous-time Markov chains (CTMCs).
 Additionally, the literature lacks a comprehensive examination of
 estimation performance for the likelihood-based general multi-state CTMC.
 Among studies attempting to assess the estimation, none have accounted for
 dependency among parameter estimates. The purpose of this research is
 twofold: (1) to develop a multivariate approach for assessing accuracy and
 precision for simulation studies (2) to add to the literature a
 comprehensive examination of the estimation of a general 3-state CTMC
 model. Simulation studies are conducted to analyze longitudinal data with
 a trinomial outcome using a CTMC with and without covariates. Measures of
 performance including bias, component-wise coverage probabilities, and
 joint coverage probabilities are calculated. An application is presented
 using Alzheimer's disease caregiver stress levels. Comparisons of joint
 and component-wise parameter estimates yield conflicting inferential
 results in simulations from models with and without covariates. In
 conclusion, caution should be taken when conducting simulation studies
 aiming to assess performance and choice of inference should properly
 reflect the purpose of the simulation.
Journal: Journal of Applied Statistics 
Pages: 2531-2538 
Issue: 12 
Volume: 42 
Year: 2015 
Month: 12 
X-DOI: 10.1080/02664763.2015.1043865 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1043865 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:12:p:2531-2538




Template-Type: ReDIF-Article 1.0
Author-Name: Pablo Mart�nez-Camblor 
Author-X-Name-First: Pablo 
Author-X-Name-Last: Mart�nez-Camblor 
Author-Name: Jacobo de U�a-�lvarez 
Author-X-Name-First: Jacobo 
Author-X-Name-Last: de U�a-�lvarez 
Author-Name: Carmen D�az Corte 
Author-X-Name-First: Carmen D�az 
Author-X-Name-Last: Corte 
Title: Expanded renal transplantation: a competing risk model approach 
Abstract:
 Multi-state models (MSMs) are useful to analyze survival data when,
 besides the event of main interest, one or more intermediate states of the
 individual are identified. These models take the several existing states
 and the possible transitions among them into account. At the same time,
 covariate effects on each transition intensity may be investigated
 separately and, therefore, MSMs are more flexible than the standard Cox
 proportional hazards model. In this work, we use MSMs to investigate the
 impact of the quality of a transplanted kidney for a group of patients at
 the Hospital Universitario Central de Asturias. Specifically, we use an
 <italic>illness-death</italic> model to study the evolution of patients
 with kidney disease who received a renal transplant after a dialysis
 period. The intermediate state is defined as the failure of the received
 organ, while the terminating state is the death of the patient. In order
 to increase the potential number of organs available for transplant, the
 standards of quality for the transplanted kidneys were relaxed (the new
 criteria are labeled <italic>expanded criteria</italic>), and these
 '<italic>expanded</italic> kidneys' were transplanted in appropriate
 candidates (older patients, with higher prevalence of diabetes mellitus).
 Results suggest that the expanded kidneys have a minor effect on survival,
 while both the kidney mortality and the risk of death increase with the
 patient's age and the serum creatinine and serum hemoglobin levels.
Journal: Journal of Applied Statistics 
Pages: 2539-2553 
Issue: 12 
Volume: 42 
Year: 2015 
Month: 12 
X-DOI: 10.1080/02664763.2015.1043866 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1043866 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:12:p:2539-2553




Template-Type: ReDIF-Article 1.0
Author-Name: E. Raffinetti 
Author-X-Name-First: E. 
Author-X-Name-Last: Raffinetti 
Author-Name: I. Romeo 
Author-X-Name-First: I. 
Author-X-Name-Last: Romeo 
Title: Dealing with the biased effects issue when handling huge datasets: the case of INVALSI data 
Abstract:
 The increasing prevalence of huge datasets addresses the research to
 appropriate statistical methods for solving troubles caused by their
 complexity. On the one hand, several techniques are mentioned in the
 literature, especially for the time-consuming and variables reduction
 issues. On the other, less debate is devoted to the statistical inference
 issue. Indeed, a large number of involved statistical units may lead to
 wrongly consider as significant variables without any actual impact on the
 phenomenon under study. This paper suggests a suitable subsampling
 procedure for the reduction of the number of statistical units and
 provides a novel index for the assessment of the significance effects. The
 proposal is validated by comparing results obtained from the analysis on
 the original data to those obtained from the proposed subsampling
 approach. The illustrative application focuses on the educational dataset
 made available by the National Committee for the Evaluation of the Italian
 Education Systems (INVALSI). This dataset collects information about the
 student features and achievements in Maths within the lower secondary
 schools of the Lombardy region (Italy). Due to the hierarchical structure
 of the data, a multilevel model is implemented with the purpose of
 investigating the effects of both individual and school factors on student
 Maths score.
Journal: Journal of Applied Statistics 
Pages: 2554-2570 
Issue: 12 
Volume: 42 
Year: 2015 
Month: 12 
X-DOI: 10.1080/02664763.2015.1043867 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1043867 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:12:p:2554-2570




Template-Type: ReDIF-Article 1.0
Author-Name: Daniel F. Linder 
Author-X-Name-First: Daniel F. 
Author-X-Name-Last: Linder 
Author-Name: Hani Samawi 
Author-X-Name-First: Hani 
Author-X-Name-Last: Samawi 
Author-Name: Lili Yu 
Author-X-Name-First: Lili 
Author-X-Name-Last: Yu 
Author-Name: Arpita Chatterjee 
Author-X-Name-First: Arpita 
Author-X-Name-Last: Chatterjee 
Author-Name: Yisong Huang 
Author-X-Name-First: Yisong 
Author-X-Name-Last: Huang 
Author-Name: Robert Vogel 
Author-X-Name-First: Robert 
Author-X-Name-Last: Vogel 
Title: On stratified bivariate ranked set sampling for regression estimators 
Abstract:
 We investigate the relative performance of stratified bivariate ranked set
 sampling (SBVRSS), with respect to stratified simple random sampling
 (SSRS) for estimating the population mean with regression methods. The
 mean and variance of the proposed estimators are derived with the mean
 being shown to be unbiased. We perform a simulation study to compare the
 relative efficiency of SBVRSS to SSRS under various data-generating
 scenarios. We also compare the two sampling schemes on a real data set
 from trauma victims in a hospital setting. The results of our simulation
 study and the real data illustration indicate that using SBVRSS for
 regression estimation provides more efficiency than SSRS in most cases.
Journal: Journal of Applied Statistics 
Pages: 2571-2583 
Issue: 12 
Volume: 42 
Year: 2015 
Month: 12 
X-DOI: 10.1080/02664763.2015.1043868 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1043868 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:12:p:2571-2583




Template-Type: ReDIF-Article 1.0
Author-Name: Ilaria L. Amerise 
Author-X-Name-First: Ilaria L. 
Author-X-Name-Last: Amerise 
Author-Name: Agostino Tarsitano 
Author-X-Name-First: Agostino 
Author-X-Name-Last: Tarsitano 
Title: Correction methods for ties in rank correlations 
Abstract:
 Equal values are common when rank methods are applied to rounded data or
 data consisting solely of small integers. A popular technique for
 resolving ties in rank correlation is the mid-rank method: the mean of the
 rankings remains unaltered, but the variance is reduced and modified
 according to the number and location of ties. Although other methods for
 breaking ties were proposed in the literature as early as 1939, no such
 procedure has gained such wide acceptance as mid-ranks. This research
 analyses various techniques for assigning ranks to tied values, with two
 objectives: (1) to enable the computation of rank correlation
 coefficients, such as those of Spearman, Kendall and Gini, by using the
 usual definition applied in the absence of ties, and (2) to determine
 whether it really makes a difference which of the various techniques is
 selected and, if so, which technique is most appropriate for a given
 application.
Journal: Journal of Applied Statistics 
Pages: 2584-2596 
Issue: 12 
Volume: 42 
Year: 2015 
Month: 12 
X-DOI: 10.1080/02664763.2015.1043870 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1043870 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:12:p:2584-2596




Template-Type: ReDIF-Article 1.0
Author-Name: Zahra Mansourvar 
Author-X-Name-First: Zahra 
Author-X-Name-Last: Mansourvar 
Author-Name: Torben Martinussen 
Author-X-Name-First: Torben 
Author-X-Name-Last: Martinussen 
Author-Name: Thomas H. Scheike 
Author-X-Name-First: Thomas H. 
Author-X-Name-Last: Scheike 
Title: Semiparametric regression for restricted mean residual life under right censoring 
Abstract:
 A mean residual life function (MRLF) is the remaining life expectancy of a
 subject who has survived to a certain time point. In the presence of
 covariates, regression models are needed to study the association between
 the MRLFs and covariates. If the survival time tends to be too long or the
 tail is not observed, the restricted mean residual life must be
 considered. In this paper, we propose the proportional restricted mean
 residual life model for fitting survival data under right censoring. For
 inference on the model parameters, martingale estimating equations are
 developed, and the asymptotic properties of the proposed estimators are
 established. In addition, a class of goodness-of-fit test is presented to
 assess the adequacy of the model. The finite sample behavior of the
 proposed estimators is evaluated through simulation studies, and the
 approach is applied to a set of real life data collected from a randomized
 clinical trial.
Journal: Journal of Applied Statistics 
Pages: 2597-2613 
Issue: 12 
Volume: 42 
Year: 2015 
Month: 12 
X-DOI: 10.1080/02664763.2015.1043871 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1043871 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:12:p:2597-2613




Template-Type: ReDIF-Article 1.0
Author-Name: Rubing Liang 
Author-X-Name-First: Rubing 
Author-X-Name-Last: Liang 
Author-Name: Cuizhen Niu 
Author-X-Name-First: Cuizhen 
Author-X-Name-Last: Niu 
Author-Name: Qiang Xia 
Author-X-Name-First: Qiang 
Author-X-Name-Last: Xia 
Author-Name: Zhiqiang Zhang 
Author-X-Name-First: Zhiqiang 
Author-X-Name-Last: Zhang 
Title: Nonlinearity testing and modeling for threshold moving average models 
Abstract:
 In this paper, we suggest a simple test and an easily applicable modeling
 procedure for threshold moving average (TMA) models. Firstly, based on the
 fitted residuals by maximum likelihood estimate (MLE) for MA models, we
 construct a simple statistic, which is obtained by linear arrange
 regression and follows <italic>F</italic>-distribution approximately, to
 test for threshold nonlinearity and specify the threshold variables. And
 then, we use some scatterplots to identify the number and locations of the
 potential thresholds. Finally, with the statistic and Akaike information
 criterion, we propose the procedure to build TMA models. Both the power of
 test statistic and the convenience of modeling procedure can work very
 well demonstrated by simulation experiments and the application to a real
 example.
Journal: Journal of Applied Statistics 
Pages: 2614-2630 
Issue: 12 
Volume: 42 
Year: 2015 
Month: 12 
X-DOI: 10.1080/02664763.2015.1043872 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1043872 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:12:p:2614-2630




Template-Type: ReDIF-Article 1.0
Author-Name: Antonio Lucadamo 
Author-X-Name-First: Antonio 
Author-X-Name-Last: Lucadamo 
Author-Name: Pietro Amenta 
Author-X-Name-First: Pietro 
Author-X-Name-Last: Amenta 
Title: A proposal for handling ordinal categorical variables in co-inertia analysis 
Abstract:
 This paper is about the problem of the treatment of ordinal qualitative
 variables in co-inertia analysis. In the literature, there are different
 proposals based on the application of known statistical techniques to
 quantify ordinal variables. Here we propose to use a new procedure for the
 coding considering the empirical distributions of the variables involved
 in the analysis. We present an application to a real dataset, comparing
 the results obtained with the different kinds of quantification.
Journal: Journal of Applied Statistics 
Pages: 2631-2638 
Issue: 12 
Volume: 42 
Year: 2015 
Month: 12 
X-DOI: 10.1080/02664763.2015.1044426 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1044426 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:12:p:2631-2638




Template-Type: ReDIF-Article 1.0
Author-Name: Antonella Plaia 
Author-X-Name-First: Antonella 
Author-X-Name-Last: Plaia 
Title: Long-term experiments and strip plot designs 
Abstract:
 In a long-term experiment usually the experimenter needs to know whether
 the effect of a treatment varies over time. But time usually has both a
 fixed and a random effects over the output and the difficulty in the
 analysis depends on the particular design considered and the availability
 of covariates. Actually, as shown in the paper, the presence of covariates
 can be very useful to model the random effect of time. In this paper a
 model to analyze data from a long-term strip plot design with covariates
 is proposed. Its effectiveness will be tested using both simulated and
 real data from a crop rotation experiment.
Journal: Journal of Applied Statistics 
Pages: 2639-2653 
Issue: 12 
Volume: 42 
Year: 2015 
Month: 12 
X-DOI: 10.1080/02664763.2015.1046821 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1046821 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:12:p:2639-2653




Template-Type: ReDIF-Article 1.0
Author-Name: Vahid Nekoukhou 
Author-X-Name-First: Vahid 
Author-X-Name-Last: Nekoukhou 
Author-Name: Hamid Bidram 
Author-X-Name-First: Hamid 
Author-X-Name-Last: Bidram 
Title: A new four-parameter discrete distribution with bathtub and unimodal failure rate 
Abstract:
 In this paper, a discrete counterpart of the general class of continuous
 <italic>beta-G</italic> distributions is introduced. A discrete analog of
 the beta generalized exponential distribution of Barreto-Souza <italic>et
 al.</italic> [2], as an important special case of the proposed class, is
 studied. This new distribution contains some previously known discrete
 distributions as well as two new models. The hazard rate function of the
 new model can be increasing, decreasing, bathtub-shaped and upside-down
 bathtub. Some distributional and moment properties of the new distribution
 as well as its order statistics are discussed. Estimation of the
 parameters is illustrated using the maximum likelihood method and,
 finally, the model with a real data set is examined.
Journal: Journal of Applied Statistics 
Pages: 2654-2670 
Issue: 12 
Volume: 42 
Year: 2015 
Month: 12 
X-DOI: 10.1080/02664763.2015.1046822 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1046822 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:12:p:2654-2670




Template-Type: ReDIF-Article 1.0
Author-Name: Sudesh Pundir 
Author-X-Name-First: Sudesh 
Author-X-Name-Last: Pundir 
Author-Name: R. Amala 
Author-X-Name-First: R. 
Author-X-Name-Last: Amala 
Title: Detecting diagnostic accuracy of two biomarkers through a bivariate log-normal ROC curve 
Abstract:
 In biomedical research, two or more biomarkers may be available for
 diagnosis of a particular disease. Selecting one single biomarker which
 ideally discriminate a diseased group from a healthy group is confront in
 a diagnostic process. Frequently, most of the people use the accuracy
 measure, area under the receiver operating characteristic (ROC) curve to
 choose the best diagnostic marker among the available markers for
 diagnosis. Some authors have tried to combine the multiple markers by an
 optimal linear combination to increase the discriminatory power. In this
 paper, we propose an alternative method that combines two continuous
 biomarkers by direct bivariate modeling of the ROC curve under
 log-normality assumption. The proposed method is applied to simulated data
 set and prostate cancer diagnostic biomarker data set.
Journal: Journal of Applied Statistics 
Pages: 2671-2685 
Issue: 12 
Volume: 42 
Year: 2015 
Month: 12 
X-DOI: 10.1080/02664763.2015.1046823 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1046823 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:12:p:2671-2685




Template-Type: ReDIF-Article 1.0
Author-Name: Nikolay K. Vitanov 
Author-X-Name-First: Nikolay K. 
Author-X-Name-Last: Vitanov 
Author-Name: Marcel Ausloos 
Author-X-Name-First: Marcel 
Author-X-Name-Last: Ausloos 
Title: Test of two hypotheses explaining the size of populations in a system of cities 
Abstract:
 Two classical hypotheses are examined about the population growth in a
 system of cities: Hypothesis 1 pertains to Gibrat's and Zipf's theory
 which states that the city growth-decay process is size independent;
 Hypothesis 2 pertains to the so-called Yule process which states that the
 growth of populations in cities happens when (i) the distribution of the
 city population initial size obeys a log-normal function, (ii) the growth
 of the settlements follows a stochastic process. The basis for the test is
 some official data on Bulgarian cities at various times. This system was
 chosen because (i) Bulgaria is a country for which one does not expect
 biased theoretical conditions; (ii) the city populations were determined
 rather precisely. The present results show that: (i) the population size
 growth of the Bulgarian cities is size dependent, whence Hypothesis 1 is
 not confirmed for Bulgaria; (ii) the population size growth of Bulgarian
 cities can be described by a double Pareto log-normal distribution, whence
 Hypothesis 2 is valid for the Bulgarian city system. It is expected that
 this fine study brings some information and light on other usually
 considered to be more pertinent countries of city systems.
Journal: Journal of Applied Statistics 
Pages: 2686-2693 
Issue: 12 
Volume: 42 
Year: 2015 
Month: 12 
X-DOI: 10.1080/02664763.2015.1047744 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1047744 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:12:p:2686-2693




Template-Type: ReDIF-Article 1.0
Author-Name: Aldo M. Garay 
Author-X-Name-First: Aldo M. 
Author-X-Name-Last: Garay 
Author-Name: Heleno Bolfarine 
Author-X-Name-First: Heleno 
Author-X-Name-Last: Bolfarine 
Author-Name: Victor H. Lachos 
Author-X-Name-First: Victor H. 
Author-X-Name-Last: Lachos 
Author-Name: Celso R.B. Cabral 
Author-X-Name-First: Celso R.B. 
Author-X-Name-Last: Cabral 
Title: Bayesian analysis of censored linear regression models with scale mixtures of normal distributions 
Abstract:
 As is the case of many studies, the data collected are limited and an
 exact value is recorded only if it falls within an interval range. Hence,
 the responses can be either left, interval or right censored. Linear (and
 nonlinear) regression models are routinely used to analyze these types of
 data and are based on normality assumptions for the errors terms. However,
 those analyzes might not provide robust inference when the normality
 assumptions are questionable. In this article, we develop a Bayesian
 framework for censored linear regression models by replacing the Gaussian
 assumptions for the random errors with scale mixtures of normal (SMN)
 distributions. The SMN is an attractive class of symmetric heavy-tailed
 densities that includes the normal, Student-<italic>t</italic>, Pearson
 type VII, slash and the contaminated normal distributions, as special
 cases. Using a Bayesian paradigm, an efficient Markov chain Monte Carlo
 algorithm is introduced to carry out posterior inference. A new
 hierarchical prior distribution is suggested for the degrees of freedom
 parameter in the Student-<italic>t</italic> distribution. The likelihood
 function is utilized to compute not only some Bayesian model selection
 measures but also to develop Bayesian case-deletion influence diagnostics
 based on the <italic>q</italic>-divergence measure. The proposed Bayesian
 methods are implemented in the R package <monospace>BayesCR</monospace>.
 The newly developed procedures are illustrated with applications using
 real and simulated data.
Journal: Journal of Applied Statistics 
Pages: 2694-2714 
Issue: 12 
Volume: 42 
Year: 2015 
Month: 12 
X-DOI: 10.1080/02664763.2015.1048671 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1048671 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:12:p:2694-2714




Template-Type: ReDIF-Article 1.0
Author-Name: H. Lu 
Author-X-Name-First: H. 
Author-X-Name-Last: Lu 
Author-Name: P. Yin 
Author-X-Name-First: P. 
Author-X-Name-Last: Yin 
Author-Name: R.X. Yue 
Author-X-Name-First: R.X. 
Author-X-Name-Last: Yue 
Author-Name: J.Q. Shi 
Author-X-Name-First: J.Q. 
Author-X-Name-Last: Shi 
Title: Robust confidence intervals for trend estimation in meta-analysis with publication bias 
Abstract:
 Confidence interval (CI) is very useful for trend estimation in
 meta-analysis. It provides a type of interval estimate of the regression
 slope as well as an indicator of the reliability of the estimate. Thus a
 precise calculation of confidence interval at an expected level is
 important. It is always difficult to explicitly quantify the CIs when
 there is publication bias in meta-analysis. Various CIs have been
 proposed, including the most widely used DerSimonian-Laird CI and the
 recently proposed Henmi-Copas CI. The latter provides a robust solution
 when there are non-ignorable missing data due to publication bias. In this
 paper we extended the idea into meta-analysis for trend estimation. We
 applied the method in different scenarios and showed that this type of CI
 is more robust than the others.
Journal: Journal of Applied Statistics 
Pages: 2715-2733 
Issue: 12 
Volume: 42 
Year: 2015 
Month: 12 
X-DOI: 10.1080/02664763.2015.1048672 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1048672 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:12:p:2715-2733




Template-Type: ReDIF-Article 1.0
Author-Name: Alan D. Hutson 
Author-X-Name-First: Alan D. 
Author-X-Name-Last: Hutson 
Author-Name: Gregory E. Wilding 
Author-X-Name-First: Gregory E. 
Author-X-Name-Last: Wilding 
Author-Name: Terry L. Mashtare 
Author-X-Name-First: Terry L. 
Author-X-Name-Last: Mashtare 
Author-Name: Albert Vexler 
Author-X-Name-First: Albert 
Author-X-Name-Last: Vexler 
Title: Measures of biomarker dependence using a copula-based multivariate epsilon-skew-normal family of distributions 
Abstract:
 In this note we develop a new multivariate copula model based on
 epsilon-skew-normal marginal densities for the purpose of examining
 biomarker dependency structures. We illustrate the flexibility and utility
 of this model via a variety of graphical tools and a data analysis example
 pertaining to salivary biomarker. The multivariate normal model is a
 sub-model of the multivariate epsilon-skew-normal distribution.
Journal: Journal of Applied Statistics 
Pages: 2734-2753 
Issue: 12 
Volume: 42 
Year: 2015 
Month: 12 
X-DOI: 10.1080/02664763.2015.1049130 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1049130 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:12:p:2734-2753




Template-Type: ReDIF-Article 1.0
Author-Name: Manisha Chakrabarty 
Author-X-Name-First: Manisha 
Author-X-Name-Last: Chakrabarty 
Author-Name: Amita Majumder 
Author-X-Name-First: Amita 
Author-X-Name-Last: Majumder 
Author-Name: Jeffrey Racine 
Author-X-Name-First: Jeffrey 
Author-X-Name-Last: Racine 
Title: Household budget-share distributions and welfare implications: an application of multivariate distributional statistics 
Abstract:
 In this paper the consequences of considering the household 'food share'
 distribution as a welfare measure, in isolation from the joint
 distribution of itemized budget shares, is examined through the
 unconditional and conditional distribution of 'food share' both
 parametrically and nonparametrically. The parametric framework uses
 Dirichlet and Beta distributions, while the nonparametric framework uses
 kernel smoothing methods. The analysis, in a three commodity setup
 ('food', 'durables', 'others'), based on household level rural data for
 West Bengal, India, for the year 2009-2010 shows significant
 underrepresentation of households by the conventional unconditional 'food
 share' distribution in the higher range of food budget shares that
 correspond to the lower end of the income profile. This may have serious
 consequences for welfare measurement.
Journal: Journal of Applied Statistics 
Pages: 2754-2768 
Issue: 12 
Volume: 42 
Year: 2015 
Month: 12  
X-DOI: 10.1080/02664763.2015.1049132 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1049132 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:12:p:2754-2768




Template-Type: ReDIF-Article 1.0
Author-Name: Markus Neuh�user 
Author-X-Name-First: Markus 
Author-X-Name-Last: Neuh�user 
Title: Combining the <italic>t</italic> test and Wilcoxon's rank-sum test 
Abstract:
 In the two-sample location-shift problem, Student's <italic>t</italic>
 test or Wilcoxon's rank-sum test are commonly applied. The latter test can
 be more powerful for non-normal data. Here, we propose to combine the two
 tests within a maximum test. We show that the constructed maximum test
 controls the type I error rate and has good power characteristics for a
 variety of distributions; its power is close to that of the more powerful
 of the two tests. Thus, irrespective of the distribution, the maximum test
 stabilizes the power. To carry out the maximum test is a more powerful
 strategy than selecting one of the single tests. The proposed test is
 applied to data of a clinical trial.
Journal: Journal of Applied Statistics 
Pages: 2769-2775 
Issue: 12 
Volume: 42 
Year: 2015 
Month: 12  
X-DOI: 10.1080/02664763.2015.1070809 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1070809 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:42:y:2015:i:12:p:2769-2775




Template-Type: ReDIF-Article 1.0
Author-Name: Massimo Attanasio 
Author-X-Name-First: Massimo 
Author-X-Name-Last: Attanasio 
Author-Name: Vincenza Capursi 
Author-X-Name-First: Vincenza 
Author-X-Name-Last: Capursi 
Title: Statistics in Education 
Journal: Journal of Applied Statistics 
Pages: 1-2 
Issue: 1 
Volume: 43 
Year: 2016 
Month: 1 
X-DOI: 10.1080/02664763.2015.1104890 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1104890 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:1:p:1-2




Template-Type: ReDIF-Article 1.0
Author-Name: Giada Adelfio 
Author-X-Name-First: Giada 
Author-X-Name-Last: Adelfio 
Author-Name: Giovanni Boscaino 
Author-X-Name-First: Giovanni 
Author-X-Name-Last: Boscaino 
Title: Degree course change and student performance: a mixed-effect model approach 
Abstract:
 This paper focuses on students credits earning speed over time and its
 determinants, dealing with the huge percentage of students who do not take
 the degree within the legal duration in the Italian University System. A
 new indicator for the performance of the student career is proposed on
 real data, concerning the cohort of students enrolled at a Faculty of the
 University of Palermo (followed for 7 years). The new indicator highlights
 a typical zero-inflated distribution and suggests to investigate the
 effect of the degree course (DC) change on the student career. A
 mixed-effect model for overdispersed data is considered, with the aim of
 taking into account the individual variability as well, due to the
 longitudinal nature of data. Results show the significant positive effect
 of the DC change on the student performance.
Journal: Journal of Applied Statistics 
Pages: 3-15 
Issue: 1 
Volume: 43 
Year: 2016 
Month: 1 
X-DOI: 10.1080/02664763.2015.1018673 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1018673 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:1:p:3-15




Template-Type: ReDIF-Article 1.0
Author-Name: F. Crippa 
Author-X-Name-First: F. 
Author-X-Name-Last: Crippa 
Author-Name: M. Mazzoleni 
Author-X-Name-First: M. 
Author-X-Name-Last: Mazzoleni 
Author-Name: M. Zenga 
Author-X-Name-First: M. 
Author-X-Name-Last: Zenga 
Title: Departures from the formal of actual students' university careers: an application of non-homogeneous fuzzy Markov chains 
Abstract:
 As in most higher education (HE) systems, the Italian university
 organisation draws paths of credit progression in formal curricula, which
 aim at framing the acquisition of knowledge and competencies within each
 specific major. The resulting yearly syllabi therefore develop in a
 sequence of examinations that are to be successfully passed, and formal
 administrative registration allows access to the following academic year.
 In general, there is a divergence between formal and actual career
 progression because each university student can proceed at her/his own
 pace, sketching her/his own trajectories, free to depart from the formal
 progression. Even if applied to various HE settings, Markov chain models
 do not fit the aforementioned situation. A methodological extension has
 been introduced, whereby progression levels are considered as fuzzy
 states. Markov chains with fuzzy states identify the latter with specified
 academic years and express each student's situation as a relational link
 to present and past academic attainments. This link is operationalised by
 means of a membership function, which is here discussed with reference to
 the Italian HE system.
Journal: Journal of Applied Statistics 
Pages: 16-30 
Issue: 1 
Volume: 43 
Year: 2016 
Month: 1 
X-DOI: 10.1080/02664763.2015.1091446 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1091446 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:1:p:16-30




Template-Type: ReDIF-Article 1.0
Author-Name: Valentina Raponi 
Author-X-Name-First: Valentina 
Author-X-Name-Last: Raponi 
Author-Name: Francesca Martella 
Author-X-Name-First: Francesca 
Author-X-Name-Last: Martella 
Author-Name: Antonello Maruotti 
Author-X-Name-First: Antonello 
Author-X-Name-Last: Maruotti 
Title: A biclustering approach to university performances: an Italian case study 
Abstract:
 University evaluation is a topic of increasing concern in Italy as well as
 in other countries. In empirical analysis, university activities and
 performances are often measured by means of indicator variables. The
 available information are then summarized to respond to different aims. We
 argue that the evaluation process is a complex phenomenon that cannot be
 addressed by a simple descriptive approach. In this paper, we used a
 model-based approach to account for association between indicators and
 similarities among the observed universities. We examine faculty-level
 data collected from different sources, covering 55 Italian Economics
 faculties in the academic year 2009/2010. Making use of a clustering
 methodology, we introduce a biclustering model that accounts for both
 homogeneity/heterogeneity among faculties and correlations between
 indicators. Our results show that there are two substantial different
 performances between universities which can be strictly related to the
 nature of the institutions, namely the <italic>Private</italic> and
 <italic>Public</italic> profiles. Each of the two groups has its own
 peculiar features and its own group-specific list of priorities, strengths
 and weaknesses. Thus, we suggest that caution should be used in
 interpreting standard university rankings as they generally do not account
 for the complex structure of the data.
Journal: Journal of Applied Statistics 
Pages: 31-45 
Issue: 1 
Volume: 43 
Year: 2016 
Month: 1 
X-DOI: 10.1080/02664763.2015.1009005 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1009005 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:1:p:31-45




Template-Type: ReDIF-Article 1.0
Author-Name: Marco Enea 
Author-X-Name-First: Marco 
Author-X-Name-Last: Enea 
Author-Name: Massimo Attanasio 
Author-X-Name-First: Massimo 
Author-X-Name-Last: Attanasio 
Title: An association model for bivariate data with application to the analysis of university students' success 
Abstract:
 The academic success of students is a priority for all universities. We
 analyze the students' success at university by considering their
 performance in terms of both &#x2018;qualitative performance&#x2019;,
 measured by their mean grade, and &#x2018;quantitative
 performance&#x2019;, measured by university credits accumulated. These
 data come from an Italian University and concern a cohort of students
 enrolled at the Faculty of Economics. To jointly model both the marginal
 relationships and the association structure with covariates, we fit a
 bivariate ordered logistic model by penalized maximum likelihood
 estimation. The penalty term we use allows us to smooth the association
 structure and enlarge the range of possible parameterizations beyond that
 provided by the usual Dale model. The advantages of our approach are also
 in terms of parsimony and parameter interpretation, while preserving the
 goodness of fit.
Journal: Journal of Applied Statistics 
Pages: 46-57 
Issue: 1 
Volume: 43 
Year: 2016 
Month: 1 
X-DOI: 10.1080/02664763.2014.998407 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.998407 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:1:p:46-57




Template-Type: ReDIF-Article 1.0
Author-Name: Hakim-Moulay Dehbi 
Author-X-Name-First: Hakim-Moulay 
Author-X-Name-Last: Dehbi 
Author-Name: Mario Cortina-Borja 
Author-X-Name-First: Mario 
Author-X-Name-Last: Cortina-Borja 
Author-Name: Marco Geraci 
Author-X-Name-First: Marco 
Author-X-Name-Last: Geraci 
Title: Aranda-Ordaz quantile regression for student performance assessment 
Abstract:
 In education research, normal regression models may not be appropriate due
 to the presence of bounded variables, which may exhibit a large variety of
 distributional shapes and present floor and ceiling effects. In this
 article a class of quantile regression models for bounded response
 variables is developed. The one-parameter Aranda-Ordaz symmetric and
 asymmetric families of transformations are applied to address modelling
 issues that arise when estimating conditional quantiles of a bounded
 response variable whose relationship with the covariates is possibly
 nonlinear. This approach exploits the equivariance property of quantiles
 and aims at achieving linearity of the predictor. This offers a flexible
 model-based alternative to nonparametric estimation of the quantile
 function. Since the transformation is quantile-specific, the modelling
 takes into account the local features of the conditional distribution of
 the response variable. Our study is motivated by the analysis of reading
 performance in seven-year old children part of the Millennium Cohort
 Study.
Journal: Journal of Applied Statistics 
Pages: 58-71 
Issue: 1 
Volume: 43 
Year: 2016 
Month: 1 
X-DOI: 10.1080/02664763.2015.1025724 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1025724 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:1:p:58-71




Template-Type: ReDIF-Article 1.0
Author-Name: Rafael Pimentel Maia 
Author-X-Name-First: Rafael Pimentel 
Author-X-Name-Last: Maia 
Author-Name: Hildete Prisco Pinheiro 
Author-X-Name-First: Hildete Prisco 
Author-X-Name-Last: Pinheiro 
Author-Name: Alu&#xed;sio Pinheiro 
Author-X-Name-First: Alu&#xed;sio 
Author-X-Name-Last: Pinheiro 
Title: Academic performance of students from entrance to graduation via quasi U-statistics: a study at a Brazilian research university 
Abstract:
 We present novel methodology to assess undergraduate students'
 performance. Emphasis is given to potential dissimilar behaviors due to
 high school background and gender. The proposed method is based on
 measures of diversity and on the decomposability of quasi
 <italic>U</italic>-statistics to define average distances between and
 within groups. One advantage of the new method over the classical analysis
 of variance is its robustness to distributional deviation from the
 normality. Moreover, compared with other nonparametric methods, it also
 includes tests for interaction effects which are not rank transform
 procedures. The variance of the test statistic is estimated by jackknife
 and <italic>p</italic>-values are computed using its asymptotic
 distribution. A college education performance data is analyzed. The data
 set is formed by students who entered in the University of Campinas,
 Brazil, between 1997 and 2000. Their academic performance has been
 recorded until graduation or drop-out. The classical ANOVA points to
 significant effects of gender, type of high school and working status.
 However, the residual analysis indicates a highly significant deviation
 from normality. The quasi <italic>U</italic>-statistics nonparametric
 tests proposed here present significant effect of interaction between type
 of high school and gender but did not present a significant effect of
 working status. The proposed nonparametric method also results in smaller
 error variances, illustrating its robustness against model
 misspecification.
Journal: Journal of Applied Statistics 
Pages: 72-86 
Issue: 1 
Volume: 43 
Year: 2016 
Month: 1 
X-DOI: 10.1080/02664763.2015.1077939 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1077939 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:1:p:72-86




Template-Type: ReDIF-Article 1.0
Author-Name: Dewi Juliah Ratnaningsih 
Author-X-Name-First: Dewi Juliah 
Author-X-Name-Last: Ratnaningsih 
Author-Name: Imas Sukaesih Sitanggang 
Author-X-Name-First: Imas Sukaesih 
Author-X-Name-Last: Sitanggang 
Title: Comparative analysis of classification methods in determining non-active student characteristics in Indonesia Open University 
Abstract:
 Classification is a data mining technique that aims to discover a model
 from training data that distinguishes records into appropriate classes.
 Classification methods can be applied in education, to classify non-active
 students in higher education programs based on their characteristics. This
 paper presents a comparison of three classification methods: Na&#xef;ve
 Bayes, Bagging, and C4.5. The criteria used to evaluate performance of
 three classifiers are stratified cross-validation, confusion matrix, ROC
 curve, recall, precision, and F-measure. The data used for this paper are
 non-active students in Indonesia Open University (IOU) for the period of
 2004--2012. The non-active students were divided into three groups:
 non-active students in the first three years, non-active students in first
 five years, and non-active students over five years. Results of the study
 show that the Bagging method provided a higher accuracy than Na&#xef;ve
 Bayes and C4.5. The accuracy of bagging classification is 82.99%, while
 the Na&#xef;ve Bayes and C4.5 are 80.04% and 82.74%, respectively. The
 classification tree resulted from the Bagging method has a large number of
 nodes, so it is quite difficult to use in decision-making. For that, the
 C4.5 tree is used to classify non-active students in IOU based in their
 characteristics.
Journal: Journal of Applied Statistics 
Pages: 87-97 
Issue: 1 
Volume: 43 
Year: 2016 
Month: 1 
X-DOI: 10.1080/02664763.2015.1077940 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1077940 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:1:p:87-97




Template-Type: ReDIF-Article 1.0
Author-Name: Dalit Contini 
Author-X-Name-First: Dalit 
Author-X-Name-Last: Contini 
Author-Name: Davide Azzolini 
Author-X-Name-First: Davide 
Author-X-Name-Last: Azzolini 
Title: Performance and decisions: immigrant--native gaps in educational transitions in Italy 
Abstract:
 Following the seminal work of Boudon [5], sociological research has
 conceptualized immigrant--native gaps in educational transitions as
 deriving from children of immigrants' poorer academic performance (primary
 effects) and from different decision models existing between native and
 immigrant families (secondary effects). The limited evidence on
 immigrant--native gaps in Europe indicates that secondary effects are
 generally positive: children of immigrants tend to make more ambitious
 educational choices than natives with the same prior performance. In this
 paper we review the different decomposition methods employed so far in the
 literature to tackle similar research questions, and extend the existing
 methodology to allow including interaction effects and taking explanatory
 variables under control. We apply this method to data coming from a unique
 Italian administrative data set. We find that children of immigrants
 exhibit higher likelihood to opt for vocational training over more
 generalist and academic programs, even when controlling for socio-economic
 background. A large share of the immigrant--native differentials in the
 probability to attend the different school programs is explained by the
 different prior performance distribution. However, decision models differ
 between groups, and, contrary to the evidence on other countries, these
 differences contribute to widening the existing gaps. If children of
 immigrants had the same social background and prior performance of their
 native peers, they still would be more likely to enroll in shorter and
 less-demanding school programs. Interestingly, these results hold true
 only for boys, while we find no evidence of decision effects for girls.
Journal: Journal of Applied Statistics 
Pages: 98-114 
Issue: 1 
Volume: 43 
Year: 2016 
Month: 1 
X-DOI: 10.1080/02664763.2015.1036845 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1036845 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:1:p:98-114




Template-Type: ReDIF-Article 1.0
Author-Name: Maria Prosperina Vitale 
Author-X-Name-First: Maria Prosperina 
Author-X-Name-Last: Vitale 
Author-Name: Giovanni C. Porzio 
Author-X-Name-First: Giovanni C. 
Author-X-Name-Last: Porzio 
Author-Name: Patrick Doreian 
Author-X-Name-First: Patrick 
Author-X-Name-Last: Doreian 
Title: Examining the effect of social influence on student performance through network autocorrelation models 
Abstract:
 The paper investigates the link between student relations and their
 performances at university. A social influence mechanism is hypothesized
 as individuals adjusting their own behaviors to those of others with whom
 they are connected. This contribution explores the effect of peers on a
 real network formed by a cohort of students enrolled at a graduate level
 in an Italian University. Specifically, by adopting a network effects
 model, the relation between interpersonal networks and university
 performance is evaluated assuming that student performance is related to
 the performance of the other students belonging to the same group. By
 controlling for individual covariates, the network results show informal
 contacts, based on mutual interests and goals, are related to performance,
 while formal groups formed temporarily by the instructor have no such
 effect.
Journal: Journal of Applied Statistics 
Pages: 115-127 
Issue: 1 
Volume: 43 
Year: 2016 
Month: 1 
X-DOI: 10.1080/02664763.2015.1049517 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1049517 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:1:p:115-127




Template-Type: ReDIF-Article 1.0
Author-Name: Simon Monyai 
Author-X-Name-First: Simon 
Author-X-Name-Last: Monyai 
Author-Name: 'Maseka Lesaoana 
Author-X-Name-First: 'Maseka 
Author-X-Name-Last: Lesaoana 
Author-Name: Timotheus Darikwa 
Author-X-Name-First: Timotheus 
Author-X-Name-Last: Darikwa 
Author-Name: Philimon Nyamugure 
Author-X-Name-First: Philimon 
Author-X-Name-Last: Nyamugure 
Title: Application of multinomial logistic regression to educational factors of the 2009 General Household Survey in South Africa 
Abstract:
 This paper combines factor analysis and multinomial logistic regression
 (MLR) in understanding the relationship between extracted factors of
 quality of life pertaining to education and variables of five key areas of
 the levels of development in the context of the South African 2009 General
 Household Survey. MLR was used to analyse the identified educational
 factors from factor analysis. It was also used to determine the extent to
 which these factors impact on educational level outcomes across South
 Africa. The overall classification accuracy rate displayed was 73.0% which
 is greater than the proportion by chance accuracy criteria of 57.0%. This
 means that the model improves on the proportion by chance accuracy rate of
 25.0% or more so that the criterion for classification accuracy is
 satisfied and the model is adequate. Evidence is that being historically
 disadvantaged, absence of parental care, violence in schools and the
 perception that fees were too high generally have a negative influence on
 educational attainment. The results of this paper compare well with other
 household surveys conducted by other researchers.
Journal: Journal of Applied Statistics 
Pages: 128-139 
Issue: 1 
Volume: 43 
Year: 2016 
Month: 1 
X-DOI: 10.1080/02664763.2015.1077941 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1077941 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:1:p:128-139




Template-Type: ReDIF-Article 1.0
Author-Name: Rosalia Castellano 
Author-X-Name-First: Rosalia 
Author-X-Name-Last: Castellano 
Author-Name: Gennaro Punzo 
Author-X-Name-First: Gennaro 
Author-X-Name-Last: Punzo 
Title: Patterns of earnings differentials across three conservative European welfare regimes with alternative education systems 
Abstract:
 The aim of this paper is to investigate, from a generational perspective,
 the effect of human capital on individual earnings and earnings
 differences in Germany, France and Italy, three developed countries in
 Western Europe with similar <italic>conservative</italic> welfare regimes
 but with important differences in their education systems. Income
 inequalities <italic>between</italic> and <italic>within</italic>
 education levels are explored using a two-stage probit model with quantile
 regressions in the second stage. More precisely, drawing upon 2005 EU-SILC
 data, returns on schooling and experience are estimated separately for
 employees and self-employed full-time workers by means of Mincerian
 earnings equations with sample selection; the sample selection correction
 accounts for the potential individual self-selection into the two labour
 force types. Although some determinants appear to be relatively similar
 across countries, state-specific differentials are drawn in light of the
 institutional features of each national context. The study reveals how
 each dimension of human capital differently affects individuals&#x2019;
 earnings and earnings inequality and, most of all, how their impacts
 differ along the conditional earnings distribution and across countries.
 In the comparative perspective, the country's leading position in terms of
 the highest rewards on education also depends on which earnings
 distribution (employee vs. self-employed) is analysed.
Journal: Journal of Applied Statistics 
Pages: 140-168 
Issue: 1 
Volume: 43 
Year: 2016 
Month: 1 
X-DOI: 10.1080/02664763.2015.1049518 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1049518 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:1:p:140-168




Template-Type: ReDIF-Article 1.0
Author-Name: Stefania Capecchi 
Author-X-Name-First: Stefania 
Author-X-Name-Last: Capecchi 
Author-Name: Domenico Piccolo 
Author-X-Name-First: Domenico 
Author-X-Name-Last: Piccolo 
Title: Investigating the determinants of job satisfaction of Italian graduates: a model-based approach 
Abstract:
 The paper explores the relationship between personal, economic and
 time-dependent covariates as determinants of the job satisfaction
 expressed by graduate workers. After discussing the main results of the
 literature, the work emphasizes a statistical modelling approach able to
 effectively estimate and visualize those determinants and their
 interactions with subjects' covariates. Interpretation and visualization
 of graduates' profiles are shown on the basis of a survey conducted in
 Italy; more specifically, the determinants of both satisfaction and
 uncertainty of the respondents are explicitly discussed.
Journal: Journal of Applied Statistics 
Pages: 169-179 
Issue: 1 
Volume: 43 
Year: 2016 
Month: 1 
X-DOI: 10.1080/02664763.2015.1036844 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1036844 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:1:p:169-179




Template-Type: ReDIF-Article 1.0
Author-Name: S. Fasola 
Author-X-Name-First: S. 
Author-X-Name-Last: Fasola 
Author-Name: O. Giambalvo 
Author-X-Name-First: O. 
Author-X-Name-Last: Giambalvo 
Author-Name: C. Romano 
Author-X-Name-First: C. 
Author-X-Name-Last: Romano 
Title: Flexible latent trait aggregation to analyze employability after the Ph.D. in Italy 
Abstract:
 The analysis of satisfaction, employability and economic perspectives
 after the Ph.D. in Italy has not received adequate attention in the past,
 especially in terms of comparison among universities. To analyze these
 aspects, in this paper we consider data from the survey &#x2018;Statistica
 in TEma di Laureati e LAvoro&#x2019; on doctors who achieved the title on
 2007, 2008 and 2009 [CILEA, <italic>Laureati STELLA, indagine
 occupazionale post-dottorato, dottori di ricerca 2007--2008</italic>,
 Tech. Rep., CILEA, Segrate, 2010; CILEA, <italic>Laureati STELLA, indagine
 occupazionale post-dottorato, dottori di ricerca 2008--2009</italic>,
 Tech. Rep., CILEA, Segrate, 2011]. To deal with the complex,
 multidimensional nature of the concept, we propose a flexible two-step
 procedure for the construction of a composite indicator, and make a first
 attempt to rank some Italian universities. In the first step, indicators
 for single dimensions are derived from cumulative link models with
 proportional odds. In the second step, aggregation through standard, ad
 hoc methods is proposed.
Journal: Journal of Applied Statistics 
Pages: 180-194 
Issue: 1 
Volume: 43 
Year: 2016 
Month: 1 
X-DOI: 10.1080/02664763.2015.1077797 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1077797 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:1:p:180-194




Template-Type: ReDIF-Article 1.0
Author-Name: D. Fouskakis 
Author-X-Name-First: D. 
Author-X-Name-Last: Fouskakis 
Author-Name: G. Petrakos 
Author-X-Name-First: G. 
Author-X-Name-Last: Petrakos 
Author-Name: I. Vavouras 
Author-X-Name-First: I. 
Author-X-Name-Last: Vavouras 
Title: A Bayesian hierarchical model for comparative evaluation of teaching quality indicators in higher education 
Abstract:
 The problem motivating the paper is the quantification of students'
 preferences regarding teaching/coursework quality, under certain numerical
 restrictions, in order to build a model for identifying, assessing and
 monitoring the major components of the overall teaching quality. We
 propose a Bayesian hierarchical beta regression model, with a Dirichlet
 prior on the model coefficients. The coefficients of the model can then be
 interpreted as weights and thus they measure the relative importance that
 students give to the different attributes. This approach not only allows
 for the incorporation of informative prior when it is available but also
 provides user-friendly interfaces and direct probability interpretations
 for all quantities. Furthermore, it is a natural way to implement the
 usual constraints for the model coefficients. This model is applied to
 data collected in 2009 and 2013 from undergraduate students in the
 Panteion University, Athens, Greece and besides the construction of an
 instrument for the assessment and monitoring of teaching quality, it gave
 some input for a preliminary discussion on the association of the
 differences in students' preferences between the two time-periods with the
 current Greek socioeconomic transformation. Results from the proposed
 approach are compared with the ones obtained by two alternative
 statistical techniques.
Journal: Journal of Applied Statistics 
Pages: 195-211 
Issue: 1 
Volume: 43 
Year: 2016 
Month: 1 
X-DOI: 10.1080/02664763.2015.1054793 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1054793 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:1:p:195-211




Template-Type: ReDIF-Article 1.0
Author-Name: Andr&#xe1;s Telcs 
Author-X-Name-First: Andr&#xe1;s 
Author-X-Name-Last: Telcs 
Author-Name: Zsolt Tibor Koszty&#xe1;n 
Author-X-Name-First: Zsolt Tibor 
Author-X-Name-Last: Koszty&#xe1;n 
Author-Name: &#xc1;d&#xe1;m T&#xf6;r&#xf6;k 
Author-X-Name-First: &#xc1;d&#xe1;m 
Author-X-Name-Last: T&#xf6;r&#xf6;k 
Title: Unbiased one-dimensional university ranking -- application-based preference ordering 
Abstract:
 Our main goal is to produce a ranking technique which overcomes
 shortcomings of the numerous university rankings published. We propose a
 ranking method that provides a one-dimensional preference list of
 universities which is solely based on the partial rankings of applicants.
 Our ranking is free of subjective weights and uncomparable dimensions.
Journal: Journal of Applied Statistics 
Pages: 212-228 
Issue: 1 
Volume: 43 
Year: 2016 
Month: 1 
X-DOI: 10.1080/02664763.2014.998180 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.998180 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:1:p:212-228




Template-Type: ReDIF-Article 1.0
Author-Name: J. Gro&#xdf; 
Author-X-Name-First: J. 
Author-X-Name-Last: Gro&#xdf; 
Author-Name: A. Robitzsch 
Author-X-Name-First: A. 
Author-X-Name-Last: Robitzsch 
Author-Name: A.C. George 
Author-X-Name-First: A.C. 
Author-X-Name-Last: George 
Title: Cognitive diagnosis models for baseline testing of educational standards in math 
Abstract:
 Cognitive diagnosis models received growing attention in recent
 psychometric literature in view of the potentiality for fine-grained
 analysis of examinees&#x2019; latent skills. Although different types and
 aspects of these models have been investigated in some detail, application
 to real-life data had so far been sparse. This paper aims at addressing
 different topics with respect to model building from a practitioner's
 perspective. The objective is to draw conclusions about examinees&#x2019;
 performance on the Austrian baseline testing of educational standards in
 math 2009. Although there is a variety of models at hand, the focus is set
 on the easy to interpret deterministic input, noisy &#x2018;and&#x2019;
 gate model. A possible course of action with respect to model fit is
 outlined in detail and some conclusions with respect to test results are
 discussed.
Journal: Journal of Applied Statistics 
Pages: 229-243 
Issue: 1 
Volume: 43 
Year: 2016 
Month: 1 
X-DOI: 10.1080/02664763.2014.1000841 
File-URL: http://hdl.handle.net/10.1080/02664763.2014.1000841 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:1:p:229-243




Template-Type: ReDIF-Article 1.0
Author-Name: Oyelola A. Adegboye 
Author-X-Name-First: Oyelola A. 
Author-X-Name-Last: Adegboye 
Author-Name: Asadullah Jawid 
Author-X-Name-First: Asadullah 
Author-X-Name-Last: Jawid 
Title: Multivariate multilevel models for attitudes toward statistics: multi-disciplinary settings in Afghanistan 
Abstract:
 The present paper focuses on examining students' attitudes and perception
 of statistics in Afghanistan universities and the factor structure of the
 statistical anxiety rating scale (STARS). In total, 209 undergraduate
 students from different disciplines in different universities in
 Afghanistan participated in the study. In addition to testing the factor
 structure of the STARS, a multivariate multilevel analysis that
 incorporates the correlation in the data was carried out on the aggregated
 subscales of the STARS scores. Results showed that the original 6-factor
 structure did not fit the Afghanistan data well. Exploratory factor
 analysis identified 5-factor constructs to best fit the data and was
 confirmed by the fit indices as well as a likelihood ratio test. Male
 students showed more positive attitudes toward statistics and a higher
 level of statistics anxiety than their female counterparts. Female
 students experienced higher levels of fear of asking for help and less
 anxiety in computation. Students who had taken at least a previous
 statistics course had lower statistics anxiety than those taking the
 course for the first time.
Journal: Journal of Applied Statistics 
Pages: 244-261 
Issue: 1 
Volume: 43 
Year: 2016 
Month: 1 
X-DOI: 10.1080/02664763.2015.1091445 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1091445 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:1:p:244-261




Template-Type: ReDIF-Article 1.0
Author-Name: Domenico De Stefano 
Author-X-Name-First: Domenico 
Author-X-Name-Last: De Stefano 
Author-Name: Susanna Zaccarin 
Author-X-Name-First: Susanna 
Author-X-Name-Last: Zaccarin 
Title: Co-authorship networks and scientific performance: an empirical analysis using the generalized extreme value distribution 
Abstract:
 This paper aims to explore the effects of collaborative behaviour on
 scholar scientific performance. Individual network measures related to
 scholar centrality as well as attitude to collaborate with others are
 derived from co-authorship networks in a given scientific community (i.e.
 Italian academic statisticians). Co-authorship information have been
 collected from three data sources of national-based, discipline-based, and
 international-based high-impact publications. Both network and individual
 covariates are used to model individual <italic>h</italic>-index by
 generalized extreme value distribution. Results show a positive
 association between performance and actors' central position in the
 network. Having a large number of co-authors and occupying central
 positions are likely to positively affect scientific performance.
Journal: Journal of Applied Statistics 
Pages: 262-279 
Issue: 1 
Volume: 43 
Year: 2016 
Month: 1 
X-DOI: 10.1080/02664763.2015.1017719 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1017719 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:1:p:262-279




Template-Type: ReDIF-Article 1.0
Author-Name: Mohammadreza Meshkani 
Author-X-Name-First: Mohammadreza 
Author-X-Name-Last: Meshkani 
Author-Name: Afshin Fallah 
Author-X-Name-First: Afshin 
Author-X-Name-Last: Fallah 
Author-Name: Amir Kavousi 
Author-X-Name-First: Amir 
Author-X-Name-Last: Kavousi 
Title: Bayesian analysis of covariance under inverse Gaussian model 
Abstract:
 This paper considers the problem of analysis of covariance (ANCOVA) under
 the assumption of inverse Gaussian distribution for response variable from
 the Bayesian point of view. We develop a fully Bayesian model for ANCOVA
 based on the conjugate prior distributions for parameters contained in the
 model. The Bayes estimator of parameters, ANCOVA model and adjusted
 effects for both treatments and covariates along with predictive
 distribution of future observations are developed. We also provide the
 essentials for comparing adjusted treatments effects and adjusted factor
 effects. A simulation study and a real world application are also
 performed to illustrate and evaluate the proposed Bayesian model.
Journal: Journal of Applied Statistics 
Pages: 280-298 
Issue: 2 
Volume: 43 
Year: 2016 
Month: 2 
X-DOI: 10.1080/02664763.2015.1049131 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1049131 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:2:p:280-298




Template-Type: ReDIF-Article 1.0
Author-Name: Sonia Ferreira Lopes Toffoli 
Author-X-Name-First: Sonia Ferreira Lopes 
Author-X-Name-Last: Toffoli 
Author-Name: Dalton Francisco de Andrade 
Author-X-Name-First: Dalton Francisco 
Author-X-Name-Last: de Andrade 
Author-Name: Antonio Cezar Bornia 
Author-X-Name-First: Antonio Cezar 
Author-X-Name-Last: Bornia 
Title: Evaluation of open items using the many-facet Rasch model 
Abstract:
 The goal of this study is to analyze the quality of ratings assigned to
 two constructed response questions for evaluating the written ability of
 essays in Portuguese language from the perspective of the many-facet Rasch
 (MFR [15]) model. The analyzed data set comes from 350 written tests with
 two open-item tasks that were developed based on a rating process
 independently marked by two rater coordinators and a group of 42 raters.
 The MFR model analysis shows the measurement quality related to the
 examinees, raters, tasks and items, and classification scale that has been
 used for the task rating process. The findings indicate significant
 differences amongst the rater severities and show that the raters cannot
 be interchanged. The results also suggest that the comparison between the
 two task difficulties needs further investigation. An additional study has
 been done on the scale structure of the classification used by each rater
 for each item. The result suggests that there have been some similarities
 amongst the tasks and a need of revision for some criteria of the rating
 process. Overall, the scale of evaluation has shown to be efficient for a
 classification of the examinees.
Journal: Journal of Applied Statistics 
Pages: 299-316 
Issue: 2 
Volume: 43 
Year: 2016 
Month: 2 
X-DOI: 10.1080/02664763.2015.1049938 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1049938 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:2:p:299-316




Template-Type: ReDIF-Article 1.0
Author-Name: H.D. Vinod 
Author-X-Name-First: H.D. 
Author-X-Name-Last: Vinod 
Title: New bootstrap inference for spurious regression problems 
Abstract:
 Phillips [11] provides asymptotic theory for regressions that relate
 nonstationary time series including those integrated of order 1,
 <inline-formula><inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1049939_ilm0001.gif"/></inline-formula>. A practical
 implication of the literature on spurious regression is that one cannot
 trust the usual confidence intervals (CIs). In the absence of prior
 knowledge that two series are cointegrated, it is therefore recommended
 that we abandon the specification in levels and work with differenced or
 detrended series. For situations when the specification in levels is
 sacrosanct we propose new CIs based on the Maximum Entropy bootstrap
 explained in Vinod and L&#xf3;pez-de-Lacalle (<italic>Maximum entropy
 bootstrap for time series: The meboot R package</italic>, J. Statist.
 Softw. 29 (2009), pp. 1--19). An extensive Monte Carlo simulation shows
 that our proposal can provide more reliable conservative CIs than
 traditional and block bootstrap intervals.
Journal: Journal of Applied Statistics 
Pages: 317-335 
Issue: 2 
Volume: 43 
Year: 2016 
Month: 2 
X-DOI: 10.1080/02664763.2015.1049939 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1049939 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:2:p:317-335




Template-Type: ReDIF-Article 1.0
Author-Name: Chandan Saha 
Author-X-Name-First: Chandan 
Author-X-Name-Last: Saha 
Author-Name: Michael P. Jones 
Author-X-Name-First: Michael P. 
Author-X-Name-Last: Jones 
Title: Type I and Type II error rates in the last observation carried forward method under informative dropout 
Abstract:
 Dropout is a persistent problem for a longitudinal study. We exhibit the
 shortcomings of the last observation carried forward method. It produces
 biased estimates of change in an outcome from baseline to study endpoint
 under informative dropout. We developed a theoretical quantification of
 the effect of such bias on type I and type II error rates. We present
 results for a setup where a subject either completes the study or drops
 out during one particular interval, and also under the setup in which
 subjects could drop out at any time during the study. The type I error
 rate steadily increases when time to dropout decreases or the common
 sample size increases. The inflation in type I error rate can be
 substantially high when reasons for dropout in the two groups differ; when
 there is a large difference in dropout rates between the control and
 treatment groups and when the common sample size is large; even when
 dropout subjects have one or two fewer observations than the completers.
 Similar results are also observed for type II error rates. A study can
 have very low power when early recovered patients in the treatment group
 and worsening patients in the control group drop out even near the end of
 the study.
Journal: Journal of Applied Statistics 
Pages: 336-350 
Issue: 2 
Volume: 43 
Year: 2016 
Month: 2 
X-DOI: 10.1080/02664763.2015.1063112 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1063112 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:2:p:336-350




Template-Type: ReDIF-Article 1.0
Author-Name: Daniel Ambach 
Author-X-Name-First: Daniel 
Author-X-Name-Last: Ambach 
Title: Short-term wind speed forecasting in Germany 
Abstract:
 The importance of renewable power production is a set goal in terms of the
 energy turnaround. Developing short-term wind speed forecasting
 improvements might increase the profitability of wind power. This article
 compares two novel approaches to model and predict wind speed. Both
 approaches incorporate periodic interactions, whereas the first model uses
 Fourier series to model the periodicity. The second model takes
 <inline-formula><inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1063113_ilm0001.gif"/></inline-formula> generalised
 trigonometric functions into consideration. The aforementioned Fourier
 series are special types of the <italic>p</italic>-generalised
 trigonometrical function and therefore model 1 is nested in model 2. The
 two models use an autoregressive fractionally integrated moving
 average--asymmetric power generalised autoregressive conditional
 heteroscedasticity process to cover the autocorrelation and the
 heteroscedasticity. A data set which consist of 10&#x2009;min data
 collected at four stations at the German--Polish border from August 2007
 to December 2012 is analysed. The most important finding is an enhancement
 of the forecasting accuracy up to three hours that is directly related to
 our new short-term forecasting model.
Journal: Journal of Applied Statistics 
Pages: 351-369 
Issue: 2 
Volume: 43 
Year: 2016 
Month: 2 
X-DOI: 10.1080/02664763.2015.1063113 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1063113 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:2:p:351-369




Template-Type: ReDIF-Article 1.0
Author-Name: Aslan Deniz Karaoglan 
Author-X-Name-First: Aslan Deniz 
Author-X-Name-Last: Karaoglan 
Author-Name: Nihat Celik 
Author-X-Name-First: Nihat 
Author-X-Name-Last: Celik 
Title: A new painting process for vessel radiators of transformer: wet-on-wet 
Abstract:
 The painting process of corrugated wall radiators of a distribution
 transformer is performed by a flow-down painting technique in the
 industrial field. This study has been prepared in accordance with ISO
 12944-5. Correspondingly, this work is motivated by Epoxy 2-pack paints
 (4.3.4.2) to obtain minimum requirements for C3 atmospheric corrosivity
 categories (5.1.1). This standard requires from the vertical surface of
 the vessel of the transformer to be painted with epoxy paints that contain
 anti-corrosive pigments with a minimum of 100&#x2005;&#xb5;m dry film
 thickness. In the present study, a new production methodology called
 wet-on-wet (WOW) painting is developed which has never been used in
 industry. In addition, a modified response surface methodology (RSM) is
 proposed for designing, modeling, and optimizing the proposed process
 under unsteady environmental effects. The results indicate that the WOW
 painting can be applied to real industrial systems successfully by the aid
 of the proposed new RSM algorithm and provide remarkable time and cost
 savings.
Journal: Journal of Applied Statistics 
Pages: 370-386 
Issue: 2 
Volume: 43 
Year: 2016 
Month: 2 
X-DOI: 10.1080/02664763.2015.1063114 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1063114 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:2:p:370-386




Template-Type: ReDIF-Article 1.0
Author-Name: Rose D. Baker 
Author-X-Name-First: Rose D. 
Author-X-Name-Last: Baker 
Author-Name: Ian G. McHale 
Author-X-Name-First: Ian G. 
Author-X-Name-Last: McHale 
Title: An empirical Bayes' procedure for ranking players in Ryder Cup golf 
Abstract:
 We describe a model to obtain strengths and rankings of players appearing
 in golf's Ryder Cup. Obtaining rankings is complicated because of two
 reasons. First, competitors do not compete on an equal number of
 occasions, with some competitors appearing too infrequently for their
 ranking to be estimated with any degree of certainty, and second,
 different competitors experience different levels of volatility in
 results. Our approach is to assume the competitor strengths are drawn from
 some common distribution. For small numbers of competitors, as is the case
 here, we fit the model using Monte-Carlo integration. Results suggest
 there is very little difference between the top performing players, though
 Scotland's Colin Montgomerie is estimated as the strongest Ryder Cup
 player.
Journal: Journal of Applied Statistics 
Pages: 387-395 
Issue: 3 
Volume: 43 
Year: 2016 
Month: 3 
X-DOI: 10.1080/02664763.2015.1043869 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1043869 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:3:p:387-395




Template-Type: ReDIF-Article 1.0
Author-Name: S.T. Boris Choy 
Author-X-Name-First: S.T. Boris 
Author-X-Name-Last: Choy 
Author-Name: Jennifer S.K. Chan 
Author-X-Name-First: Jennifer S.K. 
Author-X-Name-Last: Chan 
Author-Name: Udi E. Makov 
Author-X-Name-First: Udi E. 
Author-X-Name-Last: Makov 
Title: Robust Bayesian analysis of loss reserving data using scale mixtures distributions 
Abstract:
 It is vital for insurance companies to have appropriate levels of loss
 reserving to pay outstanding claims and related settlement costs. With
 many uncertainties and time lags inherently involved in the claims
 settlement process, loss reserving therefore must be based on estimates.
 Existing models and methods cannot cope with irregular and extreme claims
 and hence do not offer an accurate prediction of loss reserving. This
 paper extends the conventional normal error distribution in loss reserving
 modeling to a range of heavy-tailed distributions which are expressed by
 certain scale mixtures forms. This extension enables robust analysis and,
 in addition, allows an efficient implementation of Bayesian analysis via
 Markov chain Monte Carlo simulations. Various models for the mean of the
 sampling distributions, including the log-Analysis of Variance (ANOVA),
 log-Analysis of Covariance (ANCOVA) and state space models, are considered
 and the straightforward implementation of scale mixtures distributions is
 demonstrated using OpenBUGS.
Journal: Journal of Applied Statistics 
Pages: 396-411 
Issue: 3 
Volume: 43 
Year: 2016 
Month: 3 
X-DOI: 10.1080/02664763.2015.1063115 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1063115 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:3:p:396-411




Template-Type: ReDIF-Article 1.0
Author-Name: Hadi Alizadeh Noughabi 
Author-X-Name-First: Hadi 
Author-X-Name-Last: Alizadeh Noughabi 
Author-Name: Narayanaswamy Balakrishnan 
Author-X-Name-First: Narayanaswamy 
Author-X-Name-Last: Balakrishnan 
Title: Tests of goodness of fit based on Phi-divergence 
Abstract:
 In this paper, we introduce a general goodness of fit test based on
 Phi-divergence. Consistency of the proposed test is established. We then
 study some special cases of tests for normal, exponential, uniform and
 Laplace distributions. Through Monte Carlo simulations, the power values
 of the proposed tests are compared with some known competing tests under
 various alternatives. Finally, some numerical examples are presented to
 illustrate the proposed procedure.
Journal: Journal of Applied Statistics 
Pages: 412-429 
Issue: 3 
Volume: 43 
Year: 2016 
Month: 3 
X-DOI: 10.1080/02664763.2015.1063116 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1063116 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:3:p:412-429




Template-Type: ReDIF-Article 1.0
Author-Name: C. Armero 
Author-X-Name-First: C. 
Author-X-Name-Last: Armero 
Author-Name: A. Forte 
Author-X-Name-First: A. 
Author-X-Name-Last: Forte 
Author-Name: H. Perpi&#xf1;&#xe1;n 
Author-X-Name-First: H. 
Author-X-Name-Last: Perpi&#xf1;&#xe1;n 
Title: Bayesian longitudinal models for paediatric kidney transplant recipients 
Abstract:
 Chronic kidney disease is a progressive loss of renal function which
 results in the inability of the kidneys to properly filter waste from the
 blood. Renal function is usually estimated by the glomerular filtration
 rate (eGFR), which decreases with the worsening of the disease. Bayesian
 longitudinal models with covariates, random effects, serial correlation
 and measurement error are discussed to analyse the progression of eGFR in
 first transplanted children taken from a study in Val&#xe8;ncia, Spain.
Journal: Journal of Applied Statistics 
Pages: 430-440 
Issue: 3 
Volume: 43 
Year: 2016 
Month: 3 
X-DOI: 10.1080/02664763.2015.1063117 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1063117 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:3:p:430-440




Template-Type: ReDIF-Article 1.0
Author-Name: Qing Li 
Author-X-Name-First: Qing 
Author-X-Name-Last: Li 
Title: Indirect membership function assignment based on ordinal regression 
Abstract:
 In many fuzzy sets applications, fuzzy membership functions are commonly
 developed based on empirical or expert knowledge. The equation of a
 membership function is usually determined somewhat arbitrarily. This paper
 explores a novel membership function design method based on ordinal
 regression analysis. The estimated thresholds between ordinal measurement
 categories are applied to calculate the intersection points between fuzzy
 sets. These intersection points are further applied to determine the
 equations of the membership functions. Information distortion due to
 empirical guess can thus be reduced and more latent information in the
 fuzzy responses can therefore be captured. A case study investigating the
 relationship between foster mothers&#x2019; satisfaction and the foster
 time and information provided has been conducted in this research. The
 applicability and effectiveness of the proposed membership function
 assignment approach have been demonstrated through several case studies.
Journal: Journal of Applied Statistics 
Pages: 441-460 
Issue: 3 
Volume: 43 
Year: 2016 
Month: 3 
X-DOI: 10.1080/02664763.2015.1070802 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1070802 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:3:p:441-460




Template-Type: ReDIF-Article 1.0
Author-Name: Chin-Shang Li 
Author-X-Name-First: Chin-Shang 
Author-X-Name-Last: Li 
Title: A test for the linearity of the nonparametric part of a semiparametric logistic regression model 
Abstract:
 A semiparametric logistic regression model is proposed in which its
 nonparametric component is approximated with fixed-knot cubic
 <italic>B</italic>-splines. To assess the linearity of the nonparametric
 component, we construct a penalized likelihood ratio test statistic. When
 the number of knots is fixed, the null distribution of the test statistic
 is shown to be asymptotically the distribution of a linear combination of
 independent chi-squared random variables, each with one degree of freedom.
 We set the asymptotic null expectation of this test statistic equal to a
 value to determine the smoothing parameter value. Monte Carlo experiments
 are conducted to investigate the performance of the proposed test. Its
 practical use is illustrated with a real-life example.
Journal: Journal of Applied Statistics 
Pages: 461-475 
Issue: 3 
Volume: 43 
Year: 2016 
Month: 3 
X-DOI: 10.1080/02664763.2015.1070803 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1070803 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:3:p:461-475




Template-Type: ReDIF-Article 1.0
Author-Name: Hadi Emami 
Author-X-Name-First: Hadi 
Author-X-Name-Last: Emami 
Author-Name: Mostafa Emami 
Author-X-Name-First: Mostafa 
Author-X-Name-Last: Emami 
Title: New influence diagnostics in ridge regression 
Abstract:
 We occasionally find that a small subset of the data exerts a
 disproportionate influence on the fitted regression model. We would like
 to locate these influential points and assess their impact on the model.
 However, the existence of influential data is complicated by the presence
 of collinearity (see, e.g. [15]). In this article we develop a new
 influence statistic for one or a set of observations in linear regression
 dealing with collinearity. We show that this statistic has asymptotically
 normal distribution and is able to detect a subset of high ridge leverage
 outliers. Using this influence statistic we also show that when ridge
 regression is used to mitigate the effects of collinearity, the influence
 of some observations can be drastically modified. As an illustrative
 example, simulation studies and a real data set are analysed.
Journal: Journal of Applied Statistics 
Pages: 476-489 
Issue: 3 
Volume: 43 
Year: 2016 
Month: 3 
X-DOI: 10.1080/02664763.2015.1070804 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1070804 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:3:p:476-489




Template-Type: ReDIF-Article 1.0
Author-Name: John Tyssedal 
Author-X-Name-First: John 
Author-X-Name-Last: Tyssedal 
Author-Name: Shahrukh Hussain 
Author-X-Name-First: Shahrukh 
Author-X-Name-Last: Hussain 
Title: Factor screening in nonregular two-level designs based on projection-based variable&#xa0;selection 
Abstract:
 In this paper, we focus on the problem of factor screening in nonregular
 two-level designs through gradually reducing the number of possible sets
 of active factors. We are particularly concerned with situations when
 three or four factors are active. Our proposed method works through
 examining fits of projection models, where variable selection techniques
 are used to reduce the number of terms. To examine the reliability of the
 methods in combination with such techniques, a panel of models consisting
 of three or four active factors with data generated from the 12-run and
 the 20-run Plackett--Burman (PB) design is used. The dependence of the
 procedure on the amount of noise, the number of active factors and the
 number of experimental factors is also investigated. For designs with few
 runs such as the 12-run PB design, variable selection should be done with
 care and default procedures in computer software may not be reliable to
 which we suggest improvements. A real example is included to show how we
 propose factor screening can be done in practice.
Journal: Journal of Applied Statistics 
Pages: 490-508 
Issue: 3 
Volume: 43 
Year: 2016 
Month: 3 
X-DOI: 10.1080/02664763.2015.1070805 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1070805 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:3:p:490-508




Template-Type: ReDIF-Article 1.0
Author-Name: A.A.M. Nurunnabi 
Author-X-Name-First: A.A.M. 
Author-X-Name-Last: Nurunnabi 
Author-Name: M. Nasser 
Author-X-Name-First: M. 
Author-X-Name-Last: Nasser 
Author-Name: A.H.M.R. Imon 
Author-X-Name-First: A.H.M.R. 
Author-X-Name-Last: Imon 
Title: Identification and classification of multiple outliers, high leverage points and influential observations in linear regression 
Abstract:
 Detection of multiple unusual observations such as outliers, high leverage
 points and influential observations (IOs) in regression is still a
 challenging task for statisticians due to the well-known masking and
 swamping effects. In this paper we introduce a robust influence distance
 that can identify multiple IOs, and propose a sixfold plotting technique
 based on the well-known group deletion approach to classify regular
 observations, outliers, high leverage points and IOs simultaneously in
 linear regression. Experiments through several well-referred data sets and
 simulation studies demonstrate that the proposed algorithm performs
 successfully in the presence of multiple unusual observations and can
 avoid masking and/or swamping effects.
Journal: Journal of Applied Statistics 
Pages: 509-525 
Issue: 3 
Volume: 43 
Year: 2016 
Month: 3 
X-DOI: 10.1080/02664763.2015.1070806 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1070806 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:3:p:509-525




Template-Type: ReDIF-Article 1.0
Author-Name: Xuejun Ma 
Author-X-Name-First: Xuejun 
Author-X-Name-Last: Ma 
Author-Name: Xiaoqun He 
Author-X-Name-First: Xiaoqun 
Author-X-Name-Last: He 
Author-Name: Xiaokang Shi 
Author-X-Name-First: Xiaokang 
Author-X-Name-Last: Shi 
Title: A variant of <italic>K</italic> nearest neighbor quantile regression 
Abstract:
 Compared with local polynomial quantile regression, <italic>K</italic>
 nearest neighbor quantile regression (KNNQR) has many advantages, such as
 not assuming smoothness of functions. The paper summarizes the research of
 KNNQR and has carried out further research on the selection of
 <italic>k</italic>, algorithm and Monte Carlo simulations. Additionally,
 simulated functions are Blocks, Bumps, HeaviSine and Doppler, which stand
 for jumping, volatility, mutagenicity slope and high frequency function.
 When function to be estimated has some jump points or catastrophe points,
 KNNQR is superior to local linear quantile regression in the sense of the
 mean squared error and mean absolute error criteria. To be mentioned, even
 high frequency, the superiority of KNNQR could be observed. A real data is
 analyzed as an illustration.
Journal: Journal of Applied Statistics 
Pages: 526-537 
Issue: 3 
Volume: 43 
Year: 2016 
Month: 3 
X-DOI: 10.1080/02664763.2015.1070807 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1070807 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:3:p:526-537




Template-Type: ReDIF-Article 1.0
Author-Name: S. Karagulle 
Author-X-Name-First: S. 
Author-X-Name-Last: Karagulle 
Author-Name: Z. Kalaylioglu 
Author-X-Name-First: Z. 
Author-X-Name-Last: Kalaylioglu 
Title: A test for detecting etiologic heterogeneity in epidemiological studies 
Abstract:
 Current statistical methods for analyzing epidemiological data with
 disease subtype information allow us to acquire knowledge not only for
 risk factor-disease subtype association but also, on a more profound
 account, heterogeneity in these associations by multiple disease
 characteristics (so-called etiologic heterogeneity of the disease).
 Current interest, particularly in cancer epidemiology, lies in obtaining a
 valid <italic>p</italic>-value for testing the hypothesis whether a
 particular cancer is etiologically heterogeneous. We consider the
 two-stage logistic regression model along with pseudo-conditional
 likelihood estimation method and design a testing strategy based on Rao's
 score test. An extensive Monte Carlo simulation study is carried out,
 false discovery rate and statistical power of the suggested test are
 investigated. Simulation results indicate that applying the proposed
 testing strategy, even a small degree of true etiologic heterogeneity can
 be recovered with a large statistical power from the sampled data. The
 strategy is then applied on a breast cancer data set to illustrate its use
 in practice where there are multiple risk factors and multiple disease
 characteristics of simultaneous concern.
Journal: Journal of Applied Statistics 
Pages: 538-549 
Issue: 3 
Volume: 43 
Year: 2016 
Month: 3 
X-DOI: 10.1080/02664763.2015.1070808 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1070808 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:3:p:538-549




Template-Type: ReDIF-Article 1.0
Author-Name: Shu-Man Shih 
Author-X-Name-First: Shu-Man 
Author-X-Name-Last: Shih 
Author-Name: Wei-Hwa Wu 
Author-X-Name-First: Wei-Hwa 
Author-X-Name-Last: Wu 
Author-Name: Hsin-Neng Hsieh 
Author-X-Name-First: Hsin-Neng 
Author-X-Name-Last: Hsieh 
Title: A non-inferiority test for diagnostic accuracy in the absence of the golden standard test based on the paired partial areas under receiver operating characteristic curves 
Abstract:
 Non-inferiority tests are often measured for the diagnostic accuracy in
 medical research. The area under the receiver operating characteristic
 (ROC)&#xa0;curve is a familiar diagnostic measure for the overall
 diagnostic accuracy. Nevertheless, since it may not differentiate the
 diverse shapes of the ROC curves with different diagnostic significance,
 the partial area under the ROC (PAUROC) curve, another summary measure
 emerges for such diagnostic processes that require the false-positive rate
 to be in the clinically interested range. Traditionally, to estimate the
 PAUROC, the golden standard (GS) test on the true disease status is
 required. Nevertheless, the GS test may sometimes be infeasible. Besides,
 in a lot of research fields such as the epidemiology field, the true
 disease status of the patients may not be known or available. Under the
 normality assumption on diagnostic test results, based on the
 expectation-maximization algorithm in combination with the bootstrap
 method, we propose the heuristic method to construct a non-inferiority
 test for the difference in the paired PAUROCs without the GS test. Through
 the simulation study, although the proposed method might provide a liberal
 test, as a whole, the empirical size of the proposed method sufficiently
 controls the size at the significance level, and the empirical power of
 the proposed method in the absence of the GS is as good as that of the
 non-inferiority in the presence of the GS. The proposed method is
 illustrated with the published data.
Journal: Journal of Applied Statistics 
Pages: 550-562 
Issue: 3 
Volume: 43 
Year: 2016 
Month: 3 
X-DOI: 10.1080/02664763.2015.1070810 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1070810 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:3:p:550-562




Template-Type: ReDIF-Article 1.0
Author-Name: Sayed Mohammad Reza Alavi 
Author-X-Name-First: Sayed Mohammad Reza 
Author-X-Name-Last: Alavi 
Author-Name: Mahboobeh Tajodini 
Author-X-Name-First: Mahboobeh 
Author-X-Name-Last: Tajodini 
Title: Maximum likelihood estimation of sensitive proportion using repeated randomized response techniques 
Abstract:
 Randomized response techniques are designed to obtain usable data on
 sensitive issues while protecting the privacy of individuals. In this
 paper, based on repeating the randomized response technique, a new
 technique called repeated randomized response is introduced to increase
 the protection of privacy and efficiency of estimator for proportion of
 sensitive attribute. By using this technique, the proportion of academic
 cheating is estimated among students of Shahid Chamran University of
 Ahvaz, Ahvaz, Iran.
Journal: Journal of Applied Statistics 
Pages: 563-571 
Issue: 3 
Volume: 43 
Year: 2016 
Month: 3 
X-DOI: 10.1080/02664763.2015.1070811 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1070811 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:3:p:563-571




Template-Type: ReDIF-Article 1.0
Author-Name: Vicente G. Cancho 
Author-X-Name-First: Vicente G. 
Author-X-Name-Last: Cancho 
Author-Name: Dipak K. Dey 
Author-X-Name-First: Dipak K. 
Author-X-Name-Last: Dey 
Author-Name: Francisco Louzada 
Author-X-Name-First: Francisco 
Author-X-Name-Last: Louzada 
Title: Unified multivariate survival model with a surviving fraction: an application to a Brazilian customer churn data 
Abstract:
 In this paper we propose a new lifetime model for multivariate survival
 data in presence of surviving fractions and examine some of its
 properties. Its genesis is based on situations in which there are
 <italic>m</italic> types of unobservable competing causes, where each
 cause is related to a time of occurrence of an event of interest. Our
 model is a multivariate extension of the univariate survival cure rate
 model proposed by Rodrigues <italic>et al.</italic> [37]. The inferential
 approach exploits the maximum likelihood tools. We perform a simulation
 study in order to verify the asymptotic properties of the maximum
 likelihood estimators. The simulation study also focus on size and power
 of the likelihood ratio test. The methodology is illustrated on a real
 data set on customer churn data.
Journal: Journal of Applied Statistics 
Pages: 572-584 
Issue: 3 
Volume: 43 
Year: 2016 
Month: 3 
X-DOI: 10.1080/02664763.2015.1071341 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1071341 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:3:p:572-584




Template-Type: ReDIF-Article 1.0
Author-Name: Cheng Wenren 
Author-X-Name-First: Cheng 
Author-X-Name-Last: Wenren 
Author-Name: Junfeng Shang 
Author-X-Name-First: Junfeng 
Author-X-Name-Last: Shang 
Title: Conditional conceptual predictive statistic for mixed model selection 
Abstract:
 In linear mixed models, making use of the prediction of the random
 effects, we propose the conditional Conceptual Predictive Statistic
 <inline-formula><inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1071342_ilm0001.gif"/></inline-formula> for mixed model
 selection based on a conditional Gauss discrepancy. We define the
 conditional Gauss discrepancy for measuring the distance between the true
 model and the candidate model under the conditional mean of response
 variables. When the variance components are known, the conditional
 <inline-formula><inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1071342_ilm0002.gif"/></inline-formula> serves as an
 unbiased estimator for the expected transformed conditional Gauss
 discrepancy; when the variance components are unknown, the conditional
 <inline-formula><inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1071342_ilm0003.gif"/></inline-formula> serves as an
 asymptotically unbiased estimator for the expected transformed conditional
 Gauss discrepancy. The best linear unbiased predictor (BLUP) is employed
 for the estimation of the random effects. The simulation results
 demonstrate that when the true model includes significant fixed effects,
 the conditional criteria perform effectively in selecting the most
 appropriate model. The penalty term in the <inline-formula><inline-graphic
 xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1071342_ilm0004.gif"/></inline-formula> computed by the
 estimated effective degrees of freedom yields a very good approximation to
 the penalty term between the target discrepancy and the goodness-of-fit
 term.
Journal: Journal of Applied Statistics 
Pages: 585-603 
Issue: 4 
Volume: 43 
Year: 2016 
Month: 3 
X-DOI: 10.1080/02664763.2015.1071342 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1071342 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:4:p:585-603




Template-Type: ReDIF-Article 1.0
Author-Name: Edoardo Otranto 
Author-X-Name-First: Edoardo 
Author-X-Name-Last: Otranto 
Author-Name: Massimo Mucciardi 
Author-X-Name-First: Massimo 
Author-X-Name-Last: Mucciardi 
Author-Name: Pietro Bertuccelli 
Author-X-Name-First: Pietro 
Author-X-Name-Last: Bertuccelli 
Title: Spatial effects in dynamic conditional correlations 
Abstract:
 The recent literature on time series has developed a lot of models for the
 analysis of the dynamic conditional correlation, involving the same
 variable observed in different locations; very often, in this framework,
 the consideration of the spatial interactions is omitted. We propose to
 extend a time-varying conditional correlation model (following an
 autoregressive moving average dynamics) to include the spatial effects,
 with a specification depending on the local spatial interactions. The
 spatial part is based on a fixed symmetric weight matrix, called Gaussian
 kernel matrix, but its effect will vary along the time depending on the
 degree of time correlation in a certain period. We show the theoretical
 aspects, with the support of simulation experiments, and apply this
 methodology to two space--time data sets, in a demographic and a financial
 framework, respectively.
Journal: Journal of Applied Statistics 
Pages: 604-626 
Issue: 4 
Volume: 43 
Year: 2016 
Month: 3 
X-DOI: 10.1080/02664763.2015.1071343 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1071343 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:4:p:604-626




Template-Type: ReDIF-Article 1.0
Author-Name: V&#xed;ctor Leiva 
Author-X-Name-First: V&#xed;ctor 
Author-X-Name-Last: Leiva 
Author-Name: Shuangzhe Liu 
Author-X-Name-First: Shuangzhe 
Author-X-Name-Last: Liu 
Author-Name: Lei Shi 
Author-X-Name-First: Lei 
Author-X-Name-Last: Shi 
Author-Name: Francisco Jos&#xe9; A. Cysneiros 
Author-X-Name-First: Francisco Jos&#xe9; A. 
Author-X-Name-Last: Cysneiros 
Title: Diagnostics in elliptical regression models with stochastic restrictions applied to econometrics 
Abstract:
 We propose an influence diagnostic methodology for linear regression
 models with stochastic restrictions and errors following elliptically
 contoured distributions. We study how a perturbation may impact on the
 mixed estimation procedure of parameters in the model. Normal curvatures
 and slopes for assessing influence under usual schemes are derived,
 including perturbations of case-weight, response variable, and explanatory
 variable. Simulations are conducted to evaluate the performance of the
 proposed methodology. An example with real-world economy data is presented
 as an illustration.
Journal: Journal of Applied Statistics 
Pages: 627-642 
Issue: 4 
Volume: 43 
Year: 2016 
Month: 3 
X-DOI: 10.1080/02664763.2015.1072140 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1072140 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:4:p:627-642




Template-Type: ReDIF-Article 1.0
Author-Name: Junying Zhang 
Author-X-Name-First: Junying 
Author-X-Name-Last: Zhang 
Author-Name: Riquan Zhang 
Author-X-Name-First: Riquan 
Author-X-Name-Last: Zhang 
Author-Name: Zhiping Lu 
Author-X-Name-First: Zhiping 
Author-X-Name-Last: Lu 
Title: Quantile-adaptive variable screening in ultra-high dimensional varying coefficient models 
Abstract:
 The varying-coefficient model is an important nonparametric statistical
 model since it allows appreciable flexibility on the structure of fitted
 model. For ultra-high dimensional heterogeneous data it is very necessary
 to examine how the effects of covariates vary with exposure variables at
 different quantile level of interest. In this paper, we extended the
 marginal screening methods to examine and select variables by ranking a
 measure of nonparametric marginal contributions of each covariate given
 the exposure variable. Spline approximations are employed to model
 marginal effects and select the set of active variables in
 quantile-adaptive framework. This ensures the sure screening property in
 quantile-adaptive varying-coefficient model. Numerical studies demonstrate
 that the proposed procedure works well for heteroscedastic data.
Journal: Journal of Applied Statistics 
Pages: 643-654 
Issue: 4 
Volume: 43 
Year: 2016 
Month: 3 
X-DOI: 10.1080/02664763.2015.1072141 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1072141 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:4:p:643-654




Template-Type: ReDIF-Article 1.0
Author-Name: Boryana Bogdanova 
Author-X-Name-First: Boryana 
Author-X-Name-Last: Bogdanova 
Author-Name: Ivan Ivanov 
Author-X-Name-First: Ivan 
Author-X-Name-Last: Ivanov 
Title: A wavelet-based approach to the analysis and modelling of financial time series exhibiting strong long-range dependence: the case of Southeast Europe 
Abstract:
 This paper demonstrates the utilization of wavelet-based tools for the
 analysis and prediction of financial time series exhibiting strong
 long-range dependence (LRD). Commonly emerging markets' stock returns are
 characterized by LRD. Therefore, we track the LRD evolvement for the
 return series of six Southeast European stock indices through the
 application of a wavelet-based semi-parametric method. We further engage
 the &#xe1; trous wavelet transform in order to extract deeper knowledge on
 the returns term structure and utilize it for prediction purposes. In
 particular, a multiscale autoregressive (MAR) model is fitted and its
 out-of-sample forecast performance is benchmarked to that of ARMA.
 Additionally, a data-driven MAR feature selection procedure is outlined.
 We find that the wavelet-based method captures adequately LRD dynamics
 both in calm as well as in turmoil periods detecting the presence of
 transitional changes. At the same time, the MAR model handles with the
 complicated autocorrelation structure implied by the LRD in a parsimonious
 way achieving better performance.
Journal: Journal of Applied Statistics 
Pages: 655-673 
Issue: 4 
Volume: 43 
Year: 2016 
Month: 3 
X-DOI: 10.1080/02664763.2015.1077370 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1077370 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:4:p:655-673




Template-Type: ReDIF-Article 1.0
Author-Name: Jan Schepers 
Author-X-Name-First: Jan 
Author-X-Name-Last: Schepers 
Title: On regression modelling with dummy variables versus separate regressions per group: Comment on Holgersson <italic>et&#xa0;al.</italic> 
Abstract:
 In a recent issue of this journal, Holgersson <italic>et&#xa0;al.</italic>
 [Dummy variables vs. category-wise models, J. Appl. Stat. 41(2) (2014),
 pp. 233--241, doi:10.1080/02664763.2013.838665] compared the use of dummy
 coding in regression analysis to the use of category-wise models (i.e.
 estimating separate regression models for each group) with respect to
 estimating and testing group differences in intercept and in slope. They
 presented three objections against the use of dummy variables in a single
 regression equation, which could be overcome by the category-wise
 approach. In this note, I first comment on each of these three objections
 and next draw attention to some other issues in comparing these two
 approaches. This commentary further clarifies the differences and
 similarities between dummy variable and category-wise approaches.
Journal: Journal of Applied Statistics 
Pages: 674-681 
Issue: 4 
Volume: 43 
Year: 2016 
Month: 3 
X-DOI: 10.1080/02664763.2015.1077371 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1077371 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:4:p:674-681




Template-Type: ReDIF-Article 1.0
Author-Name: S. Zinn 
Author-X-Name-First: S. 
Author-X-Name-Last: Zinn 
Author-Name: A. W&#xfc;rbach 
Author-X-Name-First: A. 
Author-X-Name-Last: W&#xfc;rbach 
Title: A statistical approach to address the problem of heaping in self-reported income data 
Abstract:
 Self-reported income information particularly suffers from an intentional
 coarsening of the data, which is called heaping or rounding. If it does
 not occur completely at random -- which is usually the case -- heaping and
 rounding have detrimental effects on the results of statistical analysis.
 Conventional statistical methods do not consider this kind of reporting
 bias, and thus might produce invalid inference. We describe a novel
 statistical modeling approach that allows us to deal with self-reported
 heaped income data in an adequate and flexible way. We suggest modeling
 heaping mechanisms and the true underlying model in combination. To
 describe the true net income distribution, we use the zero-inflated
 log-normal distribution. Heaping points are identified from the data by
 applying a heuristic procedure comparing a hypothetical income
 distribution and the empirical one. To determine heaping behavior, we
 employ two distinct models: either we assume piecewise constant heaping
 probabilities, or heaping probabilities are considered to increase
 steadily with proximity to a heaping point. We validate our approach by
 some examples. To illustrate the capacity of the proposed method, we
 conduct a case study using income data from the German National
 Educational Panel Study.
Journal: Journal of Applied Statistics 
Pages: 682-703 
Issue: 4 
Volume: 43 
Year: 2016 
Month: 3 
X-DOI: 10.1080/02664763.2015.1077372 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1077372 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:4:p:682-703




Template-Type: ReDIF-Article 1.0
Author-Name: Yue Zhang 
Author-X-Name-First: Yue 
Author-X-Name-Last: Zhang 
Author-Name: Kiros Berhane 
Author-X-Name-First: Kiros 
Author-X-Name-Last: Berhane 
Title: Dynamic latent trait models with mixed hidden Markov structure for mixed longitudinal outcomes 
Abstract:
 We propose a general Bayesian joint modeling approach to model mixed
 longitudinal outcomes from the exponential family for taking into account
 any differential misclassification that may exist among categorical
 outcomes. Under this framework, outcomes observed without measurement
 error are related to latent trait variables through generalized linear
 mixed effect models. The misclassified outcomes are related to the latent
 class variables, which represent unobserved real states, using mixed
 hidden Markov models (MHMMs). In addition to enabling the estimation of
 parameters in prevalence, transition and misclassification probabilities,
 MHMMs capture cluster level heterogeneity. A transition modeling structure
 allows the latent trait and latent class variables to depend on observed
 predictors at the same time period and also on latent trait and latent
 class variables at previous time periods for each individual. Simulation
 studies are conducted to make comparisons with traditional models in order
 to illustrate the gains from the proposed approach. The new approach is
 applied to data from the Southern California Children Health Study to
 jointly model questionnaire-based asthma state and multiple lung function
 measurements in order to gain better insight about the underlying
 biological mechanism that governs the inter-relationship between asthma
 state and lung function development.
Journal: Journal of Applied Statistics 
Pages: 704-720 
Issue: 4 
Volume: 43 
Year: 2016 
Month: 3 
X-DOI: 10.1080/02664763.2015.1077373 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1077373 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:4:p:704-720




Template-Type: ReDIF-Article 1.0
Author-Name: J. Andr&#xe9;s Christen 
Author-X-Name-First: J. Andr&#xe9;s 
Author-X-Name-Last: Christen 
Author-Name: Bruno Sans&#xf3; 
Author-X-Name-First: Bruno 
Author-X-Name-Last: Sans&#xf3; 
Author-Name: Mario Santana-Cibrian 
Author-X-Name-First: Mario 
Author-X-Name-Last: Santana-Cibrian 
Author-Name: Jorge X. Velasco-Hern&#xe1;ndez 
Author-X-Name-First: Jorge X. 
Author-X-Name-Last: Velasco-Hern&#xe1;ndez 
Title: Bayesian deconvolution of oil well test data using Gaussian processes 
Abstract:
 We use Bayesian methods to infer an unobserved function that is convolved
 with a known kernel. Our method is based on the assumption that the
 function of interest is a Gaussian process and, assuming a particular
 correlation structure, the resulting convolution is also a Gaussian
 process. This fact is used to obtain inferences regarding the unobserved
 process, effectively providing a deconvolution method. We apply the
 methodology to the problem of estimating the parameters of an oil
 reservoir from well-test pressure data. Here, the unknown process
 describes the structure of the well. Applications to data from Mexican oil
 wells show very accurate results.
Journal: Journal of Applied Statistics 
Pages: 721-737 
Issue: 4 
Volume: 43 
Year: 2016 
Month: 3 
X-DOI: 10.1080/02664763.2015.1077374 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1077374 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:4:p:721-737




Template-Type: ReDIF-Article 1.0
Author-Name: &#xdc;mit Kuvvetli 
Author-X-Name-First: &#xdc;mit 
Author-X-Name-Last: Kuvvetli 
Author-Name: Ali R&#x131;za Firuzan 
Author-X-Name-First: Ali R&#x131;za 
Author-X-Name-Last: Firuzan 
Author-Name: S&#xfc;leyman Alpaykut 
Author-X-Name-First: S&#xfc;leyman 
Author-X-Name-Last: Alpaykut 
Author-Name: Atakan Gerger 
Author-X-Name-First: Atakan 
Author-X-Name-Last: Gerger 
Title: Determining Six Sigma success factors in Turkey by using structural equation modeling 
Abstract:
 Since it includes strong statistical and executive techniques, Six Sigma
 (SS) succeeded in many countries and different sectors. Especially
 successful SS applications of many international companies have increased
 the interest of other companies. As a result of this, the number of
 implemented SS projects in various countries has increased. Although
 successful SS projects are often in mind, the number of failed projects
 because of various reasons is not as low as to be ignored. As well as
 there are many factors that affect the success level of SS projects, and
 these factors vary according to countries. In this study, a survey was
 applied to 117 people who have 1 of SS belts in order to determine success
 levels of the SS projects in Turkey. By using explanatory factor analysis
 and structural equation modeling, critical success factors were
 determined. According to the results, project selection and its scope,
 quality culture and defining and measuring of metrics were determined as
 the top factors that are affecting success levels of SS projects applied
 in Turkey. The results of the study were also compared with the results of
 similar projects implemented in other countries.
Journal: Journal of Applied Statistics 
Pages: 738-753 
Issue: 4 
Volume: 43 
Year: 2016 
Month: 3 
X-DOI: 10.1080/02664763.2015.1077375 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1077375 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:4:p:738-753




Template-Type: ReDIF-Article 1.0
Author-Name: Andrea Beccarini 
Author-X-Name-First: Andrea 
Author-X-Name-Last: Beccarini 
Title: Bias correction through filtering omitted variables and instruments 
Abstract:
 This paper proposes a combination of the particle-filter-based method and
 the expectation-maximization algorithm (PFEM), in order to filter
 unobservable variables and hence, to reduce the omitted variables bias.
 Furthermore, I consider as an unobservable variable, an exogenous one that
 can be used as an instrument in the instrumental variable (IV)
 methodology. The aim is to show that the PFEM is able to eliminate or
 reduce both the omitted variable bias and the simultaneous equation bias
 by filtering the omitted variable and the unobserved instrument,
 respectively. In other words, the procedure provides (at least
 approximately) consistent estimates, without using additional information
 embedded in the omitted variable or in the instruments, since they are
 filtered by the observable variables. The validity of the procedure is
 shown both through simulations and through a comparison to an IV analysis
 which appeared in an important previous publication. As regards the latter
 point, I demonstrate that the procedure developed in this article yields
 similar results to those of the original IV analysis.
Journal: Journal of Applied Statistics 
Pages: 754-766 
Issue: 4 
Volume: 43 
Year: 2016 
Month: 3 
X-DOI: 10.1080/02664763.2015.1077376 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1077376 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:4:p:754-766




Template-Type: ReDIF-Article 1.0
Author-Name: Xiaofei Ma 
Author-X-Name-First: Xiaofei 
Author-X-Name-Last: Ma 
Author-Name: Qiuyan Zhong 
Author-X-Name-First: Qiuyan 
Author-X-Name-Last: Zhong 
Title: Missing value imputation method for disaster decision-making using K nearest neighbor 
Abstract:
 Due to destructiveness of natural disasters, restriction of disaster
 scenarios and some human causes, missing data usually occur in disaster
 decision-making problems. In order to estimate missing values of
 alternatives, this paper focuses on imputing heterogeneous attribute
 values of disaster based on an improved K nearest neighbor imputation
 (KNNI) method. Firstly, some definitions of trapezoidal fuzzy numbers
 (TFNs) are introduced and three types of attributes (i.e. linguistic term
 sets, intervals and real numbers) are converted to TFNs. Then the
 correlated degree model is utilized to extract related attributes to form
 instances that will be used in K nearest neighbor algorithm, and a novel
 KNNI method merging with correlated degree model is presented. Finally, an
 illustrative example is given to verify the proposed method and to
 demonstrate its feasibility and effectiveness.
Journal: Journal of Applied Statistics 
Pages: 767-781 
Issue: 4 
Volume: 43 
Year: 2016 
Month: 3 
X-DOI: 10.1080/02664763.2015.1077377 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1077377 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:4:p:767-781




Template-Type: ReDIF-Article 1.0
Author-Name: Hsiu-Wen Chen 
Author-X-Name-First: Hsiu-Wen 
Author-X-Name-Last: Chen 
Author-Name: Weng Kee Wong 
Author-X-Name-First: Weng Kee 
Author-X-Name-Last: Wong 
Author-Name: Hongquan Xu 
Author-X-Name-First: Hongquan 
Author-X-Name-Last: Xu 
Title: Data-driven desirability function to measure patients&#x2019; disease progression in a longitudinal study 
Abstract:
 Multiple outcomes are increasingly used to assess chronic disease
 progression. We discuss and show how desirability functions can be used to
 assess a patient overall response to a treatment using multiple outcome
 measures and each of them may contribute unequally to the final
 assessment. Because judgments on disease progression and the relative
 contribution of each outcome can be subjective, we propose a data-driven
 approach to minimize the biases by using desirability functions with
 estimated shapes and weights based on a given gold standard. Our method
 provides each patient with a meaningful overall progression score that
 facilitates comparison and clinical interpretation. We also extend the
 methodology in a novel way to monitor patients&#x2019; disease progression
 when there are multiple time points and illustrate our method using a
 longitudinal data set from a randomized two-arm clinical trial for
 scleroderma patients.
Journal: Journal of Applied Statistics 
Pages: 783-795 
Issue: 5 
Volume: 43 
Year: 2016 
Month: 4 
X-DOI: 10.1080/02664763.2015.1077378 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1077378 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:5:p:783-795




Template-Type: ReDIF-Article 1.0
Author-Name: Xiuli Wang 
Author-X-Name-First: Xiuli 
Author-X-Name-Last: Wang 
Author-Name: Mingqiu Wang 
Author-X-Name-First: Mingqiu 
Author-X-Name-Last: Wang 
Title: Variable selection for high-dimensional generalized linear models with the weighted elastic-net procedure 
Abstract:
 High-dimensional data arise frequently in modern applications such as
 biology, chemometrics, economics, neuroscience and other scientific
 fields. The common features of high-dimensional data are that many of
 predictors may not be significant, and there exists high correlation among
 predictors. Generalized linear models, as the generalization of linear
 models, also suffer from the collinearity problem. In this paper,
 combining the nonconvex penalty and ridge regression, we propose the
 weighted elastic-net to deal with the variable selection of generalized
 linear models on high dimension and give the theoretical properties of the
 proposed method with a diverging number of parameters. The finite sample
 behavior of the proposed method is illustrated with simulation studies and
 a real data example.
Journal: Journal of Applied Statistics 
Pages: 796-809 
Issue: 5 
Volume: 43 
Year: 2016 
Month: 4 
X-DOI: 10.1080/02664763.2015.1078300 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1078300 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:5:p:796-809




Template-Type: ReDIF-Article 1.0
Author-Name: M.L. Nores 
Author-X-Name-First: M.L. 
Author-X-Name-Last: Nores 
Author-Name: M.P. D&#xed;az 
Author-X-Name-First: M.P. 
Author-X-Name-Last: D&#xed;az 
Title: Bootstrap hypothesis testing in generalized additive models for comparing curves of treatments in longitudinal studies 
Abstract:
 The study of the effect of a treatment may involve the evaluation of a
 variable at a number of moments. When assuming a smooth curve for the mean
 response along time, estimation can be afforded by spline regression, in
 the context of generalized additive models. The novelty of our work lies
 in the construction of hypothesis tests to compare two curves of
 treatments in any interval of time for several types of response
 variables. The within-subject correlation is not modeled but is considered
 to obtain valid inferences by the use of bootstrap. We propose both
 semiparametric and nonparametric bootstrap approaches, based on resampling
 vectors of residuals or responses, respectively. Simulation studies
 revealed a good performance of the tests, considering, for the outcome,
 different distribution functions in the exponential family and varying the
 correlation between observations along time. We show that the sizes of
 bootstrap tests are close to the nominal value, with tests based on a
 standardized statistic having slightly better size properties. The power
 increases as the distance between curves increases and decreases when
 correlation gets higher. The usefulness of these statistical tools was
 confirmed using real data, thus allowing to detect changes in fish
 behavior when exposed to the toxin microcystin-RR.
Journal: Journal of Applied Statistics 
Pages: 810-826 
Issue: 5 
Volume: 43 
Year: 2016 
Month: 4 
X-DOI: 10.1080/02664763.2015.1078301 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1078301 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:5:p:810-826




Template-Type: ReDIF-Article 1.0
Author-Name: Meng-Ning Lyu 
Author-X-Name-First: Meng-Ning 
Author-X-Name-Last: Lyu 
Author-Name: Qing-Shan Yang 
Author-X-Name-First: Qing-Shan 
Author-X-Name-Last: Yang 
Author-Name: Na Yang 
Author-X-Name-First: Na 
Author-X-Name-Last: Yang 
Author-Name: Siu-Seong Law 
Author-X-Name-First: Siu-Seong 
Author-X-Name-Last: Law 
Title: Tourist number prediction of historic buildings by singular spectrum analysis 
Abstract:
 A wooden historic building located in Tibet, China, experienced structural
 damage when subjected to tourists visit. This kind of ancient building
 attends to too many visitors every day because heritage sites never fail
 to attract tourists. There should be a balance between accepting the
 visitors and the protection of historic buildings considering the
 importance of the cultural relics. In this paper, the singular spectrum
 analysis (SSA) is used for forecasting the number of tourist for the
 building management to exercise maintenance measures to the structure. The
 analyzed results can be used to control the tourist flow to avoid
 excessive pedestrian loading on the structure. The relationship between
 the measured acceleration from the structure and the tourist number is
 firstly studied. The root-mean-square (RMS) value of the measured
 acceleration in the passage route of the tourist is selected for
 forecasting future tourist number. The forecasting results from different
 methods are compared. The SSA is found slightly outperforms the
 autoregressive integrated moving average model (ARIMA), the X-11-ARIMA
 model and the cubic spline extrapolation in terms of the RMS error, mean
 absolute error and mean absolute percentage error for long-term
 prediction, whereas the opposite is observed for short-term forecasting.
Journal: Journal of Applied Statistics 
Pages: 827-846 
Issue: 5 
Volume: 43 
Year: 2016 
Month: 4 
X-DOI: 10.1080/02664763.2015.1078302 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1078302 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:5:p:827-846




Template-Type: ReDIF-Article 1.0
Author-Name: Leonardo Costa 
Author-X-Name-First: Leonardo 
Author-X-Name-Last: Costa 
Author-Name: Adrian Pizzinga 
Author-X-Name-First: Adrian 
Author-X-Name-Last: Pizzinga 
Author-Name: Rodrigo Atherino 
Author-X-Name-First: Rodrigo 
Author-X-Name-Last: Atherino 
Title: Modeling and predicting IBNR reserve: extended chain ladder and heteroscedastic regression analysis 
Abstract:
 This work deals with two methodologies for predicting <italic>incurred but
 not reported</italic> (IBNR) actuarial reserves. The first is the
 traditional chain ladder, which is extended for dealing with the calendar
 year IBNR reserve. The second is based on heteroscedastic regression
 models suitable to deal with the tail effect of the runoff triangle -- and
 to forecast calendar year IBNR reserves as well. Theoretical results
 regarding closed expressions for IBNR predictors and mean squared errors
 are established -- for the case of the second methodology, a Monte Carlo
 study is designed and implemented for accessing finite sample performances
 of feasible mean squared error formulae. Finally, the methods are
 implemented with two real data sets. The main conclusions: (i) considering
 tail effects does not imply theoretical and/or computational problems; and
 (ii) both methodologies are interesting to design softwares for IBNR
 reserve prediction.
Journal: Journal of Applied Statistics 
Pages: 847-870 
Issue: 5 
Volume: 43 
Year: 2016 
Month: 4 
X-DOI: 10.1080/02664763.2015.1079305 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1079305 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:5:p:847-870




Template-Type: ReDIF-Article 1.0
Author-Name: Stavros Degiannakis 
Author-X-Name-First: Stavros 
Author-X-Name-Last: Degiannakis 
Author-Name: Alexandra Livada 
Author-X-Name-First: Alexandra 
Author-X-Name-Last: Livada 
Title: Evaluation of realized volatility predictions from models with leptokurtically and asymmetrically distributed forecast errors 
Abstract:
 Accurate volatility forecasting is a key determinant for portfolio
 management, risk management and economic policy. The paper provides
 evidence that the sum of squared standardized forecast errors is a
 reliable measure for model evaluation when the predicted variable is the
 intra-day realized volatility. The forecasting evaluation is valid for
 standardized forecast errors with leptokurtic distribution as well as with
 leptokurtic and asymmetric distributions. Additionally, the widely applied
 forecasting evaluation function, the predicted mean-squared error, fails
 to select the adequate model in the case of models with residuals that are
 leptokurtically and asymmetrically distributed. Hence, the realized
 volatility forecasting evaluation should be based on the standardized
 forecast errors instead of their unstandardized version.
Journal: Journal of Applied Statistics 
Pages: 871-892 
Issue: 5 
Volume: 43 
Year: 2016 
Month: 4 
X-DOI: 10.1080/02664763.2015.1079306 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1079306 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:5:p:871-892




Template-Type: ReDIF-Article 1.0
Author-Name: Rosa Aghdam 
Author-X-Name-First: Rosa 
Author-X-Name-Last: Aghdam 
Author-Name: Mojtaba Ganjali 
Author-X-Name-First: Mojtaba 
Author-X-Name-Last: Ganjali 
Author-Name: Parisa Niloofar 
Author-X-Name-First: Parisa 
Author-X-Name-Last: Niloofar 
Author-Name: Changiz Eslahchi 
Author-X-Name-First: Changiz 
Author-X-Name-Last: Eslahchi 
Title: Inferring gene regulatory networks by an order independent algorithm using incomplete data sets 
Abstract:
 Analyzing incomplete data for inferring the structure of gene regulatory
 networks (GRNs) is a challenging task in bioinformatic. Bayesian network
 can be successfully used in this field. <italic>k</italic>-nearest
 neighbor, singular value decomposition (SVD)-based and multiple imputation
 by chained equations are three fundamental imputation methods to deal with
 missing values. Path consistency (PC) algorithm based on conditional
 mutual information (PCA--CMI) is a famous algorithm for inferring GRNs.
 This algorithm needs the data set to be complete. However, the problem is
 that PCA--CMI is not a stable algorithm and when applied on permuted gene
 orders, different networks are obtained. We propose an order independent
 algorithm, PCA--CMI--OI, for inferring GRNs. After imputation of missing
 data, the performances of PCA--CMI and PCA--CMI--OI are compared. Results
 show that networks constructed from data imputed by the SVD-based method
 and PCA--CMI--OI algorithm outperform other imputation methods and
 PCA--CMI. An undirected or partially directed network is resulted by
 PC-based algorithms. Mutual information test (MIT) score, which can deal
 with discrete data, is one of the famous methods for directing the edges
 of resulted networks. We also propose a new score, ConMIT, which is
 appropriate for analyzing continuous data. Results shows that the
 precision of directing the edges of skeleton is improved by applying the
 ConMIT score.
Journal: Journal of Applied Statistics 
Pages: 893-913 
Issue: 5 
Volume: 43 
Year: 2016 
Month: 4 
X-DOI: 10.1080/02664763.2015.1079307 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1079307 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:5:p:893-913




Template-Type: ReDIF-Article 1.0
Author-Name: Ismail Onur Baycan 
Author-X-Name-First: Ismail Onur 
Author-X-Name-Last: Baycan 
Title: The effects of exchange rate regimes on economic growth: evidence from propensity score matching estimates 
Abstract:
 This is the first study that employs the propensity score matching
 framework to examine the average treatment effect of exchange rate regimes
 on economic growth. Previous studies examining the effects of different
 exchange regimes on growth often apply time series or panel data
 techniques and provide mixed results. This study employs a variety of
 non-parametric matching methods to address the self-selection problem,
 which potentially causes a bias in the traditional linear regressions. We
 evaluate the average treatment effect of the floating exchange rate regime
 on economic growth in 164 countries. Time period of the quasi experiment
 starts in 1970, capturing the collapse of the Bretton Woods fixed exchange
 rate commitment system. Results show that the average treatment effect of
 floating exchange rate regimes on economic growth is statistically
 insignificant. Verifying the results with the Rosenbaum's bounds, our
 findings are strong and robust. The research states that there is no
 evidence that employing a floating exchange rate regime compared to a
 fixed one leads to a higher economic growth for the countries that use
 this particular policy.
Journal: Journal of Applied Statistics 
Pages: 914-924 
Issue: 5 
Volume: 43 
Year: 2016 
Month: 4 
X-DOI: 10.1080/02664763.2015.1080669 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1080669 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:5:p:914-924




Template-Type: ReDIF-Article 1.0
Author-Name: &#xd6;zg&#xfc;r Asar 
Author-X-Name-First: &#xd6;zg&#xfc;r 
Author-X-Name-Last: Asar 
Author-Name: Ozlem Ilk 
Author-X-Name-First: Ozlem 
Author-X-Name-Last: Ilk 
Title: First-order marginalised transition random effects models with probit link function 
Abstract:
 Marginalised models, also known as marginally specified models, have
 recently become a popular tool for analysis of discrete longitudinal data.
 Despite being a novel statistical methodology, these models introduce
 complex constraint equations and model fitting algorithms. On the other
 hand, there is a lack of publicly available software to fit these models.
 In this paper, we propose a three-level marginalised model for analysis of
 multivariate longitudinal binary outcome. The implicit function theorem is
 introduced to approximately solve the marginal constraint equations
 explicitly. <italic>probit</italic> link enables direct solutions to the
 convolution equations. Parameters are estimated by maximum likelihood via
 a Fisher--Scoring algorithm. A simulation study is conducted to examine
 the finite-sample properties of the estimator. We illustrate the model
 with an application to the data set from the Iowa Youth and Families
 Project. The R package <monospace>pnmtrem</monospace> is prepared to fit
 the model.
Journal: Journal of Applied Statistics 
Pages: 925-942 
Issue: 5 
Volume: 43 
Year: 2016 
Month: 4 
X-DOI: 10.1080/02664763.2015.1080670 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1080670 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:5:p:925-942




Template-Type: ReDIF-Article 1.0
Author-Name: Diwei Zhou 
Author-X-Name-First: Diwei 
Author-X-Name-Last: Zhou 
Author-Name: Ian L. Dryden 
Author-X-Name-First: Ian L. 
Author-X-Name-Last: Dryden 
Author-Name: Alexey A. Koloydenko 
Author-X-Name-First: Alexey A. 
Author-X-Name-Last: Koloydenko 
Author-Name: Koenraad M.R. Audenaert 
Author-X-Name-First: Koenraad M.R. 
Author-X-Name-Last: Audenaert 
Author-Name: Li Bai 
Author-X-Name-First: Li 
Author-X-Name-Last: Bai 
Title: Regularisation, interpolation and visualisation of diffusion tensor images using non-Euclidean statistics 
Abstract:
 Practical statistical analysis of diffusion tensor images is considered,
 and we focus primarily on methods that use metrics based on Euclidean
 distances between powers of diffusion tensors. First, we describe a family
 of anisotropy measures based on a scale invariant power-Euclidean metric,
 which are useful for visualisation. Some properties of the measures are
 derived and practical considerations are discussed, with some examples.
 Second, we discuss weighted Procrustes methods for diffusion tensor
 imaging interpolation and smoothing, and we compare methods based on
 different metrics on a set of examples as well as analytically. We
 establish a key relationship between the principal-square-root-Euclidean
 metric and the size-and-shape Procrustes metric on the space of symmetric
 positive semi-definite tensors. We explain, both analytically and by
 experiments, why the size-and-shape Procrustes metric may be preferred in
 practical tasks of interpolation, extrapolation and smoothing, especially
 when observed tensors are degenerate or when a moderate degree of tensor
 swelling is desirable. Third, we introduce regularisation methodology,
 which is demonstrated to be useful for highlighting features of prior
 interest and potentially for segmentation. Finally, we compare several
 metrics in a data set of human brain diffusion-weighted magnetic resonance
 imaging, and point out similarities between several of the non-Euclidean
 metrics but important differences with the commonly used Euclidean metric.
Journal: Journal of Applied Statistics 
Pages: 943-978 
Issue: 5 
Volume: 43 
Year: 2016 
Month: 4 
X-DOI: 10.1080/02664763.2015.1080671 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1080671 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:5:p:943-978




Template-Type: ReDIF-Article 1.0
Author-Name: T. Chen 
Author-X-Name-First: T. 
Author-X-Name-Last: Chen 
Author-Name: K. Knox 
Author-X-Name-First: K. 
Author-X-Name-Last: Knox 
Author-Name: J. Arora 
Author-X-Name-First: J. 
Author-X-Name-Last: Arora 
Author-Name: W. Tang 
Author-X-Name-First: W. 
Author-X-Name-Last: Tang 
Author-Name: J. Kowalski 
Author-X-Name-First: J. 
Author-X-Name-Last: Kowalski 
Author-Name: X.M. Tu 
Author-X-Name-First: X.M. 
Author-X-Name-Last: Tu 
Title: Power analysis for clustered non-continuous responses in multicenter trials 
Abstract:
 Power analysis for multi-center randomized control trials is quite
 difficult to perform for non-continuous responses when site differences
 are modeled by random effects using the generalized linear mixed-effects
 model (GLMM). First, it is not possible to construct power functions
 analytically, because of the extreme complexity of the sampling
 distribution of parameter estimates. Second, Monte Carlo (MC) simulation,
 a popular option for estimating power for complex models, does not work
 within the current context because of a lack of methods and software
 packages that would provide reliable estimates for fitting such GLMMs. For
 example, even statistical packages from software giants like SAS do not
 provide reliable estimates at the time of writing. Another major
 limitation of MC simulation is the lengthy running time, especially for
 complex models such as GLMM, especially when estimating power for multiple
 scenarios of interest. We present a new approach to address such
 limitations. The proposed approach defines a marginal model to approximate
 the GLMM and estimates power without relying on MC simulation. The
 approach is illustrated with both real and simulated data, with the
 simulation study demonstrating good performance of the method.
Journal: Journal of Applied Statistics 
Pages: 979-995 
Issue: 6 
Volume: 43 
Year: 2016 
Month: 5 
X-DOI: 10.1080/02664763.2015.1089218 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1089218 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:6:p:979-995




Template-Type: ReDIF-Article 1.0
Author-Name: Pao-sheng Shen 
Author-X-Name-First: Pao-sheng 
Author-X-Name-Last: Shen 
Title: Estimation of association parameters in copula models for bivariate left-truncated and right-censored data 
Abstract:
 We investigate the problem of estimating the association between two
 related survival variables when they follow a copula model and bivariate
 left-truncated and right-censored data are available. By expressing
 truncation probability as the functional of marginal survival functions,
 we propose a two-stage estimation procedure for estimating the parameters
 of Archimedean copulas. The asymptotic properties of the proposed
 estimators are established. Simulation studies are conducted to
 investigate the finite sample properties of the proposed estimators. The
 proposed method is applied to a bivariate RNA data.
Journal: Journal of Applied Statistics 
Pages: 996-1010 
Issue: 6 
Volume: 43 
Year: 2016 
Month: 5 
X-DOI: 10.1080/02664763.2015.1089219 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1089219 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:6:p:996-1010




Template-Type: ReDIF-Article 1.0
Author-Name: Rijan Shrestha 
Author-X-Name-First: Rijan 
Author-X-Name-Last: Shrestha 
Author-Name: Tomasz Kozlowski 
Author-X-Name-First: Tomasz 
Author-X-Name-Last: Kozlowski 
Title: Inverse uncertainty quantification of input model parameters for thermal-hydraulics simulations using expectation--maximization under Bayesian framework 
Abstract:
 Quantification of uncertainties in code responses necessitates knowledge
 of input model parameter uncertainties. However, nuclear
 thermal-hydraulics code such as RELAP5 and TRACE do not provide any
 information on input model parameter uncertainties. Moreover, the input
 model parameters for physical models in these legacy codes were derived
 under steady-state flow conditions and hence might not be accurate to use
 in the analysis of transients without accounting for uncertainties. We
 present a Bayesian framework to estimate the posterior mode of input model
 parameters' mean and variance by implementing the iterative
 expectation--maximization algorithm. For this, we introduce the idea of
 model parameter multiplier. A log-normal transformation is used to
 transform the model parameter multiplier to pseudo-parameter. Our analysis
 is based on two main assumptions on pseudo-parameter. First, a first-order
 linear relationship is assumed between code responses and
 pseudo-parameters. Second, the pseudo-parameters are assumed to be
 normally distributed. The problem is formulated to express the scalar
 random variable, the difference between experimental result and base
 (nominal) code-calculated value as a linear combination of
 pseudo-parameters.
Journal: Journal of Applied Statistics 
Pages: 1011-1026 
Issue: 6 
Volume: 43 
Year: 2016 
Month: 5 
X-DOI: 10.1080/02664763.2015.1089220 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1089220 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:6:p:1011-1026




Template-Type: ReDIF-Article 1.0
Author-Name: Bao Yiqi 
Author-X-Name-First: Bao 
Author-X-Name-Last: Yiqi 
Author-Name: Cibele Maria Russo 
Author-X-Name-First: Cibele 
Author-X-Name-Last: Maria Russo 
Author-Name: Vicente G. Cancho 
Author-X-Name-First: Vicente G. 
Author-X-Name-Last: Cancho 
Author-Name: Francisco Louzada 
Author-X-Name-First: Francisco 
Author-X-Name-Last: Louzada 
Title: Influence diagnostics for the Weibull-Negative-Binomial regression model with cure rate under latent failure causes 
Abstract:
 In this paper, we propose a flexible cure rate survival model by assuming
 that the number of competing causes of the event of interest follows the
 Negative Binomial distribution and the time to event follows a Weibull
 distribution. Indeed, we introduce the Weibull-Negative-Binomial (WNB)
 distribution, which can be used in order to model survival data when the
 hazard rate function is increasing, decreasing and some non-monotonous
 shaped. Another advantage of the proposed model is that it has some
 distributions commonly used in lifetime analysis as particular cases.
 Moreover, the proposed model includes as special cases some of the
 well-know cure rate models discussed in the literature. We consider a
 frequentist analysis for parameter estimation of a WNB model with cure
 rate. Then, we derive the appropriate matrices for assessing local
 influence on the parameter estimates under different perturbation schemes
 and present some ways to perform global influence analysis. Finally, the
 methodology is illustrated on a medical data.
Journal: Journal of Applied Statistics 
Pages: 1027-1060 
Issue: 6 
Volume: 43 
Year: 2016 
Month: 5 
X-DOI: 10.1080/02664763.2015.1089221 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1089221 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:6:p:1027-1060




Template-Type: ReDIF-Article 1.0
Author-Name: Sang Eun Lee 
Author-X-Name-First: Sang Eun 
Author-X-Name-Last: Lee 
Author-Name: Key-Il Shin 
Author-X-Name-First: Key-Il 
Author-X-Name-Last: Shin 
Title: The cut-off point based on underlying distribution and cost function 
Abstract:
 Cut-off sampling has been widely used for business survey which has the
 right-skewed population with a long tail. Several methods are suggested to
 obtain the optimal cut-off point. The LH algorithm suggested by Lavallee
 and Hidiroglou [6] is commonly used to get the optimum boundaries by
 minimizing the total sample size with a given precision. In this paper, we
 suggest a new cut-off point determination method which minimizes a cost
 function. And that leads to reducing the size of take-all stratum. Also we
 investigate an optimal cut-off point using a typical parametric estimation
 method under the assumptions of underlying distributions. Small
 Monte-Carlo simulation studies are performed in order to compare the new
 cut-off point method to the LH algorithm. The Korea Transportation Origin
 -- Destination data are used for real data analysis.
Journal: Journal of Applied Statistics 
Pages: 1061-1073 
Issue: 6 
Volume: 43 
Year: 2016 
Month: 5 
X-DOI: 10.1080/02664763.2015.1089222 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1089222 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:6:p:1061-1073




Template-Type: ReDIF-Article 1.0
Author-Name: Guohua Yan 
Author-X-Name-First: Guohua 
Author-X-Name-Last: Yan 
Author-Name: M. Tariqul Hasan 
Author-X-Name-First: M. Tariqul 
Author-X-Name-Last: Hasan 
Author-Name: Renjun Ma 
Author-X-Name-First: Renjun 
Author-X-Name-Last: Ma 
Title: Modeling proportions and marginal counts simultaneously for clustered multinomial data with random cluster sizes 
Abstract:
 Clustered multinomial data with random cluster sizes commonly appear in
 health, environmental and ecological studies. Traditional approaches for
 analyzing clustered multinomial data contemplate two assumptions. One of
 these assumptions is that cluster sizes are fixed, whereas the other
 demands cluster sizes to be positive. Randomness of the cluster sizes may
 be the determinant of the within-cluster correlation and between-cluster
 variation. We propose a baseline-category mixed model for clustered
 multinomial data with random cluster sizes based on Poisson mixed models.
 Our orthodox best linear unbiased predictor approach to this model depends
 only on the moment structure of unobserved distribution-free random
 effects. Our approach also consolidates the marginal and conditional
 modeling interpretations. Unlike the traditional methods, our approach can
 accommodate both random and zero cluster sizes. Two real-life multinomial
 data examples, crime data and food contamination data, are used to
 manifest our proposed methodology.
Journal: Journal of Applied Statistics 
Pages: 1074-1087 
Issue: 6 
Volume: 43 
Year: 2016 
Month: 5 
X-DOI: 10.1080/02664763.2015.1089223 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1089223 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:6:p:1074-1087




Template-Type: ReDIF-Article 1.0
Author-Name: B. Martin-Barragan 
Author-X-Name-First: B. 
Author-X-Name-Last: Martin-Barragan 
Author-Name: R.E. Lillo 
Author-X-Name-First: R.E. 
Author-X-Name-Last: Lillo 
Author-Name: J. Romo 
Author-X-Name-First: J. 
Author-X-Name-Last: Romo 
Title: Functional boxplots based on epigraphs and hypographs 
Abstract:
 Functional boxplot is an attractive technique to visualize data that come
 from functions. We propose an alternative to the functional boxplot based
 on depth measures. Our proposal generalizes the usual construction of the
 box-plot in one dimension related to the down-upward orderings of the data
 by considering two intuitive pre-orders in the functional context. These
 orderings are based on the epigraphs and hypographs of the data that allow
 a new definition of functional quartiles which is more robust to shape
 outliers. Simulated and real examples show that this proposal provides a
 convenient visualization technique with a great potential for analyzing
 functional data and illustrate its usefulness to detect outliers that
 other procedures do not detect.
Journal: Journal of Applied Statistics 
Pages: 1088-1103 
Issue: 6 
Volume: 43 
Year: 2016 
Month: 5 
X-DOI: 10.1080/02664763.2015.1092108 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1092108 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:6:p:1088-1103




Template-Type: ReDIF-Article 1.0
Author-Name: T. Chen 
Author-X-Name-First: T. 
Author-X-Name-Last: Chen 
Author-Name: N. Lu 
Author-X-Name-First: N. 
Author-X-Name-Last: Lu 
Author-Name: J. Arora 
Author-X-Name-First: J. 
Author-X-Name-Last: Arora 
Author-Name: I. Katz 
Author-X-Name-First: I. 
Author-X-Name-Last: Katz 
Author-Name: R. Bossarte 
Author-X-Name-First: R. 
Author-X-Name-Last: Bossarte 
Author-Name: H. He 
Author-X-Name-First: H. 
Author-X-Name-Last: He 
Author-Name: Y. Xia 
Author-X-Name-First: Y. 
Author-X-Name-Last: Xia 
Author-Name: H. Zhang 
Author-X-Name-First: H. 
Author-X-Name-Last: Zhang 
Author-Name: X.M. Tu 
Author-X-Name-First: X.M. 
Author-X-Name-Last: Tu 
Title: Power analysis for cluster randomized trials with binary outcomes modeled by generalized linear mixed-effects models 
Abstract:
 Power analysis for cluster randomized control trials is difficult to
 perform when a binary response is modeled using the generalized linear
 mixed-effects model (GLMM). Although methods for clustered binary
 responses exist such as the generalized estimating equations, they do not
 apply to the context of GLMM. Also, because popular statistical packages
 such as R and SAS do not provide correct estimates of parameters for the
 GLMM for binary responses, Monte Carlo simulation, a popular ad-hoc method
 for estimating power when the power function is too complex to evaluate
 analytically or numerically, fails to provide correct power estimates
 within the current context as well. In this paper, a new approach is
 developed to estimate power for cluster randomized control trials when a
 binary response is modeled by the GLMM. The approach is easy to implement
 and seems to work quite well, as assessed by simulation studies. The
 approach is illustrated with a real intervention study to reduce suicide
 reattempt rates among US Veterans.
Journal: Journal of Applied Statistics 
Pages: 1104-1118 
Issue: 6 
Volume: 43 
Year: 2016 
Month: 5 
X-DOI: 10.1080/02664763.2015.1092109 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1092109 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:6:p:1104-1118




Template-Type: ReDIF-Article 1.0
Author-Name: Kristofer M&#xe5;nsson 
Author-X-Name-First: Kristofer 
Author-X-Name-Last: M&#xe5;nsson 
Author-Name: B.M. Golam Kibria 
Author-X-Name-First: B.M. 
Author-X-Name-Last: Golam Kibria 
Author-Name: Ghazi Shukur 
Author-X-Name-First: Ghazi 
Author-X-Name-Last: Shukur 
Title: A restricted Liu estimator for binary regression models and its application to an applied demand system 
Abstract:
 In this article, we propose a restricted Liu regression estimator (RLRE)
 for estimating the parameter vector, <italic>&#x3b2;</italic>, in the
 presence of multicollinearity, when the dependent variable is binary and
 it is suspected that <italic>&#x3b2;</italic> may belong to a linear
 subspace defined by
 <italic>R&#x3b2;</italic>&#x2009;<italic>=</italic>&#x2009;<italic>r</ital
 ic>. First, we investigate the mean squared error (MSE) properties of the
 new estimator and compare them with those of the restricted maximum
 likelihood estimator (RMLE). Then we suggest some estimators of the
 shrinkage parameter, and a simulation study is conducted to compare the
 performance of the different estimators. Finally, we show the benefit of
 using RLRE instead of RMLE when estimating how changes in price affect
 consumer demand for a specific product.
Journal: Journal of Applied Statistics 
Pages: 1119-1127 
Issue: 6 
Volume: 43 
Year: 2016 
Month: 5 
X-DOI: 10.1080/02664763.2015.1092110 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1092110 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:6:p:1119-1127




Template-Type: ReDIF-Article 1.0
Author-Name: Himadri Ghosh 
Author-X-Name-First: Himadri 
Author-X-Name-Last: Ghosh 
Author-Name: S. Chowdhury 
Author-X-Name-First: S. 
Author-X-Name-Last: Chowdhury 
Author-Name:  Prajneshu 
Author-X-Name-First:  
Author-X-Name-Last: Prajneshu 
Title: An improved fuzzy time-series method of forecasting based on <italic>L</italic>--<italic>R</italic> fuzzy sets and its application 
Abstract:
 Classical time-series theory assumes values of the response variable to be
 &#x2018;crisp&#x2019; or &#x2018;precise&#x2019;, which is quite often
 violated in reality. However, forecasting of such data can be carried out
 through fuzzy time-series analysis. This article presents an improved
 method of forecasting based on <italic>L</italic>--<italic>R</italic>
 fuzzy sets as membership functions. As an illustration, the methodology is
 employed for forecasting India's total foodgrain production. For the data
 under consideration, superiority of proposed method over other competing
 methods is demonstrated in respect of modelling and forecasting on the
 basis of mean square error and average relative error criteria. Finally,
 out-of-sample forecasts are also obtained.
Journal: Journal of Applied Statistics 
Pages: 1128-1139 
Issue: 6 
Volume: 43 
Year: 2016 
Month: 5 
X-DOI: 10.1080/02664763.2015.1092111 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1092111 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:6:p:1128-1139




Template-Type: ReDIF-Article 1.0
Author-Name: Ay&#xe7;a &#xc7;akmak Pehlivanl&#x131; 
Author-X-Name-First: Ay&#xe7;a &#xc7;akmak 
Author-X-Name-Last: Pehlivanl&#x131; 
Title: A novel feature selection scheme for high-dimensional data sets: four-Staged Feature Selection 
Abstract:
 Classification of high-dimensional data set is a big challenge for
 statistical learning and data mining algorithms. To effectively apply
 classification methods to high-dimensional data sets, feature selection is
 an indispensable pre-processing step of learning process. In this study,
 we consider the problem of constructing an effective feature selection and
 classification scheme for data set which has a small number of sample size
 with a large number of features. A novel feature selection approach, named
 four-Staged Feature Selection, has been proposed to overcome
 high-dimensional data classification problem by selecting informative
 features. The proposed method first selects candidate features with number
 of filtering methods which are based on different metrics, and then it
 applies semi-wrapper, union and voting stages, respectively, to obtain
 final feature subsets. Several statistical learning and data mining
 methods have been carried out to verify the efficiency of the selected
 features. In order to test the adequacy of the proposed method, 10
 different microarray data sets are employed due to their high number of
 features and small sample size.
Journal: Journal of Applied Statistics 
Pages: 1140-1154 
Issue: 6 
Volume: 43 
Year: 2016 
Month: 5 
X-DOI: 10.1080/02664763.2015.1092112 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1092112 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:6:p:1140-1154




Template-Type: ReDIF-Article 1.0
Author-Name: E.F. Saraiva 
Author-X-Name-First: E.F. 
Author-X-Name-Last: Saraiva 
Author-Name: A.K. Suzuki 
Author-X-Name-First: A.K. 
Author-X-Name-Last: Suzuki 
Author-Name: F. Louzada 
Author-X-Name-First: F. 
Author-X-Name-Last: Louzada 
Author-Name: L.A. Milan 
Author-X-Name-First: L.A. 
Author-X-Name-Last: Milan 
Title: Partitioning gene expression data by data-driven Markov chain Monte Carlo 
Abstract:
 In this paper we introduce a Bayesian mixture model with an unknown number
 of components for partitioning gene expression data. Inferences about all
 the unknown parameters involved are made by using the proposed data-driven
 Markov chain Monte Carlo. This algorithm is essentially a
 Metropolis--Hastings within Gibbs sampling. The Metropolis--Hastings is
 performed to change the number of partitions <italic>k</italic> in the
 neighborhood <inline-formula><inline-graphic
 xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1092113_ilm0001.gif"/></inline-formula> and
 <inline-formula><inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1092113_ilm0002.gif"/></inline-formula> using a pair of
 split-merge moves. Our strategy for splitting is based on data in which
 allocation probabilities are calculated based on marginal likelihood
 function from the previously allocated observations. Conditional on
 <italic>k</italic>, the partitions labels are updated via Gibbs sampling.
 The two main advantages of the proposed algorithm is that it is easy to be
 implemented and the acceptance probability for split-merge movements
 depends only on the observed data. We examine the performance of the
 proposed algorithm on simulated data and then analyze two publicly
 available gene expression data sets.
Journal: Journal of Applied Statistics 
Pages: 1155-1173 
Issue: 6 
Volume: 43 
Year: 2016 
Month: 5 
X-DOI: 10.1080/02664763.2015.1092113 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1092113 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:6:p:1155-1173




Template-Type: ReDIF-Article 1.0
Author-Name: Getachew A. Dagne 
Author-X-Name-First: Getachew A. 
Author-X-Name-Last: Dagne 
Title: A growth mixture Tobit model: application to AIDS studies 
Abstract:
 This paper presents an alternative analysis approach to modeling data
 where a lower detection limit (LOD) and unobserved population
 heterogeneity exist in a longitudinal data set. Longitudinal data on viral
 loads in HIV/AIDS studies, for instance, show strong positive skewness and
 left-censoring. Normalizing such data using a logarithmic transformation
 seems to be unsuccessful. An alternative to such a transformation is to
 use a finite mixture model which is suitable for analyzing data which have
 skewed or multi-modal distributions. There is little work done to
 simultaneously take into account these features of longitudinal data. This
 paper develops a growth mixture Tobit model that deals with a LOD and
 heterogeneity among growth trajectories. The proposed methods are
 illustrated using simulated and real data from an AIDS clinical study.
Journal: Journal of Applied Statistics 
Pages: 1174-1185 
Issue: 7 
Volume: 43 
Year: 2016 
Month: 7 
X-DOI: 10.1080/02664763.2015.1092114 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1092114 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:7:p:1174-1185




Template-Type: ReDIF-Article 1.0
Author-Name: Tobias Voigt 
Author-X-Name-First: Tobias 
Author-X-Name-Last: Voigt 
Author-Name: Roland Fried 
Author-X-Name-First: Roland 
Author-X-Name-Last: Fried 
Author-Name: Wolfgang Rhode 
Author-X-Name-First: Wolfgang 
Author-X-Name-Last: Rhode 
Author-Name: Fabian Temme 
Author-X-Name-First: Fabian 
Author-X-Name-Last: Temme 
Title: Distance-based variable generation with applications to the FACT experiment 
Abstract:
 We introduce a new way to construct variables for classification in a
 setting of astronomy. The newly constructed variables complement the
 currently used Hillas parameters and are specifically designed to improve
 the classification. They are based on fitting elliptic or skewed bivariate
 distributions to images gathered by imaging atmospheric Cherenkov
 telescopes and evaluating the distance between the observed and the fitted
 distribution. As distance measures we use the Chi-square distance, the
 Kullback--Leibler divergence and the Hellinger distance. The new variables
 lead to an improved classification in terms of misclassification errors.
Journal: Journal of Applied Statistics 
Pages: 1186-1197 
Issue: 7 
Volume: 43 
Year: 2016 
Month: 7 
X-DOI: 10.1080/02664763.2015.1092115 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1092115 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:7:p:1186-1197




Template-Type: ReDIF-Article 1.0
Author-Name: Oscar O. Melo 
Author-X-Name-First: Oscar O. 
Author-X-Name-Last: Melo 
Author-Name: Jorge Mateu 
Author-X-Name-First: Jorge 
Author-X-Name-Last: Mateu 
Author-Name: Carlos E. Melo 
Author-X-Name-First: Carlos E. 
Author-X-Name-Last: Melo 
Title: A generalised linear space--time autoregressive model with space--time autoregressive disturbances 
Abstract:
 We present a solution to problems where the response variable is a count,
 a rate or binary using a generalised linear space--time autoregressive
 model with space--time autoregressive disturbances (GLSTARAR). The
 possibility to test the fixed effect specification against the random
 effect specification of the panel data model is extended to include
 space--time error autocorrelation or a space--time lagged dependent
 variable. Space-time generalised estimating equations are used to estimate
 the spatio-temporal parameters in the model. We also present a measure of
 goodness of fit, and show the pseudo-best linear unbiased predictor for
 prediction purposes. Additionally, we propose a joint space--time
 modelling of mean and dispersion to give a solution when the variance is
 not constant. In the application, we use social, economic, geographic and
 state presence variables for 32 Colombian departments in order to analyse
 the relationship between the number of armed actions (AAs) per
 1000&#x2009;km<inline-formula><inline-graphic
 xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1092506_ilm0001.gif"/></inline-formula> committed by
 the guerrillas of the FARC-EP and ELN during the years 2003--2009, and a
 set of covariates given by attention rate to victims of violence, forced
 displacement-households expelled, forced displacement-households received,
 total armed confrontations per year, number of AAs by military forces and
 percentage of people living in urban area.
Journal: Journal of Applied Statistics 
Pages: 1198-1225 
Issue: 7 
Volume: 43 
Year: 2016 
Month: 7 
X-DOI: 10.1080/02664763.2015.1092506 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1092506 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:7:p:1198-1225




Template-Type: ReDIF-Article 1.0
Author-Name: Chiara Bocci 
Author-X-Name-First: Chiara 
Author-X-Name-Last: Bocci 
Author-Name: Emilia Rocco 
Author-X-Name-First: Emilia 
Author-X-Name-Last: Rocco 
Title: Modelling the location decisions of manufacturing firms with a spatial point process approach 
Abstract:
 The paper is devoted to explore how the increasing availability of spatial
 micro-data, jointly with the diffusion of GIS software, allows to exploit
 micro-econometric methods based on stochastic spatial point processes in
 order to understand the factors that may influence the location decisions
 of new firms. By using the knowledge of the geographical coordinates of
 the newborn firms, their spatial distribution is treated as a realization
 of an inhomogeneous marked point process in the continuous space and the
 effect of spatial-varying factors on the location decisions is evaluated
 by parametrically modelling the intensity of the process. The study is
 motivated by the real issue of analysing the birth process of small and
 medium manufacturing firms in Tuscany, an Italian region, and it shows
 that the location choices of the new Tuscan firms is influenced on the one
 hand by the availability of infrastructures and the level of
 accessibility, and on the other by the presence and the characteristics of
 the existing firms. Moreover, the effect of these factors varies with the
 size and the level of technology of the new firms. Besides the specific
 Tuscan result, the study shows the potentiality of the described
 micro-econometric approach for the analysis of the spatial dynamics of
 firms.
Journal: Journal of Applied Statistics 
Pages: 1226-1239 
Issue: 7 
Volume: 43 
Year: 2016 
Month: 7 
X-DOI: 10.1080/02664763.2015.1093612 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1093612 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:7:p:1226-1239




Template-Type: ReDIF-Article 1.0
Author-Name: Yuzhu Tian 
Author-X-Name-First: Yuzhu 
Author-X-Name-Last: Tian 
Author-Name: Manlai Tang 
Author-X-Name-First: Manlai 
Author-X-Name-Last: Tang 
Author-Name: Maozai Tian 
Author-X-Name-First: Maozai 
Author-X-Name-Last: Tian 
Title: A class of finite mixture of quantile regressions with its applications 
Abstract:
 Mixture of linear regression models provide a popular treatment for
 modeling nonlinear regression relationship. The traditional estimation of
 mixture of regression models is based on Gaussian error assumption. It is
 well known that such assumption is sensitive to outliers and extreme
 values. To overcome this issue, a new class of finite mixture of quantile
 regressions (FMQR) is proposed in this article. Compared with the existing
 Gaussian mixture regression models, the proposed FMQR model can provide a
 complete specification on the conditional distribution of response
 variable for each component. From the likelihood point of view, the FMQR
 model is equivalent to the finite mixture of regression models based on
 errors following asymmetric Laplace distribution (ALD), which can be
 regarded as an extension to the traditional mixture of regression models
 with normal error terms. An EM algorithm is proposed to obtain the
 parameter estimates of the FMQR model by combining a hierarchical
 representation of the ALD. Finally, the iterated weighted least square
 estimation for each mixture component of the FMQR model is derived.
 Simulation studies are conducted to illustrate the finite sample
 performance of the estimation procedure. Analysis of an aphid data set is
 used to illustrate our methodologies.
Journal: Journal of Applied Statistics 
Pages: 1240-1252 
Issue: 7 
Volume: 43 
Year: 2016 
Month: 7 
X-DOI: 10.1080/02664763.2015.1094035 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1094035 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:7:p:1240-1252




Template-Type: ReDIF-Article 1.0
Author-Name: Steven B. Caudill 
Author-X-Name-First: Steven B. 
Author-X-Name-Last: Caudill 
Author-Name: Franklin G. Mixon 
Author-X-Name-First: Franklin G. 
Author-X-Name-Last: Mixon 
Title: Estimating class-specific parametric models using finite mixtures: an application to a hedonic model of wine prices 
Abstract:
 Hedonic price models are commonly used in the study of markets for various
 goods, most notably those for wine, art, and jewelry. These models were
 developed to estimate implicit prices of product attributes
 <italic>within</italic> a given product class, where in the case of some
 goods, such as wine, substantial product differentiation exists. To
 address this issue, recent research on wine prices employs local
 polynomial regression clustering (LPRC) for estimating regression models
 under class uncertainty. This study demonstrates that a superior empirical
 approach -- estimation of a mixture model -- is applicable to a hedonic
 model of wine prices, provided only that the dependent variable in the
 model is rescaled. The present study also catalogues several of the
 advantages over LPRC modeling of estimating mixture models.
Journal: Journal of Applied Statistics 
Pages: 1253-1261 
Issue: 7 
Volume: 43 
Year: 2016 
Month: 7 
X-DOI: 10.1080/02664763.2015.1094036 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1094036 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:7:p:1253-1261




Template-Type: ReDIF-Article 1.0
Author-Name: Jimoh Olawale Ajadi 
Author-X-Name-First: Jimoh Olawale 
Author-X-Name-Last: Ajadi 
Author-Name: Muhammad Riaz 
Author-X-Name-First: Muhammad 
Author-X-Name-Last: Riaz 
Author-Name: Khalid Al-Ghamdi 
Author-X-Name-First: Khalid 
Author-X-Name-Last: Al-Ghamdi 
Title: On increasing the sensitivity of mixed EWMA--CUSUM control charts for location&#xa0;parameter 
Abstract:
 Control chart is an important statistical technique that is used to
 monitor the quality of a process. Shewhart control charts are used to
 detect larger disturbances in the process parameters, whereas cumulative
 sum (CUSUM) and exponential weighted moving average (EWMA) are meant for
 smaller and moderate changes. In this study, we enhanced mixed EWMA--CUSUM
 control charts with varying fast initial response (FIR) features and also
 with a runs rule of two out of three successive points that fall above the
 upper control limit. We investigate their run-length properties. The
 proposed control charting schemes are compared with the existing
 counterparts including classical CUSUM, classical EWMA, FIR CUSUM, FIR
 EWMA, mixed EWMA--CUSUM, 2/3 modified EWMA, and 2/3 CUSUM control charting
 schemes. A case study is presented for practical considerations using a
 real data set.
Journal: Journal of Applied Statistics 
Pages: 1262-1278 
Issue: 7 
Volume: 43 
Year: 2016 
Month: 7 
X-DOI: 10.1080/02664763.2015.1094453 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1094453 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:7:p:1262-1278




Template-Type: ReDIF-Article 1.0
Author-Name: Guogen Shan 
Author-X-Name-First: Guogen 
Author-X-Name-Last: Shan 
Title: Exact confidence intervals for randomized response strategies 
Abstract:
 For surveys with sensitive questions, randomized response sampling
 strategies are often used to increase the response rate and encourage
 participants to provide the truth of the question while participants'
 privacy and confidentiality are protected. The proportion of responding
 &#x2018;yes&#x2019; to the sensitive question is the parameter of
 interest. Asymptotic confidence intervals for this proportion are
 calculated from the limiting distribution of the test statistic, and are
 traditionally used in practice for statistical inference. It is well known
 that these intervals do not guarantee the coverage probability. For this
 reason, we apply the exact approach, adjusting the critical value as in
 [10], to construct the exact confidence interval of the proportion based
 on the likelihood ratio test and three Wilson-type tests. Two randomized
 response sampling strategies are studied: the Warner model and the
 unrelated model. The exact interval based on the likelihood ratio test has
 shorter average length than others when the probability of the sensitive
 question is low. Exact Wilson intervals have good performance in other
 cases. A real example from a survey study is utilized to illustrate the
 application of these exact intervals.
Journal: Journal of Applied Statistics 
Pages: 1279-1290 
Issue: 7 
Volume: 43 
Year: 2016 
Month: 7 
X-DOI: 10.1080/02664763.2015.1094454 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1094454 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:7:p:1279-1290




Template-Type: ReDIF-Article 1.0
Author-Name: M. Roth 
Author-X-Name-First: M. 
Author-X-Name-Last: Roth 
Author-Name: G. Jongbloed 
Author-X-Name-First: G. 
Author-X-Name-Last: Jongbloed 
Author-Name: T.A. Buishand 
Author-X-Name-First: T.A. 
Author-X-Name-Last: Buishand 
Title: Threshold selection for regional peaks-over-threshold data 
Abstract:
 A hurdle in the peaks-over-threshold approach for analyzing extreme values
 is the selection of the threshold. A method is developed to reduce this
 obstacle in the presence of multiple, similar data samples. This is for
 instance the case in many environmental applications. The idea is to
 combine threshold selection methods into a regional method. Regionalized
 versions of the threshold stability and the mean excess plot are presented
 as graphical tools for threshold selection. Moreover, quantitative
 approaches based on the bootstrap distribution of the spatially averaged
 Kolmogorov--Smirnov and Anderson--Darling test statistics are introduced.
 It is demonstrated that the proposed regional method leads to an increased
 sensitivity for too low thresholds, compared to methods that do not take
 into account the regional information. The approach can be used for a wide
 range of univariate threshold selection methods. We test the methods using
 simulated data and present an application to rainfall data from the Dutch
 water board <italic>Vallei en Veluwe.</italic>
Journal: Journal of Applied Statistics 
Pages: 1291-1309 
Issue: 7 
Volume: 43 
Year: 2016 
Month: 7 
X-DOI: 10.1080/02664763.2015.1100589 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1100589 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:7:p:1291-1309




Template-Type: ReDIF-Article 1.0
Author-Name: Marcel de Toledo Vieira 
Author-X-Name-First: Marcel de Toledo 
Author-X-Name-Last: Vieira 
Author-Name: Maria de F&#xe1;tima Salgueiro 
Author-X-Name-First: Maria de F&#xe1;tima 
Author-X-Name-Last: Salgueiro 
Author-Name: Peter W. F. Smith 
Author-X-Name-First: Peter W. F. 
Author-X-Name-Last: Smith 
Title: Investigating impacts of complex sampling on latent growth curve modelling 
Abstract:
 We investigate the impacts of complex sampling on point and standard error
 estimates in latent growth curve modelling of survey data. Methodological
 issues are illustrated with empirical evidence from the analysis of
 longitudinal data on life satisfaction trajectories using data from the
 British Household Panel Survey, a national representative survey in Great
 Britain. A multi-process second-order latent growth curve model with
 conditional linear growth is used to study variation in the two perceived
 life satisfaction latent factors considered. The benefits of accounting
 for the complex survey design are considered, including obtaining unbiased
 both point and standard error estimates, and therefore correctly specified
 confidence intervals and statistical tests. We conclude that, even for the
 rather elaborated longitudinal data models that were considered,
 estimation procedures are affected by variance-inflating impacts of
 complex sampling.
Journal: Journal of Applied Statistics 
Pages: 1310-1321 
Issue: 7 
Volume: 43 
Year: 2016 
Month: 7 
X-DOI: 10.1080/02664763.2015.1100590 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1100590 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:7:p:1310-1321




Template-Type: ReDIF-Article 1.0
Author-Name: Husam Awni Bayoud 
Author-X-Name-First: Husam Awni 
Author-X-Name-Last: Bayoud 
Title: Testing the similarity of two normal populations with application to the bioequivalence problem 
Abstract:
 The problem of testing the similarity of two normal populations is
 reconsidered, in this article, from a nonclassical point of view. We
 introduce a test statistic based on the maximum likelihood estimate of
 Weitzman's overlapping coefficient. Simulated critical points are provided
 for the proposed test for various sample sizes and significance levels.
 Statistical powers of the proposed test are computed via simulation
 studies and compared to those of the existing tests. Furthermore, Type-I
 error robustness of the proposed and the existing tests are studied via
 simulation studies when the underlying distributions are non-normal. Two
 data sets are analyzed for illustration purposes. Finally, the proposed
 test has been implemented to assess the bioequivalence of two drug
 formulations.
Journal: Journal of Applied Statistics 
Pages: 1322-1334 
Issue: 7 
Volume: 43 
Year: 2016 
Month: 7 
X-DOI: 10.1080/02664763.2015.1100591 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1100591 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:7:p:1322-1334




Template-Type: ReDIF-Article 1.0
Author-Name: Firoozeh Rivaz 
Author-X-Name-First: Firoozeh 
Author-X-Name-Last: Rivaz 
Title: Optimal network design for Bayesian spatial prediction of multivariate non-Gaussian environmental data 
Abstract:
 This paper deals with the problem of increasing air pollution monitoring
 stations in Tehran city for efficient spatial prediction. As the data are
 multivariate and skewed, we introduce two multivariate skew models through
 developing the univariate skew Gaussian random field proposed by Zareifard
 and Jafari Khaledi [21]. These models provide extensions of the linear
 model of coregionalization for non-Gaussian data. In the Bayesian
 framework, the optimal network design is found based on the maximum
 entropy criterion. A Markov chain Monte Carlo algorithm is developed to
 implement posterior inference. Finally, the applicability of two proposed
 models is demonstrated by analyzing an air pollution data set.
Journal: Journal of Applied Statistics 
Pages: 1335-1348 
Issue: 7 
Volume: 43 
Year: 2016 
Month: 7 
X-DOI: 10.1080/02664763.2015.1100592 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1100592 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:7:p:1335-1348




Template-Type: ReDIF-Article 1.0
Author-Name: Jan Lasek 
Author-X-Name-First: Jan 
Author-X-Name-Last: Lasek 
Author-Name: Zolt&#xe1;n Szl&#xe1;vik 
Author-X-Name-First: Zolt&#xe1;n 
Author-X-Name-Last: Szl&#xe1;vik 
Author-Name: Marek Gagolewski 
Author-X-Name-First: Marek 
Author-X-Name-Last: Gagolewski 
Author-Name: Sandjai Bhulai 
Author-X-Name-First: Sandjai 
Author-X-Name-Last: Bhulai 
Title: How to improve a team's position in the FIFA ranking? A simulation study 
Abstract:
 In this paper, we study the efficacy of the official ranking for
 international football teams compiled by FIFA, the body governing football
 competition around the globe. We present strategies for improving a team's
 position in the ranking. By combining several statistical techniques, we
 derive an objective function in a decision problem of optimal scheduling
 of future matches. The presented results display how a team's position can
 be improved. Along the way, we compare the official procedure to the
 famous Elo rating system. Although it originates from chess, it has been
 successfully tailored to ranking football teams as well.
Journal: Journal of Applied Statistics 
Pages: 1349-1368 
Issue: 7 
Volume: 43 
Year: 2016 
Month: 7 
X-DOI: 10.1080/02664763.2015.1100593 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1100593 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:7:p:1349-1368




Template-Type: ReDIF-Article 1.0
Author-Name: Baba B. Alhaji 
Author-X-Name-First: Baba B. 
Author-X-Name-Last: Alhaji 
Author-Name: Hongsheng Dai 
Author-X-Name-First: Hongsheng 
Author-X-Name-Last: Dai 
Author-Name: Yoshiko Hayashi 
Author-X-Name-First: Yoshiko 
Author-X-Name-Last: Hayashi 
Author-Name: Veronica Vinciotti 
Author-X-Name-First: Veronica 
Author-X-Name-Last: Vinciotti 
Author-Name: Andrew Harrison 
Author-X-Name-First: Andrew 
Author-X-Name-Last: Harrison 
Author-Name: Berthold Lausen 
Author-X-Name-First: Berthold 
Author-X-Name-Last: Lausen 
Title: Bayesian analysis for mixtures of discrete distributions with a non-parametric component 
Abstract:
 Bayesian finite mixture modelling is a flexible parametric modelling
 approach for classification and density fitting. Many areas of application
 require distinguishing a <italic>signal</italic> from a
 <italic>noise</italic> component. In practice, it is often difficult to
 justify a specific distribution for the <italic>signal</italic> component;
 therefore, the <italic>signal</italic> distribution is usually further
 modelled via a mixture of distributions. However, modelling the
 <italic>signal</italic> as a mixture of distributions is computationally
 non-trivial due to the difficulties in justifying the exact number of
 components to be used and due to the label switching problem. This paper
 proposes the use of a non-parametric distribution to model the
 <italic>signal</italic> component. We consider the case of discrete data
 and show how this new methodology leads to more accurate parameter
 estimation and smaller false non-discovery rate. Moreover, it does not
 incur the label switching problem. We show an application of the method to
 data generated by ChIP-sequencing experiments.
Journal: Journal of Applied Statistics 
Pages: 1369-1385 
Issue: 8 
Volume: 43 
Year: 2016 
Month: 6 
X-DOI: 10.1080/02664763.2015.1100594 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1100594 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:8:p:1369-1385




Template-Type: ReDIF-Article 1.0
Author-Name: Fedya Telmoudi 
Author-X-Name-First: Fedya 
Author-X-Name-Last: Telmoudi 
Author-Name: Mohamed EL Ghourabi 
Author-X-Name-First: Mohamed 
Author-X-Name-Last: EL Ghourabi 
Author-Name: Mohamed Limam 
Author-X-Name-First: Mohamed 
Author-X-Name-Last: Limam 
Title: On conditional risk estimation considering model risk 
Abstract:
 Usually, parametric procedures used for conditional variance modelling are
 associated with model risk. Model risk may affect the volatility and
 conditional value at risk estimation process either due to estimation or
 misspecification risks. Hence, non-parametric artificial intelligence
 models can be considered as alternative models given that they do not rely
 on an explicit form of the volatility. In this paper, we consider the
 least-squares support vector regression (LS-SVR), weighted LS-SVR and
 Fixed size LS-SVR models in order to handle the problem of conditional
 risk estimation taking into account issues of model risk. A simulation
 study and a real application show the performance of proposed volatility
 and VaR models.
Journal: Journal of Applied Statistics 
Pages: 1386-1399 
Issue: 8 
Volume: 43 
Year: 2016 
Month: 6 
X-DOI: 10.1080/02664763.2015.1100595 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1100595 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:8:p:1386-1399




Template-Type: ReDIF-Article 1.0
Author-Name: M.J. Ershadi 
Author-X-Name-First: M.J. 
Author-X-Name-Last: Ershadi 
Author-Name: R. Noorossana 
Author-X-Name-First: R. 
Author-X-Name-Last: Noorossana 
Author-Name: S.T.A Niaki 
Author-X-Name-First: S.T.A 
Author-X-Name-Last: Niaki 
Title: Economic-statistical design of simple linear profiles with variable sampling interval 
Abstract:
 Control charts are statistical tools to monitor a process or a product.
 However, some processes cannot be controlled by monitoring a
 characteristic; instead, they need to be monitored using profiles.
 Economic-statistical design of profile monitoring means determining the
 parameters of a profile monitoring scheme such that total costs are
 minimized while statistical measures maintain proper values. While varying
 sampling interval usually increases the effectiveness of profile
 monitoring, economic-statistical design of variable sampling interval
 (VSI) profile monitoring is investigated in this paper. An extended
 Lorenzen--Vance function is used for modeling total costs in VSI model
 where the average time to signal is employed for depicting the statistical
 measure of the obtained profile monitoring scheme. Two sampling intervals;
 number of set points and the parameters of control charts that are used in
 profile monitoring are the variables that are obtained thorough the
 economic-statistical model. A genetic algorithm is employed to optimize
 the model and an experimental design approach is used for tuning its
 parameters. Sensitivity analysis and numerical results indicate
 satisfactory performance for the proposed model.
Journal: Journal of Applied Statistics 
Pages: 1400-1418 
Issue: 8 
Volume: 43 
Year: 2016 
Month: 6 
X-DOI: 10.1080/02664763.2015.1103705 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1103705 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:8:p:1400-1418




Template-Type: ReDIF-Article 1.0
Author-Name: J. Machalov&#xe1; 
Author-X-Name-First: J. 
Author-X-Name-Last: Machalov&#xe1; 
Author-Name: K. Hron 
Author-X-Name-First: K. 
Author-X-Name-Last: Hron 
Author-Name: G.S. Monti 
Author-X-Name-First: G.S. 
Author-X-Name-Last: Monti 
Title: Preprocessing of centred logratio transformed density functions using smoothing splines 
Abstract:
 With large-scale database systems, statistical analysis of data, occurring
 in the form of probability distributions, becomes an important task in
 explorative data analysis. Nevertheless, due to specific properties of
 density functions, their proper statistical treatment of these data still
 represents a challenging task in functional data analysis. Namely, the
 usual <inline-formula><inline-graphic
 xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1103706_ilm0001.gif"/></inline-formula> metric does not
 fully accounts for the relative character of information, carried by
 density functions; instead, their geometrical features are captured by
 Bayes spaces of measures. The easiest possibility of expressing density
 functions in an <inline-formula><inline-graphic
 xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1103706_ilm0002.gif"/></inline-formula> space is to use
 centred logratio transformation, even though this results in functional
 data with a constant integral constraint that needs to be taken into
 account in further analysis. While theoretical background for reasonable
 analysis of density functions is already provided comprehensively by Bayes
 spaces themselves, preprocessing issues still need to be developed. The
 aim of this paper is to introduce optimal smoothing splines for centred
 logratio transformed density functions that take all their specific
 features into account and provide a concise methodology for reasonable
 preprocessing of raw (discretized) distributional observations.
 Theoretical developments are illustrated with a real-world data set from
 official statistics and with a simulation study.
Journal: Journal of Applied Statistics 
Pages: 1419-1435 
Issue: 8 
Volume: 43 
Year: 2016 
Month: 6 
X-DOI: 10.1080/02664763.2015.1103706 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1103706 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:8:p:1419-1435




Template-Type: ReDIF-Article 1.0
Author-Name: Nd&#xe9;n&#xe9; Ka 
Author-X-Name-First: Nd&#xe9;n&#xe9; 
Author-X-Name-Last: Ka 
Author-Name: St&#xe9;phane Mussard 
Author-X-Name-First: St&#xe9;phane 
Author-X-Name-Last: Mussard 
Title: &#x2113;<sub>1</sub> regressions: Gini estimators for fixed effects panel data 
Abstract:
 Panel data, frequently employed in empirical investigations, provide
 estimators being strongly biased in the presence of atypical observations.
 The aim of this work is to propose a <inline-formula><inline-graphic
 xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1103707_ilm0001.gif"/></inline-formula> Gini regression
 for panel data. It is shown that the fixed effects within-group Gini
 estimator is more robust than the ordinary least squares one when the data
 are contaminated by outliers. This semi-parametric Gini estimator is
 proven to be an <italic>U</italic>-statistics, consequently, it is
 asymptotically normal.
Journal: Journal of Applied Statistics 
Pages: 1436-1446 
Issue: 8 
Volume: 43 
Year: 2016 
Month: 6 
X-DOI: 10.1080/02664763.2015.1103707 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1103707 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:8:p:1436-1446




Template-Type: ReDIF-Article 1.0
Author-Name: Miran A. Jaffa 
Author-X-Name-First: Miran A. 
Author-X-Name-Last: Jaffa 
Author-Name: Mulugeta Gebregziabher 
Author-X-Name-First: Mulugeta 
Author-X-Name-Last: Gebregziabher 
Author-Name: Deirdre K. Luttrell 
Author-X-Name-First: Deirdre K. 
Author-X-Name-Last: Luttrell 
Author-Name: Louis M. Luttrell 
Author-X-Name-First: Louis M. 
Author-X-Name-Last: Luttrell 
Author-Name: Ayad A. Jaffa 
Author-X-Name-First: Ayad A. 
Author-X-Name-Last: Jaffa 
Title: Multivariate generalized linear mixed models with random intercepts to analyze cardiovascular risk markers in type-1 diabetic patients 
Abstract:
 Statistical approaches tailored to analyzing longitudinal data that have
 multiple outcomes with different distributions are scarce. This paucity is
 due to the non-availability of multivariate distributions that jointly
 model outcomes with different distributions other than the multivariate
 normal. A plethora of research has been done on the specific combination
 of binary-Gaussian bivariate outcomes but a more general approach that
 allows other mixtures of distributions for multiple longitudinal outcomes
 has not been thoroughly demonstrated and examined. Here, we study a
 multivariate generalized linear mixed models approach that jointly models
 multiple longitudinal outcomes with different combinations of
 distributions and incorporates the correlations between the various
 outcomes through separate yet correlated random intercepts. Every outcome
 is linked to the set of covariates through a proper link function that
 allows the incorporation and joint modeling of different distributions. A
 novel application was demonstrated on a cohort study of Type-1 diabetic
 patients to jointly model a mix of longitudinal cardiovascular outcomes
 and to explore for the first time the effect of glycemic control
 treatment, plasma prekallikrein biomarker, gender and age on
 cardiovascular risk factors collectively.
Journal: Journal of Applied Statistics 
Pages: 1447-1464 
Issue: 8 
Volume: 43 
Year: 2016 
Month: 6 
X-DOI: 10.1080/02664763.2015.1103708 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1103708 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:8:p:1447-1464




Template-Type: ReDIF-Article 1.0
Author-Name: Sylvain Robbiano 
Author-X-Name-First: Sylvain 
Author-X-Name-Last: Robbiano 
Author-Name: Matthieu Saumard 
Author-X-Name-First: Matthieu 
Author-X-Name-Last: Saumard 
Author-Name: Michel Cur&#xe9; 
Author-X-Name-First: Michel 
Author-X-Name-Last: Cur&#xe9; 
Title: Improving prediction performance of stellar parameters using functional models 
Abstract:
 This paper investigates the problem of prediction of stellar parameters,
 based on the star's electromagnetic spectrum. The knowledge of these
 parameters permits to infer on the evolutionary state of the star. From a
 statistical point of view, the spectra of different stars can be
 represented as functional data. Therefore, a two-step procedure
 decomposing the spectra in a functional basis combined with a regression
 method of prediction is proposed. We also use a bootstrap methodology to
 build prediction intervals for the stellar parameters. A practical
 application is also provided to illustrate the numerical performance of
 our approach.
Journal: Journal of Applied Statistics 
Pages: 1465-1476 
Issue: 8 
Volume: 43 
Year: 2016 
Month: 6 
X-DOI: 10.1080/02664763.2015.1106448 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1106448 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:8:p:1465-1476




Template-Type: ReDIF-Article 1.0
Author-Name: Soumya Roy 
Author-X-Name-First: Soumya 
Author-X-Name-Last: Roy 
Author-Name: Chiranjit Mukhopadhyay 
Author-X-Name-First: Chiranjit 
Author-X-Name-Last: Mukhopadhyay 
Title: Bayesian <italic>D</italic>-optimal Accelerated Life Test plans for series systems with competing exponential causes of failure 
Abstract:
 This paper provides methods of obtaining Bayesian
 <italic>D</italic>-optimal Accelerated Life Test (ALT) plans for series
 systems with independent exponential component lives under the Type-I
 censoring scheme. Two different Bayesian <italic>D</italic>-optimality
 design criteria are considered. For both the criteria, first optimal
 designs for a given number of experimental points are found by solving a
 finite-dimensional constrained optimization problem. Next, the global
 optimality of such an ALT plan is ensured by applying the General
 Equivalence Theorem. A detailed sensitivity analysis is also carried out
 to investigate the effect of different planning inputs on the resulting
 optimal ALT plans. Furthermore, these Bayesian optimal plans are also
 compared with the corresponding (frequentist) locally
 <italic>D</italic>-optimal ALT plans.
Journal: Journal of Applied Statistics 
Pages: 1477-1493 
Issue: 8 
Volume: 43 
Year: 2016 
Month: 6 
X-DOI: 10.1080/02664763.2015.1106449 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1106449 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:8:p:1477-1493




Template-Type: ReDIF-Article 1.0
Author-Name: Richard J. Cebula 
Author-X-Name-First: Richard J. 
Author-X-Name-Last: Cebula 
Author-Name: Fiorentina Angjellari-Dajci 
Author-X-Name-First: Fiorentina 
Author-X-Name-Last: Angjellari-Dajci 
Author-Name: Russell Kashian 
Author-X-Name-First: Russell 
Author-X-Name-Last: Kashian 
Title: Are there interregional differences in the response of cigarette smoking to state cigarette excise taxes in the USA? Exploratory analysis 
Abstract:
 Within the context of the period fixed-effects model, this study uses a
 2002--2009 state-level panel data set of the USA to investigate the
 relative impact of state cigarette excise taxation across the nation in
 reducing cigarette smoking. In particular, by focusing upon the state
 cigarette excise taxation levels within each of the nine US Census
 Divisions, this study investigates whether there are inter-regional
 differences in the rate of responsiveness of cigarette consumption to
 increased state cigarette taxes. The initial empirical estimates reveal
 that although the per capita number of packs of cigarettes smoked annually
 is a decreasing function of the state cigarette excise tax in all nine
 Census Regions, the relative response of cigarette smoking to state
 cigarette tax increases varies considerably from one region to the next.
 Reinforcing this conclusion, in one specification of the model, the number
 of packs of cigarettes smoked in response to a higher state cigarette tax
 is statistically significant and negative in only eight of the nine Census
 Divisions. Furthermore, when cigarette smoking is measured in terms of the
 percentage of the population classified as smokers, interregional
 differentials in the response of smokers to higher state cigarette taxes
 are much greater. Thus, there is evidence that cigarette excise taxation
 exercises rather different impacts on the propensity to smoke across
 Census Regions.
Journal: Journal of Applied Statistics 
Pages: 1494-1507 
Issue: 8 
Volume: 43 
Year: 2016 
Month: 6 
X-DOI: 10.1080/02664763.2015.1106451 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1106451 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:8:p:1494-1507




Template-Type: ReDIF-Article 1.0
Author-Name: Luz Marina Rondon 
Author-X-Name-First: Luz Marina 
Author-X-Name-Last: Rondon 
Author-Name: Heleno Bolfarine 
Author-X-Name-First: Heleno 
Author-X-Name-Last: Bolfarine 
Title: Bayesian analysis of generalized elliptical semi-parametric models 
Abstract:
 In this paper, we study the statistical inference based on the Bayesian
 approach for regression models with the assumption that independent
 additive errors follow normal, Student-<italic>t</italic>, slash,
 contaminated normal, Laplace or symmetric hyperbolic distribution, where
 both location and dispersion parameters of the response variable
 distribution include nonparametric additive components approximated by
 <italic>B</italic>-splines. This class of models provides a rich set of
 symmetric distributions for the model error. Some of these distributions
 have heavier or lighter tails than the normal as well as different levels
 of kurtosis. In order to draw samples of the posterior distribution of the
 interest parameters, we propose an efficient Markov Chain Monte Carlo
 (MCMC) algorithm, which combines Gibbs sampler and Metropolis--Hastings
 algorithms. The performance of the proposed MCMC algorithm is assessed
 through simulation experiments. We apply the proposed methodology to a
 real data set. The proposed methodology is implemented in the R package
 BayesGESM using the function <monospace>gesm()</monospace>.
Journal: Journal of Applied Statistics 
Pages: 1508-1524 
Issue: 8 
Volume: 43 
Year: 2016 
Month: 6 
X-DOI: 10.1080/02664763.2015.1109070 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1109070 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:8:p:1508-1524




Template-Type: ReDIF-Article 1.0
Author-Name: Junying Chen 
Author-X-Name-First: Junying 
Author-X-Name-Last: Chen 
Author-Name: Haoyu Zeng 
Author-X-Name-First: Haoyu 
Author-X-Name-Last: Zeng 
Author-Name: Fei Yang 
Author-X-Name-First: Fei 
Author-X-Name-Last: Yang 
Title: Parameter estimation for employee stock ownerships preference experimental design 
Abstract:
 The experimental design method is a pivotal factor for the reliability of
 the parameters estimation in the discrete choice model. The traditional
 orthogonal design is used widely, but insufficient empirical research has
 been conducted on the effectiveness of these new design methods. Several
 new experimental design methods, such as D-efficient, Bayesian
 D-efficient, have been proposed recently. This study finds that the
 D-adoption has statistically insignificant effect on the growth of
 productivity. This study is motivated by the lack of documented evidence
 on the effect of Chinese ESOS. This study contributes to the body of
 knowledge by documenting evidence on the impact of ESOS on productivity
 enhancement and earnings management practices. The existing literature on
 productivity effect and earnings management effect of ESOS falls under two
 isolated strands of research. No documented studies have been done to
 investigate these two issues simultaneously using the same dataset. As a
 result, the existing literature fails to identify which of these two
 countervailing effects of ESOS is more dominant.
Journal: Journal of Applied Statistics 
Pages: 1525-1540 
Issue: 8 
Volume: 43 
Year: 2016 
Month: 6 
X-DOI: 10.1080/02664763.2015.1117583 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1117583 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:8:p:1525-1540




Template-Type: ReDIF-Article 1.0
Author-Name: Philip Pallmann 
Author-X-Name-First: Philip 
Author-X-Name-Last: Pallmann 
Author-Name: Ludwig A. Hothorn 
Author-X-Name-First: Ludwig A. 
Author-X-Name-Last: Hothorn 
Title: Analysis of means: a generalized approach using R 
Abstract:
 Papers on the analysis of means (ANOM) have been circulating in the
 quality control literature for decades, routinely describing it as a
 statistical stand-alone concept. Therefore, we clarify that ANOM should
 rather be regarded as a special case of a much more universal approach
 known as multiple contrast tests (MCTs). Perceiving ANOM as a
 grand-mean-type MCT paves the way for implementing it in the open-source
 software R. We give a brief tutorial on how to exploit R's versatility and
 introduce the R package <monospace>ANOM</monospace> for drawing the
 familiar decision charts. Beyond that, we illustrate two practical aspects
 of data analysis with ANOM: firstly, we compare merits and drawbacks of
 ANOM-type MCTs and ANOVA <italic>F</italic>-test and assess their
 respective statistical powers, and secondly, we show that the benefit of
 using critical values from multivariate <italic>t</italic>-distributions
 for ANOM instead of simple Bonferroni quantiles is oftentimes negligible.
Journal: Journal of Applied Statistics 
Pages: 1541-1560 
Issue: 8 
Volume: 43 
Year: 2016 
Month: 6 
X-DOI: 10.1080/02664763.2015.1117584 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1117584 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:8:p:1541-1560




Template-Type: ReDIF-Article 1.0
Author-Name: Silvia Bozza 
Author-X-Name-First: Silvia 
Author-X-Name-Last: Bozza 
Author-Name: Franco Taroni 
Author-X-Name-First: Franco 
Author-X-Name-Last: Taroni 
Title: Posterior likelihood ratios for evaluation of forensic trace evidence given a two-level model on the data by Alberink <italic>et al.</italic> (2013) 
Journal: Journal of Applied Statistics 
Pages: 1561-1563 
Issue: 8 
Volume: 43 
Year: 2016 
Month: 6 
X-DOI: 10.1080/02664763.2015.1106450 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1106450 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:8:p:1561-1563




Template-Type: ReDIF-Article 1.0
Author-Name: Thomas Holgersson 
Author-X-Name-First: Thomas 
Author-X-Name-Last: Holgersson 
Author-Name: Louise Nordstr&#xf6;m 
Author-X-Name-First: Louise 
Author-X-Name-Last: Nordstr&#xf6;m 
Author-Name: &#xd6;zge &#xd6;ner 
Author-X-Name-First: &#xd6;zge 
Author-X-Name-Last: &#xd6;ner 
Title: On regression modelling with dummy variables versus separate regressions per group: comment on Holgersson <italic>et al</italic>. 
Journal: Journal of Applied Statistics 
Pages: 1564-1565 
Issue: 8 
Volume: 43 
Year: 2016 
Month: 6 
X-DOI: 10.1080/02664763.2015.1092711 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1092711 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:8:p:1564-1565




Template-Type: ReDIF-Article 1.0
Author-Name: F.A. Alawadhi 
Author-X-Name-First: F.A. 
Author-X-Name-Last: Alawadhi 
Author-Name: D. Alhulail 
Author-X-Name-First: D. 
Author-X-Name-Last: Alhulail 
Title: Bayesian change points analysis for earthquakes body wave magnitude 
Abstract:
 Recently, the world has experienced an increased number of major
 earthquakes. The Zagros belt is among the most seismically active mountain
 ranges in the world. Due to Kuwait's location in the southwest of the
 Zagros belt, it is affected by relative tectonic movements in the
 neighboring region. It is vital to assess the Zagros seismic risks in
 Kuwait using recent data and coordinate with the competent authorities to
 reduce those risks. Using the body wave magnitude (Mb) data collected in
 Kuwait, we want to assess the recent changes in the magnitude of
 earthquakes and its variations in Kuwait's vicinity. We built a change
 point model to detect the significant changes in its parameters. This
 paper applies a hierarchical Bayesian technique and derives the marginal
 posterior density function for the Mb. Our interest lies in identifying a
 shift in the mean of a single or multiple change points as well as the
 changes in the variation. Building upon the model and its parameters for
 the 2002--2003 data, we detected three change points. The first, second
 and third change points occurred in September 2002, April 2003 and August
 2003, respectively.
Journal: Journal of Applied Statistics 
Pages: 1567-1582 
Issue: 9 
Volume: 43 
Year: 2016 
Month: 7 
X-DOI: 10.1080/02664763.2015.1117585 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1117585 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:9:p:1567-1582




Template-Type: ReDIF-Article 1.0
Author-Name: Mahmood Ul Hassan 
Author-X-Name-First: Mahmood 
Author-X-Name-Last: Ul Hassan 
Author-Name: P&#xe4;r Stockhammar 
Author-X-Name-First: P&#xe4;r 
Author-X-Name-Last: Stockhammar 
Title: Fitting probability distributions to economic growth: a maximum likelihood approach 
Abstract:
 The growth rate of the gross domestic product (GDP) usually carries
 heteroscedasticity, asymmetry and fat-tails. In this study three important
 and significantly heteroscedastic GDP series are examined. A Normal,
 normal-mixture, normal-asymmetric Laplace distribution and a Student's
 t-Asymmetric Laplace (TAL) distribution mixture are considered for
 distributional fit comparison of GDP growth series after removing
 heteroscedasticity. The parameters of the distributions have been
 estimated using maximum likelihood method. Based on the results of
 different accuracy measures, goodness-of-fit tests and plots, we find out
 that in the case of asymmetric, heteroscedastic and highly leptokurtic
 data the TAL-distribution fits better than the alternatives. In the case
 of asymmetric, heteroscedastic but less leptokurtic data the NM fit is
 superior. Furthermore, a simulation study has been carried out to obtain
 standard errors for the estimated parameters. The results of this study
 might be used in e.g. density forecasting of GDP growth series or to
 compare different economies.
Journal: Journal of Applied Statistics 
Pages: 1583-1603 
Issue: 9 
Volume: 43 
Year: 2016 
Month: 7 
X-DOI: 10.1080/02664763.2015.1117586 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1117586 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:9:p:1583-1603




Template-Type: ReDIF-Article 1.0
Author-Name: Alexander Ludwig 
Author-X-Name-First: Alexander 
Author-X-Name-Last: Ludwig 
Title: On the usability of the fluctuation test statistic to identify multiple cointegration break points 
Abstract:
 The fluctuation test suggested by Hansen and Johansen [<italic>Some tests
 for parameter constancy in cointegrated VAR models</italic>, Econometrics
 J. 2 (1999), pp. 306--333] intends to distinguish between the presence of
 zero and one break in cointegration relations. In this article, we provide
 evidence by Monte Carlo simulations that it also serves as a graphical
 device to detect even multiple break locations. It suffices to consider a
 simplified and easy-to-implement version of the original fluctuation test.
 Its break detection performance depends on the sign of change in
 cointegration parameters and the break height. The sign issue can be
 approached successfully by a backward application of the test statistic.
 If breaks are observable, the break locations are detected at the true
 location on average. We apply the graphical procedure to assess the
 cointegration of bond yields of Spain, Italy and Portugal with German
 yields for the period 1995--2013 which is surprisingly supported by the
 trace test. However, the recursive cointegration approach shows that a
 stable relationship with German yields is only present for sub-periods
 between the introduction of the Euro and the global financial crisis which
 is in line with expectations. The statistical robustness of these results
 is supported by a forward and backward application of the cointegration
 breakdown test by Andrews and Kim [<italic>Tests for cointegration
 breakdown over a short time period</italic>, J. Bus. Econom. Stat. 24
 (2006), pp. 379--394].
Journal: Journal of Applied Statistics 
Pages: 1604-1624 
Issue: 9 
Volume: 43 
Year: 2016 
Month: 7 
X-DOI: 10.1080/02664763.2015.1117587 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1117587 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:9:p:1604-1624




Template-Type: ReDIF-Article 1.0
Author-Name: Austin L. Hand 
Author-X-Name-First: Austin L. 
Author-X-Name-Last: Hand 
Author-Name: John A. Scott 
Author-X-Name-First: John A. 
Author-X-Name-Last: Scott 
Author-Name: Phil D. Young 
Author-X-Name-First: Phil D. 
Author-X-Name-Last: Young 
Author-Name: James D. Stamey 
Author-X-Name-First: James D. 
Author-X-Name-Last: Stamey 
Author-Name: Dean M. Young 
Author-X-Name-First: Dean M. 
Author-X-Name-Last: Young 
Title: Bayesian adaptive two-stage design for determining person-time in Phase II clinical trials with Poisson data 
Abstract:
 Adaptive clinical trial designs can often improve drug-study efficiency by
 utilizing data obtained during the course of the trial. We present a novel
 Bayesian two-stage adaptive design for Phase II clinical trials with
 Poisson-distributed outcomes that allows for person-observation-time
 adjustments for early termination due to either futility or efficacy. Our
 design is motivated by the adaptive trial from [9], which uses binomial
 data. Although many frequentist and Bayesian two-stage adaptive designs
 for count data have been proposed in the literature, many designs do not
 allow for person-time adjustments after the first stage. This restriction
 limits flexibility in the study design. However, our proposed design
 allows for such flexibility by basing the second-stage person-time on the
 first-stage observed-count data. We demonstrate the implementation of our
 Bayesian predictive adaptive two-stage design using a hypothetical Phase
 II trial of Immune Globulin (Intravenous).
Journal: Journal of Applied Statistics 
Pages: 1625-1635 
Issue: 9 
Volume: 43 
Year: 2016 
Month: 7 
X-DOI: 10.1080/02664763.2015.1117588 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1117588 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:9:p:1625-1635




Template-Type: ReDIF-Article 1.0
Author-Name: J. A. Achcar 
Author-X-Name-First: J. A. 
Author-X-Name-Last: Achcar 
Author-Name: N. Davarzani 
Author-X-Name-First: N. 
Author-X-Name-Last: Davarzani 
Author-Name: R. M. Souza 
Author-X-Name-First: R. M. 
Author-X-Name-Last: Souza 
Title: Basu--Dhar bivariate geometric distribution in the presence of covariates and censored data: a Bayesian approach 
Abstract:
 In this paper, we introduce classical and Bayesian approaches for the
 Basu--Dhar bivariate geometric distribution in the presence of covariates
 and censored data. This distribution is considered for the analysis of
 bivariate lifetime as an alternative to some existing bivariate lifetime
 distributions assuming continuous lifetimes as the Block and Basu or
 Marshall and Olkin bivariate distributions. Maximum likelihood and
 Bayesian estimators are presented. Two examples are considered to
 illustrate the proposed methodology: an example with simulated data and an
 example with medical bivariate lifetime data.
Journal: Journal of Applied Statistics 
Pages: 1636-1648 
Issue: 9 
Volume: 43 
Year: 2016 
Month: 7 
X-DOI: 10.1080/02664763.2015.1117589 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1117589 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:9:p:1636-1648




Template-Type: ReDIF-Article 1.0
Author-Name: Zheng Xu 
Author-X-Name-First: Zheng 
Author-X-Name-Last: Xu 
Title: An alternative circular smoothing method to nonparametric estimation of periodic functions 
Abstract:
 This article provides alternative circular smoothing methods in
 nonparametric estimation of periodic functions. By treating the data as
 &#x2018;circular&#x2019;, we solve the &#x201c;boundary issue&#x201d; in
 the nonparametric estimation treating the data as &#x2018;linear&#x2019;.
 By redefining the distance metric and signed distance, we modify many
 estimators used in the situations involving periodic patterns. In the
 perspective of &#x2018;nonparametric estimation of periodic
 functions&#x2019;, we present the examples in nonparametric estimation of
 (1) a periodic function, (2) multiple periodic functions, (3) an evolving
 function, (4) a periodically varying-coefficient model and (5) a
 generalized linear model with periodically varying coefficient. In the
 perspective of &#x2018;circular statistics&#x2019;, we provide alternative
 approaches to calculate the weighted average and evaluate the
 &#x2018;linear/circular--linear/circular&#x2019; association and
 regression. Simulation studies and an empirical study of electricity price
 index have been conducted to illustrate and compare our methods with other
 methods in the literature.
Journal: Journal of Applied Statistics 
Pages: 1649-1672 
Issue: 9 
Volume: 43 
Year: 2016 
Month: 7 
X-DOI: 10.1080/02664763.2015.1117590 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1117590 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:9:p:1649-1672




Template-Type: ReDIF-Article 1.0
Author-Name: Hongjian Zhu 
Author-X-Name-First: Hongjian 
Author-X-Name-Last: Zhu 
Author-Name: Dejian Lai 
Author-X-Name-First: Dejian 
Author-X-Name-Last: Lai 
Author-Name: Nils P. Johnson 
Author-X-Name-First: Nils P. 
Author-X-Name-Last: Johnson 
Title: Agreement between two diagnostic tests when accounting for test--retest variation: application to FFR versus iFR 
Abstract:
 In medicine, there are often two diagnostic tests that serve the same
 purpose. Typically, one of the tests will have a lower diagnostic
 performance but be less invasive, easier to perform, or cheaper.
 Clinicians must assess the agreement between the tests while accounting
 for test--retest variation in both techniques. In this paper, we
 investigate a specific example from interventional cardiology, studying
 the agreement between the fractional flow reserve and the instantaneous
 wave-free ratio. We analyze potential definitions of the agreement
 (accuracy) between the two tests and compare five families of statistical
 estimators. We contrast their statistical behavior both theoretically and
 using numerical simulations. Surprisingly for clinicians, seemingly
 natural and equivalent definitions of the concept of agreement can lead to
 discordant and even nonsensical estimates.
Journal: Journal of Applied Statistics 
Pages: 1673-1689 
Issue: 9 
Volume: 43 
Year: 2016 
Month: 7 
X-DOI: 10.1080/02664763.2015.1117591 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1117591 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:9:p:1673-1689




Template-Type: ReDIF-Article 1.0
Author-Name: Jaehee Kim 
Author-X-Name-First: Jaehee 
Author-X-Name-Last: Kim 
Author-Name: Chulwoo Jeong 
Author-X-Name-First: Chulwoo 
Author-X-Name-Last: Jeong 
Title: A Bayesian multiple structural change regression model with autocorrelated errors 
Abstract:
 This paper develops a new Bayesian approach to change-point modeling that
 allows the number of change-points in the observed autocorrelated times
 series to be unknown. The model we develop assumes that the number of
 change-points have a truncated Poisson distribution. A genetic algorithm
 is used to estimate a change-point model, which allows for structural
 changes with autocorrelated errors. We focus considerable attention on the
 construction of autocorrelated structure for each regime and for the
 parameters that characterize each regime. Our techniques are found to work
 well in the simulation with a few change-points. An empirical analysis is
 provided involving the annual flow of the Nile River and the monthly total
 energy production in South Korea to lead good estimates for structural
 change-points.
Journal: Journal of Applied Statistics 
Pages: 1690-1705 
Issue: 9 
Volume: 43 
Year: 2016 
Month: 7 
X-DOI: 10.1080/02664763.2015.1117592 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1117592 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:9:p:1690-1705




Template-Type: ReDIF-Article 1.0
Author-Name: Joshua N. Sampson 
Author-X-Name-First: Joshua N. 
Author-X-Name-Last: Sampson 
Author-Name: Charles E. Matthews 
Author-X-Name-First: Charles E. 
Author-X-Name-Last: Matthews 
Author-Name: Laurence S. Freedman 
Author-X-Name-First: Laurence S. 
Author-X-Name-Last: Freedman 
Author-Name: Raymond J. Carroll 
Author-X-Name-First: Raymond J. 
Author-X-Name-Last: Carroll 
Author-Name: Victor Kipnis 
Author-X-Name-First: Victor 
Author-X-Name-Last: Kipnis 
Title: Methods to assess measurement error in questionnaires of sedentary behavior 
Abstract:
 Sedentary behavior has already been associated with mortality,
 cardiovascular disease, and cancer. Questionnaires are an affordable tool
 for measuring sedentary behavior in large epidemiological studies. Here,
 we introduce and evaluate two statistical methods for quantifying
 measurement error in questionnaires. Accurate estimates are needed for
 assessing questionnaire quality. The two methods would be applied to
 validation studies that measure a sedentary behavior by both questionnaire
 and accelerometer on multiple days. The first method fits a reduced model
 by assuming the accelerometer is without error, while the second method
 fits a more complete model that allows both measures to have error.
 Because accelerometers tend to be highly accurate, we show that ignoring
 the accelerometer's measurement error, can result in more accurate
 estimates of measurement error in some scenarios. In this article, we
 derive asymptotic approximations for the mean-squared error of the
 estimated parameters from both methods, evaluate their dependence on study
 design and behavior characteristics, and offer an R package so
 investigators can make an informed choice between the two methods. We
 demonstrate the difference between the two methods in a recent validation
 study comparing previous day recalls to an accelerometer-based ActivPal.
Journal: Journal of Applied Statistics 
Pages: 1706-1721 
Issue: 9 
Volume: 43 
Year: 2016 
Month: 7 
X-DOI: 10.1080/02664763.2015.1117593 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1117593 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:9:p:1706-1721




Template-Type: ReDIF-Article 1.0
Author-Name: Trias Wahyuni Rakhmawati 
Author-X-Name-First: Trias Wahyuni 
Author-X-Name-Last: Rakhmawati 
Author-Name: Geert Molenberghs 
Author-X-Name-First: Geert 
Author-X-Name-Last: Molenberghs 
Author-Name: Geert Verbeke 
Author-X-Name-First: Geert 
Author-X-Name-Last: Verbeke 
Author-Name: Christel Faes 
Author-X-Name-First: Christel 
Author-X-Name-Last: Faes 
Title: Local influence diagnostics for incomplete overdispersed longitudinal counts 
Abstract:
 We develop local influence diagnostics to detect influential subjects when
 generalized linear mixed models are fitted to incomplete longitudinal
 overdispersed count data. The focus is on the influence stemming from the
 dropout model specification. In particular, the effect of small
 perturbations around an MAR specification are examined. The method is
 applied to data from a longitudinal clinical trial in epileptic patients.
 The effect on models allowing for overdispersion is contrasted with that
 on models that do not.
Journal: Journal of Applied Statistics 
Pages: 1722-1737 
Issue: 9 
Volume: 43 
Year: 2016 
Month: 7 
X-DOI: 10.1080/02664763.2015.1117594 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1117594 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:9:p:1722-1737




Template-Type: ReDIF-Article 1.0
Author-Name: M. &#xc1;lvarez Hern&#xe1;ndez 
Author-X-Name-First: M. 
Author-X-Name-Last: &#xc1;lvarez Hern&#xe1;ndez 
Author-Name: A. Mart&#xed;n Andr&#xe9;s 
Author-X-Name-First: A. Mart&#xed;n 
Author-X-Name-Last: Andr&#xe9;s 
Author-Name: I. Herranz Tejedor 
Author-X-Name-First: I. 
Author-X-Name-Last: Herranz Tejedor 
Title: One-sided asymptotic inferences for a proportion 
Abstract:
 Two-sided asymptotic confidence intervals for an unknown proportion
 <italic>p</italic> have been the subject of a great deal of literature.
 Surprisingly, there are very few papers devoted, like this article, to the
 case of one tail, despite its great importance in practice and the fact
 that its behavior is usually different from that of the case with two
 tails. This paper evaluates 47 methods and concludes that (1) the optimal
 method is the classic Wilson method with a correction for continuity and
 (2) a simpler option, almost as good as the first, is the new adjusted
 Wald method (Wald's classic method applied to the data increased in the
 values proposed by Borkowf: adding a single imaginary failure or success).
Journal: Journal of Applied Statistics 
Pages: 1738-1752 
Issue: 9 
Volume: 43 
Year: 2016 
Month: 7 
X-DOI: 10.1080/02664763.2015.1117595 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1117595 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:9:p:1738-1752




Template-Type: ReDIF-Article 1.0
Author-Name: Saima Afzal 
Author-X-Name-First: Saima 
Author-X-Name-Last: Afzal 
Author-Name: Muhammad Mutahir Iqbal 
Author-X-Name-First: Muhammad Mutahir 
Author-X-Name-Last: Iqbal 
Title: A new way to order independent components 
Abstract:
 A relatively newer computational technique adopted by statisticians is
 known as independent component analysis (ICA) which is used to analyze
 complex multidimensional data with the objective to separate it into
 components that are independent to each other. Quite often the main
 interest for conducting ICA is to identify a small number of significant
 independent components (ICs) to replace the original complex dimensions
 with. For this, determining the order of identified ICs is a
 pre-requisite. The area is not unaddressed but it does deserve a careful
 revisiting. This is the subject matter of the paper which introduces a new
 method to order ICs. The proposed method is based upon regression
 approach. It compares the magnitude of the mixing coefficients and
 regression coefficients of the regression of the original series on ICs.
 Their compatibility determines the order.
Journal: Journal of Applied Statistics 
Pages: 1753-1764 
Issue: 9 
Volume: 43 
Year: 2016 
Month: 7 
X-DOI: 10.1080/02664763.2015.1120709 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1120709 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:9:p:1753-1764




Template-Type: ReDIF-Article 1.0
Author-Name: M. I. S&#xe1;nchez-Rodr&#xed;guez 
Author-X-Name-First: M. I. 
Author-X-Name-Last: S&#xe1;nchez-Rodr&#xed;guez 
Author-Name: E. M. S&#xe1;nchez-L&#xf3;pez 
Author-X-Name-First: E. M. 
Author-X-Name-Last: S&#xe1;nchez-L&#xf3;pez 
Author-Name: A. Marinas 
Author-X-Name-First: A. 
Author-X-Name-Last: Marinas 
Author-Name: J. M. Caridad 
Author-X-Name-First: J. M. 
Author-X-Name-Last: Caridad 
Author-Name: F. J. Urbano 
Author-X-Name-First: F. J. 
Author-X-Name-Last: Urbano 
Author-Name: J. M. Marinas 
Author-X-Name-First: J. M. 
Author-X-Name-Last: Marinas 
Title: Improving the estimations of fatty acids in several Andalusian PDO olive oils from NMR spectral data 
Abstract:
 The aim of this paper is to determine the fatty acid profile of diverse
 Andalusian extra-virgin olive oils from different protected designations
 of origin (PDO). The available data for the statistical multivariate
 analysis have been obtained from gas chromatography (GC, used as classical
 reference analytical technique) and nuclear magnetic resonance (NMR)
 spectroscopy : <inline-formula><inline-graphic
 xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1119808_ilm0001.gif"/></inline-formula>-NMR and
 <inline-formula><inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1119808_ilm0002.gif"/></inline-formula>-NMR (in the
 carbonyl, C-16 y aliphatic carbon regions). The diverse percentages of
 fatty acids approximated by the above-mentioned chemical procedures are
 summarized by using a statistical treatment, which presents a some
 weighted averages to obtain the closest fatty acid profile to the one
 provided by the GC reference technique, with weights being inversely
 proportional to some measures of the calibration errors. Besides, the work
 shows that the PDO of an olive oil conditions the NMR region
 (<inline-formula><inline-graphic
 xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1119808_ilm0003.gif"/></inline-formula>-NMR or
 carbonyl, C-16 or aliphatic <inline-formula><inline-graphic
 xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1119808_ilm0004.gif"/></inline-formula>-NMR) which
 provides the best estimation of each type of fatty acid. Finally,
 procedures of cross-validation are implemented in order to generalize the
 previous results.
Journal: Journal of Applied Statistics 
Pages: 1765-1793 
Issue: 10 
Volume: 43 
Year: 2016 
Month: 8 
X-DOI: 10.1080/02664763.2015.1119808 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1119808 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:10:p:1765-1793




Template-Type: ReDIF-Article 1.0
Author-Name: Francisco J. Rubio 
Author-X-Name-First: Francisco J. 
Author-X-Name-Last: Rubio 
Author-Name: Yili Hong 
Author-X-Name-First: Yili 
Author-X-Name-Last: Hong 
Title: Survival and lifetime data analysis with a flexible class of distributions 
Abstract:
 We introduce a general class of continuous univariate distributions with
 positive support obtained by transforming the class of two-piece
 distributions. We show that this class of distributions is very flexible,
 easy to implement, and contains members that can capture different tail
 behaviours and shapes, producing also a variety of hazard functions. The
 proposed distributions represent a flexible alternative to the classical
 choices such as the log-normal, Gamma, and Weibull distributions. We
 investigate empirically the inferential properties of the proposed models
 through an extensive simulation study. We present some applications using
 real data in the contexts of time-to-event and accelerated failure time
 models. In the second kind of applications, we explore the use of these
 models in the estimation of the distribution of the individual remaining
 life.
Journal: Journal of Applied Statistics 
Pages: 1794-1813 
Issue: 10 
Volume: 43 
Year: 2016 
Month: 8 
X-DOI: 10.1080/02664763.2015.1120710 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1120710 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:10:p:1794-1813




Template-Type: ReDIF-Article 1.0
Author-Name: Cristian L. Bayes 
Author-X-Name-First: Cristian L. 
Author-X-Name-Last: Bayes 
Author-Name: Luis Valdivieso 
Author-X-Name-First: Luis 
Author-X-Name-Last: Valdivieso 
Title: A beta inflated mean regression model for fractional response variables 
Abstract:
 This article proposes a new regression model for a dependent fractional
 random variable on the interval <inline-formula><inline-graphic
 xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1120711_ilm0001.gif"/></inline-formula> that takes with
 positive probability the extreme values 0 or 1. Our model relates the
 expected value of this variable with a linear predictor through a special
 parametrization that let the parameters free in the parameter space. A
 simulation-based study and an application to capital structure choices
 were conducted to analyze the performance of the likelihood estimators in
 the model. The results show not only accurate estimations and a better fit
 than other traditional models but also a more straightforward and clear
 way to estimate the effects of a set of covariates over the mean of a
 fractional response.
Journal: Journal of Applied Statistics 
Pages: 1814-1830 
Issue: 10 
Volume: 43 
Year: 2016 
Month: 8 
X-DOI: 10.1080/02664763.2015.1120711 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1120711 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:10:p:1814-1830




Template-Type: ReDIF-Article 1.0
Author-Name: Roman Salmer&#xf3;n G&#xf3;mez 
Author-X-Name-First: Roman 
Author-X-Name-Last: Salmer&#xf3;n G&#xf3;mez 
Author-Name: Jos&#xe9; Garc&#xed;a P&#xe9;rez 
Author-X-Name-First: Jos&#xe9; 
Author-X-Name-Last: Garc&#xed;a P&#xe9;rez 
Author-Name: Mar&#xed;a Del Mar L&#xf3;pez Mart&#xed;n 
Author-X-Name-First: Mar&#xed;a Del Mar 
Author-X-Name-Last: L&#xf3;pez Mart&#xed;n 
Author-Name: Catalina Garc&#xed;a Garc&#xed;a 
Author-X-Name-First: Catalina Garc&#xed;a 
Author-X-Name-Last: Garc&#xed;a 
Title: Collinearity diagnostic applied in ridge estimation through the variance inflation factor 
Abstract:
 The variance inflation factor (VIF) is used to detect the presence of
 linear relationships between two or more independent variables (i.e.
 collinearity) in the multiple linear regression model. However, the
 traditionally used VIF definitions encounter some problems when extended
 to the case of the ridge estimation (RE). This paper presents an extension
 of the VIF in RE by providing two alternative VIF expressions that
 overcome these problems in the general case. Some characteristics of these
 expressions are also presented and compared with the traditional
 expression. The results are illustrated with an economic example in the
 case of three independent variables and with a Monte Carlo simulation for
 the general case.
Journal: Journal of Applied Statistics 
Pages: 1831-1849 
Issue: 10 
Volume: 43 
Year: 2016 
Month: 8 
X-DOI: 10.1080/02664763.2015.1120712 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1120712 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:10:p:1831-1849




Template-Type: ReDIF-Article 1.0
Author-Name: S. Noorian 
Author-X-Name-First: S. 
Author-X-Name-Last: Noorian 
Author-Name: M. Ganjali 
Author-X-Name-First: M. 
Author-X-Name-Last: Ganjali 
Author-Name: E. Bahrami Samani 
Author-X-Name-First: E. 
Author-X-Name-Last: Bahrami Samani 
Title: A Bayesian test of homogeneity of association parameter using transition modelling of longitudinal mixed responses 
Abstract:
 In this paper, a Bayesian framework using a joint transition model for
 analysing longitudinal mixed ordinal and continuous responses is
 considered. The joint model considers a multivariate mixed model for the
 responses in which a transitive cumulative logistic regression model and
 an autoregressive regression model are used to model ordinal and
 continuous responses, respectively. Also, to take into account the
 association between longitudinal ordinal and continuous responses, a
 dynamic association parameter is used. A test is conducted to see whether
 this parameter is time-invariant and another test is presented to see
 whether this parameter is equal to zero or significantly far from zero.
 Our approach is applied to longitudinal PIAT (Peabody Individual
 Achievement Test) data where the Bayesian estimates of parameters are
 obtained.
Journal: Journal of Applied Statistics 
Pages: 1850-1863 
Issue: 10 
Volume: 43 
Year: 2016 
Month: 8 
X-DOI: 10.1080/02664763.2015.1125858 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1125858 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:10:p:1850-1863




Template-Type: ReDIF-Article 1.0
Author-Name: Gafar Matanmi Oyeyemi 
Author-X-Name-First: Gafar Matanmi 
Author-X-Name-Last: Oyeyemi 
Author-Name: George Chinanu Mbaeyi 
Author-X-Name-First: George Chinanu 
Author-X-Name-Last: Mbaeyi 
Author-Name: Saheed Ishola Salawu 
Author-X-Name-First: Saheed Ishola 
Author-X-Name-Last: Salawu 
Author-Name: Bernard Olagboyega Muse 
Author-X-Name-First: Bernard Olagboyega 
Author-X-Name-Last: Muse 
Title: On discrimination procedure with mixtures of continuous and categorical variables 
Abstract:
 A discrimination procedure, based on the location model is described and
 suggested for use in situation where the discriminating variables are
 mixtures of continuous and binary variables. Some procedures that have
 been previously employed, in a similar situation, like Fisher's linear
 discriminant function and the logistic regression were compared with this
 method using error rate (ER). Optimal ERs for these procedures are
 reported using real and simulated data for the case of varying sample size
 and number of continuous and binary variables and were used as a measure
 for assessing the performance of the various procedures. The suggested
 procedure performed considerably better in the cases considered and never
 did produce a result that is poor when compared with other procedures.
 Hence, the suggested procedure might be considered for such situations.
Journal: Journal of Applied Statistics 
Pages: 1864-1873 
Issue: 10 
Volume: 43 
Year: 2016 
Month: 8 
X-DOI: 10.1080/02664763.2015.1125859 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1125859 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:10:p:1864-1873




Template-Type: ReDIF-Article 1.0
Author-Name: Ji Hwan Cha 
Author-X-Name-First: Ji Hwan 
Author-X-Name-Last: Cha 
Title: Analysis of reliability characteristics in the acceptance sampling tests 
Abstract:
 Until now, various acceptance reliability sampling plans have been
 developed based on different life tests of items. However, the statistical
 effect of the acceptance sampling tests on the reliability characteristic
 of the lots accepted in the test has not been appropriately addressed. In
 this paper, we deal with an acceptance reliability sampling plan under a
 &#x2018;general framework&#x2019; and discuss the corresponding
 statistical effect of the acceptance sampling tests. The lifetime of the
 population before the acceptance test and that of population
 &#x2018;conditional on the acceptance&#x2019; in the sampling test are
 stochastically compared. The improvement of reliability characteristics of
 the population conditional on the acceptance in the sampling test is
 precisely analyzed.
Journal: Journal of Applied Statistics 
Pages: 1874-1891 
Issue: 10 
Volume: 43 
Year: 2016 
Month: 8 
X-DOI: 10.1080/02664763.2015.1125860 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1125860 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:10:p:1874-1891




Template-Type: ReDIF-Article 1.0
Author-Name: Semra T&#xfc;rkan 
Author-X-Name-First: Semra 
Author-X-Name-Last: T&#xfc;rkan 
Author-Name: Gamze &#xd6;zel 
Author-X-Name-First: Gamze 
Author-X-Name-Last: &#xd6;zel 
Title: A new modified Jackknifed estimator for the Poisson regression model 
Abstract:
 The Poisson regression is very popular in applied researches when
 analyzing the count data. However, multicollinearity problem arises for
 the Poisson regression model when the independent variables are highly
 intercorrelated. Shrinkage estimator is a commonly applied solution to the
 general problem caused by multicollinearity. Recently, the ridge
 regression (RR) estimators and some methods for estimating the ridge
 parameter <italic>k</italic> in the Poisson regression have been proposed.
 It has been found that some estimators are better than the commonly used
 maximum-likelihood (ML) estimator and some other RR estimators. In this
 study, the modified Jackknifed Poisson ridge regression (MJPR) estimator
 is proposed to remedy the multicollinearity. A simulation study and a real
 data example are provided to evaluate the performance of estimators. Both
 mean-squared error and the percentage relative error are considered as the
 performance criteria. The simulation study and the real data example
 results show that the proposed MJPR method outperforms the Poisson ridge
 regression, Jackknifed Poisson ridge regression and the ML in all of the
 different situations evaluated in this paper.
Journal: Journal of Applied Statistics 
Pages: 1892-1905 
Issue: 10 
Volume: 43 
Year: 2016 
Month: 8 
X-DOI: 10.1080/02664763.2015.1125861 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1125861 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:10:p:1892-1905




Template-Type: ReDIF-Article 1.0
Author-Name: Didit B. Nugroho 
Author-X-Name-First: Didit B. 
Author-X-Name-Last: Nugroho 
Author-Name: Takayuki Morimoto 
Author-X-Name-First: Takayuki 
Author-X-Name-Last: Morimoto 
Title: Box--Cox realized asymmetric stochastic volatility models with generalized Student's <italic>t</italic>-error distributions 
Abstract:
 This study proposes a class of non-linear realized stochastic volatility
 (SV) model by applying the Box--Cox (BC) transformation, instead of the
 logarithmic transformation, to the realized estimator. The non-Gaussian
 distributions such as Student's <italic>t</italic>, non-central Student's
 <italic>t</italic>, and generalized hyperbolic skew Student's
 <italic>t</italic>-distributions are applied to accommodate
 heavy-tailedness and skewness in returns. The proposed models are fitted
 to daily returns and realized kernel of six stocks: SP500, FTSE100,
 Nikkei225, Nasdaq100, DAX, and DJIA using an Markov chain Monte Carlo
 Bayesian method, in which the Hamiltonian Monte Carlo (HMC) algorithm
 updates BC parameter and the Riemann manifold HMC algorithm updates latent
 variables and other parameters that are unable to be sampled directly.
 Empirical studies provide evidence against both the logarithmic
 transformation and raw versions of realized SV model.
Journal: Journal of Applied Statistics 
Pages: 1906-1927 
Issue: 10 
Volume: 43 
Year: 2016 
Month: 8 
X-DOI: 10.1080/02664763.2015.1125862 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1125862 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:10:p:1906-1927




Template-Type: ReDIF-Article 1.0
Author-Name: Melody Denhere 
Author-X-Name-First: Melody 
Author-X-Name-Last: Denhere 
Author-Name: Huybrechts F. Bindele 
Author-X-Name-First: Huybrechts F. 
Author-X-Name-Last: Bindele 
Title: Rank estimation for the functional linear model 
Abstract:
 This article discusses the estimation of the parameter function for a
 functional linear regression model under heavy-tailed errors'
 distributions and in the presence of outliers. Standard approaches of
 reducing the high dimensionality, which is inherent in functional data,
 are considered. After reducing the functional model to a standard multiple
 linear regression model, a weighted rank-based procedure is carried out to
 estimate the regression parameters. A Monte Carlo simulation and a
 real-world example are used to show the performance of the proposed
 estimator and a comparison made with the least-squares and least absolute
 deviation estimators.
Journal: Journal of Applied Statistics 
Pages: 1928-1944 
Issue: 10 
Volume: 43 
Year: 2016 
Month: 8 
X-DOI: 10.1080/02664763.2015.1125863 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1125863 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:10:p:1928-1944




Template-Type: ReDIF-Article 1.0
Author-Name: Housila P. Singh 
Author-X-Name-First: Housila P. 
Author-X-Name-Last: Singh 
Author-Name: Surya K. Pal 
Author-X-Name-First: Surya K. 
Author-X-Name-Last: Pal 
Title: An efficient class of estimators of finite population variance using quartiles 
Abstract:
 In this paper, we have proposed a class of estimators of finite population
 variance using known values of parameters related to an auxiliary variable
 such as quartiles and its properties are studied in simple random
 sampling. The suggested class of ratio-type estimators has been compared
 with the usual unbiased, ratio estimators and the class of ratio-type
 estimators due to Singh <italic>et al.</italic> [<italic>Improved
 estimation of finite population variance using quartiles</italic>,
 Istatistik -- J. Turkish Stat. Assoc. 6(3) (2013), pp. 166--121] and
 Solanki <italic>et al.</italic> [<italic>Improved ratio-type estimators of
 finite population variance using quartiles</italic>, Hacettepe J. Math.
 Stat. 44(3) (2015), pp. 747--754]. An empirical study is also carried out
 to judge the merits of the proposed estimator over other existing
 estimators of population variance using natural data set. It is found that
 the proposed class of ratio-type estimators
 &#x2018;<inline-formula><inline-graphic
 xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1125865_ilm0001.gif"/></inline-formula>&#x2019; is
 superior to the usual unbiased estimator <inline-formula><inline-graphic
 xmlns:xlink="http://www.w3.org/1999/xlink"
 xlink:href="cjas_a_1125865_ilm0002.gif"/></inline-formula> and the
 estimators recently proposed by Singh <italic>et al.</italic>
 [<italic>Improved estimation of finite population variance using
 quartiles</italic>, Istatistik -- J. Turkish Stat. Assoc. 6(3) (2013), pp.
 166--121] and Solanki <italic>et al.</italic> [<italic>Improved ratio-type
 estimators of finite population variance using quartiles</italic>,
 Hacettepe J. Math. Stat. 44(3) (2015), pp. 747--754].
Journal: Journal of Applied Statistics 
Pages: 1945-1958 
Issue: 10 
Volume: 43 
Year: 2016 
Month: 8 
X-DOI: 10.1080/02664763.2015.1125865 
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1125865 
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers. 
Handle: RePEc:taf:japsta:v:43:y:2016:i:10:p:1945-1958




Template-Type: ReDIF-Article 1.0
Author-Name: Douglas M. Hawkins
Author-X-Name-First: Douglas M.
Author-X-Name-Last: Hawkins
Author-Name: F. Lombard
Author-X-Name-First: F.
Author-X-Name-Last: Lombard
Title: Cusum control for data following the von Mises distribution
Abstract: 
 The von Mises distribution is widely used for modeling angular data. When such data are seen in a quality control setting, there may be interest in checking whether the values are in statistical control or have gone out of control. A cumulative sum (cusum) control chart has desirable properties for checking whether the distribution has changed from an in-control to an out-of-control setting. This paper develops cusums for a change in the mean direction and concentration of angular data and illustrates some of their properties.
Journal: Journal of Applied Statistics
Pages: 1319-1332
Issue: 8
Volume: 44
Year: 2017
Month: 6
X-DOI: 10.1080/02664763.2016.1202217
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1202217
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:8:p:1319-1332




Template-Type: ReDIF-Article 1.0
Author-Name: Rebecca M. Baker
Author-X-Name-First: Rebecca M.
Author-X-Name-Last: Baker
Author-Name: Tahani Coolen-Maturi
Author-X-Name-First: Tahani
Author-X-Name-Last: Coolen-Maturi
Author-Name: Frank P. A. Coolen
Author-X-Name-First: Frank P. A.
Author-X-Name-Last: Coolen
Title: Nonparametric predictive inference for stock returns
Abstract: 
 In finance, inferences about future asset returns are typically quantified with the use of parametric distributions and single-valued probabilities. It is attractive to use less restrictive inferential methods, including nonparametric methods which do not require distributional assumptions about variables, and imprecise probability methods which generalize the classical concept of probability to set-valued quantities. Main attractions include the flexibility of the inferences to adapt to the available data and that the level of imprecision in inferences can reflect the amount of data on which these are based. This paper introduces nonparametric predictive inference (NPI) for stock returns. NPI is a statistical approach based on few assumptions, with inferences strongly based on data and with uncertainty quantified via lower and upper probabilities. NPI is presented for inference about future stock returns, as a measure for risk and uncertainty, and for pairwise comparison of two stocks based on their future aggregate returns. The proposed NPI methods are illustrated using historical stock market data.
Journal: Journal of Applied Statistics
Pages: 1333-1349
Issue: 8
Volume: 44
Year: 2017
Month: 6
X-DOI: 10.1080/02664763.2016.1204429
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1204429
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:8:p:1333-1349




Template-Type: ReDIF-Article 1.0
Author-Name: Zhongxian Men
Author-X-Name-First: Zhongxian
Author-X-Name-Last: Men
Author-Name: Don McLeish
Author-X-Name-First: Don
Author-X-Name-Last: McLeish
Author-Name: Adam W. Kolkiewicz
Author-X-Name-First: Adam W.
Author-X-Name-Last: Kolkiewicz
Author-Name: Tony S. Wirjanto
Author-X-Name-First: Tony S.
Author-X-Name-Last: Wirjanto
Title: Comparison of asymmetric stochastic volatility models under different correlation structures
Abstract: 
 This paper conducts simulation-based comparison of several stochastic volatility models with leverage effects. Two new variants of asymmetric stochastic volatility models, which are subject to a logarithmic transformation on the squared asset returns, are proposed. The leverage effect is introduced into the model through correlation either between the innovations of the observation equation and the latent process, or between the logarithm of squared asset returns and the latent process. Suitable Markov Chain Monte Carlo algorithms are developed for parameter estimation and model comparison. Simulation results show that our proposed formulation of the leverage effect and the accompanying inference methods give rise to reasonable parameter estimates. Applications to two data sets uncover a negative correlation (which can be interpreted as a leverage effect) between the observed returns and volatilities, and a negative correlation between the logarithm of squared returns and volatilities.
Journal: Journal of Applied Statistics
Pages: 1350-1368
Issue: 8
Volume: 44
Year: 2017
Month: 6
X-DOI: 10.1080/02664763.2016.1204596
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1204596
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:8:p:1350-1368




Template-Type: ReDIF-Article 1.0
Author-Name: N. Davarzani
Author-X-Name-First: N.
Author-X-Name-Last: Davarzani
Author-Name: L. Golparvar
Author-X-Name-First: L.
Author-X-Name-Last: Golparvar
Author-Name: A. Parsian
Author-X-Name-First: A.
Author-X-Name-Last: Parsian
Author-Name: R. Peeters
Author-X-Name-First: R.
Author-X-Name-Last: Peeters
Title: Estimation on dependent right censoring scheme in an ordinary bivariate geometric distribution
Abstract: 
 Discrete lifetime data are very common in engineering and medical researches. In many cases the lifetime is censored at a random or predetermined time and we do not know the complete survival time. There are many situations that the lifetime variable could be dependent on the time of censoring. In this paper we propose the dependent right censoring scheme in discrete setup when the lifetime and censoring variables have a bivariate geometric distribution. We obtain the maximum likelihood estimators of the unknown parameters with their risks in closed forms. The Bayes estimators as well as the constrained Bayes estimates of the unknown parameters under the squared error loss function are also obtained. We considered an extension to the case where covariates are present along with the data. Finally we provided a simulation study and an illustrative example with a real data.
Journal: Journal of Applied Statistics
Pages: 1369-1384
Issue: 8
Volume: 44
Year: 2017
Month: 6
X-DOI: 10.1080/02664763.2016.1206064
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1206064
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:8:p:1369-1384




Template-Type: ReDIF-Article 1.0
Author-Name: A. Mahabbati
Author-X-Name-First: A.
Author-X-Name-Last: Mahabbati
Author-Name: A. Izady
Author-X-Name-First: A.
Author-X-Name-Last: Izady
Author-Name: M. Mousavi Baygi
Author-X-Name-First: M.
Author-X-Name-Last: Mousavi Baygi
Author-Name: K. Davary
Author-X-Name-First: K.
Author-X-Name-Last: Davary
Author-Name: S. M. Hasheminia
Author-X-Name-First: S. M.
Author-X-Name-Last: Hasheminia
Title: Daily soil temperature modeling using ‘panel-data’ concept
Abstract: 
 The purpose of this research was to predict soil temperature profile using ‘panel-data’ models. Panel-data analysis endows regression analysis with both spatial and temporal dimensions. The spatial dimension pertains to a set of cross-sectional units of observation. The temporal dimension pertains to periodic observations of a set of variables characterizing these cross-sectional units over a particular time-span. This study was conducted in Khorasan-Razavi Province, Iran. Daily mean soil temperatures for 9 years (2001–2009), in 6 different depths (5, 10, 20, 30, 50 and 100 cm) under bare soil surface at 10 meteorological stations were used. The data were divided into two sub-sets for training (parameter training) over the period of 2001–2008, and validation over the period of the year 2009. The panel-data models were developed using the average air temperature and rainfall of the day before ( $ {T_{d - 1}} $ Td−1 and  $ {R_{t - 1}} $ Rt−1, respectively) and the average air temperature of the past 7 days (Tw) as inputs in order to predict the average soil temperature of the next day. The results showed that the two-way fixed effects models were superior. The performance indicators (R2 = 0.94 to 0.99, RMSE = 0.46 to 1.29 and MBE = −0.83 and 0.74) revealed the effectiveness of this model. In addition, these results were compared with the results of classic linear regression models using t-test, which showed the superiority of the panel-data models.
Journal: Journal of Applied Statistics
Pages: 1385-1401
Issue: 8
Volume: 44
Year: 2017
Month: 6
X-DOI: 10.1080/02664763.2016.1214240
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1214240
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:8:p:1385-1401




Template-Type: ReDIF-Article 1.0
Author-Name: Philip L.H. Yu
Author-X-Name-First: Philip L.H.
Author-X-Name-Last: Yu
Author-Name: Thomas Mathew
Author-X-Name-First: Thomas
Author-X-Name-Last: Mathew
Author-Name: Yuanyuan Zhu
Author-X-Name-First: Yuanyuan
Author-X-Name-Last: Zhu
Title: A generalized pivotal quantity approach to portfolio selection
Abstract: 
 The major problem of mean–variance portfolio optimization is parameter uncertainty. Many methods have been proposed to tackle this problem, including shrinkage methods, resampling techniques, and imposing constraints on the portfolio weights, etc. This paper suggests a new estimation method for mean–variance portfolio weights based on the concept of generalized pivotal quantity (GPQ) in the case when asset returns are multivariate normally distributed and serially independent. Both point and interval estimations of the portfolio weights are considered. Comparing with Markowitz's mean–variance model, resampling and shrinkage methods, we find that the proposed GPQ method typically yields the smallest mean-squared error for the point estimate of the portfolio weights and obtains a satisfactory coverage rate for their simultaneous confidence intervals. Finally, we apply the proposed methodology to address a portfolio rebalancing problem.
Journal: Journal of Applied Statistics
Pages: 1402-1420
Issue: 8
Volume: 44
Year: 2017
Month: 6
X-DOI: 10.1080/02664763.2016.1214241
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1214241
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:8:p:1402-1420




Template-Type: ReDIF-Article 1.0
Author-Name: Xiaojuan Zhu
Author-X-Name-First: Xiaojuan
Author-X-Name-Last: Zhu
Author-Name: William Seaver
Author-X-Name-First: William
Author-X-Name-Last: Seaver
Author-Name: Rapinder Sawhney
Author-X-Name-First: Rapinder
Author-X-Name-Last: Sawhney
Author-Name: Shuguang Ji
Author-X-Name-First: Shuguang
Author-X-Name-Last: Ji
Author-Name: Bruce Holt
Author-X-Name-First: Bruce
Author-X-Name-Last: Holt
Author-Name: Gurudatt Bhaskar Sanil
Author-X-Name-First: Gurudatt Bhaskar
Author-X-Name-Last: Sanil
Author-Name: Girish Upreti
Author-X-Name-First: Girish
Author-X-Name-Last: Upreti
Title: Employee turnover forecasting for human resource management based on time series analysis
Abstract: 
 In some organizations, the hiring lead time is often long due to responding to human resource requirements associated with technical and security constrains. Thus, the human resource departments in these organizations are pretty interested in forecasting employee turnover since a good prediction of employee turnover could help the organizations to minimize the costs and impacts from the turnover on the operational capabilities and the budget. This study aims to enhance the ability to forecast employee turnover with or without considering the impact of economic indicators. Various time series modelling techniques were used to identify optimal models for effective employee turnover prediction. More than 11-years of monthly turnover data were used to build and validate the proposed models. Compared with other models, a dynamic regression model with additive trend, seasonality, interventions, and a very important economic indicator effectively predicted the turnover with training R2 = 0.77 and holdout R2 = 0.59. The forecasting performance of optimal models confirms that time series modelling approach has the ability to predict employee turnover for the specific scenario observed in our analysis.
Journal: Journal of Applied Statistics
Pages: 1421-1440
Issue: 8
Volume: 44
Year: 2017
Month: 6
X-DOI: 10.1080/02664763.2016.1214242
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1214242
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:8:p:1421-1440




Template-Type: ReDIF-Article 1.0
Author-Name: Safwan A. Altarazi
Author-X-Name-First: Safwan A.
Author-X-Name-Last: Altarazi
Author-Name: Rula M. Allaf
Author-X-Name-First: Rula M.
Author-X-Name-Last: Allaf
Title: Designing and analyzing a mixture experiment to optimize the mixing proportions of polyvinyl chloride composites
Abstract: 
 Polyvinyl chloride (PVC) products are typically complex composites, whose quality characteristics vary widely depending on the types and proportions of their components, as well as other processing factors. It is often required to optimize PVC production for specific applications at the highest cost efficiency. This study describes the design and analysis of a statistical experiment to investigate the effects of different parameters over the mechanical properties of PVC intended for use in electrical wire insulation. Four commonly used mixture components, namely, virgin PVC, recycled PVC, calcium carbonate, and a plasticizer, and two process variables, type of plasticizer and filler particle size, were examined. Statistical tools were utilized to analyze and optimize the mixture while simultaneously finding the proper process parameters. The mix was optimized to achieve required strength and ductility, as per ASTM D6096 while minimizing cost. The paper demonstrates how statistical models can help tailor complex polymeric composites in the presence of variations created by process variables.
Journal: Journal of Applied Statistics
Pages: 1441-1465
Issue: 8
Volume: 44
Year: 2017
Month: 6
X-DOI: 10.1080/02664763.2016.1214243
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1214243
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:8:p:1441-1465




Template-Type: ReDIF-Article 1.0
Author-Name: Yang Lei
Author-X-Name-First: Yang
Author-X-Name-Last: Lei
Author-Name: Susan Carlson
Author-X-Name-First: Susan
Author-X-Name-Last: Carlson
Author-Name: Lisa N. Yelland
Author-X-Name-First: Lisa N.
Author-X-Name-Last: Yelland
Author-Name: Maria Makrides
Author-X-Name-First: Maria
Author-X-Name-Last: Makrides
Author-Name: Robert Gibson
Author-X-Name-First: Robert
Author-X-Name-Last: Gibson
Author-Name: Byron J. Gajewski
Author-X-Name-First: Byron J.
Author-X-Name-Last: Gajewski
Title: Comparison of dichotomized and distributional approaches in rare event clinical trial design: a fixed Bayesian design
Abstract: 
 This research was motivated by our goal to design an efficient clinical trial to compare two doses of docosahexaenoic acid supplementation for reducing the rate of earliest preterm births (ePTB) and/or preterm births (PTB). Dichotomizing continuous gestational age (GA) data using a classic binomial distribution will result in a loss of information and reduced power. A distributional approach is an improved strategy to retain statistical power from the continuous distribution. However, appropriate distributions that fit the data properly, particularly in the tails, must be chosen, especially when the data are skewed. A recent study proposed a skew-normal method. We propose a three-component normal mixture model and introduce separate treatment effects at different components of GA. We evaluate operating characteristics of mixture model, beta-binomial model, and skew-normal model through simulation. We also apply these three methods to data from two completed clinical trials from the USA and Australia. Finite mixture models are shown to have favorable properties in PTB analysis but minimal benefit for ePTB analysis. Normal models on log-transformed data have the largest bias. Therefore we recommend finite mixture model for PTB study. Either finite mixture model or beta-binomial model is acceptable for ePTB study.
Journal: Journal of Applied Statistics
Pages: 1466-1478
Issue: 8
Volume: 44
Year: 2017
Month: 6
X-DOI: 10.1080/02664763.2016.1214244
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1214244
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:8:p:1466-1478




Template-Type: ReDIF-Article 1.0
Author-Name: Heba S. Mohammed
Author-X-Name-First: Heba S.
Author-X-Name-Last: Mohammed
Author-Name: Saieed F. Ateya
Author-X-Name-First: Saieed F.
Author-X-Name-Last: Ateya
Author-Name: Essam K. AL-Hussaini
Author-X-Name-First: Essam K.
Author-X-Name-Last: AL-Hussaini
Title: Estimation based on progressive first-failure censoring from exponentiated exponential distribution
Abstract: 
 In this paper, point and interval estimations for the parameters of the exponentiated exponential (EE) distribution are studied based on progressive first-failure-censored data. The Bayes estimates are computed based on squared error and Linex loss functions and using Markov Chain Monte Carlo (MCMC) algorithm. Also, based on this censoring scheme, approximate confidence intervals for the parameters of EE distribution are developed. Monte Carlo simulation study is carried out to compare the performances of the different methods by computing the estimated risks (ERs), as well as Akaike's information criteria (AIC) and Bayesian information criteria (BIC) of the estimates. Finally, a real data set is introduced and analyzed using EE and Weibull distributions. A comparison is carried out between the mentioned models based on the corresponding Kolmogorov–Smirnov (K–S) test statistic to emphasize that the EE model fits the data with the same efficiency as the other model. Point and interval estimation of all parameters are studied based on this real data set as illustrative example.
Journal: Journal of Applied Statistics
Pages: 1479-1494
Issue: 8
Volume: 44
Year: 2017
Month: 6
X-DOI: 10.1080/02664763.2016.1214245
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1214245
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:8:p:1479-1494




Template-Type: ReDIF-Article 1.0
Author-Name: Farag Shuweihdi
Author-X-Name-First: Farag
Author-X-Name-Last: Shuweihdi
Author-Name: Charles C. Taylor
Author-X-Name-First: Charles C.
Author-X-Name-Last: Taylor
Author-Name: Arief Gusnanto
Author-X-Name-First: Arief
Author-X-Name-Last: Gusnanto
Title: Classification of form under heterogeneity and non-isotropic errors
Abstract: 
 A number of areas related to learning under supervision have not been fully investigated, particularly the possibility of incorporating the method of classification into shape analysis. In this regard, practical ideas conducive to the improvement of form classification are the focus of interest. Our proposal is to employ a hybrid classifier built on Euclidean Distance Matrix Analysis (EDMA) and Procrustes distance, rather than generalised Procrustes analysis (GPA). In empirical terms, it has been demonstrated that there is notable difference between the estimated form and the true form when EDMA is used as the basis for computation. However, this does not seem to be the case when GPA is employed. With the assumption that no association exists between landmarks, EDMA and GPA are used to calculate the mean form and diagonal weighting matrix to build superimposing classifiers. As our findings indicate, with the use of EDMA estimators, the superimposing classifiers we propose work extremely well, as opposed to the use of GPA, as far as both simulated and real datasets are concerned.
Journal: Journal of Applied Statistics
Pages: 1495-1508
Issue: 8
Volume: 44
Year: 2017
Month: 6
X-DOI: 10.1080/02664763.2016.1214246
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1214246
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:8:p:1495-1508




Template-Type: ReDIF-Article 1.0
Author-Name: R. Scott Hacker
Author-X-Name-First: R.
Author-X-Name-Last: Scott Hacker
Author-Name: Abdulnasser Hatemi-J
Author-X-Name-First: Abdulnasser
Author-X-Name-Last: Hatemi-J
Title: Optimal lag-length choice in stable and unstable VAR models under situations of homoscedasticity and ARCH
Abstract: The performance of different information criteria – namely Akaike, corrected Akaike (AICC), Schwarz–Bayesian (SBC), and Hannan–Quinn – is investigated so as to choose the optimal lag length in stable and unstable vector autoregressive (VAR) models both when autoregressive conditional heteroscedasticity (ARCH) is present and when it is not. The investigation covers both large and small sample sizes. The Monte Carlo simulation results show that SBC has relatively better performance in lag-choice accuracy in many situations. It is also generally the least sensitive to ARCH regardless of stability or instability of the VAR model, especially in large sample sizes. These appealing properties of SBC make it the optimal criterion for choosing lag length in many situations, especially in the case of financial data, which are usually characterized by occasional periods of high volatility. SBC also has the best forecasting abilities in the majority of situations in which we vary sample size, stability, variance structure (ARCH or not), and forecast horizon (one period or five). frequently, AICC also has good lag-choosing and forecasting properties. However, when ARCH is present, the five-period forecast performance of all criteria in all situations worsens.
Journal: Journal of Applied Statistics
Pages: 601-615
Issue: 6
Volume: 35
Year: 2008
X-DOI: 10.1080/02664760801920473
File-URL: http://hdl.handle.net/10.1080/02664760801920473
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:35:y:2008:i:6:p:601-615




Template-Type: ReDIF-Article 1.0
Author-Name: J. López-Fidalgo
Author-X-Name-First: J.
Author-X-Name-Last: López-Fidalgo
Author-Name: R. Martín-Martín
Author-X-Name-First: R.
Author-X-Name-Last: Martín-Martín
Author-Name: M. Stehlík
Author-X-Name-First: M.
Author-X-Name-Last: Stehlík
Title: Marginally restricted D-optimal designs for correlated observations
Abstract: Two practical degrees of complexity may arise when designing an experiment for a model of a real life case. First, some explanatory variables may not be under the control of the practitioner. Secondly, the responses may be correlated. In this paper three real life cases in this situation are considered. Different covariance structures are studied and some designs are computed adapting the theory of marginally restricted designs for correlated observations. An exchange algorithm given by Brimkulov's algorithm is also adapted to marginally restricted D–optimality and it is applied to a complex situation.
Journal: Journal of Applied Statistics
Pages: 617-632
Issue: 6
Volume: 35
Year: 2008
X-DOI: 10.1080/02664760801920556
File-URL: http://hdl.handle.net/10.1080/02664760801920556
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:35:y:2008:i:6:p:617-632




Template-Type: ReDIF-Article 1.0
Author-Name: Rahul Mazumder
Author-X-Name-First: Rahul
Author-X-Name-Last: Mazumder
Title: Fluid flow pattern analysis in a trough region: a nonparametric approach
Abstract: This paper aims at identifying statistically different circulation patterns characterising fluid flow in the trough region between two adjacent asymmetric waveforms, using the velocity data collected by 3D acoustic Doppler velocimeter. Statistical clustering has been performed using ideas originating from information theory and scale space theory in computer vision for splitting the trough region into different spatially connected segments (identifying the circulation bubble in the process) on the basis of circulation patterns. The paper attempts to visualise the fluid fluctuations in the trough region, with emphasis on the circulation region, by simulating the directional fluctuations of fluid particles from the kernel density estimates learned from the experimental data. The image representation of the estimate of the spatial turbulent kinetic energy (TKE) function reveals interesting features corresponding to the regions of high TKE, suggesting the possibilities for further research in this area along the lines of feature extraction and image analysis.
Journal: Journal of Applied Statistics
Pages: 633-645
Issue: 6
Volume: 35
Year: 2008
X-DOI: 10.1080/02664760801920671
File-URL: http://hdl.handle.net/10.1080/02664760801920671
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:35:y:2008:i:6:p:633-645




Template-Type: ReDIF-Article 1.0
Author-Name: H. Jiang
Author-X-Name-First: H.
Author-X-Name-Last: Jiang
Author-Name: M. Xie
Author-X-Name-First: M.
Author-X-Name-Last: Xie
Author-Name: L.C. Tang
Author-X-Name-First: L.C.
Author-X-Name-Last: Tang
Title: Markov chain Monte Carlo methods for parameter estimation of the modified Weibull distribution
Abstract: In this paper, the Markov chain Monte Carlo (MCMC) method is used to estimate the parameters of a modified Weibull distribution based on a complete sample. While maximum-likelihood estimation (MLE) is the most used method for parameter estimation, MCMC has recently emerged as a good alternative. When applied to parameter estimation, MCMC methods have been shown to be easy to implement computationally, the estimates always exist and are statistically consistent, and their probability intervals are convenient to construct. Details of applying MCMC to parameter estimation for the modified Weibull model are elaborated and a numerical example is presented to illustrate the methods of inference discussed in this paper. To compare MCMC with MLE, a simulation study is provided, and the differences between the estimates obtained by the two algorithms are examined.
Journal: Journal of Applied Statistics
Pages: 647-658
Issue: 6
Volume: 35
Year: 2008
X-DOI: 10.1080/02664760801920846
File-URL: http://hdl.handle.net/10.1080/02664760801920846
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:35:y:2008:i:6:p:647-658




Template-Type: ReDIF-Article 1.0
Author-Name: Valentin Rousson
Author-X-Name-First: Valentin
Author-X-Name-Last: Rousson
Title: Monotone fitting for developmental variables
Abstract: In order to study developmental variables, for example, neuromotor development of children and adolescents, monotone fitting is typically needed. Most methods, to estimate a monotone regression function non-parametrically, however, are not straightforward to implement, a difficult issue being the choice of smoothing parameters. In this paper, a convenient implementation of the monotone B-spline estimates of Ramsay [Monotone regression splines in action (with discussion), Stat. Sci. 3 (1988), pp. 425–461] and Kelly and Rice [Montone smoothing with application to dose-response curves and the assessment of synergism, Biometrics 46 (1990), pp. 1071–1085] is proposed and applied to neuromotor data. Knots are selected adaptively using ideas found in Friedman and Silverman [Flexible parsimonous smoothing and additive modelling (with discussion), Technometrics 31 (1989), pp. 3–39] yielding a flexible algorithm to automatically and accurately estimate a monotone regression function. Using splines also simultaneously allows to include other aspects in the estimation problem, such as modeling a constant difference between two groups or a known jump in the regression function. Finally, an estimate which is not only monotone but also has a ‘levelling-off’ (i.e. becomes constant after some point) is derived. This is useful when the developmental variable is known to attain a maximum/minimum within the interval of observation.
Journal: Journal of Applied Statistics
Pages: 659-670
Issue: 6
Volume: 35
Year: 2008
X-DOI: 10.1080/02664760801920960
File-URL: http://hdl.handle.net/10.1080/02664760801920960
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:35:y:2008:i:6:p:659-670




Template-Type: ReDIF-Article 1.0
Author-Name: C. Lai
Author-X-Name-First: C.
Author-X-Name-Last: Lai
Author-Name: K. Govindaraju
Author-X-Name-First: K.
Author-X-Name-Last: Govindaraju
Title: Reduction of control-chart signal variablity for high-quality processes
Abstract: The design of a control chart is often based on the statistical measure of average run length (ARL). A longer in-control ARL is ensured by the design, but the variance run length distribution may also be large for such a design. In practical terms, the variability in false alarms and true signals may be large. If the sample size for plotting a point is not constant, then the focus is on the average number inspected as against the ARL. This article considers two well-known attribute control chart procedures for monitoring high quality based on the number inspected, and shows how the variability in false alarms and correct signals can be reduced.
Journal: Journal of Applied Statistics
Pages: 671-679
Issue: 6
Volume: 35
Year: 2008
X-DOI: 10.1080/02664760801921232
File-URL: http://hdl.handle.net/10.1080/02664760801921232
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:35:y:2008:i:6:p:671-679




Template-Type: ReDIF-Article 1.0
Author-Name: Tapio Nummi
Author-X-Name-First: Tapio
Author-X-Name-Last: Nummi
Author-Name: Laura Koskela
Author-X-Name-First: Laura
Author-X-Name-Last: Koskela
Title: Analysis of growth curve data by using cubic smoothing splines
Abstract: Longitudinal data frequently arises in various fields of applied sciences where individuals are measured according to some ordered variable, e.g. time. A common approach used to model such data is based on the mixed models for repeated measures. This model provides an eminently flexible approach to modeling of a wide range of mean and covariance structures. However, such models are forced into a rigidly defined class of mathematical formulas which may not be well supported by the data within the whole sequence of observations. A possible non-parametric alternative is a cubic smoothing spline, which is highly flexible and has useful smoothing properties. It can be shown that under normality assumption, the solution of the penalized log-likelihood equation is the cubic smoothing spline, and this solution can be further expressed as a solution of the linear mixed model. It is shown here how cubic smoothing splines can be easily used in the analysis of complete and balanced data. Analysis can be greatly simplified by using the unweighted estimator studied in the paper. It is shown that if the covariance structure of random errors belong to certain class of matrices, the unweighted estimator is the solution to the penalized log-likelihood function. This result is new in smoothing spline context and it is not only confined to growth curve settings. The connection to mixed models is used in developing a rough testing of group profiles. Numerical examples are presented to illustrate the techniques proposed.
Journal: Journal of Applied Statistics
Pages: 681-691
Issue: 6
Volume: 35
Year: 2008
X-DOI: 10.1080/02664760801923964
File-URL: http://hdl.handle.net/10.1080/02664760801923964
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:35:y:2008:i:6:p:681-691




Template-Type: ReDIF-Article 1.0
Author-Name: Ashis Sengupta
Author-X-Name-First: Ashis
Author-X-Name-Last: Sengupta
Author-Name: Arnab Kumar Laha
Author-X-Name-First: Arnab
Author-X-Name-Last: Kumar Laha
Title: A Bayesian analysis of the change-point problem for directional data
Abstract: In this paper, we discuss a simple fully Bayesian analysis of the change-point problem for the directional data in the parametric framework with von Mises or circular normal distribution as the underlying distribution. We first discuss the problem of detecting change in the mean direction of the circular normal distribution using a latent variable approach when the concentration parameter is unknown. Then, a simpler approach, beginning with proper priors for all the unknown parameters – the sampling importance resampling technique – is used to obtain the posterior marginal distribution of the change-point. The method is illustrated using the wind data [E.P. Weijers, A. Van Delden, H.F. Vugts and A.G.C.A. Meesters, The composite horizontal wind field within convective structures of the atmospheric surface layer, J. Atmos. Sci. 52 (1995. 3866–3878]. The method can be adapted for a variety of situations involving both angular and linear data and can be used with profit in the context of statistical process control in Phase I of control charting and also in Phase II in conjunction with control charts.
Journal: Journal of Applied Statistics
Pages: 693-700
Issue: 6
Volume: 35
Year: 2008
X-DOI: 10.1080/02664760801924004
File-URL: http://hdl.handle.net/10.1080/02664760801924004
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:35:y:2008:i:6:p:693-700




Template-Type: ReDIF-Article 1.0
Author-Name: Byoung Cheol Jung
Author-X-Name-First: Byoung
Author-X-Name-Last: Cheol Jung
Author-Name: André Khuri
Author-X-Name-First: André
Author-X-Name-Last: Khuri
Author-Name: Juneyoung Lee
Author-X-Name-First: Juneyoung
Author-X-Name-Last: Lee
Title: Comparison of designs for the three-fold nested random model
Abstract: The quality of estimation of variance components depends on the design used as well as on the unknown values of the variance components. In this article, three designs are compared, namely, the balanced, staggered, and inverted nested designs for the three-fold nested random model. The comparison is based on the so-called quantile dispersion graphs using analysis of variance (ANOVA) and maximum likelihood (ML) estimates of the variance components. It is demonstrated that the staggered nested design gives more stable estimates of the variance component for the highest nesting factor than the balanced design. The reverse, however, is true in case of lower nested factors. A comparison between ANOVA and ML estimation of the variance components is also made using each of the aforementioned designs.
Journal: Journal of Applied Statistics
Pages: 701-715
Issue: 6
Volume: 35
Year: 2008
X-DOI: 10.1080/02664760801924079
File-URL: http://hdl.handle.net/10.1080/02664760801924079
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:35:y:2008:i:6:p:701-715




Template-Type: ReDIF-Article 1.0
Author-Name: Rubén Manso
Author-X-Name-First: Rubén
Author-X-Name-Last: Manso
Author-Name: Rafael Calama
Author-X-Name-First: Rafael
Author-X-Name-Last: Calama
Author-Name: Marta Pardos
Author-X-Name-First: Marta
Author-X-Name-Last: Pardos
Author-Name: Mathieu Fortin
Author-X-Name-First: Mathieu
Author-X-Name-Last: Fortin
Title: A maximum likelihood estimator for left-truncated lifetimes based on probabilistic prior information about time of occurrence
Abstract: 
 In forestry, many processes of interest are binary and they can be modeled using lifetime analysis. However, available data are often incomplete, being interval- and right-censored as well as left-truncated, which may lead to biased parameter estimates. While censoring can be easily considered in lifetime analysis, left truncation is more complicated when individual age at selection is unknown. In this study, we designed and tested a maximum likelihood estimator that deals with left truncation by taking advantage of prior knowledge about the time when the individuals enter the experiment. Whenever a model is available for predicting the time of selection, the distribution of the delayed entries can be obtained using Bayes' theorem. It is then possible to marginalize the likelihood function over the distribution of the delayed entries in the experiment to assess the joint distribution of time of selection and time to event. This estimator was tested with continuous and discrete Gompertz-distributed lifetimes. It was then compared with two other estimators: a standard one in which left truncation was not considered and a second estimator that implemented an analytical correction. Our new estimator yielded unbiased parameter estimates with empirical coverage of confidence intervals close to their nominal value. The standard estimator leaded to an overestimation of the long-term probability of survival.
Journal: Journal of Applied Statistics
Pages: 2107-2127
Issue: 12
Volume: 45
Year: 2018
Month: 9
X-DOI: 10.1080/02664763.2017.1410527
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1410527
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:12:p:2107-2127




Template-Type: ReDIF-Article 1.0
Author-Name: Xin Zhao
Author-X-Name-First: Xin
Author-X-Name-Last: Zhao
Author-Name: Zhensheng Huang
Author-X-Name-First: Zhensheng
Author-X-Name-Last: Huang
Title: Varying-coefficient single-index measurement error model
Abstract: 
 This paper proposes a varying-coefficient single-index measurement error model, which consists of measurement error in the index covariates. We combine the simulation-extrapolation technique, the local linear regression and the weighted least-squares method to estimate the unknowns of the current model, and develop the asymptotic properties of the resulting estimators under some conditions. A simulation study is conducted to evaluate the proposed methodology, and a real example is also studied to illustrate our given methodology.
Journal: Journal of Applied Statistics
Pages: 2128-2144
Issue: 12
Volume: 45
Year: 2018
Month: 9
X-DOI: 10.1080/02664763.2017.1410528
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1410528
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:12:p:2128-2144




Template-Type: ReDIF-Article 1.0
Author-Name: Sang Gyu Kwak
Author-X-Name-First: Sang Gyu
Author-X-Name-Last: Kwak
Author-Name: Balgobin Nandram
Author-X-Name-First: Balgobin
Author-X-Name-Last: Nandram
Author-Name: Dal Ho Kim
Author-X-Name-First: Dal Ho
Author-X-Name-Last: Kim
Title: Bayesian inference on contingency tables with uncertainty about independence for small areas
Abstract: 
 A scientist might have vague information about independence/dependence in a two-way table, and a statistician might proceed with estimation conditional on this piece of information. However, one needs to take into account the uncertainty in this information which can increase variability. We develop a Bayesian method to solve this problem when estimation is needed for the cells of a  $ r \times c $ r×c contingency table and there is uncertainty about independence or dependence. In our problem, there are several small areas and a  $ r \times c $ r×c table is constructed for each area. We use the hierarchical Dirichlet-multinomial model to analyze the counts from these small areas. The key idea in our method is that the cell probabilities of each area is expressed as a convex combination of the cell probabilities under independence and the cell probabilities under dependence, where each area has its own unknown weight. We show how to fit the model using the Gibbs sampler even though many of the conditional posterior densities are nonstandard. As a by product of our method, we have actually produced a test of independence which is competitive to the chi-square test for a single table. To illustrate our method, we have used an example on body mass index and bone mineral density data obtained from NHANES III. We have shown some important differences among the three scenarios (independence, dependence and the convex combination of these two) when Bayesian predictive inference is done for the finite population means corresponding to each cell of the  $ r \times c $ r×c table.
Journal: Journal of Applied Statistics
Pages: 2145-2163
Issue: 12
Volume: 45
Year: 2018
Month: 9
X-DOI: 10.1080/02664763.2017.1413074
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1413074
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:12:p:2145-2163




Template-Type: ReDIF-Article 1.0
Author-Name: Jiajia Chen
Author-X-Name-First: Jiajia
Author-X-Name-Last: Chen
Author-Name: Xiaoqin Zhang
Author-X-Name-First: Xiaoqin
Author-X-Name-Last: Zhang
Author-Name: Shengjia Li
Author-X-Name-First: Shengjia
Author-X-Name-Last: Li
Title: Heteroskedastic linear regression model with compositional response and covariates
Abstract: 
 Compositional data are known as a sort of complex multidimensional data with the feature that reflect the relative information rather than absolute information. There are a variety of models for regression analysis with compositional variables. Similar to the traditional regression analysis, the heteroskedasticity still exists in these models. However, the existing heteroskedastic regression analysis methods cannot apply in these models with compositional error term. In this paper, we mainly study the heteroskedastic linear regression model with compositional response and covariates. The parameter estimator is obtained through weighted least squares method. For the hypothesis test of parameter, the test statistic is based on the original least squares estimator and corresponding heteroskedasticity-consistent covariance matrix estimator. When the proposed method is applied to both simulation and real example, we use the original least squares method as a comparison during the whole process. The results implicate the model's practicality and effectiveness in regression analysis with heteroskedasticity.
Journal: Journal of Applied Statistics
Pages: 2164-2181
Issue: 12
Volume: 45
Year: 2018
Month: 9
X-DOI: 10.1080/02664763.2017.1413075
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1413075
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:12:p:2164-2181




Template-Type: ReDIF-Article 1.0
Author-Name: S. Eftekhari Mahabadi
Author-X-Name-First: S.
Author-X-Name-Last: Eftekhari Mahabadi
Author-Name: E. Rahimi Jafari
Author-X-Name-First: E.
Author-X-Name-Last: Rahimi Jafari
Title: Skew-mixed effects model for multivariate longitudinal data with categorical outcomes and missingness
Abstract: 
 A longitudinal study commonly follows a set of variables, measured for each individual repeatedly over time, and usually suffers from incomplete data problem. A common approach for dealing with longitudinal categorical responses is to use the Generalized Linear Mixed Model (GLMM). This model induces the potential relation between response variables over time via a vector of random effects, assumed to be shared parameters in the non-ignorable missing mechanism. Most GLMMs assume that the random-effects parameters follow a normal or symmetric distribution and this leads to serious problems in real applications. In this paper, we propose GLMMs for the analysis of incomplete multivariate longitudinal categorical responses with a non-ignorable missing mechanism based on a shared parameter framework with the less restrictive assumption of skew-normality for the random effects. These models may contain incomplete data with monotone and non-monotone missing patterns. The performance of the model is evaluated using simulation studies and a well-known longitudinal data set extracted from a fluvoxamine trial is analyzed to determine the profile of fluvoxamine in ambulatory clinical psychiatric practice.
Journal: Journal of Applied Statistics
Pages: 2182-2201
Issue: 12
Volume: 45
Year: 2018
Month: 9
X-DOI: 10.1080/02664763.2017.1413076
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1413076
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:12:p:2182-2201




Template-Type: ReDIF-Article 1.0
Author-Name: Marcel Ausloos
Author-X-Name-First: Marcel
Author-X-Name-Last: Ausloos
Author-Name: Roy Cerqueti
Author-X-Name-First: Roy
Author-X-Name-Last: Cerqueti
Title: Intriguing yet simple skewness: kurtosis relation in economic and demographic data distributions, pointing to preferential attachment processes
Abstract: 
 In this paper, we propose that relations between high-order moments of data distributions, for example, between the skewness (S) and kurtosis (K), allow to point to theoretical models with understandable structural parameters. The illustrative data concern two cases: (i) the distribution of income taxes and (ii) that of inhabitants, after aggregation over each city in each province of Italy in 2011. Moreover, from the rank-size relationship, for either S or K, in both cases, it is shown that one obtains the parameters of the underlying (hypothetical) modeling distribution: in the present cases, the 2-parameter Beta function, itself related to the Yule–Simon distribution function, whence suggesting a growth model based on the preferential attachment process.
Journal: Journal of Applied Statistics
Pages: 2202-2218
Issue: 12
Volume: 45
Year: 2018
Month: 9
X-DOI: 10.1080/02664763.2017.1413077
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1413077
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:12:p:2202-2218




Template-Type: ReDIF-Article 1.0
Author-Name: Anthony Zullo
Author-X-Name-First: Anthony
Author-X-Name-Last: Zullo
Author-Name: Mathieu Fauvel
Author-X-Name-First: Mathieu
Author-X-Name-Last: Fauvel
Author-Name: Frédéric Ferraty
Author-X-Name-First: Frédéric
Author-X-Name-Last: Ferraty
Title: Experimental comparison of functional and multivariate spectral-based supervised classification methods in hyperspectral image
Abstract: 
 The aim of this article is to assess and compare several statistical methods for hyperspectral image supervised classification only using the spectral dimension. Since hyperspectral profiles may be viewed either as a random vector or a random curve, we propose to confront various multivariate discriminating procedures with functional alternatives. Eight methods representing three important statistical communities (mixture models, machine learning and functional data analysis) have been applied on three hyperspectral datasets following three protocols studying the influence of size and composition of the learning sample, with or without noised labels. Besides this comparative study, this work proposes a functional extension of multinomial logit model as well as a fast computing adaptation of the nonparametric functional discrimination. As a by-product, this work provides a useful comprehensive bibliography and also supplemental material especially oriented towards practitioners.
Journal: Journal of Applied Statistics
Pages: 2219-2237
Issue: 12
Volume: 45
Year: 2018
Month: 9
X-DOI: 10.1080/02664763.2017.1414162
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1414162
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:12:p:2219-2237




Template-Type: ReDIF-Article 1.0
Author-Name: Julie Ann Lorah
Author-X-Name-First: Julie Ann
Author-X-Name-Last: Lorah
Title: Estimating individual-level interaction effects in multilevel models: a Monte Carlo simulation study with application
Abstract: 
 Moderated multiple regression provides a useful framework for understanding moderator variables. These variables can also be examined within multilevel datasets, although the literature is not clear on the best way to assess data for significant moderating effects, particularly within a multilevel modeling framework. This study explores potential ways to test moderation at the individual level (level one) within a 2-level multilevel modeling framework, with varying effect sizes, cluster sizes, and numbers of clusters. The study examines five potential methods for testing interaction effects: the Wald test, F-test, likelihood ratio test, Bayesian information criterion (BIC), and Akaike information criterion (AIC). For each method, the simulation study examines Type I error rates and power. Following the simulation study, an applied study uses real data to assess interaction effects using the same five methods. Results indicate that the Wald test, F-test, and likelihood ratio test all perform similarly in terms of Type I error rates and power. Type I error rates for the AIC are more liberal, and for the BIC typically more conservative. A four-step procedure for applied researchers interested in examining interaction effects in multi-level models is provided.
Journal: Journal of Applied Statistics
Pages: 2238-2255
Issue: 12
Volume: 45
Year: 2018
Month: 9
X-DOI: 10.1080/02664763.2017.1414163
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1414163
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:12:p:2238-2255




Template-Type: ReDIF-Article 1.0
Author-Name: Ken J. Beath
Author-X-Name-First: Ken J.
Author-X-Name-Last: Beath
Title: A mixture-based approach to robust analysis of generalised linear models
Abstract: 
 A method for robustness in linear models is to assume that there is a mixture of standard and outlier observations with a different error variance for each class. For generalised linear models (GLMs) the mixture model approach is more difficult as the error variance for many distributions has a fixed relationship to the mean. This model is extended to GLMs by changing the classes to one where the standard class is a standard GLM and the outlier class which is an overdispersed GLM achieved by including a random effect term in the linear predictor. The advantages of this method are it can be extended to any model with a linear predictor, and outlier observations can be easily identified. Using simulation the model is compared to an M-estimator, and found to have improved bias and coverage. The method is demonstrated on three examples.
Journal: Journal of Applied Statistics
Pages: 2256-2268
Issue: 12
Volume: 45
Year: 2018
Month: 9
X-DOI: 10.1080/02664763.2017.1414164
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1414164
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:12:p:2256-2268




Template-Type: ReDIF-Article 1.0
Author-Name: Achilleas Vassilopoulos
Author-X-Name-First: Achilleas
Author-X-Name-Last: Vassilopoulos
Author-Name: Andreas C. Drichoutis
Author-X-Name-First: Andreas C.
Author-X-Name-Last: Drichoutis
Author-Name: Rodolfo M. Nayga
Author-X-Name-First: Rodolfo M.
Author-X-Name-Last: Nayga
Author-Name: Panagiotis Lazaridis
Author-X-Name-First: Panagiotis
Author-X-Name-Last: Lazaridis
Title: Does the supplemental nutrition assistance program really increase obesity? The importance of accounting for misclassification errors
Abstract: 
 The prevalence of obesity among US citizens has grown rapidly over the last few decades, especially among low-income individuals. This has led to questions about the effectiveness of nutritional assistance programs such as the Supplemental Nutrition Assistance Program (SNAP). Previous results on the effect of SNAP participation on obesity are mixed. These findings are however based on the assumption that participation status can be accurately observed, despite significant misclassification errors reported in the literature. Using propensity score matching, we conclude that there seems to be a positive effect of SNAP participation on obesity rates for female participants and no such effect for males, a result that is consistent with several previous studies. However, an extensive sensitivity analysis reveals that the positive effect for females is sensitive to misclassification errors and to the conditional independence assumption. Thus analogous findings should also be used with caution unless examined under the prism of classification errors and of other assumptions used for the identification of causal parameters.
Journal: Journal of Applied Statistics
Pages: 2269-2278
Issue: 12
Volume: 45
Year: 2018
Month: 9
X-DOI: 10.1080/02664763.2017.1414165
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1414165
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:12:p:2269-2278




Template-Type: ReDIF-Article 1.0
Author-Name: Guy Cafri
Author-X-Name-First: Guy
Author-X-Name-Last: Cafri
Author-Name: Luo Li
Author-X-Name-First: Luo
Author-X-Name-Last: Li
Author-Name: Elizabeth W. Paxton
Author-X-Name-First: Elizabeth W.
Author-X-Name-Last: Paxton
Author-Name: Juanjuan Fan
Author-X-Name-First: Juanjuan
Author-X-Name-Last: Fan
Title: Predicting risk for adverse health events using random forest
Abstract: 
 Estimation of person-specific risk for adverse health events in medicine has been approached almost exclusively using parametric statistical methods. Random forest is a machine learning method based on tree ensembles that is completely nonparametric and for this reason may be better suited for risk prediction. An introduction to a random forest is provided with a focus on its application to risk prediction. Using data from a total joint replacement registry, we illustrate risk prediction for the binary outcome of 90-day mortality following implantation, as well as time to device failure for aseptic reasons with the competing risk of mortality. Using the methods described in this paper, the random forest could be applied to risk prediction in a wide variety of medical fields. Issues related to implementation are discussed.
Journal: Journal of Applied Statistics
Pages: 2279-2294
Issue: 12
Volume: 45
Year: 2018
Month: 9
X-DOI: 10.1080/02664763.2017.1414166
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1414166
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:12:p:2279-2294




Template-Type: ReDIF-Article 1.0
Author-Name: Thomas Kirschenmann
Author-X-Name-First: Thomas
Author-X-Name-Last: Kirschenmann
Author-Name: Paul Damien
Author-X-Name-First: Paul
Author-X-Name-Last: Damien
Author-Name: Stephen Walker
Author-X-Name-First: Stephen
Author-X-Name-Last: Walker
Title: Bayesian estimation of the Cox model under different hazard rate shape assumptions via slice sampling
Abstract: 
 In this paper, we provide a full Bayesian analysis for Cox's proportional hazards model under different hazard rate shape assumptions. To this end, we select the modified Weibull distribution family to model failure rates. A novel Markov chain Monte Carlo method allows one to tackle both exact and right-censored failure time data. Both simulated and real data are used to illustrate the methods.
Journal: Journal of Applied Statistics
Pages: 2295-2306
Issue: 12
Volume: 45
Year: 2018
Month: 9
X-DOI: 10.1080/02664763.2017.1420147
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1420147
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:12:p:2295-2306




Template-Type: ReDIF-Article 1.0
Author-Name: Da Xu
Author-X-Name-First: Da
Author-X-Name-Last: Xu
Author-Name: Shishun Zhao
Author-X-Name-First: Shishun
Author-X-Name-Last: Zhao
Author-Name: Tao Hu
Author-X-Name-First: Tao
Author-X-Name-Last: Hu
Author-Name: Mengzhu Yu
Author-X-Name-First: Mengzhu
Author-X-Name-Last: Yu
Author-Name: Jianguo Sun
Author-X-Name-First: Jianguo
Author-X-Name-Last: Sun
Title: Regression analysis of informative current status data with the semiparametric linear transformation model
Abstract: 
 Many methods have been developed in the literature for regression analysis of current status data with noninformative censoring and also some approaches have been proposed for semiparametric regression analysis of current status data with informative censoring. However, the existing approaches for the latter situation are mainly on specific models such as the proportional hazards model and the additive hazard model. Corresponding to this, in this paper, we consider a general class of semiparametric linear transformation models and develop a sieve maximum likelihood estimation approach for the inference. In the method, the copula model is employed to describe the informative censoring or relationship between the failure time of interest and the censoring time, and Bernstein polynomials are used to approximate the nonparametric functions involved. The asymptotic consistency and normality of the proposed estimators are established, and an extensive simulation study is conducted and indicates that the proposed approach works well for practical situations. In addition, an illustrative example is provided.
Journal: Journal of Applied Statistics
Pages: 187-202
Issue: 2
Volume: 46
Year: 2019
Month: 1
X-DOI: 10.1080/02664763.2018.1466870
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1466870
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:2:p:187-202




Template-Type: ReDIF-Article 1.0
Author-Name: Henok Woldu
Author-X-Name-First: Henok
Author-X-Name-Last: Woldu
Author-Name: Timothy G. Heckman
Author-X-Name-First: Timothy G.
Author-X-Name-Last: Heckman
Author-Name: Andreas Handel
Author-X-Name-First: Andreas
Author-X-Name-Last: Handel
Author-Name: Ye Shen
Author-X-Name-First: Ye
Author-X-Name-Last: Shen
Title: Applying functional data analysis to assess tele-interpersonal psychotherapy's efficacy to reduce depression
Abstract: 
 The use of parametric linear mixed models and generalized linear mixed models to analyze longitudinal data collected during randomized control trials (RCT) is conventional. The application of these methods, however, is restricted due to various assumptions required by these models. When the number of observations per subject is sufficiently large, and individual trajectories are noisy, functional data analysis (FDA) methods serve as an alternative to parametric longitudinal data analysis techniques. However, the use of FDA in RCTs is rare. In this paper, the effectiveness of FDA and linear mixed models (LMMs) was compared by analyzing data from rural persons living with HIV and comorbid depression enrolled in a depression treatment randomized clinical trial. Interactive voice response systems were used for weekly administrations of the 10-item Self-Administered Depression Scale (SADS) over 41 weeks. Functional principal component analysis and functional regression analysis methods detected a statistically significant difference in SADS between telphone-administered interpersonal psychotherapy (tele-IPT) and controls but linear mixed effects model results did not. Additional simulation studies were conducted to compare FDA and LMMs under a different nonlinear trajectory assumption. In this clinical trial with sufficient per subject measured outcomes and individual trajectories that are noisy and nonlinear, we found FDA methods to be a better alternative to LMMs.
Journal: Journal of Applied Statistics
Pages: 203-216
Issue: 2
Volume: 46
Year: 2019
Month: 1
X-DOI: 10.1080/02664763.2018.1470231
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1470231
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:2:p:203-216




Template-Type: ReDIF-Article 1.0
Author-Name: John Mashford
Author-X-Name-First: John
Author-X-Name-Last: Mashford
Author-Name: Yong Song
Author-X-Name-First: Yong
Author-X-Name-Last: Song
Author-Name: Q. J. Wang
Author-X-Name-First: Q. J.
Author-X-Name-Last: Wang
Author-Name: David Robertson
Author-X-Name-First: David
Author-X-Name-Last: Robertson
Title: A Bayesian hierarchical spatio-temporal rainfall model
Abstract: 
 A Bayesian hierarchical spatio-temporal rainfall model is presented and analysed. The model has the ability to deal with extensive missing or null values, uses a sophisticated variance stabilising rainfall pre-transformation, incorporates a new elevation model and can provide sub-catchment rainfall estimation and interpolation using a sequential kriging scheme. The model uses a vector autoregressive stochastic process to represent the time dependence of the rainfall field and an exponential covariogram to model the spatial correlation of the rainfall field. The model can be readily generalised to other types of stochastic processes. In this paper, some results of applying the model to a particular rainfall catchment are presented.
Journal: Journal of Applied Statistics
Pages: 217-229
Issue: 2
Volume: 46
Year: 2019
Month: 1
X-DOI: 10.1080/02664763.2018.1473347
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1473347
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:2:p:217-229




Template-Type: ReDIF-Article 1.0
Author-Name: Xiao Li
Author-X-Name-First: Xiao
Author-X-Name-Last: Li
Author-Name: Michele Guindani
Author-X-Name-First: Michele
Author-X-Name-Last: Guindani
Author-Name: Chaan S. Ng
Author-X-Name-First: Chaan S.
Author-X-Name-Last: Ng
Author-Name: Brian P. Hobbs
Author-X-Name-First: Brian P.
Author-X-Name-Last: Hobbs
Title: Spatial Bayesian modeling of GLCM with application to malignant lesion characterization
Abstract: 
 The emerging field of cancer radiomics endeavors to characterize intrinsic patterns of tumor phenotypes and surrogate markers of response by transforming medical images into objects that yield quantifiable summary statistics to which regression and machine learning algorithms may be applied for statistical interrogation. Recent literature has identified clinicopathological association based on textural features deriving from gray-level co-occurrence matrices (GLCM) which facilitate evaluations of gray-level spatial dependence within a delineated region of interest. GLCM-derived features, however, tend to contribute highly redundant information. Moreover, when reporting selected feature sets, investigators often fail to adjust for multiplicities and commonly fail to convey the predictive power of their findings. This article presents a Bayesian probabilistic modeling framework for the GLCM as a multivariate object as well as describes its application within a cancer detection context based on computed tomography. The methodology, which circumvents processing steps and avoids evaluations of reductive and highly correlated feature sets, uses latent Gaussian Markov random field structure to characterize spatial dependencies among GLCM cells and facilitates classification via predictive probability. Correctly predicting the underlying pathology of 81% of the adrenal lesions in our case study, the proposed method outperformed current practices which achieved a maximum accuracy of only 59%. Simulations and theory are presented to further elucidate this comparison as well as ascertain the utility of applying multivariate Gaussian spatial processes to GLCM objects.
Journal: Journal of Applied Statistics
Pages: 230-246
Issue: 2
Volume: 46
Year: 2019
Month: 1
X-DOI: 10.1080/02664763.2018.1473348
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1473348
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:2:p:230-246




Template-Type: ReDIF-Article 1.0
Author-Name: Li-Chu Chien
Author-X-Name-First: Li-Chu
Author-X-Name-Last: Chien
Title: A method for combining -values in meta-analysis by gamma distributions
Abstract: 
 Combining p-values from statistical tests across different studies is the most commonly used approach in meta-analysis for evolutionary biology. The most commonly used p-value combination methods mainly incorporate the z-transform tests (e.g., the un-weighted z-test and the weighted z-test) and the gamma-transform tests (e.g., the CZ method [Z. Chen, W. Yang, Q. Liu, J.Y. Yang, J. Li, and M.Q. Yang, A new statistical approach to combining p-values using gamma distribution and its application to genomewide association study, Bioinformatics 15 (2014), p. S3]). However, among these existing p-value combination methods, no method is uniformly most powerful in all situations [Chen et al. 2014]. In this paper, we propose a meta-analysis method based on the gamma distribution, MAGD, by pooling the p-values from independent studies. The newly proposed test, MAGD, allows for flexible accommodating of the different levels of heterogeneity of effect sizes across individual studies. The MAGD simultaneously retains all the characters of the z-transform tests and the gamma-transform tests. We also propose an easy-to-implement resampling approach for estimating the empirical p-values of MAGD for the finite sample size. Simulation studies and two data applications show that the proposed method MAGD is essentially as powerful as the z-transform tests (the gamma-transform tests) under the circumstance with the homogeneous (heterogeneous) effect sizes across studies.
Journal: Journal of Applied Statistics
Pages: 247-261
Issue: 2
Volume: 46
Year: 2019
Month: 1
X-DOI: 10.1080/02664763.2018.1474857
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1474857
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:2:p:247-261




Template-Type: ReDIF-Article 1.0
Author-Name: J. Peng
Author-X-Name-First: J.
Author-X-Name-Last: Peng
Author-Name: W. Liu
Author-X-Name-First: W.
Author-X-Name-Last: Liu
Author-Name: F. Bretz
Author-X-Name-First: F.
Author-X-Name-Last: Bretz
Author-Name: A. J. Hayter
Author-X-Name-First: A. J.
Author-X-Name-Last: Hayter
Title: Counting by weighing: construction of two-sided confidence intervals
Abstract: 
 Counting by weighing is widely used in industry and often more efficient than counting manually which is time consuming and prone to human errors especially when the number of items is large. Lower confidence bounds on the numbers of items in infinitely many future bags based on the weights of the bags have been proposed recently in Liu et&#xA0;al. [Counting by weighing: Know your numbers with confidence, J. Roy. Statist. Soc. Ser. C 65(4) (2016), pp. 641&#x2013;648]. These confidence bounds are constructed using the data from one calibration experiment and for different parameters (or numbers), but have the frequency interpretation similar to a usual confidence set for one parameter only. In this paper, the more challenging problem of constructing two-sided confidence intervals is studied. A simulation-based method for computing the critical constant is proposed. This method is proven to give the required critical constant when the number of simulations goes to infinity, and shown to be easily implemented on an ordinary computer to compute the critical constant accurately and quickly. The methodology is illustrated with a real data example.
Journal: Journal of Applied Statistics
Pages: 262-271
Issue: 2
Volume: 46
Year: 2019
Month: 1
X-DOI: 10.1080/02664763.2018.1475553
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1475553
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:2:p:262-271




Template-Type: ReDIF-Article 1.0
Author-Name: David Gold
Author-X-Name-First: David
Author-X-Name-Last: Gold
Author-Name: Lixin Lang
Author-X-Name-First: Lixin
Author-X-Name-Last: Lang
Author-Name: Kim Zerba
Author-X-Name-First: Kim
Author-X-Name-Last: Zerba
Title: Practical statistical considerations for investigating anti-tumor treatments in mice
Abstract: 
 Anti-tumor treatment outcomes in mouse experiments can be challenging to interpret and communicate accurately. In reporting these experiments, rigorous statistical considerations are commonly absent, although statistical applications have been proposed. We investigated the practicality and utility of different statistical strategies for the analysis of anti-tumor responses in a longitudinal mouse case study. Each analysis that we performed had different endpoints, investigated different questions, and was based on different assumptions. We found rudimentary visual and risk analysis insufficient without additional considerations, and upon further investigation we found improvements in key anti-tumor parameter estimates associated with a drug combination in our case study. We offer practical statistical considerations for investigating anti-cancer treatments in mice, applying a multi-tier statistical approach.
Journal: Journal of Applied Statistics
Pages: 272-285
Issue: 2
Volume: 46
Year: 2019
Month: 1
X-DOI: 10.1080/02664763.2018.1477925
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1477925
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:2:p:272-285




Template-Type: ReDIF-Article 1.0
Author-Name: Brook T. Russell
Author-X-Name-First: Brook T.
Author-X-Name-Last: Russell
Title: Investigating precipitation extremes in South Carolina with focus on the state's October 2015 precipitation event
Abstract: 
 The October 2015 precipitation event in the Southeastern United States brought large amounts of rainfall to South Carolina, with particularly heavy amounts in Charleston and Columbia. The subsequent flooding resulted in numerous casualties and hundreds of millions of dollars in property damage. Precipitation levels were so severe that media outlets and government agencies labeled this storm as a 1 in 1000-year event in parts of the state. Two points of discussion emerged as a result of this event. The first was related to understanding the degree to which this event was anomalous; the second was related to understanding whether precipitation extremes in South Carolina have changed over recent time. In this work, 50 years of daily precipitation data at 28 locations are used to fit a spatiotemporal hierarchical model, with the ultimate goal of addressing these two points of discussion. Bayesian inference is used to estimate return levels and to perform a severity-area-frequency analysis, and it is determined that precipitation levels related to this event were atypical throughout much of the state, but were particularly unusual in the Columbia area. This analysis also finds marginal evidence in favor of the claim that precipitation extremes in the Carolinas have become more intense over the last 50 years.
Journal: Journal of Applied Statistics
Pages: 286-303
Issue: 2
Volume: 46
Year: 2019
Month: 1
X-DOI: 10.1080/02664763.2018.1477926
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1477926
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:2:p:286-303




Template-Type: ReDIF-Article 1.0
Author-Name: Yuvraj Sunecher
Author-X-Name-First: Yuvraj
Author-X-Name-Last: Sunecher
Author-Name: Naushad Mamode Khan
Author-X-Name-First: Naushad
Author-X-Name-Last: Mamode Khan
Author-Name: Vandna Jowaheer
Author-X-Name-First: Vandna
Author-X-Name-Last: Jowaheer
Title: A case study of MCB and SBMH stock transaction using a novel BINMA(1) with non-stationary NB correlated innovations
Abstract: 
 This paper focuses on the modeling of the intra-day transactions at the Stock Exchange Mauritius (SEM) of the two major banking companies: Mauritius Commercial Bank Group Limited (MCB) and State Bank of Mauritius Holdings Ltd (SBMH) in Mauritius using a flexible non-stationary bivariate integer-valued moving average of order 1 (BINMA(1)) process with negative binomial (NB) innovations that may cater for different levels of over-dispersion. The generalized quasi-likelihood (GQL) approach is used to estimate the regression, dependence and over-dispersion effects. However, for the over-dispersion parameters, the auto-covariance structure in the GQL is constructed using some higher order moments. This new model is tested over some Monte-Carlo experiments and is applied to analyze the inter-related intra-day series of volume of stocks for the two banking institutions using data collected from 3 August to 16 October 2015 in the presence of some time-varying covariates such as the news effect, Friday effect and time of the day effect.
Journal: Journal of Applied Statistics
Pages: 304-323
Issue: 2
Volume: 46
Year: 2019
Month: 1
X-DOI: 10.1080/02664763.2018.1477927
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1477927
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:2:p:304-323




Template-Type: ReDIF-Article 1.0
Author-Name: K. Drosou
Author-X-Name-First: K.
Author-X-Name-Last: Drosou
Author-Name: C. Koukouvinos
Author-X-Name-First: C.
Author-X-Name-Last: Koukouvinos
Author-Name: A. Lappa
Author-X-Name-First: A.
Author-X-Name-Last: Lappa
Title: Sure independence screening for real medical Poisson data
Abstract: 
 The statistical modeling of big data bases constitutes one of the most challenging issues, especially nowadays. The issue is even more critical in case of a complicated correlation structure. Variable selection plays a vital role in statistical analysis of large data bases and many methods have been proposed so far to deal with the aforementioned problem. One of such methods is the Sure Independence Screening which has been introduced to reduce dimensionality to a relatively smaller scale. This method, though simple, produces remarkable results even under both ultra high dimensionality and big scale in terms of sample size problems. In this paper we dealt with the analysis of a big real medical data set assuming a Poisson regression model. We support the analysis by conducting simulated experiments taking into consideration the correlation structure of the design matrix.
Journal: Journal of Applied Statistics
Pages: 324-350
Issue: 2
Volume: 46
Year: 2019
Month: 1
X-DOI: 10.1080/02664763.2018.1480708
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1480708
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:2:p:324-350




Template-Type: ReDIF-Article 1.0
Author-Name: R. N. Montgomery
Author-X-Name-First: R. N.
Author-X-Name-Last: Montgomery
Author-Name: A. S. Watts
Author-X-Name-First: A. S.
Author-X-Name-Last: Watts
Author-Name: N. C. Burns
Author-X-Name-First: N. C.
Author-X-Name-Last: Burns
Author-Name: E. D. Vidoni
Author-X-Name-First: E. D.
Author-X-Name-Last: Vidoni
Author-Name: J. D. Mahnken
Author-X-Name-First: J. D.
Author-X-Name-Last: Mahnken
Title: Evaluating paired categorical data when the pairing is lost
Abstract: 
 We encountered a problem in which a study's experimental design called for the use of paired data, but the pairing between subjects had been lost during the data collection procedure. Thus we were presented with a data set consisting of pre and post responses but with no way of determining the dependencies between our observed pre and post values. The aim of the study was to assess whether an intervention called Self-Revelatory Performance had an impact on participant's perceptions of Alzheimer's disease. The participant's responses were measured on an Affect grid before the intervention and on a separate grid after. To address the underlying question in light of the lost pairing we utilized a modified bootstrap approach to create a null hypothesized distribution for our test statistic, which was the distance between the two Affect Grids' Centers of Mass. Using this approach we were able to reject our null hypothesis and conclude that there was evidence the intervention influenced perceptions about the disease.
Journal: Journal of Applied Statistics
Pages: 351-363
Issue: 2
Volume: 46
Year: 2019
Month: 1
X-DOI: 10.1080/02664763.2018.1485013
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1485013
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:2:p:351-363




Template-Type: ReDIF-Article 1.0
Author-Name: Marcus B. Perry
Author-X-Name-First: Marcus B.
Author-X-Name-Last: Perry
Title: On the detection of transitive clusters in undirected networks
Abstract: 
 A network cluster is defined as a set of nodes with &#x2018;strong&#x2019; within group ties and &#x2018;weak&#x2019; between group ties. Most clustering methods focus on finding groups of &#x2018;densely connected&#x2019; nodes, where the dyad (or tie between two nodes) serves as the building block for forming clusters. However, since the unweighted dyad cannot distinguish strong relationships from weak ones, it then seems reasonable to consider an alternative building block, i.e. one involving more than two nodes. In the simplest case, one can consider the triad (or three nodes), where the fully connected triad represents the basic unit of transitivity in an undirected network. In this effort we propose a clustering framework for finding highly transitive subgraphs in an undirected/unweighted network, where the fully connected triad (or triangle configuration) is used as the building block for forming clusters. We apply our methodology to four real networks with encouraging results. Monte Carlo simulation results suggest that, on average, the proposed method yields good clustering performance on synthetic benchmark graphs, relative to other popular methods.
Journal: Journal of Applied Statistics
Pages: 364-384
Issue: 2
Volume: 46
Year: 2019
Month: 1
X-DOI: 10.1080/02664763.2018.1491535
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1491535
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:2:p:364-384




Template-Type: ReDIF-Article 1.0
Author-Name: Patrick Aboagye-Sarfo
Author-X-Name-First: Patrick
Author-X-Name-Last: Aboagye-Sarfo
Author-Name: James Cross
Author-X-Name-First: James
Author-X-Name-Last: Cross
Author-Name: Ute Mueller
Author-X-Name-First: Ute
Author-X-Name-Last: Mueller
Title: Intervention time series analysis of voluntary, counselling and testing on HIV infections in West African sub-region: the case of Ghana
Abstract: 
 In this paper, intervention time series models were developed to examine the effectiveness of the voluntary counselling and testing (VCT) programme in the northern and southern sectors of Ghana. Pre-intervention data of HIV reported cases in the northern and southern sectors were first modelled as Box–Jenkins univariate time series. Second, the adopted models from the pre-intervention data were extended to include the intervention variable. The intervention variable was coded as zero for the pre-intervention period (1 January 1996–31 December 2002) and one for the post-intervention period (1 January 2003–31 December 2007). The models developed were applied to the entire data for the two sectors to estimate the effect of the VCT programme. Our findings indicate that the VCT programme was found to be associated with detection of 20 and 40 new HIV infections per 100,000 persons per month in the northern and southern sectors (p &lt; .10), respectively. The VCT programme in Ghana, like most West African nations, has insignificant impact. Intervention time series models can be used to reliably examine the impact of the VCT programme. The impact of the VCT programme is minimal and we therefore recommend that the National AIDS Control Programme and other stakeholders re-double their efforts to maximise the impact of the programme.
Journal: Journal of Applied Statistics
Pages: 571-582
Issue: 4
Volume: 44
Year: 2017
Month: 3
X-DOI: 10.1080/02664763.2016.1177501
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1177501
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:4:p:571-582




Template-Type: ReDIF-Article 1.0
Author-Name: Thao Nguyentrang
Author-X-Name-First: Thao
Author-X-Name-Last: Nguyentrang
Author-Name: Tai Vovan
Author-X-Name-First: Tai
Author-X-Name-Last: Vovan
Title: Fuzzy clustering of probability density functions
Abstract: 
 Basing on L1-distance and representing element of cluster, the article proposes new three algorithms in Fuzzy Clustering of probability density Functions (FCF). They are hierarchical approach, non-hierarchical approach and the algorithm to determine the optimal number of clusters and the initial partition matrix to improve the qualities of established clusters in non-hierarchical approach. With proposed algorithms, FCF has more advantageous than Non-fuzzy Clustering of probability density Functions. These algorithms are applied for recognizing images from Texture and Corel database and practical problem about studying and training marks of students at an university. Many Matlab programs are established for computation in proposed algorithms. These programs are not only used to compute effectively the numerical examples of this article but also to be applied for many different realistic problems.
Journal: Journal of Applied Statistics
Pages: 583-601
Issue: 4
Volume: 44
Year: 2017
Month: 3
X-DOI: 10.1080/02664763.2016.1177502
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1177502
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:4:p:583-601




Template-Type: ReDIF-Article 1.0
Author-Name: Michele Rienzner
Author-X-Name-First: Michele
Author-X-Name-Last: Rienzner
Author-Name: Francesca Ieva
Author-X-Name-First: Francesca
Author-X-Name-Last: Ieva
Title: Critical values improvement for the standard normal homogeneity test by combining Monte Carlo and regression approaches
Abstract: 
 The distribution of the test statistics of homogeneity tests is often unknown, requiring the estimation of the critical values through Monte Carlo (MC) simulations. The computation of the critical values at low α, especially when the distribution of the statistics changes with the series length (sample cardinality), requires a considerable number of simulations to achieve a reasonable precision of the estimates (i.e. 106 simulations or more for each series length). If, in addition, the test requires a noteworthy computational effort, the estimation of the critical values may need unacceptably long runtimes.To overcome the problem, the paper proposes a regression-based refinement of an initial MC estimate of the critical values, also allowing an approximation of the achieved improvement. Moreover, the paper presents an application of the method to two tests: SNHT (standard normal homogeneity test, widely used in climatology), and SNH2T (a version of SNHT showing a squared numerical complexity). For both, the paper reports the critical values for α ranging between 0.1 and 0.0001 (useful for the p-value estimation), and the series length ranging from 10 (widely adopted size in climatological change-point detection literature) to 70,000 elements (nearly the length of a daily data time series 200 years long), estimated with coefficients of variation within 0.22%. For SNHT, a comparison of our results with approximated, theoretically derived, critical values is also performed; we suggest adopting those values for the series exceeding 70,000 elements.
Journal: Journal of Applied Statistics
Pages: 602-619
Issue: 4
Volume: 44
Year: 2017
Month: 3
X-DOI: 10.1080/02664763.2016.1182127
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1182127
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:4:p:602-619




Template-Type: ReDIF-Article 1.0
Author-Name: Trias Wahyuni Rakhmawati
Author-X-Name-First: Trias Wahyuni
Author-X-Name-Last: Rakhmawati
Author-Name: Geert Molenberghs
Author-X-Name-First: Geert
Author-X-Name-Last: Molenberghs
Author-Name: Geert Verbeke
Author-X-Name-First: Geert
Author-X-Name-Last: Verbeke
Author-Name: Christel Faes
Author-X-Name-First: Christel
Author-X-Name-Last: Faes
Title: Local influence diagnostics for generalized linear mixed models with overdispersion
Abstract: 
 Since the seminal paper by Cook and Weisberg [9], local influence, next to case deletion, has gained popularity as a tool to detect influential subjects and measurements for a variety of statistical models. For the linear mixed model the approach leads to easily interpretable and computationally convenient expressions, not only highlighting influential subjects, but also which aspect of their profile leads to undue influence on the model's fit [17]. Ouwens et al. [24] applied the method to the Poisson-normal generalized linear mixed model (GLMM). Given the model's nonlinear structure, these authors did not derive interpretable components but rather focused on a graphical depiction of influence. In this paper, we consider GLMMs for binary, count, and time-to-event data, with the additional feature of accommodating overdispersion whenever necessary. For each situation, three approaches are considered, based on: (1) purely numerical derivations; (2) using a closed-form expression of the marginal likelihood function; and (3) using an integral representation of this likelihood. Unlike when case deletion is used, this leads to interpretable components, allowing not only to identify influential subjects, but also to study the cause thereof. The methodology is illustrated in case studies that range over the three data types mentioned.
Journal: Journal of Applied Statistics
Pages: 620-641
Issue: 4
Volume: 44
Year: 2017
Month: 3
X-DOI: 10.1080/02664763.2016.1182128
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1182128
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:4:p:620-641




Template-Type: ReDIF-Article 1.0
Author-Name: Feng Xu
Author-X-Name-First: Feng
Author-X-Name-Last: Xu
Title: Statistical measurement of the inventory shortage cost
Abstract: 
 In searching for the optimal inventory control policy, the objective is to minimize the expected total costs related, of which the shortage cost is an important element. Due to the difficulty in calculating the indirect cost of the loss of goodwill resulted from the shortage, practitioners and researchers often simply assume a fixed penalty cost on the inventory shortage or switch to the alternative method by assigning a specific customer service level. The development of an appropriate tool for measuring the shortage cost can help a business control the total costs and improve the productivity more effectively. This paper proposes probabilistic measurements of the shortage cost, based on mathematical relationship between the cost and the shortage amount. The derived closed-form estimates of the expected shortage cost value can then be applied to support the determination of the optimal inventory control policy.
Journal: Journal of Applied Statistics
Pages: 642-648
Issue: 4
Volume: 44
Year: 2017
Month: 3
X-DOI: 10.1080/02664763.2016.1182129
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1182129
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:4:p:642-648




Template-Type: ReDIF-Article 1.0
Author-Name: M. Afshari
Author-X-Name-First: M.
Author-X-Name-Last: Afshari
Author-Name: F. Lak
Author-X-Name-First: F.
Author-X-Name-Last: Lak
Author-Name: B. Gholizadeh
Author-X-Name-First: B.
Author-X-Name-Last: Gholizadeh
Title: A new Bayesian wavelet thresholding estimator of nonparametric regression
Abstract: 
 The methods of estimation of nonparametric regression function are quite common in statistical application. In this paper, the new Bayesian wavelet thresholding estimation is considered. The new mixture prior distributions for the estimation of nonparametric regression function by applying wavelet transformation are investigated. The reversible jump algorithm to obtain the appropriate prior distributions and value of thresholding is used. The performance of the proposed estimator is assessed with simulated data from well-known test functions by comparing the convergence rate of the proposed estimator with respect to another by evaluating the average mean square error and standard deviations. Finally by applying the developed method, density function of galaxy data is estimated.
Journal: Journal of Applied Statistics
Pages: 649-666
Issue: 4
Volume: 44
Year: 2017
Month: 3
X-DOI: 10.1080/02664763.2016.1182130
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1182130
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:4:p:649-666




Template-Type: ReDIF-Article 1.0
Author-Name: Eudoxia Kakarantza
Author-X-Name-First: Eudoxia
Author-X-Name-Last: Kakarantza
Author-Name: Spyridon D. Symeonides
Author-X-Name-First: Spyridon D.
Author-X-Name-Last: Symeonides
Title: Seemingly unrelated systems of econometric equations
Abstract: 
 A generalization of Zellner's SUR model is derived for sets of seemingly unrelated systems of econometric equations. The resulting structural form – worked out for a set of Cowles Commission-type simultaneous equations systems – is general enough to include any SUR-type or panel-type specification of systems of econometric equations with contemporaneously correlated errors. Maximum estimation efficiency is obtained by treating all the individual subsystems at once rather than in a subsystem-by-subsystem fashion.
Journal: Journal of Applied Statistics
Pages: 667-684
Issue: 4
Volume: 44
Year: 2017
Month: 3
X-DOI: 10.1080/02664763.2016.1182131
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1182131
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:4:p:667-684




Template-Type: ReDIF-Article 1.0
Author-Name: Seunghon Ham
Author-X-Name-First: Seunghon
Author-X-Name-Last: Ham
Author-Name: Sunju Kim
Author-X-Name-First: Sunju
Author-X-Name-Last: Kim
Author-Name: Naroo Lee
Author-X-Name-First: Naroo
Author-X-Name-Last: Lee
Author-Name: Pilje Kim
Author-X-Name-First: Pilje
Author-X-Name-Last: Kim
Author-Name: Igchun Eom
Author-X-Name-First: Igchun
Author-X-Name-Last: Eom
Author-Name: Byoungcheun Lee
Author-X-Name-First: Byoungcheun
Author-X-Name-Last: Lee
Author-Name: Perng-Jy Tsai
Author-X-Name-First: Perng-Jy
Author-X-Name-Last: Tsai
Author-Name: Kiyoung Lee
Author-X-Name-First: Kiyoung
Author-X-Name-Last: Lee
Author-Name: Chungsik Yoon
Author-X-Name-First: Chungsik
Author-X-Name-Last: Yoon
Title: Comparison of data analysis procedures for real-time nanoparticle sampling data using classical regression and ARIMA models
Abstract: 
 Real-time monitoring is necessary for nanoparticle exposure assessment to characterize the exposure profile, but the data produced are autocorrelated. This study was conducted to compare three statistical methods used to analyze data, which constitute autocorrelated time series, and to investigate the effect of averaging time on the reduction of the autocorrelation using field data. First-order autoregressive (AR(1)) and autoregressive-integrated moving average (ARIMA) models are alternative methods that remove autocorrelation. The classical regression method was compared with AR(1) and ARIMA. Three data sets were used. Scanning mobility particle sizer data were used. We compared the results of regression, AR(1), and ARIMA with averaging times of 1, 5, and 10 min. AR(1) and ARIMA models had similar capacities to adjust autocorrelation of real-time data. Because of the non-stationary of real-time monitoring data, the ARIMA was more appropriate. When using the AR(1), transformation into stationary data was necessary. There was no difference with a longer averaging time. This study suggests that the ARIMA model could be used to process real-time monitoring data especially for non-stationary data, and averaging time setting is flexible depending on the data interval required to capture the effects of processes for occupational and environmental nano measurements.
Journal: Journal of Applied Statistics
Pages: 685-699
Issue: 4
Volume: 44
Year: 2017
Month: 3
X-DOI: 10.1080/02664763.2016.1182132
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1182132
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:4:p:685-699




Template-Type: ReDIF-Article 1.0
Author-Name: Waleed Dhhan
Author-X-Name-First: Waleed
Author-X-Name-Last: Dhhan
Author-Name: Sohel Rana
Author-X-Name-First: Sohel
Author-X-Name-Last: Rana
Author-Name: Habshah Midi
Author-X-Name-First: Habshah
Author-X-Name-Last: Midi
Title: A high breakdown, high efficiency and bounded influence modified GM estimator based on support vector regression
Abstract: 
 Regression analysis aims to estimate the approximate relationship between the response variable and the explanatory variables. This can be done using classical methods such as ordinary least squares. Unfortunately, these methods are very sensitive to anomalous points, often called outliers, in the data set. The main contribution of this article is to propose a new version of the Generalized M-estimator that provides good resistance against vertical outliers and bad leverage points. The advantage of this method over the existing methods is that it does not minimize the weight of the good leverage points, and this increases the efficiency of this estimator. To achieve this goal, the fixed parameters support vector regression technique is used to identify and minimize the weight of outliers and bad leverage points. The effectiveness of the proposed estimator is investigated using real and simulated data sets.
Journal: Journal of Applied Statistics
Pages: 700-714
Issue: 4
Volume: 44
Year: 2017
Month: 3
X-DOI: 10.1080/02664763.2016.1182133
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1182133
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:4:p:700-714




Template-Type: ReDIF-Article 1.0
Author-Name: Maosheng Li
Author-X-Name-First: Maosheng
Author-X-Name-Last: Li
Author-Name: Zhengqiu Liu
Author-X-Name-First: Zhengqiu
Author-X-Name-Last: Liu
Author-Name: Yonghong Zhang
Author-X-Name-First: Yonghong
Author-X-Name-Last: Zhang
Author-Name: Weijun Liu
Author-X-Name-First: Weijun
Author-X-Name-Last: Liu
Author-Name: Feng Shi
Author-X-Name-First: Feng
Author-X-Name-Last: Shi
Title: Distribution analysis of train interval journey time employing the censored model with shifting character
Abstract: 
 The theoretical framework of limited dependent variable models is extended to accommodate a shifting character and thus fit the distribution of train journey time on sections of urban rail network. Data of actual train arrival and departure time at each station are used to calculate the journey time of each railway interval of multi-class trains. The log-normal distribution and normal distribution among a group of theoretical distributions are the most and second most suitable latent distributions of the train interval journey time in the censored models with shifting character. This modified distribution is described by four parameters, namely, the expectation and variance of the latent distribution and the upper and lower bound of the migration interval. The square root of the least square measurement (SRLSM) is taken as a measure, and a traversal search is adopted to determine the above four parameters according to the SRLSM. The average of the SRLSM of the theoretical train interval journey time distribution obtained by using the proposed method on all railway sections is 0.0905. The theoretical framework is the basis of storing hidden rules in data instead of past data of train travel time and optimizing the existing management of rail transit operation.
Journal: Journal of Applied Statistics
Pages: 715-733
Issue: 4
Volume: 44
Year: 2017
Month: 3
X-DOI: 10.1080/02664763.2016.1182134
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1182134
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:4:p:715-733




Template-Type: ReDIF-Article 1.0
Author-Name: M. Templ
Author-X-Name-First: M.
Author-X-Name-Last: Templ
Author-Name: K. Hron
Author-X-Name-First: K.
Author-X-Name-Last: Hron
Author-Name: P. Filzmoser
Author-X-Name-First: P.
Author-X-Name-Last: Filzmoser
Title: Exploratory tools for outlier detection in compositional data with structural zeros
Abstract: 
 The analysis of compositional data using the log-ratio approach is based on ratios between the compositional parts. Zeros in the parts thus cause serious difficulties for the analysis. This is a particular problem in case of structural zeros, which cannot be simply replaced by a non-zero value as it is done, e.g. for values below detection limit or missing values. Instead, zeros to be incorporated into further statistical processing. The focus is on exploratory tools for identifying outliers in compositional data sets with structural zeros. For this purpose, Mahalanobis distances are estimated, computed either directly for subcompositions determined by their zero patterns, or by using imputation to improve the efficiency of the estimates, and then proceed to the subcompositional and subgroup level. For this approach, new theory is formulated that allows to estimate covariances for imputed compositional data and to apply estimations on subgroups using parts of this covariance matrix. Moreover, the zero pattern structure is analyzed using principal component analysis for binary data to achieve a comprehensive view of the overall multivariate data structure. The proposed tools are applied to larger compositional data sets from official statistics, where the need for an appropriate treatment of zeros is obvious.
Journal: Journal of Applied Statistics
Pages: 734-752
Issue: 4
Volume: 44
Year: 2017
Month: 3
X-DOI: 10.1080/02664763.2016.1182135
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1182135
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:4:p:734-752




Template-Type: ReDIF-Article 1.0
Author-Name: Hani M. Samawi
Author-X-Name-First: Hani M.
Author-X-Name-Last: Samawi
Author-Name: Haresh Rochani
Author-X-Name-First: Haresh
Author-X-Name-Last: Rochani
Author-Name: Daniel Linder
Author-X-Name-First: Daniel
Author-X-Name-Last: Linder
Author-Name: Arpita Chatterjee
Author-X-Name-First: Arpita
Author-X-Name-Last: Chatterjee
Title: More efficient logistic analysis using moving extreme ranked set sampling
Abstract: 
 Logistic regression is the most popular technique available for modeling dichotomous-dependent variables. It has intensive application in the field of social, medical, behavioral and public health sciences. In this paper we propose a more efficient logistic regression analysis based on moving extreme ranked set sampling (MERSSmin) scheme with ranking based on an easy-to-available auxiliary variable known to be associated with the variable of interest (response variable). The paper demonstrates that this approach will provide more powerful testing procedure as well as more efficient odds ratio and parameter estimation than using simple random sample (SRS). Theoretical derivation and simulation studies will be provided. Real data from 2011 Youth Risk Behavior Surveillance System (YRBSS) data are used to illustrate the procedures developed in this paper.
Journal: Journal of Applied Statistics
Pages: 753-766
Issue: 4
Volume: 44
Year: 2017
Month: 3
X-DOI: 10.1080/02664763.2016.1182136
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1182136
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:4:p:753-766




Template-Type: ReDIF-Article 1.0
Author-Name: Brenda Betancourt
Author-X-Name-First: Brenda
Author-X-Name-Last: Betancourt
Author-Name: Abel Rodríguez
Author-X-Name-First: Abel
Author-X-Name-Last: Rodríguez
Author-Name: Naomi Boyd
Author-X-Name-First: Naomi
Author-X-Name-Last: Boyd
Title: Investigating competition in financial markets: a sparse autologistic model for dynamic network data
Abstract: 
 We develop a sparse autologistic model for investigating the impact of diversification and disintermediation strategies in the evolution of financial trading networks. In order to induce sparsity in the model estimates and address substantive questions about the underlying processes the model includes an  $ L^1 $ L1 regularization penalty. This makes implementation feasible for complex dynamic networks in which the number of parameters is considerably greater than the number of observations over time. We use the model to characterize trader behavior in the NYMEX natural gas futures market, where we find that disintermediation and not diversification or momentum tend to drive market microstructure.
Journal: Journal of Applied Statistics
Pages: 1157-1172
Issue: 7
Volume: 45
Year: 2018
Month: 5
X-DOI: 10.1080/02664763.2017.1357684
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1357684
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:7:p:1157-1172




Template-Type: ReDIF-Article 1.0
Author-Name: Jairo A. Fúquene Patiño
Author-X-Name-First: Jairo A.
Author-X-Name-Last: Fúquene Patiño
Author-Name: Brenda Betancourt
Author-X-Name-First: Brenda
Author-X-Name-Last: Betancourt
Author-Name: João B. M. Pereira
Author-X-Name-First: João B. M.
Author-X-Name-Last: Pereira
Title: A weakly informative prior for Bayesian dynamic model selection with applications in fMRI
Abstract: 
 In recent years, Bayesian statistics methods in neuroscience have been showing important advances. In particular, detection of brain signals for studying the complexity of the brain is an active area of research. Functional magnetic resonance imagining (fMRI) is an important tool to determine which parts of the brain are activated by different types of physical behavior. According to recent results, there is evidence that the values of the connectivity brain signal parameters are close to zero and due to the nature of time series fMRI data with high-frequency behavior, Bayesian dynamic models for identifying sparsity are indeed far-reaching. We propose a multivariate Bayesian dynamic approach for model selection and shrinkage estimation of the connectivity parameters. We describe the coupling or lead-lag between any pair of regions by using mixture priors for the connectivity parameters and propose a new weakly informative default prior for the state variances. This framework produces one-step-ahead proper posterior predictive results and induces shrinkage and robustness suitable for fMRI data in the presence of sparsity. To explore the performance of the proposed methodology, we present simulation studies and an application to functional magnetic resonance imaging data.
Journal: Journal of Applied Statistics
Pages: 1173-1192
Issue: 7
Volume: 45
Year: 2018
Month: 5
X-DOI: 10.1080/02664763.2017.1363161
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1363161
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:7:p:1173-1192




Template-Type: ReDIF-Article 1.0
Author-Name: Pasquale Sarnacchiaro
Author-X-Name-First: Pasquale
Author-X-Name-Last: Sarnacchiaro
Author-Name: Flavio Boccia
Author-X-Name-First: Flavio
Author-X-Name-Last: Boccia
Title: Some remarks on measurement models in the structural equation model: an application for socially responsible food consumption
Abstract: 
 Considering the structural equation model (SEM), usually the main researches are based on the structural model rather than on the measurement one. So, this context implies some problems: construct misspecification, identification and validation. Starting from the most recent articles in terms of these issues, we achieve – and formalize through two tables – a general framework that could help researchers select and assess both formative and reflective measurement models with special attention on statistical implications. To show this general framework, we present a survey on customer behaviours for socially responsible food consumption. The survey was carried out by delivering a questionnaire administered to a representative sample of 332 families. In order to detect the main aspects impacting consumers’ preferences, a factor analysis has been performed. Then the general framework has been used to select and assess the measurement models in SEM. The estimation of the SEM has been worked out by partial least squares. The significance of the indicators has been tested using bootstrap. As far as we know, it is the first time that a model for the analysis of the consumers’ behaviour for social responsibility is formalized through a SEM.
Journal: Journal of Applied Statistics
Pages: 1193-1208
Issue: 7
Volume: 45
Year: 2018
Month: 5
X-DOI: 10.1080/02664763.2017.1363162
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1363162
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:7:p:1193-1208




Template-Type: ReDIF-Article 1.0
Author-Name: Hamid Jamalinia
Author-X-Name-First: Hamid
Author-X-Name-Last: Jamalinia
Author-Name: Saber Khalouei
Author-X-Name-First: Saber
Author-X-Name-Last: Khalouei
Author-Name: Vahideh Rezaie
Author-X-Name-First: Vahideh
Author-X-Name-Last: Rezaie
Author-Name: Samad Nejatian
Author-X-Name-First: Samad
Author-X-Name-Last: Nejatian
Author-Name: Karamolah Bagheri-Fard
Author-X-Name-First: Karamolah
Author-X-Name-Last: Bagheri-Fard
Author-Name: Hamid Parvin
Author-X-Name-First: Hamid
Author-X-Name-Last: Parvin
Title: Diverse classifier ensemble creation based on heuristic dataset modification
Abstract: 
 Bagging and Boosting are two main ensemble approaches consolidating the decisions of several hypotheses. The diversity of the ensemble members is considered to be a significant element to obtain generalization error. Here, an inventive method called EBAGTS (ensemble-based artificially generated training samples) is proposed to generate ensembles. It manipulates training examples in three ways in order to build various hypotheses straightforwardly: drawing a sub-sample from training set, reducing/raising error-prone training instances, and reducing/raising local instances around error-prone regions. The proposed method is a straightforward, generic framework utilizing any base classifier as its ensemble members to assemble a powerfully built combinational classifier. Decision-tree classifier and multilayer perceptron classifier as some basic classifiers have been employed in the experiments to indicate the proposed method accomplish higher predictive accuracy compared to meta-learning algorithms like Boosting and Bagging. Furthermore, EBAGTS outperforms Boosting more impressively as the training data set gets broader. It is illustrated that EBAGTS can fulfill better performance comparing to the state of the art.
Journal: Journal of Applied Statistics
Pages: 1209-1226
Issue: 7
Volume: 45
Year: 2018
Month: 5
X-DOI: 10.1080/02664763.2017.1363163
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1363163
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:7:p:1209-1226




Template-Type: ReDIF-Article 1.0
Author-Name: Chalani Prematilake
Author-X-Name-First: Chalani
Author-X-Name-Last: Prematilake
Author-Name: Leif Ellingson
Author-X-Name-First: Leif
Author-X-Name-Last: Ellingson
Title: Evaluation and prediction of polygon approximations of planar contours for shape analysis
Abstract: 
 Contours may be viewed as the 2D outline of the image of an object. This type of data arises in medical imaging as well as in computer vision and can be modeled as data on a manifold and can be studied using statistical shape analysis. Practically speaking, each observed contour, while theoretically infinite dimensional, must be discretized for computations. As such, the coordinates for each contour as obtained at k sampling times, resulting in the contour being represented as a k-dimensional complex vector. While choosing large values of k will result in closer approximations to the original contour, this will also result in higher computational costs in the subsequent analysis. The goal of this study is to determine reasonable values for k so as to keep the computational cost low while maintaining accuracy. To do this, we consider two methods for selecting sample points and determine lower bounds for k for obtaining a desired level of approximation error using two different criteria. Because this process is computationally inefficient to perform on a large scale, we then develop models for predicting the lower bounds for k based on simple characteristics of the contours.
Journal: Journal of Applied Statistics
Pages: 1227-1246
Issue: 7
Volume: 45
Year: 2018
Month: 5
X-DOI: 10.1080/02664763.2017.1364716
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1364716
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:7:p:1227-1246




Template-Type: ReDIF-Article 1.0
Author-Name: Felix Famoye
Author-X-Name-First: Felix
Author-X-Name-Last: Famoye
Author-Name: John S. Preisser
Author-X-Name-First: John S.
Author-X-Name-Last: Preisser
Title: Marginalized zero-inflated generalized Poisson regression
Abstract: 
 The generalized Poisson (GP) regression model has been used to model count data that exhibit over-dispersion or under-dispersion. The zero-inflated GP (ZIGP) regression model can additionally handle count data characterized by many zeros. However, the parameters of ZIGP model cannot easily be used for inference on overall exposure effects. In order to address this problem, a marginalized ZIGP is proposed to directly model the population marginal mean count. The parameters of the marginalized zero-inflated GP model are estimated by the method of maximum likelihood. The regression model is illustrated by three real-life data sets.
Journal: Journal of Applied Statistics
Pages: 1247-1259
Issue: 7
Volume: 45
Year: 2018
Month: 5
X-DOI: 10.1080/02664763.2017.1364717
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1364717
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:7:p:1247-1259




Template-Type: ReDIF-Article 1.0
Author-Name: Li-An Lin
Author-X-Name-First: Li-An
Author-X-Name-Last: Lin
Author-Name: Sheng Luo
Author-X-Name-First: Sheng
Author-X-Name-Last: Luo
Author-Name: Barry R. Davis
Author-X-Name-First: Barry R.
Author-X-Name-Last: Davis
Title: Bayesian regression model for recurrent event data with event-varying covariate effects and event effect
Abstract: 
 In the course of hypertension, cardiovascular disease events (e.g. stroke, heart failure) occur frequently and recurrently. The scientific interest in such study may lie in the estimation of treatment effect while accounting for the correlation among event times. The correlation among recurrent event times comes from two sources: subject-specific heterogeneity (e.g. varied lifestyles, genetic variations, and other unmeasurable effects) and event dependence (i.e. event incidences may change the risk of future recurrent events). Moreover, event incidences may change the disease progression so that there may exist event-varying covariate effects (the covariate effects may change after each event) and event effect (the effect of prior events on the future events). In this article, we propose a Bayesian regression model that not only accommodates correlation among recurrent events from both sources, but also explicitly characterizes the event-varying covariate effects and event effect. This model is especially useful in quantifying how the incidences of events change the effects of covariates and risk of future events. We compare the proposed model with several commonly used recurrent event models and apply our model to the motivating lipid-lowering trial (LLT) component of the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT) (ALLHAT-LLT).
Journal: Journal of Applied Statistics
Pages: 1260-1276
Issue: 7
Volume: 45
Year: 2018
Month: 5
X-DOI: 10.1080/02664763.2017.1367368
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1367368
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:7:p:1260-1276




Template-Type: ReDIF-Article 1.0
Author-Name: David M. Zimmer
Author-X-Name-First: David M.
Author-X-Name-Last: Zimmer
Title: The heterogeneous impact of insurance on health care demand among young adults: a panel data analysis
Abstract: 
 Success of the recently implemented Affordable Care Act hinges on previously uninsured young adults enrolling in coverage. How will increased coverage, in turn, affect health care utilization? This paper applies variable coefficient panel models to estimate the impact of insurance on health care utilization among young adults. The econometric setup, which accommodates nonlinear usage measures, attempts to address the potential endogeneity of insurance status. The main finding is that, for approximately one-fifth of young adults, insurance does not substantially alter health care consumption. On the other hand, another one-fifth of young adults have large moral hazard effects. Among that group, insurance increases the probability of having a routine checkup by 71–120%, relative to mean probabilities, and insurance increases the number of curative-based doctor office visits by 67–181%, relative to the mean number of visits.
Journal: Journal of Applied Statistics
Pages: 1277-1291
Issue: 7
Volume: 45
Year: 2018
Month: 5
X-DOI: 10.1080/02664763.2017.1369497
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1369497
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:7:p:1277-1291




Template-Type: ReDIF-Article 1.0
Author-Name: Gary Madden
Author-X-Name-First: Gary
Author-X-Name-Last: Madden
Author-Name: Nicholas Apergis
Author-X-Name-First: Nicholas
Author-X-Name-Last: Apergis
Author-Name: Paul Rappoport
Author-X-Name-First: Paul
Author-X-Name-Last: Rappoport
Author-Name: Aniruddha Banerjee
Author-X-Name-First: Aniruddha
Author-X-Name-Last: Banerjee
Title: An application of nonparametric regression to missing data in large market surveys
Abstract: 
 Non-response (or missing data) is often encountered in large-scale surveys. To enable the behavioural analysis of these data sets, statistical treatments are commonly applied to complete or remove these data. However, the correctness of such procedures critically depends on the nature of the underlying missingness generation process. Clearly, the efficacy of applying either case deletion or imputation procedures rests on the unknown missingness generation mechanism. The contribution of this paper is twofold. The study is the first to propose a simple sequential method to attempt to identify the form of missingness. Second, the effectiveness of the tests is assessed by generating (experimentally) nine missing data sets by imposed MCAR, MAR and NMAR processes, with data removed.
Journal: Journal of Applied Statistics
Pages: 1292-1302
Issue: 7
Volume: 45
Year: 2018
Month: 5
X-DOI: 10.1080/02664763.2017.1369498
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1369498
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:7:p:1292-1302




Template-Type: ReDIF-Article 1.0
Author-Name: Thiago G. Ramires
Author-X-Name-First: Thiago G.
Author-X-Name-Last: Ramires
Author-Name: Edwin M. M. Ortega
Author-X-Name-First: Edwin M. M.
Author-X-Name-Last: Ortega
Author-Name: Niel Hens
Author-X-Name-First: Niel
Author-X-Name-Last: Hens
Author-Name: Gauss M. Cordeiro
Author-X-Name-First: Gauss M.
Author-X-Name-Last: Cordeiro
Author-Name: Gilberto A. Paula
Author-X-Name-First: Gilberto A.
Author-X-Name-Last: Paula
Title: A flexible semiparametric regression model for bimodal, asymmetric and censored data
Abstract: 
 In this paper, we propose a new semiparametric heteroscedastic regression model allowing for positive and negative skewness and bimodal shapes using the B-spline basis for nonlinear effects. The proposed distribution is based on the generalized additive models for location, scale and shape framework in order to model any or all parameters of the distribution using parametric linear and/or nonparametric smooth functions of explanatory variables. We motivate the new model by means of Monte Carlo simulations, thus ignoring the skewness and bimodality of the random errors in semiparametric regression models, which may introduce biases on the parameter estimates and/or on the estimation of the associated variability measures. An iterative estimation process and some diagnostic methods are investigated. Applications to two real data sets are presented and the method is compared to the usual regression methods.
Journal: Journal of Applied Statistics
Pages: 1303-1324
Issue: 7
Volume: 45
Year: 2018
Month: 5
X-DOI: 10.1080/02664763.2017.1369499
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1369499
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:7:p:1303-1324




Template-Type: ReDIF-Article 1.0
Author-Name: Ao Yuan
Author-X-Name-First: Ao
Author-X-Name-Last: Yuan
Author-Name: Yuan Guo
Author-X-Name-First: Yuan
Author-X-Name-Last: Guo
Author-Name: Nawar M. Shara
Author-X-Name-First: Nawar M.
Author-X-Name-Last: Shara
Author-Name: Barbara V. Howard
Author-X-Name-First: Barbara V.
Author-X-Name-Last: Howard
Author-Name: Ming T. Tan
Author-X-Name-First: Ming T.
Author-X-Name-Last: Tan
Title: An additive Cox model for coronary heart disease study
Abstract: 
 Existing models for coronary heart disease study use a set of common risk factors to predict the survival time of the disease, via the standard Cox regression model. For complex relationships between the survival time and risk factors, the linear regression specification in the existing Cox model is not flexible enough to accounts for such relationships. Also, the risk factors are actually risky only when they fall in some risk ranges. For more flexibility in modelling and characterize the risk factors more accurately, we study a semi-parametric additive Cox model, using basis splines and LASSO technique. The proposed model is evaluated by simulation studies and is used for the analysis of a real data in the Strong Heart Study.
Journal: Journal of Applied Statistics
Pages: 1325-1346
Issue: 7
Volume: 45
Year: 2018
Month: 5
X-DOI: 10.1080/02664763.2017.1369500
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1369500
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:7:p:1325-1346




Template-Type: ReDIF-Article 1.0
Author-Name: Yang Li
Author-X-Name-First: Yang
Author-X-Name-Last: Li
Author-Name: Yichen Qin
Author-X-Name-First: Yichen
Author-X-Name-Last: Qin
Author-Name: Yanming Xie
Author-X-Name-First: Yanming
Author-X-Name-Last: Xie
Author-Name: Feng Tian
Author-X-Name-First: Feng
Author-X-Name-Last: Tian
Title: Grouped penalization estimation of the osteoporosis data in the traditional Chinese medicine
Abstract: Both continuous and categorical covariates are common in traditional Chinese medicine (TCM) research, especially in the clinical syndrome identification and in the risk prediction research. For groups of dummy variables which are generated by the same categorical covariate, it is important to penalize them group-wise rather than individually. In this paper, we discuss the group lasso method for a risk prediction analysis in TCM osteoporosis research. It is the first time to apply such a group-wise variable selection method in this field. It may lead to new insights of using the grouped penalization method to select appropriate covariates in the TCM research. The introduced methodology can select categorical and continuous variables, and estimate their parameters simultaneously. In our application of the osteoporosis data, four covariates (including both categorical and continuous covariates) are selected out of 52 covariates. The accuracy of the prediction model is excellent. Compared with the prediction model with different covariates, the group lasso risk prediction model can significantly decrease the error rate and help TCM doctors to identify patients with a high risk of osteoporosis in clinical practice. Simulation results show that the application of the group lasso method is reasonable for the categorical covariates selection model in this TCM osteoporosis research.
Journal: Journal of Applied Statistics
Pages: 699-711
Issue: 4
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2012.724660
File-URL: http://hdl.handle.net/10.1080/02664763.2012.724660
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:4:p:699-711




Template-Type: ReDIF-Article 1.0
Author-Name: Kassim Mwitondi
Author-X-Name-First: Kassim
Author-X-Name-Last: Mwitondi
Title: Statistical computing in C++ and R
Journal: Journal of Applied Statistics
Pages: 916-916
Issue: 4
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2012.749033
File-URL: http://hdl.handle.net/10.1080/02664763.2012.749033
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:4:p:916-916




Template-Type: ReDIF-Article 1.0
Author-Name: Pieter Bastiaan Ober
Author-X-Name-First: Pieter
Author-X-Name-Last: Bastiaan Ober
Title: A practitioner's guide to resampling for data analysis, data mining, and modeling
Journal: Journal of Applied Statistics
Pages: 917-917
Issue: 4
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2012.749035
File-URL: http://hdl.handle.net/10.1080/02664763.2012.749035
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:4:p:917-917




Template-Type: ReDIF-Article 1.0
Author-Name: Pieter Bastiaan Ober
Author-X-Name-First: Pieter
Author-X-Name-Last: Bastiaan Ober
Title: Data driven business decisions
Journal: Journal of Applied Statistics
Pages: 917-918
Issue: 4
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2012.749041
File-URL: http://hdl.handle.net/10.1080/02664763.2012.749041
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:4:p:917-918




Template-Type: ReDIF-Article 1.0
Author-Name: Anouar Ben Mabrouk
Author-X-Name-First: Anouar
Author-X-Name-Last: Ben Mabrouk
Title: Time series modelling of neuroscience data
Journal: Journal of Applied Statistics
Pages: 918-919
Issue: 4
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2012.749044
File-URL: http://hdl.handle.net/10.1080/02664763.2012.749044
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:4:p:918-919




Template-Type: ReDIF-Article 1.0
Author-Name: Yves Laberge
Author-X-Name-First: Yves
Author-X-Name-Last: Laberge
Title: Simulating nature: a philosophical study of computer-simulation uncertainties and their role in climate science and policy advice
Journal: Journal of Applied Statistics
Pages: 919-920
Issue: 4
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2012.749047
File-URL: http://hdl.handle.net/10.1080/02664763.2012.749047
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:4:p:919-920




Template-Type: ReDIF-Article 1.0
Author-Name: Mariano Ruiz Espejo
Author-X-Name-First: Mariano
Author-X-Name-Last: Ruiz Espejo
Title: Sampling
Journal: Journal of Applied Statistics
Pages: 920-921
Issue: 4
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2012.749048
File-URL: http://hdl.handle.net/10.1080/02664763.2012.749048
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:4:p:920-921




Template-Type: ReDIF-Article 1.0
Author-Name: Yiannis Kamarianakis
Author-X-Name-First: Yiannis
Author-X-Name-Last: Kamarianakis
Title: Ergodic control of diffusion processes
Journal: Journal of Applied Statistics
Pages: 921-922
Issue: 4
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2012.750440
File-URL: http://hdl.handle.net/10.1080/02664763.2012.750440
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:4:p:921-922




Template-Type: ReDIF-Article 1.0
Author-Name: Chris Beeley
Author-X-Name-First: Chris
Author-X-Name-Last: Beeley
Title: Behavioural research data analysis with R
Journal: Journal of Applied Statistics
Pages: 922-922
Issue: 4
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2012.750441
File-URL: http://hdl.handle.net/10.1080/02664763.2012.750441
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:4:p:922-922




Template-Type: ReDIF-Article 1.0
Author-Name: A. Mosammam
Author-X-Name-First: A.
Author-X-Name-Last: Mosammam
Title: Geostatistics: modeling spatial uncertainty, second edition
Journal: Journal of Applied Statistics
Pages: 923-923
Issue: 4
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2012.750474
File-URL: http://hdl.handle.net/10.1080/02664763.2012.750474
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:4:p:923-923




Template-Type: ReDIF-Article 1.0
Author-Name: Nilgun Fescioglu-Unver
Author-X-Name-First: Nilgun
Author-X-Name-Last: Fescioglu-Unver
Author-Name: Başak Tanyeri
Author-X-Name-First: Başak
Author-X-Name-Last: Tanyeri
Title: A comparison of artificial neural network and multinomial logit models in predicting mergers
Abstract: A merger proposal discloses a bidder firm's desire to purchase the control rights in a target firm. Predicting who will propose (bidder candidacy) and who will receive (target candidacy) merger bids is important to investigate why firms merge and to measure the price impact of mergers. This study investigates the performance of artificial neural networks and multinomial logit models in predicting bidder and target candidacy. We use a comprehensive data set that covers the years 1979–2004 and includes all deals with publicly listed bidders and targets. We find that both models perform similarly while predicting target and non-merger firms. The multinomial logit model performs slightly better in predicting bidder firms.
Journal: Journal of Applied Statistics
Pages: 712-720
Issue: 4
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2012.750717
File-URL: http://hdl.handle.net/10.1080/02664763.2012.750717
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:4:p:712-720




Template-Type: ReDIF-Article 1.0
Author-Name: B. Kibria
Author-X-Name-First: B.
Author-X-Name-Last: Kibria
Author-Name: Kristofer Månsson
Author-X-Name-First: Kristofer
Author-X-Name-Last: Månsson
Author-Name: Ghazi Shukur
Author-X-Name-First: Ghazi
Author-X-Name-Last: Shukur
Title: Some ridge regression estimators for the zero-inflated Poisson model
Abstract: The zero-inflated Poisson regression model is commonly used when analyzing economic data that come in the form of non-negative integers since it accounts for excess zeros and overdispersion of the dependent variable. However, a problem often encountered when analyzing economic data that has not been addressed for this model is multicollinearity. This paper proposes ridge regression (RR) estimators and some methods for estimating the ridge parameter k for a non-negative model. A simulation study has been conducted to compare the performance of the estimators. Both mean squared error and mean absolute error are considered as the performance criteria. The simulation study shows that some estimators are better than the commonly used maximum-likelihood estimator and some other RR estimators. Based on the simulation study and an empirical application, some useful estimators are recommended for practitioners.
Journal: Journal of Applied Statistics
Pages: 721-735
Issue: 4
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2012.752448
File-URL: http://hdl.handle.net/10.1080/02664763.2012.752448
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:4:p:721-735




Template-Type: ReDIF-Article 1.0
Author-Name: Hui Lin
Author-X-Name-First: Hui
Author-X-Name-Last: Lin
Author-Name: Chong Wang
Author-X-Name-First: Chong
Author-X-Name-Last: Wang
Author-Name: Peng Liu
Author-X-Name-First: Peng
Author-X-Name-Last: Liu
Author-Name: Derald Holtkamp
Author-X-Name-First: Derald
Author-X-Name-Last: Holtkamp
Title: Construction of disease risk scoring systems using logistic group lasso: application to porcine reproductive and respiratory syndrome survey data
Abstract: We propose to utilize the group lasso algorithm for logistic regression to construct a risk scoring system for predicting disease in swine. This work is motivated by the need to develop a risk scoring system from survey data on risk factor for porcine reproductive and respiratory syndrome (PRRS), which is a major health, production and financial problem for swine producers in nearly every country. Group lasso provides an attractive solution to this research question because of its ability to achieve group variable selection and stabilize parameter estimates at the same time. We propose to choose the penalty parameter for group lasso through leave-one-out cross-validation, using the criterion of the area under the receiver operating characteristic curve. Survey data for 896 swine breeding herd sites in the USA and Canada completed between March 2005 and March 2009 are used to construct the risk scoring system for predicting PRRS outbreaks in swine. We show that our scoring system for PRRS significantly improves the current scoring system that is based on an expert opinion. We also show that our proposed scoring system is superior in terms of area under the curve to that developed using multiple logistic regression model selected based on variable significance.
Journal: Journal of Applied Statistics
Pages: 736-746
Issue: 4
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2012.752449
File-URL: http://hdl.handle.net/10.1080/02664763.2012.752449
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:4:p:736-746




Template-Type: ReDIF-Article 1.0
Author-Name: Marco Marozzi
Author-X-Name-First: Marco
Author-X-Name-Last: Marozzi
Title: Adaptive choice of scale tests in flexible two-stage designs with applications in experimental ecology and clinical trials
Abstract: In this paper, the two-sample scale problem is addressed within the rank framework which does not require to specify the underlying continuous distribution. However, since the power of a rank test depends on the underlying distribution, it would be very useful for the researcher to have some information on it in order to use the possibly most suitable test. A two-stage adaptive design is used with adaptive tests where the data from the first stage are used to compute a selector statistic to select the test statistic for stage 2. More precisely, an adaptive scale test due to Hall and Padmanabhan and its components are considered in one-stage and several adaptive and non-adaptive two-stage procedures. A simulation study shows that the two-stage test with the adaptive choice in the second stage and with Liptak combination, when it is not more powerful than the corresponding one-stage test, shows, however, a quite similar power behavior. The test procedures are illustrated using two ecological applications and a clinical trial.
Journal: Journal of Applied Statistics
Pages: 747-762
Issue: 4
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2012.752796
File-URL: http://hdl.handle.net/10.1080/02664763.2012.752796
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:4:p:747-762




Template-Type: ReDIF-Article 1.0
Author-Name: T. Górecki
Author-X-Name-First: T.
Author-X-Name-Last: Górecki
Title: Sequential correction of linear classifiers
Abstract: In this article, a sequential correction of two linear methods: linear discriminant analysis (LDA) and perceptron is proposed. This correction relies on sequential joining of additional features on which the classifier is trained. These new features are posterior probabilities determined by a basic classification method such as LDA and perceptron. In each step, we add the probabilities obtained on a slightly different data set, because the vector of added probabilities varies at each step. We therefore have many classifiers of the same type trained on slightly different data sets. Four different sequential correction methods are presented based on different combining schemas (e.g. mean rule and product rule). Experimental results on different data sets demonstrate that the improvements are efficient, and that this approach outperforms classical linear methods, providing a significant reduction in the mean classification error rate.
Journal: Journal of Applied Statistics
Pages: 763-776
Issue: 4
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2012.753041
File-URL: http://hdl.handle.net/10.1080/02664763.2012.753041
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:4:p:763-776




Template-Type: ReDIF-Article 1.0
Author-Name: Gavin Shaddick
Author-X-Name-First: Gavin
Author-X-Name-Last: Shaddick
Author-Name: Haojie Yan
Author-X-Name-First: Haojie
Author-X-Name-Last: Yan
Author-Name: Ruth Salway
Author-X-Name-First: Ruth
Author-X-Name-Last: Salway
Author-Name: Danielle Vienneau
Author-X-Name-First: Danielle
Author-X-Name-Last: Vienneau
Author-Name: Daphne Kounali
Author-X-Name-First: Daphne
Author-X-Name-Last: Kounali
Author-Name: David Briggs
Author-X-Name-First: David
Author-X-Name-Last: Briggs
Title: Large-scale Bayesian spatial modelling of air pollution for policy support
Abstract: The potential effects of air pollution are a major concern both in terms of the environment and in relation to human health. In order to support environmental policy, there is a need for accurate measurements of the concentrations of pollutants at high geographical resolution over large regions. However, within such regions, there are likely to be areas where the monitoring information will be sparse and so methods are required to accurately predict concentrations. Set within a Bayesian framework, models are developed which exploit the relationships between pollution and geographical covariate information, such as land use, climate and transport variables together with spatial structure. Candidate models are compared based on their ability to predict a set of validation sites. The chosen model is used to perform large-scale prediction of nitrogen dioxide at a 1×1 km resolution for the entire EU. The models allow probabilistic statements to be made with regard to the levels of air pollution that might be experienced in each area. When combined with population data, such information can be invaluable in informing policy by indicating areas for which improvements may be given priority.
Journal: Journal of Applied Statistics
Pages: 777-794
Issue: 4
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2012.754851
File-URL: http://hdl.handle.net/10.1080/02664763.2012.754851
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:4:p:777-794




Template-Type: ReDIF-Article 1.0
Author-Name: Mariantonietta Ruggieri
Author-X-Name-First: Mariantonietta
Author-X-Name-Last: Ruggieri
Author-Name: Antonella Plaia
Author-X-Name-First: Antonella
Author-X-Name-Last: Plaia
Author-Name: Francesca Di Salvo
Author-X-Name-First: Francesca
Author-X-Name-Last: Di Salvo
Author-Name: Gianna Agró
Author-X-Name-First: Gianna
Author-X-Name-Last: Agró
Title: Functional principal component analysis for the explorative analysis of multisite–multivariate air pollution time series with long gaps
Abstract: The knowledge of the urban air quality represents the first step to face air pollution issues. For the last decades many cities can rely on a network of monitoring stations recording concentration values for the main pollutants. This paper focuses on functional principal component analysis (FPCA) to investigate multiple pollutant datasets measured over time at multiple sites within a given urban area. Our purpose is to extend what has been proposed in the literature to data that are multisite and multivariate at the same time. The approach results to be effective to highlight some relevant statistical features of the time series, giving the opportunity to identify significant pollutants and to know the evolution of their variability along time. The paper also deals with missing value issue. As it is known, very long gap sequences can often occur in air quality datasets, due to long time failures not easily solvable or to data coming from a mobile monitoring station. In the considered dataset, large and continuous gaps are imputed by empirical orthogonal function procedure, after denoising raw data by functional data analysis and before performing FPCA, in order to further improve the reconstruction.
Journal: Journal of Applied Statistics
Pages: 795-807
Issue: 4
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2012.754852
File-URL: http://hdl.handle.net/10.1080/02664763.2012.754852
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:4:p:795-807




Template-Type: ReDIF-Article 1.0
Author-Name: Sandra De Iaco
Author-X-Name-First: Sandra
Author-X-Name-Last: De Iaco
Title: On the use of different metrics for assessing complex pattern reproductions
Abstract: Nowadays, there is an increasing interest in multi-point models and their applications in Earth sciences. However, users not only ask for multi-point methods able to capture the uncertainties of complex structures and to reproduce the properties of a training image, but also they need quantitative tools for assessing whether a set of realizations have the properties required. Moreover, it is crucial to study the sensitivity of the realizations to the size of the data template and to analyze how fast realization-based statistics converge on average toward training-based statistics. In this paper, some similarity measures and convergence indexes, based on some physically measurable quantities and cumulants of high-order, are presented. In the case study, multi-point simulations of the spatial distribution of coarse-grained limestone and calcareous rock, generated by using three templates of different sizes, are compared and convergence toward training-based statistics is analyzed by taking into account increasing numbers of realizations.
Journal: Journal of Applied Statistics
Pages: 808-822
Issue: 4
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2012.754853
File-URL: http://hdl.handle.net/10.1080/02664763.2012.754853
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:4:p:808-822




Template-Type: ReDIF-Article 1.0
Author-Name: Hukum Chandra
Author-X-Name-First: Hukum
Author-X-Name-Last: Chandra
Title: Exploring spatial dependence in area-level random effect model for disaggregate-level crop yield estimation
Abstract: This paper describes an application of small area estimation (SAE) techniques under area-level spatial random effect models when only area (or district or aggregated) level data are available. In particular, the SAE approach is applied to produce district-level model-based estimates of crop yield for paddy in the state of Uttar Pradesh in India using the data on crop-cutting experiments supervised under the Improvement of Crop Statistics scheme and the secondary data from the Population Census. The diagnostic measures are illustrated to examine the model assumptions as well as reliability and validity of the generated model-based small area estimates. The results show a considerable gain in precision in model-based estimates produced applying SAE. Furthermore, the model-based estimates obtained by exploiting spatial information are more efficient than the one obtained by ignoring this information. However, both of these model-based estimates are more efficient than the direct survey estimate. In many districts, there is no survey data and therefore it is not possible to produce direct survey estimates for these districts. The model-based estimates generated using SAE are still reliable for such districts. These estimates produced by using SAE will provide invaluable information to policy-analysts and decision-makers.
Journal: Journal of Applied Statistics
Pages: 823-842
Issue: 4
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2012.756858
File-URL: http://hdl.handle.net/10.1080/02664763.2012.756858
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:4:p:823-842




Template-Type: ReDIF-Article 1.0
Author-Name: Ole Klungsøyr
Author-X-Name-First: Ole
Author-X-Name-Last: Klungsøyr
Author-Name: Joe Sexton
Author-X-Name-First: Joe
Author-X-Name-Last: Sexton
Author-Name: Inger Sandanger
Author-X-Name-First: Inger
Author-X-Name-Last: Sandanger
Author-Name: Jan Nygård
Author-X-Name-First: Jan
Author-X-Name-Last: Nygård
Title: A time-varying measurement error model for age of onset of a psychiatric diagnosis: applied to first depressive episode diagnosed by the Composite International Diagnostic Interview (CIDI)
Abstract: A substantial degree of uncertainty exists surrounding the reconstruction of events based on memory recall. This form of measurement error affects the performance of structured interviews such as the Composite International Diagnostic Interview (CIDI), an important tool to assess mental health in the community. Measurement error probably explains the discrepancy in estimates between longitudinal studies with repeated assessments (the gold-standard), yielding approximately constant rates of depression, versus cross-sectional studies which often find increasing rates closer in time to the interview. Repeated assessments of current status (or recent history) are more reliable than reconstruction of a person's psychiatric history based on a single interview. In this paper, we demonstrate a method of estimating a time-varying measurement error distribution in the age of onset of an initial depressive episode, as diagnosed by the CIDI, based on an assumption regarding age-specific incidence rates. High-dimensional non-parametric estimation is achieved by the EM-algorithm with smoothing. The method is applied to data from a Norwegian mental health survey in 2000. The measurement error distribution changes dramatically from 1980 to 2000, with increasing variance and greater bias further away in time from the interview. Some influence of the measurement error on already published results is found.
Journal: Journal of Applied Statistics
Pages: 843-861
Issue: 4
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2012.756859
File-URL: http://hdl.handle.net/10.1080/02664763.2012.756859
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:4:p:843-861




Template-Type: ReDIF-Article 1.0
Author-Name: Muhammad Faisal
Author-X-Name-First: Muhammad
Author-X-Name-Last: Faisal
Author-Name: Andreas Futschik
Author-X-Name-First: Andreas
Author-X-Name-Last: Futschik
Author-Name: Ijaz Hussain
Author-X-Name-First: Ijaz
Author-X-Name-Last: Hussain
Title: A new approach to choose acceptance cutoff for approximate Bayesian computation
Abstract: The approximate Bayesian computation (ABC) algorithm is used to estimate parameters from complicated phenomena, where likelihood is intractable. Here, we report the development of an algorithm to choose the tolerance level for ABC. We have illustrated the performance of our proposed method by simulating the estimation of scaled mutation and recombination rates. The result shows that the proposed algorithm performs well.
Journal: Journal of Applied Statistics
Pages: 862-869
Issue: 4
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2012.756860
File-URL: http://hdl.handle.net/10.1080/02664763.2012.756860
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:4:p:862-869




Template-Type: ReDIF-Article 1.0
Author-Name: Zaizai Yan
Author-X-Name-First: Zaizai
Author-X-Name-Last: Yan
Author-Name: Miaomiao Li
Author-X-Name-First: Miaomiao
Author-X-Name-Last: Li
Author-Name: Yalu Yan
Author-X-Name-First: Yalu
Author-X-Name-Last: Yan
Title: An efficient non-rejective implementation of the πps sampling designs
Abstract: Poisson sampling is a method for unequal probabilities sampling with random sample size. There exist several implementations of the Poisson sampling design, with fixed sample size, which almost all are rejective methods, that is, the sample is not always accepted. Thus, the existing methods can be time-consuming or even infeasible in some situations. In this paper, a fast and non-rejective method, which is efficient even for large populations, is proposed and studied. The method is a new design for selecting a sample of fixed size with unequal inclusion probabilities. For the population of large size, the proposed design is very close to the strict πps sampling which is similar to the conditional Poisson (CP) sampling design, but the implementation of the design is much more efficient than the CP sampling. And the inclusion probabilities can be calculated recursively.
Journal: Journal of Applied Statistics
Pages: 870-886
Issue: 4
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2012.756861
File-URL: http://hdl.handle.net/10.1080/02664763.2012.756861
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:4:p:870-886




Template-Type: ReDIF-Article 1.0
Author-Name: A. Hayter
Author-X-Name-First: A.
Author-X-Name-Last: Hayter
Title: Inferences on the difference between future observations for comparing two treatments
Abstract: The comparison of two treatments with normally distributed data is considered. Inferences are considered based upon the difference between single potential future observations from each of the two treatments, which provides a useful and easily interpretable assessment of the difference between the two treatments. These methodologies combine information from a standard confidence interval analysis of the difference between the two treatment means, with information available from standard prediction intervals of future observations. Win-probabilities, which are the probabilities that a future observation from one treatment will be superior to a future observation from the other treatment, are a special case of these methodologies. The theoretical derivation of these methodologies is based upon inferences about the non-centrality parameter of a non-central t-distribution. Equal and unequal variance situations are addressed, and extensions to groups of future observations from the two treatments are also considered. Some examples and discussions of the methodologies are presented.
Journal: Journal of Applied Statistics
Pages: 887-900
Issue: 4
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2012.758245
File-URL: http://hdl.handle.net/10.1080/02664763.2012.758245
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:4:p:887-900




Template-Type: ReDIF-Article 1.0
Author-Name: Edoardo Otranto
Author-X-Name-First: Edoardo
Author-X-Name-Last: Otranto
Title: Volatility clustering in the presence of time-varying model parameters
Abstract: The volatility pattern of financial time series is often characterized by several peaks and abrupt changes, consistent with the time-varying coefficients of the underlying data-generating process. As a consequence, the model-based classification of the volatility of a set of assets could vary over a period of time. We propose a procedure to classify the unconditional volatility obtained from an extended family of Multiplicative Error Models with time-varying coefficients to verify if it changes in correspondence with different regimes or particular dates. The proposed procedure is experimented on 15 stock indices.
Journal: Journal of Applied Statistics
Pages: 901-915
Issue: 4
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2012.759191
File-URL: http://hdl.handle.net/10.1080/02664763.2012.759191
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:4:p:901-915




Template-Type: ReDIF-Article 1.0
Author-Name: Hassan Bakouch
Author-X-Name-First: Hassan
Author-X-Name-Last: Bakouch
Title: R for statistics
Journal: Journal of Applied Statistics
Pages: 924-924
Issue: 4
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2012.761325
File-URL: http://hdl.handle.net/10.1080/02664763.2012.761325
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:4:p:924-924




Template-Type: ReDIF-Article 1.0
Author-Name: The Editors
Title: Erratum
Journal: Journal of Applied Statistics
Pages: v-v
Issue: 4
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2013.765664
File-URL: http://hdl.handle.net/10.1080/02664763.2013.765664
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:4:p:v-v




Template-Type: ReDIF-Article 1.0
Author-Name: I.S. Dhindsa
Author-X-Name-First: I.S.
Author-X-Name-Last: Dhindsa
Author-Name: R. Agarwal
Author-X-Name-First: R.
Author-X-Name-Last: Agarwal
Author-Name: H.S. Ryait
Author-X-Name-First: H.S.
Author-X-Name-Last: Ryait
Title: Principal component analysis-based muscle identification for myoelectric-controlled exoskeleton knee
Abstract: 
 This paper is an attempt to identify a set of muscles which are sufficient to control a myoelectic-controlled exoskeleton knee. A musculoskeletal model of the human body available in the anybody modelling system was scaled to match the subject-specific parameters. It was made to perform a task of sitting in a squat position from a standing position. Internal forces developed in 18 muscles of lower limb during the task were predicted by the inverse dynamic analysis. Principal component analysis was then conducted on the predicted force variable. The eigenvector coefficients of the principal components were evaluated. Significant variables were retained and redundant variables were rejected by the method of principal variable. Subjects were asked to perform the same task of sitting in a squat position from a standing position. Surface-electromyography (sEMG) signals were recorded from the selected muscles. The force developed in the subject's muscles were obtained from the sEMG signals. Force developed in the selected muscle was compared with the force obtained from the musculoskeletal model. A four channel system VastusLateralis, RectusFemoris, Semitendinosus and GluteusMedius and a five channel system VastusLateralis, BicepsFemoris, RectusFemoris, Semitendinosus and GluteusMedius are suitable muscles to control exoskeleton knee.
Journal: Journal of Applied Statistics
Pages: 1707-1720
Issue: 10
Volume: 44
Year: 2017
Month: 7
X-DOI: 10.1080/02664763.2016.1221907
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1221907
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:10:p:1707-1720




Template-Type: ReDIF-Article 1.0
Author-Name: JinXing Che
Author-X-Name-First: JinXing
Author-X-Name-Last: Che
Author-Name: YouLong Yang
Author-X-Name-First: YouLong
Author-X-Name-Last: Yang
Title: Stochastic correlation coefficient ensembles for variable selection
Abstract: 
 In this paper, we propose a novel Max-Relevance and Min-Common-Redundancy criterion for variable selection in linear models. Considering that the ensemble approach for variable selection has been proven to be quite effective in linear regression models, we construct a variable selection ensemble (VSE) by combining the presented stochastic correlation coefficient algorithm with a stochastic stepwise algorithm. We conduct extensive experimental comparison of our algorithm and other methods using two simulation studies and four real-life data sets. The results confirm that the proposed VSE leads to promising improvement on variable selection and regression accuracy.
Journal: Journal of Applied Statistics
Pages: 1721-1742
Issue: 10
Volume: 44
Year: 2017
Month: 7
X-DOI: 10.1080/02664763.2016.1221913
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1221913
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:10:p:1721-1742




Template-Type: ReDIF-Article 1.0
Author-Name: Nicholas T. Longford
Author-X-Name-First: Nicholas T.
Author-X-Name-Last: Longford
Title: Inflated assessments of disability
Abstract: 
 A medical examination provides a key input into decisions about disability pension and other forms of income support or compensation that are justified on medical grounds. The result of examining an individual is often communicated by means of a score, and inflation of such scores is a well-known problem. We estimate the extent of inflation of scores from a set of disability assessments using a model based on the discrete linear distribution. We explore some extensions within the framework of a sensitivity analysis.
Journal: Journal of Applied Statistics
Pages: 1743-1760
Issue: 10
Volume: 44
Year: 2017
Month: 7
X-DOI: 10.1080/02664763.2016.1221914
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1221914
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:10:p:1743-1760




Template-Type: ReDIF-Article 1.0
Author-Name: Geoffrey Colin L. Peterson
Author-X-Name-First: Geoffrey Colin L.
Author-X-Name-Last: Peterson
Author-Name: Dong Li
Author-X-Name-First: Dong
Author-X-Name-Last: Li
Author-Name: Brian J. Reich
Author-X-Name-First: Brian J.
Author-X-Name-Last: Reich
Author-Name: Donald Brenner
Author-X-Name-First: Donald
Author-X-Name-Last: Brenner
Title: Spatial prediction of crystalline defects observed in molecular dynamic simulations of plastic damage
Abstract: 
 Molecular dynamic computer simulation is an essential tool in materials science to study atomic properties of materials in extreme environments and guide development of new materials. We propose a statistical analysis to emulate simulation output with the ultimate goal of efficiently approximating the computationally intensive simulation. We compare several spatial regression approaches including conditional autoregression (CAR), discrete wavelets transform (DWT), and principle components analysis (PCA). The methods are applied to simulation of copper atoms with twin wall and dislocation loop defects, under varying tilt tension angles. We find that CAR and DWT yield accurate results but fail to capture extreme defects, yet PCA better captures defect structure.
Journal: Journal of Applied Statistics
Pages: 1761-1784
Issue: 10
Volume: 44
Year: 2017
Month: 7
X-DOI: 10.1080/02664763.2016.1221915
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1221915
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:10:p:1761-1784




Template-Type: ReDIF-Article 1.0
Author-Name: F. Cugnata
Author-X-Name-First: F.
Author-X-Name-Last: Cugnata
Author-Name: G. Perucca
Author-X-Name-First: G.
Author-X-Name-Last: Perucca
Author-Name: S. Salini
Author-X-Name-First: S.
Author-X-Name-Last: Salini
Title: Bayesian networks and the assessment of universities' value added
Abstract: 
 A broad literature focused on the effectiveness of tertiary education. In classical models, a performance indicator is regressed on a set of characteristics of the individuals and fixed effects at the institution level. The FE coefficients are interpreted as the pure value added of the universities. The innovative contribution of the present paper resides in the use of Bayesian network (BN) analysis to assess the effectiveness of tertiary education. The results of an empirical study focused on Italian universities are discussed, to present the use of BN as a decision support tool for policy-making purposes.
Journal: Journal of Applied Statistics
Pages: 1785-1806
Issue: 10
Volume: 44
Year: 2017
Month: 7
X-DOI: 10.1080/02664763.2016.1223839
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1223839
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:10:p:1785-1806




Template-Type: ReDIF-Article 1.0
Author-Name: Jacob Martin
Author-X-Name-First: Jacob
Author-X-Name-Last: Martin
Author-Name: Daniel B. Hall
Author-X-Name-First: Daniel B.
Author-X-Name-Last: Hall
Title: Marginal zero-inflated regression models for count data
Abstract: 
 Data sets with excess zeroes are frequently analyzed in many disciplines. A common framework used to analyze such data is the zero-inflated (ZI) regression model. It mixes a degenerate distribution with point mass at zero with a non-degenerate distribution. The estimates from ZI models quantify the effects of covariates on the means of latent random variables, which are often not the quantities of primary interest. Recently, marginal zero-inflated Poisson (MZIP; Long et al. [A marginalized zero-inflated Poisson regression model with overall exposure effects. Stat. Med. 33 (2014), pp. 5151–5165]) and negative binomial (MZINB; Preisser et al., 2016) models have been introduced that model the mean response directly. These models yield covariate effects that have simple interpretations that are, for many applications, more appealing than those available from ZI regression. This paper outlines a general framework for marginal zero-inflated models where the latent distribution is a member of the exponential dispersion family, focusing on common distributions for count data. In particular, our discussion includes the marginal zero-inflated binomial (MZIB) model, which has not been discussed previously. The details of maximum likelihood estimation via the EM algorithm are presented and the properties of the estimators as well as Wald and likelihood ratio-based inference are examined via simulation. Two examples presented illustrate the advantages of MZIP, MZINB, and MZIB models for practical data analysis.
Journal: Journal of Applied Statistics
Pages: 1807-1826
Issue: 10
Volume: 44
Year: 2017
Month: 7
X-DOI: 10.1080/02664763.2016.1225018
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1225018
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:10:p:1807-1826




Template-Type: ReDIF-Article 1.0
Author-Name: André G. F. C. Costa
Author-X-Name-First: André G. F. C.
Author-X-Name-Last: Costa
Author-Name: Enrico A. Colosimo
Author-X-Name-First: Enrico A.
Author-X-Name-Last: Colosimo
Author-Name: Aline B. M. Vaz
Author-X-Name-First: Aline B. M.
Author-X-Name-Last: Vaz
Author-Name: José Luiz P. Silva
Author-X-Name-First: José Luiz P.
Author-X-Name-Last: Silva
Author-Name: Leila D. Amorim
Author-X-Name-First: Leila D.
Author-X-Name-Last: Amorim
Title: Marginal models for the association structure of hierarchical binary responses
Abstract: 
 Clustered binary responses are often found in ecological studies. Data analysis may include modeling the marginal probability response. However, when the association is the main scientific focus, modeling the correlation structure between pairs of responses is the key part of the analysis. Second-order generalized estimating equations (GEE) are established in the literature. Some of them are more efficient in computational terms, especially facing large clusters. Alternating logistic regression (ALR) and orthogonalized residual (ORTH) GEE methods are presented and compared in this paper. Simulation results show a slightly superiority of ALR over ORTH. Marginal probabilities and odds ratios are also estimated and compared in a real ecological study involving a three-level hierarchical clustering. ALR and ORTH models are useful for modeling complex association structure with large cluster sizes.
Journal: Journal of Applied Statistics
Pages: 1827-1838
Issue: 10
Volume: 44
Year: 2017
Month: 7
X-DOI: 10.1080/02664763.2016.1238042
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1238042
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:10:p:1827-1838




Template-Type: ReDIF-Article 1.0
Author-Name: Tao Wang
Author-X-Name-First: Tao
Author-X-Name-Last: Wang
Author-Name: Lin Zheng
Author-X-Name-First: Lin
Author-X-Name-Last: Zheng
Author-Name: Zhonghua Li
Author-X-Name-First: Zhonghua
Author-X-Name-Last: Li
Author-Name: Haiyang Liu
Author-X-Name-First: Haiyang
Author-X-Name-Last: Liu
Title: A robust variable screening method for high-dimensional data
Abstract: 
 In practice, the presence of influential observations may lead to misleading results in variable screening problems. We, therefore, propose a robust variable screening procedure for high-dimensional data analysis in this paper. Our method consists of two steps. The first step is to define a new high-dimensional influence measure and propose a novel influence diagnostic procedure to remove those unusual observations. The second step is to utilize the sure independence screening procedure based on distance correlation to select important variables in high-dimensional regression analysis. The new influence measure and diagnostic procedure that we developed are model free. To confirm the effectiveness of the proposed method, we conduct simulation studies and a real-life data analysis to illustrate the merits of the proposed approach over some competing methods. Both the simulation results and the real-life data analysis demonstrate that the proposed method can greatly control the adverse effect after detecting and removing those unusual observations, and performs better than the competing methods.
Journal: Journal of Applied Statistics
Pages: 1839-1855
Issue: 10
Volume: 44
Year: 2017
Month: 7
X-DOI: 10.1080/02664763.2016.1238044
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1238044
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:10:p:1839-1855




Template-Type: ReDIF-Article 1.0
Author-Name: P.G. Sankaran
Author-X-Name-First: P.G.
Author-X-Name-Last: Sankaran
Author-Name: N.N. Midhu
Author-X-Name-First: N.N.
Author-X-Name-Last: Midhu
Title: Nonparametric estimation of mean residual quantile function under right censoring
Abstract: 
 In this paper, we develop non-parametric estimation of the mean residual quantile function based on right-censored data. Two non-parametric estimators, one based on the empirical quantile function and the other using the kernel smoothing method, are proposed. Asymptotic properties of the estimators are discussed. Monte Carlo simulation studies are conducted to compare the two estimators. The method is illustrated with the aid of two real data sets.
Journal: Journal of Applied Statistics
Pages: 1856-1874
Issue: 10
Volume: 44
Year: 2017
Month: 7
X-DOI: 10.1080/02664763.2016.1238046
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1238046
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:10:p:1856-1874




Template-Type: ReDIF-Article 1.0
Author-Name: Fabio Baione
Author-X-Name-First: Fabio
Author-X-Name-Last: Baione
Author-Name: Paolo De Angelis
Author-X-Name-First: Paolo
Author-X-Name-Last: De Angelis
Author-Name: Massimiliano Menzietti
Author-X-Name-First: Massimiliano
Author-X-Name-Last: Menzietti
Author-Name: Agostino Tripodi
Author-X-Name-First: Agostino
Author-X-Name-Last: Tripodi
Title: A comparison of risk transfer strategies for a portfolio of life annuities based on RORAC
Abstract: 
 This paper aims to compare different reinsurance arrangements in order to reduce the longevity and financial risk originated by a life insurer while managing a portfolio of annuities policies. Linear and nonlinear reinsurance strategies as well as swap like agreements are evaluated via a discrete-time actuarial risk model. Specifically, longevity dynamics are represented by Lee–Carter type models, while interest rate is modeled by Cox–Ingersoll–Ross model. The reinsurance strategies effectiveness is evaluated according to the Return on Risk Adjusted Capital under a ruin probability constrain.
Journal: Journal of Applied Statistics
Pages: 1875-1892
Issue: 10
Volume: 44
Year: 2017
Month: 7
X-DOI: 10.1080/02664763.2016.1238047
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1238047
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:10:p:1875-1892




Template-Type: ReDIF-Article 1.0
Author-Name: Ying-Hsiu Chen
Author-X-Name-First: Ying-Hsiu
Author-X-Name-Last: Chen
Author-Name: Po-Lin Lai
Author-X-Name-First: Po-Lin
Author-X-Name-Last: Lai
Title: Does diversification promote risk reduction and profitability raise? Estimation of dynamic impacts using the pooled mean group model
Abstract: 
 This paper utilizes the pooled mean group model to explore the dynamic effects of revenue diversification on the operational risks and profitability of banks. The sample consisted of unbalanced panel data of 25 listed Taiwanese banks for the period from 1998 to 2013. The results reveal a divergence in the long- and short-run effects of revenue diversification on credit risk by the banks, and the benefits of diversification on two other operational risks and profitability are deferred. This paper provides dynamic evidence of diversification, which has been typically evaluated in previous studies, to release the aggregate effect and to explain the ambiguity in the results in the current literature.
Journal: Journal of Applied Statistics
Pages: 1893-1901
Issue: 10
Volume: 44
Year: 2017
Month: 7
X-DOI: 10.1080/02664763.2016.1252729
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1252729
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:10:p:1893-1901




Template-Type: ReDIF-Article 1.0
Author-Name: Jingheng Cai
Author-X-Name-First: Jingheng
Author-X-Name-Last: Cai
Author-Name: Zhibin Liang
Author-X-Name-First: Zhibin
Author-X-Name-Last: Liang
Author-Name: Rongqian Sun
Author-X-Name-First: Rongqian
Author-X-Name-Last: Sun
Author-Name: Chenyi Liang
Author-X-Name-First: Chenyi
Author-X-Name-Last: Liang
Author-Name: Junhao Pan
Author-X-Name-First: Junhao
Author-X-Name-Last: Pan
Title: Bayesian analysis of latent Markov models with non-ignorable missing data
Abstract: 
 Latent Markov models (LMMs) are widely used in the analysis of heterogeneous longitudinal data. However, most existing LMMs are developed in fully observed data without missing entries. The main objective of this study is to develop a Bayesian approach for analyzing the LMMs with non-ignorable missing data. Bayesian methods for estimation and model comparison are discussed. The empirical performance of the proposed methodology is evaluated through simulation studies. An application to a data set derived from National Longitudinal Survey of Youth 1997 is presented.
Journal: Journal of Applied Statistics
Pages: 2299-2313
Issue: 13
Volume: 46
Year: 2019
Month: 10
X-DOI: 10.1080/02664763.2019.1584162
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1584162
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:13:p:2299-2313




Template-Type: ReDIF-Article 1.0
Author-Name: P. Mozgunov
Author-X-Name-First: P.
Author-X-Name-Last: Mozgunov
Author-Name: T. Jaki
Author-X-Name-First: T.
Author-X-Name-Last: Jaki
Author-Name: M. Gasparini
Author-X-Name-First: M.
Author-X-Name-Last: Gasparini
Title: Loss functions in restricted parameter spaces and their Bayesian applications
Abstract: 
 Squared error loss remains the most commonly used loss function for constructing a Bayes estimator of the parameter of interest. However, it can lead to suboptimal solutions when a parameter is defined on a restricted space. It can also be an inappropriate choice in the context when an extreme overestimation and/or underestimation results in severe consequences and a more conservative estimator is preferred. We advocate a class of loss functions for parameters defined on restricted spaces which infinitely penalize boundary decisions like the squared error loss does on the real line. We also recall several properties of loss functions such as symmetry, convexity and invariance. We propose generalizations of the squared error loss function for parameters defined on the positive real line and on an interval. We provide explicit solutions for corresponding Bayes estimators and discuss multivariate extensions. Four well-known Bayesian estimation problems are used to demonstrate inferential benefits the novel Bayes estimators can provide in the context of restricted estimation.
Journal: Journal of Applied Statistics
Pages: 2314-2337
Issue: 13
Volume: 46
Year: 2019
Month: 10
X-DOI: 10.1080/02664763.2019.1586848
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1586848
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:13:p:2314-2337




Template-Type: ReDIF-Article 1.0
Author-Name: Cord A. Müller
Author-X-Name-First: Cord A.
Author-X-Name-Last: Müller
Title: Optimal acceptance sampling for modules F and F1 of the European Measuring Instruments Directive
Abstract: 
 Acceptance sampling plans offered by ISO 2859-1 are far from optimal under the conditions for statistical verification in modules F and F1 as prescribed by Annex II of the Measuring Instruments Directive (MID) 2014/32/EU, resulting in sample sizes that are larger than necessary. An optimised single-sampling scheme is derived, both for large lots using the binomial distribution and for finite-sized lots using the exact hypergeometric distribution, resulting in smaller sample sizes that are economically more efficient while offering the full statistical protection required by the MID.
Journal: Journal of Applied Statistics
Pages: 2338-2356
Issue: 13
Volume: 46
Year: 2019
Month: 10
X-DOI: 10.1080/02664763.2019.1588235
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1588235
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:13:p:2338-2356




Template-Type: ReDIF-Article 1.0
Author-Name: Yi-Ting Hwang
Author-X-Name-First: Yi-Ting
Author-X-Name-Last: Hwang
Author-Name: Chia-Hui Huang
Author-X-Name-First: Chia-Hui
Author-X-Name-Last: Huang
Author-Name: Chun-Chao Wang
Author-X-Name-First: Chun-Chao
Author-X-Name-Last: Wang
Author-Name: Tzu-Yin Lin
Author-X-Name-First: Tzu-Yin
Author-X-Name-Last: Lin
Author-Name: Yi-Kuan Tseng
Author-X-Name-First: Yi-Kuan
Author-X-Name-Last: Tseng
Title: Joint modelling of longitudinal binary data and survival data
Abstract: 
 The medical costs in an ageing society substantially increase when the incidences of chronic diseases, disabilities and inability to live independently are high. Healthy lifestyles not only affect elderly individuals but also influence the entire community. When assessing treatment efficacy, survival and quality of life should be considered simultaneously. This paper proposes the joint likelihood approach for modelling survival and longitudinal binary covariates simultaneously. Because some unobservable information is present in the model, the Monte Carlo EM algorithm and Metropolis-Hastings algorithm are used to find the estimators. Monte Carlo simulations are performed to evaluate the performance of the proposed model based on the accuracy and precision of the estimates. Real data are used to demonstrate the feasibility of the proposed model.
Journal: Journal of Applied Statistics
Pages: 2357-2371
Issue: 13
Volume: 46
Year: 2019
Month: 10
X-DOI: 10.1080/02664763.2019.1590540
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1590540
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:13:p:2357-2371




Template-Type: ReDIF-Article 1.0
Author-Name: Marcelo A. da Silva
Author-X-Name-First: Marcelo A.
Author-X-Name-Last: da Silva
Author-Name: Anne C. Huggins-Manley
Author-X-Name-First: Anne C.
Author-X-Name-Last: Huggins-Manley
Author-Name: José A. Mazzon
Author-X-Name-First: José A.
Author-X-Name-Last: Mazzon
Author-Name: Jorge L. Bazán
Author-X-Name-First: Jorge L.
Author-X-Name-Last: Bazán
Title: Bayesian estimation of a flexible bifactor generalized partial credit model to survey data
Abstract: 
 Item response theory (IRT) models provide an important contribution in the analysis of polytomous items, such as Likert scale items in survey data. We propose a bifactor generalized partial credit model (bifac-GPC model) with flexible link functions - probit, logit and complementary log-log - for use in analysis of ordered polytomous item scale data. In order to estimate the parameters of the proposed model, we use a Bayesian approach through the NUTS algorithm and show the advantages of implementing IRT models through the Stan language. We present an application to marketing scale data. Specifically, we apply the model to a dataset of non-users of a mobile banking service in order to highlight the advantages of this model. The results show important managerial implications resulting from consumer perceptions. We provide a discussion of the methodology for this type of data and extensions. Codes are available for practitioners and researchers to replicate the application.
Journal: Journal of Applied Statistics
Pages: 2372-2387
Issue: 13
Volume: 46
Year: 2019
Month: 10
X-DOI: 10.1080/02664763.2019.1592125
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1592125
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:13:p:2372-2387




Template-Type: ReDIF-Article 1.0
Author-Name: Chih-Chun Tsai
Author-X-Name-First: Chih-Chun
Author-X-Name-Last: Tsai
Title: Optimal lamination test of ethylene vinyl acetate sheets for solar modules
Abstract: 
 Solar power is inexhaustible and has become one of the most appreciated alternative energy choices. In the development stage, solar modules are subjected to relevant reliability tests to ensure a long lifetime and optimal power generation efficiency. After the lamination process, the performance of solar modules is closely related to the degree of crosslinking of ethylene vinyl acetate (EVA) sheets. Traditionally, the degree of crosslinking on EVA sheets is obtained using the chemical extraction method to measure the gel content of these sheets. Motivated by lamination data, this study first constructed a statistical model to describe the relationship between the degree of crosslinking on EVA sheets and lamination time. Next, under the specification limits of the gel content of EVA sheets, the optimal lamination time of solar modules was derived, and the optimal allocation for measuring EVA sheets was addressed. The chemical extraction method is time consuming and leads to high pollution. The latest method is differential scanning calorimetric (DSC), which measures the curing degree of EVA sheets as the degree of crosslinking on these sheets. This study determined the specification limits of the curing degree under the DSC method. An example is presented to elucidate the proposed procedure.
Journal: Journal of Applied Statistics
Pages: 2388-2408
Issue: 13
Volume: 46
Year: 2019
Month: 10
X-DOI: 10.1080/02664763.2019.1596230
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1596230
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:13:p:2388-2408




Template-Type: ReDIF-Article 1.0
Author-Name: A. R. Baghestani
Author-X-Name-First: A. R.
Author-X-Name-Last: Baghestani
Author-Name: F. S. Hosseini-Baharanchi
Author-X-Name-First: F. S.
Author-X-Name-Last: Hosseini-Baharanchi
Title: An improper form of Weibull distribution for competing risks analysis with Bayesian approach
Abstract: 
 In survival analysis, individuals may fail due to multiple causes of failure called competing risks setting. Parametric models such as Weibull model are not improper that ignore the assumption of multiple failure times. In this study, a novel extension of Weibull distribution is proposed which is improper and then can incorporate to the competing risks framework. This model includes the original Weibull model before a pre-specified time point and an exponential form for the tail of the time axis. A Bayesian approach is used for parameter estimation. A simulation study is performed to evaluate the proposed model. The conducted simulation study showed identifiability and appropriate convergence of the proposed model. The proposed model and the 3-parameter Gompertz model, another improper parametric distribution, are fitted to the acute lymphoblastic leukemia dataset.
Journal: Journal of Applied Statistics
Pages: 2409-2417
Issue: 13
Volume: 46
Year: 2019
Month: 10
X-DOI: 10.1080/02664763.2019.1597027
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1597027
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:13:p:2409-2417




Template-Type: ReDIF-Article 1.0
Author-Name: Eliana Christou
Author-X-Name-First: Eliana
Author-X-Name-Last: Christou
Author-Name: Michael Grabchak
Author-X-Name-First: Michael
Author-X-Name-Last: Grabchak
Title: Estimation of value-at-risk using single index quantile regression
Abstract: 
 Value-at-Risk (VaR) is one of the best known and most heavily used measures of financial risk. In this paper, we introduce a non-iterative semiparametric model for VaR estimation called the single index quantile regression time series (SIQRTS) model. To test its performance, we give an application to four major US market indices: the S&P 500 Index, the Russell 2000 Index, the Dow Jones Industrial Average, and the NASDAQ Composite Index. Our results suggest that this method has a good finite sample performance and often outperforms a number of commonly used methods.
Journal: Journal of Applied Statistics
Pages: 2418-2433
Issue: 13
Volume: 46
Year: 2019
Month: 10
X-DOI: 10.1080/02664763.2019.1597028
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1597028
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:13:p:2418-2433




Template-Type: ReDIF-Article 1.0
Author-Name: Vinicius F. Calsavara
Author-X-Name-First: Vinicius F.
Author-X-Name-Last: Calsavara
Author-Name: Agatha S. Rodrigues
Author-X-Name-First: Agatha S.
Author-X-Name-Last: Rodrigues
Author-Name: Ricardo Rocha
Author-X-Name-First: Ricardo
Author-X-Name-Last: Rocha
Author-Name: Francisco Louzada
Author-X-Name-First: Francisco
Author-X-Name-Last: Louzada
Author-Name: Vera Tomazella
Author-X-Name-First: Vera
Author-X-Name-Last: Tomazella
Author-Name: Ana C. R. L. A. Souza
Author-X-Name-First: Ana C. R. L. A.
Author-X-Name-Last: Souza
Author-Name: Rafaela A. Costa
Author-X-Name-First: Rafaela A.
Author-X-Name-Last: Costa
Author-Name: Rossana P. V. Francisco
Author-X-Name-First: Rossana P. V.
Author-X-Name-Last: Francisco
Title: Zero-adjusted defective regression models for modeling lifetime data
Abstract: 
 In this paper, we introduce a defective regression model for survival data modeling with a proportion of early failures or zero-adjusted. Our approach enables us to accommodate three types of units, that is, patients with ‘zero’ survival times (early failures) and those who are susceptible or not susceptible to the event of interest. Defective distributions are obtained from standard distributions by changing the domain of the parameters of the latter in such a way that their survival functions are limited to  $ p\in (0, 1) $ p∈(0,1). We consider the Gompertz and inverse Gaussian defective distributions, which allow modeling of data containing a cure fraction. Parameter estimation is performed by maximum likelihood estimation, and Monte Carlo simulation studies are conducted to evaluate the performance of the proposed models. We illustrate the practical relevance of the proposed models on two real data sets. The first is from a study of occlusion of endoscopic stenting in patients with pancreatic cancer performed at A.C.Camargo Cancer Center, and the other is from a study on insulin use in pregnant women diagnosed with gestational diabetes performed at São Paulo University Medical School. Both studies were performed in São Paulo, Brazil.
Journal: Journal of Applied Statistics
Pages: 2434-2459
Issue: 13
Volume: 46
Year: 2019
Month: 10
X-DOI: 10.1080/02664763.2019.1597029
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1597029
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:13:p:2434-2459




Template-Type: ReDIF-Article 1.0
Author-Name: Meena Badade
Author-X-Name-First: Meena
Author-X-Name-Last: Badade
Author-Name: T. V. Ramanathan
Author-X-Name-First: T. V.
Author-X-Name-Last: Ramanathan
Title: Probabilistic frontier regression models for binary type output data
Abstract: 
 This paper proposes a probabilistic frontier regression model for binary type output data in a production process setup. We consider one of the two categories of outputs as ‘selected’ category and the reduction in probability of falling in this category is attributed to the reduction in technical efficiency (TE) of the decision-making unit. An efficiency measure is proposed to determine the deviations of individual units from the probabilistic frontier. Simulation results show that the average estimated TE component is close to its true value. An application of the proposed method to the data related to the Indian public sector banking system is provided where the output variable is the indicator of level of non-performing assets. Individual TE is obtained for each of the banks under consideration. Among the public sector banks, Andhra bank is found to be the most efficient, whereas the United Bank of India is the least.
Journal: Journal of Applied Statistics
Pages: 2460-2480
Issue: 13
Volume: 46
Year: 2019
Month: 10
X-DOI: 10.1080/02664763.2019.1597838
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1597838
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:13:p:2460-2480




Template-Type: ReDIF-Article 1.0
Author-Name: Stavros Kourtzidis
Author-X-Name-First: Stavros
Author-X-Name-Last: Kourtzidis
Author-Name: Panayiotis Tzeremes
Author-X-Name-First: Panayiotis
Author-X-Name-Last: Tzeremes
Author-Name: Nickolaos G. Tzeremes
Author-X-Name-First: Nickolaos G.
Author-X-Name-Last: Tzeremes
Title: Conditional time-dependent nonparametric estimators with an application to healthcare production function
Abstract: 
 By using the probabilistic framework of production efficiency, the paper develops time-dependent conditional efficiency estimators performing a non-parametric frontier analysis. Specifically, by applying both full and quantile (robust) time-dependent conditional estimators, it models the dynamic effect of health expenditure on countries’ technological change and technological catch-up levels. The results from the application reveal that the effect of per capita health expenditure on countries’ technological change and technological catch-up is nonlinear and is subject to countries’ specific income levels.
Journal: Journal of Applied Statistics
Pages: 2481-2490
Issue: 13
Volume: 46
Year: 2019
Month: 10
X-DOI: 10.1080/02664763.2019.1588234
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1588234
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:13:p:2481-2490




Template-Type: ReDIF-Article 1.0
Author-Name: Eugenio Brentari
Author-X-Name-First: Eugenio
Author-X-Name-Last: Brentari
Author-Name: Livia Dancelli
Author-X-Name-First: Livia
Author-X-Name-Last: Dancelli
Author-Name: Marica Manisera
Author-X-Name-First: Marica
Author-X-Name-Last: Manisera
Title: Clustering ranking data in market segmentation: a case study on the Italian McDonald's customers’ preferences
Abstract: 
 Cluster analysis is often used for market segmentation. When the inputs in the clustering algorithm are ranking data, the intersubject (dis)similarities must be measured by matching-type measures, able to take account of the ordinal nature of the data. Among them, we used a Weighted Spearman's rho, suitably transformed into a (dis)similarity measure, in order to emphasize the concordance on the top ranks. This allows creating clusters grouping customers that place the same items (products, services, etc.) higher in their rankings. Also the statistical instruments used to interpret the clusters must be conceived to deal with ordinal data. The median and other location measures are appropriate but not always able to clearly differentiate groups. The so-called bipolar mean, with its related variability measure, may reveal some additional features. A case study on real data from a survey carried out in the Italian McDonald's restaurants is presented.
Journal: Journal of Applied Statistics
Pages: 1959-1976
Issue: 11
Volume: 43
Year: 2016
Month: 8
X-DOI: 10.1080/02664763.2015.1125864
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1125864
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:11:p:1959-1976




Template-Type: ReDIF-Article 1.0
Author-Name: Pelin Kasap
Author-X-Name-First: Pelin
Author-X-Name-Last: Kasap
Author-Name: Birdal Senoglu
Author-X-Name-First: Birdal
Author-X-Name-Last: Senoglu
Author-Name: Olcay Arslan
Author-X-Name-First: Olcay
Author-X-Name-Last: Arslan
Title: Stochastic analysis of covariance when the error distribution is long-tailed symmetric
Abstract: 
 In this study, we consider stochastic one-way analysis of covariance model when the distribution of the error terms is long-tailed symmetric. Estimators of the unknown model parameters are obtained by using the maximum likelihood (ML) methodology. Iteratively reweighting algorithm is used to compute the ML estimates of the parameters. We also propose new test statistic based on ML estimators for testing the linear contrasts of the treatment effects. In the simulation study, we compare the efficiencies of the traditional least-squares (LS) estimators of the model parameters with the corresponding ML estimators. We also compare the power of the test statistics based on LS and ML estimators, respectively. A real-life example is given at the end of the study.
Journal: Journal of Applied Statistics
Pages: 1977-1997
Issue: 11
Volume: 43
Year: 2016
Month: 8
X-DOI: 10.1080/02664763.2015.1125866
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1125866
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:11:p:1977-1997




Template-Type: ReDIF-Article 1.0
Author-Name: R. Lombardo
Author-X-Name-First: R.
Author-X-Name-Last: Lombardo
Author-Name: E.J. Beh
Author-X-Name-First: E.J.
Author-X-Name-Last: Beh
Title: The prediction index of aggregate data
Abstract: 
 The analysis of the association between the two dichotomous variables of a  $ 2\times 2 $ 2×2 table arises as an important statistical issue in a number of diverse settings, such as in biomedical, medical, epidemiological, pharmaceutical or environmental research. When only the aggregate (or marginal) information is available, the analyst may determine the likely strength of the association between the variables. In this paper, we propose a new measure, called aggregate prediction index, that assesses the likely statistical significance of the association between the rows and columns of a  $ 2\times 2 $ 2×2 table where one variable is treated as a predictor variable and the other is treated as a response variable. Further insight into the predictor's potential strength can be visually obtained by performing an asymmetric version of correspondence analysis and considering a biplot display of the two variables – this issue shall also be explored in light of the new index.
Journal: Journal of Applied Statistics
Pages: 1998-2018
Issue: 11
Volume: 43
Year: 2016
Month: 8
X-DOI: 10.1080/02664763.2015.1125867
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1125867
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:11:p:1998-2018




Template-Type: ReDIF-Article 1.0
Author-Name: Hsiao-Hsian Gao
Author-X-Name-First: Hsiao-Hsian
Author-X-Name-Last: Gao
Author-Name: Li-Shan Huang
Author-X-Name-First: Li-Shan
Author-X-Name-Last: Huang
Title: Sample size planning for testing significance of curves
Abstract: 
 Smoothing methods for curve estimation have received considerable attention in statistics with a wide range of applications. However, to our knowledge, sample size planning for testing significance of curves has not been discussed in the literature. This paper focuses on sample size calculations for nonparametric regression and partially linear models based on local linear estimators. We describe explicit procedures for sample size calculations based on non- and semi-parametric F-tests. Data examples are provided to demonstrate the use of the procedures.
Journal: Journal of Applied Statistics
Pages: 2019-2028
Issue: 11
Volume: 43
Year: 2016
Month: 8
X-DOI: 10.1080/02664763.2015.1126238
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1126238
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:11:p:2019-2028




Template-Type: ReDIF-Article 1.0
Author-Name: Chien-Chia L. Huang
Author-X-Name-First: Chien-Chia L.
Author-X-Name-Last: Huang
Author-Name: Yow-Jen Jou
Author-X-Name-First: Yow-Jen
Author-X-Name-Last: Jou
Author-Name: Hsun-Jung Cho
Author-X-Name-First: Hsun-Jung
Author-X-Name-Last: Cho
Title: A new multicollinearity diagnostic for generalized linear models
Abstract: 
 We propose a new collinearity diagnostic tool for generalized linear models. The new diagnostic tool is termed the weighted variance inflation factor (WVIF) behaving exactly the same as the traditional variance inflation factor in the context of regression diagnostic, given data matrix normalized. Compared to the use of condition number (CN), WVIF shows more reliable information on how severe the situation is, when data collinearity does exist. An alternative estimator, a by-product of the new diagnostic, outperforms the ridge estimator in the presence of data collinearity in both aspects of WVIF and CN. Evidences are given through analyzing various real-world numerical examples.
Journal: Journal of Applied Statistics
Pages: 2029-2043
Issue: 11
Volume: 43
Year: 2016
Month: 8
X-DOI: 10.1080/02664763.2015.1126239
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1126239
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:11:p:2029-2043




Template-Type: ReDIF-Article 1.0
Author-Name: Michael Lechner
Author-X-Name-First: Michael
Author-X-Name-Last: Lechner
Author-Name: Nuria Rodriguez-Planas
Author-X-Name-First: Nuria
Author-X-Name-Last: Rodriguez-Planas
Author-Name: Daniel Fernández Kranz
Author-X-Name-First: Daniel
Author-X-Name-Last: Fernández Kranz
Title: Difference-in-difference estimation by FE and OLS when there is panel non-response
Abstract: 
 We show that the ordinary least squares (OLS) and fixed-effects (FE) estimators of the popular difference-in-differences model may deviate when there is time-varying panel non-response. If such non-response does not affect the common-trend assumption, then OLS and FE are consistent, but OLS is more precise. However, if non-response is affecting the common-trend assumption, then FE estimation may still be consistent, while OLS will be inconsistent. We provide simulation as well as empirical evidence for this phenomenon to occur. We conclude that in case of unbalanced panels, deviating OLS and FE estimates should be considered as evidence that non-response is not ignorable for the differences-in-differences estimation.
Journal: Journal of Applied Statistics
Pages: 2044-2052
Issue: 11
Volume: 43
Year: 2016
Month: 8
X-DOI: 10.1080/02664763.2015.1126240
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1126240
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:11:p:2044-2052




Template-Type: ReDIF-Article 1.0
Author-Name: Kuo-Chin Lin
Author-X-Name-First: Kuo-Chin
Author-X-Name-Last: Lin
Author-Name: Yi-Ju Chen
Author-X-Name-First: Yi-Ju
Author-X-Name-Last: Chen
Title: Goodness-of-fit tests of generalized linear mixed models for repeated ordinal responses
Abstract: 
 Categorical longitudinal data are frequently applied in a variety of fields, and are commonly fitted by generalized linear mixed models (GLMMs) and generalized estimating equations models. The cumulative logit is one of the useful link functions to deal with the problem involving repeated ordinal responses. To check the adequacy of the GLMMs with cumulative logit link function, two goodness-of-fit tests constructed by the unweighted sum of squared model residuals using numerical integration and bootstrap resampling technique are proposed. The empirical type I error rates and powers of the proposed tests are examined by simulation studies. The ordinal longitudinal studies are utilized to illustrate the application of the two proposed tests.
Journal: Journal of Applied Statistics
Pages: 2053-2064
Issue: 11
Volume: 43
Year: 2016
Month: 8
X-DOI: 10.1080/02664763.2015.1126568
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1126568
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:11:p:2053-2064




Template-Type: ReDIF-Article 1.0
Author-Name: Christophe Corbier
Author-X-Name-First: Christophe
Author-X-Name-Last: Corbier
Title: Huberian function applied to neurodegenerative disorder gait rhythm
Abstract: 
 Huberian statistical approach is applied to differentiate three neurodegenerative disorder gait rhythm and presents a method reducing the number of parameters of an autoregressive moving average (ARMA) modeling of the walking signal. Gait rhythm dynamics differ between healthy control, Parkinson's disease, Huntington's disease and amyotrophic lateral sclerosis. Random variables such as the stride interval and its two sub-phases (i.e. swing and stance) present a great variability with natural outliers. Huberian function as a mixture of  $ L_2 $ L2 and  $ L_1 $ L1 norms with low threshold γ is used to present new statistical indicators by deducing the corresponding skewness and kurtosis. The choice of γ is discussed to ensure consistency and convergence of a low-order ARMA estimator of the gait rhythm signal. A mathematical point of view is developed and experimental results are presented.
Journal: Journal of Applied Statistics
Pages: 2065-2084
Issue: 11
Volume: 43
Year: 2016
Month: 8
X-DOI: 10.1080/02664763.2015.1126811
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1126811
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:11:p:2065-2084




Template-Type: ReDIF-Article 1.0
Author-Name: Fernanda B. Rizzato
Author-X-Name-First: Fernanda B.
Author-X-Name-Last: Rizzato
Author-Name: Roseli A. Leandro
Author-X-Name-First: Roseli A.
Author-X-Name-Last: Leandro
Author-Name: Clarice G.B. Demétrio
Author-X-Name-First: Clarice G.B.
Author-X-Name-Last: Demétrio
Author-Name: Geert Molenberghs
Author-X-Name-First: Geert
Author-X-Name-Last: Molenberghs
Title: A Bayesian approach to analyse overdispersed longitudinal count data
Abstract: 
 In this paper, we consider a model for repeated count data, with within-subject correlation and/or overdispersion. It extends both the generalized linear mixed model and the negative-binomial model. This model, proposed in a likelihood context [17,18] is placed in a Bayesian inferential framework. An important contribution takes the form of Bayesian model assessment based on pivotal quantities, rather than the often less adequate DIC. By means of a real biological data set, we also discuss some Bayesian model selection aspects, using a pivotal quantity proposed by Johnson [12].
Journal: Journal of Applied Statistics
Pages: 2085-2109
Issue: 11
Volume: 43
Year: 2016
Month: 8
X-DOI: 10.1080/02664763.2015.1126812
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1126812
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:11:p:2085-2109




Template-Type: ReDIF-Article 1.0
Author-Name: Xun Xiao
Author-X-Name-First: Xun
Author-X-Name-Last: Xiao
Author-Name: Amitava Mukherjee
Author-X-Name-First: Amitava
Author-X-Name-Last: Mukherjee
Author-Name: Min Xie
Author-X-Name-First: Min
Author-X-Name-Last: Xie
Title: Estimation procedures for grouped data – a comparative study
Abstract: 
 Interval-censored data are very common in the reliability and lifetime data analysis. This paper investigates the performance of different estimation procedures for a special type of interval-censored data, i.e. grouped data, from three widely used lifetime distributions. The approaches considered here include the maximum likelihood estimation, the minimum distance estimation based on chi-square criterion, the moment estimation based on imputation (IM) method and an ad hoc estimation procedure. Although IM-based techniques are extensively used recently, we show that this method is not always effective. It is found that the ad hoc estimation procedure is equivalent to the minimum distance estimation with another distance metric and more effective in the simulation. The procedures of different approaches are presented and their performances are investigated by Monte Carlo simulation for various combinations of sample sizes and parameter settings. The numerical results provide guidelines to analyse grouped data for practitioners when they need to choose a good estimation approach.
Journal: Journal of Applied Statistics
Pages: 2110-2130
Issue: 11
Volume: 43
Year: 2016
Month: 8
X-DOI: 10.1080/02664763.2015.1130801
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1130801
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:11:p:2110-2130




Template-Type: ReDIF-Article 1.0
Author-Name: Jiguang Shao
Author-X-Name-First: Jiguang
Author-X-Name-Last: Shao
Author-Name: Sheng Fu
Author-X-Name-First: Sheng
Author-X-Name-Last: Fu
Title: On the modes of the negative binomial distribution of order 
Abstract: 
 In this paper, the modes of the negative binomial distribution of order k are studied. Firstly, the method of transition probability flow graphs is introduced to deal with the probability-generating function of the geometric distribution of order k, which is a special case of the negative binomial distribution of the same order. And then, the general negative binomial distribution of order k is investigated. By means of probability distribution function, the mode of the geometric distribution of order k is derived, i.e.  $ m_{X_{(k)}}=k $ mX(k)=k. Based on the Fibonacci sequence and Poly-nacci sequence, the modes of the negative binomial distribution of order k in some cases are obtained: (1)  $ m_{X_{(2,2)}}=6, 7, 8 $ mX(2,2)=6,7,8 and  $ m_{X_{(3,2)}}=16 $ mX(3,2)=16, for p=0.5; (2)  $ m_{X_{(2,3)}}=13 $ mX(2,3)=13 for p=0.5. Finally, an application of negative binomial distribution of order k in continuous sampling plans is given.
Journal: Journal of Applied Statistics
Pages: 2131-2149
Issue: 11
Volume: 43
Year: 2016
Month: 8
X-DOI: 10.1080/02664763.2015.1130802
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1130802
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:11:p:2131-2149




Template-Type: ReDIF-Article 1.0
Author-Name: Ahmed Ghorbel
Author-X-Name-First: Ahmed
Author-X-Name-Last: Ghorbel
Author-Name: Wajdi Hamma
Author-X-Name-First: Wajdi
Author-X-Name-Last: Hamma
Author-Name: Anis Jarboui
Author-X-Name-First: Anis
Author-X-Name-Last: Jarboui
Title: Dependence between oil and commodities markets using time-varying Archimedean copulas and effectiveness of hedging strategies
Abstract: 
 The aim of this work is to study in a first step the dependence between oil and some commodity prices (cotton, rice, wheat, sucre, coffee, and silver) using copula theory, and then in a second step to determine the optimal hedging strategy for oil–commodity portfolio against the risk of negative variation in commodity markets prices. The model is implemented with an AR-GARCH model with innovations that follow t distribution for the marginal distribution and the extreme value copula for the joint distribution and parameters and dependence indices are re-estimated in each new day which allow taking into account nonlinear dependence, tails behavior, and their development over time. Various copula functions are used to model the dependence structure between oil and commodity markets. Empirical results show an increase in the dependence during the last 6 years. Volatility for commodity prices registered record levels in the same time with the increase in uncertainty. Optimal hedging ratio varies over time as a consequence of the change in the dependence structure.
Journal: Journal of Applied Statistics
Pages: 1509-1542
Issue: 9
Volume: 44
Year: 2017
Month: 7
X-DOI: 10.1080/02664763.2016.1155107
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1155107
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:9:p:1509-1542




Template-Type: ReDIF-Article 1.0
Author-Name: Fang Liu
Author-X-Name-First: Fang
Author-X-Name-Last: Liu
Author-Name: Peng Zhang
Author-X-Name-First: Peng
Author-X-Name-Last: Zhang
Author-Name: Ibrahim Erkan
Author-X-Name-First: Ibrahim
Author-X-Name-Last: Erkan
Author-Name: Dylan S. Small
Author-X-Name-First: Dylan S.
Author-X-Name-Last: Small
Title: Bayesian inference for random coefficient dynamic panel data models
Abstract: 
 We develop a hierarchical Bayesian approach for inference in random coefficient dynamic panel data models. Our approach allows for the initial values of each unit's process to be correlated with the unit-specific coefficients. We impose a stationarity assumption for each unit's process by assuming that the unit-specific autoregressive coefficient is drawn from a logitnormal distribution. Our method is shown to have favorable properties compared to the mean group estimator in a Monte Carlo study. We apply our approach to analyze energy and protein intakes among individuals from the Philippines.
Journal: Journal of Applied Statistics
Pages: 1543-1559
Issue: 9
Volume: 44
Year: 2017
Month: 7
X-DOI: 10.1080/02664763.2016.1214248
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1214248
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:9:p:1543-1559




Template-Type: ReDIF-Article 1.0
Author-Name: Giuseppe Arbia
Author-X-Name-First: Giuseppe
Author-X-Name-Last: Arbia
Author-Name: Giuseppe Espa
Author-X-Name-First: Giuseppe
Author-X-Name-Last: Espa
Author-Name: Diego Giuliani
Author-X-Name-First: Diego
Author-X-Name-Last: Giuliani
Author-Name: Rocco Micciolo
Author-X-Name-First: Rocco
Author-X-Name-Last: Micciolo
Title: A spatial analysis of health and pharmaceutical firm survival
Abstract: 
 The presence of knowledge spillovers and shared human capital is at the heart of the Marhall–Arrow–Romer externalities hypothesis. Most of the earlier empirical contributions on knowledge externalities; however, considered data aggregated at a regional level so that conclusions are based on the arbitrary definition of jurisdictional spatial units: this is the essence of the so-called modifiable areal unit problem. A second limitation of these studies is constituted by the fact that, somewhat surprisingly, while concentrating on the effects of agglomeration on firm creation and growth, the literature has, conversely, largely ignored its effects on firm survival. The present paper aims at contributing to the existing literature by answering to some of the open methodological questions reconciling the literature of Cox proportional hazards model with that on point pattern and thus capturing the true nature of spatial information. We also present some empirical results based on Italian firm demography data collected and managed by the Italian National Institute of Statistics (ISTAT).
Journal: Journal of Applied Statistics
Pages: 1560-1575
Issue: 9
Volume: 44
Year: 2017
Month: 7
X-DOI: 10.1080/02664763.2016.1214249
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1214249
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:9:p:1560-1575




Template-Type: ReDIF-Article 1.0
Author-Name: Essam A. Ahmed
Author-X-Name-First: Essam A.
Author-X-Name-Last: Ahmed
Title: Estimation and prediction for the generalized inverted exponential distribution based on progressively first-failure-censored data with application
Abstract: 
 In this paper, the estimation of parameters for a generalized inverted exponential distribution based on the progressively first-failure type-II right-censored sample is studied. An expectation–maximization (EM) algorithm is developed to obtain maximum likelihood estimates of unknown parameters as well as reliability and hazard functions. Using the missing value principle, the Fisher information matrix has been obtained for constructing asymptotic confidence intervals. An exact interval and an exact confidence region for the parameters are also constructed. Bayesian procedures based on Markov Chain Monte Carlo methods have been developed to approximate the posterior distribution of the parameters of interest and in addition to deduce the corresponding credible intervals. The performances of the maximum likelihood and Bayes estimators are compared in terms of their mean-squared errors through the simulation study. Furthermore, Bayes two-sample point and interval predictors are obtained when the future sample is ordinary order statistics. The squared error, linear-exponential and general entropy loss functions have been considered for obtaining the Bayes estimators and predictors. To illustrate the discussed procedures, a set of real data is analyzed.
Journal: Journal of Applied Statistics
Pages: 1576-1608
Issue: 9
Volume: 44
Year: 2017
Month: 7
X-DOI: 10.1080/02664763.2016.1214692
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1214692
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:9:p:1576-1608




Template-Type: ReDIF-Article 1.0
Author-Name: Eran A. Barnoy
Author-X-Name-First: Eran A.
Author-X-Name-Last: Barnoy
Author-Name: Hyun J. Kim
Author-X-Name-First: Hyun J.
Author-X-Name-Last: Kim
Author-Name: David W. Gjertson
Author-X-Name-First: David W.
Author-X-Name-Last: Gjertson
Title: Complexity in applying spatial analysis to describe heterogeneous air-trapping in thoracic imaging data
Abstract: 
 In this paper we consider a novel approach to analyzing medical images by applying a concept typically employed in geospatial studies. For certain diseases, such as asthma, there is a relevant distinction between the heterogeneity of constriction in airways for patients compared to healthy individuals. In order to describe such heterogeneities quantitatively, we utilize spatial correlation in the realm of lung computer tomography (CT). Specifically, we apply the approximate profile-likelihood estimator (APLE) to simulated lung air-trapping data selected based on potential interest to pulmonologists, and we explore reference values obtainable through this statistic. Results indicate that APLE values are independent of air-trapping values, and can provide useful insight into spatial patterns of these values within the lungs in situations where other common metrics, such as the coefficient of variation, reveal little. The APLE relies on a neighborhood weights matrix to define spatial relatedness of considered regions, and among a few weight structures explored, a working optimal choice seems to be one based on the inverse distance squared between regions of interest. The application yields a new method to help analyze the degree of heterogeneity in lung CT images, which can be generalized to other medical images as well.
Journal: Journal of Applied Statistics
Pages: 1609-1629
Issue: 9
Volume: 44
Year: 2017
Month: 7
X-DOI: 10.1080/02664763.2016.1221901
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1221901
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:9:p:1609-1629




Template-Type: ReDIF-Article 1.0
Author-Name: Rosineide Fernando da Paz
Author-X-Name-First: Rosineide Fernando
Author-X-Name-Last: da Paz
Author-Name: Jorge Luis Bazán
Author-X-Name-First: Jorge
Author-X-Name-Last: Luis Bazán
Author-Name: Luis Aparecido Milan
Author-X-Name-First: Luis
Author-X-Name-Last: Aparecido Milan
Title: Bayesian estimation for a mixture of simplex distributions with an unknown number of components: HDI analysis in Brazil
Abstract: 
 Variables taking value in  $ (0, 1) $ (0,1), such as rates or proportions, are frequently analyzed by researchers, for instance, political and social data, as well as the Human Development Index (HDI). However, sometimes this type of data cannot be modeled adequately using a unique distribution. In this case, we can use a mixture of distributions, which is a powerful and flexible probabilistic tool. This manuscript deals with a mixture of simplex distributions to model proportional data. A fully Bayesian approach is proposed for inference which includes a reversible-jump Markov Chain Monte Carlo procedure. The usefulness of the proposed approach is confirmed by using of the simulated mixture data from several different scenarios and by using the methodology to analyze municipal HDI data of cities (or towns) in the Northeast region and São Paulo state in Brazil. The analysis shows that among the cities in the Northeast, some appear to have a similar HDI to other cities in São Paulo state.
Journal: Journal of Applied Statistics
Pages: 1630-1643
Issue: 9
Volume: 44
Year: 2017
Month: 7
X-DOI: 10.1080/02664763.2016.1221903
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1221903
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:9:p:1630-1643




Template-Type: ReDIF-Article 1.0
Author-Name: Ying Liu
Author-X-Name-First: Ying
Author-X-Name-Last: Liu
Author-Name: Fang Luo
Author-X-Name-First: Fang
Author-X-Name-Last: Luo
Author-Name: Danhui Zhang
Author-X-Name-First: Danhui
Author-X-Name-Last: Zhang
Author-Name: Hongyun Liu
Author-X-Name-First: Hongyun
Author-X-Name-Last: Liu
Title: Comparison and robustness of the REML, ML, MIVQUE estimators for multi-level random mediation model
Abstract: 
 This article concentrates on the multi-level random mediation effects model (1-1-1) and reviews the maximum likelihood (ML), restricted maximum likelihood (REML), and minimum variance quadratic unbiased estimation (MIVQUE) estimation methods provided by the SAS MIXED process. This paper uses Monte Carlo simulation to make a comparison of the performance of these estimators under a wide variety of different conditions. First, REML and ML produced equivalent results and both of them outperformed MIVQUE, no matter whether the normality assumption was satisfied. Second, the results indicated that the distribution of the  $ {\boldsymbol{e}_{\boldsymbol{Yij}}} $ eYij does not influence the mediation effect. The deviation of the normal distribution of  $ {\boldsymbol{b}_{\boldsymbol{j}}} $ bj or ‘ $ {\boldsymbol{a}_{\boldsymbol{j}}} $ aj and  $ \boldsymbol{b}_{\boldsymbol{j}} $ bj’ affected the mediation effect, particularly in condition that not only the magnitude of the deviation but also the covariance between these two effects were large. This thesis ends with the implications, suggestions and recommendations for the application.
Journal: Journal of Applied Statistics
Pages: 1644-1661
Issue: 9
Volume: 44
Year: 2017
Month: 7
X-DOI: 10.1080/02664763.2016.1221904
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1221904
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:9:p:1644-1661




Template-Type: ReDIF-Article 1.0
Author-Name: Eliud Silva
Author-X-Name-First: Eliud
Author-X-Name-Last: Silva
Author-Name: Víctor M. Guerrero
Author-X-Name-First: Víctor M.
Author-X-Name-Last: Guerrero
Title: Penalized least squares smoothing of two-dimensional mortality tables with imposed smoothness
Abstract: 
 This paper presents a method to estimate mortality trends of two-dimensional mortality tables. Comparability of mortality trends for two or more of such tables is enhanced by applying penalized least squares and imposing a desired percentage of smoothness to be attained by the trends. The smoothing procedure is basically determined by the smoothing parameters that are related to the percentage of smoothness. To quantify smoothness, we employ an index defined first for the one-dimensional case and then generalized to the two-dimensional one. The proposed method is applied to data from member countries of the OECD. We establish as goal the smoothed mortality surface for one of those countries and compare it with some other mortality surfaces smoothed with the same percentage of two-dimensional smoothness. Our aim is to be able to see whether convergence exists in the mortality trends of the countries under study, in both year and age dimensions.
Journal: Journal of Applied Statistics
Pages: 1662-1679
Issue: 9
Volume: 44
Year: 2017
Month: 7
X-DOI: 10.1080/02664763.2016.1221905
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1221905
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:9:p:1662-1679




Template-Type: ReDIF-Article 1.0
Author-Name: Fragkiskos Bersimis
Author-X-Name-First: Fragkiskos
Author-X-Name-Last: Bersimis
Author-Name: Demosthenes Panagiotakos
Author-X-Name-First: Demosthenes
Author-X-Name-Last: Panagiotakos
Author-Name: Malvina Vamvakari
Author-X-Name-First: Malvina
Author-X-Name-Last: Vamvakari
Title: Investigating the sensitivity function's monotony of a health-related index
Abstract: 
 In this work it is investigated theoretically whether the support's length of a continuous variable, which represents a simple health-related index, affects the index's diagnostic ability of a binary health outcome. The aforementioned is attempted by studying the monotony of the index's sensitivity function, which is a measure of its diagnostic ability, in the cases that the index's distribution was either unknown or the uniform. The case of a composite health-related index which is formed by the sum of m component variables is also presented when the distribution of its component variables was either unknown or the uniform. It is proved that a health-related index's sensitivity is a non-decreasing function as to the finite length of its components' support, under certain condition. In addition, similar propositions are presented in the case that a health-related index is distributed normally according to its distribution parameters.
Journal: Journal of Applied Statistics
Pages: 1680-1706
Issue: 9
Volume: 44
Year: 2017
Month: 7
X-DOI: 10.1080/02664763.2016.1221906
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1221906
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:9:p:1680-1706




Template-Type: ReDIF-Article 1.0
Author-Name: A. Desgagné
Author-X-Name-First: A.
Author-X-Name-Last: Desgagné
Author-Name: P. Lafaye de Micheaux
Author-X-Name-First: P.
Author-X-Name-Last: Lafaye de Micheaux
Title: A powerful and interpretable alternative to the Jarque–Bera test of normality based on 2nd-power skewness and kurtosis, using the Rao's score test on the APD family
Abstract: 
 We introduce the 2nd-power skewness and kurtosis, which are interesting alternatives to the classical Pearson's skewness and kurtosis, called 3rd-power skewness and 4th-power kurtosis in our terminology. We use the sample 2nd-power skewness and kurtosis to build a powerful test of normality. This test can also be derived as Rao's score test on the asymmetric power distribution, which combines the large range of exponential tail behavior provided by the exponential power distribution family with various levels of asymmetry. We find that our test statistic is asymptotically chi-squared distributed. We also propose a modified test statistic, for which we show numerically that the distribution can be approximated for finite sample sizes with very high precision by a chi-square. Similarly, we propose a directional test based on sample 2nd-power kurtosis only, for the situations where the true distribution is known to be symmetric. Our tests are very similar in spirit to the famous Jarque–Bera test, and as such are also locally optimal. They offer the same nice interpretation, with in addition the gold standard power of the regression and correlation tests. An extensive empirical power analysis is performed, which shows that our tests are among the most powerful normality tests. Our test is implemented in an R package called PoweR.
Journal: Journal of Applied Statistics
Pages: 2307-2327
Issue: 13
Volume: 45
Year: 2018
Month: 10
X-DOI: 10.1080/02664763.2017.1415311
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1415311
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:13:p:2307-2327




Template-Type: ReDIF-Article 1.0
Author-Name: Raushan Bokusheva
Author-X-Name-First: Raushan
Author-X-Name-Last: Bokusheva
Title: Using copulas for rating weather index insurance contracts
Abstract: 
 This study develops a methodology for a copula-based weather index insurance design. Because the copula approach is better suited for modeling tail dependence than the standard linear correlation approach, its use may increase the effectiveness of weather insurance contracts designed to provide protection against extreme weather events. In our study, we employ three selected Archimedean copulas to capture the left-tail dependence in the joint distribution of the farm yield and a specific weather index. A hierarchical Bayesian model is applied to obtain consistent estimates of tail dependence using relatively short time series. Our empirical results for 47 large grain-producing farms from Kazakhstan indicate that, given the choice of an appropriate weather index to signal catastrophic events, such as a severe drought, copula-based weather insurance contracts may provide significantly higher risk reductions than regression-based indemnification schemes.
Journal: Journal of Applied Statistics
Pages: 2328-2356
Issue: 13
Volume: 45
Year: 2018
Month: 10
X-DOI: 10.1080/02664763.2017.1420146
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1420146
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:13:p:2328-2356




Template-Type: ReDIF-Article 1.0
Author-Name: Musie Ghebremichael
Author-X-Name-First: Musie
Author-X-Name-Last: Ghebremichael
Author-Name: Semere Habtemicael
Author-X-Name-First: Semere
Author-X-Name-Last: Habtemicael
Title: Effect of tuberculosis on immune restoration among HIV-infected patients receiving antiretroviral therapy
Abstract: 
 In this article, time to immune recovery during antiretroviral therapy was estimated and compared between HIV-infected children with and without tuberculosis (TB). CD4 T-cell restoration was used as a criterion for determining immune recovery. The median residual lifetime function, which is more intuitive and robust compared to the frequently used measures of lifetime data, was used to estimate time to CD4 T-cell restoration. The median residual lifetime is not influenced by extreme observations and heavy-tailed distributions which are commonly encountered in clinical studies. Permutation-based methods were used to compare the CD4 T-cell restoration times between the two groups of patients. Our results indicate that children with TB had uniformly higher median residual lifetimes to immune recovery compared to those without TB. Although TB was associated with slower CD4 T-cell restoration, the differences between the restoration times of the two groups were not statistically significant.
Journal: Journal of Applied Statistics
Pages: 2357-2364
Issue: 13
Volume: 45
Year: 2018
Month: 10
X-DOI: 10.1080/02664763.2017.1420758
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1420758
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:13:p:2357-2364




Template-Type: ReDIF-Article 1.0
Author-Name: Dilli Bhatta
Author-X-Name-First: Dilli
Author-X-Name-Last: Bhatta
Author-Name: Balgobin Nandram
Author-X-Name-First: Balgobin
Author-X-Name-Last: Nandram
Author-Name: Joseph Sedransk
Author-X-Name-First: Joseph
Author-X-Name-Last: Sedransk
Title: Bayesian testing for independence of two categorical variables under two-stage cluster sampling with covariates
Abstract: 
 We consider Bayesian testing for independence of two categorical variables with covariates for a two-stage cluster sample. This is a difficult problem because we have a complex sample (i.e. cluster sample), not a simple random sample. Our approach is to convert the cluster sample with covariates into an equivalent simple random sample without covariates, which provides a surrogate of the original sample. Then, this surrogate sample is used to compute the Bayes factor to make an inference about independence. We apply our methodology to the data from the Trend in International Mathematics and Science Study [30] for fourth grade US students to assess the association between the mathematics and science scores represented as categorical variables. We show that if there is strong association between two categorical variables, there is no significant difference between the tests with and without the covariates. We also performed a simulation study to further understand the effect of covariates in various situations. We found that for borderline cases (moderate association between the two categorical variables), there are noticeable differences in the test with and without covariates.
Journal: Journal of Applied Statistics
Pages: 2365-2393
Issue: 13
Volume: 45
Year: 2018
Month: 10
X-DOI: 10.1080/02664763.2017.1421914
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1421914
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:13:p:2365-2393




Template-Type: ReDIF-Article 1.0
Author-Name: Deepesh Bhati
Author-X-Name-First: Deepesh
Author-X-Name-Last: Bhati
Author-Name: Sreenivasan Ravi
Author-X-Name-First: Sreenivasan
Author-X-Name-Last: Ravi
Title: Diagnostic plots for identifying max domains of attraction under power normalization
Abstract: 
 Diagnostic plots for determining the max domains of attraction of power normalized partial maxima are proposed. A test to ascertain the veracity of the claim that data distribution belongs to a max domain of attraction under power normalization is given. The performance of this test is demonstrated using data simulated from many well-known distributions. Furthermore, two real-world datasets are analysed using the proposed procedure.
Journal: Journal of Applied Statistics
Pages: 2394-2410
Issue: 13
Volume: 45
Year: 2018
Month: 10
X-DOI: 10.1080/02664763.2017.1421915
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1421915
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:13:p:2394-2410




Template-Type: ReDIF-Article 1.0
Author-Name: Oliver Cencic
Author-X-Name-First: Oliver
Author-X-Name-Last: Cencic
Author-Name: Rudolf Frühwirth
Author-X-Name-First: Rudolf
Author-X-Name-Last: Frühwirth
Title: Data reconciliation of nonnormal observations with nonlinear constraints
Abstract: 
 This paper presents a new method for the reconciliation of data described by arbitrary continuous probability distributions, with the focus on nonlinear constraints. The main idea, already applied to linear constraints in a previous paper, is to restrict the joint prior probability distribution of the observed variables with model constraints to get a joint posterior probability distribution. Because in general the posterior probability density function cannot be calculated analytically, it is shown that it has decisive advantages to sample from the posterior distribution by a Markov chain Monte Carlo (MCMC) method. From the resulting sample of observed and unobserved variables various characteristics of the posterior distribution can be estimated, such as the mean, the full covariance matrix, marginal posterior densities, as well as marginal moments, quantiles, and HPD intervals. The procedure is illustrated by examples from material flow analysis and chemical engineering.
Journal: Journal of Applied Statistics
Pages: 2411-2428
Issue: 13
Volume: 45
Year: 2018
Month: 10
X-DOI: 10.1080/02664763.2017.1421916
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1421916
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:13:p:2411-2428




Template-Type: ReDIF-Article 1.0
Author-Name: Jun Zhang
Author-X-Name-First: Jun
Author-X-Name-Last: Zhang
Author-Name: Jing Zhang
Author-X-Name-First: Jing
Author-X-Name-Last: Zhang
Author-Name: Xuehu Zhu
Author-X-Name-First: Xuehu
Author-X-Name-Last: Zhu
Author-Name: Tao Lu
Author-X-Name-First: Tao
Author-X-Name-Last: Lu
Title: Testing symmetry based on empirical likelihood
Abstract: 
 In this paper, we propose a general kth correlation coefficient between the density function and distribution function of a continuous variable as a measure of symmetry and asymmetry. We first propose a root-n moment-based estimator of the kth correlation coefficient and present its asymptotic results. Next, we consider statistical inference of the kth correlation coefficient by using the empirical likelihood (EL) method. The EL statistic is shown to be asymptotically a standard chi-squared distribution. Last, we propose a residual-based estimator of the kth correlation coefficient for a parametric regression model to test whether the density function of the true model error is symmetric or not. We present the asymptotic results of the residual-based kth correlation coefficient estimator and also construct its EL-based confidence intervals. Simulation studies are conducted to examine the performance of the proposed estimators, and we also use our proposed estimators to analyze the air quality dataset.
Journal: Journal of Applied Statistics
Pages: 2429-2454
Issue: 13
Volume: 45
Year: 2018
Month: 10
X-DOI: 10.1080/02664763.2017.1421917
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1421917
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:13:p:2429-2454




Template-Type: ReDIF-Article 1.0
Author-Name: Xiaojie Xu
Author-X-Name-First: Xiaojie
Author-X-Name-Last: Xu
Title: Causal structure among US corn futures and regional cash prices in the time and frequency domain
Abstract: 
 This study investigates causal structure among daily Chicago Board of Trade corn futures prices and seven regional cash series from Iowa, Illinois, Indiana, Ohio, Minnesota, Nebraska, and Kansas for January 2006–March 2011. Their wavelet transformed series are further analyzed for causal relationships at different time scales. Empirical results indicate no causality among states or between the futures and a cash series for time scales shorter than one month. As scales increase but do not exceed a year, bidirectional causal flows are determined among all prices. The information leadership role of the futures against a cash price is identified for the scale longer than one year and raw series, at which no interstate causality is found.
Journal: Journal of Applied Statistics
Pages: 2455-2480
Issue: 13
Volume: 45
Year: 2018
Month: 10
X-DOI: 10.1080/02664763.2017.1423044
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1423044
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:13:p:2455-2480




Template-Type: ReDIF-Article 1.0
Author-Name: Piotr Sulewski
Author-X-Name-First: Piotr
Author-X-Name-Last: Sulewski
Title: Power analysis of independence testing for three-way contingency tables of small sizes
Abstract: 
 The first aim of this paper is to introduce a modular test for the three-way contingency table (TT). The second aim is to describe the procedure of generating TT using the bar method. The third aim is on the one hand to suggest the measure of untruthfulness of H0 and on the other hand to compare the quality of independence tests by using their power. Critical values for analyzed statistics were determined by simulating the Monte Carlo method.
Journal: Journal of Applied Statistics
Pages: 2481-2498
Issue: 13
Volume: 45
Year: 2018
Month: 10
X-DOI: 10.1080/02664763.2018.1424122
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1424122
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:13:p:2481-2498




Template-Type: ReDIF-Article 1.0
Author-Name: Caleb Phillips
Author-X-Name-First: Caleb
Author-X-Name-Last: Phillips
Author-Name: Ryan Elmore
Author-X-Name-First: Ryan
Author-X-Name-Last: Elmore
Author-Name: Jenny Melius
Author-X-Name-First: Jenny
Author-X-Name-Last: Melius
Author-Name: Pieter Gagnon
Author-X-Name-First: Pieter
Author-X-Name-Last: Gagnon
Author-Name: Robert Margolis
Author-X-Name-First: Robert
Author-X-Name-Last: Margolis
Title: A data mining approach to estimating rooftop photovoltaic potential in the US
Abstract: 
 This paper aims to quantify the amount of suitable rooftop area for photovoltaic (PV) energy generation in the continental United States (US). The approach is data-driven, combining Geographic Information Systems analysis of an extensive dataset of Light Detection and Ranging (LiDAR) measurements collected by the Department of Homeland Security with a statistical model trained on these same data. The model developed herein can predict the quantity of suitable roof area where LiDAR data is not available. This analysis focuses on small buildings (1000 to 5000 square feet) which account for more than half of the total available rooftop space in these data (58%) and demonstrate a greater variability in suitability compared to larger buildings which are nearly all suitable for PV installations. This paper presents new results characterizing the size, shape and suitability of US rooftops with respect to PV installations. Overall 28% of small building roofs appear suitable in the continental United States for rooftop solar. Nationally, small building rooftops could accommodate an expected 731 GW of PV capacity and generate 926&#x2009;TWh/year of PV energy on 4920&#x2009; $ {\rm km}^2 $ km2 of suitable rooftop space which equates to 25% the current US electricity sales.
Journal: Journal of Applied Statistics
Pages: 385-394
Issue: 3
Volume: 46
Year: 2019
Month: 2
X-DOI: 10.1080/02664763.2018.1492525
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1492525
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:3:p:385-394




Template-Type: ReDIF-Article 1.0
Author-Name: Eliane R. Rodrigues
Author-X-Name-First: Eliane R.
Author-X-Name-Last: Rodrigues
Author-Name: Mario H. Tarumoto
Author-X-Name-First: Mario H.
Author-X-Name-Last: Tarumoto
Author-Name: Guadalupe Tzintzun
Author-X-Name-First: Guadalupe
Author-X-Name-Last: Tzintzun
Title: Application of a non-homogeneous Markov chain with seasonal transition probabilities to ozone data
Abstract: 
 In this work, we assume that the sequence recording whether or not an ozone exceedance of an environmental threshold has occurred in a given day is ruled by a non-homogeneous Markov chain of order one. In order to account for the possible presence of cycles in the empirical transition probabilities, a parametric form incorporating seasonal components is considered. Results show that even though some covariates (namely, relative humidity and temperature) are not included explicitly in the model, their influence is captured in the behavior of the transition probabilities. Parameters are estimated using the Bayesian point of view via Markov chain Monte Carlo algorithms. The model is applied to ozone data obtained from the monitoring network of Mexico City, Mexico. An analysis of how the methodology could be used as an aid in the decision-making is also given.
Journal: Journal of Applied Statistics
Pages: 395-415
Issue: 3
Volume: 46
Year: 2019
Month: 2
X-DOI: 10.1080/02664763.2018.1492527
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1492527
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:3:p:395-415




Template-Type: ReDIF-Article 1.0
Author-Name: Luiz R. Nakamura
Author-X-Name-First: Luiz R.
Author-X-Name-Last: Nakamura
Author-Name: Pedro H. R. Cerqueira
Author-X-Name-First: Pedro H. R.
Author-X-Name-Last: Cerqueira
Author-Name: Thiago G. Ramires
Author-X-Name-First: Thiago G.
Author-X-Name-Last: Ramires
Author-Name: Rodrigo R. Pescim
Author-X-Name-First: Rodrigo R.
Author-X-Name-Last: Pescim
Author-Name: R. A. Rigby
Author-X-Name-First: R. A.
Author-X-Name-Last: Rigby
Author-Name: Dimitrios M. Stasinopoulos
Author-X-Name-First: Dimitrios M.
Author-X-Name-Last: Stasinopoulos
Title: A new continuous distribution on the unit interval applied to modelling the points ratio of football teams
Abstract: 
 We introduce a new flexible distribution to deal with variables on the unit interval based on a transformation of the sinh&#x2013;arcsinh distribution, which accommodates different degrees of skewness and kurtosis and becomes an interesting alternative to model this type of data. We also include this new distribution into the generalised additive models for location, scale and shape (GAMLSS) framework in order to develop and fit its regression model. For different parameter settings, some simulations are performed to investigate the behaviour of the estimators. The potentiality of the new regression model is illustrated by means of a real dataset related to the points rate of football teams at the end of a championship from the four most important leagues in the world: Barclays Premier League (England), Bundesliga (Germany), Serie A (Italy) and BBVA league (Spain) during three seasons (2011&#x2013;2012, 2012&#x2013;2013 and 2013&#x2013;2014).
Journal: Journal of Applied Statistics
Pages: 416-431
Issue: 3
Volume: 46
Year: 2019
Month: 2
X-DOI: 10.1080/02664763.2018.1495699
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1495699
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:3:p:416-431




Template-Type: ReDIF-Article 1.0
Author-Name: Haiqing Chen
Author-X-Name-First: Haiqing
Author-X-Name-Last: Chen
Author-Name: Weihu Cheng
Author-X-Name-First: Weihu
Author-X-Name-Last: Cheng
Author-Name: Yaohua Rong
Author-X-Name-First: Yaohua
Author-X-Name-Last: Rong
Author-Name: Xu Zhao
Author-X-Name-First: Xu
Author-X-Name-Last: Zhao
Title: Fitting the generalized Pareto distribution to data based on transformations of order statistics
Abstract: 
 Generalized Pareto distribution (GPD) has been widely used to model exceedances over thresholds. In this article we propose a new method called weighted nonlinear least squares (WNLS) to estimate the parameters of the GPD. The WNLS estimators always exist and are simple to compute. Some asymptotic results of the proposed method are provided. The simulation results indicate that the proposed method performs well compared to existing methods in terms of mean squared error and bias. Its advantages are further illustrated through the analysis of two real data sets.
Journal: Journal of Applied Statistics
Pages: 432-448
Issue: 3
Volume: 46
Year: 2019
Month: 2
X-DOI: 10.1080/02664763.2018.1495700
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1495700
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:3:p:432-448




Template-Type: ReDIF-Article 1.0
Author-Name: Ricardo Puziol de Oliveira
Author-X-Name-First: Ricardo Puziol
Author-X-Name-Last: de Oliveira
Author-Name: Jorge Alberto Achcar
Author-X-Name-First: Jorge Alberto
Author-X-Name-Last: Achcar
Author-Name: Danielle Peralta
Author-X-Name-First: Danielle
Author-X-Name-Last: Peralta
Author-Name: Josmar Mazucheli
Author-X-Name-First: Josmar
Author-X-Name-Last: Mazucheli
Title: Discrete and continuous bivariate lifetime models in presence of cure rate: a comparative study under Bayesian approach
Abstract: 
 The modeling and analysis of lifetime data in which the main endpoints are the times when an event of interest occurs is of great interest in medical studies. In these studies, it is common that two or more lifetimes associated with the same unit such as the times to deterioration levels or the times to reaction to a treatment in pairs of organs like lungs, kidneys, eyes or ears. In medical applications, it is also possible that a cure rate is present and needed to be modeled with lifetime data with long-term survivors. This paper presented a comparative study under a Bayesian approach among some existing continuous and discrete bivariate distributions such as the bivariate exponential distributions and the bivariate geometric distributions in presence of cure rate, censored data and covariates. In presence of lifetimes related to cured patients, it is assumed standard mixture cure rate models in the data analysis. The posterior summaries of interest are obtained using Markov Chain Monte Carlo methods. To illustrate the proposed methodology two real medical data sets are considered.
Journal: Journal of Applied Statistics
Pages: 449-467
Issue: 3
Volume: 46
Year: 2019
Month: 2
X-DOI: 10.1080/02664763.2018.1495701
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1495701
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:3:p:449-467




Template-Type: ReDIF-Article 1.0
Author-Name: Yihong Zhan
Author-X-Name-First: Yihong
Author-X-Name-Last: Zhan
Author-Name: Yanan Zhang
Author-X-Name-First: Yanan
Author-X-Name-Last: Zhang
Author-Name: Jiajia Zhang
Author-X-Name-First: Jiajia
Author-X-Name-Last: Zhang
Author-Name: Bo Cai
Author-X-Name-First: Bo
Author-X-Name-Last: Cai
Author-Name: James W. Hardin
Author-X-Name-First: James W.
Author-X-Name-Last: Hardin
Title: Sample size calculation for a proportional hazards mixture cure model with nonbinary covariates
Abstract: 
 Sample size calculation is a critical issue in clinical trials because a small sample size leads to a biased inference and a large sample size increases the cost. With the development of advanced medical technology, some patients can be cured of certain chronic diseases, and the proportional hazards mixture cure model has been developed to handle survival data with potential cure information. Given the needs of survival trials with potential cure proportions, a corresponding sample size formula based on the log-rank test statistic for binary covariates has been proposed by Wang et&#xA0;al. [25]. However, a sample size formula based on continuous variables has not been developed. Herein, we presented sample size and power calculations for the mixture cure model with continuous variables based on the log-rank method and further modified it by Ewell's method. The proposed approaches were evaluated using simulation studies for synthetic data from exponential and Weibull distributions. A program for calculating necessary sample size for continuous covariates in a mixture cure model was implemented in R.
Journal: Journal of Applied Statistics
Pages: 468-483
Issue: 3
Volume: 46
Year: 2019
Month: 2
X-DOI: 10.1080/02664763.2018.1498463
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1498463
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:3:p:468-483




Template-Type: ReDIF-Article 1.0
Author-Name: Juliana Scudilio
Author-X-Name-First: Juliana
Author-X-Name-Last: Scudilio
Author-Name: Vinicius F. Calsavara
Author-X-Name-First: Vinicius F.
Author-X-Name-Last: Calsavara
Author-Name: Ricardo Rocha
Author-X-Name-First: Ricardo
Author-X-Name-Last: Rocha
Author-Name: Francisco Louzada
Author-X-Name-First: Francisco
Author-X-Name-Last: Louzada
Author-Name: Vera Tomazella
Author-X-Name-First: Vera
Author-X-Name-Last: Tomazella
Author-Name: Agatha S. Rodrigues
Author-X-Name-First: Agatha S.
Author-X-Name-Last: Rodrigues
Title: Defective models induced by gamma frailty term for survival data with cured fraction
Abstract: 
 In this paper, we propose a defective model induced by a frailty term for modeling the proportion of cured. Unlike most of the cure rate models, defective models have advantage of modeling the cure rate without adding any extra parameter in model. The introduction of an unobserved heterogeneity among individuals has bring advantages for the estimated model. The influence of unobserved covariates is incorporated using a proportional hazard model. The frailty term assumed to follow a gamma distribution is introduced on the hazard rate to control the unobservable heterogeneity of the patients. We assume that the baseline distribution follows a Gompertz and inverse Gaussian defective distributions. Thus we propose and discuss two defective distributions: the defective gamma-Gompertz and gamma-inverse Gaussian regression models. Simulation studies are performed to verify the asymptotic properties of the maximum likelihood estimator. Lastly, in order to illustrate the proposed model, we present three applications in real data sets, in which one of them we are using for the first time, related to a study about breast cancer in the A.C.Camargo Cancer Center, S&#xE3;o Paulo, Brazil.
Journal: Journal of Applied Statistics
Pages: 484-507
Issue: 3
Volume: 46
Year: 2019
Month: 2
X-DOI: 10.1080/02664763.2018.1498464
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1498464
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:3:p:484-507




Template-Type: ReDIF-Article 1.0
Author-Name: Wei Wang
Author-X-Name-First: Wei
Author-X-Name-Last: Wang
Author-Name: Zhuo Fang
Author-X-Name-First: Zhuo
Author-X-Name-Last: Fang
Title: Linear scalar-on-surface random effects regression models
Abstract: 
 Many research fields increasingly involve analyzing data of a complex structure. Models investigating the dependence of a response on a predictor have moved beyond the ordinary scalar-on-vector regression. We propose a regression model for a scalar response and a surface (or a bivariate function) predictor. The predictor has a random component and the regression model falls in the framework of linear random effects models. We estimate the model parameters via maximizing the log-likelihood with the ECME (Expectation/Conditional Maximization Either) algorithm. We use the approach to analyze a data set where the response is the neuroticism score and the predictor is the resting-state brain function image. In the simulations we tried, the approach has better performance than two other approaches, a functional principal component regression approach and a smooth scalar-on-image regression approach.
Journal: Journal of Applied Statistics
Pages: 508-521
Issue: 3
Volume: 46
Year: 2019
Month: 2
X-DOI: 10.1080/02664763.2018.1502262
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1502262
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:3:p:508-521




Template-Type: ReDIF-Article 1.0
Author-Name: Weiwei Wang
Author-X-Name-First: Weiwei
Author-X-Name-Last: Wang
Author-Name: Xianyi Wu
Author-X-Name-First: Xianyi
Author-X-Name-Last: Wu
Author-Name: Xiaoqi Zhang
Author-X-Name-First: Xiaoqi
Author-X-Name-Last: Zhang
Author-Name: Xiaobing Zhao
Author-X-Name-First: Xiaobing
Author-X-Name-Last: Zhao
Author-Name: Xian Zhou
Author-X-Name-First: Xian
Author-X-Name-Last: Zhou
Title: Partial sufficient dimension reduction on joint model of recurrent and terminal events
Abstract: 
 Joint modeling of recurrent and terminal events has attracted considerable interest and extensive investigations by many authors. The assumption of low-dimensional covariates has been usually applied in the existing studies, which is however inapplicable in many practical situations. In this paper, we consider a partial sufficient dimension reduction approach for a joint model with high-dimensional covariates. Some simulations as well as three real data applications are presented to confirm and assess the performance of the proposed model and approach.
Journal: Journal of Applied Statistics
Pages: 522-541
Issue: 3
Volume: 46
Year: 2019
Month: 2
X-DOI: 10.1080/02664763.2018.1506019
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1506019
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:3:p:522-541




Template-Type: ReDIF-Article 1.0
Author-Name: Fatemeh Hosseini
Author-X-Name-First: Fatemeh
Author-X-Name-Last: Hosseini
Author-Name: Omid Karimi
Author-X-Name-First: Omid
Author-X-Name-Last: Karimi
Title: Approximate composite marginal likelihood inference in spatial generalized linear mixed models
Abstract: 
 Non-Gaussian spatial responses are usually modeled using spatial generalized linear mixed model with spatial random effects. The likelihood function of this model cannot usually be given in a closed form, thus the maximum likelihood approach is very challenging. There are numerical ways to maximize the likelihood function, such as Monte Carlo Expectation Maximization and Quadrature Pairwise Expectation Maximization algorithms. They can be applied but may in such cases be computationally very slow or even prohibitive. Gauss&#x2013;Hermite quadrature approximation only suitable for low-dimensional latent variables and its accuracy depends on the number of quadrature points. Here, we propose a new approximate pairwise maximum likelihood method to the inference of the spatial generalized linear mixed model. This approximate method is fast and deterministic, using no sampling-based strategies. The performance of the proposed method is illustrated through two simulation examples and practical aspects are investigated through a case study on a rainfall data set.
Journal: Journal of Applied Statistics
Pages: 542-558
Issue: 3
Volume: 46
Year: 2019
Month: 2
X-DOI: 10.1080/02664763.2018.1506020
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1506020
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:3:p:542-558




Template-Type: ReDIF-Article 1.0
Author-Name: Ozgur Danisman
Author-X-Name-First: Ozgur
Author-X-Name-Last: Danisman
Author-Name: Umay Uzunoglu Kocer
Author-X-Name-First: Umay
Author-X-Name-Last: Uzunoglu Kocer
Title: Construction of a semi-Markov model for the performance of a football team in the presence of missing data
Abstract: 
 Using play-by-play data from the very beginning of the professional football league in Turkey, a semi-Markov model is presented for describing the performance of football teams. The official match results of the selected teams during 55 football seasons are used and winning, drawing and losing are considered as Markov states. The semi-Markov model is constructed with transition rates inferred from the official match results. The duration between the last match of a season and the very first match of the following season is much longer than any other duration during the season. Therefore these values are considered as missing values and estimated by using expectation&#x2013;maximization algorithm. The effect of the sojourn time in a state to the performance of a team is discussed as well as mean sojourn times after losing/winning are estimated. The limiting probabilities of winning, drawing and losing are calculated. Some insights about the performance of the selected teams are presented.
Journal: Journal of Applied Statistics
Pages: 559-576
Issue: 3
Volume: 46
Year: 2019
Month: 2
X-DOI: 10.1080/02664763.2018.1508556
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1508556
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:3:p:559-576




Template-Type: ReDIF-Article 1.0
Author-Name: The Editors
Title: Corrigendum
Journal: Journal of Applied Statistics
Pages: 577-579
Issue: 3
Volume: 46
Year: 2019
Month: 2
X-DOI: 10.1080/02664763.2018.1505203
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1505203
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:3:p:577-579




Template-Type: ReDIF-Article 1.0
Author-Name: Kuangnan Fang
Author-X-Name-First: Kuangnan
Author-X-Name-Last: Fang
Author-Name: Shuangge Ma
Author-X-Name-First: Shuangge
Author-X-Name-Last: Ma
Title: Three-part model for fractional response variables with application to Chinese household health insurance coverage
Abstract: A survey on health insurance was conducted in July and August of 2011 in three major cities in China. In this study, we analyze the household coverage rate, which is an important index of the quality of health insurance. The coverage rate is restricted to the unit interval [0, 1], and it may differ from other rate data in that the “two corners” are nonzero. That is, there are nonzero probabilities of zero and full coverage. Such data may also be encountered in economics, finance, medicine, and many other areas. The existing approaches may not be able to properly accommodate such data. In this study, we develop a three-part model that properly describes fractional response variables with non-ignorable zeros and ones. We investigate estimation and inference under two proportional constraints on the regression parameters. Such constraints may lead to more lucid interpretations and fewer unknown parameters and hence more accurate estimation. A simulation study is conducted to compare the performance of constrained and unconstrained models and show that estimation under constraint can be more efficient. The analysis of household health insurance coverage data suggests that household size, income, expense, and presence of chronic disease are associated with insurance coverage.
Journal: Journal of Applied Statistics
Pages: 925-940
Issue: 5
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2012.758246
File-URL: http://hdl.handle.net/10.1080/02664763.2012.758246
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:5:p:925-940




Template-Type: ReDIF-Article 1.0
Author-Name: Seung-Gu Kim
Author-X-Name-First: Seung-Gu
Author-X-Name-Last: Kim
Author-Name: Jeong-Soo Park
Author-X-Name-First: Jeong-Soo
Author-X-Name-Last: Park
Author-Name: Yung-Seop Lee
Author-X-Name-First: Yung-Seop
Author-X-Name-Last: Lee
Title: Identification of target clusters by using the restricted normal mixture model
Abstract: This paper addresses the problem of identifying groups that satisfy the specific conditions for the means of feature variables. In this study, we refer to the identified groups as “target clusters” (TCs). To identify TCs, we propose a method based on the normal mixture model (NMM) restricted by a linear combination of means. We provide an expectation–maximization (EM) algorithm to fit the restricted NMM by using the maximum-likelihood method. The convergence property of the EM algorithm and a reasonable set of initial estimates are presented. We demonstrate the method's usefulness and validity through a simulation study and two well-known data sets. The proposed method provides several types of useful clusters, which would be difficult to achieve with conventional clustering or exploratory data analysis methods based on the ordinary NMM. A simple comparison with another target clustering approach shows that the proposed method is promising in the identification.
Journal: Journal of Applied Statistics
Pages: 941-960
Issue: 5
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2012.759192
File-URL: http://hdl.handle.net/10.1080/02664763.2012.759192
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:5:p:941-960




Template-Type: ReDIF-Article 1.0
Author-Name: B. Houlding
Author-X-Name-First: B.
Author-X-Name-Last: Houlding
Author-Name: J. Haslett
Author-X-Name-First: J.
Author-X-Name-Last: Haslett
Title: Scheduling parallel conference sessions: an application of a novel hybrid clustering algorithm for ensuring constrained cardinality
Abstract: The 2011 World Statistics Congress included approximately 1150 oral and 250 poster presentations over approximately 250 sessions, with as many as 20 sessions running in parallel at any one time. Scheduling a timetable for such a conference is hence a complicated task, as ideally, talks on similar topics should be scheduled in the same session, and similar session topics should not be presented at identical times, allowing participants to easily decide which of the number of sessions to attend. Here, we consider a novel hybrid clustering algorithm that allows a solution under a constraint of fixed cardinality, and which is designed to find clusters of highly dense regions whilst forming others from the remaining outlying regions, hence providing a simple, yet data-generated, solution to such scheduling problems.
Journal: Journal of Applied Statistics
Pages: 961-971
Issue: 5
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2012.760239
File-URL: http://hdl.handle.net/10.1080/02664763.2012.760239
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:5:p:961-971




Template-Type: ReDIF-Article 1.0
Author-Name: I-Tang Yu
Author-X-Name-First: I-Tang
Author-X-Name-Last: Yu
Title: A modification of the Box–Meyer method for finding the active factors in screening experiments
Abstract: Screening experiments are conducted to identify a few active factors among a large number of factors. For the objective of identifying active factors, Box and Meyer provided an innovative approach, the Box–Meyer method (BMM). With the use of means models, we propose a modification of the BMM in this paper. Compared with the original BMM, the modified BMM (MBMM) can circumvent the problem that the original BMM runs into, namely that it may fail to identify some active factors due to the ignorance of higher order interactions. Furthermore, the number of explanatory variables in the MBMM is smaller. Therefore, the computational complexity is reduced. Finally, three examples with different types of designs are used to demonstrate the wide applicability of the MBMM.
Journal: Journal of Applied Statistics
Pages: 972-984
Issue: 5
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2012.761181
File-URL: http://hdl.handle.net/10.1080/02664763.2012.761181
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:5:p:972-984




Template-Type: ReDIF-Article 1.0
Author-Name: Connie Stewart
Author-X-Name-First: Connie
Author-X-Name-Last: Stewart
Title: Zero-inflated beta distribution for modeling the proportions in quantitative fatty acid signature analysis
Abstract: Quantitative fatty acid signature analysis (QFASA) produces diet estimates containing the proportion of each species of prey in a predator's diet. Since the diet estimates are compositional, often contain an abundance of zeros (signifying the absence of a species in the diet), and samples sizes are generally small, inference problems require the use of nonstandard statistical methodology. Recently, a mixture distribution involving the multiplicative logistic normal distribution (and its skew-normal extension) was introduced in relation to QFASA to manage the problematic zeros. In this paper, we examine an alternative mixture distribution, namely, the recently proposed zero-inflated beta (ZIB) distribution. A potential advantage of using the ZIB distribution over the previously considered mixture models is that it does not require transformation of the data. To assess the usefulness of the ZIB distribution in QFASA inference problems, a simulation study is first carried out which compares the small sample properties of the maximum likelihood estimators of the means. The fit of the distributions is then examined using ‘pseudo-predators’ generated from a large real-life prey base. Finally, confidence intervals for the true diet based on the ZIB distribution are compared with earlier results through a simulation study and harbor seal data.
Journal: Journal of Applied Statistics
Pages: 985-992
Issue: 5
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2013.769088
File-URL: http://hdl.handle.net/10.1080/02664763.2013.769088
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:5:p:985-992




Template-Type: ReDIF-Article 1.0
Author-Name: Yan Zhou
Author-X-Name-First: Yan
Author-X-Name-Last: Zhou
Author-Name: John Aston
Author-X-Name-First: John
Author-X-Name-Last: Aston
Author-Name: Adam Johansen
Author-X-Name-First: Adam
Author-X-Name-Last: Johansen
Title: Bayesian model comparison for compartmental models with applications in positron emission tomography
Abstract: We develop strategies for Bayesian modelling as well as model comparison, averaging and selection for compartmental models with particular emphasis on those that occur in the analysis of positron emission tomography (PET) data. Both modelling and computational issues are considered. Biophysically inspired informative priors are developed for the problem at hand, and by comparison with default vague priors it is shown that the proposed modelling is not overly sensitive to prior specification. It is also shown that an additive normal error structure does not describe measured PET data well, despite being very widely used, and that within a simple Bayesian framework simultaneous parameter estimation and model comparison can be performed with a more general noise model. The proposed approach is compared with standard techniques using both simulated and real data. In addition to good, robust estimation performance, the proposed technique provides, automatically, a characterisation of the uncertainty in the resulting estimates which can be considerable in applications such as PET.
Journal: Journal of Applied Statistics
Pages: 993-1016
Issue: 5
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2013.772569
File-URL: http://hdl.handle.net/10.1080/02664763.2013.772569
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:5:p:993-1016




Template-Type: ReDIF-Article 1.0
Author-Name: Nagatomo Nakamura
Author-X-Name-First: Nagatomo
Author-X-Name-Last: Nakamura
Author-Name: Takahiro Tsuchiya
Author-X-Name-First: Takahiro
Author-X-Name-Last: Tsuchiya
Title: A model of regression lines through a common point: estimation of the focal point in wind-blown sand phenomena
Abstract: This paper discusses a model in which the regression lines will be passing through a common point. This point exists as a focal point in the wind-blown sand phenomena. The model of regression lines will be called ‘the focal point regression model’. The focal point will move according to the conditions of the experiments or the measurement site, so it must be estimated together with regression coefficients. The existence of the focal point is mathematically proved in the research field of coastal engineering, but its physical meaning and exact estimation method have not been established. Considering the experimental and/or measurement conditions, five models, that is, common or different error variance(s), passing through or not the centroid and Bayes-like approach are proposed. Moreover, the formulae of direct computation for a focal point under some conditions are given for engineering purpose. The models are applied to the wind-blown sand data, and behaviors of the models are verified by numerical experiments.
Journal: Journal of Applied Statistics
Pages: 1017-1031
Issue: 5
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2013.772570
File-URL: http://hdl.handle.net/10.1080/02664763.2013.772570
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:5:p:1017-1031




Template-Type: ReDIF-Article 1.0
Author-Name: Antonio Gonçalves
Author-X-Name-First: Antonio
Author-X-Name-Last: Gonçalves
Author-Name: Renan Almeida
Author-X-Name-First: Renan
Author-X-Name-Last: Almeida
Author-Name: Marcos Lins
Author-X-Name-First: Marcos
Author-X-Name-Last: Lins
Author-Name: Carlos Samanez
Author-X-Name-First: Carlos
Author-X-Name-Last: Samanez
Title: Canonical correlation analysis in the definition of weight restrictions for data envelopment analysis
Abstract: This work investigates the use of canonical correlation analysis (CCA) in the definition of weight restrictions for data envelopment analysis (DEA). With this purpose, CCA limits are introduced into Wong and Beasley's DEA model. An application of the method is made over data from hospitals in 27 Brazilian cities, producing as outputs average payment (average admission values) and percentage of hospital admissions according to disease groups (International Classification of Diseases, 9th Edition), and having as inputs mortality rates and average stay (length of stay after admission (days)). In this application, performance scores were calculated for both the (CCA) restricted and unrestricted DEA models. It can be concluded that the use of CCA-based weight limits for DEA models increases the consistency of the estimated DEA scores (more homogenous weights) and that these limits do not present mathematical infeasibility problems while avoiding the need for subjectively restricting weight variation in DEA.
Journal: Journal of Applied Statistics
Pages: 1032-1043
Issue: 5
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2013.772571
File-URL: http://hdl.handle.net/10.1080/02664763.2013.772571
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:5:p:1032-1043




Template-Type: ReDIF-Article 1.0
Author-Name: David Zimmer
Author-X-Name-First: David
Author-X-Name-Last: Zimmer
Title: Intertemporal persistence in healthcare spending and utilization: the role of insurance
Abstract: This paper develops a dynamic panel model of healthcare demand, with particular emphasis on the relationship between insurance and intertemporal persistence in demand. The model combines flexible marginal distributions for single-period demand with a tight intertemporal dynamic structure. The dynamic component follows a first-order nonlinear Markov process, which is constructed using copula functions. The model considers different insurance plans, including private plans with gatekeepers, private plans without gatekeepers, and public plans. Results indicate that individuals who lack insurance exhibit higher intertemporal persistence in medical spending compared to those with insurance coverage.
Journal: Journal of Applied Statistics
Pages: 1044-1063
Issue: 5
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2013.780155
File-URL: http://hdl.handle.net/10.1080/02664763.2013.780155
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:5:p:1044-1063




Template-Type: ReDIF-Article 1.0
Author-Name: M. Islam
Author-X-Name-First: M.
Author-X-Name-Last: Islam
Author-Name: Abdulhamid Alzaid
Author-X-Name-First: Abdulhamid
Author-X-Name-Last: Alzaid
Author-Name: Rafiqul Chowdhury
Author-X-Name-First: Rafiqul
Author-X-Name-Last: Chowdhury
Author-Name: Khalaf Sultan
Author-X-Name-First: Khalaf
Author-X-Name-Last: Sultan
Title: A generalized bivariate Bernoulli model with covariate dependence
Abstract: Dependence in outcome variables may pose formidable difficulty in analyzing data in longitudinal studies. In the past, most of the studies made attempts to address this problem using the marginal models. However, using the marginal models alone, it is difficult to specify the measures of dependence in outcomes due to association between outcomes as well as between outcomes and explanatory variables. In this paper, a generalized approach is demonstrated using both the conditional and marginal models. This model uses link functions to test for dependence in outcome variables. The estimation and test procedures are illustrated with an application to the mobility index data from the Health and Retirement Survey and also simulations are performed for correlated binary data generated from the bivariate Bernoulli distributions. The results indicate the usefulness of the proposed method.
Journal: Journal of Applied Statistics
Pages: 1064-1075
Issue: 5
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2013.780156
File-URL: http://hdl.handle.net/10.1080/02664763.2013.780156
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:5:p:1064-1075




Template-Type: ReDIF-Article 1.0
Author-Name: Farzana Noor
Author-X-Name-First: Farzana
Author-X-Name-Last: Noor
Author-Name: Muhammad Aslam
Author-X-Name-First: Muhammad
Author-X-Name-Last: Aslam
Title: Bayesian inference of the inverse Weibull mixture distribution using type-I censoring
Abstract: A large number of models have been derived from the two-parameter Weibull distribution including the inverse Weibull (IW) model which is found suitable for modeling the complex failure data set. In this paper, we present the Bayesian inference for the mixture of two IW models. For this purpose, the Bayes estimates of the parameters of the mixture model along with their posterior risks using informative as well as the non-informative prior are obtained. These estimates have been attained considering two cases: (a) when the shape parameter is known and (b) when all parameters are unknown. For the former case, Bayes estimates are obtained under three loss functions while for the latter case only the squared error loss function is used. Simulation study is carried out in order to explore numerical aspects of the proposed Bayes estimators. A real-life data set is also presented for both cases, and parameters obtained under case when shape parameter is known are tested through testing of hypothesis procedure.
Journal: Journal of Applied Statistics
Pages: 1076-1089
Issue: 5
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2013.780157
File-URL: http://hdl.handle.net/10.1080/02664763.2013.780157
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:5:p:1076-1089




Template-Type: ReDIF-Article 1.0
Author-Name: Vahid Nassiri
Author-X-Name-First: Vahid
Author-X-Name-Last: Nassiri
Author-Name: Ignace Loris
Author-X-Name-First: Ignace
Author-X-Name-Last: Loris
Title: A generalized quantile regression model
Abstract: A new class of probability distributions, the so-called connected double truncated gamma distribution, is introduced. We show that using this class as the error distribution of a linear model leads to a generalized quantile regression model that combines desirable properties of both least-squares and quantile regression methods: robustness to outliers and differentiable loss function.
Journal: Journal of Applied Statistics
Pages: 1090-1105
Issue: 5
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2013.780158
File-URL: http://hdl.handle.net/10.1080/02664763.2013.780158
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:5:p:1090-1105




Template-Type: ReDIF-Article 1.0
Author-Name: Muhammad Danish
Author-X-Name-First: Muhammad
Author-X-Name-Last: Danish
Author-Name: Muhammad Aslam
Author-X-Name-First: Muhammad
Author-X-Name-Last: Aslam
Title: Bayesian estimation for randomly censored generalized exponential distribution under asymmetric loss functions
Abstract: This paper deals with the Bayesian estimation of generalized exponential distribution in the proportional hazards model of random censorship under asymmetric loss functions. It is well known for the two-parameter lifetime distributions that the continuous conjugate priors for parameters do not exist; we assume independent gamma priors for the scale and the shape parameters. It is observed that the closed-form expressions for the Bayes estimators cannot be obtained; we propose Tierney–Kadane's approximation and Gibbs sampling to approximate the Bayes estimates. Monte Carlo simulation is carried out to observe the behavior of the proposed methods and one real data analysis is performed for illustration. Bayesian methods are compared with maximum likelihood and it is observed that the Bayes estimators perform better than the maximum-likelihood estimators in some cases.
Journal: Journal of Applied Statistics
Pages: 1106-1119
Issue: 5
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2013.780159
File-URL: http://hdl.handle.net/10.1080/02664763.2013.780159
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:5:p:1106-1119




Template-Type: ReDIF-Article 1.0
Author-Name: Michael McAssey
Author-X-Name-First: Michael
Author-X-Name-Last: McAssey
Title: An empirical goodness-of-fit test for multivariate distributions
Abstract: An empirical test is presented as a tool for assessing whether a specified multivariate probability model is suitable to describe the underlying distribution of a set of observations. This test is based on the premise that, given any probability distribution, the Mahalanobis distances corresponding to data generated from that distribution will likewise follow a distinct distribution that can be estimated well by means of a large sample. We demonstrate the effectiveness of the test for detecting departures from several multivariate distributions. We then apply the test to a real multivariate data set to confirm that it is consistent with a multivariate beta model.
Journal: Journal of Applied Statistics
Pages: 1120-1131
Issue: 5
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2013.780160
File-URL: http://hdl.handle.net/10.1080/02664763.2013.780160
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:5:p:1120-1131




Template-Type: ReDIF-Article 1.0
Author-Name: Jiong Luo
Author-X-Name-First: Jiong
Author-X-Name-Last: Luo
Author-Name: Zheng Su
Author-X-Name-First: Zheng
Author-X-Name-Last: Su
Title: A note on variance estimation in the Cox proportional hazards model
Abstract: The Cox proportional hazards model is widely used in clinical trials with time-to-event outcomes to compare an experimental treatment with the standard of care. At the design stage of a trial the number of events required to achieve a desired power needs to be determined, which is frequently based on estimating the variance of the maximum partial likelihood estimate of the regression parameter with a function of the number of events. Underestimating the variance at the design stage will lead to insufficiently powered studies, and overestimating the variance will lead to unnecessarily large trials. A simple approach to estimating the variance is introduced, which is compared with two widely adopted approaches in practice. Simulation results show that the proposed approach outperforms the standard ones and gives nearly unbiased estimates of the variance.
Journal: Journal of Applied Statistics
Pages: 1132-1139
Issue: 5
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2013.780161
File-URL: http://hdl.handle.net/10.1080/02664763.2013.780161
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:5:p:1132-1139




Template-Type: ReDIF-Article 1.0
Author-Name: Steven Hua
Author-X-Name-First: Steven
Author-X-Name-Last: Hua
Author-Name: D. Hawkins
Author-X-Name-First: D.
Author-X-Name-Last: Hawkins
Author-Name: Jihao Zhou
Author-X-Name-First: Jihao
Author-X-Name-Last: Zhou
Title: Statistical considerations in bioequivalence of two area under the concentration–time curves obtained from serial sampling data
Abstract: In this paper, we study the bioequivalence (BE) inference problem motivated by pharmacokinetic data that were collected using the serial sampling technique. In serial sampling designs, subjects are independently assigned to one of the two drugs; each subject can be sampled only once, and data are collected at K distinct timepoints from multiple subjects. We consider design and hypothesis testing for the parameter of interest: the area under the concentration–time curve (AUC). Decision rules in demonstrating BE were established using an equivalence test for either the ratio or logarithmic difference of two AUCs. The proposed t-test can deal with cases where two AUCs have unequal variances. To control for the type I error rate, the involved degrees-of-freedom were adjusted using Satterthwaite's approximation. A power formula was derived to allow the determination of necessary sample sizes. Simulation results show that, when the two AUCs have unequal variances, the type I error rate is better controlled by the proposed method compared with a method that only handles equal variances. We also propose an unequal subject allocation method that improves the power relative to that of the equal and symmetric allocation. The methods are illustrated using practical examples.
Journal: Journal of Applied Statistics
Pages: 1140-1154
Issue: 5
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2013.780234
File-URL: http://hdl.handle.net/10.1080/02664763.2013.780234
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:5:p:1140-1154




Template-Type: ReDIF-Article 1.0
Author-Name: Himel Mallick
Author-X-Name-First: Himel
Author-X-Name-Last: Mallick
Author-Name: Nengjun Yi
Author-X-Name-First: Nengjun
Author-X-Name-Last: Yi
Title: Bayesian bridge regression
Abstract: 
 Classical bridge regression is known to possess many desirable statistical properties such as oracle, sparsity, and unbiasedness. One outstanding disadvantage of bridge regularization, however, is that it lacks a systematic approach to inference, reducing its flexibility in practical applications. In this study, we propose bridge regression from a Bayesian perspective. Unlike classical bridge regression that summarizes inference using a single point estimate, the proposed Bayesian method provides uncertainty estimates of the regression parameters, allowing coherent inference through the posterior distribution. Under a sparsity assumption on the high-dimensional parameter, we provide sufficient conditions for strong posterior consistency of the Bayesian bridge prior. On simulated datasets, we show that the proposed method performs well compared to several competing methods across a wide range of scenarios. Application to two real datasets further revealed that the proposed method performs as well as or better than published methods while offering the advantage of posterior inference.
Journal: Journal of Applied Statistics
Pages: 988-1008
Issue: 6
Volume: 45
Year: 2018
Month: 4
X-DOI: 10.1080/02664763.2017.1324565
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1324565
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:6:p:988-1008




Template-Type: ReDIF-Article 1.0
Author-Name: Emmanuel O. Ogundimu
Author-X-Name-First: Emmanuel O.
Author-X-Name-Last: Ogundimu
Author-Name: Gary S. Collins
Author-X-Name-First: Gary S.
Author-X-Name-Last: Collins
Title: Predictive performance of penalized beta regression model for continuous bounded outcomes
Abstract: 
 Prediction models for continuous bounded outcomes are often developed by fitting ordinary least-square regression. However, predicted values from such method may lie outside the range of the outcome as it is bounded within a fixed range, with nonlinear expectation due to the ceiling and floor effects of the bounds. Thus, regular regression models such as normal linear or nonlinear models, are inadequate for prediction purposes for bounded response variable and the use of distributions that can model different shapes are essential. Beta regression, apart from modeling different shapes and constraining predictions to an admissible range, has been shown to be superior to alternative methods for data fitting but not for prediction purposes. We take data structures into account and compared various penalized beta regression method on predictive accuracy for bounded outcome variables using optimism corrected measures. Contrary to results obtained under many regression contexts, the classical maximum likelihood method produced good predictive accuracy in terms of  $ R^{2} $ R2 and RMSE. The ridge penalized beta regression performed better in terms of g-index, which is a measure of performance of the methods in external data sets. We restricted attention to prespecified models throughout and as such variable selection methods are not evaluated.
Journal: Journal of Applied Statistics
Pages: 1030-1040
Issue: 6
Volume: 45
Year: 2018
Month: 4
X-DOI: 10.1080/02664763.2017.1339024
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1339024
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:6:p:1030-1040




Template-Type: ReDIF-Article 1.0
Author-Name: Daniel Müllensiefen
Author-X-Name-First: Daniel
Author-X-Name-Last: Müllensiefen
Author-Name: Christian Hennig
Author-X-Name-First: Christian
Author-X-Name-Last: Hennig
Author-Name: Hedie Howells
Author-X-Name-First: Hedie
Author-X-Name-Last: Howells
Title: Using clustering of rankings to explain brand preferences with personality and socio-demographic variables
Abstract: 
 The primary aim of market segmentation is to identify relevant groups of consumers that can be addressed efficiently by marketing or advertising campaigns. This paper addresses the issue whether consumer groups can be identified from background variables that are not brand-related, and how much personality vs. socio-demographic variables contribute to the identification of consumer clusters. This is done by clustering aggregated preferences for 25 brands across 5 different product categories, and by relating socio-demographic and personality variables to the clusters using logistic regression and random forests over a range of different numbers of clusters. Results indicate that some personality variables contribute significantly to the identification of consumer groups in one sample. However, these results were not replicated on a second sample that was more heterogeneous in terms of socio-demographic characteristics and not representative of the brands target audience.
Journal: Journal of Applied Statistics
Pages: 1009-1029
Issue: 6
Volume: 45
Year: 2018
Month: 4
X-DOI: 10.1080/02664763.2017.1339025
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1339025
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:6:p:1009-1029




Template-Type: ReDIF-Article 1.0
Author-Name: Nurkhairany Amyra Mokhtar
Author-X-Name-First: Nurkhairany Amyra
Author-X-Name-Last: Mokhtar
Author-Name: Yong Zulina Zubairi
Author-X-Name-First: Yong Zulina
Author-X-Name-Last: Zubairi
Author-Name: Abdul Ghapor Hussin
Author-X-Name-First: Abdul Ghapor
Author-X-Name-Last: Hussin
Title: A clustering approach to detect multiple outliers in linear functional relationship model for circular data
Abstract: 
 Outlier detection has been used extensively in data analysis to detect anomalous observation in data. It has important applications such as in fraud detection and robust analysis, among others. In this paper, we propose a method in detecting multiple outliers in linear functional relationship model for circular variables. Using the residual values of the Caires and Wyatt model, we applied the hierarchical clustering approach. With the use of a tree diagram, we illustrate the detection of outliers graphically. A Monte Carlo simulation study is done to verify the accuracy of the proposed method. Low probability of masking and swamping effects indicate the validity of the proposed approach. Also, the illustrations to two sets of real data are given to show its practical applicability.
Journal: Journal of Applied Statistics
Pages: 1041-1051
Issue: 6
Volume: 45
Year: 2018
Month: 4
X-DOI: 10.1080/02664763.2017.1342779
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1342779
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:6:p:1041-1051




Template-Type: ReDIF-Article 1.0
Author-Name: Adam L. Smith
Author-X-Name-First: Adam L.
Author-X-Name-Last: Smith
Author-Name: Sofía S. Villar
Author-X-Name-First: Sofía S.
Author-X-Name-Last: Villar
Title: Bayesian adaptive bandit-based designs using the Gittins index for multi-armed trials with normally distributed endpoints
Abstract: 
 Adaptive designs for multi-armed clinical trials have become increasingly popular recently because of their potential to shorten development times and to increase patient response. However, developing response-adaptive designs that offer patient-benefit while ensuring the resulting trial provides a statistically rigorous and unbiased comparison of the different treatments included is highly challenging. In this paper, the theory of Multi-Armed Bandit Problems is used to define near optimal adaptive designs in the context of a clinical trial with a normally distributed endpoint with known variance. We report the operating characteristics (type I error, power, bias) and patient-benefit of these approaches and alternative designs using simulation studies based on an ongoing trial. These results are then compared to those recently published in the context of Bernoulli endpoints. Many limitations and advantages are similar in both cases but there are also important differences, specially with respect to type I error control. This paper proposes a simulation-based testing procedure to correct for the observed type I error inflation that bandit-based and adaptive rules can induce.
Journal: Journal of Applied Statistics
Pages: 1052-1076
Issue: 6
Volume: 45
Year: 2018
Month: 4
X-DOI: 10.1080/02664763.2017.1342780
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1342780
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:6:p:1052-1076




Template-Type: ReDIF-Article 1.0
Author-Name: Yingzhen Chen
Author-X-Name-First: Yingzhen
Author-X-Name-Last: Chen
Author-Name: Xuejun Ma
Author-X-Name-First: Xuejun
Author-X-Name-Last: Ma
Author-Name: Jingke Zhou
Author-X-Name-First: Jingke
Author-X-Name-Last: Zhou
Title: Variable selection for mode regression
Abstract: 
 From the prediction viewpoint, mode regression is more attractive since it pay attention to the most probable value of response variable given regressors. On the other hand, high-dimensional data are very prevalent as the advance of the technology of collecting and storing data. Variable selection is an important strategy to deal with high-dimensional regression problem. This paper aims to propose a variable selection procedure for high-dimensional mode regression via combining nonparametric kernel estimation method with sparsity penalty tactics. We also establish the asymptotic properties under certain technical conditions. The effectiveness and flexibility of the proposed methods are further illustrated by numerical studies and the real data application.
Journal: Journal of Applied Statistics
Pages: 1077-1084
Issue: 6
Volume: 45
Year: 2018
Month: 4
X-DOI: 10.1080/02664763.2017.1342781
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1342781
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:6:p:1077-1084




Template-Type: ReDIF-Article 1.0
Author-Name: H. Haselimashhadi
Author-X-Name-First: H.
Author-X-Name-Last: Haselimashhadi
Author-Name: V. Vinciotti
Author-X-Name-First: V.
Author-X-Name-Last: Vinciotti
Author-Name: K. Yu
Author-X-Name-First: K.
Author-X-Name-Last: Yu
Title: A novel Bayesian regression model for counts with an application to health data
Abstract: 
 Discrete data are collected in many application areas and are often characterised by highly-skewed distributions. An example of this, which is considered in this paper, is the number of visits to a specialist, often taken as a measure of demand in healthcare. A discrete Weibull regression model was recently proposed for regression problems with a discrete response and it was shown to possess desirable properties. In this paper, we propose the first Bayesian implementation of this model. We consider a general parametrization, where both parameters of the discrete Weibull distribution can be conditioned on the predictors, and show theoretically how, under a uniform non-informative prior, the posterior distribution is proper with finite moments. In addition, we consider closely the case of Laplace priors for parameter shrinkage and variable selection. Parameter estimates and their credible intervals can be readily calculated from their full posterior distribution. A simulation study and the analysis of four real datasets of medical records show promises for the wide applicability of this approach to the analysis of count data. The method is implemented in the R package
BDWreg.
Journal: Journal of Applied Statistics
Pages: 1085-1105
Issue: 6
Volume: 45
Year: 2018
Month: 4
X-DOI: 10.1080/02664763.2017.1342782
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1342782
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:6:p:1085-1105




Template-Type: ReDIF-Article 1.0
Author-Name: Olusola Samuel Makinde
Author-X-Name-First: Olusola Samuel
Author-X-Name-Last: Makinde
Author-Name: Olusoga Akin Fasoranbaku
Author-X-Name-First: Olusoga Akin
Author-X-Name-Last: Fasoranbaku
Title: On maximum depth classifiers: depth distribution approach
Abstract: 
 In this paper, we consider the notions of data depth for ordering multivariate data and propose a classification rule based on the distribution of some depth functions in  $ \mathbb {R}^d $ Rd. The equivalence of the proposed classification rule to optimal Bayes rule is discussed under suitable conditions. The performance of the proposed classification method is investigated in low- and high-dimensional setting using real datasets. Also, the performance of the proposed classification method is illustrated in comparison to some other depth-based classifiers using simulated data sets.
Journal: Journal of Applied Statistics
Pages: 1106-1117
Issue: 6
Volume: 45
Year: 2018
Month: 4
X-DOI: 10.1080/02664763.2017.1342783
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1342783
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:6:p:1106-1117




Template-Type: ReDIF-Article 1.0
Author-Name: Jisu Yoon
Author-X-Name-First: Jisu
Author-X-Name-Last: Yoon
Author-Name: Tatyana Krivobokova
Author-X-Name-First: Tatyana
Author-X-Name-Last: Krivobokova
Title: Treatments of non-metric variables in partial least squares and principal component analysis
Abstract: 
 This paper reviews various treatments of non-metric variables in partial least squares (PLS) and principal component analysis (PCA) algorithms. The performance of different treatments is compared in an extensive simulation study under several typical data generating processes and associated recommendations are made. Moreover, we find that PLS-based methods are to prefer in practice, since, independent of the data generating process, PLS performs either as good as PCA or significantly outperforms it. As an application of PLS and PCA algorithms with non-metric variables we consider construction of a wealth index to predict household expenditures. Consistent with our simulation study, we find that a PLS-based wealth index with dummy coding outperforms PCA-based ones.
Journal: Journal of Applied Statistics
Pages: 971-987
Issue: 6
Volume: 45
Year: 2018
Month: 4
X-DOI: 10.1080/02664763.2017.1346065
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1346065
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:6:p:971-987




Template-Type: ReDIF-Article 1.0
Author-Name: Tarald O. Kvålseth
Author-X-Name-First: Tarald O.
Author-X-Name-Last: Kvålseth
Title: Measuring association between nominal categorical variables: an alternative to the Goodman–Kruskal lambda
Abstract: 
 As a measure of association between two nominal categorical variables, the lambda coefficient or Goodman–Kruskal's lambda has become a most popular measure. Its popularity is primarily due to its simple and meaningful definition and interpretation in terms of the proportional reduction in error when predicting a random observation's category for one variable given (versus not knowing) its category for the other variable. It is an asymmetric measure, although a symmetric version is available. The lambda coefficient does, however, have a widely recognized limitation: it can equal zero even when there is no independence between the variables and when all other measures take on positive values. In order to mitigate this problem, an alternative lambda coefficient is introduced in this paper as a slight modification of the Goodman–Kruskal lambda. The properties of the new measure are discussed and a symmetric form is introduced. A statistical inference procedure is developed and a numerical example is provided.
Journal: Journal of Applied Statistics
Pages: 1118-1132
Issue: 6
Volume: 45
Year: 2018
Month: 4
X-DOI: 10.1080/02664763.2017.1346066
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1346066
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:6:p:1118-1132




Template-Type: ReDIF-Article 1.0
Author-Name: Hugo Lewi Hammer
Author-X-Name-First: Hugo Lewi
Author-X-Name-Last: Hammer
Title: Statistical models for short- and long-term forecasts of snow depth
Abstract: 
 Forecasting of future snow depths is useful for many applications like road safety, winter sport activities, avalanche risk assessment and hydrology. Motivated by the lack of statistical forecasts models for snow depth, in this paper we present a set of models to fill this gap. First, we present a model to do short-term forecasts when we assume that reliable weather forecasts of air temperature and precipitation are available. The covariates are included nonlinearly into the model following basic physical principles of snowfall, snow aging and melting. Due to the large set of observations with snow depth equal to zero, we use a zero-inflated gamma regression model, which is commonly used to similar applications like precipitation. We also do long-term forecasts of snow depth and much further than traditional weather forecasts for temperature and precipitation. The long-term forecasts are based on fitting models to historic time series of precipitation, temperature and snow depth. We fit the models to data from six locations in Norway with different climatic and vegetation properties. Forecasting five days into the future, the results showed that, given reliable weather forecasts of temperature and precipitation, the forecast errors in absolute value was between 3 and 7 cm for different locations in Norway. Forecasting three weeks into the future, the forecast errors were between 7 and 16 cm.
Journal: Journal of Applied Statistics
Pages: 1133-1156
Issue: 6
Volume: 45
Year: 2018
Month: 4
X-DOI: 10.1080/02664763.2017.1357683
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1357683
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:6:p:1133-1156




Template-Type: ReDIF-Article 1.0
Author-Name: Mojtaba Alizadeh
Author-X-Name-First: Mojtaba
Author-X-Name-Last: Alizadeh
Author-Name: Seyyed Fazel Bagheri
Author-X-Name-First: Seyyed
Author-X-Name-Last: Fazel Bagheri
Author-Name: Mohammad Alizadeh
Author-X-Name-First: Mohammad
Author-X-Name-Last: Alizadeh
Author-Name: Saralees Nadarajah
Author-X-Name-First: Saralees
Author-X-Name-Last: Nadarajah
Title: A new four-parameter lifetime distribution
Abstract: 
 Generalizing lifetime distributions is always precious for applied statisticians. In this paper, we introduce a new four-parameter generalization of the exponentiated power Lindley (EPL) distribution, called the exponentiated power Lindley geometric (EPLG) distribution, obtained by compounding EPL and geometric distributions. The new distribution arises in a latent complementary risks scenario, in which the lifetime associated with a particular risk is not observable; rather, we observe only the maximum lifetime value among all risks. The distribution exhibits decreasing, increasing, unimodal and bathtub-shaped hazard rate functions, depending on its parameters. It contains several lifetime distributions as particular cases: EPL, new generalized Lindley, generalized Lindley, power Lindley and Lindley geometric distributions. We derive several properties of the new distribution such as closed-form expressions for the density, cumulative distribution function, survival function, hazard rate function, the rth raw moment, and also the moments of order statistics. Moreover, we discuss maximum likelihood estimation and provide formulas for the elements of the Fisher information matrix. Simulation studies are also provided. Finally, two real data applications are given for showing the flexibility and potentiality of the new distribution.
Journal: Journal of Applied Statistics
Pages: 767-797
Issue: 5
Volume: 44
Year: 2017
Month: 4
X-DOI: 10.1080/02664763.2016.1182137
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1182137
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:5:p:767-797




Template-Type: ReDIF-Article 1.0
Author-Name: Francisco J. Rubio
Author-X-Name-First: Francisco J.
Author-X-Name-Last: Rubio
Author-Name: Keming Yu
Author-X-Name-First: Keming
Author-X-Name-Last: Yu
Title: Flexible objective Bayesian linear regression with applications in survival analysis
Abstract: 
 We study objective Bayesian inference for linear regression models with residual errors distributed according to the class of two-piece scale mixtures of normal distributions. These models allow for capturing departures from the usual assumption of normality of the errors in terms of heavy tails, asymmetry, and certain types of heteroscedasticity. We propose a general non-informative, scale-invariant, prior structure and provide sufficient conditions for the propriety of the posterior distribution of the model parameters, which cover cases when the response variables are censored. These results allow us to apply the proposed models in the context of survival analysis. This paper represents an extension to the Bayesian framework of the models proposed in [16]. We present a simulation study that shows good frequentist properties of the posterior credible intervals as well as point estimators associated to the proposed priors. We illustrate the performance of these models with real data in the context of survival analysis of cancer patients.
Journal: Journal of Applied Statistics
Pages: 798-810
Issue: 5
Volume: 44
Year: 2017
Month: 4
X-DOI: 10.1080/02664763.2016.1182138
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1182138
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:5:p:798-810




Template-Type: ReDIF-Article 1.0
Author-Name: Kyeongjun Lee
Author-X-Name-First: Kyeongjun
Author-X-Name-Last: Lee
Author-Name: Youngseuk Cho
Author-X-Name-First: Youngseuk
Author-X-Name-Last: Cho
Title: Bayesian and maximum likelihood estimations of the inverted exponentiated half logistic distribution under progressive Type II censoring
Abstract: 
 In this paper, the estimation of parameters, reliability and hazard functions of a inverted exponentiated half logistic distribution (IEHLD) from progressive Type II censored data has been considered. The Bayes estimates for progressive Type II censored IEHLD under asymmetric and symmetric loss functions such as squared error, general entropy and linex loss function are provided. The Bayes estimates for progressive Type II censored IEHLD parameters, reliability and hazard functions are also obtained under the balanced loss functions. However, the Bayes estimates cannot be obtained explicitly, Lindley approximation method and importance sampling procedure are considered to obtain the Bayes estimates. Furthermore, the asymptotic normality of the maximum likelihood estimates is used to obtain the approximate confidence intervals. The highest posterior density credible intervals of the parameters based on importance sampling procedure are computed. Simulations are performed to see the performance of the proposed estimates. For illustrative purposes, two data sets have been analyzed.
Journal: Journal of Applied Statistics
Pages: 811-832
Issue: 5
Volume: 44
Year: 2017
Month: 4
X-DOI: 10.1080/02664763.2016.1183602
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1183602
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:5:p:811-832




Template-Type: ReDIF-Article 1.0
Author-Name: Nels Johnson
Author-X-Name-First: Nels
Author-X-Name-Last: Johnson
Author-Name: Inyoung Kim
Author-X-Name-First: Inyoung
Author-X-Name-Last: Kim
Title: Generalized linear models with covariate measurement error and unknown link function
Abstract: 
 Generalized linear models (GLMs) with error-in-covariates are useful in epidemiological research due to the ubiquity of non-normal response variables and inaccurate measurements. The link function in GLMs is chosen by the user depending on the type of response variable, frequently the canonical link function. When covariates are measured with error, incorrect inference can be made, compounded by incorrect choice of link function. In this article we propose three flexible approaches for handling error-in-covariates and estimating an unknown link simultaneously. The first approach uses a fully Bayesian (FB) hierarchical framework, treating the unobserved covariate as a latent variable to be integrated over. The second and third are approximate Bayesian approach which use a Laplace approximation to marginalize the variables measured with error out of the likelihood. Our simulation results show support that the FB approach is often a better choice than the approximate Bayesian approaches for adjusting for measurement error, particularly when the measurement error distribution is misspecified. These approaches are demonstrated on an application with binary response.
Journal: Journal of Applied Statistics
Pages: 833-852
Issue: 5
Volume: 44
Year: 2017
Month: 4
X-DOI: 10.1080/02664763.2016.1183603
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1183603
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:5:p:833-852




Template-Type: ReDIF-Article 1.0
Author-Name: Taha Alshaybawee
Author-X-Name-First: Taha
Author-X-Name-Last: Alshaybawee
Author-Name: Habshah Midi
Author-X-Name-First: Habshah
Author-X-Name-Last: Midi
Author-Name: Rahim Alhamzawi
Author-X-Name-First: Rahim
Author-X-Name-Last: Alhamzawi
Title: Bayesian elastic net single index quantile regression
Abstract: 
 Single index model conditional quantile regression is proposed in order to overcome the dimensionality problem in nonparametric quantile regression. In the proposed method, the Bayesian elastic net is suggested for single index quantile regression for estimation and variables selection. The Gaussian process prior is considered for unknown link function and a Gibbs sampler algorithm is adopted for posterior inference. The results of the simulation studies and numerical example indicate that our propose method, BENSIQReg, offers substantial improvements over two existing methods, SIQReg and BSIQReg. The BENSIQReg has consistently show a good convergent property, has the least value of median of mean absolute deviations and smallest standard deviations, compared to the other two methods.
Journal: Journal of Applied Statistics
Pages: 853-871
Issue: 5
Volume: 44
Year: 2017
Month: 4
X-DOI: 10.1080/02664763.2016.1189515
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1189515
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:5:p:853-871




Template-Type: ReDIF-Article 1.0
Author-Name: Mian Arif Shams Adnan
Author-X-Name-First: Mian Arif Shams
Author-X-Name-Last: Adnan
Author-Name: Shongkour Roy
Author-X-Name-First: Shongkour
Author-X-Name-Last: Roy
Title: A sequential discrimination procedure for two almost identically shaped wrapped distributions
Abstract: 
 The way of investigating a distribution knowing its interesting properties might be often inadequate when the shapes of two distributions are almost similar. In each of these circumstances, the accurate decision about the genesis of a random sample from any of the two parent distributions will be very much ambiguous even with the availability of the existing testing procedure of the circular data. A sequential discrimination procedure has been suggested which is also invariant to the sample size. The performance of the proposed discrimination procedure has been evaluated by checking its capability of detecting the genesis of the known samples from the two identically shaped wrapped distributions.
Journal: Journal of Applied Statistics
Pages: 872-881
Issue: 5
Volume: 44
Year: 2017
Month: 4
X-DOI: 10.1080/02664763.2016.1189516
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1189516
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:5:p:872-881




Template-Type: ReDIF-Article 1.0
Author-Name: Douglas Moura Miranda
Author-X-Name-First: Douglas Moura
Author-X-Name-Last: Miranda
Author-Name: Samuel Vieira Conceição
Author-X-Name-First: Samuel Vieira
Author-X-Name-Last: Conceição
Title: A practical method to calculate probabilities: illustrative example from the electronic industry business
Abstract: 
 The real-life environment is made of probabilistic data by nature and the ability to make decisions based on probabilities is crucial in the business world. It is common to have a set of data and the need of calculating the probability of taking a value greater or less than a specific value. It is also common in many companies the unavailability of a statistical software or a specialized professional in statistics. The purpose of this paper is to present a practical and simple method to calculate probabilities from normal or non-normal distributed data set and illustrate it with an application from the electronic industry. The method does not demand statistical knowledge from the user; there is no need of normality assumptions, goodness test or transformations. The proposed method is easy to implement, robust and the experiments have evidenced its quality. The technique is validated with a large variety of instances and compared with the well-known Johnson system of distributions.
Journal: Journal of Applied Statistics
Pages: 882-896
Issue: 5
Volume: 44
Year: 2017
Month: 4
X-DOI: 10.1080/02664763.2016.1189517
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1189517
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:5:p:882-896




Template-Type: ReDIF-Article 1.0
Author-Name: Rabindra Nath Das
Author-X-Name-First: Rabindra
Author-X-Name-Last: Nath Das
Author-Name: Anis Chandra Mukhopadhyay
Author-X-Name-First: Anis Chandra
Author-X-Name-Last: Mukhopadhyay
Title: Correlated random effects regression analysis for a log-normally distributed variable
Abstract: 
 In regression analysis, it is assumed that the response (or dependent variable) distribution is Normal, and errors are homoscedastic and uncorrelated. However, in practice, these assumptions are rarely satisfied by a real data set. To stabilize the heteroscedastic response variance, generally, log-transformation is suggested. Consequently, the response variable distribution approaches nearer to the Normal distribution. As a result, the model fit of the data is improved. Practically, a proper (seems to be suitable) transformation may not always stabilize the variance, and the response distribution may not reduce to Normal distribution. The present article assumes that the response distribution is log-normal with compound autocorrelated errors. Under these situations, estimation and testing of hypotheses regarding regression parameters have been derived. From a set of reduced data, we have derived the best linear unbiased estimators of all the regression coefficients, except the intercept which is often unimportant in practice. Unknown correlation parameters have been estimated. In this connection, we have derived a test rule for testing any set of linear hypotheses of the unknown regression coefficients. In addition, we have developed the confidence ellipsoids of a set of estimable functions of regression coefficients. For the fitted regression equation, an index of fit has been proposed. A simulated study illustrates the results derived in this report.
Journal: Journal of Applied Statistics
Pages: 897-915
Issue: 5
Volume: 44
Year: 2017
Month: 4
X-DOI: 10.1080/02664763.2016.1189518
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1189518
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:5:p:897-915




Template-Type: ReDIF-Article 1.0
Author-Name: Sukhdev Singh
Author-X-Name-First: Sukhdev
Author-X-Name-Last: Singh
Author-Name: Yogesh Mani Tripathi
Author-X-Name-First: Yogesh
Author-X-Name-Last: Mani Tripathi
Author-Name: Shuo-Jye Wu
Author-X-Name-First: Shuo-Jye
Author-X-Name-Last: Wu
Title: Bayesian estimation and prediction based on lognormal record values
Abstract: 
 In this paper we consider the problems of estimation and prediction when observed data from a lognormal distribution are based on lower record values and lower record values with inter-record times. We compute maximum likelihood estimates and asymptotic confidence intervals for model parameters. We also obtain Bayes estimates and the highest posterior density (HPD) intervals using noninformative and informative priors under square error and LINEX loss functions. Furthermore, for the problem of Bayesian prediction under one-sample and two-sample framework, we obtain predictive estimates and the associated predictive equal-tail and HPD intervals. Finally for illustration purpose a real data set is analyzed and simulation study is conducted to compare the methods of estimation and prediction.
Journal: Journal of Applied Statistics
Pages: 916-940
Issue: 5
Volume: 44
Year: 2017
Month: 4
X-DOI: 10.1080/02664763.2016.1189520
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1189520
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:5:p:916-940




Template-Type: ReDIF-Article 1.0
Author-Name: Xu Guo
Author-X-Name-First: Xu
Author-X-Name-Last: Guo
Author-Name: Hecheng Wu
Author-X-Name-First: Hecheng
Author-X-Name-Last: Wu
Author-Name: Gaorong Li
Author-X-Name-First: Gaorong
Author-X-Name-Last: Li
Author-Name: Qiuyue Li
Author-X-Name-First: Qiuyue
Author-X-Name-Last: Li
Title: Inference for the common mean of several Birnbaum–Saunders populations
Abstract: 
 The Birnbaum–Saunders distribution is a widely used distribution in reliability applications to model failure times. For several samples from possible different Birnbaum–Saunders distributions, if their means can be considered as the same, it is of importance to make inference for the common mean. This paper presents procedures for interval estimation and hypothesis testing for the common mean of several Birnbaum–Saunders populations. The proposed approaches are hybrids between the generalized inference method and the large sample theory. Some simulation results are conducted to present the performance of the proposed approaches. The simulation results indicate that our proposed approaches perform well. Finally, the proposed approaches are applied to analyze a real example on the fatigue life of 6061-T6 aluminum coupons for illustration.
Journal: Journal of Applied Statistics
Pages: 941-954
Issue: 5
Volume: 44
Year: 2017
Month: 4
X-DOI: 10.1080/02664763.2016.1189521
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1189521
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:5:p:941-954




Template-Type: ReDIF-Article 1.0
Author-Name: Zahra Sadat Meshkani Farahani
Author-X-Name-First: Zahra Sadat
Author-X-Name-Last: Meshkani Farahani
Author-Name: Esmaile Khorram
Author-X-Name-First: Esmaile
Author-X-Name-Last: Khorram
Author-Name: Mojtaba Ganjali
Author-X-Name-First: Mojtaba
Author-X-Name-Last: Ganjali
Author-Name: Taban Baghfalaki
Author-X-Name-First: Taban
Author-X-Name-Last: Baghfalaki
Title: Longitudinal data analysis in the presence of informative sampling: weighted distribution or joint modelling
Abstract: 
 Weighted distributions, as an example of informative sampling, work appropriately under the missing at random mechanism since they neglect missing values and only completely observed subjects are used in the study plan. However, length-biased distributions, as a special case of weighted distributions, remove the subjects with short length deliberately, which surely meet the missing not at random mechanism. Accordingly, applying length-biased distributions jeopardizes the results by producing biased estimates. Hence, an alternate method has to be used such that the results are improved by means of valid inferences. We propose methods that are based on weighted distributions and joint modelling procedure and compare them in analysing longitudinal data. After introducing three methods in use, a set of simulation studies and analysis of two real longitudinal datasets affirm our claim.
Journal: Journal of Applied Statistics
Pages: 2111-2127
Issue: 12
Volume: 46
Year: 2019
Month: 9
X-DOI: 10.1080/02664763.2019.1576599
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1576599
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:12:p:2111-2127




Template-Type: ReDIF-Article 1.0
Author-Name: Minjung Lee
Author-X-Name-First: Minjung
Author-X-Name-Last: Lee
Title: Parametric inference for quantile event times with adjustment for covariates on competing risks data
Abstract: 
 We propose parametric inferences for quantile event times with adjustment for covariates on competing risks data. We develop parametric quantile inferences using parametric regression modeling of the cumulative incidence function from the cause-specific hazard and direct approaches. Maximum likelihood inferences are developed for estimation of the cumulative incidence function and quantiles. We develop the construction of parametric confidence intervals for quantiles. Simulation studies show that the proposed methods perform well. We illustrate the methods using early stage breast cancer data.
Journal: Journal of Applied Statistics
Pages: 2128-2144
Issue: 12
Volume: 46
Year: 2019
Month: 9
X-DOI: 10.1080/02664763.2019.1577370
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1577370
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:12:p:2128-2144




Template-Type: ReDIF-Article 1.0
Author-Name: M. Shafiqur Rahman
Author-X-Name-First: M. Shafiqur
Author-X-Name-Last: Rahman
Author-Name: Afrin Sadia Rumana
Author-X-Name-First: Afrin Sadia
Author-X-Name-Last: Rumana
Title: A model-based concordance-type index for evaluating the added predictive ability of novel risk factors and markers in the logistic regression models
Abstract: 
 The Concordance statistic (C-statistic) is commonly used to assess the predictive performance (discriminatory ability) of logistic regression model. Although there are several approaches for the C-statistic, their performance in quantifying the subsequent improvement in predictive accuracy due to inclusion of novel risk factors or biomarkers in the model has been extremely criticized in literature. This paper proposed a model-based concordance-type index, CK, for use with logistic regression model. The CK and its asymptotic sampling distribution is derived following Gonen and Heller's approach for Cox PH model for survival data but taking necessary modifications for use with binary data. Unlike the existing C-statistics for logistic model, it quantifies the concordance probability by taking the difference in the predicted risks between two subjects in a pair rather than ranking them and hence is able to quantify the equivalent incremental value from the new risk factor or marker. The simulation study revealed that the CK performed well when the model parameters are correctly estimated for large sample and showed greater improvement in quantifying the additional predictive value from the new risk factor or marker than the existing C-statistics. Furthermore, the illustration using three datasets supports the findings from simulation study.
Journal: Journal of Applied Statistics
Pages: 2145-2163
Issue: 12
Volume: 46
Year: 2019
Month: 9
X-DOI: 10.1080/02664763.2019.1580253
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1580253
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:12:p:2145-2163




Template-Type: ReDIF-Article 1.0
Author-Name: Young H. Chun
Author-X-Name-First: Young H.
Author-X-Name-Last: Chun
Title: Generalized run tests for statistical process control
Abstract: 
 In a sequence of elements, a run is defined as a maximal subsequence of like elements. The number of runs or the length of the longest run has been widely used to test the randomness of an ordered sequence. Based on two different sampling methods and two types of test statistics used, run tests can be classified into one of four cases. Numerous researchers have derived the probability distributions in many different ways, treating each case separately. In the paper, we propose a unified approach which is based on recurrence arguments of two mutually exclusive sub-sequences. We also consider the sequence of nominal data that has more than two classes. Thus, the traditional run tests for a binary sequence are special cases of our generalized run tests. We finally show that the generalized run tests can be applied to many quality management areas, such as testing changes in process variation, developing non-parametric multivariate control charts, and comparing the shapes and locations of more than two process distributions.
Journal: Journal of Applied Statistics
Pages: 2164-2179
Issue: 12
Volume: 46
Year: 2019
Month: 9
X-DOI: 10.1080/02664763.2019.1581147
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1581147
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:12:p:2164-2179




Template-Type: ReDIF-Article 1.0
Author-Name: O. Theodosiadou
Author-X-Name-First: O.
Author-X-Name-Last: Theodosiadou
Author-Name: V. Polimenis
Author-X-Name-First: V.
Author-X-Name-Last: Polimenis
Author-Name: G. Tsaklidis
Author-X-Name-First: G.
Author-X-Name-Last: Tsaklidis
Title: A semi-parametric method for estimating the beta coefficients of the hidden two-sided asset return jumps
Abstract: 
 We introduce a new methodology for estimating the parameters of a two-sided jump model, which aims at decomposing the daily stock return evolution into (unobservable) positive and negative jumps as well as Brownian noise. The parameters of interest are the jump beta coefficients which measure the influence of the market jumps on the stock returns, and are latent components. For this purpose, at first we use the Variance Gamma (VG) distribution which is frequently used in modeling financial time series and leads to the revelation of the hidden market jumps' distributions. Then, our method is based on the central moments of the stock returns for estimating the parameters of the model. It is proved that the proposed method provides always a solution in terms of the jump beta coefficients. We thus achieve a semi-parametric fit to the empirical data. The methodology itself serves as a criterion to test the fit of any sets of parameters to the empirical returns. The analysis is applied to NASDAQ and Google returns during the 2006–2008 period.
Journal: Journal of Applied Statistics
Pages: 2180-2197
Issue: 12
Volume: 46
Year: 2019
Month: 9
X-DOI: 10.1080/02664763.2019.1581734
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1581734
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:12:p:2180-2197




Template-Type: ReDIF-Article 1.0
Author-Name: Lizbeth Naranjo
Author-X-Name-First: Lizbeth
Author-X-Name-Last: Naranjo
Author-Name: Carlos J. Pérez
Author-X-Name-First: Carlos J.
Author-X-Name-Last: Pérez
Author-Name: Jacinto Martín
Author-X-Name-First: Jacinto
Author-X-Name-Last: Martín
Author-Name: Timothy Mutsvari
Author-X-Name-First: Timothy
Author-X-Name-Last: Mutsvari
Author-Name: Emmanuel Lesaffre
Author-X-Name-First: Emmanuel
Author-X-Name-Last: Lesaffre
Title: A Bayesian approach for misclassified ordinal response data
Abstract: 
 Motivated by a longitudinal oral health study, the Signal-Tandmobiel® study, a Bayesian approach has been developed to model misclassified ordinal response data. Two regression models have been considered to incorporate misclassification in the categorical response. Specifically, probit and logit models have been developed. The computational difficulties have been avoided by using data augmentation. This idea is exploited to derive efficient Markov chain Monte Carlo methods. Although the method is proposed for ordered categories, it can also be implemented for unordered ones in a simple way. The model performance is shown through a simulation-based example and the analysis of the motivating study.
Journal: Journal of Applied Statistics
Pages: 2198-2215
Issue: 12
Volume: 46
Year: 2019
Month: 9
X-DOI: 10.1080/02664763.2019.1582613
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1582613
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:12:p:2198-2215




Template-Type: ReDIF-Article 1.0
Author-Name: Cheng Ju
Author-X-Name-First: Cheng
Author-X-Name-Last: Ju
Author-Name: Mary Combs
Author-X-Name-First: Mary
Author-X-Name-Last: Combs
Author-Name: Samuel D. Lendle
Author-X-Name-First: Samuel D.
Author-X-Name-Last: Lendle
Author-Name: Jessica M. Franklin
Author-X-Name-First: Jessica M.
Author-X-Name-Last: Franklin
Author-Name: Richard Wyss
Author-X-Name-First: Richard
Author-X-Name-Last: Wyss
Author-Name: Sebastian Schneeweiss
Author-X-Name-First: Sebastian
Author-X-Name-Last: Schneeweiss
Author-Name: Mark J. van der Laan
Author-X-Name-First: Mark J.
Author-X-Name-Last: van der Laan
Title: Propensity score prediction for electronic healthcare databases using super learner and high-dimensional propensity score methods
Abstract: 
 The optimal learner for prediction modeling varies depending on the underlying data-generating distribution. Super Learner (SL) is a generic ensemble learning algorithm that uses cross-validation to select among a ‘library’ of candidate prediction models. While SL has been widely studied in a number of settings, it has not been thoroughly evaluated in large electronic healthcare databases that are common in pharmacoepidemiology and comparative effectiveness research. In this study, we applied and evaluated the performance of SL in its ability to predict the propensity score (PS), the conditional probability of treatment assignment given baseline covariates, using three electronic healthcare databases. We considered a library of algorithms that consisted of both nonparametric and parametric models. We also proposed a novel strategy for prediction modeling that combines SL with the high-dimensional propensity score (hdPS) variable selection algorithm. Predictive performance was assessed using three metrics: the negative log-likelihood, area under the curve (AUC), and time complexity. Results showed that the best individual algorithm, in terms of predictive performance, varied across datasets. The SL was able to adapt to the given dataset and optimize predictive performance relative to any individual learner. Combining the SL with the hdPS was the most consistent prediction method and may be promising for PS estimation and prediction modeling in electronic healthcare databases.
Journal: Journal of Applied Statistics
Pages: 2216-2236
Issue: 12
Volume: 46
Year: 2019
Month: 9
X-DOI: 10.1080/02664763.2019.1582614
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1582614
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:12:p:2216-2236




Template-Type: ReDIF-Article 1.0
Author-Name: Asma Nani
Author-X-Name-First: Asma
Author-X-Name-Last: Nani
Author-Name: Imed Gamoudi
Author-X-Name-First: Imed
Author-X-Name-Last: Gamoudi
Author-Name: Mohamed El Ghourabi
Author-X-Name-First: Mohamed
Author-X-Name-Last: El Ghourabi
Title: Value-at-risk estimation by LS-SVR and FS-LS-SVR based on GAS model
Abstract: 
 Conditional risk measuring plays an important role in financial regulation and depends on volatility estimation. A new class of parameter models called Generalized Autoregressive Score (GAS) model has been successfully applied for different error's densities and for different problems of time series prediction in particular for volatility modeling and VaR estimation. To improve the estimating accuracy of the GAS model, this study proposed a semi-parametric method, LS-SVR and FS-LS-SVR applied to the GAS model to estimate the conditional VaR. In particular, we fit the GAS(1,1) model to the return series using three different distributions. Then, LS-SVR and FS-LS-SVR approximate the GAS(1,1) model. An empirical research was performed to illustrate the effectiveness of the proposed method. More precisely, the experimental results from four stock indexes returns suggest that using hybrid models, GAS-LS-SVR and GAS-FS-LS-SVR provides improved performances in the VaR estimation.
Journal: Journal of Applied Statistics
Pages: 2237-2253
Issue: 12
Volume: 46
Year: 2019
Month: 9
X-DOI: 10.1080/02664763.2019.1584161
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1584161
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:12:p:2237-2253




Template-Type: ReDIF-Article 1.0
Author-Name: Yonggang Lu
Author-X-Name-First: Yonggang
Author-X-Name-Last: Lu
Author-Name: Peter Westfall
Author-X-Name-First: Peter
Author-X-Name-Last: Westfall
Title: Simple and flexible Bayesian inferences for standardized regression coefficients
Abstract: 
 In statistical practice, inferences on standardized regression coefficients are often required, but complicated by the fact that they are nonlinear functions of the parameters, and thus standard textbook results are simply wrong. Within the frequentist domain, asymptotic delta methods can be used to construct confidence intervals of the standardized coefficients with proper coverage probabilities. Alternatively, Bayesian methods solve similar and other inferential problems by simulating data from the posterior distribution of the coefficients. In this paper, we present Bayesian procedures that provide comprehensive solutions for inferences on the standardized coefficients. Simple computing algorithms are developed to generate posterior samples with no autocorrelation and based on both noninformative improper and informative proper prior distributions. Simulation studies show that Bayesian credible intervals constructed by our approaches have comparable and even better statistical properties than their frequentist counterparts, particularly in the presence of collinearity. In addition, our approaches solve some meaningful inferential problems that are difficult if not impossible from the frequentist standpoint, including identifying joint rankings of multiple standardized coefficients and making optimal decisions concerning their sizes and comparisons. We illustrate applications of our approaches through examples and make sample R functions available for implementing our proposed methods.
Journal: Journal of Applied Statistics
Pages: 2254-2288
Issue: 12
Volume: 46
Year: 2019
Month: 9
X-DOI: 10.1080/02664763.2019.1584609
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1584609
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:12:p:2254-2288




Template-Type: ReDIF-Article 1.0
Author-Name: Nikolay Miller
Author-X-Name-First: Nikolay
Author-X-Name-Last: Miller
Author-Name: Yiming Yang
Author-X-Name-First: Yiming
Author-X-Name-Last: Yang
Author-Name: Bruce Sun
Author-X-Name-First: Bruce
Author-X-Name-Last: Sun
Author-Name: Guoyi Zhang
Author-X-Name-First: Guoyi
Author-X-Name-Last: Zhang
Title: Identification of technical analysis patterns with smoothing splines for bitcoin prices
Abstract: 
 This research studies automatic price pattern search procedure for bitcoin cryptocurrency based on 1-min price data. To achieve this, search algorithm is proposed based on nonparametric regression method of smoothing splines. We investigate some well-known technical analysis patterns and construct algorithmic trading strategy to evaluate the effectiveness of the patterns. We found that method of smoothing splines for identifying the technical analysis patterns and that strategies based on certain technical analysis patterns yield returns that significantly exceed results of unconditional trading strategies.
Journal: Journal of Applied Statistics
Pages: 2289-2297
Issue: 12
Volume: 46
Year: 2019
Month: 9
X-DOI: 10.1080/02664763.2019.1580251
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1580251
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:12:p:2289-2297




Template-Type: ReDIF-Article 1.0
Author-Name: Xiaoqi Jiang
Author-X-Name-First: Xiaoqi
Author-X-Name-Last: Jiang
Author-Name: Steven Wink
Author-X-Name-First: Steven
Author-X-Name-Last: Wink
Author-Name: Bob van de Water
Author-X-Name-First: Bob
Author-X-Name-Last: van de Water
Author-Name: Annette Kopp-Schneider
Author-X-Name-First: Annette
Author-X-Name-Last: Kopp-Schneider
Title: Functional analysis of high-content high-throughput imaging data
Abstract: 
 High-content automated imaging platforms allow the multiplexing of several targets simultaneously to generate multi-parametric single-cell data sets over extended periods of time. Typically, standard simple measures such as mean value of all cells at every time point are calculated to summarize the temporal process, resulting in loss of time dynamics of the single cells. Multiple experiments are performed but observation time points are not necessarily identical, leading to difficulties when integrating summary measures from different experiments. We used functional data analysis to analyze continuous curve data, where the temporal process of a response variable for each single cell can be described using a smooth curve. This allows analyses to be performed on continuous functions, rather than on original discrete data points. Functional regression models were applied to determine common temporal characteristics of a set of single cell curves and random effects were employed in the models to explain variation between experiments. The aim of the multiplexing approach is to simultaneously analyze the effect of a large number of compounds in comparison to control to discriminate between their mode of action. Functional principal component analysis based on T-statistic curves for pairwise comparison to control was used to study time-dependent compound effects.
Journal: Journal of Applied Statistics
Pages: 1903-1919
Issue: 11
Volume: 44
Year: 2017
Month: 8
X-DOI: 10.1080/02664763.2016.1238048
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1238048
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:11:p:1903-1919




Template-Type: ReDIF-Article 1.0
Author-Name: G. Inan
Author-X-Name-First: G.
Author-X-Name-Last: Inan
Author-Name: R. Yucel
Author-X-Name-First: R.
Author-X-Name-Last: Yucel
Title: Joint GEEs for multivariate correlated data with incomplete binary outcomes
Abstract: 
 This study considers a fully-parametric but uncongenial multiple imputation (MI) inference to jointly analyze incomplete binary response variables observed in a correlated data settings. Multiple imputation model is specified as a fully-parametric model based on a multivariate extension of mixed-effects models. Dichotomized imputed datasets are then analyzed using joint GEE models where covariates are associated with the marginal mean of responses with response-specific regression coefficients and a Kronecker product is accommodated for cluster-specific correlation structure for a given response variable and correlation structure between multiple response variables. The validity of the proposed MI-based JGEE (MI-JGEE) approach is assessed through a Monte Carlo simulation study under different scenarios. The simulation results, which are evaluated in terms of bias, mean-squared error, and coverage rate, show that MI-JGEE has promising inferential properties even when the underlying multiple imputation is misspecified. Finally, Adolescent Alcohol Prevention Trial data are used for illustration.
Journal: Journal of Applied Statistics
Pages: 1920-1937
Issue: 11
Volume: 44
Year: 2017
Month: 8
X-DOI: 10.1080/02664763.2016.1238049
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1238049
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:11:p:1920-1937




Template-Type: ReDIF-Article 1.0
Author-Name: Wei Wang
Author-X-Name-First: Wei
Author-X-Name-Last: Wang
Title: Checking identifiability of covariance parameters in linear mixed effects models
Abstract: 
 To build a linear mixed effects model, one needs to specify the random effects and often the associated parametrized covariance matrix structure. Inappropriate specification of the structures can result in the covariance parameters of the model not identifiable. Non-identifiability can result in extraordinary wide confidence intervals, and unreliable parameter inference. Sometimes software produces implication of model non-identifiability, but not always. In the simulation of fitting non-identifiable models we tried, about half of the times the software output did not look abnormal. We derive necessary and sufficient conditions of covariance parameters identifiability which does not require any prior model fitting. The results are easy to implement and are applicable to commonly used covariance matrix structures.
Journal: Journal of Applied Statistics
Pages: 1938-1946
Issue: 11
Volume: 44
Year: 2017
Month: 8
X-DOI: 10.1080/02664763.2016.1238050
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1238050
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:11:p:1938-1946




Template-Type: ReDIF-Article 1.0
Author-Name: Rahim Alhamzawi
Author-X-Name-First: Rahim
Author-X-Name-Last: Alhamzawi
Title: Inference with three-level prior distributions in quantile regression problems
Abstract: 
 In this paper, we propose a three level hierarchical Bayesian model for variable selection and estimation in quantile regression problems. Specifically, at the first level we consider a zero mean normal priors for the coefficients with unknown variance parameters. At the second level, we specify two different priors for the unknown variance parameters which introduce two different models producing different levels of sparsity. Then, at the third level we suggest joint improper priors for the unknown hyperparameters assuming they are independent. Simulations and Boston Housing data are utilized to compare the performance of our models with six existing models. The results indicate that our models perform good in the simulations and Boston Housing data.
Journal: Journal of Applied Statistics
Pages: 1947-1959
Issue: 11
Volume: 44
Year: 2017
Month: 8
X-DOI: 10.1080/02664763.2016.1238051
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1238051
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:11:p:1947-1959




Template-Type: ReDIF-Article 1.0
Author-Name: Hongmei Lin
Author-X-Name-First: Hongmei
Author-X-Name-Last: Lin
Author-Name: Wenchao Xu
Author-X-Name-First: Wenchao
Author-X-Name-Last: Xu
Author-Name: Riquan Zhang
Author-X-Name-First: Riquan
Author-X-Name-Last: Zhang
Author-Name: Jianhong Shi
Author-X-Name-First: Jianhong
Author-X-Name-Last: Shi
Author-Name: Yuedong Wang
Author-X-Name-First: Yuedong
Author-X-Name-Last: Wang
Title: Multiple-index varying-coefficient models for longitudinal data
Abstract: 
 In haemodialysis patients, vascular access type is of paramount importance. Although recent studies have found that central venous catheter is often associated with poor outcomes and switching to arteriovenous fistula is beneficial, studies have not fully elucidated how the effect of switching of access on outcomes changes over time for patients on dialysis and whether the effect depends on switching time. In this paper, we characterise the switching access type effect on outcomes for haemodialysis patients. This is achieved by using a new class of multiple-index varying-coefficient (MIVC) models. We develop a new estimation procedure for MIVC models based on local linear, profile least-square method and Cholesky decomposition. Monte Carlo simulation studies show excellent finite sample performance. Finally, we analyse the dialysis data using our method.
Journal: Journal of Applied Statistics
Pages: 1960-1978
Issue: 11
Volume: 44
Year: 2017
Month: 8
X-DOI: 10.1080/02664763.2016.1238052
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1238052
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:11:p:1960-1978




Template-Type: ReDIF-Article 1.0
Author-Name: Alan T. K. Wan
Author-X-Name-First: Alan T. K.
Author-X-Name-Last: Wan
Author-Name: Shangyu Xie
Author-X-Name-First: Shangyu
Author-X-Name-Last: Xie
Author-Name: Yong Zhou
Author-X-Name-First: Yong
Author-X-Name-Last: Zhou
Title: A varying coefficient approach to estimating hedonic housing price functions and their quantiles
Abstract: 
 The varying coefficient (VC) model introduced by Hastie and Tibshirani [26] is arguably one of the most remarkable recent developments in nonparametric regression theory. The VC model is an extension of the ordinary regression model where the coefficients are allowed to vary as smooth functions of an effect modifier possibly different from the regressors. The VC model reduces the modelling bias with its unique structure while also avoiding the ‘curse of dimensionality’ problem. While the VC model has been applied widely in a variety of disciplines, its application in economics has been minimal. The central goal of this paper is to apply VC modelling to the estimation of a hedonic house price function using data from Hong Kong, one of the world's most buoyant real estate markets. We demonstrate the advantages of the VC approach over traditional parametric and semi-parametric regressions in the face of a large number of regressors. We further combine VC modelling with quantile regression to examine the heterogeneity of the marginal effects of attributes across the distribution of housing prices.
Journal: Journal of Applied Statistics
Pages: 1979-1999
Issue: 11
Volume: 44
Year: 2017
Month: 8
X-DOI: 10.1080/02664763.2016.1238053
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1238053
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:11:p:1979-1999




Template-Type: ReDIF-Article 1.0
Author-Name: Guohai Zhou
Author-X-Name-First: Guohai
Author-X-Name-Last: Zhou
Author-Name: Lang Wu
Author-X-Name-First: Lang
Author-X-Name-Last: Wu
Author-Name: Rollin Brant
Author-X-Name-First: Rollin
Author-X-Name-Last: Brant
Author-Name: J. Mark Ansermino
Author-X-Name-First: J. Mark
Author-X-Name-Last: Ansermino
Title: A likelihood-based approach for multivariate one-sided tests with missing data
Abstract: 
 Inequality-restricted hypotheses testing methods containing multivariate one-sided testing methods are useful in practice, especially in multiple comparison problems. In practice, multivariate and longitudinal data often contain missing values since it may be difficult to observe all values for each variable. However, although missing values are common for multivariate data, statistical methods for multivariate one-sided tests with missing values are quite limited. In this article, motivated by a dataset in a recent collaborative project, we develop two likelihood-based methods for multivariate one-sided tests with missing values, where the missing data patterns can be arbitrary and the missing data mechanisms may be non-ignorable. Although non-ignorable missing data are not testable based on observed data, statistical methods addressing this issue can be used for sensitivity analysis and might lead to more reliable results, since ignoring informative missingness may lead to biased analysis. We analyse the real dataset in details under various possible missing data mechanisms and report interesting findings which are previously unavailable. We also derive some asymptotic results and evaluate our new tests using simulations.
Journal: Journal of Applied Statistics
Pages: 2000-2016
Issue: 11
Volume: 44
Year: 2017
Month: 8
X-DOI: 10.1080/02664763.2016.1238054
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1238054
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:11:p:2000-2016




Template-Type: ReDIF-Article 1.0
Author-Name: Milan Stojković
Author-X-Name-First: Milan
Author-X-Name-Last: Stojković
Author-Name: Stevan Prohaska
Author-X-Name-First: Stevan
Author-X-Name-Last: Prohaska
Author-Name: Nikola Zlatanović
Author-X-Name-First: Nikola
Author-X-Name-Last: Zlatanović
Title: Estimation of flood frequencies from data sets with outliers using mixed distribution functions
Abstract: 
 In this paper the estimation of high return period quantiles of the flood peak and volume in the Kolubara River basin are carried out. Estimation of flood frequencies is carried out on a data set containing high outliers which are identified by the Rosner’s test. Simultaneously, low outliers are determined by the multiple Grubbs–Beck. The next step involved the usage of the mixed distribution functions applied to a data set from three populations: floods with low outliers, normal floods and floods with high outliers. The contribution of the data set with low outliers is neglected, since it should underestimate the flood quantiles with large return periods. Consequently, the best fitted mixed distribution from the applied types (EV1, GEV, P3 and LP3) was determined by using the minimum standard error of fit.
Journal: Journal of Applied Statistics
Pages: 2017-2035
Issue: 11
Volume: 44
Year: 2017
Month: 8
X-DOI: 10.1080/02664763.2016.1238055
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1238055
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:11:p:2017-2035




Template-Type: ReDIF-Article 1.0
Author-Name: Lucy Kerns
Author-X-Name-First: Lucy
Author-X-Name-Last: Kerns
Author-Name: John T. Chen
Author-X-Name-First: John T.
Author-X-Name-Last: Chen
Title: Simultaneous confidence bands for restricted logistic regression models
Abstract: 
 The hyperbolic  $ 1-\alpha $ 1−α confidence bands for one logistic regression model with restricted predictors have been considered in the statistical literature. At times, one wishes to construct simultaneous confidence bands for comparing several logistic regression models. It seems that Liu's book [Simultaneous Inference in Regression, Chapman &amp; Hall, 2010, Chapter 8] is the only published work that has addressed this problem. Liu suggested simulation-based methods for constructing simultaneous confidence bands for comparing several logistic models, but further research was warranted to assess the conservativeness of the bands. In this paper, we propose a dimension-wise partitioning method to construct a set of simultaneous confidence bands for the comparisons of several logistic regression functions with a pre-specified function in a stepwise fashion. In addition, simulation studies cast new light on the assumption of predetermined testing order for the stepwise procedures presented in this paper and by Hsu and Berger [Stepwise confidence intervals without multiplicity adjustment for dose–response and toxicity studies, J. Amer. Statist. Assoc. 94 (1999), pp. 468–482]. As an illustration, we include an example on the success rate of thrombolysis associated with patient characteristics regarding post-thrombotic syndrome.
Journal: Journal of Applied Statistics
Pages: 2036-2051
Issue: 11
Volume: 44
Year: 2017
Month: 8
X-DOI: 10.1080/02664763.2016.1238056
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1238056
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:11:p:2036-2051




Template-Type: ReDIF-Article 1.0
Author-Name: Filidor Vilca
Author-X-Name-First: Filidor
Author-X-Name-Last: Vilca
Author-Name: Caio L. N. Azevedo
Author-X-Name-First: Caio L. N.
Author-X-Name-Last: Azevedo
Author-Name: N. Balakrishnan
Author-X-Name-First: N.
Author-X-Name-Last: Balakrishnan
Title: Bayesian inference for sinh-normal/independent nonlinear regression models
Abstract: 
 Sinh-normal/independent distributions are a class of symmetric heavy-tailed distributions that include the sinh-normal distribution as a special case, which has been used extensively in Birnbaum–Saunders regression models. Here, we explore the use of Markov Chain Monte Carlo methods to develop a Bayesian analysis in nonlinear regression models when Sinh-normal/independent distributions are assumed for the random errors term, and it provides a robust alternative to the sinh-normal nonlinear regression model. Bayesian mechanisms for parameter estimation, residual analysis and influence diagnostics are then developed, which extend the results of Farias and Lemonte [Bayesian inference for the Birnbaum-Saunders nonlinear regression model, Stat. Methods Appl. 20 (2011), pp. 423-438] who used the Sinh-normal/independent distributions with known scale parameter. Some special cases, based on the sinh-Student-t (sinh-St), sinh-slash (sinh-SL) and sinh-contaminated normal (sinh-CN) distributions are discussed in detail. Two real datasets are finally analyzed to illustrate the developed procedures.
Journal: Journal of Applied Statistics
Pages: 2052-2074
Issue: 11
Volume: 44
Year: 2017
Month: 8
X-DOI: 10.1080/02664763.2016.1238058
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1238058
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:11:p:2052-2074




Template-Type: ReDIF-Article 1.0
Author-Name: Wenjuan Liang
Author-X-Name-First: Wenjuan
Author-X-Name-Last: Liang
Author-Name: Xiaolong Pu
Author-X-Name-First: Xiaolong
Author-X-Name-Last: Pu
Author-Name: Dongdong Xiang
Author-X-Name-First: Dongdong
Author-X-Name-Last: Xiang
Title: A distribution-free multivariate CUSUM control chart using dynamic control limits
Abstract: 
 In modern quality control, it is becoming common to simultaneously monitor several quality characteristics of a process with rapid evolving data-acquisition technology. When the multivariate process distribution is unknown and only a set of in-control data is available, the bootstrap technique can be used to adjust the constant limit of the multivariate cumulative sum (MCUSUM) control chart. To further improve the performance of the control chart, we extend the constant control limit to a sequence of dynamic control limits which are determined by the conditional distribution of the charting statistics given the sprint length. Simulation results show that the novel control chart with dynamic control limits offers a better ARL performance, compared with the traditional MCUSUM control chart. Despite it, the proposed control chart is considerably computer-intensive. This leads to the development of a more flexible control chart which uses a continuous function of the sprint length as the control limit sequences. More importantly, the control chart is easy to implement and can reduce the computational time significantly. A white wine data illustrates that the novel control chart performs quite well in applications.
Journal: Journal of Applied Statistics
Pages: 2075-2093
Issue: 11
Volume: 44
Year: 2017
Month: 8
X-DOI: 10.1080/02664763.2016.1247784
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1247784
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:11:p:2075-2093




Template-Type: ReDIF-Article 1.0
Author-Name: Chun-Xia Zhang
Author-X-Name-First: Chun-Xia
Author-X-Name-Last: Zhang
Author-Name: Jiang-She Zhang
Author-X-Name-First: Jiang-She
Author-X-Name-Last: Zhang
Author-Name: Guan-Wei Wang
Author-X-Name-First: Guan-Wei
Author-X-Name-Last: Wang
Author-Name: Nan-Nan Ji
Author-X-Name-First: Nan-Nan
Author-X-Name-Last: Ji
Title: A novel bagging approach for variable ranking and selection via a mixed importance measure
Abstract: 
 At present, ensemble learning has exhibited its great power in stabilizing and enhancing the performance of some traditional variable selection methods such as lasso and genetic algorithm. In this paper, a novel bagging ensemble method called BSSW is developed to implement variable ranking and selection in linear regression models. Its main idea is to execute stepwise search algorithm on multiple bootstrap samples. In each trial, a mixed importance measure is assigned to each variable according to the order that it is selected into final model as well as the improvement of model fitting resulted from its inclusion. Based on the importance measure averaged across some bootstrapping trials, all candidate variables are ranked and then decided to be important or not. To extend the scope of application, BSSW is extended to the situation of generalized linear models. Experiments carried out with some simulated and real data indicate that BSSW achieves better performance in most studied cases when compared with several other existing methods.
Journal: Journal of Applied Statistics
Pages: 1734-1755
Issue: 10
Volume: 45
Year: 2018
Month: 7
X-DOI: 10.1080/02664763.2017.1391181
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1391181
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:10:p:1734-1755




Template-Type: ReDIF-Article 1.0
Author-Name: Brandi N. Falley
Author-X-Name-First: Brandi N.
Author-X-Name-Last: Falley
Author-Name: James D. Stamey
Author-X-Name-First: James D.
Author-X-Name-Last: Stamey
Author-Name: A. Alexander Beaujean
Author-X-Name-First: A. Alexander
Author-X-Name-Last: Beaujean
Title: Bayesian estimation of logistic regression with misclassified covariates and response
Abstract: 
 Measurement error is a commonly addressed problem in psychometrics and the behavioral sciences, particularly where gold standard data either does not exist or are too expensive. The Bayesian approach can be utilized to adjust for the bias that results from measurement error in tests. Bayesian methods offer other practical advantages for the analysis of epidemiological data including the possibility of incorporating relevant prior scientific information and the ability to make inferences that do not rely on large sample assumptions. In this paper we consider a logistic regression model where both the response and a binary covariate are subject to misclassification. We assume both a continuous measure and a binary diagnostic test are available for the response variable but no gold standard test is assumed available. We consider a fully Bayesian analysis that affords such adjustments, accounting for the sources of error and correcting estimates of the regression parameters. Based on the results from our example and simulations, the models that account for misclassification produce more statistically significant results, than the models that ignore misclassification. A real data example on math disorders is considered.
Journal: Journal of Applied Statistics
Pages: 1756-1769
Issue: 10
Volume: 45
Year: 2018
Month: 7
X-DOI: 10.1080/02664763.2017.1391182
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1391182
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:10:p:1756-1769




Template-Type: ReDIF-Article 1.0
Author-Name: Jin-Jian Hsieh
Author-X-Name-First: Jin-Jian
Author-X-Name-Last: Hsieh
Author-Name: Jian-Lin Wang
Author-X-Name-First: Jian-Lin
Author-X-Name-Last: Wang
Title: Quantile residual life regression based on semi-competing risks data
Abstract: 
 This paper investigates the quantile residual life regression based on semi-competing risk data. Because the terminal event time dependently censors the non-terminal event time, the inference on the non-terminal event time is not available without extra assumption. Therefore, we assume that the non-terminal event time and the terminal event time follow an Archimedean copula. Then, we apply the inverse probability weight technique to construct an estimating equation of quantile residual life regression coefficients. But, the estimating equation may not be continuous in coefficients. Thus, we apply the generalized solution approach to overcome this problem. Since the variance estimation of the proposed estimator is difficult to obtain, we use the bootstrap resampling method to estimate it. From simulations, it shows the performance of the proposed method is well. Finally, we analyze the Bone Marrow Transplant data for illustrations.
Journal: Journal of Applied Statistics
Pages: 1770-1780
Issue: 10
Volume: 45
Year: 2018
Month: 7
X-DOI: 10.1080/02664763.2017.1391183
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1391183
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:10:p:1770-1780




Template-Type: ReDIF-Article 1.0
Author-Name: Osvaldo Loquiha
Author-X-Name-First: Osvaldo
Author-X-Name-Last: Loquiha
Author-Name: Niel Hens
Author-X-Name-First: Niel
Author-X-Name-Last: Hens
Author-Name: Emilia Martins-Fonteyn
Author-X-Name-First: Emilia
Author-X-Name-Last: Martins-Fonteyn
Author-Name: Herman Meulemans
Author-X-Name-First: Herman
Author-X-Name-Last: Meulemans
Author-Name: Edwin Wouters
Author-X-Name-First: Edwin
Author-X-Name-Last: Wouters
Author-Name: Marleen Temmerman
Author-X-Name-First: Marleen
Author-X-Name-Last: Temmerman
Author-Name: Nafissa Osman
Author-X-Name-First: Nafissa
Author-X-Name-Last: Osman
Author-Name: Marc Aerts
Author-X-Name-First: Marc
Author-X-Name-Last: Aerts
Title: Joint models for mixed categorical outcomes: a study of HIV risk perception and disease status in Mozambique
Abstract: 
 Two types of bivariate models for categorical response variables are introduced to deal with special categories such as ‘unsure’ or ‘unknown’ in combination with other ordinal categories, while taking additional hierarchical data structures into account. The latter is achieved by the use of different covariance structures for a trivariate random effect. The models are applied to data from the INSIDA survey, where interest goes to the effect of covariates on the association between HIV risk perception (quadrinomial with an ‘unknown risk’ category) and HIV infection status (binary). The final model combines continuation-ratio with cumulative link logits for the risk perception, together with partly correlated and partly shared trivariate random effects for the household level. The results indicate that only age has a significant effect on the association between HIV risk perception and infection status. The proposed models may be useful in various fields of application such as social and biomedical sciences, epidemiology and public health.
Journal: Journal of Applied Statistics
Pages: 1781-1798
Issue: 10
Volume: 45
Year: 2018
Month: 7
X-DOI: 10.1080/02664763.2017.1391184
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1391184
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:10:p:1781-1798




Template-Type: ReDIF-Article 1.0
Author-Name: Shahedul A. Khan
Author-X-Name-First: Shahedul A.
Author-X-Name-Last: Khan
Author-Name: Setu C. Kar
Author-X-Name-First: Setu C.
Author-X-Name-Last: Kar
Title: Generalized bent-cable methodology for changepoint data: a Bayesian approach
Abstract: 
 The choice of the model framework in a regression setting depends on the nature of the data. The focus of this study is on changepoint data, exhibiting three phases: incoming and outgoing, both of which are linear, joined by a curved transition. Bent-cable regression is an appealing statistical tool to characterize such trajectories, quantifying the nature of the transition between the two linear phases by modeling the transition as a quadratic phase with unknown width. We demonstrate that a quadratic function may not be appropriate to adequately describe many changepoint data. We then propose a generalization of the bent-cable model by relaxing the assumption of the quadratic bend. The properties of the generalized model are discussed and a Bayesian approach for inference is proposed. The generalized model is demonstrated with applications to three data sets taken from environmental science and economics. We also consider a comparison among the quadratic bent-cable, generalized bent-cable and piecewise linear models in terms of goodness of fit in analyzing both real-world and simulated data. This study suggests that the proposed generalization of the bent-cable model can be valuable in adequately describing changepoint data that exhibit either an abrupt or gradual transition over time.
Journal: Journal of Applied Statistics
Pages: 1799-1812
Issue: 10
Volume: 45
Year: 2018
Month: 7
X-DOI: 10.1080/02664763.2017.1391754
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1391754
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:10:p:1799-1812




Template-Type: ReDIF-Article 1.0
Author-Name: Airlane P. Alencar
Author-X-Name-First: Airlane P.
Author-X-Name-Last: Alencar
Title: Seasonality of hospitalizations due to respiratory diseases: modelling serial correlation all we need is Poisson
Abstract: 
 The identification of seasonality and trend patterns of the weekly number of hospitalizations may be useful to plan the structure of health care and the vaccination calendar. A generalized additive model with the negative binomial distribution and a generalized additive model with autoregressive terms (GAMAR) and Poisson distribution are fitted including seasonal parameters and nonlinear trend using splines. The GAMAR includes autoregressive terms to take into account the serial correlation, yielding correct standard errors and reducing overdispersion. For the number of hospitalizations of people older than 60 years due to respiratory diseases in São Paulo city, both models present similar estimates but the Poisson-GAMAR presents uncorrelated residuals, no overdispersion and provides smaller confidence intervals for the weekly percentage changes. Forecasts for the next year based on both models are obtained by simulation and the Poisson-GAMAR presented better performance.
Journal: Journal of Applied Statistics
Pages: 1813-1822
Issue: 10
Volume: 45
Year: 2018
Month: 7
X-DOI: 10.1080/02664763.2017.1396295
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1396295
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:10:p:1813-1822




Template-Type: ReDIF-Article 1.0
Author-Name: M. S. Paez
Author-X-Name-First: M. S.
Author-X-Name-Last: Paez
Author-Name: S. G. Walker
Author-X-Name-First: S. G.
Author-X-Name-Last: Walker
Title: Modeling with a large class of unimodal multivariate distributions
Abstract: 
 In this paper we introduce a new class of multivariate unimodal distributions, motivated by Khintchine's representation for unimodal densities on the real line. We start by introducing a new class of unimodal distributions which can then be naturally extended to higher dimensions, using the multivariate Gaussian copula. Under both univariate and multivariate settings, we provide MCMC algorithms to perform inference about the model parameters and predictive densities. The methodology is illustrated with univariate and bivariate examples, and with variables taken from a real data set.
Journal: Journal of Applied Statistics
Pages: 1823-1845
Issue: 10
Volume: 45
Year: 2018
Month: 7
X-DOI: 10.1080/02664763.2017.1396296
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1396296
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:10:p:1823-1845




Template-Type: ReDIF-Article 1.0
Author-Name: D. S. Gonçalves
Author-X-Name-First: D. S.
Author-X-Name-Last: Gonçalves
Author-Name: C. L. N. Azevedo
Author-X-Name-First: C. L. N.
Author-X-Name-Last: Azevedo
Author-Name: C. Lavor
Author-X-Name-First: C.
Author-X-Name-Last: Lavor
Author-Name: M. A. Gomes-Ruggiero
Author-X-Name-First: M. A.
Author-X-Name-Last: Gomes-Ruggiero
Title: Bayesian inference for quantum state tomography
Abstract: 
 We present a Bayesian approach to the problem of estimating density matrices in quantum state tomography. A general framework is presented based on a suitable mathematical formulation, where a study of the convergence of the Monte Carlo Markov Chain algorithm is given, including a comparison with other estimation methods, such as maximum likelihood estimation and linear inversion. This analysis indicates that our approach not only recovers the underlying parameters quite properly, but also produces physically acceptable punctual and interval estimates. A prior sensitive study was conducted indicating that when useful prior information is available and incorporated, more accurate results are obtained. This general framework, which is based on a reparameterization of the model, allows an easier choice of the prior and proposal distributions for the Metropolis–Hastings algorithm.
Journal: Journal of Applied Statistics
Pages: 1846-1871
Issue: 10
Volume: 45
Year: 2018
Month: 7
X-DOI: 10.1080/02664763.2017.1401049
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1401049
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:10:p:1846-1871




Template-Type: ReDIF-Article 1.0
Author-Name: Mansi Ghodsi
Author-X-Name-First: Mansi
Author-X-Name-Last: Ghodsi
Author-Name: Hossein Hassani
Author-X-Name-First: Hossein
Author-X-Name-Last: Hassani
Author-Name: Donya Rahmani
Author-X-Name-First: Donya
Author-X-Name-Last: Rahmani
Author-Name: Emmanuel Sirimal Silva
Author-X-Name-First: Emmanuel Sirimal
Author-X-Name-Last: Silva
Title: Vector and recurrent singular spectrum analysis: which is better at forecasting?
Abstract: 
 Singular spectrum analysis (SSA) is an increasingly popular and widely adopted filtering and forecasting technique which is currently exploited in a variety of fields. Given its increasing application and superior performance in comparison to other methods, it is pertinent to study and distinguish between the two forecasting variations of SSA. These are referred to as Vector SSA (SSA-V) and Recurrent SSA (SSA-R). The general notion is that SSA-V is more robust and provides better forecasts than SSA-R. This is especially true when faced with time series which are non-stationary and asymmetric, or affected by unit root problems, outliers or structural breaks. However, currently there exists no empirical evidence for proving the above notions or suggesting that SSA-V is better than SSA-R. In this paper, we evaluate out-of-sample forecasting capabilities of the optimised SSA-V and SSA-R forecasting algorithms via a simulation study and an application to 100 real data sets with varying structures, to provide a statistically reliable answer to the question of which SSA algorithm is best for forecasting at both short and long run horizons based on several important criteria.
Journal: Journal of Applied Statistics
Pages: 1872-1899
Issue: 10
Volume: 45
Year: 2018
Month: 7
X-DOI: 10.1080/02664763.2017.1401050
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1401050
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:10:p:1872-1899




Template-Type: ReDIF-Article 1.0
Author-Name: Jiang Du
Author-X-Name-First: Jiang
Author-X-Name-Last: Du
Author-Name: Xiuping Chen
Author-X-Name-First: Xiuping
Author-X-Name-Last: Chen
Author-Name: Eddy Kwessi
Author-X-Name-First: Eddy
Author-X-Name-Last: Kwessi
Author-Name: Zhimeng Sun
Author-X-Name-First: Zhimeng
Author-X-Name-Last: Sun
Title: Model averaging based on rank
Abstract: 
 In this paper, we investigate model selection and model averaging based on rank regression. Under mild conditions, we propose a focused information criterion and a frequentist model averaging estimator for the focused parameters in rank regression model. Compared to the least squares method, the new method is not only highly efficient but also robust. The large sample properties of the proposed procedure are established. The finite sample properties are investigated via extensive Monte Claro simulation study. Finally, we use the Boston Housing Price Dataset to illustrate the use of the proposed rank methods.
Journal: Journal of Applied Statistics
Pages: 1900-1919
Issue: 10
Volume: 45
Year: 2018
Month: 7
X-DOI: 10.1080/02664763.2017.1401051
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1401051
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:10:p:1900-1919




Template-Type: ReDIF-Article 1.0
Author-Name: Qi Zhou
Author-X-Name-First: Qi
Author-X-Name-Last: Zhou
Author-Name: Yoo-Mi Chin
Author-X-Name-First: Yoo-Mi
Author-X-Name-Last: Chin
Author-Name: James D. Stamey
Author-X-Name-First: James D.
Author-X-Name-Last: Stamey
Author-Name: Joon Jin Song
Author-X-Name-First: Joon Jin
Author-X-Name-Last: Song
Title: Bayesian misclassification and propensity score methods for clustered observational studies
Abstract: 
 Bayesian propensity score regression analysis with misclassified binary responses is proposed to analyse clustered observational data. This approach utilizes multilevel models and corrects for misclassification in the responses. Using the deviance information criterion (DIC), the performance of the approach is compared with approaches without correcting for misclassification, multilevel structure specification, or both in the study of the impact of female employment on the likelihood of physical violence. The smallest DIC confirms that our proposed model best fits the data. We conclude that female employment has an insignificant impact on the likelihood of physical spousal violence towards women. In addition, a simulation study confirms that the proposed approach performed best in terms of bias and coverage rate. Ignoring misclassification in response or multilevel structure of data would yield biased estimation of the exposure effect.
Journal: Journal of Applied Statistics
Pages: 1547-1560
Issue: 9
Volume: 45
Year: 2018
Month: 7
X-DOI: 10.1080/02664763.2017.1380786
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1380786
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:9:p:1547-1560




Template-Type: ReDIF-Article 1.0
Author-Name: Gunther Schauberger
Author-X-Name-First: Gunther
Author-X-Name-Last: Schauberger
Author-Name: Andreas Groll
Author-X-Name-First: Andreas
Author-X-Name-Last: Groll
Author-Name: Gerhard Tutz
Author-X-Name-First: Gerhard
Author-X-Name-Last: Tutz
Title: Analysis of the importance of on-field covariates in the German Bundesliga
Abstract: 
 In modern football, various variables as, for example, the distance a team runs or its percentage of ball possession, are collected throughout a match. However, there is a lack of methods to make use of these on-field variables simultaneously and to connect them with the final result of the match. This paper considers data from the German Bundesliga season 2015/2016. The objective is to identify the on-field variables that are connected to the sportive success or failure of the single teams. An extended Bradley–Terry model for football matches is proposed that is able to take into account on-field covariates. Penalty terms are used to reduce the complexity of the model and to find clusters of teams with equal covariate effects. The model identifies the running distance to be the on-field covariate that is most strongly connected to the match outcome.
Journal: Journal of Applied Statistics
Pages: 1561-1578
Issue: 9
Volume: 45
Year: 2018
Month: 7
X-DOI: 10.1080/02664763.2017.1383370
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1383370
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:9:p:1561-1578




Template-Type: ReDIF-Article 1.0
Author-Name: Samer A. Kharroubi
Author-X-Name-First: Samer A.
Author-X-Name-Last: Kharroubi
Title: Valuations of EQ-5D health states: could United Kingdom results be used as informative priors for the United States
Abstract: 
 Valuations of health state descriptors such as the generic EuroQol five-dimensional (EQ-5D) or the six-dimensional short form (SF-6D) have been conducted in different countries. There is a scope to make use of the results in one country as informative priors to help with the analysis of a study in another, for this to enable better estimation to be obtained in the new country than analysing its data separately. This article analyses data from 2 EQ-5D valuation studies where, using similar time trade-off protocols, values for 42 common health states were elicited from representative samples of the US and UK general adult populations. We apply a nonparametric Bayesian method to improve the accuracy of predictions of the US population utility function where the UK results were used as informative priors. The results suggest that drawing extra information from the UK data produces a better estimation of the US population utility than analysing its data separately. The implications of these results will be extremely crucial in countries where valuation studies are limited.
Journal: Journal of Applied Statistics
Pages: 1579-1594
Issue: 9
Volume: 45
Year: 2018
Month: 7
X-DOI: 10.1080/02664763.2017.1386770
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1386770
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:9:p:1579-1594




Template-Type: ReDIF-Article 1.0
Author-Name: Luigi Spezia
Author-X-Name-First: Luigi
Author-X-Name-Last: Spezia
Author-Name: Nial Friel
Author-X-Name-First: Nial
Author-X-Name-Last: Friel
Author-Name: Alessandro Gimona
Author-X-Name-First: Alessandro
Author-X-Name-Last: Gimona
Title: Spatial hidden Markov models and species distributions
Abstract: 
 A spatial hidden Markov model (SHMM) is introduced to analyse the distribution of a species on an atlas, taking into account that false observations and false non-detections of the species can occur during the survey, blurring the true map of presence and absence of the species. The reconstruction of the true map is tackled as the restoration of a degraded pixel image, where the true map is an autologistic model, hidden behind the observed map, whose normalizing constant is efficiently computed by simulating an auxiliary map. The distribution of the species is explained under the Bayesian paradigm and Markov chain Monte Carlo (MCMC) algorithms are developed. We are interested in the spatial distribution of the bird species Greywing Francolin in the south of Africa. Many climatic and land-use explanatory variables are also available: they are included in the SHMM and a subset of them is selected by the mutation operators within the MCMC algorithm.
Journal: Journal of Applied Statistics
Pages: 1595-1615
Issue: 9
Volume: 45
Year: 2018
Month: 7
X-DOI: 10.1080/02664763.2017.1386771
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1386771
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:9:p:1595-1615




Template-Type: ReDIF-Article 1.0
Author-Name: Akash Malhotra
Author-X-Name-First: Akash
Author-X-Name-Last: Malhotra
Author-Name: Shailesh Krishna
Author-X-Name-First: Shailesh
Author-X-Name-Last: Krishna
Title: Release velocities and bowler performance in cricket
Abstract: 
 There is a widespread notion in the cricketing world that with increasing pace the performance of a bowler improves. Additionally, many cricket experts believe faster bowlers to be more effective against lower order batters than bowlers who bowl at slower speeds. The present study puts these two ubiquitous notions under test by statistically analysing the differences in performance of bowlers from three subpopulations based on average release velocities. Results from one-way ANOVA (and its modified versions), for international test matches, reveal faster bowlers to be performing better, in terms of Average and Strike-rate, but no significant differences in the Economy rate and Dynamic Bowling rate. Faster bowlers were found to be more effective in taking wickets of lower and middle order batters as compared to bowlers with less pace. However, there was no statistically significant difference in performance of Fast and Fast-Medium bowlers against a top-order batter.
Journal: Journal of Applied Statistics
Pages: 1616-1627
Issue: 9
Volume: 45
Year: 2018
Month: 7
X-DOI: 10.1080/02664763.2017.1386772
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1386772
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:9:p:1616-1627




Template-Type: ReDIF-Article 1.0
Author-Name: Kelley M. Kidwell
Author-X-Name-First: Kelley M.
Author-X-Name-Last: Kidwell
Author-Name: Nicholas J. Seewald
Author-X-Name-First: Nicholas J.
Author-X-Name-Last: Seewald
Author-Name: Qui Tran
Author-X-Name-First: Qui
Author-X-Name-Last: Tran
Author-Name: Connie Kasari
Author-X-Name-First: Connie
Author-X-Name-Last: Kasari
Author-Name: Daniel Almirall
Author-X-Name-First: Daniel
Author-X-Name-Last: Almirall
Title: Design and analysis considerations for comparing dynamic treatment regimens with binary outcomes from sequential multiple assignment randomized trials
Abstract: 
 In behavioral, educational and medical practice, interventions are often personalized over time using strategies that are based on individual behaviors and characteristics and changes in symptoms, severity, or adherence that are a result of one's treatment. Such strategies that more closely mimic real practice, are known as dynamic treatment regimens (DTRs). A sequential multiple assignment randomized trial (SMART) is a multi-stage trial design that can be used to construct effective DTRs. This article reviews a simple to use ‘weighted and replicated’ estimation technique for comparing DTRs embedded in a SMART design using logistic regression for a binary, end-of-study outcome variable. Based on a Wald test that compares two embedded DTRs of interest from the ‘weighted and replicated’ regression model, a sample size calculation is presented with a corresponding user-friendly applet to aid in the process of designing a SMART. The analytic models and sample size calculations are presented for three of the more commonly used two-stage SMART designs. Simulations for the sample size calculation show the empirical power reaches expected levels. A data analysis example with corresponding code is presented in the appendix using data from a SMART developing an effective DTR in autism.
Journal: Journal of Applied Statistics
Pages: 1628-1651
Issue: 9
Volume: 45
Year: 2018
Month: 7
X-DOI: 10.1080/02664763.2017.1386773
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1386773
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:9:p:1628-1651




Template-Type: ReDIF-Article 1.0
Author-Name: Yajie Zou
Author-X-Name-First: Yajie
Author-X-Name-Last: Zou
Author-Name: John E. Ash
Author-X-Name-First: John E.
Author-X-Name-Last: Ash
Author-Name: Byung-Jung Park
Author-X-Name-First: Byung-Jung
Author-X-Name-Last: Park
Author-Name: Dominique Lord
Author-X-Name-First: Dominique
Author-X-Name-Last: Lord
Author-Name: Lingtao Wu
Author-X-Name-First: Lingtao
Author-X-Name-Last: Wu
Title: Empirical Bayes estimates of finite mixture of negative binomial regression models and its application to highway safety
Abstract: 
 The empirical Bayes (EB) method is commonly used by transportation safety analysts for conducting different types of safety analyses, such as before–after studies and hotspot analyses. To date, most implementations of the EB method have been applied using a negative binomial (NB) model, as it can easily accommodate the overdispersion commonly observed in crash data. Recent studies have shown that a generalized finite mixture of NB models with K mixture components (GFMNB-K) can also be used to model crash data subjected to overdispersion and generally offers better statistical performance than the traditional NB model. So far, nobody has developed how the EB method could be used with finite mixtures of NB models. The main objective of this study is therefore to use a GFMNB-K model in the calculation of EB estimates. Specifically, GFMNB-K models with varying weight parameters are developed to analyze crash data from Indiana and Texas. The main finding shows that the rankings produced by the NB and GFMNB-2 models for hotspot identification are often quite different, and this was especially noticeable with the Texas dataset. Finally, a simulation study designed to examine which model formulation can better identify the hotspot is recommended as our future research.
Journal: Journal of Applied Statistics
Pages: 1652-1669
Issue: 9
Volume: 45
Year: 2018
Month: 7
X-DOI: 10.1080/02664763.2017.1389863
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1389863
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:9:p:1652-1669




Template-Type: ReDIF-Article 1.0
Author-Name: Joanna Morais
Author-X-Name-First: Joanna
Author-X-Name-Last: Morais
Author-Name: Christine Thomas-Agnan
Author-X-Name-First: Christine
Author-X-Name-Last: Thomas-Agnan
Author-Name: Michel Simioni
Author-X-Name-First: Michel
Author-X-Name-Last: Simioni
Title: Using compositional and Dirichlet models for market share regression
Abstract: 
 When the aim is to model market shares, the marketing literature proposes some regression models which can be qualified as attraction models. They are generally derived from an aggregated version of the multinomial logit model. But aggregated multinomial logit models (MNL) and the so-called generalized multiplicative competitive interaction models (GMCI) present some limitations: in their simpler version they do not specify brand-specific and cross effect parameters. In this paper, we consider alternative models: the Dirichlet model (DIR) and the compositional model (CODA). DIR allows to introduce brand-specific parameters and CODA allows additionally to consider cross effect parameters. We show that these two models can be written in a similar fashion, called attraction form, as the MNL and the GMCI models. As market share models are usually interpreted in terms of elasticities, we also use this notion to interpret the DIR and CODA models. We compare the properties of the models in order to explain why CODA and DIR models can outperform traditional market share models. An application to the automobile market is presented where we model brands market shares as a function of media investments, controlling for the brands price and scrapping incentive. We compare the quality of the models using measures adapted to shares.
Journal: Journal of Applied Statistics
Pages: 1670-1689
Issue: 9
Volume: 45
Year: 2018
Month: 7
X-DOI: 10.1080/02664763.2017.1389864
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1389864
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:9:p:1670-1689




Template-Type: ReDIF-Article 1.0
Author-Name: Bogumił Kamiński
Author-X-Name-First: Bogumił
Author-X-Name-Last: Kamiński
Author-Name: Przemysław Szufel
Author-X-Name-First: Przemysław
Author-X-Name-Last: Szufel
Title: On parallel policies for ranking and selection problems
Abstract: 
 In this paper we develop and test experimental methodologies for selection of the best alternative among a discrete number of available treatments. We consider a scenario where a researcher sequentially decides which treatments are assigned to experimental units. This problem is particularly challenging if a single measurement of the response to a treatment is time-consuming and there is a limited time for experimentation. This time can be decreased if it is possible to perform measurements in parallel. In this work we propose and discuss asynchronous extensions of two well-known Ranking &amp; Selection policies, namely, Optimal Computing Budget Allocation (OCBA) and Knowledge Gradient (KG) policy. Our extensions (Asynchronous Optimal Computing Budget Allocation (AOCBA) and Asynchronous Knowledge Gradient (AKG), respectively) allow for parallel asynchronous allocation of measurements. Additionally, since the standard KG method is sequential (it can only allocate one experiment at a time) we propose a parallel synchronous extension of KG policy – Synchronous Knowledge Gradient (SKG). Computer simulations of our algorithms indicate that our parallel KG-based policies (AKG, SKG) outperform the standard OCBA method as well as AOCBA, if the number of evaluated alternatives is small or the computing/experimental budget is limited. For experimentations with large budgets and big sets of alternatives, both the OCBA and AOCBA policies are more efficient.
Journal: Journal of Applied Statistics
Pages: 1690-1713
Issue: 9
Volume: 45
Year: 2018
Month: 7
X-DOI: 10.1080/02664763.2017.1390555
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1390555
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:9:p:1690-1713




Template-Type: ReDIF-Article 1.0
Author-Name: W. Tang
Author-X-Name-First: W.
Author-X-Name-Last: Tang
Author-Name: H. He
Author-X-Name-First: H.
Author-X-Name-Last: He
Author-Name: W.J. Wang
Author-X-Name-First: W.J.
Author-X-Name-Last: Wang
Author-Name: D.G. Chen
Author-X-Name-First: D.G.
Author-X-Name-Last: Chen
Title: Untangle the structural and random zeros in statistical modelings
Abstract: 
 Count data with structural zeros are common in public health applications. There are considerable researches focusing on zero-inflated models such as zero-inflated Poisson (ZIP) and zero-inflated Negative Binomial (ZINB) models for such zero-inflated count data when used as response variable. However, when such variables are used as predictors, the difference between structural and random zeros is often ignored and may result in biased estimates. One remedy is to include an indicator of the structural zero in the model as a predictor if observed. However, structural zeros are often not observed in practice, in which case no statistical method is available to address the bias issue. This paper is aimed to fill this methodological gap by developing parametric methods to model zero-inflated count data when used as predictors based on the maximum likelihood approach. The response variable can be any type of data including continuous, binary, count or even zero-inflated count responses. Simulation studies are performed to assess the numerical performance of this new approach when sample size is small to moderate. A real data example is also used to demonstrate the application of this method.
Journal: Journal of Applied Statistics
Pages: 1714-1733
Issue: 9
Volume: 45
Year: 2018
Month: 7
X-DOI: 10.1080/02664763.2017.1391180
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1391180
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:9:p:1714-1733




Template-Type: ReDIF-Article 1.0
Author-Name: D. Kurz
Author-X-Name-First: D.
Author-X-Name-Last: Kurz
Author-Name: H. Lewitschnig
Author-X-Name-First: H.
Author-X-Name-Last: Lewitschnig
Author-Name: J. Pilz
Author-X-Name-First: J.
Author-X-Name-Last: Pilz
Title: Failure probability estimation under additional subsystem information with application to semiconductor burn-in
Abstract: 
 In the classical approach to qualitative reliability demonstration, system failure probabilities are estimated based on a binomial sample drawn from the running production. In this paper, we show how to take account of additional available sampling information for some or even all subsystems of a current system under test with serial reliability structure. In that connection, we present two approaches, a frequentist and a Bayesian one, for assessing an upper bound for the failure probability of serial systems under binomial subsystem data. In the frequentist approach, we introduce (i) a new way of deriving the probability distribution for the number of system failures, which might be randomly assembled from the failed subsystems and (ii) a more accurate estimator for the Clopper–Pearson upper bound using a beta mixture distribution. In the Bayesian approach, however, we infer the posterior distribution for the system failure probability on the basis of the system/subsystem testing results and a prior distribution for the subsystem failure probabilities. We propose three different prior distributions and compare their performances in the context of high reliability testing. Finally, we apply the proposed methods to reduce the efforts of semiconductor burn-in studies by considering synergies such as comparable chip layers, among different chip technologies.
Journal: Journal of Applied Statistics
Pages: 955-967
Issue: 6
Volume: 44
Year: 2017
Month: 4
X-DOI: 10.1080/02664763.2016.1189522
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1189522
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:6:p:955-967




Template-Type: ReDIF-Article 1.0
Author-Name: Yunlu Jiang
Author-X-Name-First: Yunlu
Author-X-Name-Last: Jiang
Title: S-estimator in partially linear regression models
Abstract: 
 In this paper, a robust estimator is proposed for partially linear regression models. We first estimate the nonparametric component using the penalized regression spline, then we construct an estimator of parametric component by using robust S-estimator. We propose an iterative algorithm to solve the proposed optimization problem, and introduce a robust generalized cross-validation to select the penalized parameter. Simulation studies and a real data analysis illustrate that the our proposed method is robust against outliers in the dataset or errors with heavy tails.
Journal: Journal of Applied Statistics
Pages: 968-977
Issue: 6
Volume: 44
Year: 2017
Month: 4
X-DOI: 10.1080/02664763.2016.1189523
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1189523
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:6:p:968-977




Template-Type: ReDIF-Article 1.0
Author-Name: Wen-Liang Hung
Author-X-Name-First: Wen-Liang
Author-X-Name-Last: Hung
Author-Name: Shou-Jen Chang-Chien
Author-X-Name-First: Shou-Jen
Author-X-Name-Last: Chang-Chien
Title: Learning-based EM algorithm for normal-inverse Gaussian mixture model with application to extrasolar planets
Abstract: 
 Karlis and Santourian [14] proposed a model-based clustering algorithm, the expectation–maximization (EM) algorithm, to fit the mixture of multivariate normal-inverse Gaussian (NIG) distribution. However, the EM algorithm for the mixture of multivariate NIG requires a set of initial values to begin the iterative process, and the number of components has to be given a priori. In this paper, we present a learning-based EM algorithm: its aim is to overcome the aforementioned weaknesses of Karlis and Santourian's EM algorithm [14]. The proposed learning-based EM algorithm was first inspired by Yang et al. [24]: the process of how they perform self-clustering was then simulated. Numerical experiments showed promising results compared to Karlis and Santourian's EM algorithm. Moreover, the methodology is applicable to the analysis of extrasolar planets. Our analysis provides an understanding of the clustering results in the ln P−ln M and ln P−e spaces, where M is the planetary mass, P is the orbital period and e is orbital eccentricity. Our identified groups interpret two phenomena: (1) the characteristics of two clusters in ln P−ln M space might be related to the tidal and disc interactions (see [9]); and (2) there are two clusters in ln P−e space.
Journal: Journal of Applied Statistics
Pages: 978-999
Issue: 6
Volume: 44
Year: 2017
Month: 4
X-DOI: 10.1080/02664763.2016.1190322
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1190322
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:6:p:978-999




Template-Type: ReDIF-Article 1.0
Author-Name: M. P. Gadre
Author-X-Name-First: M. P.
Author-X-Name-Last: Gadre
Author-Name: S. B. Adnaik
Author-X-Name-First: S. B.
Author-X-Name-Last: Adnaik
Author-Name: R.N. Rattihalli
Author-X-Name-First: R.N.
Author-X-Name-Last: Rattihalli
Title: Continuous single attribute control chart for Markov-dependent processes
Abstract: 
 Most of the times, the observations related to the quality characteristic of a process do not need to be independent. In such cases, control charts based on the assumption of independence of the observations are not appropriate. When the characteristic under study is qualitative, Markov model serves as a simple model to account for the dependency of the observations. For this purpose, we develop an attribute control chart under 100% inspection for a Markov dependent process by controlling the error probabilities. This chart consists of two sub-charts. For a given sample, depending upon the state of the last observation of previous sample (if any), one of these two will be used. Optimal values of the design parameters of the control chart are obtained. Chart’s performance is studied by using its capability (probability) of detecting a shift in process parameters.
Journal: Journal of Applied Statistics
Pages: 1000-1012
Issue: 6
Volume: 44
Year: 2017
Month: 4
X-DOI: 10.1080/02664763.2016.1191621
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1191621
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:6:p:1000-1012




Template-Type: ReDIF-Article 1.0
Author-Name: S. Wang
Author-X-Name-First: S.
Author-X-Name-Last: Wang
Author-Name: N. G. Cadigan
Author-X-Name-First: N. G.
Author-X-Name-Last: Cadigan
Author-Name: H. P. Benoît
Author-X-Name-First: H. P.
Author-X-Name-Last: Benoît
Title: Inference about regression parameters using highly stratified survey count data with over-dispersion and repeated measurements
Abstract: 
 We study methods to estimate regression and variance parameters for over-dispersed and correlated count data from highly stratified surveys. Our application involves counts of fish catches from stratified research surveys and we propose a novel model in fisheries science to address changes in survey protocols. A challenge with this model is the large number of nuisance parameters which leads to computational issues and biased statistical inferences. We use a computationally efficient profile generalized estimating equation method and compare it to marginal maximum likelihood (MLE) and restricted MLE (REML) methods. We use REML to address bias and inaccurate confidence intervals because of many nuisance parameters. The marginal MLE and REML approaches involve intractable integrals and we used a new R package that is designed for estimating complex nonlinear models that may include random effects. We conclude from simulation analyses that the REML method provides more reliable statistical inferences among the three methods we investigated.
Journal: Journal of Applied Statistics
Pages: 1013-1030
Issue: 6
Volume: 44
Year: 2017
Month: 4
X-DOI: 10.1080/02664763.2016.1191622
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1191622
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:6:p:1013-1030




Template-Type: ReDIF-Article 1.0
Author-Name: I. A. R. de Lara
Author-X-Name-First: I. A. R.
Author-X-Name-Last: de Lara
Author-Name: J. P. Hinde
Author-X-Name-First: J. P.
Author-X-Name-Last: Hinde
Author-Name: A. C. de Castro
Author-X-Name-First: A. C.
Author-X-Name-Last: de Castro
Author-Name: I. J. O. da Silva
Author-X-Name-First: I. J. O.
Author-X-Name-Last: da Silva
Title: A proportional odds transition model for ordinal responses with an application to pig behaviour
Abstract: 
 Categorical data are quite common in many fields of science including in behaviour studies in animal science. In this article, the data concern the degree of lesions in pigs, related to the behaviour of these animals. The experimental design corresponded to two levels of environmental enrichment and four levels of genetic lineages in a completely randomized  $ 2 \times 4 $ 2×4 factorial with data collected longitudinally over four time occasions. The transition models used for the data analysis are based on stochastic processes and Generalized Linear Models. In general, these are not used for analysis of longitudinal data but they are useful in many situations as in this study. We present some aspects of this class of models for the stationary case. The proportional odds transition model is used to construct the matrix of transition probabilities and a function was developed in the R system to fit this model. The likelihood ratio test was used to verify the assumption of odds ratio proportionality and to select the structure of the linear predictor. The methodology used allowed for the choice of a model that can be used to explain the relationship between the severity of lesions in pigs and the use of the environmental enrichment.
Journal: Journal of Applied Statistics
Pages: 1031-1046
Issue: 6
Volume: 44
Year: 2017
Month: 4
X-DOI: 10.1080/02664763.2016.1191623
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1191623
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:6:p:1031-1046




Template-Type: ReDIF-Article 1.0
Author-Name: I. R. C. Oliveira
Author-X-Name-First: I. R. C.
Author-X-Name-Last: Oliveira
Author-Name: G. Molenberghs
Author-X-Name-First: G.
Author-X-Name-Last: Molenberghs
Author-Name: G. Verbeke
Author-X-Name-First: G.
Author-X-Name-Last: Verbeke
Author-Name: C. G. B. Demétrio
Author-X-Name-First: C. G. B.
Author-X-Name-Last: Demétrio
Author-Name: C. T. S. Dias
Author-X-Name-First: C. T. S.
Author-X-Name-Last: Dias
Title: Negative variance components for non-negative hierarchical data with correlation, over-, and/or underdispersion
Abstract: 
 The concept of negative variance components in linear mixed-effects models, while confusing at first sight, has received considerable attention in the literature, for well over half a century, following the early work of Chernoff [7] and Nelder [21]. Broadly, negative variance components in linear mixed models are allowable if inferences are restricted to the implied marginal model. When a hierarchical view-point is adopted, in the sense that outcomes are specified conditionally upon random effects, the variance–covariance matrix of the random effects must be positive-definite (positive-semi-definite is also possible, but raises issues of degenerate distributions). Many contemporary software packages allow for this distinction. Less work has been done for generalized linear mixed models. Here, we study such models, with extension to allow for overdispersion, for non-negative outcomes (counts). Using a study of trichomes counts on tomato plants, it is illustrated how such negative variance components play a natural role in modeling both the correlation between repeated measures on the same experimental unit and over- or underdispersion.
Journal: Journal of Applied Statistics
Pages: 1047-1063
Issue: 6
Volume: 44
Year: 2017
Month: 4
X-DOI: 10.1080/02664763.2016.1191624
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1191624
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:6:p:1047-1063




Template-Type: ReDIF-Article 1.0
Author-Name: C. E. Pertsinidou
Author-X-Name-First: C. E.
Author-X-Name-Last: Pertsinidou
Author-Name: G. Tsaklidis
Author-X-Name-First: G.
Author-X-Name-Last: Tsaklidis
Author-Name: E. Papadimitriou
Author-X-Name-First: E.
Author-X-Name-Last: Papadimitriou
Author-Name: N. Limnios
Author-X-Name-First: N.
Author-X-Name-Last: Limnios
Title: Application of hidden semi-Markov models for the seismic hazard assessment of the North and South Aegean Sea, Greece
Abstract: 
 The real stress field in an area associated with earthquake generation cannot be directly observed. For that purpose we apply hidden semi-Markov models (HSMMs) for strong  $ (M\ge 5.5) $ (M≥5.5) earthquake occurrence in the areas of North and South Aegean Sea considering that the stress field constitutes the hidden process. The advantage of HSMMs compared to hidden Markov models (HMMs) is that they allow any arbitrary distribution for the sojourn times. Poisson, Logarithmic and Negative Binomial distributions as well as different model dimensions are tested. The parameter estimation is achieved via the EM algorithm. For the decoding procedure, a new Viterbi algorithm with a simple form is applied detecting precursory phases (hidden stress variations) and warning for anticipated earthquake occurrences. The optimal HSMM provides an alarm period for 70 out of 88 events. HMMs are also studied presenting poor results compared to these obtained via HSMMs. Bootstrap standard errors and confidence intervals for the parameters are evaluated and the forecasting ability of the Poisson models is examined.
Journal: Journal of Applied Statistics
Pages: 1064-1085
Issue: 6
Volume: 44
Year: 2017
Month: 4
X-DOI: 10.1080/02664763.2016.1193724
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1193724
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:6:p:1064-1085




Template-Type: ReDIF-Article 1.0
Author-Name: Byron C. Jaeger
Author-X-Name-First: Byron C.
Author-X-Name-Last: Jaeger
Author-Name: Lloyd J. Edwards
Author-X-Name-First: Lloyd J.
Author-X-Name-Last: Edwards
Author-Name: Kalyan Das
Author-X-Name-First: Kalyan
Author-X-Name-Last: Das
Author-Name: Pranab K. Sen
Author-X-Name-First: Pranab K.
Author-X-Name-Last: Sen
Title: An  statistic for fixed effects in the generalized linear mixed model
Abstract: 
 Measuring the proportion of variance explained ( $ R^2 $ R2) by a statistical model and the relative importance of specific predictors (semi-partial  $ R^2 $ R2) can be essential considerations when building a parsimonious statistical model. The  $ R^2 $ R2 statistic is a familiar summary of goodness-of-fit for normal linear models and has been extended in various ways to more general models. In particular, the generalized linear mixed model (GLMM) extends the normal linear model and is used to analyze correlated (hierarchical), non-normal data structures. Although various  $ R^2 $ R2 statistics have been proposed, there is no consensus in statistical literature for the most sensible definition of  $ R^2 $ R2 in this context. This research aims to build upon existing knowledge and definitions of  $ R^2 $ R2 and to concisely define the statistic for the GLMM. Here, we derive a model and semi-partial  $ R^2 $ R2 statistic for fixed (population) effects in the GLMM by utilizing the penalized quasi-likelihood estimation method based on linearization. We show that our proposed  $ R^2 $ R2 statistic generalizes the widely used marginal  $ R^2 $ R2 statistic introduced by Nakagawa and Schielzeth, demonstrate our statistics capability in model selection, show the utility of semi-partial  $ R^2 $ R2 statistics in longitudinal data analysis, and provide software that computes the proposed  $ R^2 $ R2 statistic along with semi-partial  $ R^2 $ R2 for individual fixed effects. The software provided is adapted for both SAS and R programming languages.
Journal: Journal of Applied Statistics
Pages: 1086-1105
Issue: 6
Volume: 44
Year: 2017
Month: 4
X-DOI: 10.1080/02664763.2016.1193725
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1193725
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:6:p:1086-1105




Template-Type: ReDIF-Article 1.0
Author-Name: M. Mahdizadeh
Author-X-Name-First: M.
Author-X-Name-Last: Mahdizadeh
Author-Name: Ehsan Zamanzade
Author-X-Name-First: Ehsan
Author-X-Name-Last: Zamanzade
Title: New goodness of fit tests for the Cauchy distribution
Abstract: 
 Some goodness-of-fit procedures for the Cauchy distribution are presented. The power comparisons indicate that the new tests possess good performances among the competitors, especially against symmetric alternatives. A financial data set is analyzed for illustration.
Journal: Journal of Applied Statistics
Pages: 1106-1121
Issue: 6
Volume: 44
Year: 2017
Month: 4
X-DOI: 10.1080/02664763.2016.1193726
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1193726
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:6:p:1106-1121




Template-Type: ReDIF-Article 1.0
Author-Name: Rui Fang
Author-X-Name-First: Rui
Author-X-Name-Last: Fang
Author-Name: Xiaohu Li
Author-X-Name-First: Xiaohu
Author-X-Name-Last: Li
Title: Nonparametric tests for strictly increasing virtual valuations
Abstract: 
 This paper shows that for absolutely continuous valuation distributions the increasing virtual valuations is equivalent to the increasing odds rate. Based on this new characterization we develop two nonparametric tests for the strictly increasing virtual valuations by using the generalized total time on test transform. The empirical type I error rate and power performance of the two tests are examined through Monte Carlo simulations. As illustrations the two tests are also applied to two real data sets collected from eBay.
Journal: Journal of Applied Statistics
Pages: 1122-1136
Issue: 6
Volume: 44
Year: 2017
Month: 4
X-DOI: 10.1080/02664763.2016.1193727
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1193727
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:6:p:1122-1136




Template-Type: ReDIF-Article 1.0
Author-Name: Anthony C. Atkinson
Author-X-Name-First: Anthony C.
Author-X-Name-Last: Atkinson
Author-Name: Marco Riani
Author-X-Name-First: Marco
Author-X-Name-Last: Riani
Author-Name: Andrea Cerioli
Author-X-Name-First: Andrea
Author-X-Name-Last: Cerioli
Title: Cluster detection and clustering with random start forward searches
Abstract: 
 The forward search is a method of robust data analysis in which outlier free subsets of the data of increasing size are used in model fitting; the data are then ordered by closeness to the model. Here the forward search, with many random starts, is used to cluster multivariate data. These random starts lead to the diagnostic identification of tentative clusters. Application of the forward search to the proposed individual clusters leads to the establishment of cluster membership through the identification of non-cluster members as outlying. The method requires no prior information on the number of clusters and does not seek to classify all observations. These properties are illustrated by the analysis of 200 six-dimensional observations on Swiss banknotes. The importance of linked plots and brushing in elucidating data structures is illustrated. We also provide an automatic method for determining cluster centres and compare the behaviour of our method with model-based clustering. In a simulated example with eight clusters our method provides more stable and accurate solutions than model-based clustering. We consider the computational requirements of both procedures.
Journal: Journal of Applied Statistics
Pages: 777-798
Issue: 5
Volume: 45
Year: 2018
Month: 4
X-DOI: 10.1080/02664763.2017.1310806
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1310806
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:5:p:777-798




Template-Type: ReDIF-Article 1.0
Author-Name: Hyoyoung Choo-Wosoba
Author-X-Name-First: Hyoyoung
Author-X-Name-Last: Choo-Wosoba
Author-Name: Somnath Datta
Author-X-Name-First: Somnath
Author-X-Name-Last: Datta
Title: Analyzing clustered count data with a cluster-specific random effect zero-inflated Conway–Maxwell–Poisson distribution
Abstract: 
 Count data analysis techniques have been developed in biological and medical research areas. In particular, zero-inflated versions of parametric count distributions have been used to model excessive zeros that are often present in these assays. The most common count distributions for analyzing such data are Poisson and negative binomial. However, a Poisson distribution can only handle equidispersed data and a negative binomial distribution can only cope with overdispersion. However, a Conway–Maxwell–Poisson (CMP) distribution [4] can handle a wide range of dispersion. We show, with an illustrative data set on next-generation sequencing of maize hybrids, that both underdispersion and overdispersion can be present in genomic data. Furthermore, the maize data set consists of clustered observations and, therefore, we develop inference procedures for a zero-inflated CMP regression that incorporates a cluster-specific random effect term. Unlike the Gaussian models, the underlying likelihood is computationally challenging. We use a numerical approximation via a Gaussian quadrature to circumvent this issue. A test for checking zero-inflation has also been developed in our setting. Finite sample properties of our estimators and test have been investigated by extensive simulations. Finally, the statistical methodology has been applied to analyze the maize data mentioned before.
Journal: Journal of Applied Statistics
Pages: 799-814
Issue: 5
Volume: 45
Year: 2018
Month: 4
X-DOI: 10.1080/02664763.2017.1312299
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1312299
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:5:p:799-814




Template-Type: ReDIF-Article 1.0
Author-Name: Rahim Alhamzawi
Author-X-Name-First: Rahim
Author-X-Name-Last: Alhamzawi
Author-Name: Haithem Taha Mohammad Ali
Author-X-Name-First: Haithem Taha Mohammad
Author-X-Name-Last: Ali
Title: Bayesian quantile regression for ordinal longitudinal data
Abstract: 
 Since the pioneering work by Koenker and Bassett [27], quantile regression models and its applications have become increasingly popular and important for research in many areas. In this paper, a random effects ordinal quantile regression model is proposed for analysis of longitudinal data with ordinal outcome of interest. An efficient Gibbs sampling algorithm was derived for fitting the model to the data based on a location-scale mixture representation of the skewed double-exponential distribution. The proposed approach is illustrated using simulated data and a real data example. This is the first work to discuss quantile regression for analysis of longitudinal data with ordinal outcome.
Journal: Journal of Applied Statistics
Pages: 815-828
Issue: 5
Volume: 45
Year: 2018
Month: 4
X-DOI: 10.1080/02664763.2017.1315059
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1315059
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:5:p:815-828




Template-Type: ReDIF-Article 1.0
Author-Name: R. J. Waken
Author-X-Name-First: R. J.
Author-X-Name-Last: Waken
Author-Name: Joon Jin Song
Author-X-Name-First: Joon Jin
Author-X-Name-Last: Song
Author-Name: Soohyun Kwon
Author-X-Name-First: Soohyun
Author-X-Name-Last: Kwon
Author-Name: Ki-Hong Min
Author-X-Name-First: Ki-Hong
Author-X-Name-Last: Min
Author-Name: GyuWon Lee
Author-X-Name-First: GyuWon
Author-X-Name-Last: Lee
Title: A flexible and efficient spatial interpolator for radar rainfall estimation
Abstract: 
 A key challenge in rainfall estimation is spatio-temporal variablility. Weather radars are used to estimate precipitation with high spatial and temporal resolution. Due to the inherent errors in radar estimates, spatial interpolation has been often employed to calibrate the estimates. Kriging is a simple and popular spatial interpolation method, but the method has several shortcomings. In particular, the prediction is quite unstable and often fails to be performed when sample size is small. In this paper, we proposed a flexible and efficient spatial interpolator for radar rainfall estimation, with several advantages over kriging. The method is illustrated using a real-world data set.
Journal: Journal of Applied Statistics
Pages: 829-844
Issue: 5
Volume: 45
Year: 2018
Month: 4
X-DOI: 10.1080/02664763.2017.1317723
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1317723
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:5:p:829-844




Template-Type: ReDIF-Article 1.0
Author-Name: Georg Man
Author-X-Name-First: Georg
Author-X-Name-Last: Man
Title: Critical appraisal of jointness concepts in Bayesian model averaging: evidence from life sciences, sociology, and other scientific fields
Abstract: 
 Jointness is a Bayesian approach to capturing dependence among regressors in multivariate data. It addresses the general issue of whether explanatory factors for a given empirical phenomenon are complements or substitutes. I ask a number of questions about existing jointness concepts: Are the patterns revealed stable across datasets? Are results robust to prior choice and do data characteristics affect results? And importantly: What do the answers imply from a practical vista? The present study takes an applied, interdisciplinary and comparative perspective, validating jointness concepts on datasets across scientific fields with focus on life sciences (Parkinson's disease) and sociology. Simulations complement the study of real-world data. My findings suggest that results depend on which jointness concept is used: Some concepts deliver jointness patterns remarkably uniform across datasets, while all concepts are fairly robust to the choice of prior structure. This can be interpreted as critique of jointness from a practical perspective, given that the patterns revealed are at times very different and no concept emerges as overall advantageous. The composite indicators approach to combining information across jointness concepts is also explored, suggesting an avenue to facilitate the application of the concepts in future research.
Journal: Journal of Applied Statistics
Pages: 845-867
Issue: 5
Volume: 45
Year: 2018
Month: 4
X-DOI: 10.1080/02664763.2017.1318839
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1318839
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:5:p:845-867




Template-Type: ReDIF-Article 1.0
Author-Name: Ramón Flores
Author-X-Name-First: Ramón
Author-X-Name-Last: Flores
Author-Name: Rosa Lillo
Author-X-Name-First: Rosa
Author-X-Name-Last: Lillo
Author-Name: Juan Romo
Author-X-Name-First: Juan
Author-X-Name-Last: Romo
Title: Homogeneity test for functional data
Abstract: 
 In the context of functional data analysis, we propose new sample tests for homogeneity. Based on some well-known depth measures, we construct four different statistics in order to measure distance between the two samples. A simulation study is performed to check the efficiency of the tests when confronted with shape and magnitude perturbation. Finally, we apply these tools to measure the homogeneity in some samples of real data, and we obtain good results using this new method.
Journal: Journal of Applied Statistics
Pages: 868-883
Issue: 5
Volume: 45
Year: 2018
Month: 4
X-DOI: 10.1080/02664763.2017.1319470
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1319470
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:5:p:868-883




Template-Type: ReDIF-Article 1.0
Author-Name: Christian Pierdzioch
Author-X-Name-First: Christian
Author-X-Name-Last: Pierdzioch
Author-Name: Monique B. Reid
Author-X-Name-First: Monique B.
Author-X-Name-Last: Reid
Author-Name: Rangan Gupta
Author-X-Name-First: Rangan
Author-X-Name-Last: Gupta
Title: On the directional accuracy of inflation forecasts: evidence from South African survey data
Abstract: 
 We study the information content of South African inflation survey data by determining the directional accuracy of both short-term and long-term forecasts. We use relative operating characteristic (ROC) curves, which have been applied in a variety of fields including weather forecasting and radiology, to ascertain the directional accuracy of the forecasts. A ROC curve summarizes the directional accuracy of forecasts by comparing the rate of true signals (sensitivity) with the rate of false signals (one minus specifity). A ROC curve goes beyond market-timing tests widely studied in earlier research as this comparison is carried out for many alternative values of a decision criterion that discriminates between signals (of a rising inflation rate) and nonsignals (of an unchanged or a falling inflation rate). We find consistent evidence that forecasts contain information with respect to the subsequent direction of change of the inflation rate.
Journal: Journal of Applied Statistics
Pages: 884-900
Issue: 5
Volume: 45
Year: 2018
Month: 4
X-DOI: 10.1080/02664763.2017.1322556
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1322556
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:5:p:884-900




Template-Type: ReDIF-Article 1.0
Author-Name: Jun Ye
Author-X-Name-First: Jun
Author-X-Name-Last: Ye
Author-Name: Juan Xi
Author-X-Name-First: Juan
Author-X-Name-Last: Xi
Author-Name: Richard L. Einsporn
Author-X-Name-First: Richard L.
Author-X-Name-Last: Einsporn
Title: Functional principal component analysis in age–period–cohort analysis of body mass index data by gender and ethnicity
Abstract: 
 In this paper, we propose a two-stage functional principal component analysis method in age–period–cohort (APC) analysis. The first stage of the method considers the age–period effect with the fitted values treated as an offset; and the second stage of the method considers the residual age–cohort effect conditional on the already estimated age-period effect. An APC version of the model in functional data analysis provides an improved fit to the data, especially when the data are sparse and irregularly spaced. We demonstrate the effectiveness of the proposed method using body mass index data stratified by gender and ethnicity.
Journal: Journal of Applied Statistics
Pages: 901-917
Issue: 5
Volume: 45
Year: 2018
Month: 4
X-DOI: 10.1080/02664763.2017.1322557
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1322557
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:5:p:901-917




Template-Type: ReDIF-Article 1.0
Author-Name: Harvey Goldstein
Author-X-Name-First: Harvey
Author-X-Name-Last: Goldstein
Author-Name: William J. Browne
Author-X-Name-First: William J.
Author-X-Name-Last: Browne
Author-Name: Christopher Charlton
Author-X-Name-First: Christopher
Author-X-Name-Last: Charlton
Title: A Bayesian model for measurement and misclassification errors alongside missing data, with an application to higher education participation in Australia
Abstract: 
 In this paper we consider the impact of both missing data and measurement errors on a longitudinal analysis of participation in higher education in Australia. We develop a general method for handling both discrete and continuous measurement errors that also allows for the incorporation of missing values and random effects in both binary and continuous response multilevel models. Measurement errors are allowed to be mutually dependent and their distribution may depend on further covariates. We show that our methodology works via two simple simulation studies. We then consider the impact of our measurement error assumptions on the analysis of the real data set.
Journal: Journal of Applied Statistics
Pages: 918-931
Issue: 5
Volume: 45
Year: 2018
Month: 4
X-DOI: 10.1080/02664763.2017.1322558
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1322558
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:5:p:918-931




Template-Type: ReDIF-Article 1.0
Author-Name: Mário F. Desousa
Author-X-Name-First: Mário F.
Author-X-Name-Last: Desousa
Author-Name: Helton Saulo
Author-X-Name-First: Helton
Author-X-Name-Last: Saulo
Author-Name: Víctor Leiva
Author-X-Name-First: Víctor
Author-X-Name-Last: Leiva
Author-Name: Paulo Scalco
Author-X-Name-First: Paulo
Author-X-Name-Last: Scalco
Title: On a tobit–Birnbaum–Saunders model with an application to medical data
Abstract: 
 The tobit model allows a censored response variable to be described by covariates. Its applications cover different areas such as economics, engineering, environment and medicine. A strong assumption of the standard tobit model is that its errors follow a normal distribution. However, not all applications are well modeled by this distribution. Some efforts have relaxed the normality assumption by considering more flexible distributions. Nevertheless, the presence of asymmetry could not be well described by these flexible distributions. A real-world data application of measles vaccine in Haiti is explored, which confirms this asymmetry. We propose a tobit model with errors following a Birnbaum–Saunders (BS) distribution, which is asymmetrical and has shown to be a good alternative for describing medical data. Inference based on the maximum likelihood method and a type of residual are derived for the tobit–BS model. We perform global and local influence diagnostics to assess the sensitivity of the maximum likelihood estimators to atypical cases. A Monte Carlo simulation study is carried out to empirically evaluate the performance of these estimators. We conduct a data analysis for the mentioned application of measles vaccine based on the proposed model with the help of the R software. The results show the good performance of the tobit–BS model.
Journal: Journal of Applied Statistics
Pages: 932-955
Issue: 5
Volume: 45
Year: 2018
Month: 4
X-DOI: 10.1080/02664763.2017.1322559
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1322559
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:5:p:932-955




Template-Type: ReDIF-Article 1.0
Author-Name: Wei-Ya Wu
Author-X-Name-First: Wei-Ya
Author-X-Name-Last: Wu
Author-Name: Wei-Hwa Wu
Author-X-Name-First: Wei-Hwa
Author-X-Name-Last: Wu
Author-Name: Hsin-Neng Hsieh
Author-X-Name-First: Hsin-Neng
Author-X-Name-Last: Hsieh
Author-Name: Meng-Chih Lee
Author-X-Name-First: Meng-Chih
Author-X-Name-Last: Lee
Title: The generalized inference on the sign testing problem about the normal variances
Abstract: 
 For the sign testing problem about the normal variances, we develop the heuristic testing procedure based on the concept of generalized test variable and generalized p-value. A detailed simulation study is conducted to empirically investigate the performance of the proposed method. Through the simulation study, especially in small sample sizes, the proposed test not only adequately controls empirical size at the nominal level, but also uniformly more powerful than likelihood ratio test, Gutmann's test, Li and Sinha's test and Liu and Chan's test, showing that the proposed method can be recommended in practice. The proposed method is illustrated with the published data.
Journal: Journal of Applied Statistics
Pages: 956-970
Issue: 5
Volume: 45
Year: 2018
Month: 4
X-DOI: 10.1080/02664763.2017.1325857
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1325857
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:5:p:956-970




Template-Type: ReDIF-Article 1.0
Author-Name: Luisa Rivas
Author-X-Name-First: Luisa
Author-X-Name-Last: Rivas
Author-Name: Manuel Galea
Author-X-Name-First: Manuel
Author-X-Name-Last: Galea
Title: Influence analysis for the generalized Waring regression model
Abstract: 
 In this paper, we consider a regression model under the generalized Waring distribution for modeling count data. We develop and implement local influence diagnostic techniques based on likelihood displacement. Also we develop case-deletion methods. The generalized Waring regression model is presented as a mixture of the Negative Binomial and the Beta II distributions, and it is compared to the Negative Binomial and Waring regression models. Estimation is performed by maximum likelihood function. The influence measures developed in this paper are applied to a Spanish football league data set. Empirical results show that the generalized Waring regression model performs better when compared to the Negative Binomial and Waring regression models. Technical details are presented in the Appendix.
Journal: Journal of Applied Statistics
Pages: 1-27
Issue: 1
Volume: 47
Year: 2020
Month: 1
X-DOI: 10.1080/02664763.2019.1670148
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1670148
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:1:p:1-27




Template-Type: ReDIF-Article 1.0
Author-Name: Mengmeng Guo
Author-X-Name-First: Mengmeng
Author-X-Name-Last: Guo
Author-Name: Jingyong Su
Author-X-Name-First: Jingyong
Author-X-Name-Last: Su
Author-Name: Li Sun
Author-X-Name-First: Li
Author-X-Name-Last: Sun
Author-Name: Guofeng Cao
Author-X-Name-First: Guofeng
Author-X-Name-Last: Cao
Title: Statistical regression analysis of functional and shape data
Abstract: 
 We develop a multivariate regression model when responses or predictors are on nonlinear manifolds, rather than on Euclidean spaces. The nonlinear constraint makes the problem challenging and needs to be studied carefully. By performing principal component analysis (PCA) on tangent space of manifold, we use principal directions instead in the model. Then, the ordinary regression tools can be utilized. We apply the framework to both shape data (ozone hole contours) and functional data (spectrums of absorbance of meat in Tecator dataset). Specifically, we adopt the square-root velocity function representation and parametrization-invariant metric. Experimental results have shown that we can not only perform powerful regression analysis on the non-Euclidean data but also achieve high prediction accuracy by the constructed model.
Journal: Journal of Applied Statistics
Pages: 28-44
Issue: 1
Volume: 47
Year: 2020
Month: 1
X-DOI: 10.1080/02664763.2019.1669541
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1669541
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:1:p:28-44




Template-Type: ReDIF-Article 1.0
Author-Name: Xiaowen Dai
Author-X-Name-First: Xiaowen
Author-X-Name-Last: Dai
Author-Name: Zhen Yan
Author-X-Name-First: Zhen
Author-X-Name-Last: Yan
Author-Name: Maozai Tian
Author-X-Name-First: Maozai
Author-X-Name-Last: Tian
Author-Name: ManLai Tang
Author-X-Name-First: ManLai
Author-X-Name-Last: Tang
Title: Quantile regression for general spatial panel data models with fixed effects
Abstract: 
 This paper considers the quantile regression model with both individual fixed effect and time period effect for general spatial panel data. Fixed effects quantile regression estimators based on instrumental variable method will be proposed. Asymptotic properties of the proposed estimators will be developed. Simulations are conducted to study the performance of the proposed method. We will illustrate our methodologies using a cigarettes demand data set.
Journal: Journal of Applied Statistics
Pages: 45-60
Issue: 1
Volume: 47
Year: 2020
Month: 1
X-DOI: 10.1080/02664763.2019.1628190
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1628190
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:1:p:45-60




Template-Type: ReDIF-Article 1.0
Author-Name: Caiyun Fan
Author-X-Name-First: Caiyun
Author-X-Name-Last: Fan
Author-Name: Gang Ding
Author-X-Name-First: Gang
Author-X-Name-Last: Ding
Author-Name: Feipeng Zhang
Author-X-Name-First: Feipeng
Author-X-Name-Last: Zhang
Title: A kernel nonparametric quantile estimator for right-censored competing risks data
Abstract: 
 In medical and epidemiological studies, it is often interest to study time-to-event distributions under competing risks that involve two or more failure types. Nonparametric analysis of competing risks is typically focused on the cumulative incidence function or nonparametric quantile function. However, the existing estimators may be very unstable due to their unsmoothness. In this paper, we propose a kernel nonparametric quantile estimator for right-censored competing risks data, which is a smoothed version of Peng and Fine's nonparametric quantile estimator. We establish the Bahadur representation of the proposed estimator. The convergence rate of the remainder term for the proposed estimator is substantially faster than Peng and Fine's quantile estimator. The pointwise confidence intervals and simultaneous confidence bands of the quantile functions are also derived. Simulation studies illustrate the good performance of the proposed estimator. The methodology is demonstrated with two applications of the Supreme Court Judge data and AIDSSI data.
Journal: Journal of Applied Statistics
Pages: 61-75
Issue: 1
Volume: 47
Year: 2020
Month: 1
X-DOI: 10.1080/02664763.2019.1631267
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1631267
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:1:p:61-75




Template-Type: ReDIF-Article 1.0
Author-Name: Xiaohui Liu
Author-X-Name-First: Xiaohui
Author-X-Name-Last: Liu
Author-Name: Yang He
Author-X-Name-First: Yang
Author-X-Name-Last: He
Title: RR-plot: a descriptive tool for regression observations
Abstract: 
 In this paper, we propose a regression depth versus regression depth plot, hereafter RR-plot, for regression observations based on the halfspace regression depth. Areas of application of this tool include: the visualization of hypothesis tests about regression coefficients, and of the comparison between regression observations from different models, etc. Some characterization theorems are also provided to address the rationale of this RR-plot.
Journal: Journal of Applied Statistics
Pages: 76-90
Issue: 1
Volume: 47
Year: 2020
Month: 1
X-DOI: 10.1080/02664763.2019.1631268
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1631268
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:1:p:76-90




Template-Type: ReDIF-Article 1.0
Author-Name: Sanying Feng
Author-X-Name-First: Sanying
Author-X-Name-Last: Feng
Author-Name: Gaorong Li
Author-X-Name-First: Gaorong
Author-X-Name-Last: Li
Author-Name: Tiejun Tong
Author-X-Name-First: Tiejun
Author-X-Name-Last: Tong
Author-Name: Shuanghua Luo
Author-X-Name-First: Shuanghua
Author-X-Name-Last: Luo
Title: Testing for heteroskedasticity in two-way fixed effects panel data models
Abstract: 
 In this paper, we propose a new method for testing heteroskedasticity in two-way fixed effects panel data models under two important scenarios where the cross-sectional dimension is large and the temporal dimension is either large or fixed. Specifically, we will develop test statistics for both cases under the conditional moment framework, and derive their asymptotic distributions under both the null and alternative hypotheses. The proposed tests are distribution free and can easily be implemented using the simple auxiliary regressions. Simulation studies and two real data analyses demonstrate that our proposed tests perform well in practice, and may have the potential for wide application in econometric models with panel data.
Journal: Journal of Applied Statistics
Pages: 91-116
Issue: 1
Volume: 47
Year: 2020
Month: 1
X-DOI: 10.1080/02664763.2019.1634682
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1634682
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:1:p:91-116




Template-Type: ReDIF-Article 1.0
Author-Name: Yuzhu Tian
Author-X-Name-First: Yuzhu
Author-X-Name-Last: Tian
Author-Name: Liyong Wang
Author-X-Name-First: Liyong
Author-X-Name-Last: Wang
Author-Name: Manlai Tang
Author-X-Name-First: Manlai
Author-X-Name-Last: Tang
Author-Name: Yanchao Zang
Author-X-Name-First: Yanchao
Author-X-Name-Last: Zang
Author-Name: Maozai Tian
Author-X-Name-First: Maozai
Author-X-Name-Last: Tian
Title: Likelihood-based quantile autoregressive distributed lag models and its applications
Abstract: 
 Time lag effect exists widely in the course of economic operation. Some economic variables are affected not only by various factors in the current period but also by various factors in the past and even their own past values. As a class of dynamical models, autoregressive distributed lag (ARDL) models are frequently used to conduct dynamic regression analysis. In this paper, we are interested in the quantile regression (QR) modeling of the ARDL model in a dynamic framework. By combining the working likelihood of asymmetric Laplace distribution (ALD) with the expectation–maximization (EM) algorithm into the considered ARDL model, the iterative weighted least square estimators (IWLSE) are derived. Some Monte Carlo simulations are implemented to evaluate the performance of the proposed estimation method. A dataset of the consumption of electricity by residential customers is analyzed to illustrate the application.
Journal: Journal of Applied Statistics
Pages: 117-131
Issue: 1
Volume: 47
Year: 2020
Month: 1
X-DOI: 10.1080/02664763.2019.1633285
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1633285
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:1:p:117-131




Template-Type: ReDIF-Article 1.0
Author-Name: Abdelghani Hamaz
Author-X-Name-First: Abdelghani
Author-X-Name-Last: Hamaz
Author-Name: Ouerdia Arezki
Author-X-Name-First: Ouerdia
Author-X-Name-Last: Arezki
Author-Name: Farida Achemine
Author-X-Name-First: Farida
Author-X-Name-Last: Achemine
Title: Impact of missing data on the prediction of random fields
Abstract: 
 The purpose of this paper is to treat the prediction problems where a number of observations are missing to the quarter-plane past of a stationary random field. Our aim is to quantify the influence of missing values on the prediction by giving the simple bounds for the prediction error variance. These bounds allow to characterize the random fields for which the missing observations do not affect the prediction. Simulation experiments and an application to real data are presented.
Journal: Journal of Applied Statistics
Pages: 132-149
Issue: 1
Volume: 47
Year: 2020
Month: 1
X-DOI: 10.1080/02664763.2019.1633286
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1633286
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:1:p:132-149




Template-Type: ReDIF-Article 1.0
Author-Name: Joonsung Kang
Author-X-Name-First: Joonsung
Author-X-Name-Last: Kang
Title: Robust estimation for longitudinal data based upon minimum Hellinger distance
Abstract: 
 Generalized linear mixed models have been widely used in the analysis of correlated data in a lot of research areas. The linear mixed model with normal errors has been a popular model for the analysis of repeated measures and longitudinal data. Outliers, however, can severely have an wrong influence on the linear mixed model. The aforementioned model has not fully taken those severe outliers into consideration. One of the popular robust estimation methods, M-estimator attains robustness at the expense of first-order or second-order efficiency whereas minimum Hellinger distance estimator is efficient and robust. In this paper, we propose more robust Bayesian version of parameter estimation via pseudo posterior distribution based on minimum Hellinger distance. It accommodates an appropriate nonparametric kernel density estimation for longitudinal data to require the proposed cross-validation estimator. We conduct simulation study and real data study with the orthodontic study data and the Alzheimers Disease (AD) study data. In simulation study, the proposed method shows smaller biases, mean squared errors, and standard errors than the (residual) maximum likelihood method (REML) in the presence of outliers or missing values. In real data analysis, standard errors and variance-covariance components for the proposed method in two data sets are shown to be lower than those for REML method.
Journal: Journal of Applied Statistics
Pages: 150-159
Issue: 1
Volume: 47
Year: 2020
Month: 1
X-DOI: 10.1080/02664763.2019.1635573
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1635573
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:1:p:150-159




Template-Type: ReDIF-Article 1.0
Author-Name: K. Krishnamoorthy
Author-X-Name-First: K.
Author-X-Name-Last: Krishnamoorthy
Author-Name: Dustin Waguespack
Author-X-Name-First: Dustin
Author-X-Name-Last: Waguespack
Author-Name: Ngan Hoang-Nguyen-Thuy
Author-X-Name-First: Ngan
Author-X-Name-Last: Hoang-Nguyen-Thuy
Title: Confidence interval, prediction interval and tolerance limits for a two-parameter Rayleigh distribution
Abstract: 
 The problems of interval estimating the parameters and the mean of a two-parameter Rayleigh distribution are considered. We propose pivotal-based methods for constructing confidence intervals for the mean, quantiles, survival probability and for constructing prediction intervals for the mean of a future sample. Pivotal quantities based on the maximum likelihood estimates (MLEs), moment estimates (MEs) and the L-moments estimates (L-MEs) are proposed. Interval estimates based on them are compared via Monte Carlo simulation. Comparison studies indicate that the results based on the MEs and the L-MEs are very similar. The results based on the MLEs are slightly better than those based on the MEs and the L-MEs for small to moderate sample sizes. The methods are illustrated using an example involving lifetime data.
Journal: Journal of Applied Statistics
Pages: 160-175
Issue: 1
Volume: 47
Year: 2020
Month: 1
X-DOI: 10.1080/02664763.2019.1634681
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1634681
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:1:p:160-175




Template-Type: ReDIF-Article 1.0
Author-Name: Suchismita Goswami
Author-X-Name-First: Suchismita
Author-X-Name-Last: Goswami
Author-Name: Edward J. Wegman
Author-X-Name-First: Edward J.
Author-X-Name-Last: Wegman
Title: Detection of excessive activities in time series of graphs
Abstract: 
 Considerable efforts have been made to apply scan statistics in detecting fraudulent or excessive activities in dynamic email networks. However, previous studies are mostly based on the fixed and disjoint windows, and on the assumption of short-term stationarity of the series, which might result in loss of information and error in detecting excessive activities. Here we devise scan statistics with variable and overlapping windows on stationary time series of organizational emails with a two-step process, and use likelihood function to rank the clusters. We initially estimate the log-likelihood ratio to obtain a primary cluster of communications using the Poisson model on email count series, and then extract neighborhood ego subnetworks around the observed primary cluster to obtain more refined cluster by invoking the graph invariant betweenness as the locality statistic using the binomial model. The results were then compared with the non-parametric maximum likelihood estimation method, and the residual analysis of ARMA model fitted to the time series of graph edit distance. We demonstrate that the scan statistics with two-step process is effective in detecting excessive activity in large dynamic social networks.
Journal: Journal of Applied Statistics
Pages: 176-200
Issue: 1
Volume: 47
Year: 2020
Month: 1
X-DOI: 10.1080/02664763.2019.1634680
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1634680
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:1:p:176-200




Template-Type: ReDIF-Article 1.0
Author-Name: M. Cannas
Author-X-Name-First: M.
Author-X-Name-Last: Cannas
Author-Name: C. Conversano
Author-X-Name-First: C.
Author-X-Name-Last: Conversano
Author-Name: F. Mola
Author-X-Name-First: F.
Author-X-Name-Last: Mola
Author-Name: E. Sironi
Author-X-Name-First: E.
Author-X-Name-Last: Sironi
Title: Variation in caesarean delivery rates across hospitals: a Bayesian semi-parametric approach
Abstract: 
 This article presents a Bayesian semi-parametric approach for modeling the occurrence of cesarean sections using a sample of women delivering in 20 hospitals of Sardinia (Italy). A multilevel logistic regression has been fitted on the data using a Dirichlet process prior for modeling the random-effects distribution of the unobserved factors at the hospital level. Using the estimated random effects at the hospital level, a partition of the hospitals in terms of similar medical practice has been obtained that identifies different profiles of hospitals in terms of caesarean section risks. The limited number of clusters may be useful for suggesting policy implications that help to reduce the heterogeneity of caesarean delivery risks.
Journal: Journal of Applied Statistics
Pages: 2095-2107
Issue: 12
Volume: 44
Year: 2017
Month: 9
X-DOI: 10.1080/02664763.2016.1247785
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1247785
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:12:p:2095-2107




Template-Type: ReDIF-Article 1.0
Author-Name: Z. Naji
Author-X-Name-First: Z.
Author-X-Name-Last: Naji
Author-Name: A. Rasekh
Author-X-Name-First: A.
Author-X-Name-Last: Rasekh
Author-Name: E. L. Boone
Author-X-Name-First: E. L.
Author-X-Name-Last: Boone
Title: Local influence in seemingly unrelated regression model with ridge estimate
Abstract: 
 Local influence is a well-known method for identifying the influential observations in a dataset and commonly needed in a statistical analysis. In this paper, we study the local influence on the parameters of interest in the seemingly unrelated regression model with ridge estimation, when there exists collinearity among the explanatory variables. We examine two types of perturbation schemes to identify influential observations: the perturbation of variance and the perturbation of individual explanatory variables. Finally, the efficacy of our proposed method is illustrated by analyzing [13] productivity dataset.
Journal: Journal of Applied Statistics
Pages: 2108-2124
Issue: 12
Volume: 44
Year: 2017
Month: 9
X-DOI: 10.1080/02664763.2016.1247787
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1247787
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:12:p:2108-2124




Template-Type: ReDIF-Article 1.0
Author-Name: Clécio S. Ferreira
Author-X-Name-First: Clécio S.
Author-X-Name-Last: Ferreira
Author-Name: Camila B. Zeller
Author-X-Name-First: Camila B.
Author-X-Name-Last: Zeller
Author-Name: Aparecida M. S. Mimura
Author-X-Name-First: Aparecida M. S.
Author-X-Name-Last: Mimura
Author-Name: Júlio C. J. Silva
Author-X-Name-First: Júlio C. J.
Author-X-Name-Last: Silva
Title: Partially linear models and their applications to change point detection of chemical process data
Abstract: 
 In many chemical data sets, the amount of radiation absorbed (absorbance) is related to the concentration of the element in the sample by Lambert–Beer's law. However, this relation changes abruptly when the variable concentration reaches an unknown threshold level, the so-called change point. In the context of analytical chemistry, there are many methods that describe the relationship between absorbance and concentration, but none of them provide inferential procedures to detect change points. In this paper, we propose partially linear models with a change point separating the parametric and nonparametric components. The Schwarz information criterion is used to locate a change point. A back-fitting algorithm is presented to obtain parameter estimates and the penalized Fisher information matrix is obtained to calculate the standard errors of the parameter estimates. To examine the proposed method, we present a simulation study. Finally, we apply the method to data sets from the chemistry area. The partially linear models with a change point developed in this paper are useful supplements to other methods of absorbance–concentration analysis in chemical studies, for example, and in many other practical applications.
Journal: Journal of Applied Statistics
Pages: 2125-2141
Issue: 12
Volume: 44
Year: 2017
Month: 9
X-DOI: 10.1080/02664763.2016.1247788
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1247788
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:12:p:2125-2141




Template-Type: ReDIF-Article 1.0
Author-Name: Giorgio Calzolari
Author-X-Name-First: Giorgio
Author-X-Name-Last: Calzolari
Author-Name: Antonino Di Pino
Author-X-Name-First: Antonino
Author-X-Name-Last: Di Pino
Title: Self-selection and direct estimation of across-regime correlation parameter
Abstract: 
 A direct maximum likelihood (ML) procedure to estimate the ‘generally unidentified’ across-regime correlation parameter in a two-regime endogenous switching model is here provided. The results of a Monte Carlo experiment confirm consistency of our direct ML procedure, and its relative efficiency over widely applied models and methods. As an empirical application, we estimate a two-regime simultaneous equation model of domestic work of Italian married women in which the two regimes are given by their working status (employed or unemployed).
Journal: Journal of Applied Statistics
Pages: 2142-2160
Issue: 12
Volume: 44
Year: 2017
Month: 9
X-DOI: 10.1080/02664763.2016.1247789
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1247789
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:12:p:2142-2160




Template-Type: ReDIF-Article 1.0
Author-Name: Chien-Chia L. Huang
Author-X-Name-First: Chien-Chia L.
Author-X-Name-Last: Huang
Author-Name: Yow-Jen Jou
Author-X-Name-First: Yow-Jen
Author-X-Name-Last: Jou
Author-Name: Hsun-Jung Cho
Author-X-Name-First: Hsun-Jung
Author-X-Name-Last: Cho
Title: Difference-based matrix perturbation method for semi-parametric regression with multicollinearity
Abstract: 
 This paper addresses the collinearity problems in semi-parametric linear models. Under the difference-based settings, we introduce a new diagnostic, the difference-based variance inflation factor (DVIF), for detecting the presence of multicollinearity in semi-parametric models. The DVIF is then used to device a difference-based matrix perturbation method for solving the problem. The electricities distribution data set is analyzed, and numerical evidences validate the effectiveness of the proposed method.
Journal: Journal of Applied Statistics
Pages: 2161-2171
Issue: 12
Volume: 44
Year: 2017
Month: 9
X-DOI: 10.1080/02664763.2016.1247790
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1247790
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:12:p:2161-2171




Template-Type: ReDIF-Article 1.0
Author-Name: T. Górecki
Author-X-Name-First: T.
Author-X-Name-Last: Górecki
Author-Name: Ł. Smaga
Author-X-Name-First: Ł.
Author-X-Name-Last: Smaga
Title: Multivariate analysis of variance for functional data
Abstract: 
 Functional data are being observed frequently in many scientific fields, and therefore most of the standard statistical methods are being adapted for functional data. The multivariate analysis of variance problem for functional data is considered. It seems to be of practical interest similarly as the one-way analysis of variance for such data. For the MANOVA problem for multivariate functional data, we propose permutation tests based on a basis function representation and tests based on random projections. Their performance is examined in comprehensive simulation studies, which provide an idea of the size control and power of the tests and identify differences between them. The simulation experiments are based on artificial data and real labeled multivariate time series data found in the literature. The results suggest that the studied testing procedures can detect small differences between vectors of curves even with small sample sizes. Illustrative real data examples of the use of the proposed testing procedures in practice are also presented.
Journal: Journal of Applied Statistics
Pages: 2172-2189
Issue: 12
Volume: 44
Year: 2017
Month: 9
X-DOI: 10.1080/02664763.2016.1247791
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1247791
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:12:p:2172-2189




Template-Type: ReDIF-Article 1.0
Author-Name: Nicholas T. Longford
Author-X-Name-First: Nicholas T.
Author-X-Name-Last: Longford
Author-Name: José Rafael Tovar Cuevas
Author-X-Name-First: José Rafael
Author-X-Name-Last: Tovar Cuevas
Author-Name: Carlos Alvear
Author-X-Name-First: Carlos
Author-X-Name-Last: Alvear
Title: Analysis of a marker for cancer of the thyroid with a limit of detection
Abstract: 
 Limit of detection (LoD) is a common problem in the analysis of data generated by instruments that cannot detect very small concentrations or other quantities, resulting in left-censored measurements. Methods intended for data that are not subject to this problem are often difficult to modify for censoring. We adapt the simulation-extrapolation method, devised originally for fitting models with measurement error, to dealing with LoD in conjunction with a mixture analysis. The application relates the levels of thyroglobulin in individuals with cancer of the thyroid before and after treatment with radioactive iodine I–131. We conclude that the fitted mixture components correspond to levels of effectiveness of the treatment.
Journal: Journal of Applied Statistics
Pages: 2190-2203
Issue: 12
Volume: 44
Year: 2017
Month: 9
X-DOI: 10.1080/02664763.2016.1247792
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1247792
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:12:p:2190-2203




Template-Type: ReDIF-Article 1.0
Author-Name: Liang Yan
Author-X-Name-First: Liang
Author-X-Name-Last: Yan
Author-Name: Rui Wang
Author-X-Name-First: Rui
Author-X-Name-Last: Wang
Author-Name: Xingzhong Xu
Author-X-Name-First: Xingzhong
Author-X-Name-Last: Xu
Title: A new confidence interval in errors-in-variables model with known error variance
Abstract: 
 This paper considers constructing a new confidence interval for the slope parameter in the structural errors-in-variables model with known error variance associated with the regressors. Existing confidence intervals are so severely affected by Gleser–Hwang effect that they are subject to have poor empirical coverage probabilities and unsatisfactory lengths. Moreover, these problems get worse with decreasing reliability ratio which also result in more frequent absence of some existing intervals. To ease these issues, this paper presents a fiducial generalized confidence interval which maintains the correct asymptotic coverage. Simulation results show that this fiducial interval is slightly conservative while often having average length comparable or shorter than the other methods. Finally, we illustrate these confidence intervals with two real data examples, and in the second example some existing intervals do not exist.
Journal: Journal of Applied Statistics
Pages: 2204-2221
Issue: 12
Volume: 44
Year: 2017
Month: 9
X-DOI: 10.1080/02664763.2016.1247793
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1247793
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:12:p:2204-2221




Template-Type: ReDIF-Article 1.0
Author-Name: Jung In Seo
Author-X-Name-First: Jung In
Author-X-Name-Last: Seo
Author-Name: Yongku Kim
Author-X-Name-First: Yongku
Author-X-Name-Last: Kim
Title: Objective Bayesian analysis based on upper record values from two-parameter Rayleigh distribution with partial information
Abstract: 
 In the life test, predicting higher failure times than the largest failure time of the observed is an important issue. Although the Rayleigh distribution is a suitable model for analyzing the lifetime of components that age rapidly over time because its failure rate function is an increasing linear function of time, the inference for a two-parameter Rayleigh distribution based on upper record values has not been addressed from the Bayesian perspective. This paper provides Bayesian analysis methods by proposing a noninformative prior distribution to analyze survival data, using a two-parameter Rayleigh distribution based on record values. In addition, we provide a pivotal quantity and an algorithm based on the pivotal quantity to predict the behavior of future survival records. We show that the proposed method is superior to the frequentist counterpart in terms of the mean-squared error and bias through Monte carlo simulations. For illustrative purposes, survival data on lung cancer patients are analyzed, and it is proved that the proposed model can be a good alternative when prior information is not given.
Journal: Journal of Applied Statistics
Pages: 2222-2237
Issue: 12
Volume: 44
Year: 2017
Month: 9
X-DOI: 10.1080/02664763.2016.1251886
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1251886
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:12:p:2222-2237




Template-Type: ReDIF-Article 1.0
Author-Name: Yeşim Güney
Author-X-Name-First: Yeşim
Author-X-Name-Last: Güney
Author-Name: Yetkin Tuaç
Author-X-Name-First: Yetkin
Author-X-Name-Last: Tuaç
Author-Name: Olcay Arslan
Author-X-Name-First: Olcay
Author-X-Name-Last: Arslan
Title: Marshall–Olkin distribution: parameter estimation and application to cancer data
Abstract: 
 In this study, as alternatives to the maximum likelihood (ML) and the frequency estimators, we propose robust estimators for the parameters of Zipf and Marshall–Olkin Zipf distributions. A small simulation study is given to illustrate the performance of the proposed estimators. We apply the proposed estimators to a real data set from cancer research to illustrate the performance of the proposed estimators over the ML, moments and frequency estimators. We observe that the robust estimators have superiority over the frequency estimators based on classical sample mean.
Journal: Journal of Applied Statistics
Pages: 2238-2250
Issue: 12
Volume: 44
Year: 2017
Month: 9
X-DOI: 10.1080/02664763.2016.1252730
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1252730
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:12:p:2238-2250




Template-Type: ReDIF-Article 1.0
Author-Name: M. Revan Özkale
Author-X-Name-First: M. Revan
Author-X-Name-Last: Özkale
Author-Name: Funda Can
Author-X-Name-First: Funda
Author-X-Name-Last: Can
Title: An evaluation of ridge estimator in linear mixed models: an example from kidney failure data
Abstract: 
 This paper is concerned with the ridge estimation of fixed and random effects in the context of Henderson's mixed model equations in the linear mixed model. For this purpose, a penalized likelihood method is proposed. A linear combination of ridge estimator for fixed and random effects is compared to a linear combination of best linear unbiased estimator for fixed and random effects under the mean-square error (MSE) matrix criterion. Additionally, for choosing the biasing parameter, a method of MSE under the ridge estimator is given. A real data analysis is provided to illustrate the theoretical results and a simulation study is conducted to characterize the performance of ridge and best linear unbiased estimators approach in the linear mixed model.
Journal: Journal of Applied Statistics
Pages: 2251-2269
Issue: 12
Volume: 44
Year: 2017
Month: 9
X-DOI: 10.1080/02664763.2016.1252732
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1252732
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:12:p:2251-2269




Template-Type: ReDIF-Article 1.0
Author-Name: Jiajia Chen
Author-X-Name-First: Jiajia
Author-X-Name-Last: Chen
Author-Name: Xiaoqin Zhang
Author-X-Name-First: Xiaoqin
Author-X-Name-Last: Zhang
Author-Name: Shengjia Li
Author-X-Name-First: Shengjia
Author-X-Name-Last: Li
Title: Multiple linear regression with compositional response and covariates
Abstract: 
 The standard regression model designed for real space is not suitable for compositional variables; it should be considered, whether the response and/or covariates are of compositional nature. There are usually three types of multiple regression model with compositional variables: Type 1 refers to the case where all the covariates are compositional data and the response is real; Type 2 is the opposite of Type 1; Type 3 relates to the model with compositional response and covariates. There have been some models for the three types. In this paper, we focus on Type 3 and propose multiple linear regression models including model in the simplex and model in isometric log-ratio (ilr) coordinates. The model in the simplex is based on matrix product, which can project a  $ D_{1} $ D1-part composition to another  $ D_{2} $ D2-part composition, and can deal with different number of parts of compositional variables. Some theorems are given to point out the relationship of parameters between the proposed models. Moreover, the inference for parameters in proposed models is also given. Real example is studied to verify the validity and usefulness of proposed models.
Journal: Journal of Applied Statistics
Pages: 2270-2285
Issue: 12
Volume: 44
Year: 2017
Month: 9
X-DOI: 10.1080/02664763.2016.1157145
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1157145
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:12:p:2270-2285




Template-Type: ReDIF-Article 1.0
Author-Name: Zheng Wei
Author-X-Name-First: Zheng
Author-X-Name-Last: Wei
Author-Name: Erin M. Conlon
Author-X-Name-First: Erin M.
Author-X-Name-Last: Conlon
Title: Parallel Markov chain Monte Carlo for Bayesian hierarchical models with big data, in two stages
Abstract: 
 Due to the escalating growth of big data sets in recent years, new Bayesian Markov chain Monte Carlo (MCMC) parallel computing methods have been developed. These methods partition large data sets by observations into subsets. However, for Bayesian nested hierarchical models, typically only a few parameters are common for the full data set, with most parameters being group specific. Thus, parallel Bayesian MCMC methods that take into account the structure of the model and split the full data set by groups rather than by observations are a more natural approach for analysis. Here, we adapt and extend a recently introduced two-stage Bayesian hierarchical modeling approach, and we partition complete data sets by groups. In stage 1, the group-specific parameters are estimated independently in parallel. The stage 1 posteriors are used as proposal distributions in stage 2, where the target distribution is the full model. Using three-level and four-level models, we show in both simulation and real data studies that results of our method agree closely with the full data analysis, with greatly increased MCMC efficiency and greatly reduced computation times. The advantages of our method versus existing parallel MCMC computing methods are also described.
Journal: Journal of Applied Statistics
Pages: 1917-1936
Issue: 11
Volume: 46
Year: 2019
Month: 8
X-DOI: 10.1080/02664763.2019.1572723
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1572723
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:11:p:1917-1936




Template-Type: ReDIF-Article 1.0
Author-Name: Dileep Kumar M.
Author-X-Name-First: Dileep
Author-X-Name-Last: Kumar M.
Author-Name: Sankaran P.G.
Author-X-Name-First: Sankaran
Author-X-Name-Last: P.G.
Author-Name: Unnikrishnan Nair N.
Author-X-Name-First: Unnikrishnan
Author-X-Name-Last: Nair N.
Title: Proportional odds model – a quantile approach
Abstract: 
 The paper discusses a quantile-based definition for the well-known proportional odds model. We present various reliability properties of the model using quantile functions. Different ageing properties are derived. A generalization for the class of distributions with bilinear hazard quantile function is established and the practical application of this model is illustrated with a real-life data set.
Journal: Journal of Applied Statistics
Pages: 1937-1955
Issue: 11
Volume: 46
Year: 2019
Month: 8
X-DOI: 10.1080/02664763.2019.1572724
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1572724
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:11:p:1937-1955




Template-Type: ReDIF-Article 1.0
Author-Name: Alka Sabharwal
Author-X-Name-First: Alka
Author-X-Name-Last: Sabharwal
Author-Name: Gurprit Grover
Author-X-Name-First: Gurprit
Author-X-Name-Last: Grover
Author-Name: Sakshi Kaushik
Author-X-Name-First: Sakshi
Author-X-Name-Last: Kaushik
Title: Testing the difference between bipolar disorder and schizophrenia on the basis of the severity of symptoms with C(α) test
Abstract: 
 Bipolar disorder and schizophrenia share some key symptoms which lead to misdiagnosis, especially on initial presentation. In this study, we have considered two categories of patients belonging to schizophrenia and bipolar disorder with (i) total duration of illness (TDI) less than or equal to 2 years and (ii) TDI greater than 2 years. We statistically test the difference between the severity of symptoms of the two groups as measured by their respective psychiatric rating scales using  $ C(\alpha ) $ C(α) (or score tests), likelihood ratio and permutation tests for both categories of patients. The unknown parameters are estimated using maximum likelihood, moments by Cran and Bayesian estimation. It is observed that there exists a significant difference between the two disorders for patients in second category based on real and simulated data. Further, performance of  $ C(\alpha ) $ C(α) statistic is compared on the basis of p-value and power performance with the other two methods. A new weight suggested in this paper is found to be as efficient as the previous weight based on simulation study. A retrospective data of 108 patients diagnosed with schizophrenia and bipolar disorders is collected from Lady Hardinge Medical College & Smt. S.K. Hospital, New Delhi, India for the calendar year 2013–2014.
Journal: Journal of Applied Statistics
Pages: 2101-2110
Issue: 11
Volume: 46
Year: 2019
Month: 8
X-DOI: 10.1080/02664763.2019.1573882
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1573882
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:11:p:2101-2110




Template-Type: ReDIF-Article 1.0
Author-Name: Fengqing Zhang
Author-X-Name-First: Fengqing
Author-X-Name-Last: Zhang
Author-Name: Jiangtao Gou
Author-X-Name-First: Jiangtao
Author-X-Name-Last: Gou
Title: Control of false positive rates in clusterwise fMRI inferences
Abstract: 
 Random field theory (RFT) provided a theoretical foundation for cluster-extent-based thresholding, the most widely used method for multiple comparison correction of statistical maps in neuroimaging research. However, several studies questioned the validity of the standard clusterwise inference in fMRI analyses and observed inflated false positive rates. In particular, Eklund et al. [Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates, Proc. Natl. Acad. Sci. 113 (2016), pp. 7900–7905. Available at http://www.pnas.org/content/113/28/7900.abstract] used resting-state fMRI as null data and found false positive rates of up to  $ 70\% $ 70%, which immediately led to many discussions. In this study, we summarize the assumptions in RFT clusterwise inference and propose new parametric ways to approximate the distribution of the cluster size by properly combining the limiting distribution of the cluster size given by Nosko [Local structure of Gaussian random fields in the vicinity of high-level shines, Sov. Math. Dokl. 10 (1969), pp. 1481–1484] and the expected value of the cluster size provided by Friston et al. [Assessing the significance of focal activations using their spatial extent, Hum. Brain Mapp. 1 (1994), pp. 210–220. Available at http://dx.doi.org/10.1002/hbm.460010306]. We evaluated our proposed method using four different classic simulation settings in published papers. Results show that our method produces a more stringent estimation of cluster extent size, which leads to a better control of false positive rates.
Journal: Journal of Applied Statistics
Pages: 1956-1972
Issue: 11
Volume: 46
Year: 2019
Month: 8
X-DOI: 10.1080/02664763.2019.1573883
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1573883
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:11:p:1956-1972




Template-Type: ReDIF-Article 1.0
Author-Name: Yunfei Wei
Author-X-Name-First: Yunfei
Author-X-Name-Last: Wei
Author-Name: Shifeng Xiong
Author-X-Name-First: Shifeng
Author-X-Name-Last: Xiong
Title: Bayesian integrative analysis for multi-fidelity computer experiments
Abstract: 
 This paper proposes a Bayesian integrative analysis method for linking multi-fidelity computer experiments. Instead of assuming covariance structures of multivariate Gaussian process models, we handle the outputs from different levels of accuracy as independent processes and link them via a penalization method that controls the distance between their overall trends. Based on the priors induced by the penalty, we build Bayesian prediction models for the output at the highest accuracy. Simulated and real examples show that the proposed method is better than existing methods in terms of prediction accuracy for many cases.
Journal: Journal of Applied Statistics
Pages: 1973-1987
Issue: 11
Volume: 46
Year: 2019
Month: 8
X-DOI: 10.1080/02664763.2019.1575340
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1575340
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:11:p:1973-1987




Template-Type: ReDIF-Article 1.0
Author-Name: Yue Shi
Author-X-Name-First: Yue
Author-X-Name-Last: Shi
Author-Name: Chi Tim Ng
Author-X-Name-First: Chi Tim
Author-X-Name-Last: Ng
Author-Name: Zhiguo Feng
Author-X-Name-First: Zhiguo
Author-X-Name-Last: Feng
Author-Name: Ka-Fai Cedric Yiu
Author-X-Name-First: Ka-Fai Cedric
Author-X-Name-Last: Yiu
Title: A descent algorithm for constrained LAD-Lasso estimation with applications in portfolio selection
Abstract: 
 To improve the out-of-sample performance of the portfolio, Lasso regularization is incorporated to the Mean Absolute Deviance (MAD)-based portfolio selection method. It is shown that such a portfolio selection problem can be reformulated as a constrained Least Absolute Deviance problem with linear equality constraints. Moreover, we propose a new descent algorithm based on the ideas of ‘nonsmooth optimality conditions’ and ‘basis descent direction set’. The resulting MAD-Lasso method enjoys at least two advantages. First, it does not involve the estimation of covariance matrix that is difficult particularly in the high-dimensional settings. Second, sparsity is encouraged. This means that assets with weights close to zero in the Markovwitz's portfolio are driven to zero automatically. This reduces the management cost of the portfolio. Extensive simulation and real data examples indicate that if the Lasso regularization is incorporated, MAD portfolio selection method is consistently improved in terms of out-of-sample performance, measured by Sharpe ratio and sparsity. Moreover, simulation results suggest that the proposed descent algorithm is more time-efficient than interior point method and ADMM algorithm.
Journal: Journal of Applied Statistics
Pages: 1988-2009
Issue: 11
Volume: 46
Year: 2019
Month: 8
X-DOI: 10.1080/02664763.2019.1575952
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1575952
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:11:p:1988-2009




Template-Type: ReDIF-Article 1.0
Author-Name: A. Hajrajabi
Author-X-Name-First: A.
Author-X-Name-Last: Hajrajabi
Author-Name: M. Maleki
Author-X-Name-First: M.
Author-X-Name-Last: Maleki
Title: Nonlinear semiparametric autoregressive model with finite mixtures of scale mixtures of skew normal innovations
Abstract: 
 We propose data generating structures which can be represented as the nonlinear autoregressive models with single and finite mixtures of scale mixtures of skew normal innovations. This class of models covers symmetric/asymmetric and light/heavy-tailed distributions, so provide a useful generalization of the symmetrical nonlinear autoregressive models. As semiparametric and nonparametric curve estimation are the approaches for exploring the structure of a nonlinear time series data set, in this article the semiparametric estimator for estimating the nonlinear function of the model is investigated based on the conditional least square method and nonparametric kernel approach. Also, an Expectation–Maximization-type algorithm to perform the maximum likelihood (ML) inference of unknown parameters of the model is proposed. Furthermore, some strong and weak consistency of the semiparametric estimator in this class of models are presented. Finally, to illustrate the usefulness of the proposed model, some simulation studies and an application to real data set are considered.
Journal: Journal of Applied Statistics
Pages: 2010-2029
Issue: 11
Volume: 46
Year: 2019
Month: 8
X-DOI: 10.1080/02664763.2019.1575953
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1575953
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:11:p:2010-2029




Template-Type: ReDIF-Article 1.0
Author-Name: Tom Fong
Author-X-Name-First: Tom
Author-X-Name-Last: Fong
Author-Name: Ceara Hui
Author-X-Name-First: Ceara
Author-X-Name-Last: Hui
Author-Name: Alfred Y.-T. Wong
Author-X-Name-First: Alfred Y.-T.
Author-X-Name-Last: Wong
Title: How might sovereign bond yields in Asia Pacific react to US monetary normalisation under turbulent market conditions?
Abstract: 
 This paper examines the potential impact of US monetary normalisation on sovereign bond yields in Asia Pacific. We apply the quantile vector autoregressive model with principal component analysis to the assessment of tail risk of sovereign debt, which may not be detectable using traditional OLS-based analysis. Our empirical evidence suggests that US Treasury bond yields can have a significant impact on sovereign bond yields in the region, an important channel through which monetary normalisation by the Fed can affect Asia-Pacific economies. Increases in sovereign bond yields will not only compromise the ability of the sovereigns in the region to service their debt but also translate into higher costs of borrowing for the rest of economy. The results show how much the outsized impact could potentially be if US monetary normalisation somehow turns out to be much more disorderly than expected.
Journal: Journal of Applied Statistics
Pages: 2030-2055
Issue: 11
Volume: 46
Year: 2019
Month: 8
X-DOI: 10.1080/02664763.2019.1579305
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1579305
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:11:p:2030-2055




Template-Type: ReDIF-Article 1.0
Author-Name: Yong Wang
Author-X-Name-First: Yong
Author-X-Name-Last: Wang
Author-Name: Xuxu Wang
Author-X-Name-First: Xuxu
Author-X-Name-Last: Wang
Title: Classification using semiparametric mixtures
Abstract: 
 A new density-based classification method that uses semiparametric mixtures is proposed. Like other density-based classifiers, it first estimates the probability density function for the observations in each class, with a semiparametric mixture, and then classifies a new observation by the highest posterior probability. By making a proper use of a multivariate nonparametric density estimator that has been developed recently, it is able to produce adaptively smooth and complicated decision boundaries in a high-dimensional space and can thus work well in such cases. Issues specific to classification are studied and discussed. Numerical studies using simulated and real-world data show that the new classifier performs very well as compared with other commonly used classification methods.
Journal: Journal of Applied Statistics
Pages: 2056-2074
Issue: 11
Volume: 46
Year: 2019
Month: 8
X-DOI: 10.1080/02664763.2019.1579306
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1579306
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:11:p:2056-2074




Template-Type: ReDIF-Article 1.0
Author-Name: Leila Amiri
Author-X-Name-First: Leila
Author-X-Name-Last: Amiri
Author-Name: Mojtaba Khazaei
Author-X-Name-First: Mojtaba
Author-X-Name-Last: Khazaei
Author-Name: Mojtaba Ganjali
Author-X-Name-First: Mojtaba
Author-X-Name-Last: Ganjali
Title: Mixtures of general location model with factor analyzer covariance structure for clustering mixed type data
Abstract: 
 Cluster analysis is one of the most widely used method in statistical analyses, in which homogeneous subgroups are identified in a heterogeneous population. Due to the existence of the continuous and discrete mixed data in many applications, so far, some ordinary clustering methods such as, hierarchical methods, k-means and model-based methods have been extended for analysis of mixed data. However, in the available model-based clustering methods, by increasing the number of continuous variables, the number of parameters increases and identifying as well as fitting an appropriate model may be difficult. In this paper, to reduce the number of the parameters, for the model-based clustering mixed data of continuous (normal) and nominal data, a set of parsimonious models is introduced. Models in this set are extended, using the general location model approach, for modeling distribution of mixed variables and applying factor analyzer structure for covariance matrices. The ECM algorithm is used for estimating the parameters of these models. In order to show the performance of the proposed models for clustering, results from some simulation studies and analyzing two real data sets are presented.
Journal: Journal of Applied Statistics
Pages: 2075-2100
Issue: 11
Volume: 46
Year: 2019
Month: 8
X-DOI: 10.1080/02664763.2019.1579307
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1579307
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:11:p:2075-2100




Template-Type: ReDIF-Article 1.0
Author-Name: Robert G. Aykroyd
Author-X-Name-First: Robert G.
Author-X-Name-Last: Aykroyd
Author-Name: M. Rosario Gonzales-Rogriguez
Author-X-Name-First: M. Rosario
Author-X-Name-Last: Gonzales-Rogriguez
Author-Name: Biagio Simonetti
Author-X-Name-First: Biagio
Author-X-Name-Last: Simonetti
Author-Name: Massimo Squillante
Author-X-Name-First: Massimo
Author-X-Name-Last: Squillante
Title: Editorial
Journal: Journal of Applied Statistics
Pages: 2347-2347
Issue: 13
Volume: 43
Year: 2016
Month: 10
X-DOI: 10.1080/02664763.2016.1213003
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1213003
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:13:p:2347-2347




Template-Type: ReDIF-Article 1.0
Author-Name: M. A. Di Palma
Author-X-Name-First: M. A.
Author-X-Name-Last: Di Palma
Author-Name: M. Gallo
Author-X-Name-First: M.
Author-X-Name-Last: Gallo
Title: A co-median approach to detect compositional outliers
Abstract: 
 Compositional data consist of vectors of positive values summing up to a unit or to some fixed constant. They find application in chemometrics, geology, economics, psychometrics and many other field of studies. In statistical analysis many theoretical efforts have been dedicated to identify procedures able to accomodate outliers included in the estimation of the model even in compositional data. The principal purpose of this work is to introduce an alternative robust procedure, defined as COMCoDa, capable to cope with compositional outliers and based on median absolute deviation (MAD) and correlation median. The new method is first evaluated in a simulation study and then on real data sets. The algorithm requires considerably less computational time than other procedures already existing in literature, it works well for huge compositional data sets at any level of contamination.
Journal: Journal of Applied Statistics
Pages: 2348-2362
Issue: 13
Volume: 43
Year: 2016
Month: 10
X-DOI: 10.1080/02664763.2016.1163525
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1163525
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:13:p:2348-2362




Template-Type: ReDIF-Article 1.0
Author-Name: A.A. Romano
Author-X-Name-First: A.A.
Author-X-Name-Last: Romano
Author-Name: G. Scandurra
Author-X-Name-First: G.
Author-X-Name-Last: Scandurra
Title: Divergences in the determinants of investments in renewable energy sources: hydroelectric vs. other renewable sources
Abstract: 
 In this paper, we analyze the drivers promoting the investments in renewable energy sources (RES) and the divergences on the basis of generation sources (hydroelectric and other renewable sources). To address these issues, a dynamic panel analysis of the renewable investments in a sample of 32 countries (Organisation for economic co-operation and development and Brasil, Russia, India, China and South Africa) with distinct economic and social structures, in the years between 2000 and 2008, is proposed. Results confirm that key factors promoting investments in RES vary according to generation sources considered. Investments in hydroelectric sources contribute to improve the environmental conditions, while the other sources are not significant. The policies are useful to support the investments in renewable energy. Results also show that share of nuclear and thermal electricity generation depress the investments in renewables.
Journal: Journal of Applied Statistics
Pages: 2363-2376
Issue: 13
Volume: 43
Year: 2016
Month: 10
X-DOI: 10.1080/02664763.2016.1163526
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1163526
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:13:p:2363-2376




Template-Type: ReDIF-Article 1.0
Author-Name: Md Hasinur Rahaman Khan
Author-X-Name-First: Md Hasinur
Author-X-Name-Last: Rahaman Khan
Author-Name: A. M. Azharul Islam
Author-X-Name-First: A. M. Azharul
Author-X-Name-Last: Islam
Author-Name: Faisal Ababneh
Author-X-Name-First: Faisal
Author-X-Name-Last: Ababneh
Title: Substantial gender gap reduction in Bangladesh explained by the proximity measure of literacy and life expectancy
Abstract: 
 The Human Development Index (HDI) is an indicator that substantially captures the overall country level status on human welfare based on issues of equity, poverty, and gender. This study uses a proximity measure of simultaneous effect of literacy and life expectancy called literate life expectancy (LLE) as a measure of human quality. This study discusses the distribution of LLE along with giving a detail gender and spatial differentials. With the proximity indicator we quantify gander gap between the year 1981 and 2008. Over the 27 years more than substantial improvement in LLE are found among women than with far less improvement rate among men in both national and residence level. We also learn that measured over time, the indicator allows statements about the rate of change and not just static differences. The LLE is useful as this index could be used to calculate future social development by adopting different mortality and educational scenarios such as health treatment facilities, nutritious food, easy access to clean drinking water, air pollution, greenhouse emissions, psychological stress, and most importantly, poverty, which can be associated with specific policy assumptions.
Journal: Journal of Applied Statistics
Pages: 2377-2395
Issue: 13
Volume: 43
Year: 2016
Month: 10
X-DOI: 10.1080/02664763.2016.1163527
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1163527
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:13:p:2377-2395




Template-Type: ReDIF-Article 1.0
Author-Name: M. Rosario González-Rodríguez
Author-X-Name-First: M. Rosario
Author-X-Name-Last: González-Rodríguez
Author-Name: M. Carmen Díaz Fernández
Author-X-Name-First: M. Carmen
Author-X-Name-Last: Díaz Fernández
Author-Name: Biagio Simonetti
Author-X-Name-First: Biagio
Author-X-Name-Last: Simonetti
Title: Corporate Social Responsibility perception versus human values: a structural equation modeling approach
Abstract: 
 In the business world, increasing importance is being given to Corporate Social Responsibility (CSR). Consumer perception of CSR is determinant on the success of CSR practices and this perception is directly influenced by individual value structures. Despite research efforts and the continued preoccupation of CSR role in business and Society, few studies to date have analyzed jointly CSR perception and the value structure. As a result, the paper brings new knowledge of the relationship between basic human values and CSR's perception under a particular social initiative carried out by a company. To reach our purpose a Hierarchical Component Model which includes the variable gender to control for heterogeneity is adopted. The model focuses on not only by analyzing the effects of human values on CSR but also analyzes the influence of values by gender on CSR perception. This approach to study the relationship of CSR versus values considering the Schwartz's higher-order values and the moderating role of gender constitutes a new perspective. The main results of this study reveal the influence of values on CSR, the strength of those relationships and the importance of analyzing the moderator effect to control for heterogeneity.
Journal: Journal of Applied Statistics
Pages: 2396-2415
Issue: 13
Volume: 43
Year: 2016
Month: 10
X-DOI: 10.1080/02664763.2016.1163528
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1163528
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:13:p:2396-2415




Template-Type: ReDIF-Article 1.0
Author-Name: Amir T. Payandeh Najafabadi
Author-X-Name-First: Amir T.
Author-X-Name-Last: Payandeh Najafabadi
Author-Name: Maryam Omidi Najafabadi
Author-X-Name-First: Maryam Omidi
Author-X-Name-Last: Najafabadi
Title: On the Bayesian estimation for Cronbach's alpha
Abstract: 
 This article considers the problem of estimating Cronbach's alpha under a Bayeisan framework. Such Bayes estimator arrives through out approximating distribution of the maximum likelihood estimator for Cronbach's alpha by an F distribution. Then, employing a noninformative prior distribution, Bayes estimator under squared-error and LINEX loss functions have been evaluated. Simulation studies suggest that the Bayes estimator under LINEX loss function reduce biasness of the ordinary maximum likelihood estimator. Moreover, The LINEX Bayes estimator does not sensitive with respect to choice of hyperparameters of prior distribution. R codes for readers to calculate Bayesian Cronbach's alpha have been given.
Journal: Journal of Applied Statistics
Pages: 2416-2441
Issue: 13
Volume: 43
Year: 2016
Month: 10
X-DOI: 10.1080/02664763.2016.1163529
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1163529
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:13:p:2416-2441




Template-Type: ReDIF-Article 1.0
Author-Name: Sergio Scippacercola
Author-X-Name-First: Sergio
Author-X-Name-Last: Scippacercola
Author-Name: Enrica Sepe
Author-X-Name-First: Enrica
Author-X-Name-Last: Sepe
Title: Ordinal principal component analysis for a common ranking of stochastic frontiers
Abstract: 
 The Stochastic Frontier Analysis (SFA) is a model to evaluate the Technical Efficiency (TE) for Production Units (PU). When SFA is applied on different output variables with same input, the analysis estimates different TEs for the PU. We refer to these TEs as the Multiple Technical Efficiency (MTE) of the PU. In this work, we present a method to unify the MTE in one ranking, in order to compute a synthetic index of the TE based on a parametric model. Our approach transforms the measures of efficiency into values on an ordinal scale. Then, using the Ordinal Principal Component Analysis and a genetic algorithm, we merge the multiple rankings.
Journal: Journal of Applied Statistics
Pages: 2442-2451
Issue: 13
Volume: 43
Year: 2016
Month: 10
X-DOI: 10.1080/02664763.2016.1163530
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1163530
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:13:p:2442-2451




Template-Type: ReDIF-Article 1.0
Author-Name: Alejandra Figliola
Author-X-Name-First: Alejandra
Author-X-Name-Last: Figliola
Author-Name: Lucas Catalano
Author-X-Name-First: Lucas
Author-X-Name-Last: Catalano
Title: Evolution of multifractal cross-correlations between the Argentina MERVAL Index and international commodities prices
Abstract: 
 We compute the auto-correlations and cross-correlations of the volatility time series of the Argentina MERVAL Index (the Buenos Aires Stock Exchange main index) and three agricultural commodities, in a multifractal context using the Detrended Cross-Correlation Analysis [12]. We observe a clear increase of the cross-correlations between the Merval series and the grain quotations which can be ascribed to a stronger coupling between the agricultural sector and the rest of the Argentinian economy. We connect this to fiscal decisions implemented since 2004 and reinforced after 2009.
Journal: Journal of Applied Statistics
Pages: 2452-2461
Issue: 13
Volume: 43
Year: 2016
Month: 10
X-DOI: 10.1080/02664763.2016.1181725
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1181725
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:13:p:2452-2461




Template-Type: ReDIF-Article 1.0
Author-Name: C.K. Chandrasekhar
Author-X-Name-First: C.K.
Author-X-Name-Last: Chandrasekhar
Author-Name: H. Bagyalakshmi
Author-X-Name-First: H.
Author-X-Name-Last: Bagyalakshmi
Author-Name: M.R. Srinivasan
Author-X-Name-First: M.R.
Author-X-Name-Last: Srinivasan
Author-Name: M. Gallo
Author-X-Name-First: M.
Author-X-Name-Last: Gallo
Title: Partial ridge regression under multicollinearity
Abstract: 
 In multiple linear regression analysis, linear dependencies in the regressor variables lead to ill-conditioning known as multicollinearity. Multicollinearity inflates variance of the estimates as well as causes changes in direction of signs of the coefficient estimates leading to unreliable, and many times erroneous inference. Principal components regression and ridge or shrinkage approach have not provided completely satisfactory results in dealing with the multicollinearity. There are host of issues in ridge regression like choosing bias k and stability or consistency of the variances which still remain unresolved. In this paper, a partial ridge regression estimation is proposed, which involves selectively adjusting the ridge constants associated with highly collinear variables to control instability in the variances of coefficient estimates. Results based on synthetic data from simulations, and a real-world data set from the manufacturing industry show that the proposed method outperforms the existing solutions in terms of bias, mean square error, and relative efficiency of the estimated parameters.
Journal: Journal of Applied Statistics
Pages: 2462-2473
Issue: 13
Volume: 43
Year: 2016
Month: 10
X-DOI: 10.1080/02664763.2016.1181726
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1181726
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:13:p:2462-2473




Template-Type: ReDIF-Article 1.0
Author-Name: Mehmet Ozer Demir
Author-X-Name-First: Mehmet Ozer
Author-X-Name-Last: Demir
Author-Name: Murat Alper Basaran
Author-X-Name-First: Murat Alper
Author-X-Name-Last: Basaran
Author-Name: Biagio Simonetti
Author-X-Name-First: Biagio
Author-X-Name-Last: Simonetti
Title: Determining factors affecting healthcare service satisfaction utilizing fuzzy rule-based systems
Abstract: 
 Health communication, which is a multi-attribute concept, is a generic title describing clinical practice. The literature shows that the relation between health communication and healthcare service satisfaction (HSS) has been found to be significant. The main objective of pursuing better health communication is to achieve the best outcome and patient satisfaction where healthcare systems are supposed to deliver. However, the health communication is a complex process. Also, measuring patients’ satisfaction is not an easy task since satisfaction is a complex notion with several factors. In this study, questions in the questionnaire directed to patients are factor-analyzed in order to obtain components which are used as independent attributes that will be modeled by fuzzy rule-based systems (FRBS) in order to explain HSS. Utilizing FRBS brings two different advantages, one of which is to use mathematical functions called membership functions for linguistically expressed responses. The second one is to observe the transition among the linguistic values expressed by patients. The four independent variables, namely, doctor–patient communication (DPC), information seeking behavior (ISB), equal behavior and tolerance to cultural differences (TCD) and the dependent variable HSS are employed in the modeling. Although both DPC and ISB have positive effects on HSS, TCD has none. One interesting finding about DPC is that if DPC scores below the average value tend to lower, it does not have a decreasing effect on HSS, which means that if a patient does expect to have average DPC, his or her evaluation on HSS does not lower, which says that if a patient knows that the doctor has a poor communication skill, the patient does not pay attention to this attribute.
Journal: Journal of Applied Statistics
Pages: 2474-2489
Issue: 13
Volume: 43
Year: 2016
Month: 10
X-DOI: 10.1080/02664763.2016.1181727
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1181727
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:13:p:2474-2489




Template-Type: ReDIF-Article 1.0
Author-Name: Pasquale Sarnacchiaro
Author-X-Name-First: Pasquale
Author-X-Name-Last: Sarnacchiaro
Author-Name: Antonello D’Ambra
Author-X-Name-First: Antonello
Author-X-Name-Last: D’Ambra
Author-Name: Luigi D’Ambra
Author-X-Name-First: Luigi
Author-X-Name-Last: D’Ambra
Title: CATANOVA for ordinal variables using orthogonal polynomials with different scoring methods
Abstract: 
 In the context of categorical data analysis, the CATegorical ANalysis Of Variance (CATANOVA) has been proposed to analyse the scheme variable-factor, both for nominal and ordinal variables. This method is based on the C statistic and allows to test the statistical significance of the tau index using its relationship with the C statistic. Through Emerson orthogonal polynomials (EOP) a useful decomposition of C statistic into bivariate moments (location, dispersion and higher order components) has been developed. In the construction of EOP the categories are replaced by scores, typically natural scores. In the paper, we provide an overview of the main scoring schemes focusing on the advantages and the statistical properties; we pay special attention to the impact of the chosen scores on the C statistic of CATANOVA and the graphical representations of doubly ordered non-symmetrical correspondence analysis. Through a real data example, we show the impact of the scoring schemes and we consider the RV and multidimensional scaling as tools to measure similarity among the results achieved with each method.
Journal: Journal of Applied Statistics
Pages: 2490-2502
Issue: 13
Volume: 43
Year: 2016
Month: 10
X-DOI: 10.1080/02664763.2016.1184627
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1184627
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:13:p:2490-2502




Template-Type: ReDIF-Article 1.0
Author-Name: Puyu Wang
Author-X-Name-First: Puyu
Author-X-Name-Last: Wang
Author-Name: Hai Zhang
Author-X-Name-First: Hai
Author-X-Name-Last: Zhang
Author-Name: Yong Liang
Author-X-Name-First: Yong
Author-X-Name-Last: Liang
Title: Model selection with distributed SCAD penalty
Abstract: 
 In this paper, we focus on the feature extraction and variable selection of massive data which is divided and stored in different linked computers. Specifically, we study the distributed model selection with the Smoothly Clipped Absolute Deviation (SCAD) penalty. Based on the Alternating Direction Method of Multipliers (ADMM) algorithm, we propose distributed SCAD algorithm and prove its convergence. The results of variable selection of the distributed approach are same with the results of the non-distributed approach. Numerical studies show that our method is both effective and efficient which performs well in distributed data analysis.
Journal: Journal of Applied Statistics
Pages: 1938-1955
Issue: 11
Volume: 45
Year: 2018
Month: 8
X-DOI: 10.1080/02664763.2017.1401052
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1401052
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:11:p:1938-1955




Template-Type: ReDIF-Article 1.0
Author-Name: D. M. Swanson
Author-X-Name-First: D. M.
Author-X-Name-Last: Swanson
Author-Name: C. D. Anderson
Author-X-Name-First: C. D.
Author-X-Name-Last: Anderson
Author-Name: R. A. Betensky
Author-X-Name-First: R. A.
Author-X-Name-Last: Betensky
Title: Hypothesis Tests for Neyman's Bias in Case–Control Studies
Abstract: 
 Survival bias is a long recognized problem in case–control studies, and many varieties of bias can come under this umbrella term. We focus on one of them, termed Neyman's bias or ‘prevalence–incidence bias’. It occurs in case–control studies when exposure affects both disease and disease-induced mortality, and we give a formula for the observed, biased odds ratio under such conditions. We compare our result with previous investigations into this phenomenon and consider models under which this bias may or may not be important. Finally, we propose three hypothesis tests to identify when Neyman's bias may be present in case–control studies. We apply these tests to three data sets, one of stroke mortality, another of brain tumors, and the last of atrial fibrillation, and find some evidence of Neyman's bias in the former two cases, but not the last case.
Journal: Journal of Applied Statistics
Pages: 1956-1977
Issue: 11
Volume: 45
Year: 2018
Month: 8
X-DOI: 10.1080/02664763.2017.1401053
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1401053
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:11:p:1956-1977




Template-Type: ReDIF-Article 1.0
Author-Name: Wen Su
Author-X-Name-First: Wen
Author-X-Name-Last: Su
Author-Name: Hangjin Jiang
Author-X-Name-First: Hangjin
Author-X-Name-Last: Jiang
Title: Semiparametric analysis of longitudinal data with informative observation times and censoring times
Abstract: 
 We focus on regression analysis of irregularly observed longitudinal data which often occur in medical follow-up studies and observational investigations. The model for such data involves two processes: a longitudinal response process of interest and an observation process controlling observation times. Restrictive models and questionable assumptions, such as Poisson assumption and independent censoring time assumption, were posed in previous works for analysing longitudinal data. In this paper, we propose a more general model together with a robust estimation approach for longitudinal data with informative observation times and censoring times, and the asymptotic normalities of the proposed estimators are established. Both simulation studies and real data application indicate that the proposed method is promising.
Journal: Journal of Applied Statistics
Pages: 1978-1993
Issue: 11
Volume: 45
Year: 2018
Month: 8
X-DOI: 10.1080/02664763.2017.1403574
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1403574
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:11:p:1978-1993




Template-Type: ReDIF-Article 1.0
Author-Name: Yunlu Jiang
Author-X-Name-First: Yunlu
Author-X-Name-Last: Jiang
Author-Name: Yu Conglian
Author-X-Name-First: Yu
Author-X-Name-Last: Conglian
Author-Name: Ji Qinghua
Author-X-Name-First: Ji
Author-X-Name-Last: Qinghua
Title: Model selection for the localized mixture of experts models
Abstract: 
 In this paper, we propose a penalized likelihood method to simultaneous select covariate, and mixing component and obtain parameter estimation in the localized mixture of experts models. We develop an expectation maximization algorithm to solve the proposed penalized likelihood procedure, and introduce a data-driven procedure to select the tuning parameters. Extensive numerical studies are carried out to compare the finite sample performances of our proposed method and other existing methods. Finally, we apply the proposed methodology to analyze the Boston housing price data set and the baseball salaries data set.
Journal: Journal of Applied Statistics
Pages: 1994-2006
Issue: 11
Volume: 45
Year: 2018
Month: 8
X-DOI: 10.1080/02664763.2017.1405914
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1405914
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:11:p:1994-2006




Template-Type: ReDIF-Article 1.0
Author-Name: Feng-shou Ko
Author-X-Name-First: Feng-shou
Author-X-Name-Last: Ko
Title: Discussion on the issue of sample size determination for a targeted to an untargeted and to a mixed effect model-based clinical trial design
Abstract: 
 More and more studies have shown that genetic determinants may mediate variability among persons in the response to a drug. In other words, some therapeutics benefit only a subset of treated patients. Genomic technologies – such as DNA sequencing, mRNA transcript profiling, and comparative genomic hybridization – are providing biomarkers that can be used to predict which patients are most likely to respond to a given drug. In this paper, the sample size determination of a targeted clinical trial, an untargeted clinical trial and a random effect model is conducted. Treatment effect for the responder and non-responder patients, the assay specificity and sensitivity, and the proportion of the population for non-responder can affect sample size determination of the experimental design.
Journal: Journal of Applied Statistics
Pages: 2007-2019
Issue: 11
Volume: 45
Year: 2018
Month: 8
X-DOI: 10.1080/02664763.2017.1405915
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1405915
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:11:p:2007-2019




Template-Type: ReDIF-Article 1.0
Author-Name: Cristina Coscia
Author-X-Name-First: Cristina
Author-X-Name-Last: Coscia
Author-Name: Roberto Fontana
Author-X-Name-First: Roberto
Author-X-Name-Last: Fontana
Author-Name: Patrizia Semeraro
Author-X-Name-First: Patrizia
Author-X-Name-Last: Semeraro
Title: Graphical models for complex networks: an application to Italian museums
Abstract: 
 This paper applies probabilistic graphical models in a new framework to study association rules driven by consumer choices in a network of Italian museums. The network consists of the museums participating in the programme of Abbonamento Musei Torino Piemonte, which is a yearly subscription managed by Associazione Torino Città Capitale Europea. It is available to people living in the Piemonte region, Italy. Consumers are card-holders, who are allowed entry to all the museums in the network for one year. We employ graphical models to highlight associations between the museums driven by card-holder visiting behaviour. We use both simple undirected graphs and more complex directed graphs, and we do not make any hypothesis on the models but rather learn their structures directly from the data. We also use methodologies and tools for robust network identification and principal component analysis to complete the analysis of the phenomenon.
Journal: Journal of Applied Statistics
Pages: 2020-2038
Issue: 11
Volume: 45
Year: 2018
Month: 8
X-DOI: 10.1080/02664763.2017.1406901
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1406901
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:11:p:2020-2038




Template-Type: ReDIF-Article 1.0
Author-Name: Gislaine V. Duarte
Author-X-Name-First: Gislaine V.
Author-X-Name-Last: Duarte
Author-Name: Altemir Braga
Author-X-Name-First: Altemir
Author-X-Name-Last: Braga
Author-Name: Daniel L. Miquelluti
Author-X-Name-First: Daniel L.
Author-X-Name-Last: Miquelluti
Author-Name: Vitor A. Ozaki
Author-X-Name-First: Vitor A.
Author-X-Name-Last: Ozaki
Title: Modeling of soybean yield using symmetric, asymmetric and bimodal distributions: implications for crop insurance
Abstract: 
 Over the years, many papers used parametric distributions to model crop yields, such as: normal (N), Beta, Log-normal and the Skew-normal (SN). These models are well-defined, mathematically and also computationally, but its do not incorporate bimodality. Therefore, it is necessary to study distributions which are more flexible in modeling, since most of crop yield data in Brazil presents evidence of asymmetry or bimodality. Thus, the aim of this study was to model and forecast soybean yields for municipalities in the State of Paran, in the period from 1980 to 2014, using the Odd log normal logistic (OLLN) distribution for the bimodal data and the Beta, SN and Skew-t distributions for the symmetrical and asymmetrical series. The OLLN model was the one which best fit the data. The results were discussed in the context of crop insurance pricing.
Journal: Journal of Applied Statistics
Pages: 1920-1937
Issue: 11
Volume: 45
Year: 2018
Month: 8
X-DOI: 10.1080/02664763.2017.1406902
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1406902
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:11:p:1920-1937




Template-Type: ReDIF-Article 1.0
Author-Name: Thalita do Bem Mattos
Author-X-Name-First: Thalita do Bem
Author-X-Name-Last: Mattos
Author-Name: Aldo M. Garay
Author-X-Name-First: Aldo M.
Author-X-Name-Last: Garay
Author-Name: Victor H. Lachos
Author-X-Name-First: Victor H.
Author-X-Name-Last: Lachos
Title: Likelihood-based inference for censored linear regression models with scale mixtures of skew-normal distributions
Abstract: 
 In many studies, the data collected are subject to some upper and lower detection limits. Hence, the responses are either left or right censored. A complication arises when these continuous measures present heavy tails and asymmetrical behavior; simultaneously. For such data structures, we propose a robust-censored linear model based on the scale mixtures of skew-normal (SMSN) distributions. The SMSN is an attractive class of asymmetrical heavy-tailed densities that includes the skew-normal, skew-t, skew-slash, skew-contaminated normal and the entire family of scale mixtures of normal (SMN) distributions as special cases. We propose a fast estimation procedure to obtain the maximum likelihood (ML) estimates of the parameters, using a stochastic approximation of the EM (SAEM) algorithm. This approach allows us to estimate the parameters of interest easily and quickly, obtaining as a byproducts the standard errors, predictions of unobservable values of the response and the log-likelihood function. The proposed methods are illustrated through real data applications and several simulation studies.
Journal: Journal of Applied Statistics
Pages: 2039-2066
Issue: 11
Volume: 45
Year: 2018
Month: 8
X-DOI: 10.1080/02664763.2017.1408788
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1408788
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:11:p:2039-2066




Template-Type: ReDIF-Article 1.0
Author-Name: Jiajia Chen
Author-X-Name-First: Jiajia
Author-X-Name-Last: Chen
Author-Name: Xiaoqin Zhang
Author-X-Name-First: Xiaoqin
Author-X-Name-Last: Zhang
Author-Name: Karel Hron
Author-X-Name-First: Karel
Author-X-Name-Last: Hron
Author-Name: Matthias Templ
Author-X-Name-First: Matthias
Author-X-Name-Last: Templ
Author-Name: Shengjia Li
Author-X-Name-First: Shengjia
Author-X-Name-Last: Li
Title: Regression imputation with Q-mode clustering for rounded zero replacement in high-dimensional compositional data
Abstract: 
 The logratio methodology is not applicable when rounded zeros occur in compositional data. There are many methods to deal with rounded zeros. However, some methods are not suitable for analyzing data sets with high dimensionality. Recently, related methods have been developed, but they cannot balance the calculation time and accuracy. For further improvement, we propose a method based on regression imputation with Q-mode clustering. This method forms the groups of parts and builds partial least squares regression with these groups using centered logratio coordinates. We also prove that using centered logratio coordinates or isometric logratio coordinates in the response of partial least squares regression have the equivalent results for the replacement of rounded zeros. Simulation study and real example are conducted to analyze the performance of the proposed method. The results show that the proposed method can reduce the calculation time in higher dimensions and improve the quality of results.
Journal: Journal of Applied Statistics
Pages: 2067-2080
Issue: 11
Volume: 45
Year: 2018
Month: 8
X-DOI: 10.1080/02664763.2017.1410524
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1410524
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:11:p:2067-2080




Template-Type: ReDIF-Article 1.0
Author-Name: Emílio A. Coelho-Barros
Author-X-Name-First: Emílio A.
Author-X-Name-Last: Coelho-Barros
Author-Name: Josmar Mazucheli
Author-X-Name-First: Josmar
Author-X-Name-Last: Mazucheli
Author-Name: Jorge A. Achcar
Author-X-Name-First: Jorge A.
Author-X-Name-Last: Achcar
Author-Name: Kelly Vanessa Parede Barco
Author-X-Name-First: Kelly Vanessa Parede
Author-X-Name-Last: Barco
Author-Name: José Rafael Tovar Cuevas
Author-X-Name-First: José Rafael
Author-X-Name-Last: Tovar Cuevas
Title: The inverse power Lindley distribution in the presence of left-censored data
Abstract: 
 In this study, classical and Bayesian inference methods are introduced to analyze lifetime data sets in the presence of left censoring considering two generalizations of the Lindley distribution: a first generalization proposed by Ghitany et al. [Power Lindley distribution and associated inference, Comput. Statist. Data Anal. 64 (2013), pp. 20–33], denoted as a power Lindley distribution and a second generalization proposed by Sharma et al. [The inverse Lindley distribution: A stress–strength reliability model with application to head and neck cancer data, J. Ind. Prod. Eng. 32 (2015), pp. 162–173], denoted as an inverse Lindley distribution. In our approach, we have used a distribution obtained from these two generalizations denoted as an inverse power Lindley distribution. A numerical illustration is presented considering a dataset of thyroglobulin levels present in a group of individuals with differentiated cancer of thyroid.
Journal: Journal of Applied Statistics
Pages: 2081-2094
Issue: 11
Volume: 45
Year: 2018
Month: 8
X-DOI: 10.1080/02664763.2017.1410525
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1410525
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:11:p:2081-2094




Template-Type: ReDIF-Article 1.0
Author-Name: Aliakbar Mastani Shirazi
Author-X-Name-First: Aliakbar Mastani
Author-X-Name-Last: Shirazi
Author-Name: Aluisio Pinheiro
Author-X-Name-First: Aluisio
Author-X-Name-Last: Pinheiro
Title: A proportional hazard cure model for ordinal responses by self-modeling regression
Abstract: 
 In a medical study, patients have various stages of illness. After treatment the patient will be cured or the stage of illness will change. Since there are suitable evidences of a susceptible population by several levels, the authors combine a Self-Modeling ordinal model for the probability of occurrence of an event with a Cox regression for the time of occurrence of an event. We proposed the use of self-modeling ordinal longitudinal where the conditional cumulative probabilities for a category of an outcome have a relation with shape-invariant model. A simulation study is carried out for justification of the methodology. A schizophrenia illness data are analyzed based on our model to see whether the treatment affects the illness.
Journal: Journal of Applied Statistics
Pages: 2095-2106
Issue: 11
Volume: 45
Year: 2018
Month: 8
X-DOI: 10.1080/02664763.2017.1410526
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1410526
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:11:p:2095-2106




Template-Type: ReDIF-Article 1.0
Author-Name: Jooyong Shim
Author-X-Name-First: Jooyong
Author-X-Name-Last: Shim
Author-Name: Changha Hwang
Author-X-Name-First: Changha
Author-X-Name-Last: Hwang
Author-Name: Sunjoo Jeong
Author-X-Name-First: Sunjoo
Author-X-Name-Last: Jeong
Author-Name: Insuk Sohn
Author-X-Name-First: Insuk
Author-X-Name-Last: Sohn
Title: Semivarying coefficient least-squares support vector regression for analyzing high-dimensional gene-environmental data
Abstract: 
 In the context of genetics and genomic medicine, gene-environment (G×E) interactions have a great impact on the risk of human diseases. Some existing methods for identifying G×E interactions are considered to be limited, since they analyze one or a few number of G factors at a time, assume linear effects of E factors, and use inefficient selection methods. In this paper, we propose a new method to identify significant main effects and G×E interactions. This is based on a semivarying coefficient least-squares support vector regression (LS-SVR) technique, which is devised by utilizing flexible semiparametric LS-SVR approach for censored survival data. This semivarying coefficient model is used to deal with the nonlinear effects of E factors. We also derive a generalized cross validation (GCV) function for determining the optimal values of hyperparameters of the proposed method. This GCV function is also used to identify significant main effects and G×E interactions. The proposed method is evaluated through numerical studies.
Journal: Journal of Applied Statistics
Pages: 1370-1381
Issue: 8
Volume: 45
Year: 2018
Month: 6
X-DOI: 10.1080/02664763.2017.1371676
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1371676
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:8:p:1370-1381




Template-Type: ReDIF-Article 1.0
Author-Name: Flavio Mignone
Author-X-Name-First: Flavio
Author-X-Name-Last: Mignone
Author-Name: Fabio Rapallo
Author-X-Name-First: Fabio
Author-X-Name-Last: Rapallo
Title: Detection of outlying proportions
Abstract: 
 In this paper we introduce a new method for detecting outliers in a set of proportions. It is based on the construction of a suitable two-way contingency table and on the application of an algorithm for the detection of outlying cells in such table. We exploit the special structure of the relevant contingency table to increase the efficiency of the method. The main properties of our algorithm, together with a guide for the choice of the parameters, are investigated through simulations, and in simple cases some theoretical justifications are provided. Several examples on synthetic data and an example based on pseudo-real data from biological experiments demonstrate the good performances of our algorithm.
Journal: Journal of Applied Statistics
Pages: 1382-1395
Issue: 8
Volume: 45
Year: 2018
Month: 6
X-DOI: 10.1080/02664763.2017.1371677
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1371677
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:8:p:1382-1395




Template-Type: ReDIF-Article 1.0
Author-Name: Philippe Casin
Author-X-Name-First: Philippe
Author-X-Name-Last: Casin
Title: Categorical multiblock linear discriminant analysis
Abstract: 
 Techniques of credit scoring have been developed these last years in order to reduce the risk taken by banks and financial institutions in the loans that they are granting. Credit Scoring is a classification problem of individuals in one of the two following groups: defaulting borrowers or non-defaulting borrowers. The aim of this paper is to propose a new method of discrimination when the dependent variable is categorical and when a large number of categorical explanatory variables are retained. This method, Categorical Multiblock Linear Discriminant Analysis, computes components which take into account both relationships between explanatory categorical variables and canonical correlation between each explanatory categorical variable and the dependent variable. A comparison with three other techniques and an application on credit scoring data are provided.
Journal: Journal of Applied Statistics
Pages: 1396-1409
Issue: 8
Volume: 45
Year: 2018
Month: 6
X-DOI: 10.1080/02664763.2017.1371678
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1371678
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:8:p:1396-1409




Template-Type: ReDIF-Article 1.0
Author-Name: Xi Shen
Author-X-Name-First: Xi
Author-X-Name-Last: Shen
Author-Name: Chang-Xing Ma
Author-X-Name-First: Chang-Xing
Author-X-Name-Last: Ma
Title: Testing homogeneity of difference of two proportions for stratified correlated paired binary data
Abstract: 
 In ophthalmologic or otolaryngologic study, each subject may contribute paired organs measurements to the analysis. A number of statistical methods have been proposed on bilateral correlated data. In practice, it is important to detect confounding effect by treatment interaction, since ignoring confounding effect may lead to unreliable conclusion. Therefore, stratified data analysis can be considered to adjust the effect of confounder on statistical inference. In this article, we investigate and derive three test procedures for testing homogeneity of difference of two proportions for stratified correlated paired binary data in the basis of equal correlation model assumption. The performance of proposed test procedures is examined through Monte Carlo simulation. The simulation results show that the Score test is usually robust on type I error control with high power, and therefore is recommended among the three methods. One example from otolaryngologic study is given to illustrate the three test procedures.
Journal: Journal of Applied Statistics
Pages: 1410-1425
Issue: 8
Volume: 45
Year: 2018
Month: 6
X-DOI: 10.1080/02664763.2017.1371679
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1371679
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:8:p:1410-1425




Template-Type: ReDIF-Article 1.0
Author-Name: Kalanka P. Jayalath
Author-X-Name-First: Kalanka P.
Author-X-Name-Last: Jayalath
Author-Name: Hon Keung Tony Ng
Author-X-Name-First: Hon Keung Tony
Author-X-Name-Last: Ng
Title: Analysis of means approach for random factor analysis
Abstract: 
 Analysis of means (ANOM) is a powerful tool for comparing means and variances in fixed-effects models. The graphical exhibit of ANOM is considered as a great advantage because of its interpretability and its ability to evaluate the practical significance of the mean effects. However, the presence of random factors may be problematic for the ANOM method. In this paper, we propose an ANOM approach that can be applied to test random effects in many different balanced statistical models including fixed-, random- and mixed-effects models. The proposed approach utilizes the range of the treatment averages for identifying the dispersions of the underlying populations. The power performance of the proposed procedure is compared to the analysis of variance (ANOVA) approach in a wide range of situations via a Monte Carlo simulation study. Illustrative examples are used to demonstrate the usefulness of the proposed approach and its graphical exhibits, provide meaningful interpretations, and discuss the statistical and practical significance of factor effects.
Journal: Journal of Applied Statistics
Pages: 1426-1446
Issue: 8
Volume: 45
Year: 2018
Month: 6
X-DOI: 10.1080/02664763.2017.1375083
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1375083
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:8:p:1426-1446




Template-Type: ReDIF-Article 1.0
Author-Name: Stijn Luca
Author-X-Name-First: Stijn
Author-X-Name-Last: Luca
Title: Modified chain sampling plans for lot inspection by variables and attributes
Abstract: 
 The purpose of acceptance sampling is to develop decision rules to accept or reject production lots based on sample data. When testing is destructive or expensive, dependent sampling procedures cumulate results from several preceding lots. This chaining of past lot results reduces the required size of the samples. A large part of these procedures only chain past lot results when defects are found in the current sample. However, such selective use of past lot results only achieves a limited reduction of sample sizes. In this article, a modified approach for chaining past lot results is proposed that is less selective in its use of quality history and, as a result, requires a smaller sample size than the one required for commonly used dependent sampling procedures, such as multiple dependent sampling plans and chain sampling plans of Dodge. The proposed plans are applicable for inspection by attributes and inspection by variables. Several properties of their operating characteristic-curves are derived, and search procedures are given to select such modified chain sampling plans by using the two-point method.
Journal: Journal of Applied Statistics
Pages: 1447-1464
Issue: 8
Volume: 45
Year: 2018
Month: 6
X-DOI: 10.1080/02664763.2017.1375084
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1375084
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:8:p:1447-1464




Template-Type: ReDIF-Article 1.0
Author-Name: James O. Chipperfield
Author-X-Name-First: James O.
Author-X-Name-Last: Chipperfield
Author-Name: Margo L. Barr
Author-X-Name-First: Margo L.
Author-X-Name-Last: Barr
Author-Name: David. G. Steel
Author-X-Name-First: David. G.
Author-X-Name-Last: Steel
Title: Split Questionnaire Designs: collecting only the data that you need through MCAR and MAR designs
Abstract: 
 We call a sample design that allows for different patterns, or sets, of data items to be collected from different sample units a Split Questionnaire Design (SQD). SQDs can be thought of as incorporating missing data into survey design. This paper examines the situation where data that are not collected by an SQD can be treated as Missing Completely At Random or Missing At Random, targets are regression coefficients in a generalised linear model fitted to binary variables, and targets are estimated using Maximum Likelihood. A key finding is that it can be easy to measure the relative contribution of a respondent to the accuracy of estimated model parameters before collecting all the respondent's model covariates. We show empirically and theoretically that we could achieve a significant reduction in respondent burden with a negligible impact on the accuracy of estimates by not collecting model covariates from respondents who we identify as contributing little to the accuracy of estimates. We discuss the general implications for SQDs.
Journal: Journal of Applied Statistics
Pages: 1465-1475
Issue: 8
Volume: 45
Year: 2018
Month: 6
X-DOI: 10.1080/02664763.2017.1375085
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1375085
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:8:p:1465-1475




Template-Type: ReDIF-Article 1.0
Author-Name: S. Reza H. Shojaei
Author-X-Name-First: S. Reza H.
Author-X-Name-Last: Shojaei
Author-Name: Yadollah Waghei
Author-X-Name-First: Yadollah
Author-X-Name-Last: Waghei
Author-Name: Mohsen Mohammadzadeh
Author-X-Name-First: Mohsen
Author-X-Name-Last: Mohammadzadeh
Title: Geostatistical analysis of disease data: a case study of tuberculosis incidence in Iran
Abstract: 
 The main objective of this study is to introduce two advanced statistical methods and to consider geographical distribution of tuberculosis incidence in Iran. With the knowledge that environmental and climatic conditions in each region are affective for the incidence and spread of the disease, the study has been taken into consideration. The disease incidences in different counties are realizations of spatial data, therefore we apply the Poisson kriging and ordinary kriging for prediction of tuberculosis incidence rates map in Iran. To identify high risk areas using statistical map of disease, our results show that tuberculosis incidences are not uniformly distributed in whole of the country and estimated risk is high in the eastern parts. Assessing geographical distribution of a disease is essential for health officials to recognize high-risk areas, and improve case management and resource allocation.
Journal: Journal of Applied Statistics
Pages: 1476-1483
Issue: 8
Volume: 45
Year: 2018
Month: 6
X-DOI: 10.1080/02664763.2017.1375468
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1375468
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:8:p:1476-1483




Template-Type: ReDIF-Article 1.0
Author-Name: David P. M. Scollnik
Author-X-Name-First: David P. M.
Author-X-Name-Last: Scollnik
Title: Bayesian analysis of a quarantine inspection model
Abstract: 
 In this paper, we propose a quarantine inspection model and examine its analysis from a Bayesian point of view. This model is a generalization of the one appearing in Decrouez and Robinson [Aust. N. Z. J. Stat., 54 (2012), pp. 281–299]. The context has to do with items approaching a border, some of which are randomly selected and inspected for contamination. A random sample of the items that pass this first inspection is submitted to a second inspection that is assumed to detect all contamination. Inference is sought with respect to the model parameters and also especially the proportion of items that pass through the border that are still contaminated. A hierarchical quarantine inspection model is also introduced and discussed. Three illustrative examples are given.
Journal: Journal of Applied Statistics
Pages: 1484-1496
Issue: 8
Volume: 45
Year: 2018
Month: 6
X-DOI: 10.1080/02664763.2017.1380785
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1380785
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:8:p:1484-1496




Template-Type: ReDIF-Article 1.0
Author-Name: Erindi Allaj
Author-X-Name-First: Erindi
Author-X-Name-Last: Allaj
Title: Two simple measures of variability for categorical data
Abstract: 
 This paper proposes two new variability measures for categorical data. The first variability measure is obtained as one minus the square root of the sum of the squares of the relative frequencies of the different categories. The second measure is obtained by standardizing the first measure. The measures proposed are functions of the variability measure proposed by Gini [Variabilitá e Mutuabilitá Contributo allo Studio delle Distribuzioni e delle Relazioni Statistiche, C. Cuppini, Bologna, 1912] and approximate the coefficient of nominal variation introduced by Kvålseth [Coefficients of variation for nominal and ordinal categorical data, Percept. Motor Skills 80 (1995), pp. 843–847] when the number of categories increases. Different mathematical properties of the proposed variability measures are studied and analyzed. Several examples illustrate how the variability measures can be interpreted and used in practice.
Journal: Journal of Applied Statistics
Pages: 1497-1516
Issue: 8
Volume: 45
Year: 2018
Month: 6
X-DOI: 10.1080/02664763.2017.1380787
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1380787
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:8:p:1497-1516




Template-Type: ReDIF-Article 1.0
Author-Name: Kung-Jong Lui
Author-X-Name-First: Kung-Jong
Author-X-Name-Last: Lui
Title: Sample size determination for testing equality in frequency data under an incomplete block crossover design
Abstract: 
 When there are more than two treatments under comparison, we may consider the use of the incomplete block crossover design (IBCD) to save the number of patients needed for a parallel groups design and reduce the duration of a crossover trial. We develop an asymptotic procedure for simultaneously testing equality of two treatments versus a control treatment (or placebo) in frequency data under the IBCD with two periods. We derive a sample size calculation procedure for the desired power of detecting the given treatment effects at a nominal-level and suggest a simple ad hoc adjustment procedure to improve the accuracy of the sample size determination when the resulting minimum required number of patients is not large. We employ Monte Carlo simulation to evaluate the finite-sample performance of the proposed test, the accuracy of the sample size calculation procedure, and that with the simple ad hoc adjustment suggested here. We use the data taken as a part of a crossover trial comparing the number of exacerbations between using salbutamol or salmeterol and a placebo in asthma patients to illustrate the sample size calculation procedure.
Journal: Journal of Applied Statistics
Pages: 1517-1529
Issue: 8
Volume: 45
Year: 2018
Month: 6
X-DOI: 10.1080/02664763.2017.1380788
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1380788
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:8:p:1517-1529




Template-Type: ReDIF-Article 1.0
Author-Name: M. A. Di Palma
Author-X-Name-First: M. A.
Author-X-Name-Last: Di Palma
Author-Name: P. Filzmoser
Author-X-Name-First: P.
Author-X-Name-Last: Filzmoser
Author-Name: M. Gallo
Author-X-Name-First: M.
Author-X-Name-Last: Gallo
Author-Name: K. Hron
Author-X-Name-First: K.
Author-X-Name-Last: Hron
Title: A robust Parafac model for compositional data
Abstract: 
 Compositional data are characterized by values containing relative information, and thus the ratios between the data values are of interest for the analysis. Due to specific features of compositional data, standard statistical methods should be applied to compositions expressed in a proper coordinate system with respect to an orthonormal basis. It is discussed how three-way compositional data can be analyzed with the Parafac model. When data are contaminated by outliers, robust estimates for the Parafac model parameters should be employed. It is demonstrated how robust estimation can be done in the context of compositional data and how the results can be interpreted. A real data example from macroeconomics underlines the usefulness of this approach.
Journal: Journal of Applied Statistics
Pages: 1347-1369
Issue: 8
Volume: 45
Year: 2018
Month: 6
X-DOI: 10.1080/02664763.2017.1381669
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1381669
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:8:p:1347-1369




Template-Type: ReDIF-Article 1.0
Author-Name: Mahesh Pandit
Author-X-Name-First: Mahesh
Author-X-Name-Last: Pandit
Author-Name: Krishna P. Paudel
Author-X-Name-First: Krishna P.
Author-X-Name-Last: Paudel
Author-Name: Roger Hinson
Author-X-Name-First: Roger
Author-X-Name-Last: Hinson
Title: Market channel selections by US nursery plant producers: a multivariate nonparametric fractional regression analysis
Abstract: 
 Availability of market channel alternatives has helped the growth of ornamental plant sales in the United States. To identify the factors affecting the choice and allocation of outputs to different market channels by nursery producers, we first use a mixture of experts model to select clusters of homogenous subpopulations of US nursery producers based on a 2009 National Nursery Survey. The impact of growers’ business characteristics on shares of sales to these channels was estimated using multivariate parametric and nonparametric fractional regression models. Specification tests indicated a nonparametric model was superior to a parametric model in some clusters. Important explanatory variables affecting the sales volume to different channels were sales of plant groups, kinds of contract sales, promotional expenses, and farm size. Results indicated the existence of clear market segmentation of nursery producers in the United States.
Journal: Journal of Applied Statistics
Pages: 1530-1546
Issue: 8
Volume: 45
Year: 2018
Month: 6
X-DOI: 10.1080/02664763.2017.1381670
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1381670
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:8:p:1530-1546




Template-Type: ReDIF-Article 1.0
Author-Name: Yan-Ting Xiao
Author-X-Name-First: Yan-Ting
Author-X-Name-Last: Xiao
Author-Name: Zhan-Shou Chen
Author-X-Name-First: Zhan-Shou
Author-X-Name-Last: Chen
Title: Bias-corrected estimations in varying-coefficient partially nonlinear models with measurement error in the nonparametric part
Abstract: 
 In this paper, we consider the statistical inference for the varying-coefficient partially nonlinear model with additive measurement errors in the nonparametric part. The local bias-corrected profile nonlinear least-squares estimation procedure for parameter in nonlinear function and nonparametric function is proposed. Then, the asymptotic normality properties of the resulting estimators are established. With the empirical likelihood method, a local bias-corrected empirical log-likelihood ratio statistic for the unknown parameter, and a corrected and residual adjusted empirical log-likelihood ratio for the nonparametric component are constructed. It is shown that the resulting statistics are asymptotically chi-square distribution under some suitable conditions. Some simulations are conducted to evaluate the performance of the proposed methods. The results indicate that the empirical likelihood method is superior to the profile nonlinear least-squares method in terms of the confidence regions of parameter and point-wise confidence intervals of nonparametric function.
Journal: Journal of Applied Statistics
Pages: 586-603
Issue: 4
Volume: 45
Year: 2018
Month: 3
X-DOI: 10.1080/02664763.2017.1288201
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1288201
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:4:p:586-603




Template-Type: ReDIF-Article 1.0
Author-Name: Qing Li
Author-X-Name-First: Qing
Author-X-Name-Last: Li
Author-Name: Feng Guo
Author-X-Name-First: Feng
Author-X-Name-Last: Guo
Author-Name: Inyoung Kim
Author-X-Name-First: Inyoung
Author-X-Name-Last: Kim
Author-Name: Sheila G. Klauer
Author-X-Name-First: Sheila G.
Author-X-Name-Last: Klauer
Author-Name: Bruce G. Simons-Morton
Author-X-Name-First: Bruce G.
Author-X-Name-Last: Simons-Morton
Title: A Bayesian finite mixture change-point model for assessing the risk of novice teenage drivers
Abstract: 
 The driving risk during the initial period after licensure for novice teenage drivers is typically the highest but decreases rapidly right after. The change-point of driving risk is a critical parameter for evaluating teenage driving risk, which also varies substantially among drivers. This paper presents latent class recurrent-event change-point models for detecting the change-points. The proposed model is applied to the Naturalist Teenage Driving Study, which continuously recorded the driving data of 42 novice teenage drivers for 18 months using advanced in-vehicle instrumentation. We propose a hierarchical BFMM to estimate the change-points by clusters of drivers with similar risk profiles. The model is based on a non-homogeneous Poisson process with piecewise-constant intensity functions. Latent variables which identify the membership of the subjects are used to detect potential clusters among subjects. Application to the Naturalistic Teenage Driving Study identifies three distinct clusters with change-points at 52.30, 108.99 and 150.20 hours of driving after first licensure, respectively. The overall intensity rate and the pattern of change also differ substantially among clusters. The results of this research provide more insight in teenagers' driving behaviour and will be critical to improve young drivers' safety education and parent management programs, as well as provide crucial reference for the GDL regulations to encourage safer driving.
Journal: Journal of Applied Statistics
Pages: 604-625
Issue: 4
Volume: 45
Year: 2018
Month: 3
X-DOI: 10.1080/02664763.2017.1288202
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1288202
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:4:p:604-625




Template-Type: ReDIF-Article 1.0
Author-Name: Kyle M. Irimata
Author-X-Name-First: Kyle M.
Author-X-Name-Last: Irimata
Author-Name: Jeffrey R. Wilson
Author-X-Name-First: Jeffrey R.
Author-X-Name-Last: Wilson
Title: Identifying intraclass correlations necessitating hierarchical modeling
Abstract: 
 Hierarchical binary outcome data with three levels, such as disease remission for patients nested within physicians, nested within clinics are frequently encountered in practice. One important aspect in such data is the correlation that occurs at each level of the data. In parametric modeling, accounting for these correlations increases the complexity. These models may also yield results that lead to the same conclusions as simpler models. We developed a measure of intraclass correlation at each stage of a three-level nested structure and identified guidelines for determining when the dependencies in hierarchical models need to be taken into account. These guidelines are supported by simulations of hierarchical data sets, as well as the analysis of AIDS knowledge in Bangladesh from the 2011 Demographic Health Survey. We also provide a simple rule of thumb to assist researchers faced with the challenge of choosing an appropriately complex model when analyzing hierarchical binary data.
Journal: Journal of Applied Statistics
Pages: 626-641
Issue: 4
Volume: 45
Year: 2018
Month: 3
X-DOI: 10.1080/02664763.2017.1288203
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1288203
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:4:p:626-641




Template-Type: ReDIF-Article 1.0
Author-Name: Oscar Palmeros
Author-X-Name-First: Oscar
Author-X-Name-Last: Palmeros
Author-Name: Jose A. Villaseñor
Author-X-Name-First: Jose A.
Author-X-Name-Last: Villaseñor
Author-Name: Elizabeth González
Author-X-Name-First: Elizabeth
Author-X-Name-Last: González
Title: On computing estimates of a change-point in the Weibull regression hazard model
Abstract: 
 The hazard function describes the instantaneous rate of failure at a time t, given that the individual survives up to t. In applications, the effect of covariates produce changes in the hazard function. When dealing with survival analysis, it is of interest to identify where a change point in time has occurred. In this work, covariates and censored variables are considered in order to estimate a change-point in the Weibull regression hazard model, which is a generalization of the exponential model. For this more general model, it is possible to obtain maximum likelihood estimators for the change-point and for the parameters involved. A Monte Carlo simulation study shows that indeed, it is possible to implement this model in practice. An application with clinical trial data coming from a treatment of chronic granulomatous disease is also included.
Journal: Journal of Applied Statistics
Pages: 642-648
Issue: 4
Volume: 45
Year: 2018
Month: 3
X-DOI: 10.1080/02664763.2017.1289366
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1289366
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:4:p:642-648




Template-Type: ReDIF-Article 1.0
Author-Name: Diego Casadei
Author-X-Name-First: Diego
Author-X-Name-Last: Casadei
Author-Name: Cornelius Grunwald
Author-X-Name-First: Cornelius
Author-X-Name-Last: Grunwald
Author-Name: Kevin Kröninger
Author-X-Name-First: Kevin
Author-X-Name-Last: Kröninger
Author-Name: Florian Mentzel
Author-X-Name-First: Florian
Author-X-Name-Last: Mentzel
Title: Objective Bayesian analysis of counting experiments with correlated sources of background
Abstract: 
 Searches for faint signals in counting experiments are often encountered in particle physics and astrophysics, as well as in other fields. Many problems can be reduced to the case of a model with independent and Poisson-distributed signal and background. Often several background contributions are present at the same time, possibly correlated. We provide the analytic solution of the statistical inference problem of estimating the signal in the presence of multiple backgrounds, in the framework of objective Bayes statistics. The model can be written in the form of a product of a single Poisson distribution with a multinomial distribution. The first is related to the total number of events, whereas the latter describes the fraction of events coming from each individual source. Correlations among different backgrounds can be included in the inference problem by a suitable choice of the priors.
Journal: Journal of Applied Statistics
Pages: 649-667
Issue: 4
Volume: 45
Year: 2018
Month: 3
X-DOI: 10.1080/02664763.2017.1289367
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1289367
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:4:p:649-667




Template-Type: ReDIF-Article 1.0
Author-Name: I. Wilms
Author-X-Name-First: I.
Author-X-Name-Last: Wilms
Author-Name: C. Croux
Author-X-Name-First: C.
Author-X-Name-Last: Croux
Title: An algorithm for the multivariate group lasso with covariance estimation
Abstract: 
 We study a group lasso estimator for the multivariate linear regression model that accounts for correlated error terms. A block coordinate descent algorithm is used to compute this estimator. We perform a simulation study with categorical data and multivariate time series data, typical settings with a natural grouping among the predictor variables. Our simulation studies show the good performance of the proposed group lasso estimator compared to alternative estimators. We illustrate the method on a time series data set of gene expressions.
Journal: Journal of Applied Statistics
Pages: 668-681
Issue: 4
Volume: 45
Year: 2018
Month: 3
X-DOI: 10.1080/02664763.2017.1289503
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1289503
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:4:p:668-681




Template-Type: ReDIF-Article 1.0
Author-Name: Bishal Gurung
Author-X-Name-First: Bishal
Author-X-Name-Last: Gurung
Author-Name: K. N. Singh
Author-X-Name-First: K. N.
Author-X-Name-Last: Singh
Author-Name: Ravindra Singh Shekhawat
Author-X-Name-First: Ravindra Singh
Author-X-Name-Last: Shekhawat
Author-Name: Md Yeasin
Author-X-Name-First: Md
Author-X-Name-Last: Yeasin
Title: An insight into technology diffusion of tractor through Weibull growth model
Abstract: 
 Most of the technological innovation diffusion follows an S-shaped curve. But, in many practical situations this may not hold true. To this end, Weibull model was proposed to capture the diffusion of new technological innovation, which does not follow any specific pattern. Nonlinear growth models play a very important role in getting an insight into the underlying mechanism. These models are generally ‘mechanistic’ as the parameters have meaningful interpretation. The nonlinear method of estimation of parameters of Weibull model fails to converge. Taking this problem into consideration, we propose the use of a powerful technique of genetic algorithm for parameter estimation. The methodology is also validated by simulation study to check whether parameter estimates are closer to the real value. For illustration purpose, we model the tractor density time-series data of India as a whole and some major states of India. It is seen that fitted Weibull model is able to capture the technology diffusion process in a reasonable manner. Further, comparison is also made with Logistic and Gompertz model; and is found to perform better for the data sets under consideration.
Journal: Journal of Applied Statistics
Pages: 682-696
Issue: 4
Volume: 45
Year: 2018
Month: 3
X-DOI: 10.1080/02664763.2017.1289504
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1289504
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:4:p:682-696




Template-Type: ReDIF-Article 1.0
Author-Name: Zicheng Hu
Author-X-Name-First: Zicheng
Author-X-Name-Last: Hu
Author-Name: Jessica N Lancaster
Author-X-Name-First: Jessica N
Author-X-Name-Last: Lancaster
Author-Name: Lauren I. R. Ehrlich
Author-X-Name-First: Lauren I. R.
Author-X-Name-Last: Ehrlich
Author-Name: Peter Müller
Author-X-Name-First: Peter
Author-X-Name-Last: Müller
Title: Detecting T cell activation using a varying dimension Bayesian model
Abstract: 
 The detection of T cell activation is critical in many immunological assays. However, detecting T cell activation in live tissues remains a challenge due to highly noisy data. We developed a Bayesian probabilistic model to identify T cell activation based on calcium flux, a increase in intracellular calcium concentration that occurs during T cell activation. Because a T cell has unknown number of flux events, the implementation of posterior inference requires trans-dimensional posterior simulation. The model is able to detect calcium flux events at the single cell level from simulated data, as well as from noisy biological data.
Journal: Journal of Applied Statistics
Pages: 697-713
Issue: 4
Volume: 45
Year: 2018
Month: 3
X-DOI: 10.1080/02664763.2017.1290789
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1290789
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:4:p:697-713




Template-Type: ReDIF-Article 1.0
Author-Name: Amanpreet Kaur
Author-X-Name-First: Amanpreet
Author-X-Name-Last: Kaur
Author-Name: Ravinder Agarwal
Author-X-Name-First: Ravinder
Author-X-Name-Last: Agarwal
Author-Name: Amod Kumar
Author-X-Name-First: Amod
Author-X-Name-Last: Kumar
Title: Adaptive threshold method for peak detection of surface electromyography signal from around shoulder muscles
Abstract: 
 This paper illustrates the accurate identification of the surface electromyography signal obtained from the shoulder muscles (Teres, Trapezius and Pectoralis) of amputee subjects with three different arm motions (elevation, protraction and retraction). During the acquisition of the signal, a variety of variations (amplitude, frequency and noise) were introduced into the acquired signal which will misguide in the prediction of motion of the shoulder. Therefore, a novel approach has been aimed to adaptively adjust the threshold of Teager energy operator in order to filter the unwanted peaks in the pre-processing stage of the surface electromyography (SEMG) signal. Results show that the proposed approach is accurate and effective in the analysis of biomedical signal where peaks are important to detect without the knowledge of the shape of the waveform. As clinical research continues, these algorithms helps us to process SEMG signal and the identified signal would be used to design more accurate and efficient controllers for the upper-limb amputee.
Journal: Journal of Applied Statistics
Pages: 714-726
Issue: 4
Volume: 45
Year: 2018
Month: 3
X-DOI: 10.1080/02664763.2017.1293624
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1293624
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:4:p:714-726




Template-Type: ReDIF-Article 1.0
Author-Name: Mariano Ruiz Espejo
Author-X-Name-First: Mariano Ruiz
Author-X-Name-Last: Espejo
Author-Name: Adalbert Marqués Vilallonga
Author-X-Name-First: Adalbert Marqués
Author-X-Name-Last: Vilallonga
Title: Principles of Scientific Methods
Journal: Journal of Applied Statistics
Pages: 775-776
Issue: 4
Volume: 45
Year: 2018
Month: 3
X-DOI: 10.1080/02664763.2017.1295522
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1295522
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:4:p:775-776




Template-Type: ReDIF-Article 1.0
Author-Name: Fadel Hamid Hadi Alhusseini
Author-X-Name-First: Fadel Hamid Hadi
Author-X-Name-Last: Alhusseini
Author-Name: Vasile Georgescu
Author-X-Name-First: Vasile
Author-X-Name-Last: Georgescu
Title: Bayesian composite Tobit quantile regression
Abstract: 
 Composite quantile regression models have been shown to be effective techniques in improving the prediction accuracy [H. Zou and M. Yuan, Composite quantile regression and the oracle model selection theory, Ann. Statist. 36 (2008), pp. 1108–1126; J. Bradic, J. Fan, and W. Wang, Penalized composite quasi-likelihood for ultrahighdimensional variable selection, J. R. Stat. Soc. Ser. B 73 (2011), pp. 325–349; Z. Zhao and Z. Xiao, Efficient regressions via optimally combining quantile information, Econometric Theory 30(06) (2014), pp. 1272–1314]. This paper studies composite Tobit quantile regression (TQReg) from a Bayesian perspective. A simple and efficient MCMC-based computation method is derived for posterior inference using a mixture of an exponential and a scaled normal distribution of the skewed Laplace distribution. The approach is illustrated via simulation studies and a real data set. Results show that combine information across different quantiles can provide a useful method in efficient statistical estimation. This is the first work to discuss composite TQReg from a Bayesian perspective.
Journal: Journal of Applied Statistics
Pages: 727-739
Issue: 4
Volume: 45
Year: 2018
Month: 3
X-DOI: 10.1080/02664763.2017.1299697
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1299697
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:4:p:727-739




Template-Type: ReDIF-Article 1.0
Author-Name: Ján Dolinský
Author-X-Name-First: Ján
Author-X-Name-Last: Dolinský
Author-Name: Kei Hirose
Author-X-Name-First: Kei
Author-X-Name-Last: Hirose
Author-Name: Sadanori Konishi
Author-X-Name-First: Sadanori
Author-X-Name-Last: Konishi
Title: Readouts for echo-state networks built using locally regularized orthogonal forward regression
Abstract: 
 Echo state network (ESN) is viewed as a temporal expansion which naturally give rise to regressors of various relevance to a teacher output. We illustrate that often only a certain amount of the generated echo-regressors effectively explain the teacher output and we propose to determine the importance of the echo-regressors by a joint calculation of the individual variance contributions and Bayesian relevance using the locally regularized orthogonal forward regression (LROFR). This information can be advantageously used in a variety of ways for an analysis of an ESN structure. We present a locally regularized linear readout built using LROFR. The readout may have a smaller dimensionality than the ESN model itself, and improves robustness and accuracy of an ESN. Its main advantage is ability to determine what type of an additional readout is suitable for a task at hand. Comparison with PCA is provided too. We also propose a radial basis function (RBF) readout built using LROFR, since flexibility of the linear readout has limitations and might be insufficient for complex tasks. Its excellent generalization abilities make it a viable alternative to feed-forward neural networks or relevance-vector-machines. For cases where more temporal capacity is required we propose well studied delay&amp;sum readout.
Journal: Journal of Applied Statistics
Pages: 740-762
Issue: 4
Volume: 45
Year: 2018
Month: 3
X-DOI: 10.1080/02664763.2017.1305331
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1305331
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:4:p:740-762




Template-Type: ReDIF-Article 1.0
Author-Name: K. Nichols
Author-X-Name-First: K.
Author-X-Name-Last: Nichols
Author-Name: E. Trevino
Author-X-Name-First: E.
Author-X-Name-Last: Trevino
Author-Name: N. Ikeda
Author-X-Name-First: N.
Author-X-Name-Last: Ikeda
Author-Name: D. Philo
Author-X-Name-First: D.
Author-X-Name-Last: Philo
Author-Name: A. Garcia
Author-X-Name-First: A.
Author-X-Name-Last: Garcia
Author-Name: D. Bowman
Author-X-Name-First: D.
Author-X-Name-Last: Bowman
Title: Interdependency amongst earthquake magnitudes in Southern California
Abstract: 
 Recent research has shown that for the larger earthquakes recorded ( $ M\ge 5.2 $ M≥5.2) within the global centroid moment tensor (gCMT) there is a positive correlation between the magnitudes of earthquakes and the magnitudes of their aftershocks [13]. Through a modification of model independent stochastic de-clustering [12] and a more localized catalog provided by the Southern California Earthquake Data Center (SCEDC), the methodologies of Nichols and Schoenberg can be extended to catalogs complete with a much lower minimum magnitude of completeness ( $ M\ge 2.2 $ M≥2.2). Results indicate that the positive correlation observed between larger earthquakes in the gCMT catalog and their aftershocks is also evident in the relationship between the magnitudes of earthquakes in the SCEDC data and their aftershocks. However, with the lower minimum magnitude of completeness found in the SCEDC catalog and with short periods of extreme earthquake activity evident within the data, the statistical power of the stochastic de-clustering algorithms to distinguish between mainshocks and aftershocks is diminished.
Journal: Journal of Applied Statistics
Pages: 763-774
Issue: 4
Volume: 45
Year: 2018
Month: 3
X-DOI: 10.1080/02664763.2017.1313965
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1313965
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:4:p:763-774




Template-Type: ReDIF-Article 1.0
Author-Name: Connie Stewart
Author-X-Name-First: Connie
Author-X-Name-Last: Stewart
Title: An approach to measure distance between compositional diet estimates containing essential zeros
Abstract: 
 For many applications involving compositional data, it is necessary to establish a valid measure of distance, yet when essential zeros are present traditional distance measures are problematic. In quantitative fatty acid signature analysis (QFASA), compositional diet estimates are produced that often contain many zeros. In order to test for a difference in diet between two populations of predators using the QFASA diet estimates, a legitimate measure of distance for use in the test statistic is necessary. Since ecologists using QFASA must first select the potential species of prey in the predator's diet, the chosen measure of distance should be such that the distance between samples does not decrease as the number of species considered increases, a property known in general as subcompositional coherence. In this paper we compare three measures of distance for compositional data capable of handling zeros, but not satisfying some of the well-accepted principles of compositional data analysis. For compositional diet estimates, the most relevant of these is the property of subcompositionally coherence and we show that this property may be approximately satisfied. Based on the results of a simulation study and an application to real-life QFASA diet estimates of grey seals, we recommend the chi-square measure of distance.
Journal: Journal of Applied Statistics
Pages: 1137-1152
Issue: 7
Volume: 44
Year: 2017
Month: 5
X-DOI: 10.1080/02664763.2016.1193846
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1193846
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:7:p:1137-1152




Template-Type: ReDIF-Article 1.0
Author-Name: Diego I. Gallardo
Author-X-Name-First: Diego I.
Author-X-Name-Last: Gallardo
Author-Name: Héctor W. Gómez
Author-X-Name-First: Héctor W.
Author-X-Name-Last: Gómez
Author-Name: Heleno Bolfarine
Author-X-Name-First: Heleno
Author-X-Name-Last: Bolfarine
Title: A new cure rate model based on the Yule–Simon distribution with application to a melanoma data set
Abstract: 
 In this paper, a new survival cure rate model is introduced considering the Yule–Simon distribution [12] to model the number of concurrent causes. We study some properties of this distribution and the model arising when the distribution of the competing causes is the Weibull model. We call this distribution the Weibull–Yule–Simon distribution. Maximum likelihood estimation is conducted for model parameters. A small scale simulation study is conducted indicating satisfactory parameter recovery by the estimation approach. Results are applied to a real data set (melanoma) illustrating the fact that the model proposed can outperform traditional alternative models in terms of model fitting.
Journal: Journal of Applied Statistics
Pages: 1153-1164
Issue: 7
Volume: 44
Year: 2017
Month: 5
X-DOI: 10.1080/02664763.2016.1194385
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1194385
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:7:p:1153-1164




Template-Type: ReDIF-Article 1.0
Author-Name: Tatsuya Kubota
Author-X-Name-First: Tatsuya
Author-X-Name-Last: Kubota
Author-Name: Takeshi Kurosawa
Author-X-Name-First: Takeshi
Author-X-Name-Last: Kurosawa
Title: Bayesian prediction of unobserved values for Type-II censored data
Abstract: 
 In this paper, we consider posterior predictive distributions of Type-II censored data for an inverse Weibull distribution. These functions are given by using conditional density functions and conditional survival functions. Although the conditional survival functions were expressed by integral forms in previous studies, we derive the conditional survival functions in closed forms and thereby reduce the computation cost. In addition, we calculate the predictive confidence intervals of unobserved values and coverage probabilities of unobserved values by using the posterior predictive survival functions.
Journal: Journal of Applied Statistics
Pages: 1165-1180
Issue: 7
Volume: 44
Year: 2017
Month: 5
X-DOI: 10.1080/02664763.2016.1201792
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1201792
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:7:p:1165-1180




Template-Type: ReDIF-Article 1.0
Author-Name: Öznur İşçi Güneri
Author-X-Name-First: Öznur İşçi
Author-X-Name-Last: Güneri
Author-Name: Atilla Göktaş
Author-X-Name-First: Atilla
Author-X-Name-Last: Göktaş
Author-Name: Uğur Kayalı
Author-X-Name-First: Uğur
Author-X-Name-Last: Kayalı
Title: Path analysis and determining the distribution of indirect effects via simulation
Abstract: 
 The difference between a path analysis and the other multivariate analyses is that the path analysis has the ability to compute the indirect effects apart from the direct effects. The aim of this study is to investigate the distribution of indirect effects that is one of the components of path analysis via generated data. To realize this, a simulation study has been conducted with four different sample sizes, three different numbers of explanatory variables and with three different correlation matrices. A replication of 1000 has been applied for every single combination. According to the results obtained, it is found that irrespective of the sample size path coefficients tend to be stable. Moreover, path coefficients are not affected by correlation types either. Since the replication number is 1000, which is fairly large, the indirect effects from the path models have been treated as normal and their confidence intervals have been presented as well. It is also found that the path analysis should not be used with three explanatory variables. We think that this study would help scientists who are working in both natural and social sciences to determine sample size and different number of variables in the path analysis.
Journal: Journal of Applied Statistics
Pages: 1181-1210
Issue: 7
Volume: 44
Year: 2017
Month: 5
X-DOI: 10.1080/02664763.2016.1201793
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1201793
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:7:p:1181-1210




Template-Type: ReDIF-Article 1.0
Author-Name: Rafael Bernardo Carmona-Benítez
Author-X-Name-First: Rafael Bernardo
Author-X-Name-Last: Carmona-Benítez
Author-Name: María Rosa Nieto
Author-X-Name-First: María Rosa
Author-X-Name-Last: Nieto
Title: Comparison of bootstrap estimation intervals to forecast arithmetic mean and median air passenger demand
Abstract: 
 The aim of this paper is to compare passenger (pax) demand between airports based on the arithmetic mean (MPD) and the median pax demand (MePD). A three phases approach is applied. First phase, we use bootstrap procedures to estimate the distribution of the arithmetic MPD and the MePD for each block of routes distance; second phase, we use percentile, standard, bias corrected, and bias corrected accelerated methods to calculate bootstrap confidence bands for the MPD and the MePD; and third phase, we implement Monte Carlo (MC) experiments to analyse the finite sample performance of the applied bootstrap. Our results conclude that it is more meaningful to use the estimation of MePD rather than the estimation of MPD in the air transport industry. By carrying out MC experiments, we demonstrate that the bootstrap methods produce coverages close to the nominal for the MPD and the MePD.
Journal: Journal of Applied Statistics
Pages: 1211-1224
Issue: 7
Volume: 44
Year: 2017
Month: 5
X-DOI: 10.1080/02664763.2016.1201794
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1201794
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:7:p:1211-1224




Template-Type: ReDIF-Article 1.0
Author-Name: Izabella A. R. A. Santos
Author-X-Name-First: Izabella A. R. A.
Author-X-Name-Last: Santos
Author-Name: Denise Duarte
Author-X-Name-First: Denise
Author-X-Name-Last: Duarte
Author-Name: Marcelo Azevedo Costa
Author-X-Name-First: Marcelo Azevedo
Author-X-Name-Last: Costa
Title: Use of jump process to model mobility in massive multiplayer on-line games
Abstract: 
 This paper proposes a methodology to model the mobility of characters in Massively Multiplayer On-line (MMO) Games. We propose to model the mobility of characters in the map of an MMO game as a jump process using two approaches to model the times spent in the states of the process: parametric and non-parametric. Furthermore, a simulator for the mobility is presented. We analyze geographic position data of the characters in the map of the game World of Warcraft and compare the observed and simulated data. The proposed methodology and the simulator can be used to optimize computing load allocation of servers, which is extremely important for game performance, service quality and cost.
Journal: Journal of Applied Statistics
Pages: 1225-1247
Issue: 7
Volume: 44
Year: 2017
Month: 5
X-DOI: 10.1080/02664763.2016.1201795
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1201795
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:7:p:1225-1247




Template-Type: ReDIF-Article 1.0
Author-Name: Jouchi Nakajima
Author-X-Name-First: Jouchi
Author-X-Name-Last: Nakajima
Author-Name: Tsuyoshi Kunihama
Author-X-Name-First: Tsuyoshi
Author-X-Name-Last: Kunihama
Author-Name: Yasuhiro Omori
Author-X-Name-First: Yasuhiro
Author-X-Name-Last: Omori
Title: Bayesian modeling of dynamic extreme values: extension of generalized extreme value distributions with latent stochastic processes
Abstract: 
 This paper develops Bayesian inference of extreme value models with a flexible time-dependent latent structure. The generalized extreme value distribution is utilized to incorporate state variables that follow an autoregressive moving average (ARMA) process with Gumbel-distributed innovations. The time-dependent extreme value distribution is combined with heavy-tailed error terms. An efficient Markov chain Monte Carlo algorithm is proposed using a state-space representation with a finite mixture of normal distributions to approximate the Gumbel distribution. The methodology is illustrated by simulated data and two different sets of real data. Monthly minima of daily returns of stock price index, and monthly maxima of hourly electricity demand are fit to the proposed model and used for model comparison. Estimation results show the usefulness of the proposed model and methodology, and provide evidence that the latent autoregressive process and heavy-tailed errors play an important role to describe the monthly series of minimum stock returns and maximum electricity demand.
Journal: Journal of Applied Statistics
Pages: 1248-1268
Issue: 7
Volume: 44
Year: 2017
Month: 5
X-DOI: 10.1080/02664763.2016.1201796
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1201796
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:7:p:1248-1268




Template-Type: ReDIF-Article 1.0
Author-Name: Camillo Cammarota
Author-X-Name-First: Camillo
Author-X-Name-Last: Cammarota
Title: Estimating the turning point location in shifted exponential model of time series
Abstract: 
 We consider the distribution of the turning point location of time series modeled as the sum of deterministic trend plus random noise. If the variables are modeled by shifted exponentials, whose location parameters define the trend, we provide a formula for computing the distribution of the turning point location and consequently to estimate a confidence interval for the location. We test this formula in simulated data series having a trend with asymmetric minimum, investigating the coverage rate as a function of a bandwidth parameter. The method is applied to estimate the confidence interval of the minimum location of two types of real-time series: the RT intervals extracted from the electrocardiogram recorded during the exercise test and an economic indicator, the current account balance. We discuss the connection with stochastic ordering.
Journal: Journal of Applied Statistics
Pages: 1269-1281
Issue: 7
Volume: 44
Year: 2017
Month: 5
X-DOI: 10.1080/02664763.2016.1201797
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1201797
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:7:p:1269-1281




Template-Type: ReDIF-Article 1.0
Author-Name: M. Moghimbeygi
Author-X-Name-First: M.
Author-X-Name-Last: Moghimbeygi
Author-Name: M. Golalizadeh
Author-X-Name-First: M.
Author-X-Name-Last: Golalizadeh
Title: Longitudinal shape analysis by using the spherical coordinates
Abstract: 
 One of the important topics in morphometry that received high attention recently is the longitudinal analysis of shape variation. According to Kendall's definition of shape, the shape of object appertains on non-Euclidean space, making the longitudinal study of configuration somehow difficult. However, to simplify this task, triangulation of the objects and then constructing a non-parametric regression-type model on the unit sphere is pursued in this paper. The prediction of the configurations in some time instances is done using both properties of triangulation and the size of great baselines. Moreover, minimizing a Euclidean risk function is proposed to select feasible weights in constructing smoother functions in a non-parametric smoothing manner. These will provide some proper shape growth models to analysis objects varying in time. The proposed models are applied to analysis of two real-life data sets.
Journal: Journal of Applied Statistics
Pages: 1282-1295
Issue: 7
Volume: 44
Year: 2017
Month: 5
X-DOI: 10.1080/02664763.2016.1201798
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1201798
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:7:p:1282-1295




Template-Type: ReDIF-Article 1.0
Author-Name: C. Masci
Author-X-Name-First: C.
Author-X-Name-Last: Masci
Author-Name: F. Ieva
Author-X-Name-First: F.
Author-X-Name-Last: Ieva
Author-Name: T. Agasisti
Author-X-Name-First: T.
Author-X-Name-Last: Agasisti
Author-Name: A. M. Paganoni
Author-X-Name-First: A. M.
Author-X-Name-Last: Paganoni
Title: Bivariate multilevel models for the analysis of mathematics and reading pupils' achievements
Abstract: 
 The purpose of this paper is to identify a relationship between pupils' mathematics and reading test scores and the characteristics of students themselves, stratifying for classes, schools and geographical areas. The data set of interest contains detailed information about more than 500,000 students at the first year of junior secondary school in the year 2012/2013, provided by the Italian Institute for the Evaluation of Educational System. The innovation of this work is in the use of multivariate multilevel models, in which the outcome is bivariate: reading and mathematics achievement. Using the bivariate outcome enables researchers to analyze the correlations between achievement levels in the two fields and to predict statistically significant school and class effects after adjusting for pupil's characteristics. The statistical model employed here explicates account for the potential covariance between the two topics, and at the same time it allows the school effect to vary among them. The results show that while for most cases the direction of school's effect is coherent for reading and mathematics (i.e. positive/negative), there are cases where internal school factors lead to different performances in the two fields.
Journal: Journal of Applied Statistics
Pages: 1296-1317
Issue: 7
Volume: 44
Year: 2017
Month: 5
X-DOI: 10.1080/02664763.2016.1201799
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1201799
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:7:p:1296-1317




Template-Type: ReDIF-Article 1.0
Author-Name: Yanqin Feng
Author-X-Name-First: Yanqin
Author-X-Name-Last: Feng
Author-Name: Shurong Lin
Author-X-Name-First: Shurong
Author-X-Name-Last: Lin
Author-Name: Yang Li
Author-X-Name-First: Yang
Author-X-Name-Last: Li
Title: Semiparametric regression of clustered current status data
Abstract: 
 This paper discusses regression analysis of clustered current status data under semiparametric additive hazards models. In particular, we consider the situation when cluster sizes can be informative about correlated failure times from the same cluster. To address the problem, we present estimating equation-based estimation procedures and establish asymptotic properties of the resulting estimates. Finite sample performance of the proposed method is assessed through an extensive simulation study, which indicates the procedure works well. The method is applied to a motivating data set from a lung tumorigenicity study.
Journal: Journal of Applied Statistics
Pages: 1724-1737
Issue: 10
Volume: 46
Year: 2019
Month: 7
X-DOI: 10.1080/02664763.2018.1564022
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1564022
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:10:p:1724-1737




Template-Type: ReDIF-Article 1.0
Author-Name: Muhammad Aslam Mohd Safari
Author-X-Name-First: Muhammad Aslam Mohd
Author-X-Name-Last: Safari
Author-Name: Nurulkamal Masseran
Author-X-Name-First: Nurulkamal
Author-X-Name-Last: Masseran
Author-Name: Kamarulzaman Ibrahim
Author-X-Name-First: Kamarulzaman
Author-X-Name-Last: Ibrahim
Title: On the identification of extreme outliers and dragon-kings mechanisms in the upper tail of income distribution
Abstract: 
 The presence of extreme outliers in the upper tail data of income distribution affects the Pareto tail modeling. A simulation study is carried out to compare the performance of three types of boxplot in the detection of extreme outliers for Pareto data, including standard boxplot, adjusted boxplot and generalized boxplot. It is found that the generalized boxplot is the best method for determining extreme outliers for Pareto distributed data. For the application, the generalized boxplot is utilized for determining the exreme outliers in the upper tail of Malaysian income distribution. In addition, for this data set, the confidence interval method is applied for examining the presence of dragon-kings, extreme outliers which are beyond the Pareto or power-laws distribution.
Journal: Journal of Applied Statistics
Pages: 1886-1902
Issue: 10
Volume: 46
Year: 2019
Month: 7
X-DOI: 10.1080/02664763.2019.1566447
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1566447
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:10:p:1886-1902




Template-Type: ReDIF-Article 1.0
Author-Name: Shuang Xu
Author-X-Name-First: Shuang
Author-X-Name-Last: Xu
Author-Name: Chun-Xia Zhang
Author-X-Name-First: Chun-Xia
Author-X-Name-Last: Zhang
Title: Robust sparse regression by modeling noise as a mixture of gaussians
Abstract: 
 Regression analysis has been proven to be a quite effective tool in a large variety of fields. In many regression models, it is often assumed that noise is with a specific distribution. Although the theoretical analysis can be greatly facilitated, the model-fitting performance may be poor since the supposed noise distribution may deviate from real noise to a large extent. Meanwhile, the model is also expected to be robust in consideration of the complexity of real-world data. Without any assumption about noise, we propose in this paper a novel sparse regression method called MoG-Lasso to directly model noise in linear regression models via a mixture of Gaussian distributions (MoG). Meanwhile, the  $ L_1 $ L1 penalty is included as a part of the loss function of MoG-Lasso to enhance its ability to identify a sparse model. As for the parameters in MoG-Lasso, we present an efficient algorithm to estimate them via the EM (expectation maximization) and ADMM (alternating direction method of multipliers) algorithms. With some simulated and real data contaminated by complex noise, the experiments show that the novel model MoG-Lasso performs better than several other popular methods in both ‘p>n’ and ‘p<n’ situations, including Lasso, LAD-Lasso and Huber-Lasso.
Journal: Journal of Applied Statistics
Pages: 1738-1755
Issue: 10
Volume: 46
Year: 2019
Month: 7
X-DOI: 10.1080/02664763.2019.1566448
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1566448
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:10:p:1738-1755




Template-Type: ReDIF-Article 1.0
Author-Name: I. Votsi
Author-X-Name-First: I.
Author-X-Name-Last: Votsi
Author-Name: A. Brouste
Author-X-Name-First: A.
Author-X-Name-Last: Brouste
Title: Confidence interval for the mean time to failure in semi-Markov models: an application to wind energy production
Abstract: 
 Mean times to failure are fundamental indicators in reliability and related fields where stochastic processes are used to describe random, real-life systems. Here we focus on the conditional mean time to failure defined in a semi-Markov context. A discrete time semi-Markov model with discrete state space is employed, which allows for realistic description of systems under risk. Our main objective is to estimate the conditional mean time to failure and provide asymptotic properties of its empirical estimator. Consistency and asymptotic normality results are provided and are validated numerically. Our methodology is tested in real and simulated wind data and indicators associated with the wind energy production are estimated.
Journal: Journal of Applied Statistics
Pages: 1756-1773
Issue: 10
Volume: 46
Year: 2019
Month: 7
X-DOI: 10.1080/02664763.2019.1566449
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1566449
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:10:p:1756-1773




Template-Type: ReDIF-Article 1.0
Author-Name: Ruhao Wu
Author-X-Name-First: Ruhao
Author-X-Name-Last: Wu
Author-Name: Bo Wang
Author-X-Name-First: Bo
Author-X-Name-Last: Wang
Title: Coherent mortality forecasting by the weighted multilevel functional principal component approach
Abstract: 
 In human mortality modelling, if a population consists of several subpopulations it can be desirable to model their mortality rates simultaneously while taking into account the heterogeneity among them. The mortality forecasting methods tend to result in divergent forecasts for subpopulations when independence is assumed. However, under closely related social, economic and biological backgrounds, mortality patterns of these subpopulations are expected to be non-divergent in the future. In this article, we propose a new method for coherent modelling and forecasting of mortality rates for multiple subpopulations, in the sense of nondivergent life expectancy among subpopulations. The mortality rates of subpopulations are treated as multilevel functional data and a weighted multilevel functional principal component (wMFPCA) approach is proposed to model and forecast them. The proposed model is applied to sex-specific data for nine developed countries, and the results show that, in terms of overall forecasting accuracy, the model outperforms the independent model and the Product-Ratio model as well as the unweighted multilevel functional principal component approach.
Journal: Journal of Applied Statistics
Pages: 1774-1791
Issue: 10
Volume: 46
Year: 2019
Month: 7
X-DOI: 10.1080/02664763.2019.1572718
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1572718
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:10:p:1774-1791




Template-Type: ReDIF-Article 1.0
Author-Name: F. Prataviera
Author-X-Name-First: F.
Author-X-Name-Last: Prataviera
Author-Name: J. C. S. Vasconcelos
Author-X-Name-First: J. C. S.
Author-X-Name-Last: Vasconcelos
Author-Name: G. M. Cordeiro
Author-X-Name-First: G. M.
Author-X-Name-Last: Cordeiro
Author-Name: E. M. Hashimoto
Author-X-Name-First: E. M.
Author-X-Name-Last: Hashimoto
Author-Name: E. M. M. Ortega
Author-X-Name-First: E. M. M.
Author-X-Name-Last: Ortega
Title: The exponentiated power exponential regression model with different regression structures: application in nursing data
Abstract: 
 We define the exponentiated power exponential distribution and propose a regression model with different systematic structures based on the new distribution. We show that the new regression model can be applied to dispersion data since it represents a parametric family of models that includes as sub-models some widely-known regression models. It then can be used more effectively in the analysis of real data. We use maximum likelihood estimation and derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes. Some global-influence measurements are also investigated and simulation studies are performed to evaluate the accuracy of the estimates. We provide an application of the regression model with four systematic structures to nursing activities score data in the Unit of the Medical Clinic of University of São Paulo (USP) Hospital.
Journal: Journal of Applied Statistics
Pages: 1792-1821
Issue: 10
Volume: 46
Year: 2019
Month: 7
X-DOI: 10.1080/02664763.2019.1572719
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1572719
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:10:p:1792-1821




Template-Type: ReDIF-Article 1.0
Author-Name: Katinka Fischer
Author-X-Name-First: Katinka
Author-X-Name-Last: Fischer
Author-Name: Christoph Bothung
Author-X-Name-First: Christoph
Author-X-Name-Last: Bothung
Author-Name: Felix Lieder
Author-X-Name-First: Felix
Author-X-Name-Last: Lieder
Author-Name: Stefan Wolfart
Author-X-Name-First: Stefan
Author-X-Name-Last: Wolfart
Author-Name: Holger Schwender
Author-X-Name-First: Holger
Author-X-Name-Last: Schwender
Title: A statistical test for detecting discordance in rankings between k groups
Abstract: 
 In several research areas such as psychology, social science, and medicine, studies are conducted in which objects should be ranked by different judges/raters and the concordance of the different rankings is then analyzed. In such studies, it is also frequently of interest to compare the rankings between different groups of judges, e.g. female vs. male judges or judges from different professions. In the two-group case, the two-group concordance test of Schucany & Frawley can be employed for such a comparison. In this article, we propose an extension of this test enabling the comparison of rankings from more than two groups of judges. This test aims to detect disagreement in the average rankings of the objects between k groups with an at least moderate intra-group concordance. We evaluate this test in an extensive simulation study and in an application to data from an aesthetics study. This simulation study shows that the proposed test is able to detect differences between average rankings and performs well even in situations in which the disagreement is comparably small or the intra-group concordance is inhomogeneous.
Journal: Journal of Applied Statistics
Pages: 1822-1842
Issue: 10
Volume: 46
Year: 2019
Month: 7
X-DOI: 10.1080/02664763.2019.1572720
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1572720
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:10:p:1822-1842




Template-Type: ReDIF-Article 1.0
Author-Name: Sanku Dey
Author-X-Name-First: Sanku
Author-X-Name-Last: Dey
Author-Name: Mahendra Saha
Author-X-Name-First: Mahendra
Author-X-Name-Last: Saha
Title: Bootstrap confidence intervals of generalized process capability index Cpyk using different methods of estimation
Abstract: 
 One of the indicators for evaluating the capability of a process is the process capability index. In this article, we consider six frequentest estimation methods, namely, the method of maximum likelihood (ML), the method of least square (LS), the method of weighted least square, method of percentile (PC), the method of maximum product of spacing and method of Cramèr-von-Mises (CM) to estimate the parameters and the generalized process capability index (GPCI)  $ C_{pyk} $ Cpyk for the logistic exponential (LE) distribution and in particular for exponential distribution. It is well known that confidence interval (CI) provides much more information about the population characteristic of interest than does a point estimate. Hence, next, we consider three bootstrap confidence intervals (BCIs) namely, standard bootstrap (s-boot), percentile bootstrap (p-boot) and bias-corrected percentile bootstrap ( $ BC_p $ BCp-boot) for obtaining CIs of GPCI  $ C_{pyk} $ Cpyk using the aforementioned methods of estimation. A Monte Carlo simulation has been used to investigate the estimated coverage probabilities and average widths of the BCIs. The performances of the estimators have been compared in terms of their mean squared error using simulated samples. Simulation results showed that the estimated coverage probabilities of the p-boot CI and the s-boot CI get closer to the nominal confidence level than those of the  $ BC_p $ BCp-boot CI for both cases. Finally, three real data sets are analyzed for illustrative purposes.
Journal: Journal of Applied Statistics
Pages: 1843-1869
Issue: 10
Volume: 46
Year: 2019
Month: 7
X-DOI: 10.1080/02664763.2019.1572721
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1572721
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:10:p:1843-1869




Template-Type: ReDIF-Article 1.0
Author-Name: Mohammad Samsul Alam
Author-X-Name-First: Mohammad Samsul
Author-X-Name-Last: Alam
Author-Name: Syed Shahadat Hossain
Author-X-Name-First: Syed Shahadat
Author-X-Name-Last: Hossain
Author-Name: Farha Ferdous Sheela
Author-X-Name-First: Farha Ferdous
Author-X-Name-Last: Sheela
Title: Spatial smoothing of low birth weight rate in Bangladesh using Bayesian hierarchical model
Abstract: 
 The term low birth weight refers an event where a newborn baby has a weight that is less than 2500 g. This is an essential indicator while the interest is in public health issues such as infant mortality, maternal complications, and antenatal care, etc. of a country, particularly, for a developing country like Bangladesh. The regional development programs are in the current priority list of Bangladesh government and other policy makers. Many of such regional development programs may need the spatial distribution of relative risk for low birth weight that can be obtained by mapping the risks over small area domains like the districts of Bangladesh. This study aims to find whether is there any spatial dependence among the relative risks of low birth weight for the districts of Bangladesh. This has been investigated using Moran's I statistic and a significant spatial dependence in the relative risks was found. Then, attempt has been made to rediscover the spatial distribution based on the idea of spatial smoothing. A Bayesian hierarchical model is used considering percent received antenatal care and female labor force participation as covariates to smooth the observed relative risks of low birth weight in 64 districts of Bangladesh. Revised spatial distribution taking the spatial dependence under consideration through intrinsic conditional autoregressive model is derived and showed in choropleth map along with its different behaviors.
Journal: Journal of Applied Statistics
Pages: 1870-1885
Issue: 10
Volume: 46
Year: 2019
Month: 7
X-DOI: 10.1080/02664763.2019.1572722
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1572722
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:10:p:1870-1885




Template-Type: ReDIF-Article 1.0
Author-Name: Thomas McAndrew
Author-X-Name-First: Thomas
Author-X-Name-Last: McAndrew
Author-Name: Bjorn Redfors
Author-X-Name-First: Bjorn
Author-X-Name-Last: Redfors
Author-Name: Aaron Crowley
Author-X-Name-First: Aaron
Author-X-Name-Last: Crowley
Author-Name: Yiran Zhang
Author-X-Name-First: Yiran
Author-X-Name-Last: Zhang
Author-Name: Shmuel Chen
Author-X-Name-First: Shmuel
Author-X-Name-Last: Chen
Author-Name: Mordechai Golomb
Author-X-Name-First: Mordechai
Author-X-Name-Last: Golomb
Author-Name: Maria C. Alu
Author-X-Name-First: Maria C.
Author-X-Name-Last: Alu
Author-Name: Dominic P. Francese
Author-X-Name-First: Dominic P.
Author-X-Name-Last: Francese
Author-Name: Ori Ben-Yehuda
Author-X-Name-First: Ori
Author-X-Name-Last: Ben-Yehuda
Author-Name: Akiko Maehara
Author-X-Name-First: Akiko
Author-X-Name-Last: Maehara
Author-Name: Gary S. Mintz
Author-X-Name-First: Gary S.
Author-X-Name-Last: Mintz
Author-Name: Gregg W. Stone
Author-X-Name-First: Gregg W.
Author-X-Name-Last: Stone
Author-Name: Paul L. Jenkins
Author-X-Name-First: Paul L.
Author-X-Name-Last: Jenkins
Title: How Cox models react to a study-specific confounder in a patient-level pooled dataset: random effects better cope with an imbalanced covariate across trials unless baseline hazards differ
Abstract: 
 Combining patient-level data from clinical trials can connect rare phenomena with clinical endpoints, but statistical techniques applied to a single trial may become problematical when trials are pooled. Estimating the hazard of a binary variable unevenly distributed across trials showcases a common pooled database issue. We studied how an unevenly distributed binary variable can compromise the integrity of fixed and random effects Cox proportional hazards (cph) models. We compared fixed effect and random effects cph models on a set of simulated datasets inspired by a 17-trial pooled database of patients presenting with ST segment elevation myocardial infarction (STEMI) and non-STEMI undergoing percutaneous coronary intervention. An unevenly distributed covariate can bias hazard ratio estimates, inflate standard errors, raise type I error, and reduce power. While uneveness causes problems for all cph models, random effects suffer least. Compared to fixed effect models, random effects suffer lower bias and trade inflated type I errors for improved power. Contrasting hazard rates between trials prevent accurate estimates from both fixed and random effects models.
Journal: Journal of Applied Statistics
Pages: 1903-1916
Issue: 10
Volume: 46
Year: 2019
Month: 7
X-DOI: 10.1080/02664763.2019.1573216
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1573216
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:10:p:1903-1916




Template-Type: ReDIF-Article 1.0
Author-Name: Anderson Ara
Author-X-Name-First: Anderson
Author-X-Name-Last: Ara
Author-Name: Francisco Louzada
Author-X-Name-First: Francisco
Author-X-Name-Last: Louzada
Author-Name: Carlos A. R. Diniz
Author-X-Name-First: Carlos A. R.
Author-X-Name-Last: Diniz
Title: Statistical monitoring of a web server for error rates: a bivariate time-series copula-based modeling approach
Abstract: 
 The monitoring of web servers through statistical frameworks is of utmost important in order to verify possible suspicious anomalies in network traffic or misuse actions that compromise integrity, confidentiality, and availability of information. In this paper, by considering the Plackett copula function, we propose a bivariate beta-autoregressive moving average time-series model for proportion data over time, which is the case for variables present in web server monitoring such as error rates. To illustrate the proposed methodology, we monitor a Brazilian web server's rate of connection synchronization and rejection errors in a web system, with error logging rate in the past 10 min. In essence, the entire methodology may be generalized to any number of time-series of error rates.
Journal: Journal of Applied Statistics
Pages: 2287-2300
Issue: 13
Volume: 44
Year: 2017
Month: 10
X-DOI: 10.1080/02664763.2016.1238041
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1238041
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:13:p:2287-2300




Template-Type: ReDIF-Article 1.0
Author-Name: Yungtai Lo
Author-X-Name-First: Yungtai
Author-X-Name-Last: Lo
Title: Joint modeling of bottle use, daily milk intake from bottles, and daily energy intake in toddlers
Abstract: 
 The current study follows an educational intervention on bottle-weaning to simultaneously evaluate effects of the bottle-weaning intervention on reducing bottle use, daily milk intake from bottles, and daily energy intake in toddlers aged 11–13 months. In this paper, we propose to use shared parameter models and random effects models to model these outcomes jointly. Our joint models consist of two submodels: a two-part submodel for modeling the odds of bottle use and the intensity of daily milk intake from bottles, and a linear mixed submodel for modeling the intensity of daily energy intake. The two submodels are linked by either shared random effects or separate but correlated random effects. We investigate whether the intervention effects, parameter estimates, and model fit differ between shared parameter models and random effects models.
Journal: Journal of Applied Statistics
Pages: 2301-2316
Issue: 13
Volume: 44
Year: 2017
Month: 10
X-DOI: 10.1080/02664763.2016.1251885
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1251885
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:13:p:2301-2316




Template-Type: ReDIF-Article 1.0
Author-Name: Leena Pasanen
Author-X-Name-First: Leena
Author-X-Name-Last: Pasanen
Author-Name: Päivi Laukkanen-Nevala
Author-X-Name-First: Päivi
Author-X-Name-Last: Laukkanen-Nevala
Author-Name: Ilkka Launonen
Author-X-Name-First: Ilkka
Author-X-Name-Last: Launonen
Author-Name: Sergey Prusov
Author-X-Name-First: Sergey
Author-X-Name-Last: Prusov
Author-Name: Lasse Holmström
Author-X-Name-First: Lasse
Author-X-Name-Last: Holmström
Author-Name: Eero Niemelä
Author-X-Name-First: Eero
Author-X-Name-Last: Niemelä
Author-Name: Jaakko Erkinaro
Author-X-Name-First: Jaakko
Author-X-Name-Last: Erkinaro
Title: Extraction of sea temperature in the Barents Sea by a scale space multiresolution method – prospects for Atlantic salmon
Abstract: 
 Variation of marine temperature at different time scales is a central environmental factor in the life cycle of marine organisms, and may have particular importance for various life stages of anadromous species, for example, Atlantic salmon. To understand the salient features of temperature variation we employ scale space multiresolution analysis, that uses differences of smooths of a time series to decompose it as a sum of scale-dependent components. The number of resolved components can be determined either automatically or by exploring a map that visualizes the structure of the time series. The statistical credibility of the features of the components is established with Bayesian inference. The method was applied to analyze a marine temperature time series measured from the Barents Sea and its correlation with the abundance of Atlantic salmon in three Barents Sea rivers. Besides the annual seasonal variation and a linear trend, the method revealed mid time-scale (∼10 years) and long time-scale (∼30 years) variation. The 10-year quasi-cyclical component of the temperature time series appears to be connected with a similar feature in Atlantic salmon abundance. These findings can provide information about the environmental factors affecting seasonal and periodic variation in survival and migrations of Atlantic salmon and other migratory fish.
Journal: Journal of Applied Statistics
Pages: 2317-2336
Issue: 13
Volume: 44
Year: 2017
Month: 10
X-DOI: 10.1080/02664763.2016.1252731
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1252731
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:13:p:2317-2336




Template-Type: ReDIF-Article 1.0
Author-Name: Haiyong Zhang
Author-X-Name-First: Haiyong
Author-X-Name-Last: Zhang
Author-Name: Xinyu Wang
Author-X-Name-First: Xinyu
Author-X-Name-Last: Wang
Title: Combined asymmetric spatial weights matrix with application to housing prices
Abstract: 
 In this study, a combined asymmetric spatial weights matrix is proposed for capturing the unequal spatial dependence of housing prices, where the advantage of this matrix was demonstrated by a non-nested hypothesis test. To explore the heterogeneous spatial impacts of urban essential characteristics on housing prices over the eastern, central, and western regions of China, after the Lagrange multiplier and likelihood ratio tests, the spatial Durbin model using the proposed weights matrix was applied to each region. The estimation results showed that the direct impacts of college and new employment were significantly negative in the eastern region, but not significant in the central and western regions. By contrast, the direct impacts of hospitals and scenic spots were significantly positive in eastern China, but not significant in central and western China. In addition, the indirect impacts of the four variables were not significant in the three regions. These results suggest that in eastern China, the government may increase the requirements for using medical resources and close tourist attractions in a single city to cool down the skyrocketing housing prices in this area.
Journal: Journal of Applied Statistics
Pages: 2337-2353
Issue: 13
Volume: 44
Year: 2017
Month: 10
X-DOI: 10.1080/02664763.2016.1254163
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1254163
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:13:p:2337-2353




Template-Type: ReDIF-Article 1.0
Author-Name: Tao Lu
Author-X-Name-First: Tao
Author-X-Name-Last: Lu
Title: Jointly modeling skew longitudinal survival data with missingness and mismeasured covariates
Abstract: 
 Jointly modeling longitudinal and survival data has been an active research area. Most researches focus on improving the estimating efficiency but ignore many data features frequently encountered in practice. In the current study, we develop the joint models that concurrently accounting for longitudinal and survival data with multiple features. Specifically, the proposed model handles skewness, missingness and measurement errors in covariates which are typically observed in the collection of longitudinal survival data from many studies. We employ a Bayesian inferential method to make inference on the proposed model. We applied the proposed model to an real data study. A few alternative models under different conditions are compared. We conduct extensive simulations in order to evaluate how the method works.
Journal: Journal of Applied Statistics
Pages: 2354-2367
Issue: 13
Volume: 44
Year: 2017
Month: 10
X-DOI: 10.1080/02664763.2016.1254728
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1254728
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:13:p:2354-2367




Template-Type: ReDIF-Article 1.0
Author-Name: A. Asgharzadeh
Author-X-Name-First: A.
Author-X-Name-Last: Asgharzadeh
Author-Name: Hassan S. Bakouch
Author-X-Name-First: Hassan S.
Author-X-Name-Last: Bakouch
Author-Name: M. Habibi
Author-X-Name-First: M.
Author-X-Name-Last: Habibi
Title: A generalized binomial exponential 2 distribution: modeling and applications to hydrologic events
Abstract: 
 Developing statistical methods to model hydrologic events is always interesting for both statisticians and hydrologists, because of its importance in hydraulic structures design and water resource planning. Because of this, a flexible 3-parameter generalization of the exponential distribution is introduced based on the binomial exponential 2 (BE2) distribution [2]. The proposed distribution involving the exponential, gamma and BE2 distributions as submodels; and it exhibits decreasing, increasing and bathtub-shaped hazard rates, so it turns out to be quite flexible for analyzing non-negative real life data. Some statistical properties, parameters estimation and information matrix of the distribution are investigated. The proposed distribution, Gumbel, generalized Logistic and other distributions are utilized to model and fit two hydrologic data sets. The distribution is shown to be more appropriate to the data than the compared distributions using the selection criteria: average scaled absolute error, Akaike information criterion, Bayesian information criterion and Kolmogorov–Smirnov statistics. As a result, some hydrologic parameters of the data are obtained such as return level, conditional mean, mean deviation about the return level and the rth moments of order statistics.
Journal: Journal of Applied Statistics
Pages: 2368-2387
Issue: 13
Volume: 44
Year: 2017
Month: 10
X-DOI: 10.1080/02664763.2016.1254729
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1254729
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:13:p:2368-2387




Template-Type: ReDIF-Article 1.0
Author-Name: Bilin Zeng
Author-X-Name-First: Bilin
Author-X-Name-Last: Zeng
Author-Name: Xuerong Meggie Wen
Author-X-Name-First: Xuerong Meggie
Author-X-Name-Last: Wen
Author-Name: Lixing Zhu
Author-X-Name-First: Lixing
Author-X-Name-Last: Zhu
Title: A link-free sparse group variable selection method for single-index model
Abstract: 
 For regression problems with grouped covariates, we adapt the idea of sparse group lasso (SGL) [10] to the framework of the sufficient dimension reduction. Assuming that the regression falls into a single-index structure, we propose a method called the sparse group sufficient dimension reduction to conduct group and within-group variable selections simultaneously without assuming a specific link function. Simulation studies show that our method is comparable to the SGL under the regular linear model setting and outperforms SGL with higher true positive rates and substantially lower false positive rates when the regression function is nonlinear. One immediate application of our method is to the gene pathway data analysis where genes naturally fall into groups (pathways). An analysis of a glioblastoma microarray data is included for illustration of our method.
Journal: Journal of Applied Statistics
Pages: 2388-2400
Issue: 13
Volume: 44
Year: 2017
Month: 10
X-DOI: 10.1080/02664763.2016.1254731
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1254731
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:13:p:2388-2400




Template-Type: ReDIF-Article 1.0
Author-Name: Abdollah Jalilian
Author-X-Name-First: Abdollah
Author-X-Name-Last: Jalilian
Title: Modelling and classification of species abundance: a case study in the Barro Colorado Island plot
Abstract: 
 Quantifying and modelling the effect of environmental variables on the abundance of species is of great importance in plant ecology and forestry. In this paper, using a log-additive model, the effect of environmental variables on distribution of five species in the Barro Colorado Island plot is modelled. The fitted log-additive models are examined and compared with conventional log-linear models. Finally, a cluster analysis is employed to classify species into groups with similar habitat preferences.
Journal: Journal of Applied Statistics
Pages: 2401-2409
Issue: 13
Volume: 44
Year: 2017
Month: 10
X-DOI: 10.1080/02664763.2016.1254732
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1254732
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:13:p:2401-2409




Template-Type: ReDIF-Article 1.0
Author-Name: Fernando Ferraz do Nascimento
Author-X-Name-First: Fernando Ferraz do
Author-X-Name-Last: Nascimento
Author-Name: Wyara Vanesa Moura e Silva
Author-X-Name-First: Wyara Vanesa
Author-X-Name-Last: Moura e Silva
Title: A Bayesian model for multiple change point to extremes, with application to environmental and financial data
Abstract: 
 Abrupt changes often occur for environmental and financial time series. Most often, these changes are due to human intervention. Change point analysis is a statistical tool used to analyze sudden changes in observations along the time series. In this paper, we propose a Bayesian model for extreme values for environmental and economic datasets that present a typical change point behavior. The model proposed in this paper addresses the situation in which more than one change point can occur in a time series. By analyzing maxima, the distribution of each regime is a generalized extreme value distribution. In this model, the change points are unknown and considered parameters to be estimated. Simulations of extremes with two change points showed that the proposed algorithm can recover the true values of the parameters, in addition to detecting the true change points in different configurations. Also, the number of change points was a problem to be considered, and the Bayesian estimation can correctly identify the correct number of change points for each application. Environmental and financial data were analyzed and results showed the importance of considering the change point in the data and revealed that this change of regime brought about an increase in the return levels, increasing the number of floods in cities around the rivers. Stock market levels showed the necessity of a model with three different regimes.
Journal: Journal of Applied Statistics
Pages: 2410-2426
Issue: 13
Volume: 44
Year: 2017
Month: 10
X-DOI: 10.1080/02664763.2016.1254733
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1254733
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:13:p:2410-2426




Template-Type: ReDIF-Article 1.0
Author-Name: Tiago R. Pellegrini
Author-X-Name-First: Tiago R.
Author-X-Name-Last: Pellegrini
Author-Name: M. Tariqul Hasan
Author-X-Name-First: M. Tariqul
Author-X-Name-Last: Hasan
Author-Name: Renjun Ma
Author-X-Name-First: Renjun
Author-X-Name-Last: Ma
Title: Modeling of paired zero-inflated continuous data without breaking down paired designs
Abstract: 
 Paired data have been widely collected in the efficiency studies of a new method against an established method in environmental, ecological and medical studies. For example, in comparative fishing studies, ability of catching target species (fish catch) or reducing the catch of non-target species (fish bycatch) is usually investigated through a paired design. These paired fish catches by weight are generally skewed and continuous, but with a significant portion of exact zeros (no catch). Such zero-inflated continuous data are traditionally handled by two-part models where the zero and positive components are handled separately; however, this separation generally destroys paired structure, and thus may result in substantial difficulty in characterizing the relative efficiency between two methods. To overcome this problem, we consider compound Poisson mixed model for paired data with which the zero and non-zero components are characterized in an integral way. In our approach, the clustering effects by pair are captured by incorporating relevant random effects. Our model is estimated using orthodox best linear unbiased predictor approach. Unlike two-part models, our approach unifies inferences of zero and positive components. Our method is illustrated with analyses of winter flounder bycatch data and ultrasound safety data.
Journal: Journal of Applied Statistics
Pages: 2427-2443
Issue: 13
Volume: 44
Year: 2017
Month: 10
X-DOI: 10.1080/02664763.2016.1254734
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1254734
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:13:p:2427-2443




Template-Type: ReDIF-Article 1.0
Author-Name: Meltem Ekiz
Author-X-Name-First: Meltem
Author-X-Name-Last: Ekiz
Author-Name: O.Ufuk Ekiz
Author-X-Name-First: O.Ufuk
Author-X-Name-Last: Ekiz
Title: Outlier detection with Mahalanobis square distance: incorporating small sample correction factor
Abstract: 
 Mahalanobis square distances (MSDs) based on robust estimators improves outlier detection performance in multivariate data. However, the unbiasedness of robust estimators are not guaranteed when the sample size is small and this reduces their performance in outlier detection. In this study, we propose a framework that uses MSDs with incorporated small sample correction factor (c) and show its impact on performance when the sample size is small. This is achieved by using two prototypes, minimum covariance determinant estimator and S-estimators with bi-weight and t-biweight functions. The results from simulations show that distribution of MSDs for non-extreme observations are more likely to fit to chi-square with p degrees of freedom and MSDs of the extreme observations fit to F distribution, when c is incorporated into the model. However, without c, the distributions deviate significantly from chi-square and F observed for the case with incorporated c. These results are even more prominent for S-estimators. We present seven distinct comparison methods with robust estimators and various cut-off values and test their outlier detection performance with simulated data. We also present an application of some of these methods to the real data.
Journal: Journal of Applied Statistics
Pages: 2444-2457
Issue: 13
Volume: 44
Year: 2017
Month: 10
X-DOI: 10.1080/02664763.2016.1255313
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1255313
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:13:p:2444-2457




Template-Type: ReDIF-Article 1.0
Author-Name: Roberto F. Manghi
Author-X-Name-First: Roberto F.
Author-X-Name-Last: Manghi
Author-Name: Gilberto A. Paula
Author-X-Name-First: Gilberto A.
Author-X-Name-Last: Paula
Author-Name: Francisco José A. Cysneiros
Author-X-Name-First: Francisco José A.
Author-X-Name-Last: Cysneiros
Title: On elliptical multilevel models
Abstract: 
 Multilevel models have been widely applied to analyze data sets which present some hierarchical structure. In this paper we propose a generalization of the normal multilevel models, named elliptical multilevel models. This proposal suggests the use of distributions in the elliptical class, thus involving all symmetric continuous distributions, including the normal distribution as a particular case. Elliptical distributions may have lighter or heavier tails than the normal ones. In the case of normal error models with the presence of outlying observations, heavy-tailed error models may be applied to accommodate such observations. In particular, we discuss some aspects of the elliptical multilevel models, such as maximum likelihood estimation and residual analysis to assess features related to the fitting and the model assumptions. Finally, two motivating examples analyzed under normal multilevel models are reanalyzed under Student-t and power exponential multilevel models. Comparisons with the normal multilevel model are performed by using residual analysis.
Journal: Journal of Applied Statistics
Pages: 2150-2171
Issue: 12
Volume: 43
Year: 2016
Month: 9
X-DOI: 10.1080/02664763.2015.1134445
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1134445
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:12:p:2150-2171




Template-Type: ReDIF-Article 1.0
Author-Name: Jung-In Seo
Author-X-Name-First: Jung-In
Author-X-Name-Last: Seo
Author-Name: Suk-Bok Kang
Author-X-Name-First: Suk-Bok
Author-X-Name-Last: Kang
Title: An objective Bayesian analysis of the two-parameter half-logistic distribution based on progressively type-II censored samples
Abstract: 
 This paper develops an objective Bayesian analysis method for estimating unknown parameters of the half-logistic distribution when a sample is available from the progressively Type-II censoring scheme. Noninformative priors such as Jeffreys and reference priors are derived. In addition, derived priors are checked to determine whether they satisfy probability-matching criteria. The Metropolis–Hasting algorithm is applied to generate Markov chain Monte Carlo samples from these posterior density functions because marginal posterior density functions of each parameter cannot be expressed in an explicit form. Monte Carlo simulations are conducted to investigate frequentist properties of estimated models under noninformative priors. For illustration purposes, a real data set is presented, and the quality of models under noninformative priors is evaluated through posterior predictive checking.
Journal: Journal of Applied Statistics
Pages: 2172-2190
Issue: 12
Volume: 43
Year: 2016
Month: 9
X-DOI: 10.1080/02664763.2015.1134446
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1134446
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:12:p:2172-2190




Template-Type: ReDIF-Article 1.0
Author-Name: Muhammad Faisal
Author-X-Name-First: Muhammad
Author-X-Name-Last: Faisal
Author-Name: Andreas Futschik
Author-X-Name-First: Andreas
Author-X-Name-Last: Futschik
Author-Name: Ijaz Hussain
Author-X-Name-First: Ijaz
Author-X-Name-Last: Hussain
Author-Name: Mitwali Abd-el.Moemen
Author-X-Name-First: Mitwali
Author-X-Name-Last: Abd-el.Moemen
Title: Choosing summary statistics by least angle regression for approximate Bayesian computation
Abstract: 
 Bayesian statistical inference relies on the posterior distribution. Depending on the model, the posterior can be more or less difficult to derive. In recent years, there has been a lot of interest in complex settings where the likelihood is analytically intractable. In such situations, approximate Bayesian computation (ABC) provides an attractive way of carrying out Bayesian inference. For obtaining reliable posterior estimates however, it is important to keep the approximation errors small in ABC. The choice of an appropriate set of summary statistics plays a crucial role in this effort. Here, we report the development of a new algorithm that is based on least angle regression for choosing summary statistics. In two population genetic examples, the performance of the new algorithm is better than a previously proposed approach that uses partial least squares.
Journal: Journal of Applied Statistics
Pages: 2191-2202
Issue: 12
Volume: 43
Year: 2016
Month: 9
X-DOI: 10.1080/02664763.2015.1134447
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1134447
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:12:p:2191-2202




Template-Type: ReDIF-Article 1.0
Author-Name: F. Razie
Author-X-Name-First: F.
Author-X-Name-Last: Razie
Author-Name: E. Bahrami Samani
Author-X-Name-First: E.
Author-X-Name-Last: Bahrami Samani
Author-Name: M. Ganjali
Author-X-Name-First: M.
Author-X-Name-Last: Ganjali
Title: A latent variable model for analyzing mixed longitudinal (,)-inflated count and ordinal responses
Abstract: 
 A random effects model for analyzing mixed longitudinal count and ordinal data is presented where the count response is inflated in two points (k and l) and an (k,l)-Inflated Power series distribution is used as its distribution. A full likelihood-based approach is used to obtain maximum likelihood estimates of parameters of the model. For data with non-ignorable missing values models with probit model for missing mechanism are used.The dependence between longitudinal sequences of responses and inflation parameters are investigated using a random effects approach. Also, to investigate the correlation between mixed ordinal and count responses of each individuals at each time, a shared random effect is used. In order to assess the performance of the model, a simulation study is performed for a case that the count response has (k,l)-Inflated Binomial distribution. Performance comparisons of count-ordinal random effect model, Zero-Inflated ordinal random effects model and (k,l)-Inflated ordinal random effects model are also given. The model is applied to a real social data set from the first two waves of the national longitudinal study of adolescent to adult health (Add Health study). In this data set, the joint responses are the number of days in a month that each individual smoked as the count response and the general health condition of each individual as the ordinal response. For the count response there is incidence of excess values of 0 and 30.
Journal: Journal of Applied Statistics
Pages: 2203-2224
Issue: 12
Volume: 43
Year: 2016
Month: 9
X-DOI: 10.1080/02664763.2015.1134448
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1134448
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:12:p:2203-2224




Template-Type: ReDIF-Article 1.0
Author-Name: Chen Chen
Author-X-Name-First: Chen
Author-X-Name-Last: Chen
Author-Name: Feng Guo
Author-X-Name-First: Feng
Author-X-Name-Last: Guo
Title: Evaluating the influence of crashes on driving risk using recurrent event models and Naturalistic Driving Study data
Abstract: 
 Dramatic events such as crashes could alter driver behavior and change driving risk during post-event period. This study investigated the influence of crashes on driving risk using the 100-Car Naturalistic Driving Study data. The analysis is based on 51 crashes from primary drivers. Driving risk is measured by the intensity of safety-critical incidents (SCI) and near-crashes (NC), which typically occur at a high frequency both before and after a crash. We applied four alternative recurrent event models to evaluate the influence of crashes based on actual driving time. The driving period was divided into several phases based on the relationship to crashes, and the event intensities of these periods were compared. Results show a reduction in SCI intensity after the first crash ( $ {\rm intensity rate ratio} = 0.82 $ intensityrateratio=0.82;  $ 95\% $ 95% CI  $ [0.693, 0.971] $ [0.693,0.971]) and the second crash ( $ {\rm intensity rate ratio} = 0.47 $ intensityrateratio=0.47;  $ 95\% $ 95% CI  $ [0.377, 0.59] $ [0.377,0.59]) for male drivers. No significant response to the first crash was observed for females, but SCI intensity decreased after the second crash ( $ {\rm intensity rate ratio} = 0.43 $ intensityrateratio=0.43;  $ 95\% $ 95% CI  $ [0.342, 0.547] $ [0.342,0.547]). The findings of this study provide crucial information for understanding driver behavior and for developing effective safety education programs as well as safety counter measures.
Journal: Journal of Applied Statistics
Pages: 2225-2238
Issue: 12
Volume: 43
Year: 2016
Month: 9
X-DOI: 10.1080/02664763.2015.1134449
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1134449
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:12:p:2225-2238




Template-Type: ReDIF-Article 1.0
Author-Name: S. R. Haile
Author-X-Name-First: S. R.
Author-X-Name-Last: Haile
Author-Name: J.-H. Jeong
Author-X-Name-First: J.-H.
Author-X-Name-Last: Jeong
Author-Name: X. Chen
Author-X-Name-First: X.
Author-X-Name-Last: Chen
Author-Name: Y. Cheng
Author-X-Name-First: Y.
Author-X-Name-Last: Cheng
Title: A 3-parameter Gompertz distribution for survival data with competing risks, with an application to breast cancer data
Abstract: 
 The cumulative incidence function is of great importance in the analysis of survival data when competing risks are present. Parametric modeling of such functions, which are by nature improper, suggests the use of improper distributions. One frequently used improper distribution is that of Gompertz, which captures only monotone hazard shapes. In some applications, however, subdistribution hazard estimates have been observed with unimodal shapes. An extension to the Gompertz distribution is presented which can capture unimodal as well as monotone hazard shapes. Important properties of the proposed distribution are discussed, and the proposed distribution is used to analyze survival data from a breast cancer clinical trial.
Journal: Journal of Applied Statistics
Pages: 2239-2253
Issue: 12
Volume: 43
Year: 2016
Month: 9
X-DOI: 10.1080/02664763.2015.1134450
File-URL: http://hdl.handle.net/10.1080/02664763.2015.1134450
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:12:p:2239-2253




Template-Type: ReDIF-Article 1.0
Author-Name: K. Vaitheeswaran
Author-X-Name-First: K.
Author-X-Name-Last: Vaitheeswaran
Author-Name: M. Subbiah
Author-X-Name-First: M.
Author-X-Name-Last: Subbiah
Author-Name: R. Ramakrishnan
Author-X-Name-First: R.
Author-X-Name-Last: Ramakrishnan
Author-Name: T. Kannan
Author-X-Name-First: T.
Author-X-Name-Last: Kannan
Title: A comparison of ordinal logistic regression models using Classical and Bayesian approaches in an analysis of factors associated with diabetic retinopathy
Abstract: 
 Estimating the risk factors of a disease such as diabetic retinopathy (DR) is one of the important research problems among bio-medical and statistical practitioners as well as epidemiologists. Incidentally many studies have focused in building models with binary outcomes, that may not exploit the available information. This article has investigated the importance of retaining the ordinal nature of the response variable (e.g. severity level of a disease) while determining the risk factors associated with DR. A generalized linear model approach with appropriate link functions has been studied using both Classical and Bayesian frameworks. From the result of this study, it can be observed that the ordinal logistic regression with probit link function could be more appropriate approach in determining the risk factors of DR. The study has emphasized the ways to handle the ordinal nature of the response variable with better model fit compared to other link functions.
Journal: Journal of Applied Statistics
Pages: 2254-2260
Issue: 12
Volume: 43
Year: 2016
Month: 9
X-DOI: 10.1080/02664763.2016.1140725
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1140725
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:12:p:2254-2260




Template-Type: ReDIF-Article 1.0
Author-Name: Tahani Coolen-Maturi
Author-X-Name-First: Tahani
Author-X-Name-Last: Coolen-Maturi
Title: New weighted rank correlation coefficients sensitive to agreement on top and bottom rankings
Abstract: 
 Three new weighted rank correlation coefficients are proposed which are sensitive to both agreement on top and bottom rankings. The first one is based on the weighted rank correlation coefficient proposed by Maturi and Abdelfattah [13], the second and the third are based on the order statistics and the quantiles of the Laplace distribution, respectively. The limiting distributions of the new correlation coefficients under the null hypothesis of no association between the rankings are presented, and a summary of the exact and approximate quantiles for these coefficients is provided. A simulation study is performed to compare the performance of Kendall's tau, Spearman's rho, and the new weighted rank correlation coefficients in detecting the agreement on the top and the bottom rankings simultaneously. Finally, examples are given for illustration purposes, including a real data set from financial market indices.
Journal: Journal of Applied Statistics
Pages: 2261-2279
Issue: 12
Volume: 43
Year: 2016
Month: 9
X-DOI: 10.1080/02664763.2016.1140726
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1140726
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:12:p:2261-2279




Template-Type: ReDIF-Article 1.0
Author-Name: A. M. Elsawah
Author-X-Name-First: A. M.
Author-X-Name-Last: Elsawah
Author-Name: Hong Qin
Author-X-Name-First: Hong
Author-X-Name-Last: Qin
Title: Asymmetric uniform designs based on mixture discrepancy
Abstract: 
 Efficient experimental design is crucial in the study of scientific problems. The uniform design is one of the most widely used approaches. The discrepancies have played an important role in quasi-Monte Carlo methods and uniform design. Zhou et al. [17] proposed a new type of discrepancy, mixture discrepancy ( $ {\mathcal {MD}} $ MD), and showed that  $ {\mathcal {MD}} $ MD may be a better uniformity measure than other discrepancies. In this paper, we discuss in depth the  $ {\mathcal{MD}} $ MD as the uniformity measure for asymmetric mixed two and three levels U-type designs. New analytical expression based on row distance and new lower bound of the  $ {\mathcal {MD}} $ MD are given for asymmetric levels designs. Using the new formulation and the new lower bound as the benchmark, we can implement a new version of the fast local search heuristic threshold accepting. By this search heuristic, we can obtain mixed two and three levels U-type designs with low discrepancy.
Journal: Journal of Applied Statistics
Pages: 2280-2294
Issue: 12
Volume: 43
Year: 2016
Month: 9
X-DOI: 10.1080/02664763.2016.1140727
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1140727
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:12:p:2280-2294




Template-Type: ReDIF-Article 1.0
Author-Name: James Sharpe
Author-X-Name-First: James
Author-X-Name-Last: Sharpe
Author-Name: Nick Fieller
Author-X-Name-First: Nick
Author-X-Name-Last: Fieller
Title: Uncertainty in functional principal component analysis
Abstract: 
 Principal component analysis (PCA) and functional principal analysis are key tools in multivariate analysis, in particular modelling yield curves, but little attention is given to questions of uncertainty, neither in the components themselves nor in any derived quantities such as scores. Actuaries using PCA to model yield curves to assess interest rate risk for insurance companies are required to show any uncertainty in their calculations. Asymptotic results based on assumptions of multivariate normality are unsatisfactory for modest samples, and application of bootstrap methods is not straightforward, with the novel pitfalls of possible inversions in order of sample components and reversals of signs. We present methods for overcoming these difficulties and discuss arising of other potential hazards.
Journal: Journal of Applied Statistics
Pages: 2295-2309
Issue: 12
Volume: 43
Year: 2016
Month: 9
X-DOI: 10.1080/02664763.2016.1140728
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1140728
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:12:p:2295-2309




Template-Type: ReDIF-Article 1.0
Author-Name: D.-C. Jhwueng
Author-X-Name-First: D.-C.
Author-X-Name-Last: Jhwueng
Author-Name: V. Maroulas
Author-X-Name-First: V.
Author-X-Name-Last: Maroulas
Title: Adaptive trait evolution in random environment
Abstract: 
 Current phylogenetic comparative methods generally employ the Ornstein–Uhlenbeck(OU) process for modeling trait evolution. Being able of tracking the optimum of a trait within a group of related species, the OU process provides information about the stabilizing selection where the population mean adopts a particular trait value. The optima of a trait may follow certain stochastic dynamics along the evolutionary history. In this paper, we extend the current framework by adopting a rate of evolution which behave according to pertinent stochastic dynamics. The novel model is applied to analyze about 225 datasets collected from the existing literature. Results validate that the new framework provides a better fit for the majority of these datasets.
Journal: Journal of Applied Statistics
Pages: 2310-2324
Issue: 12
Volume: 43
Year: 2016
Month: 9
X-DOI: 10.1080/02664763.2016.1140729
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1140729
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:12:p:2310-2324




Template-Type: ReDIF-Article 1.0
Author-Name: Yu-Mei Chang
Author-X-Name-First: Yu-Mei
Author-X-Name-Last: Chang
Author-Name: Pao-Sheng Shen
Author-X-Name-First: Pao-Sheng
Author-X-Name-Last: Shen
Author-Name: Guan-Wei Liu
Author-X-Name-First: Guan-Wei
Author-X-Name-Last: Liu
Title: Confidence intervals for the difference between two median survival times for clustered survival data
Abstract: 
 Clustered survival data arise often in clinical trial design, where the correlated subunits from the same cluster are randomized to different treatment groups. Under such design, we consider the problem of constructing confidence interval for the difference of two median survival time given the covariates. We use Cox gamma frailty model to account for the within-cluster correlation. Based on the conditional confidence intervals, we can identify the possible range of covariates over which the two groups would provide different median survival times. The associated coverage probability and the expected length of the proposed interval are investigated via a simulation study. The implementation of the confidence intervals is illustrated using a real data set.
Journal: Journal of Applied Statistics
Pages: 2325-2345
Issue: 12
Volume: 43
Year: 2016
Month: 9
X-DOI: 10.1080/02664763.2016.1140730
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1140730
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:12:p:2325-2345




Template-Type: ReDIF-Article 1.0
Author-Name: Edwin M. M. Ortega
Author-X-Name-First: Edwin M. M.
Author-X-Name-Last: Ortega
Author-Name: Artur J. Lemonte
Author-X-Name-First: Artur J.
Author-X-Name-Last: Lemonte
Author-Name: Gauss M. Cordeiro
Author-X-Name-First: Gauss M.
Author-X-Name-Last: Cordeiro
Author-Name: Vicente G. Cancho
Author-X-Name-First: Vicente G.
Author-X-Name-Last: Cancho
Author-Name: Fábio L. Mialhe
Author-X-Name-First: Fábio L.
Author-X-Name-Last: Mialhe
Title: Heteroscedastic log-exponentiated Weibull regression model
Abstract: 
 We introduce a new class of heteroscedastic log-exponentiated Weibull (LEW) regression models. The class of regression models can be applied to censored data and be used more effectively in survival analysis. Maximum likelihood estimation of the model parameters with censored data as well as influence diagnostics for the new regression model is investigated. For different parameter settings, sample sizes and censoring percentages, various simulation studies are performed and compared to the performance of the heteroscedastic LEW regression model. The normal curvatures for studying local influence are derived under various perturbation schemes. An empirical application to a real data set is provided to illustrate the usefulness of the new class of heteroscedastic regression models.
Journal: Journal of Applied Statistics
Pages: 384-408
Issue: 3
Volume: 45
Year: 2018
Month: 2
X-DOI: 10.1080/02664763.2016.1277192
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1277192
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:3:p:384-408




Template-Type: ReDIF-Article 1.0
Author-Name: Bei Wang
Author-X-Name-First: Bei
Author-X-Name-Last: Wang
Author-Name: Jeffrey R. Wilson
Author-X-Name-First: Jeffrey R.
Author-X-Name-Last: Wilson
Title: Comparative GMM and GQL logistic regression models on hierarchical data
Abstract: 
 We often rely on the likelihood to obtain estimates of regression parameters but it is not readily available for generalized linear mixed models (GLMMs). Inferences for the regression coefficients and the covariance parameters are key in these models. We presented alternative approaches for analyzing binary data from a hierarchical structure that do not rely on any distributional assumptions: a generalized quasi-likelihood (GQL) approach and a generalized method of moments (GMM) approach. These are alternative approaches to the typical maximum-likelihood approximation approach in Statistical Analysis System (SAS) such as Laplace approximation (LAP). We examined and compared the performance of GQL and GMM approaches with multiple random effects to the LAP approach as used in PROC GLIMMIX, SAS. The GQL approach tends to produce unbiased estimates, whereas the LAP approach can lead to highly biased estimates for certain scenarios. The GQL approach produces more accurate estimates on both the regression coefficients and the covariance parameters with smaller standard errors as compared to the GMM approach. We found that both GQL and GMM approaches are less likely to result in non-convergence as opposed to the LAP approach. A simulation study was conducted and a numerical example was presented for illustrative purposes.
Journal: Journal of Applied Statistics
Pages: 409-425
Issue: 3
Volume: 45
Year: 2018
Month: 2
X-DOI: 10.1080/02664763.2017.1279595
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1279595
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:3:p:409-425




Template-Type: ReDIF-Article 1.0
Author-Name: Ehsan Zamanzade
Author-X-Name-First: Ehsan
Author-X-Name-Last: Zamanzade
Author-Name: M. Mahdizadeh
Author-X-Name-First: M.
Author-X-Name-Last: Mahdizadeh
Title: Estimating the population proportion in pair ranked set sampling with application to air quality monitoring
Abstract: 
 In this paper, we consider the problem of estimating the population proportion in pair ranked set sampling design. An unbiased estimator for the population proportion is proposed, and its theoretical properties are studied. It is shown that the estimator is more (less) efficient than its counterpart in simple random sampling (ranked set sampling). Asymptotic normality of the estimator is also established. Application of the suggested procedure is illustrated using a data set from an environmental study.
Journal: Journal of Applied Statistics
Pages: 426-437
Issue: 3
Volume: 45
Year: 2018
Month: 2
X-DOI: 10.1080/02664763.2017.1279596
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1279596
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:3:p:426-437




Template-Type: ReDIF-Article 1.0
Author-Name: Dale Bowman
Author-X-Name-First: Dale
Author-X-Name-Last: Bowman
Author-Name: E. Olusegun George
Author-X-Name-First: E. Olusegun
Author-X-Name-Last: George
Title: A Bayesian analysis of clustered discrete and continuous outcomes
Abstract: 
 Many study designs yield a variety of outcomes from each subject clustered within an experimental unit. When these outcomes are of mixed data types, it is challenging to jointly model the effects of covariates on the responses using traditional methods. In this paper, we develop a Bayesian approach for a joint regression model of the different outcome variables and show that the fully conditional posterior distributions obtained under the model assumptions allow for estimation of posterior distributions using Gibbs sampling algorithm.
Journal: Journal of Applied Statistics
Pages: 438-449
Issue: 3
Volume: 45
Year: 2018
Month: 2
X-DOI: 10.1080/02664763.2017.1280003
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1280003
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:3:p:438-449




Template-Type: ReDIF-Article 1.0
Author-Name: Wenlin Dai
Author-X-Name-First: Wenlin
Author-X-Name-Last: Dai
Author-Name: Yuejin Zhou
Author-X-Name-First: Yuejin
Author-X-Name-Last: Zhou
Author-Name: Tiejun Tong
Author-X-Name-First: Tiejun
Author-X-Name-Last: Tong
Title: Testing discontinuities in nonparametric regression
Abstract: 
 In nonparametric regression, it is often needed to detect whether there are jump discontinuities in the mean function. In this paper, we revisit the difference-based method in [13] and propose to further improve it. To achieve the goal, we first reveal that their method is less efficient due to the inappropriate choice of the response variable in their linear regression model. We then propose a new regression model for estimating the residual variance and the total amount of discontinuities simultaneously. In both theory and simulation, we show that the proposed variance estimator has a smaller mean-squared error compared to the existing estimator, whereas the estimation efficiency for the total amount of discontinuities remains unchanged. Finally, we construct a new test procedure for detection of discontinuities using the proposed method; and via simulation studies, we demonstrate that our new test procedure outperforms the existing one in most settings.
Journal: Journal of Applied Statistics
Pages: 450-473
Issue: 3
Volume: 45
Year: 2018
Month: 2
X-DOI: 10.1080/02664763.2017.1280004
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1280004
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:3:p:450-473




Template-Type: ReDIF-Article 1.0
Author-Name: Shuo Wang
Author-X-Name-First: Shuo
Author-X-Name-Last: Wang
Author-Name: Sungsu Kim
Author-X-Name-First: Sungsu
Author-X-Name-Last: Kim
Author-Name: Thelge Buddika Peiris
Author-X-Name-First: Thelge Buddika
Author-X-Name-Last: Peiris
Title: An improved meta-analysis for analyzing cylindrical-type time series data with applications to forecasting problem in environmental study
Abstract: 
 In this paper, we propose an improved generalized least square (GLS) meta-analysis in a linear-circular regression, and show its utility in the analysis of a certain environmental issue. The existing GLS meta-analysis proposed in Becker and Wu has a serious flaw since information about the covariance among coefficients across studies is not utilized. In our proposed meta-analysis, we take the correlations between adjacent studies into account, and improve the existing GLS meta-analysis. We provide numerical examples to compare the proposed method with several other existing methods by using Akaike's Information Criterion, Bayesian Information Criterion and mean square prediction errors with applications to forecasting problem in Environmental study.
Journal: Journal of Applied Statistics
Pages: 474-486
Issue: 3
Volume: 45
Year: 2018
Month: 2
X-DOI: 10.1080/02664763.2017.1280451
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1280451
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:3:p:474-486




Template-Type: ReDIF-Article 1.0
Author-Name: Eftychia Solea
Author-X-Name-First: Eftychia
Author-X-Name-Last: Solea
Author-Name: Bing Li
Author-X-Name-First: Bing
Author-X-Name-Last: Li
Author-Name: Aleksandra Slavković
Author-X-Name-First: Aleksandra
Author-X-Name-Last: Slavković
Title: Statistical learning on emerging economies
Abstract: 
 BRIC is an acronym coined by Jim O'Neill from Goldman Sachs in 2001 to abbreviate four emerging economies, Brazil, Russia, India and China, based on economic data at the time. Later, as new data became available, Goldman Sachs updated this list to include Mexico, Indonesia, Nigeria and Turkey, which was referred to as MINT. This list, as well as some other similar lists of emerging economies, is based on descriptive statistics of the economic data combined with economists' insights. The purpose of this study is twofold: to see if these insights into the global economic trends can be learned with statistical learning tools, and, if so, to identify the next emerging countries. We apply both unsupervised and supervised learning methods, which include linear and nonlinear principle component analysis, and nonlinear sufficient dimension reduction, to 13 years worth of economic data. Our results show that these statistical learning techniques, and in particular the kernel sliced inverse regression algorithm, can serve as a useful tool for economists and policy-makers for analyzing global economic trends, by its ability to incorporate large amount of economic data and previous experts' judgments, which otherwise may take years of experiences to acquire.
Journal: Journal of Applied Statistics
Pages: 487-507
Issue: 3
Volume: 45
Year: 2018
Month: 2
X-DOI: 10.1080/02664763.2017.1280452
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1280452
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:3:p:487-507




Template-Type: ReDIF-Article 1.0
Author-Name: Samantha Chadee
Author-X-Name-First: Samantha
Author-X-Name-Last: Chadee
Author-Name: Valerie Stoute
Author-X-Name-First: Valerie
Author-X-Name-Last: Stoute
Title: Development of an urban intensity index to facilitate urban ecosystem studies in Trinidad and Tobago
Abstract: 
 Urban ecosystems, considered centres of economic, social and cultural development, face a multitude of environmental and socio-economic challenges, which impact on quality of life. Effective management of the urbanization process is believed critical to improving quality of life and realizing sustainable development. The ecosystem perspective provides a holistic approach, needed to address the complexly interconnected issues, which arise from urban development. Central to the mapping and characterization of urban ecosystems is the delineation of their boundaries, which are made less transparent by growing urbanization. This exposes the limitations of a dichotomous approach. An urban intensity index is a critical tool which supports urban ecosystem studies by facilitating analysis of effects along the urban–rural gradient. In this study, Urban Intensity is estimated and ranked from most to least intense for communities across Trinidad and Tobago, using multivariate statistical analysis of physical data from the built environment. This statistically validated index, designed for Trinidad and Tobago, should have wider applicability to other disciplines and countries.
Journal: Journal of Applied Statistics
Pages: 508-527
Issue: 3
Volume: 45
Year: 2018
Month: 2
X-DOI: 10.1080/02664763.2017.1282440
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1282440
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:3:p:508-527




Template-Type: ReDIF-Article 1.0
Author-Name: Jason S. Bergtold
Author-X-Name-First: Jason S.
Author-X-Name-Last: Bergtold
Author-Name: Elizabeth A. Yeager
Author-X-Name-First: Elizabeth A.
Author-X-Name-Last: Yeager
Author-Name: Allen M. Featherstone
Author-X-Name-First: Allen M.
Author-X-Name-Last: Featherstone
Title: Inferences from logistic regression models in the presence of small samples, rare events, nonlinearity, and multicollinearity with observational data
Abstract: 
 The logistic regression model has been widely used in the social and natural sciences and results from studies using this model can have significant policy impacts. Thus, confidence in the reliability of inferences drawn from these models is essential. The robustness of such inferences is dependent on sample size. The purpose of this article is to examine the impact of alternative data sets on the mean estimated bias and efficiency of parameter estimation and inference for the logistic regression model with observational data. A number of simulations are conducted examining the impact of sample size, nonlinear predictors, and multicollinearity on substantive inferences (e.g. odds ratios, marginal effects) when using logistic regression models. Findings suggest that small sample size can negatively affect the quality of parameter estimates and inferences in the presence of rare events, multicollinearity, and nonlinear predictor functions, but marginal effects estimates are relatively more robust to sample size.
Journal: Journal of Applied Statistics
Pages: 528-546
Issue: 3
Volume: 45
Year: 2018
Month: 2
X-DOI: 10.1080/02664763.2017.1282441
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1282441
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:3:p:528-546




Template-Type: ReDIF-Article 1.0
Author-Name: Saiedeh Haji-Maghsoudi
Author-X-Name-First: Saiedeh
Author-X-Name-Last: Haji-Maghsoudi
Author-Name: Azam Rastegari
Author-X-Name-First: Azam
Author-X-Name-Last: Rastegari
Author-Name: Behshid Garrusi
Author-X-Name-First: Behshid
Author-X-Name-Last: Garrusi
Author-Name: Mohammad Reza Baneshi
Author-X-Name-First: Mohammad Reza
Author-X-Name-Last: Baneshi
Title: Addressing the problem of missing data in decision tree modeling
Abstract: 
 Tree-based models (TBMs) can substitute missing data using the surrogate approach (SUR). The aim of this study is to compare the performance of statistical imputation against the performance of SUR in TBMs. Employing empirical data, a TBM was constructed. Thereafter, 10%, 20%, and 40% of variable values appeared as the first split was deleted, and imputed with and without the use of outcome variables in the imputation model (IMP− and IMP+). This was repeated one thousand times. Absolute relative bias above 0.10 was defined as sever (SARB). Subsequently, in a series of simulations, the following parameters were changed: the degree of correlation among variables, the number of variables truly associated with the outcome, and the missing rate. At a 10% missing rate, the proportion of times SARB was observed in either SUR or IMP− was two times higher than in IMP+ (28% versus 13%). When the missing rate was increased to 20%, all these proportions were approximately doubled. Irrespective of the missing rate, IMP+ was about 65% less likely to produce SARB than SUR. Results of IMP− and SUR were comparable up to a 20% missing rate. At a high missing rate, IMP− was 76% more likely to provide SARB estimates. Statistical imputation of missing data and the use of outcome variable in the imputation model is recommended, even in the content of TBM.
Journal: Journal of Applied Statistics
Pages: 547-557
Issue: 3
Volume: 45
Year: 2018
Month: 2
X-DOI: 10.1080/02664763.2017.1284184
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1284184
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:3:p:547-557




Template-Type: ReDIF-Article 1.0
Author-Name: M. F. de Almeida
Author-X-Name-First: M. F.
Author-X-Name-Last: de Almeida
Author-Name: Aldina Correia
Author-X-Name-First: Aldina
Author-X-Name-Last: Correia
Author-Name: Eliana Costa e Silva
Author-X-Name-First: Eliana
Author-X-Name-Last: Costa e Silva
Title: Layered clays in PP polymer dispersion: the effect of the processing conditions
Abstract: 
 The effect of processing conditions of material and interfacial/ interphase properties in clay/polymer nanocomposites are usually studied through experimental results of X-ray diffraction, transmission electron microscopy and rheology mechanical tests. In this paper, the interaction of the experimental parameters and properties measure results are studied by employing multivariate analysis of variance technique. This model is used to establish relations between several material and interfacial/interphase characteristics with the processing conditions. These results are then analyzed in detail, using univariate approach (analysis of variance), to point out the existing significant relations. In general, agreement with experimental observations was found, but few other nonagreeable things are found out, which must be studied in the future. The effects of all parameters are thus determined, using these new approach, to predict the relations between material and interfacial/interphase properties, without the need to duplicate experiments in laboratory.
Journal: Journal of Applied Statistics
Pages: 558-567
Issue: 3
Volume: 45
Year: 2018
Month: 2
X-DOI: 10.1080/02664763.2017.1284185
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1284185
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:3:p:558-567




Template-Type: ReDIF-Article 1.0
Author-Name: Georgiana Onicescu
Author-X-Name-First: Georgiana
Author-X-Name-Last: Onicescu
Author-Name: Andrew B. Lawson
Author-X-Name-First: Andrew B.
Author-X-Name-Last: Lawson
Author-Name: Jiajia Zhang
Author-X-Name-First: Jiajia
Author-X-Name-Last: Zhang
Author-Name: Mulugeta Gebregziabher
Author-X-Name-First: Mulugeta
Author-X-Name-Last: Gebregziabher
Author-Name: Kristin Wallace
Author-X-Name-First: Kristin
Author-X-Name-Last: Wallace
Author-Name: Jan M. Eberth
Author-X-Name-First: Jan M.
Author-X-Name-Last: Eberth
Title: Spatially explicit survival modeling for small area cancer data
Abstract: 
 In this paper we propose a novel Bayesian statistical methodology for spatial survival data. Our methodology broadens the definition of the survival, density and hazard functions by explicitly modeling the spatial dependency using direct derivations of these functions and their marginals and conditionals. We also derive spatially dependent likelihood functions. Finally we examine the applications of these derivations with geographically augmented survival distributions in the context of the Louisiana Surveillance, Epidemiology, and End Results registry prostate cancer data.
Journal: Journal of Applied Statistics
Pages: 568-585
Issue: 3
Volume: 45
Year: 2018
Month: 2
X-DOI: 10.1080/02664763.2017.1288200
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1288200
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:3:p:568-585




Template-Type: ReDIF-Article 1.0
Author-Name: Xuwen Zhu
Author-X-Name-First: Xuwen
Author-X-Name-Last: Zhu
Title: Variable diagnostics in model-based clustering through variation partition
Abstract: 
 Model-based clustering is a flexible grouping technique based on fitting finite mixture models to data groups. Despite its rapid development in recent years, there is rather limited literature devoted to developing diagnostic tools for obtained clustering solutions. In this paper, a new method through fuzzy variation decomposition is proposed for probabilistic assessing contribution of variables to a detected dataset partition. Correlation between-variable contributions reveals the underlying variable interaction structure. A visualization tool illustrates whether two variables work collaboratively or exclusively in the model. Elimination of negative-effect variables in the partition leads to better classification results. The developed technique is employed on real-life datasets with promising results.
Journal: Journal of Applied Statistics
Pages: 2888-2905
Issue: 16
Volume: 45
Year: 2018
Month: 12
X-DOI: 10.1080/02664763.2018.1444740
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1444740
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:16:p:2888-2905




Template-Type: ReDIF-Article 1.0
Author-Name: Durba Bhattacharya
Author-X-Name-First: Durba
Author-X-Name-Last: Bhattacharya
Author-Name: Sourabh Bhattacharya
Author-X-Name-First: Sourabh
Author-X-Name-Last: Bhattacharya
Title: A Bayesian semiparametric approach to learning about gene–gene interactions in case-control studies
Abstract: 
 Gene–gene interactions are often regarded as playing significant roles in influencing variabilities of complex traits. Although much research has been devoted to this area, to date a comprehensive statistical model that addresses the various sources of uncertainties, seem to be lacking. In this paper, we propose and develop a novel Bayesian semiparametric approach composed of finite mixtures based on Dirichlet processes and a hierarchical matrix-normal distribution that can comprehensively account for the unknown number of sub-populations and gene–gene interactions. Then, by formulating novel and suitable Bayesian tests of hypotheses we attempt to single out the roles of the genes, individually, and in interaction with other genes, in case-control studies. We also attempt to identify the significant loci associated with the disease. Our model facilitates a highly efficient parallel computing methodology, combining Gibbs sampling and Transformation-based MCMC (TMCMC). Application of our ideas to biologically realistic data sets revealed quite encouraging performance. We also applied our ideas to a real, myocardial infarction dataset, and obtained interesting results that partly agree with, and also complement, the existing works in this area, to reveal the importance of sophisticated and realistic modeling of gene–gene interactions.
Journal: Journal of Applied Statistics
Pages: 2906-2928
Issue: 16
Volume: 45
Year: 2018
Month: 12
X-DOI: 10.1080/02664763.2018.1444741
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1444741
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:16:p:2906-2928




Template-Type: ReDIF-Article 1.0
Author-Name: Dal Ho Kim
Author-X-Name-First: Dal Ho
Author-X-Name-Last: Kim
Author-Name: Jayoun Lee
Author-X-Name-First: Jayoun
Author-X-Name-Last: Lee
Author-Name: Yongku Kim
Author-X-Name-First: Yongku
Author-X-Name-Last: Kim
Title: Information analysis of local suppression scheme based on a spatial-temporal model
Abstract: 
 In a wireless sensor network, data collection is relatively cheap whereas data transmission is relatively expensive. Thus, preserving battery life is critical. If the process of interest is sufficiently predictable, the suppression in transmission can be adopted to improve efficiency of sensor networks because the loss of information is not great. The prime interest lies in finding an inference-efficient way to support suppressed data collection application. In this paper, we present a suppression scheme for a multiple nodes setting with spatio-temporal processes, especially when process knowledge is insufficient. We also explore the impact of suppression schemes on the inference of the regional processes under various suppression levels. Finally, we formalize the hierarchical Bayesian model for these schemes.
Journal: Journal of Applied Statistics
Pages: 2929-2942
Issue: 16
Volume: 45
Year: 2018
Month: 12
X-DOI: 10.1080/02664763.2018.1445703
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1445703
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:16:p:2929-2942




Template-Type: ReDIF-Article 1.0
Author-Name: Mike G. Tsionas
Author-X-Name-First: Mike G.
Author-X-Name-Last: Tsionas
Author-Name: Marwan Izzeldin
Author-X-Name-First: Marwan
Author-X-Name-Last: Izzeldin
Title: Bayesian CV@R/super-quantile regression
Abstract: 
 In this paper we provide a Bayesian interpretation of the conditional value at risk, CV@R, or super-quantile regression recently developed by Rockafellar et al. [Super-quantile regression with applications to buffered reliability, uncertainty quantification, and conditional value-at-risk, Eur. J. Oper. Res. 234 (2014), pp. 140–154]. Computations are based on particle filtering using a special posterior distribution consistent with the super-quantile concept. An empirical application to data used by RRM as well to another data set on energy prices confirms their results and shows the applicability of the new techniques.
Journal: Journal of Applied Statistics
Pages: 2943-2957
Issue: 16
Volume: 45
Year: 2018
Month: 12
X-DOI: 10.1080/02664763.2018.1450363
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1450363
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:16:p:2943-2957




Template-Type: ReDIF-Article 1.0
Author-Name: Gerd K. Rosenkranz
Author-X-Name-First: Gerd K.
Author-X-Name-Last: Rosenkranz
Title: Empirical Bayes estimators in hierarchical models with mixture priors
Abstract: 
 We consider subgroup analyses within the framework of hierarchical modeling and empirical Bayes (EB) methodology for general priors, thereby generalizing the normal–normal model. By doing this one obtains greater flexibility in modeling. We focus on mixture priors, that is, on the situation where group effects are exchangeable within clusters of subgroups only. We establish theoretical results on accuracy, precision, shrinkage and selection bias of EB estimators under the general priors. The impact of model misspecification is investigated and the applicability of the methodology is illustrated with datasets from the (medical) literature.
Journal: Journal of Applied Statistics
Pages: 2958-2980
Issue: 16
Volume: 45
Year: 2018
Month: 12
X-DOI: 10.1080/02664763.2018.1450364
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1450364
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:16:p:2958-2980




Template-Type: ReDIF-Article 1.0
Author-Name: J. A. A. Andrade
Author-X-Name-First: J. A. A.
Author-X-Name-Last: Andrade
Author-Name: J. P. Gosling
Author-X-Name-First: J. P.
Author-X-Name-Last: Gosling
Title: Expert knowledge elicitation using item response theory
Abstract: 
 In an expert knowledge elicitation exercise, experts face a carefully constructed list of questions that they answer according to their knowledge. The elicitation process concludes when a probability distribution is found that adequately captures the experts' beliefs in the light of those answers. In many situations, it is very difficult to create a set of questions that will efficiently capture the experts' knowledge, since experts might not be able to make precise probabilistic statements about the parameter of interest. We present an approach for capturing expert knowledge based on item response theory, in which a set of binary response questions is proposed to the expert, trying to capture responses directly related to the quantity of interest. As a result, the posterior distribution of the parameter of interest will represent the elicited prior distribution that does not assume any particular parametric form. The method is illustrated by a simulated example and by an application involving the elicitation of rain prophets' predictions for the rainy season in the north-east of Brazil.
Journal: Journal of Applied Statistics
Pages: 2981-2998
Issue: 16
Volume: 45
Year: 2018
Month: 12
X-DOI: 10.1080/02664763.2018.1450365
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1450365
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:16:p:2981-2998




Template-Type: ReDIF-Article 1.0
Author-Name: Jae-Cheol Park
Author-X-Name-First: Jae-Cheol
Author-X-Name-Last: Park
Author-Name: Jea-Young Lee
Author-X-Name-First: Jea-Young
Author-X-Name-Last: Lee
Title: How to build nomogram for type 2 diabetes using a naïve Bayesian classifier technique
Abstract: 
 In this study, we introduced a method for building a Bayesian nomogram and proposed an appropriate nomogram for type 2 diabetes (T2D) using data from 13,474 subjects collected from the 2013–2015 Korean National Health and Nutrition Examination Survey (KNHANES) data. We identified risk factors related to T2D, proposed a visual nomogram for T2D from a naïve Bayesian classifier model, and predicted incidence rates. Additionally, we computed confidence intervals for the influence of risk factors (attributes) and verified the proposed Bayesian nomogram using a receiver operating characteristic curve. Finally, we compared logistic regression and the Bayesian nomogram for T2D. The results of the analysis of the T2D data showed that the most influential factor among all attributes in the Bayesian nomogram was age group, and the highest risk factor for T2D incidence was cardiovascular disease. Dyslipidemia and hypertension also had significant impacts on T2D incidence while the effects of sex, smoking status, and employment status were relatively small compared to those of other variables. Using the proposed Bayesian nomogram, we can easily predict the incidence rate of T2D in an individual, and treatment plans can be established based on this information.
Journal: Journal of Applied Statistics
Pages: 2999-3011
Issue: 16
Volume: 45
Year: 2018
Month: 12
X-DOI: 10.1080/02664763.2018.1450366
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1450366
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:16:p:2999-3011




Template-Type: ReDIF-Article 1.0
Author-Name: Longmei Chen
Author-X-Name-First: Longmei
Author-X-Name-Last: Chen
Author-Name: Alan T. K. Wan
Author-X-Name-First: Alan T. K.
Author-X-Name-Last: Wan
Author-Name: Geoffrey Tso
Author-X-Name-First: Geoffrey
Author-X-Name-Last: Tso
Author-Name: Xinyu Zhang
Author-X-Name-First: Xinyu
Author-X-Name-Last: Zhang
Title: A model averaging approach for the ordered probit and nested logit models with applications
Abstract: 
 This paper considers model averaging for the ordered probit and nested logit models, which are widely used in empirical research. Within the frameworks of these models, we examine a range of model averaging methods, including the jackknife method, which is proved to have an optimal asymptotic property in this paper. We conduct a large-scale simulation study to examine the behaviour of these model averaging estimators in finite samples, and draw comparisons with model selection estimators. Our results show that while neither averaging nor selection is a consistently better strategy, model selection results in the poorest estimates far more frequently than averaging, and more often than not, averaging yields superior estimates. Among the averaging methods considered, the one based on a smoothed version of the Bayesian Information criterion frequently produces the most accurate estimates. In three real data applications, we demonstrate the usefulness of model averaging in mitigating problems associated with the ‘replication crisis’ that commonly arises with model selection.
Journal: Journal of Applied Statistics
Pages: 3012-3052
Issue: 16
Volume: 45
Year: 2018
Month: 12
X-DOI: 10.1080/02664763.2018.1450367
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1450367
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:16:p:3012-3052




Template-Type: ReDIF-Article 1.0
Author-Name: The Editors
Title: List of Reviewers
Journal: Journal of Applied Statistics
Pages: 3053-3056
Issue: 16
Volume: 45
Year: 2018
Month: 12
X-DOI: 10.1080/02664763.2018.1530909
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1530909
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:16:p:3053-3056




Template-Type: ReDIF-Article 1.0
Author-Name: Nabakumar Jana
Author-X-Name-First: Nabakumar
Author-X-Name-Last: Jana
Author-Name: Somesh Kumar
Author-X-Name-First: Somesh
Author-X-Name-Last: Kumar
Author-Name: Kashinath Chatterjee
Author-X-Name-First: Kashinath
Author-X-Name-Last: Chatterjee
Title: Bayes estimation for exponential distributions with common location parameter and applications to multi-state reliability models
Abstract: 
 This paper considers the estimation of the stress–strength reliability of a multi-state component or of a multi-state system where its states depend on the ratio of the strength and stress variables through a kernel function. The article presents a Bayesian approach assuming the stress and strength as exponentially distributed with a common location parameter but different scale parameters. We show that the limits of the Bayes estimators of both location and scale parameters under suitable priors are the maximum likelihood estimators as given by Ghosh and Razmpour [15]. We use the Bayes estimators to determine the multi-state stress–strength reliability of a system having states between 0 and 1. We derive the uniformly minimum variance unbiased estimators of the reliability function. Interval estimation using the bootstrap method is also considered. Under the squared error loss function and linex loss function, risk comparison of the reliability estimators is carried out using extensive simulations.
Journal: Journal of Applied Statistics
Pages: 2697-2712
Issue: 15
Volume: 43
Year: 2016
Month: 11
X-DOI: 10.1080/02664763.2016.1142950
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1142950
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:15:p:2697-2712




Template-Type: ReDIF-Article 1.0
Author-Name: Xiaosun Lu
Author-X-Name-First: Xiaosun
Author-X-Name-Last: Lu
Author-Name: Yangxin Huang
Author-X-Name-First: Yangxin
Author-X-Name-Last: Huang
Author-Name: Rong Zhou
Author-X-Name-First: Rong
Author-X-Name-Last: Zhou
Title: Joint analysis of nonlinear heterogeneous longitudinal data and binary outcome: an application to AIDS clinical studies
Abstract: 
 Finite mixture models are currently used to analyze heterogeneous longitudinal data. By releasing the homogeneity restriction of nonlinear mixed-effects (NLME) models, finite mixture models not only can estimate model parameters but also cluster individuals into one of the pre-specified classes with class membership probabilities. This clustering may have clinical significance, which might be associated with a clinically important binary outcome. This article develops a joint modeling of a finite mixture of NLME models for longitudinal data in the presence of covariate measurement errors and a logistic regression for a binary outcome, linked by individual latent class indicators, under a Bayesian framework. Simulation studies are conducted to assess the performance of the proposed joint model and a naive two-step model, in which finite mixture model and logistic regression are fitted separately, followed by an application to a real data set from an AIDS clinical trial, in which the viral dynamics and dichotomized time to the first decline of CD4/CD8 ratio are analyzed jointly.
Journal: Journal of Applied Statistics
Pages: 2713-2728
Issue: 15
Volume: 43
Year: 2016
Month: 11
X-DOI: 10.1080/02664763.2016.1142951
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1142951
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:15:p:2713-2728




Template-Type: ReDIF-Article 1.0
Author-Name: Visalakshi Jeyaseelan
Author-X-Name-First: Visalakshi
Author-X-Name-Last: Jeyaseelan
Author-Name: Tunny Sebastian
Author-X-Name-First: Tunny
Author-X-Name-Last: Sebastian
Author-Name: Jeyaseelan Lakshmanan
Author-X-Name-First: Jeyaseelan
Author-X-Name-Last: Lakshmanan
Author-Name: Shrikant I Bangdiwala
Author-X-Name-First: Shrikant I
Author-X-Name-Last: Bangdiwala
Title: Longitudinal data analysis of mean passage time among malnutrition states: an application of Markov chains
Abstract: 
 Clinical prognosis of patients can be best described from a longitudinal study and a Markov regression model is an appropriate way of analyzing the prognosis of disease when the outcomes are serially dependent. Mean first passage time (MFPT) is a method to estimate the average number of transitions between the states of a Markov chain. The present study used the secondary data from a longitudinal study which was done during 1982–1986. This study was to illustrate the MFPT among the states of malnutrition, which were classified as Normal, Mild/Moderate and
Severe among children aged 5–7 years, in South India. The 95% confidence interval (CI) for the MFPT was calculated using Monte Carlo simulation. Markov regression models were used to test for the association of state transitions across the risk factors. The average time taken for an underweight child to transit from Severe state of malnutrition to become Normal was nearly 2.73 (95% CI 2.60–2.86) years and 3.41 (95% CI 3.25–3.58) years in Rural area and 2.31(95% CI 2.20–2.42) in Urban area. The significant difference between the MFPT for some risk factors are useful to plan interventions. It will especially be useful to find the impact of duration among school-going children on their cognitive disorders.
Journal: Journal of Applied Statistics
Pages: 2729-2739
Issue: 15
Volume: 43
Year: 2016
Month: 11
X-DOI: 10.1080/02664763.2016.1143454
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1143454
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:15:p:2729-2739




Template-Type: ReDIF-Article 1.0
Author-Name: Xia Cai
Author-X-Name-First: Xia
Author-X-Name-Last: Cai
Author-Name: Khamis Khalid Said
Author-X-Name-First: Khamis Khalid
Author-X-Name-Last: Said
Author-Name: Wei Ning
Author-X-Name-First: Wei
Author-X-Name-Last: Ning
Title: Change-point analysis with bathtub shape for the exponential distribution
Abstract: 
 Likelihood ratio type test statistic and Schwarz information criterion statistics are proposed for detecting possible bathtub-shaped changes in the parameter in a sequence of exponential distributions. The asymptotic distribution of likelihood ratio type statistic under the null hypothesis and the testing procedure based on Schwarz information criterion are derived. Numerical critical values and powers of two methods are tabulated for certain selected values of the parameters. The tests are applied to detect the change points for the predator data and Stanford heart transplant data.
Journal: Journal of Applied Statistics
Pages: 2740-2750
Issue: 15
Volume: 43
Year: 2016
Month: 11
X-DOI: 10.1080/02664763.2016.1143455
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1143455
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:15:p:2740-2750




Template-Type: ReDIF-Article 1.0
Author-Name: Taeryon Choi
Author-X-Name-First: Taeryon
Author-X-Name-Last: Choi
Author-Name: Hea-Jung Kim
Author-X-Name-First: Hea-Jung
Author-X-Name-Last: Kim
Author-Name: Seongil Jo
Author-X-Name-First: Seongil
Author-X-Name-Last: Jo
Title: Bayesian variable selection approach to a Bernstein polynomial regression model with stochastic constraints
Abstract: 
 This paper provides a Bayesian estimation procedure for monotone regression models incorporating the monotone trend constraint subject to uncertainty. For monotone regression modeling with stochastic restrictions, we propose a Bayesian Bernstein polynomial regression model using two-stage hierarchical prior distributions based on a family of rectangle-screened multivariate Gaussian distributions extended from the work of Gurtis and Ghosh [7]. This approach reflects the uncertainty about the prior constraint, and thus proposes a regression model subject to monotone restriction with uncertainty. Based on the proposed model, we derive the posterior distributions for unknown parameters and present numerical schemes to generate posterior samples. We show the empirical performance of the proposed model based on synthetic data and real data applications and compare the performance to the Bernstein polynomial regression model of Curtis and Ghosh [7] for the shape restriction with certainty. We illustrate the effectiveness of our proposed method that incorporates the uncertainty of the monotone trend and automatically adapts the regression function to the monotonicity, through empirical analysis with synthetic data and real data applications.
Journal: Journal of Applied Statistics
Pages: 2751-2771
Issue: 15
Volume: 43
Year: 2016
Month: 11
X-DOI: 10.1080/02664763.2016.1143456
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1143456
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:15:p:2751-2771




Template-Type: ReDIF-Article 1.0
Author-Name: Tommi Härkänen
Author-X-Name-First: Tommi
Author-X-Name-Last: Härkänen
Author-Name: Juha Karvanen
Author-X-Name-First: Juha
Author-X-Name-Last: Karvanen
Author-Name: Hanna Tolonen
Author-X-Name-First: Hanna
Author-X-Name-Last: Tolonen
Author-Name: Risto Lehtonen
Author-X-Name-First: Risto
Author-X-Name-Last: Lehtonen
Author-Name: Kari Djerf
Author-X-Name-First: Kari
Author-X-Name-Last: Djerf
Author-Name: Teppo Juntunen
Author-X-Name-First: Teppo
Author-X-Name-Last: Juntunen
Author-Name: Seppo Koskinen
Author-X-Name-First: Seppo
Author-X-Name-Last: Koskinen
Title: Systematic handling of missing data in complex study designs – experiences from the Health 2000 and 2011 Surveys
Abstract: 
 We present a systematic approach to the practical and comprehensive handling of missing data motivated by our experiences of analyzing longitudinal survey data. We consider the Health 2000 and 2011 Surveys (BRIF8901) where increased non-response and non-participation from 2000 to 2011 was a major issue. The model assumptions involved in the complex sampling design, repeated measurements design, non-participation mechanisms and associations are presented graphically using methodology previously defined as a causal model with design, i.e. a functional causal model extended with the study design. This tool forces the statistician to make the study design and the missing-data mechanism explicit. Using the systematic approach, the sampling probabilities and the participation probabilities can be considered separately. This is beneficial when the performance of missing-data methods are to be compared. Using data from Health 2000 and 2011 Surveys and from national registries, it was found that multiple imputation removed almost all differences between full sample and estimated prevalences. The inverse probability weighting removed more than half and the doubly robust method 60% of the differences. These findings are encouraging since decreasing participation rates are a major problem in population surveys worldwide.
Journal: Journal of Applied Statistics
Pages: 2772-2790
Issue: 15
Volume: 43
Year: 2016
Month: 11
X-DOI: 10.1080/02664763.2016.1144725
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1144725
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:15:p:2772-2790




Template-Type: ReDIF-Article 1.0
Author-Name: T. Söküt Açar
Author-X-Name-First: T. Söküt
Author-X-Name-Last: Açar
Author-Name: M.R. Özkale
Author-X-Name-First: M.R.
Author-X-Name-Last: Özkale
Title: Influence measures based on confidence ellipsoids in general linear regression model with correlated regressors
Abstract: 
 In this paper, we investigated the Andrews–Pregibon (AP), COVRATIO and Cook–Weisberg (CW) statistics to determine the influential observations on the confidence ellipsoids in linear regression model with correlated errors and correlated regressors. A real example and a Monte Carlo simulation study are given to detect the effects of autocorrelation coefficient and ridge parameter on the AP, COVRATIO and CW statistics.
Journal: Journal of Applied Statistics
Pages: 2791-2812
Issue: 15
Volume: 43
Year: 2016
Month: 11
X-DOI: 10.1080/02664763.2016.1144726
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1144726
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:15:p:2791-2812




Template-Type: ReDIF-Article 1.0
Author-Name: Dawit G. Ayele
Author-X-Name-First: Dawit G.
Author-X-Name-Last: Ayele
Author-Name: Temesgen T. Zewotir
Author-X-Name-First: Temesgen T.
Author-X-Name-Last: Zewotir
Title: Childhood mortality spatial distribution in Ethiopia
Abstract: 
 The risk of a child dying before completing five years of age is highest in Sub-Saharan African countries. But Child mortality rates have shown substantial decline in Ethiopia. For this study, the 2000, 2005 and 2011 Ethiopian Demographic Survey (EDHS) was used. Generalized linear mixed model with spatial covariance structure was adapted. The model allowed for spatial correlation, and leads to the more realistic estimate for under-five mortality risk factors. The analysis showed that the risk of under-five mortality shows decline in years. But, some regions showed increase in years. The study highlights the need to implement better education for family planning and child care to improve the under-five mortality situation in some administrative areas.
Journal: Journal of Applied Statistics
Pages: 2813-2828
Issue: 15
Volume: 43
Year: 2016
Month: 11
X-DOI: 10.1080/02664763.2016.1144727
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1144727
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:15:p:2813-2828




Template-Type: ReDIF-Article 1.0
Author-Name: Carolina Marchant
Author-X-Name-First: Carolina
Author-X-Name-Last: Marchant
Author-Name: Víctor Leiva
Author-X-Name-First: Víctor
Author-X-Name-Last: Leiva
Author-Name: Francisco José A. Cysneiros
Author-X-Name-First: Francisco José A.
Author-X-Name-Last: Cysneiros
Author-Name: Juan F. Vivanco
Author-X-Name-First: Juan F.
Author-X-Name-Last: Vivanco
Title: Diagnostics in multivariate generalized Birnbaum-Saunders regression models
Abstract: 
 Birnbaum–Saunders (BS) models are receiving considerable attention in the literature. Multivariate regression models are a useful tool of the multivariate analysis, which takes into account the correlation between variables. Diagnostic analysis is an important aspect to be considered in the statistical modeling. In this paper, we formulate multivariate generalized BS regression models and carry out a diagnostic analysis for these models. We consider the Mahalanobis distance as a global influence measure to detect multivariate outliers and use it for evaluating the adequacy of the distributional assumption. We also consider the local influence approach and study how a perturbation may impact on the estimation of model parameters. We implement the obtained results in the R software, which are illustrated with real-world multivariate data to show their potential applications.
Journal: Journal of Applied Statistics
Pages: 2829-2849
Issue: 15
Volume: 43
Year: 2016
Month: 11
X-DOI: 10.1080/02664763.2016.1148671
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1148671
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:15:p:2829-2849




Template-Type: ReDIF-Article 1.0
Author-Name: Kiranmoy Das
Author-X-Name-First: Kiranmoy
Author-X-Name-Last: Das
Title: A semiparametric Bayesian approach for joint modeling of longitudinal trait and event time
Abstract: 
 Inference on the whole biological system is the recent focus in bioscience. Different biomarkers, although seem to function separately, can actually control some event(s) of interest simultaneously. This fundamental biological principle has motivated the researchers for developing joint models which can explain the biological system efficiently. Because of the advanced biotechnology, huge amount of biological information can be easily obtained in current years. Hence dimension reduction is one of the major issues in current biological research. In this article, we propose a Bayesian semiparametric approach of jointly modeling observed longitudinal trait and event-time data. A sure independence screening procedure based on the distance correlation and a modified version of Bayesian Lasso are used for dimension reduction. Traditional Cox proportional hazards model is used for modeling the event-time. Our proposed model is used for detecting marker genes controlling the biomass and first flowering time of soybean plants. Simulation studies are performed for assessing the practical usefulness of the proposed model. Proposed model can be used for the joint analysis of traits and diseases for humans, animals and plants.
Journal: Journal of Applied Statistics
Pages: 2850-2865
Issue: 15
Volume: 43
Year: 2016
Month: 11
X-DOI: 10.1080/02664763.2016.1155108
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1155108
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:15:p:2850-2865




Template-Type: ReDIF-Article 1.0
Author-Name: Gerhard Tutz
Author-X-Name-First: Gerhard
Author-X-Name-Last: Tutz
Author-Name: Sebastian Petry
Author-X-Name-First: Sebastian
Author-X-Name-Last: Petry
Title: Generalized additive models with unknown link function including variable selection
Abstract: 
 The generalized additive model is a well established and strong tool that allows modelling smooth effects of predictors on the response. However, if the link function, which is typically chosen as the canonical link, is misspecified, estimates can be biased. A procedure is proposed that simultaneously estimates the form of the link function and the unknown form of the predictor functions including selection of predictors. The procedure is based on boosting methodology, which obtains estimates by using a sequence of weak learners. It strongly dominates fitting procedures that are unable to modify a given link function if the true link function deviates from the fixed function. The performance of the procedure is shown in simulation studies and illustrated by real world examples.
Journal: Journal of Applied Statistics
Pages: 2866-2885
Issue: 15
Volume: 43
Year: 2016
Month: 11
X-DOI: 10.1080/02664763.2016.1155109
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1155109
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:15:p:2866-2885




Template-Type: ReDIF-Article 1.0
Author-Name: Folefac D. Atem
Author-X-Name-First: Folefac D.
Author-X-Name-Last: Atem
Author-Name: Jing Qian
Author-X-Name-First: Jing
Author-X-Name-Last: Qian
Author-Name: Jacqueline E. Maye
Author-X-Name-First: Jacqueline E.
Author-X-Name-Last: Maye
Author-Name: Keith A. Johnson
Author-X-Name-First: Keith A.
Author-X-Name-Last: Johnson
Author-Name: Rebecca A. Betensky
Author-X-Name-First: Rebecca A.
Author-X-Name-Last: Betensky
Title: Multiple imputation of a randomly censored covariate improves logistic regression analysis
Abstract: 
 Randomly censored covariates arise frequently in epidemiologic studies. The most commonly used methods, including complete case and single imputation or substitution, suffer from inefficiency and bias. They make strong parametric assumptions or they consider limit of detection censoring only. We employ multiple imputation, in conjunction with semi-parametric modeling of the censored covariate, to overcome these shortcomings and to facilitate robust estimation. We develop a multiple imputation approach for randomly censored covariates within the framework of a logistic regression model. We use the non-parametric estimate of the covariate distribution or the semi-parametric Cox model estimate in the presence of additional covariates in the model. We evaluate this procedure in simulations, and compare its operating characteristics to those from the complete case analysis and a survival regression approach. We apply the procedures to an Alzheimer's study of the association between amyloid positivity and maternal age of onset of dementia. Multiple imputation achieves lower standard errors and higher power than the complete case approach under heavy and moderate censoring and is comparable under light censoring. The survival regression approach achieves the highest power among all procedures, but does not produce interpretable estimates of association. Multiple imputation offers a favorable alternative to complete case analysis and ad hoc substitution methods in the presence of randomly censored covariates within the framework of logistic regression.
Journal: Journal of Applied Statistics
Pages: 2886-2896
Issue: 15
Volume: 43
Year: 2016
Month: 11
X-DOI: 10.1080/02664763.2016.1155110
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1155110
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:15:p:2886-2896




Template-Type: ReDIF-Article 1.0
Author-Name: Johann Ospína-Galindez
Author-X-Name-First: Johann
Author-X-Name-Last: Ospína-Galindez
Author-Name: Ramón Giraldo
Author-X-Name-First: Ramón
Author-X-Name-Last: Giraldo
Author-Name: Mercedes Andrade-Bejarano
Author-X-Name-First: Mercedes
Author-X-Name-Last: Andrade-Bejarano
Title: Functional regression concurrent model with spatially correlated errors: application to rainfall ground validation
Abstract: 
 In this paper, we give an extension of the functional regression concurrent model to the case of spatially correlated errors. We propose estimating the spatial correlation structure by using functional geostatistics. The estimation of the regression parameters is carried out by feasible generalized least squares. This modeling approach is motivated by the problem of validating rainfall data retrieved from satellite sensors. In this sense, we use the methodology to study the relationship between satellite and ground rainfall time series recorded in 82 weather stations from Department of Valle del Cauca, Colombia. The model obtained allows predicting pentadal rainfall curves in many sites of the region of interest by using as input the satellite information. A residual analysis shows a good performance of the methodology proposed.
Journal: Journal of Applied Statistics
Pages: 1350-1363
Issue: 8
Volume: 46
Year: 2019
Month: 6
X-DOI: 10.1080/02664763.2018.1544231
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1544231
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:8:p:1350-1363




Template-Type: ReDIF-Article 1.0
Author-Name: Daniel Fernández
Author-X-Name-First: Daniel
Author-X-Name-Last: Fernández
Author-Name: Daphna Harel
Author-X-Name-First: Daphna
Author-X-Name-Last: Harel
Author-Name: Panos Ipeirotis
Author-X-Name-First: Panos
Author-X-Name-Last: Ipeirotis
Author-Name: Tara McAllister
Author-X-Name-First: Tara
Author-X-Name-Last: McAllister
Title: Statistical considerations for crowdsourced perceptual ratings of human speech productions
Abstract: 
 Crowdsourcing has become a major tool for scholarly research since its introduction to the academic sphere in 2008. However, unlike in traditional laboratory settings, it is nearly impossible to control the conditions under which workers on crowdsourcing platforms complete tasks. In the study of communication disorders, crowdsourcing has provided a novel solution to the collection of perceptual ratings of human speech production. Such ratings allow researchers to gauge whether a treatment yields meaningful change in how human listeners' perceive disordered speech. This paper will explore some statistical considerations of crowdsourced data with specific focus on collecting perceptual ratings of human speech productions. Random effects models are applied to crowdsourced perceptual ratings collected in both a continuous and binary fashion. A simulation study is conducted to test the reliability of the proposed models under differing numbers of workers and tasks. Finally, this methodology is applied to a data set from the study of communication disorders.
Journal: Journal of Applied Statistics
Pages: 1364-1384
Issue: 8
Volume: 46
Year: 2019
Month: 6
X-DOI: 10.1080/02664763.2018.1547692
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1547692
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:8:p:1364-1384




Template-Type: ReDIF-Article 1.0
Author-Name: M. Kazemi
Author-X-Name-First: M.
Author-X-Name-Last: Kazemi
Author-Name: D. Shahsavani
Author-X-Name-First: D.
Author-X-Name-Last: Shahsavani
Author-Name: M. Arashi
Author-X-Name-First: M.
Author-X-Name-Last: Arashi
Title: A sure independence screening procedure for ultra-high dimensional partially linear additive models
Abstract: 
 We introduce a two-step procedure, in the context of ultra-high dimensional additive models, which aims to reduce the size of covariates vector and distinguish linear and nonlinear effects among nonzero components. Our proposed screening procedure, in the first step, is constructed based on the concept of cumulative distribution function and conditional expectation of response in the framework of marginal correlation. B-splines and empirical distribution functions are used to estimate the two above measures. The sure screening property of this procedure is also established. In the second step, a double penalization based procedure is applied to identify nonzero and linear components, simultaneously. The performance of the designed method is examined by several test functions to show its capabilities against competitor methods when the distribution of errors is varied. Simulation studies imply that the proposed screening procedure can be applied to the ultra-high dimensional data and well detect the influential covariates. It also demonstrate the superiority in comparison with the existing methods. This method is also applied to identify most influential genes for overexpression of a G protein-coupled receptor in mice.
Journal: Journal of Applied Statistics
Pages: 1385-1403
Issue: 8
Volume: 46
Year: 2019
Month: 6
X-DOI: 10.1080/02664763.2018.1548583
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1548583
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:8:p:1385-1403




Template-Type: ReDIF-Article 1.0
Author-Name: Rafel Xampeny
Author-X-Name-First: Rafel
Author-X-Name-Last: Xampeny
Author-Name: Pere Grima
Author-X-Name-First: Pere
Author-X-Name-Last: Grima
Author-Name: Xavier Tort-Martorell
Author-X-Name-First: Xavier
Author-X-Name-Last: Tort-Martorell
Title: Selecting significant effects in factorial designs: Lenth’s method versus the Box-Meyer approach
Abstract: 
 The Lenth method is conceptually simple and probably the most common approach to analyzing the significance of the effects in factorial designs. Here, we compare it with a Bayesian approach proposed by Box and Meyer and which does not appear in the usual software packages. The comparison is made by simulating the results of 4, 8 and 16 run designs in a set of scenarios that mirror practical situations and analyzing the results provided by both methods. Although the results depend on the number of runs and the scenario considered, the use of the Box and Meyer method generally produces better results.
Journal: Journal of Applied Statistics
Pages: 1404-1416
Issue: 8
Volume: 46
Year: 2019
Month: 6
X-DOI: 10.1080/02664763.2018.1548584
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1548584
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:8:p:1404-1416




Template-Type: ReDIF-Article 1.0
Author-Name: Russell J. Bowater
Author-X-Name-First: Russell J.
Author-X-Name-Last: Bowater
Author-Name: Ludmila E. Guzmán-Pantoja
Author-X-Name-First: Ludmila E.
Author-X-Name-Last: Guzmán-Pantoja
Title: Bayesian, classical and hybrid methods of inference when one parameter value is special
Abstract: 
 This paper considers the problem of making statistical inferences about a parameter when a narrow interval centred at a given value of the parameter is considered special, which is interpreted as meaning that there is a substantial degree of prior belief that the true value of the parameter lies in this interval. A clear justification of the practical importance of this problem is provided. The main difficulty with the standard Bayesian solution to this problem is discussed and, as a result, a pseudo-Bayesian solution is put forward based on determining lower limits for the posterior probability of the parameter lying in the special interval by means of a sensitivity analysis. Since it is not assumed that prior beliefs necessarily need to be expressed in terms of prior probabilities, nor that post-data probabilities must be Bayesian posterior probabilities, hybrid methods of inference are also proposed that are based on specific ways of measuring and interpreting the classical concept of significance. The various methods that are outlined are compared and contrasted at both a foundational level, and from a practical viewpoint by applying them to real data from meta-analyses that appeared in a well-known medical article.
Journal: Journal of Applied Statistics
Pages: 1417-1437
Issue: 8
Volume: 46
Year: 2019
Month: 6
X-DOI: 10.1080/02664763.2018.1548585
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1548585
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:8:p:1417-1437




Template-Type: ReDIF-Article 1.0
Author-Name: Piotr Sulewski
Author-X-Name-First: Piotr
Author-X-Name-Last: Sulewski
Title: Some contributions to practice of 2 × 2 contingency tables
Abstract: 
 This paper focuses on  $ 2 \times 2 $ 2×2 contingency tables (CTs) used to accept or reject  $ {H_0} $ H0 hypothesis that two categorical features are independent. This paper promotes a new view of the essence of CTs. Particular CTs are created as a result of phenomena governed by random factors. We put forward four simple scenarios under which CTs may be created. Further in the paper, a measure of untruthfulness of  $ {H_0} $ H0, which also may serve as the test statistics, is defined. The paper recalls several CT-related test statistics. Each test statistics produces its own power-of-the-test (PoT) function. The PoTs were determined with the Monte Carlo method and then compared to each other. In order to ease such a PoT-to-PoT comparison, the measure named ‘average outgoing untruthfulness level’ is put forward. Derivation, properties and example how to apply the measure in practice are given in details.
Journal: Journal of Applied Statistics
Pages: 1438-1455
Issue: 8
Volume: 46
Year: 2019
Month: 6
X-DOI: 10.1080/02664763.2018.1552665
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1552665
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:8:p:1438-1455




Template-Type: ReDIF-Article 1.0
Author-Name: Yoonsuh Jung
Author-X-Name-First: Yoonsuh
Author-X-Name-Last: Jung
Title: Nonlinear regression models for heterogeneous data with massive outliers
Abstract: 
 The income or expenditure-related data sets are often nonlinear, heteroscedastic, skewed even after the transformation, and contain numerous outliers. We propose a class of robust nonlinear models that treat outlying observations effectively without removing them. For this purpose, case-specific parameters and a related penalty are employed to detect and modify the outliers systematically. We show how the existing nonlinear models such as smoothing splines and generalized additive models can be robustified by the case-specific parameters. Next, we extend the proposed methods to the heterogeneous models by incorporating unequal weights. The details of estimating the weights are provided. Two real data sets and simulated data sets show the potential of the proposed methods when the nature of the data is nonlinear with outlying observations.
Journal: Journal of Applied Statistics
Pages: 1456-1477
Issue: 8
Volume: 46
Year: 2019
Month: 6
X-DOI: 10.1080/02664763.2018.1552666
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1552666
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:8:p:1456-1477




Template-Type: ReDIF-Article 1.0
Author-Name: Xiaoqing Niu
Author-X-Name-First: Xiaoqing
Author-X-Name-Last: Niu
Author-Name: Pengfei Li
Author-X-Name-First: Pengfei
Author-X-Name-Last: Li
Author-Name: Yuejiao Fu
Author-X-Name-First: Yuejiao
Author-X-Name-Last: Fu
Title: Testing homogeneity in a heteroscedastic contaminated normal mixture
Abstract: 
 Large-scale simultaneous hypothesis testing appears in many areas. A well-known inference method is to control the false discovery rate. One popular approach is to model the z-scores derived from the individual t-tests and then use this model to control the false discovery rate. We propose a heteroscedastic contaminated normal mixture to describe the distribution of z-scores and design an EM-test for testing homogeneity in this class of mixture models. The proposed EM-test can be used to investigate whether a collection of z-scores has arisen from a single normal distribution or whether a heteroscedastic contaminated normal mixture is more appropriate. We show that the EM-test statistic has a shifted mixture of chi-squared limiting distribution. Simulation results show that the proposed testing procedure has accurate type-I error and significantly larger power than its competitors under a variety of model specifications. A real-data example is analysed to exemplify the application of the proposed method.
Journal: Journal of Applied Statistics
Pages: 1478-1491
Issue: 8
Volume: 46
Year: 2019
Month: 6
X-DOI: 10.1080/02664763.2018.1552668
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1552668
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:8:p:1478-1491




Template-Type: ReDIF-Article 1.0
Author-Name: Sonal Budhiraja
Author-X-Name-First: Sonal
Author-X-Name-Last: Budhiraja
Author-Name: Biswabrata Pradhan
Author-X-Name-First: Biswabrata
Author-X-Name-Last: Pradhan
Title: Optimum reliability acceptance sampling plans under progressive type-I interval censoring with random removal using a cost model
Abstract: 
 A reliability acceptance sampling plan (RASP) is a variable sampling plan, which is used for lot sentencing based on the lifetime of the product under consideration. If a good lot is rejected then there is a loss of sales, whereas if a bad lot is accepted then the post sale cost increases and the brand image of the product is affected. Since cost is an important decision-making factor, adopting an economically optimal RASP is indispensable. This work considers the determination of an asymptotically optimum RASP under progressive type-I interval censoring scheme with random removal (PICR-I). We formulate a decision model for lot sentencing and a cost function is proposed that quantifies the losses. The cost function includes the cost of conducting the life test and warranty cost when the lot is accepted, and the cost of batch disposition when it is rejected. The asymptotically optimal RASP is obtained by minimizing the Bayes risk in a set of decision rules based on the maximum likelihood estimator of the mean lifetime of the items in the lot. For numerical illustration, we consider that lifetimes follow exponential or Weibull distributions.
Journal: Journal of Applied Statistics
Pages: 1492-1517
Issue: 8
Volume: 46
Year: 2019
Month: 6
X-DOI: 10.1080/02664763.2018.1554626
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1554626
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:8:p:1492-1517




Template-Type: ReDIF-Article 1.0
Author-Name: Marcella Corduas
Author-X-Name-First: Marcella
Author-X-Name-Last: Corduas
Author-Name: Giancarlo Ragozini
Author-X-Name-First: Giancarlo
Author-X-Name-Last: Ragozini
Title: A statistical procedure for representing state fragility and transition paths
Abstract: 
 State fragility is a concept that entered the political discourse in the last decades producing remarkable implications for aid allocation and international policies. The operationalization of this concept has generated a number of composite indices to produce rankings of fragile states. However, the temporal dimension of the driving forces leading to fragility has been rather neglected. This article discusses a statistical procedure that helps to represent the global fragility of a country and the path that a country has followed or will follow in the future when possibly entering into (or escaping from) a fragility condition. Specifically, multiple factor analysis is applied to depict vulnerable and weak countries, and to identify the fundamental forces that determine their overall fragility. Moreover, the trajectories of countries along the years are estimated using partial factor scores. Finally, the path of each country is predicted by means of parsimonious regression models, based on a reduced set of explanatory variables, and according to scenarios elaborated from available international outlooks.
Journal: Journal of Applied Statistics
Pages: 1518-1528
Issue: 8
Volume: 46
Year: 2019
Month: 6
X-DOI: 10.1080/02664763.2018.1552667
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1552667
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:8:p:1518-1528




Template-Type: ReDIF-Article 1.0
Author-Name: Selen Cakmakyapan
Author-X-Name-First: Selen
Author-X-Name-Last: Cakmakyapan
Author-Name: Haydar Demirhan
Author-X-Name-First: Haydar
Author-X-Name-Last: Demirhan
Title: A Monte Carlo-based pseudo-coefficient of determination for generalized linear models with binary outcome
Abstract: 
 In this article, we focus on a pseudo-coefficient of determination for generalized linear models with binary outcome. Although there are numerous coefficients of determination proposed in the literature, none of them is identified as the best in terms of estimation accuracy, or incorporates all desired characteristics of a precise coefficient of determination. Considering this, we propose a new coefficient of determination by using a computational Monte Carlo approach, and exhibit main characteristics of the proposed coefficient of determination both analytically and numerically. We evaluate and compare performances of the proposed and nine existing coefficients of determination by a comprehensive Monte Carlo simulation study. The proposed measure is found superior to the existent measures when dependent variable is balanced or moderately unbalanced for probit, logit, and complementary log–log link functions and a wide range of sample sizes. Due to the extensive design space of our simulation study, we identify new conditions in which previously recommended coefficients of determination should be used carefully.
Journal: Journal of Applied Statistics
Pages: 2458-2482
Issue: 14
Volume: 44
Year: 2017
Month: 10
X-DOI: 10.1080/02664763.2016.1257585
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1257585
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:14:p:2458-2482




Template-Type: ReDIF-Article 1.0
Author-Name: Xiaoqin Wang
Author-X-Name-First: Xiaoqin
Author-X-Name-Last: Wang
Author-Name: Weimin Ye
Author-X-Name-First: Weimin
Author-X-Name-Last: Ye
Author-Name: Li Yin
Author-X-Name-First: Li
Author-X-Name-Last: Yin
Title: Measuring and estimating the interaction between exposures on a dichotomous outcome for observational studies
Abstract: 
 In observational studies for the interaction between exposures on a dichotomous outcome of a certain population, usually one parameter of a regression model is used to describe the interaction, leading to one measure of the interaction. In this article we use the conditional risk of an outcome given exposures and covariates to describe the interaction and obtain five different measures of the interaction, that is, difference between the marginal risk differences, ratio of the marginal risk ratios, ratio of the marginal odds ratios, ratio of the conditional risk ratios, and ratio of the conditional odds ratios. These measures reflect different aspects of the interaction. By using only one regression model for the conditional risk, we obtain the maximum-likelihood (ML)-based point and interval estimates of these measures, which are most efficient due to the nature of ML. We use the ML estimates of the model parameters to obtain the ML estimates of these measures. We use the approximate normal distribution of the ML estimates of the model parameters to obtain approximate non-normal distributions of the ML estimates of these measures and then confidence intervals of these measures. The method can be easily implemented and is presented via a medical example.
Journal: Journal of Applied Statistics
Pages: 2483-2498
Issue: 14
Volume: 44
Year: 2017
Month: 10
X-DOI: 10.1080/02664763.2016.1257587
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1257587
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:14:p:2483-2498




Template-Type: ReDIF-Article 1.0
Author-Name: Arwa Nasr
Author-X-Name-First: Arwa
Author-X-Name-Last: Nasr
Author-Name: Soufiane Gasmi
Author-X-Name-First: Soufiane
Author-X-Name-Last: Gasmi
Author-Name: Fayçal Ben Hmida
Author-X-Name-First: Fayçal
Author-X-Name-Last: Ben Hmida
Title: Parameter estimation of the flexible Weibull distribution for type I censored samples
Abstract: 
 Often in practice one is interested in the situation where the lifetime data are censored. Censorship is a common phenomenon frequently encountered when analyzing lifetime data due to time constraints. In this paper, the flexible Weibull distribution proposed in Bebbington et al. [A flexible Weibull extension, Reliab. Eng. Syst. Safety 92 (2007), pp. 719–726] is studied using maximum likelihood technics based on three different algorithms: Newton Raphson, Levenberg Marquardt and Trust Region reflective. The proposed parameter estimation method is introduced and proved to work from theoretical and practical point of view. On one hand, we apply a maximum likelihood estimation method using complete simulated and real data. On the other hand, we study for the first time the model using simulated and real data for type I censored samples. The estimation results are approved by a statistical test.
Journal: Journal of Applied Statistics
Pages: 2499-2512
Issue: 14
Volume: 44
Year: 2017
Month: 10
X-DOI: 10.1080/02664763.2016.1257588
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1257588
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:14:p:2499-2512




Template-Type: ReDIF-Article 1.0
Author-Name: Abdul Suleman
Author-X-Name-First: Abdul
Author-X-Name-Last: Suleman
Title: A fuzzy clustering approach to evaluate individual competencies from REFLEX data
Abstract: 
 We empirically illustrate how concepts and methods involved in a grade of membership (GoM) analysis can be used to sort individuals by competence. Our study relies on a data set compiled from the international survey on higher education graduates called REFLEX. We focus on the subset of data related to the perception of own competencies. It is first decomposed into fuzzy clusters that form a hierarchical fuzzy partition. Then, we calculate a scalar measure of competencies for each fuzzy cluster, and subsequently use the individual GoM scores to combine cluster-based competencies to position individuals on a scale from 0 to 1.
Journal: Journal of Applied Statistics
Pages: 2513-2533
Issue: 14
Volume: 44
Year: 2017
Month: 10
X-DOI: 10.1080/02664763.2016.1257589
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1257589
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:14:p:2513-2533




Template-Type: ReDIF-Article 1.0
Author-Name: Chen Peng
Author-X-Name-First: Chen
Author-X-Name-Last: Peng
Author-Name: Maochao Xu
Author-X-Name-First: Maochao
Author-X-Name-Last: Xu
Author-Name: Shouhuai Xu
Author-X-Name-First: Shouhuai
Author-X-Name-Last: Xu
Author-Name: Taizhong Hu
Author-X-Name-First: Taizhong
Author-X-Name-Last: Hu
Title: Modeling and predicting extreme cyber attack rates via marked point processes
Abstract: 
 Cyber attacks have become a problem that is threatening the economy, human privacy, and even national security. Before we can adequately address the problem, we need to have a crystal clear understanding about cyber attacks from various perspectives. This is a challenge because the Internet is a large-scale complex system with humans in the loop. In this paper, we investigate a particular perspective of the problem, namely the extreme value phenomenon that is exhibited by cyber attack rates, which are the numbers of attacks against a system of interest per time unit. It is important to explore this perspective because understanding the statistical properties of extreme cyber attack rates will pave the way for cost-effective, if not optimal, allocation of resources in real-life cyber defense operations. Specifically, we propose modeling and predicting extreme cyber attack rates via marked point processes, while using the Value-at-Risk as a natural measure of intense cyber attacks. The point processes are then applied to analyze some real data sets. Our analysis shows that the point processes can describe and predict extreme cyber attack rates at a very satisfactory accuracy.
Journal: Journal of Applied Statistics
Pages: 2534-2563
Issue: 14
Volume: 44
Year: 2017
Month: 10
X-DOI: 10.1080/02664763.2016.1257590
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1257590
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:14:p:2534-2563




Template-Type: ReDIF-Article 1.0
Author-Name: Rand R. Wilcox
Author-X-Name-First: Rand R.
Author-X-Name-Last: Wilcox
Title: Linear regression: robust heteroscedastic confidence bands that have some specified simultaneous probability coverage
Abstract: 
 Let  $ M(Y\,|\,X) = \beta _0 + \beta _1X $ M(Y|X)=β0+β1X be some conditional measure of location associated with the random variable Y, given X, where the unknown parameters  $ \beta _0 $ β0 and  $ \beta _1 $ β1 are estimated based on the random sample  $ (X_1, Y_1), \ldots, (X_n, Y_n) $ (X1,Y1),…,(Xn,Yn). When using the ordinary least squares (OLS) estimator and  $ M(Y\,|\,X)=E(Y\,|\,X) $ M(Y|X)=E(Y|X), several methods for computing a confidence band have been derived that are aimed at achieving some specified simultaneous probability coverage assuming a homoscedastic error term and normality. There is an extant technique that allows heteroscedasticity, but a remaining concern is that the OLS estimator is not robust. Extant results indicate how a confidence interval can be computed via a robust regression estimator when there is heteroscedasticity and attention is focused on a single value of X. The paper extends this method by describing a heteroscedastic technique for computing a confidence interval for each  $ M(Y\,|\,X=X_i) $ M(Y|X=Xi) ( $ i=1, \ldots, n $ i=1,…,n) such that the simultaneous probability coverage has some specified value. The small-sample properties of the method are studied when using the OLS estimators as well as three robust regression estimators.
Journal: Journal of Applied Statistics
Pages: 2564-2574
Issue: 14
Volume: 44
Year: 2017
Month: 10
X-DOI: 10.1080/02664763.2016.1257591
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1257591
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:14:p:2564-2574




Template-Type: ReDIF-Article 1.0
Author-Name: Hanieh Panahi
Author-X-Name-First: Hanieh
Author-X-Name-Last: Panahi
Title: Estimation of the Burr type III distribution with application in unified hybrid censored sample of fracture toughness
Abstract: 
 In this paper, the statistical inference of the unknown parameters of a Burr Type III (BIII) distribution based on the unified hybrid censored sample is studied. The maximum likelihood estimators of the unknown parameters are obtained using the Expectation–Maximization algorithm. It is observed that the Bayes estimators cannot be obtained in explicit forms, hence Lindley's approximation and the Markov Chain Monte Carlo (MCMC) technique are used to compute the Bayes estimators. Further the highest posterior density credible intervals of the unknown parameters based on the MCMC samples are provided. The new model selection test is developed in discriminating between two competing models under unified hybrid censoring scheme. Finally, the potentiality of the BIII distribution to analyze the real data is illustrated by using the fracture toughness data of the three different materials namely silicon nitride (Si3N4), Zirconium dioxide (ZrO2) and sialon (Si6−xAlxOxN8−x). It is observed that for the present data sets, the BIII distribution has the better fit than the Weibull distribution which is frequently used in the fracture toughness data analysis.
Journal: Journal of Applied Statistics
Pages: 2575-2592
Issue: 14
Volume: 44
Year: 2017
Month: 10
X-DOI: 10.1080/02664763.2016.1258549
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1258549
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:14:p:2575-2592




Template-Type: ReDIF-Article 1.0
Author-Name: Xiaojie Xu
Author-X-Name-First: Xiaojie
Author-X-Name-Last: Xu
Title: Short-run price forecast performance of individual and composite models for 496 corn cash markets
Abstract: 
 Using daily prices from 496 corn cash markets for July 2006–February 2011, this study investigates short-run forecast performance of 31 individual and 10 composite models for each market at horizons of 5, 10, and 30 days. Over the performance evaluation period September 2010–February 2011, two composite models are optimal across horizons for different markets based on the mean-squared error. For around half of the markets at the horizon of 5 days and most of them at 10 and 30 days, the mean-squared error of a market's optimal model is significantly different from those of at least other 23 models evaluated for it. Root-mean-squared error reductions through switching from non-optimal models to the optimal are generally around 0.40%, 0.55%, and 0.87% at horizons of 5, 10, and 30 days.
Journal: Journal of Applied Statistics
Pages: 2593-2620
Issue: 14
Volume: 44
Year: 2017
Month: 10
X-DOI: 10.1080/02664763.2016.1259399
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1259399
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:14:p:2593-2620




Template-Type: ReDIF-Article 1.0
Author-Name: Amadou Sawadogo
Author-X-Name-First: Amadou
Author-X-Name-Last: Sawadogo
Author-Name: Simplice Dossou-Gbété
Author-X-Name-First: Simplice
Author-X-Name-Last: Dossou-Gbété
Author-Name: Dominique Lafon
Author-X-Name-First: Dominique
Author-X-Name-Last: Lafon
Title: Ties in one block comparison experiments: a generalization of the Mallows–Bradley–Terry ranking model
Abstract: 
 This study is concerned with the extension of the Mallows–Bradley–Terry ranking model for one block comparison consisting of all the items of interest to situations which allow an expression of no preference. We consider a modification of the Mallows–Bradley–Terry ranking model by introducing an additional parameter, called an index of discrimination, in the model. This permits ties in the model. The maximum likelihood estimates of the parameters are found using a Maximization–Minimization algorithm: the evaluation of the mathematical expectations involved in the log-likelihood equation is obtained by generating samples of Monte Carlo Markov chain from the stationary distribution. In addition, a simulation study for asymptotic properties assessment has been made. The proposed method is applied to analyze data election.
Journal: Journal of Applied Statistics
Pages: 2621-2644
Issue: 14
Volume: 44
Year: 2017
Month: 10
X-DOI: 10.1080/02664763.2016.1259400
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1259400
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:14:p:2621-2644




Template-Type: ReDIF-Article 1.0
Author-Name: Jie Chen
Author-X-Name-First: Jie
Author-X-Name-Last: Chen
Title: Editorial
Journal: Journal of Applied Statistics
Pages: 1-2
Issue: 1
Volume: 44
Year: 2017
Month: 1
X-DOI: 10.1080/02664763.2016.1264701
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1264701
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:1:p:1-2




Template-Type: ReDIF-Article 1.0
Author-Name: Jing Cai
Author-X-Name-First: Jing
Author-X-Name-Last: Cai
Author-Name: Yimin Shi
Author-X-Name-First: Yimin
Author-X-Name-Last: Shi
Author-Name: Bin Liu
Author-X-Name-First: Bin
Author-X-Name-Last: Liu
Title: Inference for a series system with dependent masked data under progressive interval censoring
Abstract: 
 This paper considers the statistical analysis of masked data in a series system with Burr-XII distributed components. Based on progressively Type-I interval censored sample, the maximum likelihood estimators for the parameters are obtained by using the expectation maximization algorithm, and the associated approximate confidence intervals are also derived. In addition, Gibbs sampling procedure using important sampling is applied for obtaining the Bayesian estimates of the parameters, and Monte Carlo method is employed to construct the credible intervals. Finally, a simulation study is proposed to illustrate the efficiency of the methods under different removal schemes and masking probabilities.
Journal: Journal of Applied Statistics
Pages: 3-15
Issue: 1
Volume: 44
Year: 2017
Month: 1
X-DOI: 10.1080/02664763.2016.1156658
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1156658
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:1:p:3-15




Template-Type: ReDIF-Article 1.0
Author-Name: D. J. Best
Author-X-Name-First: D. J.
Author-X-Name-Last: Best
Author-Name: J. C. W. Rayner
Author-X-Name-First: J. C. W.
Author-X-Name-Last: Rayner
Title: Analysis of ranked data in randomized blocks when there are missing values
Abstract: 
 Data consisting of ranks within blocks are considered for randomized block designs when there are missing values. Tied ranks are possible. Such data can be analysed using the Skillings–Mack test. Here we suggest a new approach based on carrying out an ANOVA on the ranks using the general linear model platform available in many statistical packages. Such a platform allows an ANOVA to be calculated when there are missing values. Indicative sizes and powers show the ANOVA approach performs better than the Skillings–Mack test.
Journal: Journal of Applied Statistics
Pages: 16-23
Issue: 1
Volume: 44
Year: 2017
Month: 1
X-DOI: 10.1080/02664763.2016.1158245
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1158245
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:1:p:16-23




Template-Type: ReDIF-Article 1.0
Author-Name: Daniel McNeish
Author-X-Name-First: Daniel
Author-X-Name-Last: McNeish
Title: Missing data methods for arbitrary missingness with small samples
Abstract: 
 Missing data are a prevalent and widespread data analytic issue and previous studies have performed simulations to compare the performance of missing data methods in various contexts and for various models; however, one such context that has yet to receive much attention in the literature is the handling of missing data with small samples, particularly when the missingness is arbitrary. Prior studies have either compared methods for small samples with monotone missingness commonly found in longitudinal studies or have investigated the performance of a single method to handle arbitrary missingness with small samples but studies have yet to compare the relative performance of commonly implemented missing data methods for small samples with arbitrary missingness. This study conducts a simulation study to compare and assess the small sample performance of maximum likelihood, listwise deletion, joint multiple imputation, and fully conditional specification multiple imputation for a single-level regression model with a continuous outcome. Results showed that, provided assumptions are met, joint multiple imputation unanimously performed best of the methods examined in the conditions under study.
Journal: Journal of Applied Statistics
Pages: 24-39
Issue: 1
Volume: 44
Year: 2017
Month: 1
X-DOI: 10.1080/02664763.2016.1158246
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1158246
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:1:p:24-39




Template-Type: ReDIF-Article 1.0
Author-Name: Gwowen Shieh
Author-X-Name-First: Gwowen
Author-X-Name-Last: Shieh
Title: The equivalence of two approaches to incorporating variance uncertainty in sample size calculations for linear statistical models
Abstract: 
 Sample size determination is one of the most commonly encountered tasks in the design of every applied research. The general guideline suggests that a pilot study can offer plausible planning values for the vital model characteristics. This article examines two viable approaches to taking into account the imprecision of a variance estimate in sample size calculations for linear statistical models. The multiplier procedure employs an adjusted sample variance in the form of a multiple of the observed sample variance. The Bayesian method accommodates the uncertainty of a sample variance through a prior distribution. It is shown that the two seemingly distinct techniques are equivalent for sample size determination under the designated assurance requirements that the actual power exceeds the planned threshold with a given tolerance probability, or the expected power attains the desired level. The selection of optimum pilot sample size for minimizing the expected total cost is also considered.
Journal: Journal of Applied Statistics
Pages: 40-56
Issue: 1
Volume: 44
Year: 2017
Month: 1
X-DOI: 10.1080/02664763.2016.1158797
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1158797
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:1:p:40-56




Template-Type: ReDIF-Article 1.0
Author-Name: Shengping Yang
Author-X-Name-First: Shengping
Author-X-Name-Last: Yang
Author-Name: Zhide Fang
Author-X-Name-First: Zhide
Author-X-Name-Last: Fang
Title: Beta approximation of ratio distribution and its application to next generation sequencing read counts
Abstract: 
 Paired sequencing data are commonly collected in genomic studies to control biological variation. However, existing data processing strategies suffer at low coverage regions, which are unavoidable due to the limitation of current sequencing technology. Furthermore, information contained in the absolute values of the read counts is commonly ignored. We propose a read count ratio processing/modification method, to not only incorporate information contained in the absolute values of paired counts into one variable, but also mitigate the discrete artifact, especially when both counts are small. Simulation shows that the processed variable fits well with a Beta distribution, thus providing an easy tool for down-stream inference analysis.
Journal: Journal of Applied Statistics
Pages: 57-70
Issue: 1
Volume: 44
Year: 2017
Month: 1
X-DOI: 10.1080/02664763.2016.1158798
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1158798
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:1:p:57-70




Template-Type: ReDIF-Article 1.0
Author-Name: Shaochuan Lu
Author-X-Name-First: Shaochuan
Author-X-Name-Last: Lu
Title: A continuous-time HMM approach to modeling the magnitude-frequency distribution of earthquakes
Abstract: 
 The magnitude-frequency distribution (MFD) of earthquake is a fundamental statistic in seismology. The so-called b-value in the MFD is of particular interest in geophysics. A continuous time hidden Markov model (HMM) is proposed for characterizing the variability of b-values. The HMM-based approach to modeling the MFD has some appealing properties over the widely used sliding-window approach. Often, large variability appears in the estimation of b-value due to window size tuning, which may cause difficulties in interpretation of b-value heterogeneities. Continuous-time hidden Markov models (CT-HMMs) are widely applied in various fields. It bears some advantages over its discrete time counterpart in that it can characterize heterogeneities appearing in time series in a finer time scale, particularly for highly irregularly-spaced time series, such as earthquake occurrences. We demonstrate an expectation–maximization algorithm for the estimation of general exponential family CT-HMM. In parallel with discrete-time hidden Markov models, we develop a continuous time version of Viterbi algorithm to retrieve the overall optimal path of the latent Markov chain. The methods are applied to New Zealand deep earthquakes. Before the analysis, we first assess the completeness of catalogue events to assure the analysis is not biased by missing data. The estimation of b-value is stable over the selection of magnitude thresholds, which is ideal for the interpretation of b-value variability.
Journal: Journal of Applied Statistics
Pages: 71-88
Issue: 1
Volume: 44
Year: 2017
Month: 1
X-DOI: 10.1080/02664763.2016.1161736
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1161736
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:1:p:71-88




Template-Type: ReDIF-Article 1.0
Author-Name: Sotirios Bersimis
Author-X-Name-First: Sotirios
Author-X-Name-Last: Bersimis
Author-Name: Stavros Degiannakis
Author-X-Name-First: Stavros
Author-X-Name-Last: Degiannakis
Author-Name: Dimitrios Georgakellos
Author-X-Name-First: Dimitrios
Author-X-Name-Last: Georgakellos
Title: Real-time monitoring of carbon monoxide using value-at-risk measure and control charting
Abstract: 
 One of the most important environmental health issues is air pollution, causing the deterioration of the population's quality of life, principally in cities where the urbanization level seems limitless. Among ambient pollutants, carbon monoxide (CO) is well known for its biological toxicity. Many studies report associations between exposure to CO and excess mortality. In this context, the present work provides an advanced modelling scheme for real-time monitoring of pollution data and especially of carbon monoxide pollution in city level. The real-time monitoring is based on an appropriately adjusted multivariate time series model that is used in finance and gives accurate one-step-ahead forecasts. On the output of the time series, we apply an empirical monitoring scheme that is used for the early detection of abnormal increases of CO levels. The proposed methodology is applied in the city of Athens and as the analysis revealed has a valuable performance.
Journal: Journal of Applied Statistics
Pages: 89-108
Issue: 1
Volume: 44
Year: 2017
Month: 1
X-DOI: 10.1080/02664763.2016.1161738
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1161738
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:1:p:89-108




Template-Type: ReDIF-Article 1.0
Author-Name: Stephan Dlugosz
Author-X-Name-First: Stephan
Author-X-Name-Last: Dlugosz
Author-Name: Simon M.S. Lo
Author-X-Name-First: Simon M.S.
Author-X-Name-Last: Lo
Author-Name: Ralf A. Wilke
Author-X-Name-First: Ralf A.
Author-X-Name-Last: Wilke
Title: Competing risks quantile regression at work: in-depth exploration of the role of public child support for the duration of maternity leave
Abstract: 
 Despite its emergence as a frequently used method for the empirical analysis of multivariate data, quantile regression is yet to become a mainstream tool for the analysis of duration data. We present a pioneering empirical study on the grounds of a competing risks quantile regression model. We use large-scale maternity duration data with multiple competing risks derived from German linked social security records to analyse how public policies are related to the length of economic inactivity of young mothers after giving birth. Our results show that the model delivers detailed insights into the distribution of transitions out of maternity leave. It is found that cumulative incidences implied by the quantile regression model differ from those implied by a proportional hazards model. To foster the use of the model, we make an R-package (cmprskQR) available.
Journal: Journal of Applied Statistics
Pages: 109-122
Issue: 1
Volume: 44
Year: 2017
Month: 1
X-DOI: 10.1080/02664763.2016.1164836
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1164836
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:1:p:109-122




Template-Type: ReDIF-Article 1.0
Author-Name: Muhammad Aslam
Author-X-Name-First: Muhammad
Author-X-Name-Last: Aslam
Author-Name: G. Srinivasa Rao
Author-X-Name-First: G.
Author-X-Name-Last: Srinivasa Rao
Author-Name: Liaquat Ahmad
Author-X-Name-First: Liaquat
Author-X-Name-Last: Ahmad
Author-Name: Chi-Hyuck Jun
Author-X-Name-First: Chi-Hyuck
Author-X-Name-Last: Jun
Title: A control chart for multivariate Poisson distribution using repetitive sampling
Abstract: 
 Control charts using repetitive group sampling have attracted a great deal of attention during the last few years. In the present article, we attempt to develop a control chart for the multivariate Poisson distribution using the repetitive group sampling scheme. In the proposed control chart, the monitoring statistic from the multivariate Poisson distribution has been used for the quick detection of the deteriorated process to avoid losses. The control coefficients have been estimated using the specified in-control average run lengths. The procedure of the proposed control chart has been explained by using the real-world example and a simulated data set. It has been observed that the proposed control chart is an efficient development for the quick detection of the nonrandom change in the manufacturing process.
Journal: Journal of Applied Statistics
Pages: 123-136
Issue: 1
Volume: 44
Year: 2017
Month: 1
X-DOI: 10.1080/02664763.2016.1164837
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1164837
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:1:p:123-136




Template-Type: ReDIF-Article 1.0
Author-Name: J. Hambuckers
Author-X-Name-First: J.
Author-X-Name-Last: Hambuckers
Author-Name: C. Heuchenne
Author-X-Name-First: C.
Author-X-Name-Last: Heuchenne
Title: A robust statistical approach to select adequate error distributions for financial returns
Abstract: 
 In this article, we propose a robust statistical approach to select an appropriate error distribution, in a classical multiplicative heteroscedastic model. In a first step, unlike to the traditional approach, we do not use any GARCH-type estimation of the conditional variance. Instead, we propose to use a recently developed nonparametric procedure [31]: the local adaptive volatility estimation. The motivation for using this method is to avoid a possible model misspecification for the conditional variance. In a second step, we suggest a set of estimation and model selection procedures (Berk–Jones tests, kernel density-based selection, censored likelihood score, and coverage probability) based on the so-obtained residuals. These methods enable to assess the global fit of a set of distributions as well as to focus on their behaviour in the tails, giving us the capacity to map the strengths and weaknesses of the candidate distributions. A bootstrap procedure is provided to compute the rejection regions in this semiparametric context. Finally, we illustrate our methodology throughout a small simulation study and an application on three time series of daily returns (UBS stock returns, BOVESPA returns and EUR/USD exchange rates).
Journal: Journal of Applied Statistics
Pages: 137-161
Issue: 1
Volume: 44
Year: 2017
Month: 1
X-DOI: 10.1080/02664763.2016.1165803
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1165803
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:1:p:137-161




Template-Type: ReDIF-Article 1.0
Author-Name: Shakhawat Hossain
Author-X-Name-First: Shakhawat
Author-X-Name-Last: Hossain
Author-Name: Hatem A. Howlader
Author-X-Name-First: Hatem A.
Author-X-Name-Last: Howlader
Title: Shrinkage estimation in lognormal regression model for censored data
Abstract: 
 We introduce in this paper, the shrinkage estimation method in the lognormal regression model for censored data involving many predictors, some of which may not have any influence on the response of interest. We develop the asymptotic properties of the shrinkage estimators (SEs) using the notion of asymptotic distributional biases and risks. We show that if the shrinkage dimension exceeds two, the asymptotic risk of the SEs is strictly less than the corresponding classical estimators. Furthermore, we study the penalty (LASSO and adaptive LASSO) estimation methods and compare their relative performance with the SEs. A simulation study for various combinations of the inactive predictors and censoring percentages shows that the SEs perform better than the penalty estimators in certain parts of the parameter space, especially when there are many inactive predictors in the model. It also shows that the shrinkage and penalty estimators outperform the classical estimators. A real-life data example using Worcester heart attack study is used to illustrate the performance of the suggested estimators.
Journal: Journal of Applied Statistics
Pages: 162-180
Issue: 1
Volume: 44
Year: 2017
Month: 1
X-DOI: 10.1080/02664763.2016.1168365
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1168365
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:1:p:162-180




Template-Type: ReDIF-Article 1.0
Author-Name: Jianmin Liu
Author-X-Name-First: Jianmin
Author-X-Name-Last: Liu
Author-Name: Xiaomei Hu
Author-X-Name-First: Xiaomei
Author-X-Name-Last: Hu
Author-Name: Jinguang Wu
Author-X-Name-First: Jinguang
Author-X-Name-Last: Wu
Title: Fiscal decentralization, financial efficiency and upgrading the industrial structure: an empirical analysis of a spatial heterogeneity model
Abstract: 
 From the perspective of spatial heterogeneity and using data from 281 prefecture-level cities in China over the 2003–2012 period, we empirically test the interactions and differences in the spatial effects through which fiscal decentralization and financial efficiency influence the upgrading of regional industrial structure. The results indicate that regional differences in fiscal decentralization and financial efficiency lead to significant spatial heterogeneity in upgrading the industrial structure. The interaction mechanism between fiscal decentralization and financial efficiency plays a positive role in promoting the upgrading of the regional industrial structure, which is subject to organic integration between fiscal systems and financial development. Moreover, there is significant spatial dependence in the effects of fiscal decentralization and financial efficiency on upgrading the regional industrial structure, the ‘high-lying-land’ effects and the ‘low-lying-land’ effects coexist in the spatial pattern.
Journal: Journal of Applied Statistics
Pages: 181-196
Issue: 1
Volume: 44
Year: 2017
Month: 1
X-DOI: 10.1080/02664763.2016.1252733
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1252733
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:1:p:181-196




Template-Type: ReDIF-Article 1.0
Author-Name: Camilo Lillo
Author-X-Name-First: Camilo
Author-X-Name-Last: Lillo
Author-Name: Víctor Leiva
Author-X-Name-First: Víctor
Author-X-Name-Last: Leiva
Author-Name: Orietta Nicolis
Author-X-Name-First: Orietta
Author-X-Name-Last: Nicolis
Author-Name: Robert G. Aykroyd
Author-X-Name-First: Robert G.
Author-X-Name-Last: Aykroyd
Title: L-moments of the Birnbaum–Saunders distribution and its extreme value version: estimation, goodness of fit and application to earthquake data
Abstract: 
 Understanding patterns in the frequency of extreme natural events, such as earthquakes, is important as it helps in the prediction of their future occurrence and hence provides better civil protection. Distributions describing these events are known to be heavy tailed and positive skew making standard distributions unsuitable for modelling the frequency of such events. The Birnbaum–Saunders distribution and its extreme value version have been widely studied and applied due to their attractive properties. We derive L-moment equations for these distributions and propose novel methods for parameter estimation, goodness-of-fit assessment and model selection. A simulation study is conducted to evaluate the performance of the L-moment estimators, which is compared to that of the maximum likelihood estimators, demonstrating the superiority of the proposed methods. To illustrate these methods in a practical application, a data analysis of real-world earthquake magnitudes, obtained from the global centroid moment tensor catalogue during 1962–2015, is carried out. This application identifies the extreme value Birnbaum–Saunders distribution as a better model than classic extreme value distributions for describing seismic events.
Journal: Journal of Applied Statistics
Pages: 187-209
Issue: 2
Volume: 45
Year: 2018
Month: 1
X-DOI: 10.1080/02664763.2016.1269729
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1269729
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:2:p:187-209




Template-Type: ReDIF-Article 1.0
Author-Name: S. Islam
Author-X-Name-First: S.
Author-X-Name-Last: Islam
Author-Name: S. Anand
Author-X-Name-First: S.
Author-X-Name-Last: Anand
Author-Name: M. McQueen
Author-X-Name-First: M.
Author-X-Name-Last: McQueen
Author-Name: J. Hamid
Author-X-Name-First: J.
Author-X-Name-Last: Hamid
Author-Name: L. Thabane
Author-X-Name-First: L.
Author-X-Name-Last: Thabane
Author-Name: S. Yusuf
Author-X-Name-First: S.
Author-X-Name-Last: Yusuf
Author-Name: J. Beyene
Author-X-Name-First: J.
Author-X-Name-Last: Beyene
Title: Classification rules for identifying individuals at high risk of developing myocardial infarction based on ApoB, ApoA1 and the ratio were determined using a Bayesian approach
Abstract: 
 We have developed a new approach to determine the threshold of a biomarker that maximizes the classification accuracy of a disease. We consider a Bayesian estimation procedure for this purpose and illustrate the method using a real data set. In particular, we determine the threshold for Apolipoprotein B (ApoB), Apolipoprotein A1 (ApoA1) and the ratio for the classification of myocardial infarction (MI). We first conduct a literature review and construct prior distributions. We then develop classification rules based on the posterior distribution of the location and scale parameters for these biomarkers. We identify the threshold for ApoB and ApoA1, and the ratio as 0.908 (gram/liter), 1.138 (gram/liter) and 0.808, respectively. We also observe that the threshold for disease classification varies substantially across different age and ethnic groups. Next, we identify the most informative predictor for MI among the three biomarkers. Based on this analysis, ApoA1 appeared to be a stronger predictor than ApoB for MI classification. Given that we have used this data set for illustration only, the results will require further investigation for use in clinical applications. However, the approach developed in this article can be used to determine the threshold of any continuous biomarker for a binary disease classification.
Journal: Journal of Applied Statistics
Pages: 210-224
Issue: 2
Volume: 45
Year: 2018
Month: 1
X-DOI: 10.1080/02664763.2016.1270912
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1270912
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:2:p:210-224




Template-Type: ReDIF-Article 1.0
Author-Name: Francisco M. M. Rocha
Author-X-Name-First: Francisco M. M.
Author-X-Name-Last: Rocha
Author-Name: Julio M. Singer
Author-X-Name-First: Julio M.
Author-X-Name-Last: Singer
Title: Selection of terms in random coefficient regression models
Abstract: 
 The selection of suitable terms in random coefficient regression models is a challenging problem to practitioners. Although many techniques, ranging from those with a theoretical flavour to those with an exploratory spirit, have been proposed for such purposes, no particular one may be considered as a paradigm. In fact, many authors advocate that they should be used in a complementary way. We consider exploratory methods based on fitting standard regression models to the individual response profiles or to the rows of the sample within-units covariance matrix (for balanced data) that may serve as additional tools in the process of selecting an appropriate model. We evaluate the performance of the proposal via a simulation study and consider applications to two examples in the field of Biostatistics.
Journal: Journal of Applied Statistics
Pages: 225-242
Issue: 2
Volume: 45
Year: 2018
Month: 1
X-DOI: 10.1080/02664763.2016.1273884
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1273884
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:2:p:225-242




Template-Type: ReDIF-Article 1.0
Author-Name: S. Lalitha
Author-X-Name-First: S.
Author-X-Name-Last: Lalitha
Author-Name: Pratyasha Tripathi
Author-X-Name-First: Pratyasha
Author-X-Name-Last: Tripathi
Title: Detection of a pair of outliers in a sample from a Gumbel distribution with known scale parameter
Abstract: 
 Test procedures on outlier detection problems for Gumbel distribution are rarely available. Hence, a test statistic is proposed here for detection of a pair of upper and lower outliers from a Gumbel distribution with known scale parameter. The critical values of the statistic are obtained and some examples are also given to highlight the use of the statistic. The advantage of the proposed statistic is that the scale parameter, though assumed to be known is not explicitly involved in the determination of the critical values.
Journal: Journal of Applied Statistics
Pages: 243-254
Issue: 2
Volume: 45
Year: 2018
Month: 1
X-DOI: 10.1080/02664763.2016.1273885
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1273885
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:2:p:243-254




Template-Type: ReDIF-Article 1.0
Author-Name: Feridun Tasdan
Author-X-Name-First: Feridun
Author-X-Name-Last: Tasdan
Author-Name: Ozgur Yeniay
Author-X-Name-First: Ozgur
Author-X-Name-Last: Yeniay
Title: A comparative simulation of multiple testing procedures in circular data problems
Abstract: 
 Bonferroni correction procedures are commonly used for performing multiple hypothesis tests in linear data problems. Moreover, several improved Bonferroni type procedures have been proposed and shown that they attain a type-I error rate  $ (\alpha ) $ (α) better than the classical Bonferroni approach, but all of the studies are considered for linear data problems. The circular data analysis is a developing field of statistics that lacks similar studies, and also lacks computer programs to implement Bonferroni procedures. The aim of this study is to perform a comparative study of improved Bonferroni procedures and also to provide a computer program in R that performs classical and improved Bonferroni procedures for circular data problems.
Journal: Journal of Applied Statistics
Pages: 255-269
Issue: 2
Volume: 45
Year: 2018
Month: 1
X-DOI: 10.1080/02664763.2016.1273886
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1273886
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:2:p:255-269




Template-Type: ReDIF-Article 1.0
Author-Name: Muhammad Yameen Danish
Author-X-Name-First: Muhammad Yameen
Author-X-Name-Last: Danish
Author-Name: Irshad Ahmad Arshad
Author-X-Name-First: Irshad Ahmad
Author-X-Name-Last: Arshad
Author-Name: Muhammad Aslam
Author-X-Name-First: Muhammad
Author-X-Name-Last: Aslam
Title: Bayesian inference for the randomly censored Burr-type XII distribution
Abstract: 
 The article presents the Bayesian inference for the parameters of randomly censored Burr-type XII distribution with proportional hazards. The joint conjugate prior of the proposed model parameters does not exist; we consider two different systems of priors for Bayesian estimation. The explicit forms of the Bayes estimators are not possible; we use Lindley's method to obtain the Bayes estimates. However, it is not possible to obtain the Bayesian credible intervals with Lindley's method; we suggest the Gibbs sampling procedure for this purpose. Numerical experiments are performed to check the properties of the different estimators. The proposed methodology is applied to a real-life data for illustrative purposes. The Bayes estimators are compared with the Maximum likelihood estimators via numerical experiments and real data analysis. The model is validated using posterior predictive simulation in order to ascertain its appropriateness.
Journal: Journal of Applied Statistics
Pages: 270-283
Issue: 2
Volume: 45
Year: 2018
Month: 1
X-DOI: 10.1080/02664763.2016.1275530
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1275530
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:2:p:270-283




Template-Type: ReDIF-Article 1.0
Author-Name: Amílcar Oliveira
Author-X-Name-First: Amílcar
Author-X-Name-Last: Oliveira
Author-Name: Teresa Oliveira
Author-X-Name-First: Teresa
Author-X-Name-Last: Oliveira
Author-Name: Antonio Seijas-Macías
Author-X-Name-First: Antonio
Author-X-Name-Last: Seijas-Macías
Title: The uniform distribution product: an approach to the  inventory model using R
Abstract: 
 In this work the probability density function (PDF) for the product of two uniformly distributed random variables is explored under the implementation of a new procedure in R language. Based on the Rohatgi theorem for the theoretical form of the product, different possibilities for the range of values of the limits of both distributions are considered. As an application, the management of a  $ (Q,r) $ (Q,r) inventory model with the presence of lead-time and uniform demand forecasts is considered. Solution to this model looks up to minimize the total costs through the variables Q (reorder quantity) and r (the reorder point), and not always exists an analytical solution of the problem. We show a graphical procedure for the simulation results and a more exactly analytical solution. Implementation in R is straightforward.
Journal: Journal of Applied Statistics
Pages: 284-297
Issue: 2
Volume: 45
Year: 2018
Month: 1
X-DOI: 10.1080/02664763.2016.1275531
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1275531
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:2:p:284-297




Template-Type: ReDIF-Article 1.0
Author-Name: J. T. Fry
Author-X-Name-First: J. T.
Author-X-Name-Last: Fry
Author-Name: Andrew Hoegh
Author-X-Name-First: Andrew
Author-X-Name-Last: Hoegh
Author-Name: Scotland Leman
Author-X-Name-First: Scotland
Author-X-Name-Last: Leman
Author-Name: Matthew Montesano
Author-X-Name-First: Matthew
Author-X-Name-Last: Montesano
Title: A Bayesian hierarchical model for estimating the cost of postponing the cyclo-cross national championships
Abstract: 
 In early January 2015, the multi-event national cyclo-cross bicycle races were set to take place in Austin, Texas. Cyclo-cross has a rich history in this country, and throughout the world, attracting huge crowds and competitors. Being primarily a winter sport, these athletes often compete in harsh conditions, which include rain, snow, mud, and revel in the excitement that comes with such elements. Unfortunately, the competition was postponed mid-event when a local arborist group protested to the parks department. The issue: there was too much mud, in an event where many spectators and racers alike hope for such conditions. For many competitors, the postponement generated additional expenses, such as flights, hotels, and car rentals. Although people on opposite sides of the debate may greatly disagree, we instead focus on the competitors themselves. We analyze the financial impact of the disagreement using a hierarchical Bayesian mixed model which accounts for heterogeneity within the costs endured by the event's participants.
Journal: Journal of Applied Statistics
Pages: 298-305
Issue: 2
Volume: 45
Year: 2018
Month: 1
X-DOI: 10.1080/02664763.2016.1276523
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1276523
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:2:p:298-305




Template-Type: ReDIF-Article 1.0
Author-Name: Mohammadreza Nassiri
Author-X-Name-First: Mohammadreza
Author-X-Name-Last: Nassiri
Author-Name: Mahdi Elahi Torshizi
Author-X-Name-First: Mahdi
Author-X-Name-Last: Elahi Torshizi
Author-Name: Shahrokh Ghovvati
Author-X-Name-First: Shahrokh
Author-X-Name-Last: Ghovvati
Author-Name: Mohammad Doosti
Author-X-Name-First: Mohammad
Author-X-Name-Last: Doosti
Title: Evaluation of different statistical methods using SAS software: an in silico approach for analysis of real-time PCR data
Abstract: 
 Real-time polymerase chain reaction (PCR) is reliable quantitative technique in gene expression studies. The statistical analysis of real-time PCR data is quite crucial for results analysis and explanation. The statistical procedures of analyzing real-time PCR data try to determine the slope of regression line and calculate the reaction efficiency. Applications of mathematical functions have been used to calculate the target gene relative to the reference gene(s). Moreover, these statistical techniques compare Ct (threshold cycle) numbers between control and treatments group. There are many different procedures in SAS for real-time PCR data evaluation. In this study, the efficiency of calibrated model and delta delta Ct model have been statistically tested and explained. Several methods were tested to compare control with treatment means of Ct. The methods tested included t-test (parametric test), Wilcoxon test (non-parametric test) and multiple regression. Results showed that applied methods led to similar results and no significant difference was observed between results of gene expression measurement by the relative method.
Journal: Journal of Applied Statistics
Pages: 306-319
Issue: 2
Volume: 45
Year: 2018
Month: 1
X-DOI: 10.1080/02664763.2016.1276890
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1276890
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:2:p:306-319




Template-Type: ReDIF-Article 1.0
Author-Name: Carolina Costa Mota Paraíba
Author-X-Name-First: Carolina
Author-X-Name-Last: Costa Mota Paraíba
Author-Name: Natalia Bochkina
Author-X-Name-First: Natalia
Author-X-Name-Last: Bochkina
Author-Name: Carlos Alberto Ribeiro Diniz
Author-X-Name-First: Carlos Alberto Ribeiro
Author-X-Name-Last: Diniz
Title: Bayesian truncated beta nonlinear mixed-effects models
Abstract: 
 Truncated regression models arise in many applications where it is not possible to observe values of the response variable that are above or below certain thresholds. We propose a Bayesian truncated beta nonlinear mixed-effects model by considering the truncated variable to follow a truncated beta distribution. The mean parameter of the distribution is modeled by a nonlinear function of unknown fixed parameters and covariates and by random effects. The proposed model is suitable for response variables, y, bounded to an interval  $ (a,b) $ (a,b) without the need to consider a transformed variable  $ y^{*} = (y-a)/(b-a) $ y∗=(y−a)/(b−a) to apply the well-known beta regression model and its extensions, which are primarily appropriate for responses in the interval  $ (0,1) $ (0,1). Bayesian estimates and credible intervals are computed based on draws from the posterior distribution of parameters generated using an MCMC procedure. Posterior predictive checks, Bayesian standardized residuals and a Bayesian influence measures are considered for model diagnostics. Model selection is performed using the sum of log-CPO metric and a Bayesian model selection criterion based on Bayesian mixture modeling. Simulated datasets are used for prior sensitivity analysis and to evaluate finite sample properties of Bayesian estimates. The model is applied to a real dataset on soil–water retention.
Journal: Journal of Applied Statistics
Pages: 320-346
Issue: 2
Volume: 45
Year: 2018
Month: 1
X-DOI: 10.1080/02664763.2016.1276891
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1276891
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:2:p:320-346




Template-Type: ReDIF-Article 1.0
Author-Name: L. C. Matioli
Author-X-Name-First: L. C.
Author-X-Name-Last: Matioli
Author-Name: S.R. Santos
Author-X-Name-First: S.R.
Author-X-Name-Last: Santos
Author-Name: M. Kleina
Author-X-Name-First: M.
Author-X-Name-Last: Kleina
Author-Name: E. A. Leite
Author-X-Name-First: E. A.
Author-X-Name-Last: Leite
Title: A new algorithm for clustering based on kernel density estimation
Abstract: 
 In this paper, we present an algorithm for clustering based on univariate kernel density estimation, named ClusterKDE. It consists of an iterative procedure that in each step a new cluster is obtained by minimizing a smooth kernel function. Although in our applications we have used the univariate Gaussian kernel, any smooth kernel function can be used. The proposed algorithm has the advantage of not requiring a priori the number of cluster. Furthermore, the ClusterKDE algorithm is very simple, easy to implement, well-defined and stops in a finite number of steps, namely, it always converges independently of the initial point. We also illustrate our findings by numerical experiments which are obtained when our algorithm is implemented in the software Matlab and applied to practical applications. The results indicate that the ClusterKDE algorithm is competitive and fast when compared with the well-known Clusterdata and K-means algorithms, used by Matlab to clustering data.
Journal: Journal of Applied Statistics
Pages: 347-366
Issue: 2
Volume: 45
Year: 2018
Month: 1
X-DOI: 10.1080/02664763.2016.1277191
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1277191
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:2:p:347-366




Template-Type: ReDIF-Article 1.0
Author-Name: Tingting Yu
Author-X-Name-First: Tingting
Author-X-Name-Last: Yu
Author-Name: Lang Wu
Author-X-Name-First: Lang
Author-X-Name-Last: Wu
Title: Robust modelling of the relationship between CD4 and viral load for complex AIDS data
Abstract: 
 CD4 and viral load play important roles in HIV/AIDS studies, and the study of their relationship has received much attention with well-known results. However, AIDS datasets are often highly complex in the sense that they typically contain outliers, measurement errors, and missing data. These data complications can greatly affect statistical analysis results, but much of the literature fail to address these issues in data analysis. In this paper, we re-visit the important relationship between CD4 and viral load and propose methods which simultaneously address outliers, measurement errors, and missing data. We find that the strength of the relationship may be severely mis-estimated if measurement errors and outliers are ignored. The proposed methods are general and can be used in other settings, where jointly modelling several different types of longitudinal data is required in the presence of data complications.
Journal: Journal of Applied Statistics
Pages: 367-383
Issue: 2
Volume: 45
Year: 2018
Month: 1
X-DOI: 10.1080/02664763.2017.1279594
File-URL: http://hdl.handle.net/10.1080/02664763.2017.1279594
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:2:p:367-383




Template-Type: ReDIF-Article 1.0
Author-Name: Alessandro Beretta
Author-X-Name-First: Alessandro
Author-X-Name-Last: Beretta
Author-Name: Cédric Heuchenne
Author-X-Name-First: Cédric
Author-X-Name-Last: Heuchenne
Title: Variable selection in proportional hazards cure model with time-varying covariates, application to US bank failures
Abstract: 
 From a survival analysis perspective, bank failure data are often characterized by small default rates and heavy censoring. This empirical evidence can be explained by the existence of a subpopulation of banks likely immune from bankruptcy. In this regard, we use a mixture cure model to separate the factors with an influence on the susceptibility to default from the ones affecting the survival time of susceptible banks. In this paper, we extend a semi-parametric proportional hazards cure model to time-varying covariates and we propose a variable selection technique based on its penalized likelihood. By means of a simulation study, we show how this technique performs reasonably well. Finally, we illustrate an application to commercial bank failures in the United States over the period 2006–2016.
Journal: Journal of Applied Statistics
Pages: 1529-1549
Issue: 9
Volume: 46
Year: 2019
Month: 7
X-DOI: 10.1080/02664763.2018.1554627
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1554627
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:9:p:1529-1549




Template-Type: ReDIF-Article 1.0
Author-Name: P. Martínez-Camblor
Author-X-Name-First: P.
Author-X-Name-Last: Martínez-Camblor
Author-Name: S. Pérez-Fernández
Author-X-Name-First: S.
Author-X-Name-Last: Pérez-Fernández
Author-Name: S. Díaz-Coto
Author-X-Name-First: S.
Author-X-Name-Last: Díaz-Coto
Title: Improving the biomarker diagnostic capacity via functional transformations
Abstract: 
 The use of the area under the receiver-operating characteristic, ROC, curve (AUC) as an index of diagnostic accuracy is overwhelming in fields such as biomedical science and machine learning. It seems that a larger AUC value has become synonymous with a better performance. The functional transformation of the marker values has been proposed in the specialized literature as a procedure for increasing the AUC and therefore the diagnostic accuracy. However, the classification process is based on some regions (classification subsets) which support the decision made; one subject is classified as positive if its marker is within this region and classified as negative otherwise. In this paper we study the capacity of improving the classification performance of univariate biomarkers via functional transformations and the impact of this transformation on the final classification regions based on a real-world dataset. Particularly, we consider the problem of determining the gender of a subject based on the Mode frequency of his/her voice. The shape of the cumulative distribution function of this characteristic in both the male and the female groups makes the resulting classification problem useful for illustrating the differences between having useful diagnostic rules and obtaining an optimal AUC value. Our point is that improving the AUC by means of a functional transformation can produce classification regions with no practical interpretability. We propose to improve the classification accuracy by making the selection of the classification subsets more flexible while preserving their interpretability. Besides, we provide different graphical approximations which allow us a better understanding of the classification problem.
Journal: Journal of Applied Statistics
Pages: 1550-1566
Issue: 9
Volume: 46
Year: 2019
Month: 7
X-DOI: 10.1080/02664763.2018.1554628
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1554628
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:9:p:1550-1566




Template-Type: ReDIF-Article 1.0
Author-Name: Shrabanti Chowdhury
Author-X-Name-First: Shrabanti
Author-X-Name-Last: Chowdhury
Author-Name: Saptarshi Chatterjee
Author-X-Name-First: Saptarshi
Author-X-Name-Last: Chatterjee
Author-Name: Himel Mallick
Author-X-Name-First: Himel
Author-X-Name-Last: Mallick
Author-Name: Prithish Banerjee
Author-X-Name-First: Prithish
Author-X-Name-Last: Banerjee
Author-Name: Broti Garai
Author-X-Name-First: Broti
Author-X-Name-Last: Garai
Title: Group regularization for zero-inflated poisson regression models with an application to insurance ratemaking
Abstract: 
 Zero-inflated count models have received considerable amount of attention in recent years, fuelled by their widespread applications in many scientific disciplines. In this paper, we consider the problem of selecting grouped variables in zero-inflated Poisson (ZIP) models via group bridge regularization. The ZIP mixture likelihood with a group-wise  $ L_1 $ L1 penalty on the coefficients is formulated using least squares approximation and then the parameters involved in the penalized likelihood are estimated by an efficient group descent algorithm. We examine the effectiveness of our modeling procedure through extensive Monte Carlo simulations. An auto insurance claim dataset from the SAS Enterprise Miner database is analyzed for illustrative purposes. Finally, we derive theoretical properties of the proposed group variable selection procedure under certain regularity conditions. The open source software implementation of this method is publicly available at https://github.com/himelmallick/Gooogle.
Journal: Journal of Applied Statistics
Pages: 1567-1581
Issue: 9
Volume: 46
Year: 2019
Month: 7
X-DOI: 10.1080/02664763.2018.1555232
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1555232
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:9:p:1567-1581




Template-Type: ReDIF-Article 1.0
Author-Name: Hrishikesh Chakraborty
Author-X-Name-First: Hrishikesh
Author-X-Name-Last: Chakraborty
Author-Name: Akhtar Hossain
Author-X-Name-First: Akhtar
Author-X-Name-Last: Hossain
Author-Name: Mahbub A.H.M. Latif
Author-X-Name-First: Mahbub A.H.M.
Author-X-Name-Last: Latif
Title: A three-state continuous time Markov chain model for HIV disease burden
Abstract: 
 Plasma HIV viral load (VL) is the clinical indicator used to evaluate disease burden for HIV-infected patients. We developed a covariate-adjusted, three-state, homogenous continuous time Markov chain model for HIV/AIDS disease burden among subgroups. We defined Detectable and Undetectable HIV VL levels as two transient states and Death as the third absorbing state. We implemented the exact maximum likelihood method to estimate the parameters with related asymptotic distribution to conduct hypothesis testing. We evaluated the proposed model using HIV-infected individuals from South Carolina (SC) HIV surveillance data. Using the developed model, we estimated and compared the transition hazards, transition probabilities, and the state-specific duration for HIV-infected individuals. We examined gender, race/ethnicity, age, CD4 count, place of residence, and antiretroviral treatment regimen prescribed at the beginning of the study period. We found that patients with a higher CD4 count, increased age, heterosexual orientation, white, and single tablet regimen users were associated with reduced risk of transitioning to a Detectable VL from an Undetectable VL, whereas shorter time since diagnosis, being male, and injection drug use increased the risk of the same transition.
Journal: Journal of Applied Statistics
Pages: 1671-1688
Issue: 9
Volume: 46
Year: 2019
Month: 7
X-DOI: 10.1080/02664763.2018.1555573
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1555573
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:9:p:1671-1688




Template-Type: ReDIF-Article 1.0
Author-Name: Gerhard Tutz
Author-X-Name-First: Gerhard
Author-X-Name-Last: Tutz
Author-Name: Micha Schneider
Author-X-Name-First: Micha
Author-X-Name-Last: Schneider
Title: Flexible uncertainty in mixture models for ordinal responses
Abstract: 
 In classical mixture models for ordinal data with an uncertainty component, the Uniform distribution is used to model indecision. In the approach proposed here, the discrete Uniform distribution is replaced by a more flexible distribution, which is centered in the middle of the response categories. The resulting model allows to distinguish between a tendency to middle categories and a tendency to extreme categories. By linking these preferences to explanatory variables, one can investigate which persons show a tendency to these response styles. It is demonstrated that severe bias might occur if inadvertently the Uniform distribution is used to model uncertainty. An application to attitudes on the performance of health services illustrates the advantages of the more flexible model.
Journal: Journal of Applied Statistics
Pages: 1582-1601
Issue: 9
Volume: 46
Year: 2019
Month: 7
X-DOI: 10.1080/02664763.2018.1555574
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1555574
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:9:p:1582-1601




Template-Type: ReDIF-Article 1.0
Author-Name: Solomon W. Harrar
Author-X-Name-First: Solomon W.
Author-X-Name-Last: Harrar
Author-Name: Fabrizio Ronchi
Author-X-Name-First: Fabrizio
Author-X-Name-Last: Ronchi
Author-Name: Luigi Salmaso
Author-X-Name-First: Luigi
Author-X-Name-Last: Salmaso
Title: A comparison of recent nonparametric methods for testing effects in two-by-two factorial designs
Abstract: 
 The two-way two-levels crossed factorial design is a commonly used design by practitioners at the exploratory phase of industrial experiments. The F-test in the usual linear model for analysis of variance (ANOVA) is a key instrument to assess the impact of each factor and of their interactions on the response variable. However, if assumptions such as normal distribution and homoscedasticity of errors are violated, the conventional wisdom is to resort to nonparametric tests. Nonparametric methods, rank-based as well as permutation, have been a subject of recent investigations to make them effective in testing the hypotheses of interest and to improve their performance in small sample situations. In this study, we assess the performances of some nonparametric methods and, more importantly, we compare their powers. Specifically, we examine three permutation methods (Constrained Synchronized Permutations, Unconstrained Synchronized Permutations and Wald-Type Permutation Test), a rank-based method (Aligned Rank Transform) and a parametric method (ANOVA-Type Test). In the simulations, we generate datasets with different configurations of distribution of errors, variance, factor's effect and number of replicates. The objective is to elicit practical advice and guides to practitioners regarding the sensitivity of the tests in the various configurations, the conditions under which some tests cannot be used, the tradeoff between power and type I error, and the bias of the power on one main factor analysis due to the presence of effect of the other factor. A dataset from an industrial engineering experiment for thermoformed packaging production is used to illustrate the application of the various methods of analysis, taking into account the power of the test suggested by the objective of the experiment.
Journal: Journal of Applied Statistics
Pages: 1649-1670
Issue: 9
Volume: 46
Year: 2019
Month: 7
X-DOI: 10.1080/02664763.2018.1555575
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1555575
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:9:p:1649-1670




Template-Type: ReDIF-Article 1.0
Author-Name: Marcos Antonio Alves Pereira
Author-X-Name-First: Marcos Antonio Alves
Author-X-Name-Last: Pereira
Author-Name: Cibele Maria Russo
Author-X-Name-First: Cibele Maria
Author-X-Name-Last: Russo
Title: Nonlinear mixed-effects models with scale mixture of skew-normal distributions
Abstract: 
 Aiming to avoid the sensitivity in the parameters estimation due to atypical observations or skewness, we develop asymmetric nonlinear regression models with mixed-effects, which provide alternatives to the use of normal distribution and other symmetric distributions. Nonlinear models with mixed-effects are explored in several areas of knowledge, especially when data are correlated, such as longitudinal data, repeated measures and multilevel data, in particular, for their flexibility in dealing with measures of areas such as economics and pharmacokinetics. The random components of the present model are assumed to follow distributions that belong to scale mixtures of skew-normal (SMSN) distribution family, that encompasses distributions with light and heavy tails, such as skew-normal, skew-Student-t, skew-contaminated normal and skew-slash, as well as symmetrical versions of these distributions. For the parameters estimation we obtain a numerical solution via the EM algorithm and its extensions, and the Newton-Raphson algorithm. An application with pharmacokinetic data shows the superiority of the proposed models, for which the skew-contaminated normal distribution has shown to be the most adequate distribution. A brief simulation study points to good properties of the parameter vector estimators obtained by the maximum likelihood method.
Journal: Journal of Applied Statistics
Pages: 1602-1620
Issue: 9
Volume: 46
Year: 2019
Month: 7
X-DOI: 10.1080/02664763.2018.1557122
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1557122
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:9:p:1602-1620




Template-Type: ReDIF-Article 1.0
Author-Name: Bernhard Haller
Author-X-Name-First: Bernhard
Author-X-Name-Last: Haller
Author-Name: Hans-Henning Eckstein
Author-X-Name-First: Hans-Henning
Author-X-Name-Last: Eckstein
Author-Name: Peter A. Ringleb
Author-X-Name-First: Peter A.
Author-X-Name-Last: Ringleb
Author-Name: Kurt Ulm
Author-X-Name-First: Kurt
Author-X-Name-Last: Ulm
Title: Investigation of age–treatment interaction in the SPACE trial using different statistical approaches
Abstract: 
 Selection of treatments to fit the specific needs for a certain patient is one major challenge in modern medicine. Personalized treatments rely on established patient–treatment interactions. In recent years, various statistical methods for the identification and estimation of interactions between relevant covariates and treatment were proposed. In this article, different available methods for detection and estimation of a covariate–treatment interaction for a time-to-event outcome, namely the standard Cox regression model assuming a linear interaction, the fractional polynomials approach for interaction, the modified outcome approach, the local partial-likelihood approach, and STEPP (Subpopulation Treatment Effect Pattern Plots) were applied to data from the SPACE trial, a randomized clinical trial comparing stent-protected angioplasty (CAS) to carotid endarterectomy (CEA) in patients with symptomatic stenosis, with the aim to analyse the interaction between age and treatment. Time from primary intervention to the first relevant event (any stroke or death) was considered as outcome parameter. The analyses suggest a qualitative interaction between patient age and treatment indicating a lower risk after treatment with CAS compared to CEA for younger patients, while for elderly patients a lower risk after CEA was observed. Differences in the statistical methods regarding the observed results, applicability, and interpretation are discussed.
Journal: Journal of Applied Statistics
Pages: 1689-1701
Issue: 9
Volume: 46
Year: 2019
Month: 7
X-DOI: 10.1080/02664763.2018.1558186
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1558186
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:9:p:1689-1701




Template-Type: ReDIF-Article 1.0
Author-Name: Abigail R. Smith
Author-X-Name-First: Abigail R.
Author-X-Name-Last: Smith
Author-Name: Nathan P. Goodrich
Author-X-Name-First: Nathan P.
Author-X-Name-Last: Goodrich
Author-Name: Charlotte A. Beil
Author-X-Name-First: Charlotte A.
Author-X-Name-Last: Beil
Author-Name: Qian Liu
Author-X-Name-First: Qian
Author-X-Name-Last: Liu
Author-Name: Robert M. Merion
Author-X-Name-First: Robert M.
Author-X-Name-Last: Merion
Author-Name: Brenda W. Gillespie
Author-X-Name-First: Brenda W.
Author-X-Name-Last: Gillespie
Author-Name: Jarcy Zee
Author-X-Name-First: Jarcy
Author-X-Name-Last: Zee
Title: Graphical representation of survival curves in the presence of time-dependent categorical covariates with application to liver transplantation
Abstract: 
 Graphical representation of survival curves is often used to illustrate associations between exposures and time-to-event outcomes. However, when exposures are time-dependent, calculation of survival probabilities is not straightforward. Our aim was to develop a method to estimate time-dependent survival probabilities and represent them graphically. Cox models with time-dependent indicators to represent state changes were fitted, and survival probabilities were plotted using pre-specified times of state changes. Time-varying hazard ratios for the state change were also explored. The method was applied to data from the Adult-to-Adult Living Donor Liver Transplantation Cohort Study (A2ALL). Survival curves showing a ‘split’ at a pre-specified time t allow for the qualitative comparison of survival probabilities between patients with similar baseline covariates who do and do not experience a state change at time t. Time since state change interactions can be visually represented to reflect changing hazard ratios over time. A2ALL study results showed differences in survival probabilities among those who did not receive a transplant, received a living donor transplant, and received a deceased donor transplant. These graphical representations of survival curves with time-dependent indicators improve upon previous methods and allow for clinically meaningful interpretation.
Journal: Journal of Applied Statistics
Pages: 1702-1713
Issue: 9
Volume: 46
Year: 2019
Month: 7
X-DOI: 10.1080/02664763.2018.1558187
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1558187
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:9:p:1702-1713




Template-Type: ReDIF-Article 1.0
Author-Name: Jose M. Carbo
Author-X-Name-First: Jose M.
Author-X-Name-Last: Carbo
Author-Name: Daniel J. Graham
Author-X-Name-First: Daniel J.
Author-X-Name-Last: Graham
Author-Name:  Anupriya
Author-X-Name-First: 
Author-X-Name-Last: Anupriya
Author-Name: Daniel Casas
Author-X-Name-First: Daniel
Author-X-Name-Last: Casas
Author-Name: Patricia C. Melo
Author-X-Name-First: Patricia C.
Author-X-Name-Last: Melo
Title: Evaluating the causal economic impacts of transport investments: evidence from the Madrid–Barcelona high speed rail corridor
Abstract: 
 This paper evaluates economic impacts arising from the introduction of high-speed rail (HSR) between Madrid and Barcelona. Using difference-in-differences estimation we estimate an average treatment effect for provinces with stops on the HSR line of 2.4% for economic output, 3.3% for numbers of firms, and 1.1% for labour productivity. We complement our DID results with a synthetic control analysis for Lleida and Tarragona, two provinces that we argue were assigned HSR stations largely due to their incidental location. We find that both the number of firms and labour productivity are substantially higher in these provinces than in their synthetic counterparts.
Journal: Journal of Applied Statistics
Pages: 1714-1723
Issue: 9
Volume: 46
Year: 2019
Month: 7
X-DOI: 10.1080/02664763.2018.1558188
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1558188
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:9:p:1714-1723




Template-Type: ReDIF-Article 1.0
Author-Name: Anjana Sukumaran
Author-X-Name-First: Anjana
Author-X-Name-Last: Sukumaran
Author-Name: Isha Dewan
Author-X-Name-First: Isha
Author-X-Name-Last: Dewan
Title: Modelling and analysis of recall-based competing risks data
Abstract: 
 In this paper we consider the analysis of recall-based competing risks data. The chance of an individual recalling the exact time to event depends on the time of occurrence of the event and time of observation of the individual. In particular, it is assumed that the probability of recall depends on the time elapsed since the occurrence of an event. In this study we consider the likelihood-based inference for the analysis of recall-based competing risks data. The likelihood function is constructed by incorporating the information about the probability of recall. We consider the maximum likelihood estimation of parameters. Simulation studies are carried out to examine the performance of the estimators. The proposed estimation procedure is applied to a real life data set.
Journal: Journal of Applied Statistics
Pages: 1621-1635
Issue: 9
Volume: 46
Year: 2019
Month: 7
X-DOI: 10.1080/02664763.2018.1561833
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1561833
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:9:p:1621-1635




Template-Type: ReDIF-Article 1.0
Author-Name: Amy M. J. O’Shea
Author-X-Name-First: Amy M. J.
Author-X-Name-Last: O’Shea
Author-Name: Jeffrey D. Dawson
Author-X-Name-First: Jeffrey D.
Author-X-Name-Last: Dawson
Title: Modeling time series data with semi-reflective boundaries
Abstract: 
 Time series data are increasingly common in many areas of the health sciences, and in some instances, may have natural boundaries serving as performance guidelines or as thresholds associated with adverse outcomes. Such boundaries may be labeled as semi-reflective, in that the time series values have an increased chance of returning towards middle levels as the boundaries are approached, but boundaries can still be breached. In this paper we review a model that was previously proposed for such data and we investigate its statistical properties. Specifically, this model consists of a third-order auto-regressive projection component, parameterized as a constrained linear combination of linear, flat, and quadratic trends, and an error term that uses a logistic regression model for its sign. We describe and compare a previously-proposed estimation method with a modified version thereof, using computer simulations, as well as data examples from heart monitoring and from a driving simulator. We find that the two methods tend to give different results, with the modified technique having lower bias and more accurate confidence intervals than the previously-proposed method.
Journal: Journal of Applied Statistics
Pages: 1636-1648
Issue: 9
Volume: 46
Year: 2019
Month: 7
X-DOI: 10.1080/02664763.2018.1561834
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1561834
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:9:p:1636-1648




Template-Type: ReDIF-Article 1.0
Author-Name: Dongbing Lai
Author-X-Name-First: Dongbing
Author-X-Name-Last: Lai
Author-Name: Huiping Xu
Author-X-Name-First: Huiping
Author-X-Name-Last: Xu
Author-Name: Daniel Koller
Author-X-Name-First: Daniel
Author-X-Name-Last: Koller
Author-Name: Tatiana Foroud
Author-X-Name-First: Tatiana
Author-X-Name-Last: Foroud
Author-Name: Sujuan Gao
Author-X-Name-First: Sujuan
Author-X-Name-Last: Gao
Title: A multivariate finite mixture latent trajectory model with application to dementia studies
Abstract: 
 Dementia patients exhibit considerable heterogeneity in individual trajectories of cognitive decline, with some patients showing rapid decline following diagnoses while others exhibiting slower decline or remaining stable for several years. Dementia studies often collect longitudinal measures of multiple neuropsychological tests aimed to measure patients’ decline across a number of cognitive domains. We propose a multivariate finite mixture latent trajectory model to identify distinct longitudinal patterns of cognitive decline simultaneously in multiple cognitive domains, each of which is measured by multiple neuropsychological tests. EM algorithm is used for parameter estimation and posterior probabilities are used to predict latent class membership. We present results of a simulation study demonstrating adequate performance of our proposed approach and apply our model to the Uniform Data Set from the National Alzheimer's Coordinating Center to identify cognitive decline patterns among dementia patients.
Journal: Journal of Applied Statistics
Pages: 2503-2523
Issue: 14
Volume: 43
Year: 2016
Month: 10
X-DOI: 10.1080/02664763.2016.1141181
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1141181
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:14:p:2503-2523




Template-Type: ReDIF-Article 1.0
Author-Name: Jianhua Ding
Author-X-Name-First: Jianhua
Author-X-Name-Last: Ding
Author-Name: Zhongzhan Zhang
Author-X-Name-First: Zhongzhan
Author-X-Name-Last: Zhang
Title: Bayesian regression on non-parametric mixed-effect models with shape-restricted Bernstein polynomials
Abstract: 
 We develop a Bayesian estimation method to non-parametric mixed-effect models under shape-constrains. The approach uses a hierarchical Bayesian framework and characterizations of shape-constrained Bernstein polynomials (BPs). We employ Markov chain Monte Carlo methods for model fitting, using a truncated normal distribution as the prior for the coefficients of BPs to ensure the desired shape constraints. The small sample properties of the Bayesian shape-constrained estimators across a range of functions are provided via simulation studies. Two real data analysis are given to illustrate the application of the proposed method.
Journal: Journal of Applied Statistics
Pages: 2524-2537
Issue: 14
Volume: 43
Year: 2016
Month: 10
X-DOI: 10.1080/02664763.2016.1142940
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1142940
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:14:p:2524-2537




Template-Type: ReDIF-Article 1.0
Author-Name: Ahmed Hossain
Author-X-Name-First: Ahmed
Author-X-Name-Last: Hossain
Author-Name: Hafiz T.A. Khan
Author-X-Name-First: Hafiz T.A.
Author-X-Name-Last: Khan
Title: Identification of genomic markers correlated with sensitivity in solid tumors to Dasatinib using sparse principal components
Abstract: 
 Differential analysis techniques are commonly used to offer scientists a dimension reduction procedure and an interpretable gateway to variable selection, especially when confronting high-dimensional genomic data. Huang et al. used a gene expression profile of breast cancer cell lines to identify genomic markers which are highly correlated with in vitro sensitivity of a drug Dasatinib. They considered three statistical methods to identify differentially expressed genes and finally used the results from the intersection. But the statistical methods that are used in the paper are not sufficient to select the genomic markers. In this paper we used three alternative statistical methods to select a combined list of genomic markers and compared the genes that were proposed by Huang et al. We then proposed to use sparse principal component analysis (Sparse PCA) to identify a final list of genomic markers. The Sparse PCA incorporates correlation into account among the genes and helps to draw a successful genomic markers discovery. We present a new and a small set of genomic markers to separate out the groups of patients effectively who are sensitive to the drug Dasatinib. The analysis procedure will also encourage scientists in identifying genomic markers that can help to separate out two groups.
Journal: Journal of Applied Statistics
Pages: 2538-2549
Issue: 14
Volume: 43
Year: 2016
Month: 10
X-DOI: 10.1080/02664763.2016.1142941
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1142941
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:14:p:2538-2549




Template-Type: ReDIF-Article 1.0
Author-Name: S. Magnussen
Author-X-Name-First: S.
Author-X-Name-Last: Magnussen
Author-Name: G. Frazer
Author-X-Name-First: G.
Author-X-Name-Last: Frazer
Author-Name: M. Penner
Author-X-Name-First: M.
Author-X-Name-Last: Penner
Title: Alternative mean-squared error estimators for synthetic estimators of domain means
Abstract: 
 In forest management surveys, the mean of a variable of interest (Y) in a population composed of N equal area spatial compact elements is increasingly estimated from a model linking Y to an auxiliary vector X known for all elements in the population. It is also desired to have synthetic estimates of the mean of Y in spatially compact domains (forest stands) with no or at most one sample-based observation of Y. We develop three alternative estimators of mean-squared errors (MSE) that reduce the risk of a serious underestimation of the uncertainty in a synthetic estimate of a domain mean in cases where the employed model does not accounts for domain effects nor spatial autocorrelation in unobserved residual errors. Expansions of the estimators including anticipated effects of a spatial autocorrelation in residual errors are also provided. Simulation results indicate that the conventional model-dependent (MD) population-level estimator of variance in a synthetic estimate of a domain mean underestimates uncertainty by a wide margin. Our alternative estimators mitigated, in settings with weak to moderate domain effects and relatively small sample sizes, to a large extent, the problem of underestimating uncertainty. We demonstrate applications with examples from two actual forest inventories.
Journal: Journal of Applied Statistics
Pages: 2550-2573
Issue: 14
Volume: 43
Year: 2016
Month: 10
X-DOI: 10.1080/02664763.2016.1142942
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1142942
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:14:p:2550-2573




Template-Type: ReDIF-Article 1.0
Author-Name: L. Bagnato
Author-X-Name-First: L.
Author-X-Name-Last: Bagnato
Author-Name: L. De Capitani
Author-X-Name-First: L.
Author-X-Name-Last: De Capitani
Author-Name: A. Punzo
Author-X-Name-First: A.
Author-X-Name-Last: Punzo
Title: The Kullback–Leibler autodependogram
Abstract: 
 The autodependogram is a graphical device recently proposed in the literature to analyze autodependencies. It is defined computing the classical Pearson  $ \chi ^2 $ χ2-statistics of independence at various lags in order to point out the presence lag-depedencies. This paper proposes an improvement of this diagram obtained by substituting the  $ \chi ^2 $ χ2-statistics with an estimator of the Kullback–Leibler divergence between the bivariate density of two delayed variables and the product of their marginal distributions. A simulation study, on well-established time series models, shows that this new autodependogram is more powerful than the previous one. An application to a well-known financial time series is also shown.
Journal: Journal of Applied Statistics
Pages: 2574-2594
Issue: 14
Volume: 43
Year: 2016
Month: 10
X-DOI: 10.1080/02664763.2016.1142943
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1142943
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:14:p:2574-2594




Template-Type: ReDIF-Article 1.0
Author-Name: Yan Fan
Author-X-Name-First: Yan
Author-X-Name-Last: Fan
Author-Name: Yukun Liu
Author-X-Name-First: Yukun
Author-X-Name-Last: Liu
Title: Evaluating the relative merits of competing models based on empirical likelihood ratio test
Abstract: 
 Competing models arise naturally in many research fields, such as survival analysis and economics, when the same phenomenon of interest is explained by different researcher using different theories or according to different experiences. The model selection problem is therefore remarkably important because of its great importance to the subsequent inference; Inference under a misspecified or inappropriate model will be risky. Existing model selection tests such as Vuong's tests [26] and Shi's non-degenerate tests [21] suffer from the variance estimation and the departure of the normality of the likelihood ratios. To circumvent these dilemmas, we propose in this paper an empirical likelihood ratio (ELR) tests for model selection. Following Shi [21], a bias correction method is proposed for the ELR tests to enhance its performance. A simulation study and a real-data analysis are provided to illustrate the performance of the proposed ELR tests.
Journal: Journal of Applied Statistics
Pages: 2595-2607
Issue: 14
Volume: 43
Year: 2016
Month: 10
X-DOI: 10.1080/02664763.2016.1142944
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1142944
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:14:p:2595-2607




Template-Type: ReDIF-Article 1.0
Author-Name: Ahmed Z. Afify
Author-X-Name-First: Ahmed Z.
Author-X-Name-Last: Afify
Author-Name: Haitham M. Yousof
Author-X-Name-First: Haitham M.
Author-X-Name-Last: Yousof
Author-Name: Gauss M. Cordeiro
Author-X-Name-First: Gauss M.
Author-X-Name-Last: Cordeiro
Author-Name: Edwin M. M. Ortega
Author-X-Name-First: Edwin M. M.
Author-X-Name-Last: Ortega
Author-Name: Zohdy M. Nofal
Author-X-Name-First: Zohdy M.
Author-X-Name-Last: Nofal
Title: The Weibull Fréchet distribution and its applications
Abstract: 
 A new four-parameter lifetime model called the Weibull Fréchet distribution is defined and studied. Various of its structural properties including ordinary and incomplete moments, quantile and generating functions, probability weighted moments, Rényi and  $ {\delta } $ δ-entropies and order statistics are investigated. The new density function can be expressed as a linear mixture of Fréchet densities. The maximum likelihood method is used to estimate the model parameters. The new distribution is applied to two real data sets to prove empirically its flexibility. It can serve as an alternative model to other lifetime distributions in the existing literature for modeling positive real data in many areas.
Journal: Journal of Applied Statistics
Pages: 2608-2626
Issue: 14
Volume: 43
Year: 2016
Month: 10
X-DOI: 10.1080/02664763.2016.1142945
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1142945
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:14:p:2608-2626




Template-Type: ReDIF-Article 1.0
Author-Name: Stefano Cabras
Author-X-Name-First: Stefano
Author-X-Name-Last: Cabras
Author-Name: Juan de Dios Tena Horrillo
Author-X-Name-First: Juan de Dios
Author-X-Name-Last: Tena Horrillo
Title: A Bayesian non-parametric modeling to estimate student response to ICT investment
Abstract: 
 This paper estimates the causal impact of investment in information and communication technologies (ICT) on student performances in mathematics as measured in the Program for International Student Assessment (PISA) 2012 for Spain. To do this we apply a new methodology in this context known as Bayesian Additive Regression Trees that has important advantages over more standard parametric specifications. Results indicate that ICT has a moderate positive effect on math scores. In addition, we analyze how this effect interacts with variables related to school features and student socioeconomic status, finding that ICT investment is especially beneficial for students from a low socioeconomic background.
Journal: Journal of Applied Statistics
Pages: 2627-2642
Issue: 14
Volume: 43
Year: 2016
Month: 10
X-DOI: 10.1080/02664763.2016.1142946
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1142946
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:14:p:2627-2642




Template-Type: ReDIF-Article 1.0
Author-Name: Cheolwoo Park
Author-X-Name-First: Cheolwoo
Author-X-Name-Last: Park
Author-Name: Yongho Jeon
Author-X-Name-First: Yongho
Author-X-Name-Last: Jeon
Author-Name: Kee-Hoon Kang
Author-X-Name-First: Kee-Hoon
Author-X-Name-Last: Kang
Title: An exploratory data analysis in scale-space for interval-valued data
Abstract: 
 We propose an exploratory data analysis approach when data are observed as intervals in a nonparametric regression setting. The interval-valued data contain richer information than single-valued data in the sense that they provide both center and range information of the underlying structure. Conventionally, these two attributes have been studied separately as traditional tools can be readily used for single-valued data analysis. We propose a unified data analysis tool that attempts to capture the relationship between response and covariate by simultaneously accounting for variability present in the data. It utilizes a kernel smoothing approach, which is conducted in scale-space so that it considers a wide range of smoothing parameters rather than selecting an optimal value. It also visually summarizes the significance of trends in the data as a color map across multiple locations and scales. We demonstrate its effectiveness as an exploratory data analysis tool for interval-valued data using simulated and real examples.
Journal: Journal of Applied Statistics
Pages: 2643-2660
Issue: 14
Volume: 43
Year: 2016
Month: 10
X-DOI: 10.1080/02664763.2016.1142947
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1142947
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:14:p:2643-2660




Template-Type: ReDIF-Article 1.0
Author-Name: Onur Çoban
Author-X-Name-First: Onur
Author-X-Name-Last: Çoban
Author-Name: Talha Kivanç
Author-X-Name-First: Talha
Author-X-Name-Last: Kivanç
Author-Name: Mustafa Özgür Bora
Author-X-Name-First: Mustafa Özgür
Author-X-Name-Last: Bora
Author-Name: Burcu Özcan
Author-X-Name-First: Burcu
Author-X-Name-Last: Özcan
Author-Name: Tamer Sinmazçelik
Author-X-Name-First: Tamer
Author-X-Name-Last: Sinmazçelik
Author-Name: Sinan Fidan
Author-X-Name-First: Sinan
Author-X-Name-Last: Fidan
Title: Thermal, viscoelastic and mechanical properties' optimization of polyphenylene sulfide via optimal processing parameters using the Taguchi method
Abstract: 
 Thermal, viscoelastic and mechanical properties of polyphenylene sulfide (PPS) were optimized as a function of extrusion and injection molding parameters. For this purpose, design of experiments approach utilizing Taguchi's L27 (37) orthogonal arrays was used. Effect of the parameters on desired properties was determined using the analysis of variance. Differential scanning calorimeter (DSC) tests were performed for the analysis of thermal properties such as melting temperature (Tm) and melting enthalpy (ΔHM). Dynamic mechanical analysis (DMA) tests were performed for the analysis of viscoelastic properties such as damping factor (tan δ) and glass transition temperature (Tg). Tensile tests were performed for the analysis of mechanical properties such as tensile strength and modulus. With optimized process parameters, verification DSC, DMA and tensile tests were performed for thermal, viscoelastic and mechanical properties, respectively. The Taguchi method showed that ‘barrel temperature’ and its level of ‘340°C’ were seen to be the most effective parameter and its level; respectively. It was suggested that PPS can be reinforced for further improvement after optimized thermal, viscoelastic and mechanical properties.
Journal: Journal of Applied Statistics
Pages: 2661-2680
Issue: 14
Volume: 43
Year: 2016
Month: 10
X-DOI: 10.1080/02664763.2016.1142948
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1142948
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:14:p:2661-2680




Template-Type: ReDIF-Article 1.0
Author-Name: C. Satheesh Kumar
Author-X-Name-First: C. Satheesh
Author-X-Name-Last: Kumar
Author-Name: A. Riyaz
Author-X-Name-First: A.
Author-X-Name-Last: Riyaz
Title: An order  version of the alternative zero-inflated logarithmic series distribution and its applications
Abstract: 
 Here we introduce an order  $ k $ k version of the alternative zero-inflated logarithmic series distribution of Kumar and Riyaz [14] and investigate some of its important properties by deriving an expression for its probability mass function, recurrence relations for its probabilities, raw moments and factorial moments. We discuss the estimation of the parameters of the distribution by the method of maximum likelihood. Certain test procedures are developed for testing the significance of the additional parameters of the model. All these procedures discussed in the paper are illustrated with the help of real life data sets. A simulation study is also considered for assessing the performance of the estimators.
Journal: Journal of Applied Statistics
Pages: 2681-2695
Issue: 14
Volume: 43
Year: 2016
Month: 10
X-DOI: 10.1080/02664763.2016.1142949
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1142949
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:14:p:2681-2695




Template-Type: ReDIF-Article 1.0
Author-Name: The Editors
Title: Corrigendum
Journal: Journal of Applied Statistics
Pages: 2696-2696
Issue: 14
Volume: 43
Year: 2016
Month: 10
X-DOI: 10.1080/02664763.2016.1162899
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1162899
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:14:p:2696-2696




Template-Type: ReDIF-Article 1.0
Author-Name: Andrew Hoegh
Author-X-Name-First: Andrew
Author-X-Name-Last: Hoegh
Author-Name: Ian Crandell
Author-X-Name-First: Ian
Author-X-Name-Last: Crandell
Author-Name: Scott Klopfer
Author-X-Name-First: Scott
Author-X-Name-Last: Klopfer
Author-Name: Mike Fies
Author-X-Name-First: Mike
Author-X-Name-Last: Fies
Title: Model selection with missing covariates for policy considerations in fox enclosures
Abstract: 
 Foxhound training enclosures are facilities where wild-trapped foxes are placed into large fenced areas for dog training purposes. Although the purpose of these facilities is to train dogs without harming foxes, dog-related mortality has been reported to be an issue in some enclosures. Using data from a fox enclosure in Virginia, we investigate factors that influence fox survival in these dog training facilities and propose a set of policies to improve fox survival. In particular, a Bayesian hierarchical model is formulated to compute fox survival probabilities based on a fox's time in the enclosure and the number of dogs allowed in the enclosure at one time. These calculations are complicated by missing information on the number of dogs in the enclosure for many days during the study. We elicit expert knowledge for a prior on the number of dogs to account for the uncertainty in the missing data. Reversible jump Markov Chain Monte Carlo is used for model selection in the presence of missing covariates. We then use our model to examine possible changes to foxhound training enclosure policy and what effect those changes may have on fox survival.
Journal: Journal of Applied Statistics
Pages: 2645-2658
Issue: 15
Volume: 44
Year: 2017
Month: 11
X-DOI: 10.1080/02664763.2016.1259401
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1259401
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:15:p:2645-2658




Template-Type: ReDIF-Article 1.0
Author-Name: Weerinrada Wongrin
Author-X-Name-First: Weerinrada
Author-X-Name-Last: Wongrin
Author-Name: Winai Bodhisuwan
Author-X-Name-First: Winai
Author-X-Name-Last: Bodhisuwan
Title: Generalized Poisson–Lindley linear model for count data
Abstract: 
 The purpose of this paper is to develop a new linear regression model for count data, namely generalized-Poisson Lindley (GPL) linear model. The GPL linear model is performed by applying generalized linear model to GPL distribution. The model parameters are estimated by the maximum likelihood estimation. We utilize the GPL linear model to fit two real data sets and compare it with the Poisson, negative binomial (NB) and Poisson-weighted exponential (P-WE) models for count data. It is found that the GPL linear model can fit over-dispersed count data, and it shows the highest log-likelihood, the smallest AIC and BIC values. As a consequence, the linear regression model from the GPL distribution is a valuable alternative model to the Poisson, NB, and P-WE models.
Journal: Journal of Applied Statistics
Pages: 2659-2671
Issue: 15
Volume: 44
Year: 2017
Month: 11
X-DOI: 10.1080/02664763.2016.1260095
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1260095
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:15:p:2659-2671




Template-Type: ReDIF-Article 1.0
Author-Name: Antonis A. Michis
Author-X-Name-First: Antonis A.
Author-X-Name-Last: Michis
Author-Name: Guy P. Nason
Author-X-Name-First: Guy P.
Author-X-Name-Last: Nason
Title: Case study: shipping trend estimation and prediction via multiscale variance stabilisation
Abstract: 
 Shipping and shipping services are a key industry of great importance to the economy of Cyprus and the wider European Union. Assessment, management and future steering of the industry, and its associated economy, is carried out by a range of organisations and is of direct interest to a number of stakeholders. This article presents an analysis of shipping credit flow data: an important and archetypal series whose analysis is hampered by rapid changes of variance. Our analysis uses the recently developed data-driven Haar–Fisz transformation that enables accurate trend estimation and successful prediction in these kinds of situation. Our trend estimation is augmented by bootstrap confidence bands, new in this context. The good performance of the data-driven Haar–Fisz transform contrasts with the poor performance exhibited by popular and established variance stabilisation alternatives: the Box–Cox, logarithm and square root transformations.
Journal: Journal of Applied Statistics
Pages: 2672-2684
Issue: 15
Volume: 44
Year: 2017
Month: 11
X-DOI: 10.1080/02664763.2016.1260096
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1260096
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:15:p:2672-2684




Template-Type: ReDIF-Article 1.0
Author-Name: A. S. Al-Moisheer
Author-X-Name-First: A. S.
Author-X-Name-Last: Al-Moisheer
Title: Updating a nonlinear discriminant function estimated from a mixture of two Burr Type III distributions
Abstract: 
 The main contribution of this paper is is updating a nonlinear discriminant function on the basis of data of unknown origin. Specifically a procedure is developed for updating the nonlinear discriminant function on the basis of two Burr Type III distributions (TBIIID) when the additional observations are mixed or classified. First the nonlinear discriminant function of the assumed model is obtained. Then the total probabilities of misclassification are calculated. In addition a Monte carlo simulation runs are used to compute the relative efficiencies in order to investigate the performance of the developed updating procedures. Finally the results obtained in this paper are illustrated through a real and simulated data set.
Journal: Journal of Applied Statistics
Pages: 2685-2696
Issue: 15
Volume: 44
Year: 2017
Month: 11
X-DOI: 10.1080/02664763.2016.1261088
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1261088
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:15:p:2685-2696




Template-Type: ReDIF-Article 1.0
Author-Name: Hiba Alawieh
Author-X-Name-First: Hiba
Author-X-Name-Last: Alawieh
Author-Name: Nicolas Wicker
Author-X-Name-First: Nicolas
Author-X-Name-Last: Wicker
Author-Name: Baydaa Al Ayoubi
Author-X-Name-First: Baydaa
Author-X-Name-Last: Al Ayoubi
Author-Name: Luc Moulinier
Author-X-Name-First: Luc
Author-X-Name-Last: Moulinier
Title: Penalized multidimensional fitting for protein movement detection
Abstract: 
 The three-dimensional structure of a given protein can take different conformations depending upon the reaction it undergoes and its substrate/cofactor/partners binding state. Various methods exist to study these conformational changes but only one, called DynDom, is clearly focused on movement detection. An alternative method is proposed, making use of multivariate data analysis, called ‘penalized Multidimensional Fitting (penalized MDF)’ based on penalized movements of points in order to approach the distances between points after movement to the distances given by the reference matrix. The objective is to detect the amino acids that undergo an important movement by fitting the distances of one conformation to the distances of the second one by modifying only the coordinates of the first one. This method is applied to three different proteins.
Journal: Journal of Applied Statistics
Pages: 2697-2715
Issue: 15
Volume: 44
Year: 2017
Month: 11
X-DOI: 10.1080/02664763.2016.1261811
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1261811
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:15:p:2697-2715




Template-Type: ReDIF-Article 1.0
Author-Name: Sayed Jamal Mirkamali
Author-X-Name-First: Sayed Jamal
Author-X-Name-Last: Mirkamali
Author-Name: Mojtaba Ganjali
Author-X-Name-First: Mojtaba
Author-X-Name-Last: Ganjali
Title: A general location model with zero-inflated counts and skew normal outcomes
Abstract: 
 This paper proposes an extension of the general location model using a joint model for analyzing inflated counting outcomes and skew continuous outcomes. A zero-inflated binomial with batches of binomials or a zero-inflated Poisson with batches of Poissons is proposed for counting outcome and a skew normal distribution is assumed for continuous outcome. The EM algorithm is developed for estimation of parameters. The accuracy of estimations is evaluated using a simulation study. An application of our models for joint analysis of the number of cigarettes smoked per day and the weights of respondents for the American's Changing Lives study is enclosed.
Journal: Journal of Applied Statistics
Pages: 2716-2728
Issue: 15
Volume: 44
Year: 2017
Month: 11
X-DOI: 10.1080/02664763.2016.1261813
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1261813
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:15:p:2716-2728




Template-Type: ReDIF-Article 1.0
Author-Name: Susana Mendes
Author-X-Name-First: Susana
Author-X-Name-Last: Mendes
Author-Name: M. José Fernández-Gómez
Author-X-Name-First: M. José
Author-X-Name-Last: Fernández-Gómez
Author-Name: Sónia Cotrim Marques
Author-X-Name-First: Sónia Cotrim
Author-X-Name-Last: Marques
Author-Name: Miguel Ângelo Pardal
Author-X-Name-First: Miguel Ângelo
Author-X-Name-Last: Pardal
Author-Name: Ulisses Miranda Azeiteiro
Author-X-Name-First: Ulisses Miranda
Author-X-Name-Last: Azeiteiro
Author-Name: M. Purificación Galindo-Villardón
Author-X-Name-First: M. Purificación
Author-X-Name-Last: Galindo-Villardón
Title: CO-tucker: a new method for the simultaneous analysis of a sequence of paired tables
Abstract: 
 Relationships between species and their environment are a key component to understand ecological communities. Usually, this kind of data are repeated over time or space for communities and their environment, which leads to a sequence of pairs of ecological tables, i.e. multi-way matrices. This work proposes a new method which is a combined approach of STATICO and Tucker3 techniques and deals to the problem of describing not only the stable part of the dynamics of structure–function relationships between communities and their environment (in different locations and/or at different times), but also the interactions and changes associated with the ecosystems’ dynamics. At the same time, emphasis is given to the comparison with the STATICO method on the same (real) data set, where advantages and drawbacks are explored and discussed. Thus, this study produces a general methodological framework and develops a new technique to facilitate the use of these practices by researchers. Furthermore, from this first approach with estuarine environmental data one of the major advantages of modeling ecological data sets with the CO-TUCKER model is the gain in interpretability.
Journal: Journal of Applied Statistics
Pages: 2729-2755
Issue: 15
Volume: 44
Year: 2017
Month: 11
X-DOI: 10.1080/02664763.2016.1261815
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1261815
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:15:p:2729-2755




Template-Type: ReDIF-Article 1.0
Author-Name: M. H. Hof
Author-X-Name-First: M. H.
Author-X-Name-Last: Hof
Author-Name: J. Z. Musoro
Author-X-Name-First: J. Z.
Author-X-Name-Last: Musoro
Author-Name: R. B. Geskus
Author-X-Name-First: R. B.
Author-X-Name-Last: Geskus
Author-Name: G. H. Struijk
Author-X-Name-First: G. H.
Author-X-Name-Last: Struijk
Author-Name: I. J. M. ten Berge
Author-X-Name-First: I. J. M.
Author-X-Name-Last: ten Berge
Author-Name: A. H. Zwinderman
Author-X-Name-First: A. H.
Author-X-Name-Last: Zwinderman
Title: Simulated maximum likelihood estimation in joint models for multiple longitudinal markers and recurrent events of multiple types, in the presence of a terminal event
Abstract: 
 In medical studies we are often confronted with complex longitudinal data. During the follow-up period, which can be ended prematurely by a terminal event (e.g. death), a subject can experience recurrent events of multiple types. In addition, we collect repeated measurements from multiple markers. An adverse health status, represented by ‘bad’ marker values and an abnormal number of recurrent events, is often associated with the risk of experiencing the terminal event. In this situation, the missingness of the data is not at random and, to avoid bias, it is necessary to model all data simultaneously using a joint model. The correlations between the repeated observations of a marker or an event type within an individual are captured by normally distributed random effects. Because the joint likelihood contains an analytically intractable integral, Bayesian approaches or quadrature approximation techniques are necessary to evaluate the likelihood. However, when the number of recurrent event types and markers is large, the dimensionality of the integral is high and these methods are too computationally expensive. As an alternative, we propose a simulated maximum-likelihood approach based on quasi-Monte Carlo integration to evaluate the likelihood of joint models with multiple recurrent event types and markers.
Journal: Journal of Applied Statistics
Pages: 2756-2777
Issue: 15
Volume: 44
Year: 2017
Month: 11
X-DOI: 10.1080/02664763.2016.1262336
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1262336
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:15:p:2756-2777




Template-Type: ReDIF-Article 1.0
Author-Name: Yang-Jin Kim
Author-X-Name-First: Yang-Jin
Author-X-Name-Last: Kim
Title: Analysis of interval censored failure time data with competing risk
Abstract: 
 In this article, we analyze interval censored failure time data with competing risks. A new estimator for the cumulative incidence function is derived using an approximate likelihood and a test statistic to compare two samples is then obtained by extending Sun's test statistic. Small sample properties of the proposed methods are examined by conducting simulations and a cohort dataset from AIDS patients is analyzed as a real example.
Journal: Journal of Applied Statistics
Pages: 2778-2790
Issue: 15
Volume: 44
Year: 2017
Month: 11
X-DOI: 10.1080/02664763.2016.1263609
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1263609
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:15:p:2778-2790




Template-Type: ReDIF-Article 1.0
Author-Name: Cristina Mollica
Author-X-Name-First: Cristina
Author-X-Name-Last: Mollica
Author-Name: Lea Petrella
Author-X-Name-First: Lea
Author-X-Name-Last: Petrella
Title: Bayesian binary quantile regression for the analysis of Bachelor-to-Master transition
Abstract: 
 The multi-cycle organization of modern university systems stimulates the interest in studying the progression to higher level degree courses during the academic career. In particular, after the achievement of the first level qualification (Bachelor degree), students have to decide whether to continue their university studies, by enrolling in a second level (Master) programme, or to conclude their training experience. In this work we propose a binary quantile regression (BQR) approach to analyse the Bachelor-to-Master transition phenomenon with the adoption of the Bayesian inferential perspective. In addition to the traditional predictors of academic outcomes, such as the personal characteristics and the field of study, different aspects of student's performance are considered. Moreover, the role of a new contextual variable, representing the type of university regulations experienced during the academic path, is investigated. The utility of the Bayesian BQR to characterize the non-continuation decision after the first cycle studies is illustrated with an application to administrative data of Bachelor graduates at the School of Economics of Sapienza University of Rome. The method favourably compares with more conventional model specifications concerning the conditional mean of the binary response.
Journal: Journal of Applied Statistics
Pages: 2791-2812
Issue: 15
Volume: 44
Year: 2017
Month: 11
X-DOI: 10.1080/02664763.2016.1263835
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1263835
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:15:p:2791-2812




Template-Type: ReDIF-Article 1.0
Author-Name: Taban Baghfalaki
Author-X-Name-First: Taban
Author-X-Name-Last: Baghfalaki
Author-Name: Mojtaba Ganjali
Author-X-Name-First: Mojtaba
Author-X-Name-Last: Ganjali
Author-Name: Geert Verbeke
Author-X-Name-First: Geert
Author-X-Name-Last: Verbeke
Title: A shared parameter model of longitudinal measurements and survival time with heterogeneous random-effects distribution
Abstract: 
 Typical joint modeling of longitudinal measurements and time to event data assumes that two models share a common set of random effects with a normal distribution assumption. But, sometimes the underlying population that the sample is extracted from is a heterogeneous population and detecting homogeneous subsamples of it is an important scientific question. In this paper, a finite mixture of normal distributions for the shared random effects is proposed for considering the heterogeneity in the population. For detecting whether the unobserved heterogeneity exits or not, we use a simple graphical exploratory diagnostic tool proposed by Verbeke and Molenberghs [34] to assess whether the traditional normality assumption for the random effects in the mixed model is adequate. In the joint modeling setting, in the case of evidence against normality (homogeneity), a finite mixture of normals is used for the shared random-effects distribution. A Bayesian MCMC procedure is developed for parameter estimation and inference. The methodology is illustrated using some simulation studies. Also, the proposed approach is used for analyzing a real HIV data set, using the heterogeneous joint model for this data set, the individuals are classified into two groups: a group with high risk and a group with moderate risk.
Journal: Journal of Applied Statistics
Pages: 2813-2836
Issue: 15
Volume: 44
Year: 2017
Month: 11
X-DOI: 10.1080/02664763.2016.1266309
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1266309
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:15:p:2813-2836




Template-Type: ReDIF-Article 1.0
Author-Name: Tian Chen
Author-X-Name-First: Tian
Author-X-Name-Last: Chen
Author-Name: Hui Zhang
Author-X-Name-First: Hui
Author-X-Name-Last: Zhang
Author-Name: Bo Zhang
Author-X-Name-First: Bo
Author-X-Name-Last: Zhang
Title: A semiparametric marginalized zero-inflated model for analyzing healthcare utilization panel data with missingness
Abstract: 
 Zero-inflated count outcomes arise quite often in research and practice. Parametric models such as the zero-inflated Poisson and zero-inflated negative binomial are widely used to model such responses. However, interpretations of those models focus on the at-risk subpopulation of a two-component population mixture and fail to provide direct inference about marginal effects for the overall population. Recently, new approaches have been proposed to facilitate such marginal inferences for count responses with excess zeros. However, they are likelihood based and impose strong assumptions on data distributions. In this paper, we propose a new distribution-free, or semiparametric, alternative to provide robust inference for marginal effects when population mixtures are defined by zero-inflated count outcomes. The proposed method also applies to longitudinal studies with missing data following the general missing at random mechanism. The proposed approach is illustrated with both simulated and real study data.
Journal: Journal of Applied Statistics
Pages: 2862-2883
Issue: 16
Volume: 46
Year: 2019
Month: 12
X-DOI: 10.1080/02664763.2019.1620705
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1620705
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:16:p:2862-2883




Template-Type: ReDIF-Article 1.0
Author-Name: Huijuan Ma
Author-X-Name-First: Huijuan
Author-X-Name-Last: Ma
Author-Name: Limin Peng
Author-X-Name-First: Limin
Author-X-Name-Last: Peng
Author-Name: Haoda Fu
Author-X-Name-First: Haoda
Author-X-Name-Last: Fu
Title: Quantile regression modeling of latent trajectory features with longitudinal data
Abstract: 
 Quantile regression has demonstrated promising utility in longitudinal data analysis. Existing work is primarily focused on modeling cross-sectional outcomes, while outcome trajectories often carry more substantive information in practice. In this work, we develop a trajectory quantile regression framework that is designed to robustly and flexibly investigate how latent individual trajectory features are related to observed subject characteristics. The proposed models are built under multilevel modeling with usual parametric assumptions lifted or relaxed. We derive our estimation procedure by novelly transforming the problem at hand to quantile regression with perturbed responses and adapting the bias correction technique for handling covariate measurement errors. We establish desirable asymptotic properties of the proposed estimator, including uniform consistency and weak convergence. Extensive simulation studies confirm the validity of the proposed method as well as its robustness. An application to the DURABLE trial uncovers sensible scientific findings and illustrates the practical value of our proposals.
Journal: Journal of Applied Statistics
Pages: 2884-2904
Issue: 16
Volume: 46
Year: 2019
Month: 12
X-DOI: 10.1080/02664763.2019.1620706
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1620706
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:16:p:2884-2904




Template-Type: ReDIF-Article 1.0
Author-Name: Christian H. Weiß
Author-X-Name-First: Christian H.
Author-X-Name-Last: Weiß
Title: On some measures of ordinal variation
Abstract: 
 A family of variation measures for ordinal data or ordinal time series is considered. The asymptotic distribution of these measures is derived under different scenarios, and the performance of the resulting approximations is investigated with simulations. Possible applications include the bias correction of point estimates, the computation of confidence intervals as well as a test for maximal dispersion. A couple of real-data examples are presented to illustrate the application and interpretation of the ordinal variation measures.
Journal: Journal of Applied Statistics
Pages: 2905-2926
Issue: 16
Volume: 46
Year: 2019
Month: 12
X-DOI: 10.1080/02664763.2019.1620707
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1620707
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:16:p:2905-2926




Template-Type: ReDIF-Article 1.0
Author-Name: Farrukh Jamal
Author-X-Name-First: Farrukh
Author-X-Name-Last: Jamal
Author-Name: Muhammad Arslan Nasir
Author-X-Name-First: Muhammad
Author-X-Name-Last: Arslan Nasir
Author-Name: Gamze Ozel
Author-X-Name-First: Gamze
Author-X-Name-Last: Ozel
Author-Name: M. Elgarhy
Author-X-Name-First: M.
Author-X-Name-Last: Elgarhy
Author-Name: Naushad Mamode Khan
Author-X-Name-First: Naushad
Author-X-Name-Last: Mamode Khan
Title: Generalized inverted Kumaraswamy generated family of distributions: theory and applications
Abstract: 
 This paper proposes a new generator function based on the inverted Kumaraswamy distribution and introduces ‘generalized inverted Kumaraswamy-G’ family of distributions. We provide a comprehensive account of some of its mathematical properties that include the ordinary and incomplete moments, quantile and generating functions and order statistics. The infinite mixture representations for probability density and cumulative distribution and entropy functions of the new family are also established. The density function of the ith-order statistics is expressed as an infinite linear combination of baseline densities and model parameters are estimated by maximum likelihood method. Four special models of this family are also derived along with their respective hazard rate functions. The maximum likelihood estimation (MLE) method is used to obtain the model parameters. Monte Carlo simulation experiments are executed to assess the performance of the ML estimators under the corresponding generated models while some data applications are also illustrated. The results of the study show that the proposed distribution is more flexible as compared to the baseline model. This distribution especially can be used to model symmetric, left-skewed, right-skewed and reversed-J data sets.
Journal: Journal of Applied Statistics
Pages: 2927-2944
Issue: 16
Volume: 46
Year: 2019
Month: 12
X-DOI: 10.1080/02664763.2019.1623867
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1623867
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:16:p:2927-2944




Template-Type: ReDIF-Article 1.0
Author-Name: A. Giussani
Author-X-Name-First: A.
Author-X-Name-Last: Giussani
Author-Name: M. Bonetti
Author-X-Name-First: M.
Author-X-Name-Last: Bonetti
Title: Marshall–Olkin frailty survival models for bivariate right-censored failure time data
Abstract: 
 The aim of this paper is to explore multivariate survival techniques for the analysis of bivariate right-censoring failure time data. In particular, a new family of parametric bivariate frailty models is investigated. To take into account the correlation between two survival times, the Marshall–Olkin Bivariate Exponential Distribution (MOBVE) is exploited to model the joint distribution of two frailties. The reason is twofold: on the one hand, it allows one to model shocks that affect individual-specific frailties; on the other hand, the parameter underlying the Poisson process characterizing the common shock is used to capture the dependence between two lifetimes. The proposed methodology is applied to the investigation of association in death on different-sex couples followed within the Cache County Study on Memory Health and Aging (CCSMHA). A cure rate extension of the model is also described.
Journal: Journal of Applied Statistics
Pages: 2945-2961
Issue: 16
Volume: 46
Year: 2019
Month: 12
X-DOI: 10.1080/02664763.2019.1624694
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1624694
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:16:p:2945-2961




Template-Type: ReDIF-Article 1.0
Author-Name: Tugba Ozkal Yildiz
Author-X-Name-First: Tugba Ozkal
Author-X-Name-Last: Yildiz
Author-Name: Yusuf Can Sevil
Author-X-Name-First: Yusuf Can
Author-X-Name-Last: Sevil
Title: Empirical distribution function estimators based on sampling designs in a finite population using single auxiliary variable
Abstract: 
 In this paper, we study empirical distribution function (EDF) estimators based on ranked set sampling (RSS). Ranked set samples are constructed by using three different sampling designs which are level-0, level-1 and level-2. These designs are different in terms of their replacement policies. The sample is constructed by sampling with replacement in level-0 sampling design. In level-1 sampling design, the sample is constructed by sampling with replacement of the unmeasured units in each set. Also, we construct the sample without replacement on the entire set in level-2 sampling design. In simulation study, we compare EDF estimators when using sampling designs with EDF estimator when using simple random sampling (SRS) in finite populations having different distribution functions. The effects of both perfect and imperfect ranking on the estimators of the sampling designs are investigated. Relative efficiencies of the EDF estimators are obtained by using their integrated mean squared errors (IMSEs), numerically. For illustrative purposes, these EDF estimators based on sampling designs are applied to a real data set. By using both the simulation data and real data, we show that these EDF estimators have higher efficiencies against EDF based on SRS.
Journal: Journal of Applied Statistics
Pages: 2962-2974
Issue: 16
Volume: 46
Year: 2019
Month: 12
X-DOI: 10.1080/02664763.2019.1625311
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1625311
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:16:p:2962-2974




Template-Type: ReDIF-Article 1.0
Author-Name: Ritwik Bhattacharya
Author-X-Name-First: Ritwik
Author-X-Name-Last: Bhattacharya
Author-Name: Muhammad Aslam
Author-X-Name-First: Muhammad
Author-X-Name-Last: Aslam
Title: Design of variables sampling plans based on lifetime-performance index in presence of hybrid censoring scheme
Abstract: 
 Design of variables sampling plans based on the lifetime-performance index for the exponentially distributed hybrid censored data is described in this article. The exact distribution of the maximum likelihood estimator of the exponential lifetime parameter is used to construct the sampling plans. A simulation based algorithm is applied to compute the sampling plans. A comparative study with the existing methods in the literature is also carried out to validate the proposed method. Finally, one real-life data set is analyzed to demonstrate the applicability of the method in practice.
Journal: Journal of Applied Statistics
Pages: 2975-2986
Issue: 16
Volume: 46
Year: 2019
Month: 12
X-DOI: 10.1080/02664763.2019.1625877
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1625877
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:16:p:2975-2986




Template-Type: ReDIF-Article 1.0
Author-Name: Evan Reynolds
Author-X-Name-First: Evan
Author-X-Name-Last: Reynolds
Author-Name: Brian Callaghan
Author-X-Name-First: Brian
Author-X-Name-Last: Callaghan
Author-Name: Mousumi Banerjee
Author-X-Name-First: Mousumi
Author-X-Name-Last: Banerjee
Title: SVM–CART for disease classification
Abstract: 
 Classification and regression trees (CART) and support vector machines (SVM) have become very popular statistical learning tools for analyzing complex data that often arise in biomedical research. While both CART and SVM serve as powerful classifiers in many clinical settings, there are some common scenarios in which each fails to meet the performance and interpretability needed for use as a clinical decision-making tool. In this paper, we propose a new classification method, SVM–CART, that combines features of SVM and CART to produce a more flexible classifier that has the potential to outperform either method in terms of interpretability and prediction accuracy. Furthermore, to enhance prediction accuracy we provide extensions of a single SVM–CART to an ensemble, and methods to extract a representative classifier from the SVM–CART ensemble. The goal is to produce a decision-making tool that can be used in the clinical setting, while still harnessing the stability and predictive improvements gained through developing the SVM–CART ensemble. An extensive simulation study is conducted to assess the performance of the methods in various settings. Finally, we illustrate our methods using a clinical neuropathy dataset.
Journal: Journal of Applied Statistics
Pages: 2987-3007
Issue: 16
Volume: 46
Year: 2019
Month: 12
X-DOI: 10.1080/02664763.2019.1625876
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1625876
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:16:p:2987-3007




Template-Type: ReDIF-Article 1.0
Author-Name: Nabakumar Jana
Author-X-Name-First: Nabakumar
Author-X-Name-Last: Jana
Author-Name: Somesh Kumar
Author-X-Name-First: Somesh
Author-X-Name-Last: Kumar
Author-Name: Kashinath Chatterjee
Author-X-Name-First: Kashinath
Author-X-Name-Last: Chatterjee
Title: Inference on stress–strength reliability for exponential distributions with a common scale parameter
Abstract: 
 This paper considers estimation of the stress–strength reliability when the stress and strength follow two-parameter exponential distributions having different location parameters and a common scale parameter. All parameters are assumed to be unknown. We derive the uniformly minimum variance unbiased estimator, Bayes estimators and an affine equivariant estimator of the stress–strength reliability. We propose confidence intervals of the stress–strength reliability based on the generalized variable approach and percentile bootstrap method. We also derive an approximate confidence interval and Bayesian intervals of the reliability parameter. Numerical comparisons among the proposed estimators are carried out using intensive simulations. Illustrative examples have been given using real data sets.
Journal: Journal of Applied Statistics
Pages: 3008-3031
Issue: 16
Volume: 46
Year: 2019
Month: 12
X-DOI: 10.1080/02664763.2019.1625878
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1625878
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:16:p:3008-3031




Template-Type: ReDIF-Article 1.0
Author-Name: Timothy M. Young
Author-X-Name-First: Timothy M.
Author-X-Name-Last: Young
Author-Name: Olga Khaliukova
Author-X-Name-First: Olga
Author-X-Name-Last: Khaliukova
Author-Name: Nicolas André
Author-X-Name-First: Nicolas
Author-X-Name-Last: André
Author-Name: Alexander Petutschnigg
Author-X-Name-First: Alexander
Author-X-Name-Last: Petutschnigg
Author-Name: Timothy G. Rials
Author-X-Name-First: Timothy G.
Author-X-Name-Last: Rials
Author-Name: Chung-Hao Chen
Author-X-Name-First: Chung-Hao
Author-X-Name-Last: Chen
Title: Detecting special-cause variation ‘events’ from process data signatures
Abstract: 
 The ability to detect the special-cause variation of incoming feedstocks from advanced sensor technology is invaluable to manufacturers. Many on-line sensors produce data signatures that require further off-line statistical processing for interpretation by operational personnel. However, early detection of changes in variation in incoming feedstocks may be imperative to promote early-stage preventive measures. A method is proposed in this applied study for developing control bands to quantify the variation of data signatures in the context of statistical process control (SPC). Control bands based on pointwise prediction intervals constructed from the Bonferroni Inequality and Bayesian smoothing splines are developed. Applications using the control band method for data signatures from near-infrared (NIR) spectroscopy scans of industrial fibers of Switchgrass (Panicum virgatum) used for biofuels production, Loblolly Pine (Pinus taeda) fibers for medium density fiberboard production, and formaldehyde (HCHO) emissions from particleboard were used. Simulations curves (k) of k = 100, k = 1000, and k = 10,000 indicate that the Bonferroni method for detecting special-cause variation is closely aligned with the Shewhart definition of control limits when the pdfs are Gaussian or lognormal.
Journal: Journal of Applied Statistics
Pages: 3032-3043
Issue: 16
Volume: 46
Year: 2019
Month: 12
X-DOI: 10.1080/02664763.2019.1622658
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1622658
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:16:p:3032-3043




Template-Type: ReDIF-Article 1.0
Author-Name: Sherzod B. Akhundjanov
Author-X-Name-First: Sherzod B.
Author-X-Name-Last: Akhundjanov
Author-Name: Lauren Chamberlain
Author-X-Name-First: Lauren
Author-X-Name-Last: Chamberlain
Title: The power-law distribution of agricultural land size
Abstract: 
 Power-law distributions explain a variety of natural and man-made processes spanning various disciplines including economics and finance. This paper demonstrates that the distribution of agricultural land size in the United States is best described by a power-law distribution. Maximum likelihood estimation is carried out using county-level data of over 3000 observations gathered at five-year intervals by the USDA Census of Agriculture. Our analysis indicates that U.S. agricultural land size is heavy-tailed, that variance estimates generally do not converge, and that the top 5% of agricultural counties account for about 25% of agricultural land between 1997 and 2012. The goodness of fit of power-law distribution is evaluated using likelihood ratio tests and regression-based diagnostics. The power-law distribution of farm size has important implications for the design of more efficient regional and national agricultural policies as counties close to the mean account for little of the cumulative distribution of total agricultural land.
Journal: Journal of Applied Statistics
Pages: 3044-3056
Issue: 16
Volume: 46
Year: 2019
Month: 12
X-DOI: 10.1080/02664763.2019.1624695
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1624695
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:16:p:3044-3056




Template-Type: ReDIF-Article 1.0
Author-Name: M. Vijayabhama
Author-X-Name-First: M.
Author-X-Name-Last: Vijayabhama
Author-Name: R. Jaisankar
Author-X-Name-First: R.
Author-X-Name-Last: Jaisankar
Author-Name: S. Varadha Raj
Author-X-Name-First: S.
Author-X-Name-Last: Varadha Raj
Author-Name: K. Baranidharan
Author-X-Name-First: K.
Author-X-Name-Last: Baranidharan
Title: Spatial–temporal variation of casuarina spread in Cauvery delta and north eastern zone of Tamil Nadu, India: a spatial autoregressive model
Abstract: 
 The low forest cover and productivity are the major obstacles for mitigating the demand supply gap of raw material for forest-based industries, which could be fulfilled from a tree outside forest area. Casuarina is a multi-utile, short rotation tree which adapts to all ecosystems. The casuarina wood is predominantly demanded for fuel, construction and paper industries which is mostly preferred by farmers, traders and industries. This study explores the spatial and temporal variability of casuarina spread in mitigating the gap of demand and supply in Tamil Nadu using a spatial autoregressive model. The spread of casuarina was spatially and temporally significant, which was negatively influenced by the gross area irrigated as main and direct effects and positively in an indirect effect. An assured irrigation forces the farmers to choose traditional agricultural crops for their livelihood in their own district. The increase in the price of casuarina would increase the spread of casuarina in both own district and neighbouring districts. The spread of casuarina would augment the supply of raw material for forest-based industries.
Journal: Journal of Applied Statistics
Pages: 1-7
Issue: 1
Volume: 45
Year: 2018
Month: 1
X-DOI: 10.1080/02664763.2016.1247786
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1247786
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:1:p:1-7




Template-Type: ReDIF-Article 1.0
Author-Name: Emre Dunder
Author-X-Name-First: Emre
Author-X-Name-Last: Dunder
Author-Name: Serpil Gumustekin
Author-X-Name-First: Serpil
Author-X-Name-Last: Gumustekin
Author-Name: Mehmet Ali Cengiz
Author-X-Name-First: Mehmet Ali
Author-X-Name-Last: Cengiz
Title: Variable selection in gamma regression models via artificial bee colony algorithm
Abstract: 
 Variable selection is an important task in regression analysis. Performance of the statistical model highly depends on the determination of the subset of predictors. There are several methods to select most relevant variables to construct a good model. However in practice, the dependent variable may have positive continuous values and not normally distributed. In such situations, gamma distribution is more suitable than normal for building a regression model. This paper introduces an heuristic approach to perform variable selection using artificial bee colony optimization for gamma regression models. We evaluated the proposed method against with classical selection methods such as backward and stepwise. Both simulation studies and real data set examples proved the accuracy of our selection procedure.
Journal: Journal of Applied Statistics
Pages: 8-16
Issue: 1
Volume: 45
Year: 2018
Month: 1
X-DOI: 10.1080/02664763.2016.1254730
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1254730
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:1:p:8-16




Template-Type: ReDIF-Article 1.0
Author-Name: Mehmet Ali Cengiz
Author-X-Name-First: Mehmet Ali
Author-X-Name-Last: Cengiz
Author-Name: Emre Dünder
Author-X-Name-First: Emre
Author-X-Name-Last: Dünder
Author-Name: Talat Şenel
Author-X-Name-First: Talat
Author-X-Name-Last: Şenel
Title: Energy performance evaluation of OECD countries using Bayesian stochastic frontier analysis and Bayesian network classifiers
Abstract: 
 More recently a large amount of interest has been devoted to the use of Bayesian methods for deriving parameter estimates of the stochastic frontier analysis. Bayesian stochastic frontier analysis (BSFA) seems to be a useful method to assess the efficiency in energy sector. However, BSFA results do not expose the multiple relationships between input and output variables and energy efficiency. This study proposes a framework to make inferences about BSFA efficiencies, recognizing the underlying relationships between variables and efficiency, using Bayesian network (BN) approach. BN classifiers are proposed as a method to analyze the results obtained from BSFA.
Journal: Journal of Applied Statistics
Pages: 17-25
Issue: 1
Volume: 45
Year: 2018
Month: 1
X-DOI: 10.1080/02664763.2016.1257586
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1257586
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:1:p:17-25




Template-Type: ReDIF-Article 1.0
Author-Name: Feng Li
Author-X-Name-First: Feng
Author-X-Name-Last: Li
Author-Name: Yiqiang Lu
Author-X-Name-First: Yiqiang
Author-X-Name-Last: Lu
Title: Lasso-type estimation for covariate-adjusted linear model
Abstract: 
 Lasso is popularly used for variable selection in recent years. In this paper, lasso-type penalty functions including lasso and adaptive lasso are employed in simultaneously variable selection and parameter estimation for covariate-adjusted linear model, where the predictors and response cannot be observed directly and distorted by some observable covariate through some unknown multiplicative smooth functions. Estimation procedures are proposed and some asymptotic properties are obtained under some mild conditions. It deserves noting that under appropriate conditions, the adaptive lasso estimator correctly select covariates with nonzero coefficients with probability converging to one and that the estimators of nonzero coefficients have the same asymptotic distribution that they would have if the zero coefficients were known in advance, i.e. the adaptive lasso estimator has the oracle property in the sense of Fan and Li [6]. Simulation studies are carried out to examine its performance in finite sample situations and the Boston Housing data is analyzed for illustration.
Journal: Journal of Applied Statistics
Pages: 26-42
Issue: 1
Volume: 45
Year: 2018
Month: 1
X-DOI: 10.1080/02664763.2016.1267121
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1267121
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:1:p:26-42




Template-Type: ReDIF-Article 1.0
Author-Name: Kartlos Joseph Kachiashvili
Author-X-Name-First: Kartlos Joseph
Author-X-Name-Last: Kachiashvili
Author-Name: Archil Iveri Prangishvili
Author-X-Name-First: Archil Iveri
Author-X-Name-Last: Prangishvili
Title: Verification in biometric systems: problems and modern methods of their solution
Abstract: 
 The paper deals with the problem of electronic verification of people on the basis of measurement information of a fingerprint reader and new approaches to its solution. The offered method guaranties the restriction of error probabilities of both type at the desired level at making a decision about permitting or rejecting the request on service in the system. On the basis of investigation of real data obtained in the real biometrical system, the choice of distribution laws is substantiated and the proper estimations of their parameters are obtained. Using chosen distribution laws, the normal distribution for measurement results of characteristics of the people having access to the system and the beta distribution for the people having no such access, the optimal rule based on the Constrained Bayesian Method (CBM) of making a decision about giving a permission of access to the users of the system is justified. The CBM, the Neyman–Pearson and classical Bayes methods are investigated and their good and negative points are examined. Computation results obtained by direct computation, by simulation and using real data completely confirm the suppositions made and the high quality of verification results obtained on their basis.
Journal: Journal of Applied Statistics
Pages: 43-62
Issue: 1
Volume: 45
Year: 2018
Month: 1
X-DOI: 10.1080/02664763.2016.1267122
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1267122
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:1:p:43-62




Template-Type: ReDIF-Article 1.0
Author-Name: Hang J. Kim
Author-X-Name-First: Hang J.
Author-X-Name-Last: Kim
Author-Name: Jerome P. Reiter
Author-X-Name-First: Jerome P.
Author-X-Name-Last: Reiter
Author-Name: Alan F. Karr
Author-X-Name-First: Alan F.
Author-X-Name-Last: Karr
Title: Simultaneous edit-imputation and disclosure limitation for business establishment data
Abstract: 
 Business establishment microdata typically are required to satisfy agency-specified edit rules, such as balance equations and linear inequalities. Inevitably some establishments' reported data violate the edit rules. Statistical agencies correct faulty values using a process known as edit-imputation. Business establishment data also must be heavily redacted before being shared with the public; indeed, confidentiality concerns lead many agencies not to share establishment microdata as unrestricted access files. When microdata must be heavily redacted, one approach is to create synthetic data, as done in the U.S. Longitudinal Business Database and the German IAB Establishment Panel. This article presents the first implementation of a fully integrated approach to edit-imputation and data synthesis. We illustrate the approach on data from the U.S. Census of Manufactures and present a variety of evaluations of the utility of the synthetic data. The paper also presents assessments of disclosure risks for several intruder attacks. We find that the synthetic data preserve important distributional features from the post-editing confidential microdata, and have low risks for the various attacks.
Journal: Journal of Applied Statistics
Pages: 63-82
Issue: 1
Volume: 45
Year: 2018
Month: 1
X-DOI: 10.1080/02664763.2016.1267123
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1267123
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:1:p:63-82




Template-Type: ReDIF-Article 1.0
Author-Name: T. F. Cox
Author-X-Name-First: T. F.
Author-X-Name-Last: Cox
Author-Name: D. S. Arnold
Author-X-Name-First: D. S.
Author-X-Name-Last: Arnold
Title: Simple components
Abstract: 
 Interpretation of principal components is difficult due to their weights (loadings, coefficients) being of various sizes. Whereas very small weights or very large weights can give clear indication of the importance of particular variables, weights that are neither large nor small (‘grey area’ weights) are problematical. This is a particular problem in the fast moving goods industries where a lot of multivariate panel data are collected on products. These panel data are subjected to univariate analyses and multivariate analyses where principal components (PCs) are key to the interpretation of the data. Several authors have suggested alternatives to PCs, seeking simplified components such as sparse PCs. Here components, termed simple components (SCs), are sought in conjunction with Thurstonian criteria that a component should have only a few variables highly weighted on it and each variable should be weighted heavily on just a few components. An algorithm is presented that finds SCs efficiently. Simple components are found for panel data consisting of the responses to a questionnaire on efficacy and other features of deodorants. It is shown that five SCs can explain an amount of variation within the data comparable to that explained by the PCs, but with easier interpretation.
Journal: Journal of Applied Statistics
Pages: 83-99
Issue: 1
Volume: 45
Year: 2018
Month: 1
X-DOI: 10.1080/02664763.2016.1268104
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1268104
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:1:p:83-99




Template-Type: ReDIF-Article 1.0
Author-Name: Rand R. Wilcox
Author-X-Name-First: Rand R.
Author-X-Name-Last: Wilcox
Title: Robust regression: an inferential method for determining which independent variables are most important
Abstract: 
 Consider the usual linear regression model consisting of two or more explanatory variables. There are many methods aimed at indicating the relative importance of the explanatory variables. But in general these methods do not address a fundamental issue: when all of the explanatory variables are included in the model, how strong is the empirical evidence that the first explanatory variable is more or less important than the second explanatory variable? How strong is the empirical evidence that the first two explanatory variables are more important than the third explanatory variable? The paper suggests a robust method for dealing with these issues. The proposed technique is based on a particular version of explanatory power used in conjunction with a modification of the basic percentile method.
Journal: Journal of Applied Statistics
Pages: 100-111
Issue: 1
Volume: 45
Year: 2018
Month: 1
X-DOI: 10.1080/02664763.2016.1268105
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1268105
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:1:p:100-111




Template-Type: ReDIF-Article 1.0
Author-Name: Tao Chen
Author-X-Name-First: Tao
Author-X-Name-Last: Chen
Author-Name: Qingliang Fan
Author-X-Name-First: Qingliang
Author-X-Name-Last: Fan
Title: A functional data approach to model score difference process in professional basketball games
Abstract: 
 In this paper, we investigate the progress of score difference (between home and away teams) in professional basketball games employing functional data analysis (FDA). The observed score difference is viewed as the realization of the latent intensity process, which is assumed to be continuous. There are two major advantages of modeling the latent score difference intensity process using FDA: (1) it allows for arbitrary dependent structure among score change increments. This removes potential model mis-specifications and accommodates momentum which is often observed in sports games. (2) further statistical inferences using FDA estimates will not suffer from inconsistency due to the issue of having a continuous model yet discretely sampled data. Based on the FDA estimates, we define and numerically characterize momentum in basketball games and demonstrate its importance in predicting game outcomes.
Journal: Journal of Applied Statistics
Pages: 112-127
Issue: 1
Volume: 45
Year: 2018
Month: 1
X-DOI: 10.1080/02664763.2016.1268106
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1268106
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:1:p:112-127




Template-Type: ReDIF-Article 1.0
Author-Name: Giovani Carrara Rodrigues
Author-X-Name-First: Giovani Carrara
Author-X-Name-Last: Rodrigues
Author-Name: Francisco Louzada
Author-X-Name-First: Francisco
Author-X-Name-Last: Louzada
Author-Name: Pedro Luiz Ramos
Author-X-Name-First: Pedro Luiz
Author-X-Name-Last: Ramos
Title: Poisson–exponential distribution: different methods of estimation
Abstract: 
 In this study, we present different estimation procedures for the parameters of the Poisson–exponential distribution, such as the maximum likelihood, method of moments, modified moments, ordinary and weighted least-squares, percentile, maximum product of spacings, Cramer–von Mises and the Anderson–Darling maximum goodness-of-fit estimators and compare them using extensive numerical simulations. We showed that the Anderson–Darling estimator is the most efficient for estimating the parameters of the proposed distribution. Our proposed methodology was also illustrated in three real data sets related to the minimum, average and the maximum flows during October at São Carlos River in Brazil demonstrating that the PE distribution is a simple alternative to be used in hydrological applications.
Journal: Journal of Applied Statistics
Pages: 128-144
Issue: 1
Volume: 45
Year: 2018
Month: 1
X-DOI: 10.1080/02664763.2016.1268571
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1268571
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:1:p:128-144




Template-Type: ReDIF-Article 1.0
Author-Name: Michelli Barros
Author-X-Name-First: Michelli
Author-X-Name-Last: Barros
Author-Name: Manuel Galea
Author-X-Name-First: Manuel
Author-X-Name-Last: Galea
Author-Name: Víctor Leiva
Author-X-Name-First: Víctor
Author-X-Name-Last: Leiva
Author-Name: Manoel Santos-Neto
Author-X-Name-First: Manoel
Author-X-Name-Last: Santos-Neto
Title: Generalized Tobit models: diagnostics and application in econometrics
Abstract: 
 The standard Tobit model is constructed under the assumption of a normal distribution and has been widely applied in econometrics. Atypical/extreme data have a harmful effect on the maximum likelihood estimates of the standard Tobit model parameters. Then, we need to count with diagnostic tools to evaluate the effect of extreme data. If they are detected, we must have available a Tobit model that is robust to this type of data. The family of elliptically contoured distributions has the Laplace, logistic, normal and Student-t cases as some of its members. This family has been largely used for providing generalizations of models based on the normal distribution, with excellent practical results. In particular, because the Student-t distribution has an additional parameter, we can adjust the kurtosis of the data, providing robust estimates against extreme data. We propose a methodology based on a generalization of the standard Tobit model with errors following elliptical distributions. Diagnostics in the Tobit model with elliptical errors are developed. We derive residuals and global/local influence methods considering several perturbation schemes. This is important because different diagnostic methods can detect different atypical data. We implement the proposed methodology in an R package. We illustrate the methodology with real-world econometrical data by using the R package, which shows its potential applications. The Tobit model based on the Student-t distribution with a small quantity of degrees of freedom displays an excellent performance reducing the influence of extreme cases in the maximum likelihood estimates in the application presented. It provides new empirical evidence on the capabilities of the Student-t distribution for accommodation of atypical data.
Journal: Journal of Applied Statistics
Pages: 145-167
Issue: 1
Volume: 45
Year: 2018
Month: 1
X-DOI: 10.1080/02664763.2016.1268572
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1268572
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:1:p:145-167




Template-Type: ReDIF-Article 1.0
Author-Name: Fernanda De Bastiani
Author-X-Name-First: Fernanda
Author-X-Name-Last: De Bastiani
Author-Name: Robert A. Rigby
Author-X-Name-First: Robert A.
Author-X-Name-Last: Rigby
Author-Name: Dimitrios M. Stasinopoulous
Author-X-Name-First: Dimitrios M.
Author-X-Name-Last: Stasinopoulous
Author-Name: Audrey H.M.A. Cysneiros
Author-X-Name-First: Audrey H.M.A.
Author-X-Name-Last: Cysneiros
Author-Name: Miguel A. Uribe-Opazo
Author-X-Name-First: Miguel A.
Author-X-Name-Last: Uribe-Opazo
Title: Gaussian Markov random field spatial models in GAMLSS
Abstract: 
 This paper describes the modelling and fitting of Gaussian Markov random field spatial components within a Generalized AdditiveModel for Location, Scale and Shape (GAMLSS) model. This allows modelling of any or all the parameters of the distribution for the response variable using explanatory variables and spatial effects. The response variable distribution is allowed to be a non-exponential family distribution. A new package developed in R to achieve this is presented. We use Gaussian Markov random fields to model the spatial effect in Munich rent data and explore some features and characteristics of the data. The potential of using spatial analysis within GAMLSS is discussed. We argue that the flexibility of parametric distributions, ability to model all the parameters of the distribution and diagnostic tools of GAMLSS provide an ideal environment for modelling spatial features of data.
Journal: Journal of Applied Statistics
Pages: 168-186
Issue: 1
Volume: 45
Year: 2018
Month: 1
X-DOI: 10.1080/02664763.2016.1269728
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1269728
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:1:p:168-186




Template-Type: ReDIF-Article 1.0
Author-Name: Tatjana Miljkovic
Author-X-Name-First: Tatjana
Author-X-Name-Last: Miljkovic
Author-Name: Saleem Shaik
Author-X-Name-First: Saleem
Author-X-Name-Last: Shaik
Author-Name: Dragan Miljkovic
Author-X-Name-First: Dragan
Author-X-Name-Last: Miljkovic
Title: Redefining standards for body mass index of the US population based on BRFSS data using mixtures
Abstract: 
 Using body mass index (BMI) data from 2012 Behavioral Risk Factor Surveillance System, we test a spectrum of single parametric skewed distributions as well as Gaussian mixture densities to determine best distributional fit. We find that a k-component Gaussian mixture is the best model to describe the distribution of BMI data for the overall US population and for the population divided by gender, race, and region. A 4-component Gaussian mixture with the following sub-population means (standard deviations) fits best the US population:  $ 22.21(\sigma =2.27) $ 22.21(σ=2.27),  $ 26.05(\sigma =2.19) $ 26.05(σ=2.19),  $ 29.83(\sigma =3.90) $ 29.83(σ=3.90),  $ 35.47(\sigma =8.45) $ 35.47(σ=8.45) with corresponding weights: 23%, 25%, 37%, and 15%. Current obesity standards are set based on a convention and they are fairly dated. Overweight population has BMI (25.0, 29.9). Obese population is subdivided into three grades based on BMI: grade 1 (30–35), grade 2 (35–40), grade 3 (40 and above). Our study shows that modeling BMI using mixtures can be used to redefine current standards and support them with actual prevalence rather than a dated convention. By redefining BMI standards and employing the mixture models by gender and race, health and food policy makers will have opportunity to diversify policies and treatments of obesity as premier public health problem in the USA.
Journal: Journal of Applied Statistics
Pages: 197-211
Issue: 2
Volume: 44
Year: 2017
Month: 1
X-DOI: 10.1080/02664763.2016.1168366
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1168366
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:2:p:197-211




Template-Type: ReDIF-Article 1.0
Author-Name: Wen-Liang Hung
Author-X-Name-First: Wen-Liang
Author-X-Name-Last: Hung
Author-Name: De-Hua Chen
Author-X-Name-First: De-Hua
Author-X-Name-Last: Chen
Author-Name: Jenn-Hwai Yang
Author-X-Name-First: Jenn-Hwai
Author-X-Name-Last: Yang
Title: Weighting variables in Kohonen competitive learning algorithms
Abstract: 
 This paper presents a new variable weight method, called the singular value decomposition (SVD) approach, for Kohonen competitive learning (KCL) algorithms based on the concept of Varshavsky et al. [18]. Integrating the weighted fuzzy c-means (FCM) algorithm with KCL, in this paper, we propose a weighted fuzzy KCL (WFKCL) algorithm. The goal of the proposed WFKCL algorithm is to reduce the clustering error rate when data contain some noise variables. Compared with the k-means, FCM and KCL with existing variable-weight methods, the proposed WFKCL algorithm with the proposed SVD's weight method provides a better clustering performance based on the error rate criterion. Furthermore, the complexity of the proposed SVD's approach is less than Pal et al. [17], Wang et al. [19] and Hung et al. [9].
Journal: Journal of Applied Statistics
Pages: 212-232
Issue: 2
Volume: 44
Year: 2017
Month: 1
X-DOI: 10.1080/02664763.2016.1168367
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1168367
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:2:p:212-232




Template-Type: ReDIF-Article 1.0
Author-Name: Rodrigo R. Pescim
Author-X-Name-First: Rodrigo R.
Author-X-Name-Last: Pescim
Author-Name: Edwin M. M. Ortega
Author-X-Name-First: Edwin M. M.
Author-X-Name-Last: Ortega
Author-Name: Gauss M. Cordeiro
Author-X-Name-First: Gauss M.
Author-X-Name-Last: Cordeiro
Author-Name: Morad Alizadeh
Author-X-Name-First: Morad
Author-X-Name-Last: Alizadeh
Title: A new log-location regression model: estimation, influence diagnostics and residual analysis
Abstract: 
 We introduce a log-linear regression model based on the odd log-logistic generalized half-normal distribution [7]. Some of its structural properties including explicit expressions for the density function, quantile and generating functions and ordinary moments are derived. We estimate the model parameters by the maximum likelihood method. For different parameter settings, proportion of censoring and sample size, some simulations are performed to investigate the behavior of the estimators. We derive the appropriate matrices for assessing local influence diagnostics on the parameter estimates under different perturbation schemes. We also define the martingale and modified deviance residuals to detect outliers and evaluate the model assumptions. In addition, we demonstrate that the extended regression model can be very useful in the analysis of real data and provide more realistic fits than other special regression models. The potentiality of the new regression model is illustrated by means of a real data set.
Journal: Journal of Applied Statistics
Pages: 233-252
Issue: 2
Volume: 44
Year: 2017
Month: 1
X-DOI: 10.1080/02664763.2016.1168368
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1168368
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:2:p:233-252




Template-Type: ReDIF-Article 1.0
Author-Name: Shiyi Tu
Author-X-Name-First: Shiyi
Author-X-Name-Last: Tu
Author-Name: Min Wang
Author-X-Name-First: Min
Author-X-Name-Last: Wang
Author-Name: Xiaoqian Sun
Author-X-Name-First: Xiaoqian
Author-X-Name-Last: Sun
Title: Bayesian variable selection and estimation in maximum entropy quantile regression
Abstract: 
 Quantile regression has gained increasing popularity as it provides richer information than the regular mean regression, and variable selection plays an important role in the quantile regression model building process, as it improves the prediction accuracy by choosing an appropriate subset of regression predictors. Unlike the traditional quantile regression, we consider the quantile as an unknown parameter and estimate it jointly with other regression coefficients. In particular, we adopt the Bayesian adaptive Lasso for the maximum entropy quantile regression. A flat prior is chosen for the quantile parameter due to the lack of information on it. The proposed method not only addresses the problem about which quantile would be the most probable one among all the candidates, but also reflects the inner relationship of the data through the estimated quantile. We develop an efficient Gibbs sampler algorithm and show that the performance of our proposed method is superior than the Bayesian adaptive Lasso and Bayesian Lasso through simulation studies and a real data analysis.
Journal: Journal of Applied Statistics
Pages: 253-269
Issue: 2
Volume: 44
Year: 2017
Month: 1
X-DOI: 10.1080/02664763.2016.1168369
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1168369
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:2:p:253-269




Template-Type: ReDIF-Article 1.0
Author-Name: A.Y. Kombo
Author-X-Name-First: A.Y.
Author-X-Name-Last: Kombo
Author-Name: H. Mwambi
Author-X-Name-First: H.
Author-X-Name-Last: Mwambi
Author-Name: G. Molenberghs
Author-X-Name-First: G.
Author-X-Name-Last: Molenberghs
Title: Multiple imputation for ordinal longitudinal data with monotone missing data patterns
Abstract: 
 Missing data often complicate the analysis of scientific data. Multiple imputation is a general purpose technique for analysis of datasets with missing values. The approach is applicable to a variety of missing data patterns but often complicated by some restrictions like the type of variables to be imputed and the mechanism underlying the missing data. In this paper, the authors compare the performance of two multiple imputation methods, namely fully conditional specification and multivariate normal imputation in the presence of ordinal outcomes with monotone missing data patterns. Through a simulation study and an empirical example, the authors show that the two methods are indeed comparable meaning any of the two may be used when faced with scenarios, at least, as the ones presented here.
Journal: Journal of Applied Statistics
Pages: 270-287
Issue: 2
Volume: 44
Year: 2017
Month: 1
X-DOI: 10.1080/02664763.2016.1168370
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1168370
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:2:p:270-287




Template-Type: ReDIF-Article 1.0
Author-Name: Y. T. Hwang
Author-X-Name-First: Y. T.
Author-X-Name-Last: Hwang
Author-Name: C. H. Huang
Author-X-Name-First: C. H.
Author-X-Name-Last: Huang
Author-Name: W. L. Yeh
Author-X-Name-First: W. L.
Author-X-Name-Last: Yeh
Author-Name: Y. D. Shen
Author-X-Name-First: Y. D.
Author-X-Name-Last: Shen
Title: The weighted general linear model for longitudinal medical cost data – an application in colorectal cancer
Abstract: 
 Identifying cost-effective decisions that can take into account of medical cost and health outcome is an important issue under very limited resources. Analyzing medical costs has been challenged owing to skewness of cost distributions, heterogeneity across samples and censoring. When censoring is due to administrative reasons, the total cost might be related to the survival time since longer survivals are likely to be censored and the corresponding total cost will be censored as well. This paper uses the general linear model for the longitudinal data to model the repeated medical cost data and the weighted estimating equation is used to find more accurate estimates for the parameter. Furthermore, the asymptotic properties for the proposed model are discussed. Simulations are used to evaluate the performance of estimators under various scenarios. Finally, the proposed model is implemented on the data extracted from National Health Insurance database for patients with the colorectal cancer.
Journal: Journal of Applied Statistics
Pages: 288-307
Issue: 2
Volume: 44
Year: 2017
Month: 1
X-DOI: 10.1080/02664763.2016.1169255
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1169255
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:2:p:288-307




Template-Type: ReDIF-Article 1.0
Author-Name: YangXia Luo
Author-X-Name-First: YangXia
Author-X-Name-Last: Luo
Title: Statistics and recognition for software birthmark based on clustering analysis
Abstract: 
 The result of feature selection for software birthmark has a direct bearing on software recognition rate. In this paper, we apply constrained clustering to analyze the software features (SF). The within-class (homogeneous software) and between-class (heterogeneous software) distances of features are measured based on mutual information. Information gain functions and penalty functions are constructed using homogeneous and heterogeneous SF, respectively; and redundancy is measured with correlation coefficients. Then the software birthmark features with high class distinction and minimum redundancy are selected. The example of extracting and detecting framework of birthmark feature is also given. The algorithm is analyzed and compared with the similar algorithms, and it is shown the algorithm provide an effective approach for software birthmark selection and optimization.
Journal: Journal of Applied Statistics
Pages: 308-324
Issue: 2
Volume: 44
Year: 2017
Month: 1
X-DOI: 10.1080/02664763.2016.1169256
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1169256
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:2:p:308-324




Template-Type: ReDIF-Article 1.0
Author-Name: Matthew J. Keefe
Author-X-Name-First: Matthew J.
Author-X-Name-Last: Keefe
Author-Name: Christopher T. Franck
Author-X-Name-First: Christopher T.
Author-X-Name-Last: Franck
Author-Name: William H. Woodall
Author-X-Name-First: William H.
Author-X-Name-Last: Woodall
Title: Monitoring foreclosure rates with a spatially risk-adjusted Bernoulli CUSUM chart for concurrent observations
Abstract: 
 Frequently in process monitoring, situations arise in which the order that events occur cannot be distinguished, motivating the need to accommodate multiple observations occurring at the same time, or concurrent observations. The risk-adjusted Bernoulli cumulative sum (CUSUM) control chart can be used to monitor the rate of an adverse event by fitting a risk-adjustment model, followed by a likelihood ratio-based scoring method that produces a statistic that can be monitored. In our paper, we develop a risk-adjusted Bernoulli CUSUM control chart for concurrent observations. Furthermore, we adopt a novel approach that uses a combined mixture model and kernel density estimation approach in order to perform risk-adjustment with regard to spatial location. Our proposed method allows for monitoring binary outcomes through time with multiple observations at each time point, where the chart is spatially adjusted for each Bernoulli observation's estimated probability of the adverse event. A simulation study is presented to assess the performance of the proposed monitoring scheme. We apply our method using data from Wayne County, Michigan between 2005 and 2014 to monitor the rate of foreclosure as a percentage of all housing transactions.
Journal: Journal of Applied Statistics
Pages: 325-341
Issue: 2
Volume: 44
Year: 2017
Month: 1
X-DOI: 10.1080/02664763.2016.1169257
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1169257
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:2:p:325-341




Template-Type: ReDIF-Article 1.0
Author-Name: Reşit Çelik
Author-X-Name-First: Reşit
Author-X-Name-Last: Çelik
Title: A new test to detect monotonic and non-monotonic types of heteroscedasticity
Abstract: 
 A direct parametric test is proposed to detect monotonic and non-monotonic types of heteroscedasticity. After giving brief information about non-monotonic types of heteroscedasticity, the test algorithm is introduced. Proposed test and usual heteroscedasticity tests are compared on monotonic and non-monotonic types of heteroscedasticity in real and artificial data.
Journal: Journal of Applied Statistics
Pages: 342-361
Issue: 2
Volume: 44
Year: 2017
Month: 1
X-DOI: 10.1080/02664763.2016.1169258
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1169258
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:2:p:342-361




Template-Type: ReDIF-Article 1.0
Author-Name: Huihui Sun
Author-X-Name-First: Huihui
Author-X-Name-Last: Sun
Title: Testing of homogeneity of variance and autocorrelation coefficients of nonlinear mixed models with AR(1) errors based on M-estimation
Abstract: 
 Homogeneity of between-individual variance and autocorrelation coefficients is one of assumptions in the study of longitudinal data. However, the assumption could be challenging due to the complexity of the dataset. In the paper we propose and analyze nonlinear mixed models with AR(1) errors for longitudinal data, intend to introduce Huber's function in the log-likelihood function and get robust estimation, which may help to reduce the influence of outliers, by Fisher scoring method. Testing of homogeneity of variance among individuals and autocorrelation coefficients on the basis of Huber's M-estimation is studied later in the paper. Simulation studies are carried to assess performance of score test we proposed. Results obtained from plasma concentrations data are reported as an illustrative example.
Journal: Journal of Applied Statistics
Pages: 362-375
Issue: 2
Volume: 44
Year: 2017
Month: 1
X-DOI: 10.1080/02664763.2016.1169259
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1169259
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:2:p:362-375




Template-Type: ReDIF-Article 1.0
Author-Name: D. Malathi
Author-X-Name-First: D.
Author-X-Name-Last: Malathi
Author-Name: S. Muthulakshmi
Author-X-Name-First: S.
Author-X-Name-Last: Muthulakshmi
Title: Economic design of acceptance sampling plans for truncated life test using Frechet distribution
Abstract: 
 In this article designing of acceptance sampling plans for the truncated life test is proposed using economic approach by assuming that the lifetime of a product follows Frechet distribution based on median. The optimal sampling plans to meet the consumer’s confidence level with minimum total cost are designed. Efficiency of the proposed plan is also analyzed.
Journal: Journal of Applied Statistics
Pages: 376-384
Issue: 2
Volume: 44
Year: 2017
Month: 1
X-DOI: 10.1080/02664763.2016.1169260
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1169260
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:2:p:376-384




Template-Type: ReDIF-Article 1.0
Author-Name: J. Kowalski
Author-X-Name-First: J.
Author-X-Name-Last: Kowalski
Author-Name: S. Hao
Author-X-Name-First: S.
Author-X-Name-Last: Hao
Author-Name: T. Chen
Author-X-Name-First: T.
Author-X-Name-Last: Chen
Author-Name: Y. Liang
Author-X-Name-First: Y.
Author-X-Name-Last: Liang
Author-Name: J. Liu
Author-X-Name-First: J.
Author-X-Name-Last: Liu
Author-Name: L. Ge
Author-X-Name-First: L.
Author-X-Name-Last: Ge
Author-Name: C. Feng
Author-X-Name-First: C.
Author-X-Name-Last: Feng
Author-Name: X. M. Tu
Author-X-Name-First: X. M.
Author-X-Name-Last: Tu
Title: Modern variable selection for longitudinal semi-parametric models with missing data
Abstract: 
 Penalized methods for variable selection such as the Smoothly Clipped Absolute Deviation penalty have been increasingly applied to aid variable section in regression analysis. Much of the literature has focused on parametric models, while a few recent studies have shifted the focus and developed their applications for the popular semi-parametric, or distribution-free, generalized estimating equations (GEEs) and weighted GEE (WGEE). However, although the WGEE is composed of one main and one missing-data module, available methods only focus on the main module, with no variable selection for the missing-data module. In this paper, we develop a new approach to further extend the existing methods to enable variable selection for both modules. The approach is illustrated by both real and simulated study data.
Journal: Journal of Applied Statistics
Pages: 2548-2562
Issue: 14
Volume: 45
Year: 2018
Month: 10
X-DOI: 10.1080/02664763.2018.1426739
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1426739
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:14:p:2548-2562




Template-Type: ReDIF-Article 1.0
Author-Name: S. Balamurali
Author-X-Name-First: S.
Author-X-Name-Last: Balamurali
Author-Name: P. Jeyadurga
Author-X-Name-First: P.
Author-X-Name-Last: Jeyadurga
Author-Name: M. Usha
Author-X-Name-First: M.
Author-X-Name-Last: Usha
Title: Optimal design of repetitive group sampling plans for Weibull and gamma distributions with applications and comparison to the Birnbaum–Saunders distribution
Abstract: 
 In this paper, we propose a multiple deferred state repetitive group sampling plan which is a new sampling plan developed by incorporating the features of both multiple deferred state sampling plan and repetitive group sampling plan, for assuring Weibull or gamma distributed mean life of the products. The quality of the product is represented by the ratio of true mean life and specified mean life of the products. Two points on the operating characteristic curve approach is used to determine the optimal parameters of the proposed plan. The plan parameters are determined by formulating an optimization problem for various combinations of producer's risk and consumer's risk for both distributions. The sensitivity analysis of the proposed plan is discussed. The implementation of the proposed plan is explained using real-life data and simulated data. The proposed plan under Weibull distribution is compared with the existing sampling plans. The average sample number (ASN) of the proposed plan and failure probability of the product are obtained under Weibull, gamma and Birnbaum–Saunders distributions for a specified value of shape parameter and compared with each other. In addition, a comparative study is made between the ASN of the proposed plan under Weibull and gamma distributions.
Journal: Journal of Applied Statistics
Pages: 2499-2520
Issue: 14
Volume: 45
Year: 2018
Month: 10
X-DOI: 10.1080/02664763.2018.1426740
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1426740
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:14:p:2499-2520




Template-Type: ReDIF-Article 1.0
Author-Name: Ruofei Du
Author-X-Name-First: Ruofei
Author-X-Name-Last: Du
Author-Name: Zhide Fang
Author-X-Name-First: Zhide
Author-X-Name-Last: Fang
Title: Statistical correction for functional metagenomic profiling of a microbial community with short NGS reads
Abstract: 
 By sequence homology search, the list of all the functions found and the counts of reads being aligned to them present the functional profile of a metagenomic sample. However, a significant obstacle has been observed in this approach due to the short read length associated with many next-generation sequencing technologies. This includes artificial families, cross-annotations, length bias and conservation bias. The widely applied cut-off methods, such as BLAST E-value, are not able to solve the problems. Following the published successful procedures on the artificial families and the cross-annotation issue, we propose in this paper to use zero-truncated Poisson and Binomial (ZTP-Bin) hierarchical modelling to correct the length bias and the conservation bias. Goodness of fit of the modelling and cross-validation for the prediction using a bioinformatic simulated sample show the validity of this approach. Evaluated on an in vitro-simulated data set, the proposed modelling method outperforms other traditional methods. All three steps were then sequentially applied on real-life metagenomic samples to show that the proposed framework will lead to a more accurate functional profile of a short-read metagenomic sample.
Journal: Journal of Applied Statistics
Pages: 2521-2535
Issue: 14
Volume: 45
Year: 2018
Month: 10
X-DOI: 10.1080/02664763.2018.1426741
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1426741
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:14:p:2521-2535




Template-Type: ReDIF-Article 1.0
Author-Name: Daniel B. Wright
Author-X-Name-First: Daniel B.
Author-X-Name-Last: Wright
Title: Estimating school effectiveness with student growth percentile and gain score models
Abstract: 
 Measuring school effectiveness using student test scores is controversial and some methods used for this can be inaccurate in some situations. The validity of two statistical models – the Student Growth Percentile (SGP) model and a multilevel gain score model – are evaluated. The SGP model conditions on previous test scores thereby unblocking a backdoor path between true school/teacher effectiveness and student test scores. When the product of the coefficients that make up this unblocked backdoor path is positive, the SGP estimates can be inaccurate. The accuracy of the multilevel gain score model was not associated with the product of this backdoor path. The gain score model appears promising in these situations where the SGP and other covariate adjusted models perform poorly.
Journal: Journal of Applied Statistics
Pages: 2536-2547
Issue: 14
Volume: 45
Year: 2018
Month: 10
X-DOI: 10.1080/02664763.2018.1426742
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1426742
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:14:p:2536-2547




Template-Type: ReDIF-Article 1.0
Author-Name: Antonio Punzo
Author-X-Name-First: Antonio
Author-X-Name-Last: Punzo
Author-Name: Angelo Mazza
Author-X-Name-First: Angelo
Author-X-Name-Last: Mazza
Author-Name: Antonello Maruotti
Author-X-Name-First: Antonello
Author-X-Name-Last: Maruotti
Title: Fitting insurance and economic data with outliers: a flexible approach based on finite mixtures of contaminated gamma distributions
Abstract: 
 Insurance and economic data are frequently characterized by positivity, skewness, leptokurtosis, and multi-modality; although many parametric models have been used in the literature, often these peculiarities call for more flexible approaches. Here, we propose a finite mixture of contaminated gamma distributions that provides a better characterization of data. It is placed in between parametric and non-parametric density estimation and strikes a balance between these alternatives, as a large class of densities can be implemented. We adopt a maximum likelihood approach to estimate the model parameters, providing the likelihood and the expected-maximization algorithm implemented to estimate all unknown parameters. We apply our approach to an artificial dataset and to two well-known datasets as the workers compensation data and the healthcare expenditure data taken from the medical expenditure panel survey. The Value-at-Risk is evaluated and comparisons with other benchmark models are provided.
Journal: Journal of Applied Statistics
Pages: 2563-2584
Issue: 14
Volume: 45
Year: 2018
Month: 10
X-DOI: 10.1080/02664763.2018.1428288
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1428288
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:14:p:2563-2584




Template-Type: ReDIF-Article 1.0
Author-Name: Rodney V. Fonseca
Author-X-Name-First: Rodney V.
Author-X-Name-Last: Fonseca
Author-Name: Francisco Cribari-Neto
Author-X-Name-First: Francisco
Author-X-Name-Last: Cribari-Neto
Title: Bimodal Birnbaum–Saunders generalized autoregressive score model
Abstract: 
 Time series models based on the Birnbaum–Saunders ( $ \mathcal{BS} $ BS) distribution have not received much attention in the literature, there being only a few articles that address such models. In the present paper, we propose a generalized autoregressive score (GAS) model based on a bimodal Birnbaum–Saunders law. The proposed model, denoted by GBS2-GAS, generalizes an existing time series  $ \mathcal{BS} $ BS model. We discuss conditional maximum likelihood parameter estimation, hypothesis testing inference, residual analysis and develop prediction intervals for the GBS2-GAS model. Additionally, we provide analytical expressions for the score vector and for the Hessian matrix. Two empirical applications, involving financial and hydrological data, are presented and discussed.
Journal: Journal of Applied Statistics
Pages: 2585-2606
Issue: 14
Volume: 45
Year: 2018
Month: 10
X-DOI: 10.1080/02664763.2018.1428734
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1428734
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:14:p:2585-2606




Template-Type: ReDIF-Article 1.0
Author-Name: Md. Moniruzzaman Moni
Author-X-Name-First: Md. Moniruzzaman
Author-X-Name-Last: Moni
Author-Name: M. Iftakhar Alam
Author-X-Name-First: M. Iftakhar
Author-X-Name-Last: Alam
Title: Patient-specific dose finding in phase I clinical trials
Abstract: 
 This article proposes an extension of the continual reassessment method to determine the maximum tolerated dose (MTD) in the presence of patients' heterogeneity in phase I clinical trials. To start with a simple case, we consider the covariate as a binary variable representing two groups of patients. A logistic regression model is used to establish the dose–response relationship and the design is based on the Bayesian framework. Simulation studies for six plausible dose–response scenarios show that the proposed design is likely to determine the MTD more accurately than the design that does not take covariate into consideration.
Journal: Journal of Applied Statistics
Pages: 2607-2618
Issue: 14
Volume: 45
Year: 2018
Month: 10
X-DOI: 10.1080/02664763.2018.1428735
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1428735
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:14:p:2607-2618




Template-Type: ReDIF-Article 1.0
Author-Name: S. Ghasemzadeh
Author-X-Name-First: S.
Author-X-Name-Last: Ghasemzadeh
Author-Name: M. Ganjali
Author-X-Name-First: M.
Author-X-Name-Last: Ganjali
Author-Name: T. Baghfalaki
Author-X-Name-First: T.
Author-X-Name-Last: Baghfalaki
Title: A Bayesian conditional model for bivariate mixed ordinal and skew continuous longitudinal responses using quantile regression
Abstract: 
 In this paper, we develop a conditional model for analyzing mixed bivariate continuous and ordinal longitudinal responses. We propose a quantile regression model with random effects for analyzing continuous responses. For this purpose, an Asymmetric Laplace Distribution (ALD) is allocated for continuous response given random effects. For modeling ordinal responses, a cumulative logit model is used, via specifying a latent variable model, with considering other random effects. Therefore, the intra-association between continuous and ordinal responses is taken into account using their own exclusive random effects. But, the inter-association between two mixed responses is taken into account by adding a continuous response term in the ordinal model. We use a Bayesian approach via Markov chain Monte Carlo method for analyzing the proposed conditional model and to estimate unknown parameters, a Gibbs sampler algorithm is used. Moreover, we illustrate an application of the proposed model using a part of the British Household Panel Survey data set. The results of data analysis show that gender, age, marital status, educational level and the amount of money spent on leisure have significant effects on annual income. Also, the associated parameter is significant in using the best fitting proposed conditional model, thus it should be employed rather than analyzing separate models.
Journal: Journal of Applied Statistics
Pages: 2619-2642
Issue: 14
Volume: 45
Year: 2018
Month: 10
X-DOI: 10.1080/02664763.2018.1431208
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1431208
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:14:p:2619-2642




Template-Type: ReDIF-Article 1.0
Author-Name: Taha Alshaybawee
Author-X-Name-First: Taha
Author-X-Name-Last: Alshaybawee
Author-Name: Rahim Alhamzawi
Author-X-Name-First: Rahim
Author-X-Name-Last: Alhamzawi
Author-Name: Habshah Midi
Author-X-Name-First: Habshah
Author-X-Name-Last: Midi
Author-Name: Intisar Ibrahim Allyas
Author-X-Name-First: Intisar Ibrahim
Author-X-Name-Last: Allyas
Title: Bayesian variable selection and coefficient estimation in heteroscedastic linear regression model
Abstract: 
 In many real applications, such as econometrics, biological sciences, radio-immunoassay, finance, and medicine, the usual assumption of constant error variance may be unrealistic. Ignoring heteroscedasticity (non-constant error variance), if it is present in the data, may lead to incorrect inferences and inefficient estimation. In this paper, a simple and effcient Gibbs sampling algorithm is proposed, based on a heteroscedastic linear regression model with an  $ {l_1} $ l1 penalty. Then, a Bayesian stochastic search variable selection method is proposed for subset selection. Simulations and real data examples are used to compare the performance of the proposed methods with other existing methods. The results indicate that the proposal performs well in the simulations and real data examples. R code is available upon request.
Journal: Journal of Applied Statistics
Pages: 2643-2657
Issue: 14
Volume: 45
Year: 2018
Month: 10
X-DOI: 10.1080/02664763.2018.1432576
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1432576
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:14:p:2643-2657




Template-Type: ReDIF-Article 1.0
Author-Name: Stephen Reid
Author-X-Name-First: Stephen
Author-X-Name-Last: Reid
Author-Name: Aaron M. Newman
Author-X-Name-First: Aaron M.
Author-X-Name-Last: Newman
Author-Name: Maximilian Diehn
Author-X-Name-First: Maximilian
Author-X-Name-Last: Diehn
Author-Name: Ash A. Alizadeh
Author-X-Name-First: Ash A.
Author-X-Name-Last: Alizadeh
Author-Name: Robert Tibshirani
Author-X-Name-First: Robert
Author-X-Name-Last: Tibshirani
Title: Genomic feature selection by coverage design optimization
Abstract: 
 We introduce a novel data reduction technique whereby we select a subset of tiles to ‘cover’ maximally events of interest in large-scale biological datasets (e.g. genetic mutations), while minimizing the number of tiles. A tile is a genomic unit capturing one or more biological events, such as a sequence of base pairs that can be sequenced and observed simultaneously. The goal is to reduce significantly the number of tiles considered to those with areas of dense events in a cohort, thus saving on cost and enhancing interpretability. However, the reduction should not come at the cost of too much information, allowing for sensible statistical analysis after its application. We envisage application of our methods to a variety of high throughput data types, particularly those produced by next-generation sequencing (NGS) experiments. The procedure is cast as a convex optimization problem, which is presented, along with methods of its solution. The method is demonstrated on a large dataset of somatic mutations spanning 5000+ patients, each having one of 29 cancer types. Applied to these data, our method dramatically reduces the number of gene locations required for broad coverage of patients and their mutations, giving subject specialists a more easily interpretable snapshot of recurrent mutational profiles in these cancers. The locations identified coincide with previously identified cancer genes. Finally, despite considerable data reduction, we show that our covering designs preserve the cancer discrimination ability of multinomial logistic regression models trained on all of the locations ( $ &gt;1M $ &gt;1M).
Journal: Journal of Applied Statistics
Pages: 2658-2676
Issue: 14
Volume: 45
Year: 2018
Month: 10
X-DOI: 10.1080/02664763.2018.1432577
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1432577
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:14:p:2658-2676




Template-Type: ReDIF-Article 1.0
Author-Name: Jihwan Oh
Author-X-Name-First: Jihwan
Author-X-Name-Last: Oh
Author-Name: Faye Zheng
Author-X-Name-First: Faye
Author-X-Name-Last: Zheng
Author-Name: R. W. Doerge
Author-X-Name-First: R. W.
Author-X-Name-Last: Doerge
Author-Name: Hyonho Chun
Author-X-Name-First: Hyonho
Author-X-Name-Last: Chun
Title: Kernel partial correlation: a novel approach to capturing conditional independence in graphical models for noisy data
Abstract: 
 Graphical models capture the conditional independence structure among random variables via existence of edges among vertices. One way of inferring a graph is to identify zero partial correlation coefficients, which is an effective way of finding conditional independence under a multivariate Gaussian setting. For more general settings, we propose kernel partial correlation which extends partial correlation with a combination of two kernel methods. First, a nonparametric function estimation is employed to remove effects from other variables, and then the dependence between remaining random components is assessed through a nonparametric association measure. The proposed approach is not only flexible but also robust under high levels of noise owing to the robustness of the nonparametric approaches.
Journal: Journal of Applied Statistics
Pages: 2677-2696
Issue: 14
Volume: 45
Year: 2018
Month: 10
X-DOI: 10.1080/02664763.2018.1437123
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1437123
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:14:p:2677-2696




Template-Type: ReDIF-Article 1.0
Author-Name: Mingfei Qiu
Author-X-Name-First: Mingfei
Author-X-Name-Last: Qiu
Author-Name: Robert Paige
Author-X-Name-First: Robert
Author-X-Name-Last: Paige
Author-Name: Vic Patrangenaru
Author-X-Name-First: Vic
Author-X-Name-Last: Patrangenaru
Title: A nonparametric approach to 3D shape analysis from digital camera images – II
Abstract: 
 With the wide availability of digital cameras and high quality smart phone cameras the world is awash in digital images. These cameras are most often uncalibrated meaning that one does not have knowledge of the internal camera parameters, such as focal length and optical center, nor information about the external camera parameters which describe the location of the camera relative to the imaged scene. In the absence of additional information about the scene one cannot parse out many common types of geometric information. For instance, one cannot calculate distances between points or angles between lines. One can, however, determine the lesser known projective shape of a scene. Projective shape data do not lie in Euclidean space  $\mathbb {R}^{p} $Rp. This presents special challenges since the overwhelming majority of statistical methods are for data in  $\mathbb {R}^{p} $Rp. Furthermore, we consider 3D projective shapes of contours extracted from digital camera images which lie in an infinite-dimensional projective shape space and as such presents an especially novel environment for doing statistics. We develop a novel nonparametric hypothesis testing method for the mean change from matched sample contours. Our methodology is applied to the two-sample problem for 3D projective shapes of contours extracted from digital camera images.
Journal: Journal of Applied Statistics
Pages: 2677-2699
Issue: 15
Volume: 46
Year: 2019
Month: 11
X-DOI: 10.1080/02664763.2019.1610163
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1610163
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:15:p:2677-2699




Template-Type: ReDIF-Article 1.0
Author-Name: P. C. Ramyamol
Author-X-Name-First: P. C.
Author-X-Name-Last: Ramyamol
Author-Name: M. Kumar
Author-X-Name-First: M.
Author-X-Name-Last: Kumar
Title: Optimal design of variable acceptance sampling plans for mixture distribution
Abstract: 
 The paper investigates the design of single and sequential variable acceptance sampling plans for a mixture distribution. Mixture distributions are seen in many practical problems such as life testing experiments of electronic components and clinical trials. The sampling plans for this kind of situations are not well addressed in the literature. We first propose a single sampling plan for a distribution which is a mixture of two exponential distributions. An optimization problem which minimizes the total cost of testing at given producer's and consumer's risks is solved to obtain the plan parameters. Two different sequential sampling plans are also defined and plan parameters are obtained by solving corresponding optimization problems. Finally, a case study, a simulation study and a sensitivity analysis are presented to illustrate our sampling plans.
Journal: Journal of Applied Statistics
Pages: 2700-2721
Issue: 15
Volume: 46
Year: 2019
Month: 11
X-DOI: 10.1080/02664763.2019.1610162
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1610162
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:15:p:2700-2721




Template-Type: ReDIF-Article 1.0
Author-Name: Irene Votsi
Author-X-Name-First: Irene
Author-X-Name-Last: Votsi
Title: Conditional failure occurrence rates for semi-Markov chains
Abstract: 
 In the present paper, we aim at providing plug-in-type empirical estimators that enable us to quantify the contribution of each operational or/and non-functioning state to the failures of a system described by a semi-Markov model. In the discrete-time and finite state space semi-Markov framework, we study different conditional versions of an important reliability measure for random repairable systems, the failure occurrence rate, which is based on counting processes. The identification of potential failure contributors through the conditional counterparts of the failure occurrence rate is of paramount importance since it could lead to corrective actions that minimize the occurrence of the more important failure modes and therefore improve the reliability of the system. The aforementioned estimators are characterized by appealing asymptotic properties such as strong consistency and asymptotic normality. We further obtain detailed analytical expressions for the covariance matrices of the random vectors describing the conditional failure occurrence rates. As particular cases we present the failure occurrence rates for hidden (semi-) Markov models. We illustrate our results by means of a simulated study. Different applications are presented based on wind, earthquake and vibration data.
Journal: Journal of Applied Statistics
Pages: 2722-2743
Issue: 15
Volume: 46
Year: 2019
Month: 11
X-DOI: 10.1080/02664763.2019.1610164
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1610164
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:15:p:2722-2743




Template-Type: ReDIF-Article 1.0
Author-Name: Thiago G. Ramires
Author-X-Name-First: Thiago G.
Author-X-Name-Last: Ramires
Author-Name: Luiz R. Nakamura
Author-X-Name-First: Luiz R.
Author-X-Name-Last: Nakamura
Author-Name: Ana J. Righetto
Author-X-Name-First: Ana J.
Author-X-Name-Last: Righetto
Author-Name: Rodrigo R. Pescim
Author-X-Name-First: Rodrigo R.
Author-X-Name-Last: Pescim
Author-Name: Josmar Mazucheli
Author-X-Name-First: Josmar
Author-X-Name-Last: Mazucheli
Author-Name: Gauss M. Cordeiro
Author-X-Name-First: Gauss M.
Author-X-Name-Last: Cordeiro
Title: A new semiparametric Weibull cure rate model: fitting different behaviors within GAMLSS
Abstract: 
 We propose a new semiparametric Weibull cure rate model for fitting nonlinear effects of explanatory variables on the mean, scale and cure rate parameters. The regression model is based on the generalized additive models for location, scale and shape, for which any or all distribution parameters can be modeled as parametric linear and/or nonparametric smooth functions of explanatory variables. We present methods to select additive terms, model estimation and validation, where all computational codes are presented in a simple way such that any R user can fit the new model. Biases of the parameter estimates caused by models specified erroneously are investigated through Monte Carlo simulations. We illustrate the usefulness of the new model by means of two applications to real data. We provide computational codes to fit the new regression model in the R software.
Journal: Journal of Applied Statistics
Pages: 2744-2760
Issue: 15
Volume: 46
Year: 2019
Month: 11
X-DOI: 10.1080/02664763.2019.1611748
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1611748
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:15:p:2744-2760




Template-Type: ReDIF-Article 1.0
Author-Name: Akinori Hidaka
Author-X-Name-First: Akinori
Author-X-Name-Last: Hidaka
Author-Name: Kenji Watanabe
Author-X-Name-First: Kenji
Author-X-Name-Last: Watanabe
Author-Name: Takio Kurita
Author-X-Name-First: Takio
Author-X-Name-Last: Kurita
Title: Sparse discriminant analysis based on estimation of posterior probabilities
Abstract: 
 Fisher's linear discriminant analysis (FLDA) is known as a method to find a discriminative feature space for multi-class classification. As a theory of extending FLDA to an ultimate nonlinear form, optimal nonlinear discriminant analysis (ONDA) has been proposed. ONDA indicates that the best theoretical nonlinear map for maximizing the Fisher's discriminant criterion is formulated by using the Bayesian a posterior probabilities. In addition, the theory proves that FLDA is equivalent to ONDA when the Bayesian a posterior probabilities are approximated by linear regression (LR). Due to some limitations of the linear model, there is room to modify FLDA by using stronger approximation/estimation methods. For the purpose of probability estimation, multi-nominal logistic regression (MLR) is more suitable than LR. Along this line, in this paper, we develop a nonlinear discriminant analysis (NDA) in which the posterior probabilities in ONDA are estimated by MLR. In addition, in this paper, we develop a way to introduce sparseness into discriminant analysis. By applying L1 or L2 regularization to LR or MLR, we can incorporate sparseness in FLDA and our NDA to increase generalization performance. The performance of these methods is evaluated by benchmark experiments using last_exam17 standard datasets and a face classification experiment.
Journal: Journal of Applied Statistics
Pages: 2761-2785
Issue: 15
Volume: 46
Year: 2019
Month: 11
X-DOI: 10.1080/02664763.2019.1613348
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1613348
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:15:p:2761-2785




Template-Type: ReDIF-Article 1.0
Author-Name: C. S. Marange
Author-X-Name-First: C. S.
Author-X-Name-Last: Marange
Author-Name: Y. Qin
Author-X-Name-First: Y.
Author-X-Name-Last: Qin
Title: A moment-based empirical likelihood ratio test for exponentiality using the probability integral transformation
Abstract: 
 A simple and efficient goodness-of-fit test for exponentiality is developed by exploiting the characterization of the exponential distribution using the probability integral transformation. We adopted the empirical likelihood methodology in constructing the test statistic. The proposed test statistic has a chi-square limiting distribution. For small to moderate sample sizes Monte-Carlo simulations revealed that our proposed tests are much more superior under increasing failure rate (IFR) and bathtub decreasing-increasing failure rate (BFR) alternatives. Real data examples were used to demonstrate the robustness and applicability of our proposed tests in practice.
Journal: Journal of Applied Statistics
Pages: 2786-2803
Issue: 15
Volume: 46
Year: 2019
Month: 11
X-DOI: 10.1080/02664763.2019.1614542
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1614542
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:15:p:2786-2803




Template-Type: ReDIF-Article 1.0
Author-Name: S. Viviani
Author-X-Name-First: S.
Author-X-Name-Last: Viviani
Title: Joint models for a GLM-type longitudinal response and a time-to-event with smooth random effects
Abstract: 
 Longitudinal studies often entail non-Gaussian primary responses. When dropout occurs, potential non-ignorability of the missingness process may occur, and a joint model for the primary response and a time-to-event may represent an appealing tool to account for dependence between the two processes. As an extension to the GLMJM, recently proposed, and based on Gaussian latent effects, we assume that the random effects follow a smooth, P-spline based density. To estimate model parameters, we adopt a two-step conditional Newton–Raphson algorithm. Since the maximization of the penalized log-likelihood requires numerical integration over the random effect, which is often cumbersome, we opt for a pseudo-adaptive Gaussian quadrature rule to approximate the model likelihood. We discuss the proposed model by analyzing an original dataset on dilated cardiomyopathies and through a simulation study.
Journal: Journal of Applied Statistics
Pages: 2804-2818
Issue: 15
Volume: 46
Year: 2019
Month: 11
X-DOI: 10.1080/02664763.2019.1617253
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1617253
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:15:p:2804-2818




Template-Type: ReDIF-Article 1.0
Author-Name: Davit Khachatryan
Author-X-Name-First: Davit
Author-X-Name-Last: Khachatryan
Author-Name: Brigitte Muehlmann
Author-X-Name-First: Brigitte
Author-X-Name-Last: Muehlmann
Title: Measuring technological breadth and depth of patent documents using Rao’s Quadratic Entropy
Abstract: 
 In this article we propose indices for technological breadth and depth of patent documents. We conceptually compare and contrast several indices that have been used in the existing literature, explaining main limitations. Motivated by the drawbacks of those we demonstrate how Rao’s Quadratic Entropy, a statistical index used in ecology for measuring biodiversity, can be decomposed to separately measure technological breadth and depth. The properties of breadth and depth are then investigated using patents for business data processing. For the technological domains with the highest patenting activity, we show how the novel measures of technological breadth and depth can be used to rank the patenting entities by average breadth, and identify ones with inventions that on average exceed the domain-specific average depth. Practical implications of the proposed indices are also exemplified and discussed in the context of competitor analysis for entrepreneurial ventures in the area of network security for business data processing.
Journal: Journal of Applied Statistics
Pages: 2819-2844
Issue: 15
Volume: 46
Year: 2019
Month: 11
X-DOI: 10.1080/02664763.2019.1619072
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1619072
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:15:p:2819-2844




Template-Type: ReDIF-Article 1.0
Author-Name: Azka Ubaidillah
Author-X-Name-First: Azka
Author-X-Name-Last: Ubaidillah
Author-Name: Khairil Anwar Notodiputro
Author-X-Name-First: Khairil Anwar
Author-X-Name-Last: Notodiputro
Author-Name: Anang Kurnia
Author-X-Name-First: Anang
Author-X-Name-Last: Kurnia
Author-Name: I. Wayan Mangku
Author-X-Name-First: I. Wayan
Author-X-Name-Last: Mangku
Title: Multivariate Fay-Herriot models for small area estimation with application to household consumption per capita expenditure in Indonesia
Abstract: 
 Multivariate Fay-Herriot (MFH) models become popular methods to produce reliable parameter estimates of some related multiple characteristics of interest that are commonly produced from many surveys. This article studies the application of MFH models for estimating household consumption per capita expenditure (HCPE) on food and HCPE of non-food. Both of those associated direct estimates, which are obtained from the National Socioeconomic Surveys conducted regularly by Statistics Indonesia, have a strong correlation. The effects of correlation in MFH models are evaluated by employing a simulation study. The simulation showed that the strength of correlation between variables of interest, instead of the number of domains, plays a prominent role in MFH models. The application showed that MFH models have more efficient than univariate models in terms of standard errors of regression parameter estimates. The roots of mean squared errors (RMSEs) of the estimates obtained from the empirical best linear unbiased prediction (EBLUP) estimators of MFH models are smaller than RMSEs obtained from the direct estimators. Based on MFH model, the HCPE estimates of food by districts in Central Java, Indonesia, are higher than the HCPE estimates of non-food. The average of HCPE estimates of food and non-food in Central Java, Indonesia in 2015 are IDR 383,100.6 and IDR 280,653.6, respectively.
Journal: Journal of Applied Statistics
Pages: 2845-2861
Issue: 15
Volume: 46
Year: 2019
Month: 11
X-DOI: 10.1080/02664763.2019.1615420
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1615420
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:15:p:2845-2861




Template-Type: ReDIF-Article 1.0
Author-Name: L. Feng
Author-X-Name-First: L.
Author-X-Name-Last: Feng
Author-Name: Y. Shi
Author-X-Name-First: Y.
Author-X-Name-Last: Shi
Title: Fractionally integrated GARCH model with tempered stable distribution: a simulation study
Abstract: 
 With the growing availability of high-frequency data, long memory has become a popular topic in finance research. Fractionally Integrated GARCH (FIGARCH) model is a standard approach to study the long memory of financial volatility. The original specification of FIGARCH model is developed using Normal distribution, which cannot accommodate fat-tailed properties commonly existing in financial time series. Traditionally, the Student-t distribution and General Error Distribution (GED) are used instead to solve that problem. However, a recent study points out that the Student-t lacks stability. Instead, the Stable distribution is introduced. The issue of this distribution is that its second moment does not exist. To overcome this new problem, the tempered stable distribution, which retains most attractive characteristics of the Stable distribution and has defined moments, is a natural candidate. In this paper, we describe the estimation procedure of the FIGARCH model with tempered stable distribution and conduct a series of simulation studies to demonstrate that it consistently outperforms FIGARCH models with the Normal, Student-t and GED distributions. An empirical evidence of the S&amp;P 500 hourly return is also provided with robust results. Therefore, we argue that the tempered stable distribution could be a widely useful tool for modelling the high-frequency financial volatility in general contexts with a FIGARCH-type specification.
Journal: Journal of Applied Statistics
Pages: 2837-2857
Issue: 16
Volume: 44
Year: 2017
Month: 12
X-DOI: 10.1080/02664763.2016.1266310
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1266310
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:16:p:2837-2857




Template-Type: ReDIF-Article 1.0
Author-Name: Ezgi Ayyıldız
Author-X-Name-First: Ezgi
Author-X-Name-Last: Ayyıldız
Author-Name: Melih Ağraz
Author-X-Name-First: Melih
Author-X-Name-Last: Ağraz
Author-Name: Vilda Purutçuoğlu
Author-X-Name-First: Vilda
Author-X-Name-Last: Purutçuoğlu
Title: MARS as an alternative approach of Gaussian graphical model for biochemical networks
Abstract: 
 The Gaussian graphical model (GGM) is one of the well-known modelling approaches to describe biological networks under the steady-state condition via the precision matrix of data. In literature there are different methods to infer model parameters based on GGM. The neighbourhood selection with the lasso regression and the graphical lasso method are the most common techniques among these alternative estimation methods. But they can be computationally demanding when the system's dimension increases. Here, we suggest a non-parametric statistical approach, called the multivariate adaptive regression splines (MARS) as an alternative of GGM. To compare the performance of both models, we evaluate the findings of normal and non-normal data via the specificity, precision, F-measures and their computational costs. From the outputs, we see that MARS performs well, resulting in, a plausible alternative approach with respect to GGM in the construction of complex biological systems.
Journal: Journal of Applied Statistics
Pages: 2858-2876
Issue: 16
Volume: 44
Year: 2017
Month: 12
X-DOI: 10.1080/02664763.2016.1266465
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1266465
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:16:p:2858-2876




Template-Type: ReDIF-Article 1.0
Author-Name: Tonghui Lian
Author-X-Name-First: Tonghui
Author-X-Name-Last: Lian
Author-Name: Caihua Yu
Author-X-Name-First: Caihua
Author-X-Name-Last: Yu
Author-Name: Zhongqun Wang
Author-X-Name-First: Zhongqun
Author-X-Name-Last: Wang
Author-Name: Zhiping Hou
Author-X-Name-First: Zhiping
Author-X-Name-Last: Hou
Title: The evaluation study on tourism websites: from the perspective of triangular intuitionistic fuzzy multiple attribute group decision making
Abstract: 
 The research on the evaluation of tourism websites can measure the websites' development in the same industry, and it is helpful to improve the quality of the websites. In view of the complexity of the tourism website evaluation, this study firstly constructs the evaluation index system of tourism websites, including subjective indicators and objective indicators. Six websites are taken for example from non-profit tourism websites, direct tourism websites and intermediary tourism websites, two from each type. And then the evaluation index data is obtained; triangular fuzzy number and subjective preference value of evaluation index are calculated; fuzzy evaluation matrix, normalization matrix and the expectation matrix are constructed; the index weight is determined; tourism website comprehensive expectation is calculated and the website ranking according to comprehensive expectation is given from high to low.
Journal: Journal of Applied Statistics
Pages: 2877-2889
Issue: 16
Volume: 44
Year: 2017
Month: 12
X-DOI: 10.1080/02664763.2016.1266466
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1266466
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:16:p:2877-2889




Template-Type: ReDIF-Article 1.0
Author-Name: M. Ataharul Islam
Author-X-Name-First: M. Ataharul
Author-X-Name-Last: Islam
Author-Name: Rafiqul I. Chowdhury
Author-X-Name-First: Rafiqul I.
Author-X-Name-Last: Chowdhury
Author-Name: K. S. Sultan
Author-X-Name-First: K. S.
Author-X-Name-Last: Sultan
Title: Analyzing dependence in incidence of diabetes and heart problem using generalized bivariate geometric models with covariates
Abstract: 
 For analyzing incidence data on diabetes and health problems, the bivariate geometric probability distribution is a natural choice but remained unexplored largely due to lack of models linking covariates with the probabilities of bivariate incidence of correlated outcomes. In this paper, bivariate geometric models are proposed for two correlated incidence outcomes. The extended generalized linear models are developed to take into account covariate dependence of the bivariate probabilities of correlated incidence outcomes for diabetes and heart diseases for the elderly population. The estimation and test procedures are illustrated using the Health and Retirement Study data. Two models are shown in this paper, one based on conditional-marginal approach and the other one based on the joint probability distribution with an association parameter. The joint model with association parameter appears to be a very good choice for analyzing the covariate dependence of the joint incidence of diabetes and heart diseases. Bootstrapping is performed to measure the accuracy of estimates and the results indicate very small bias.
Journal: Journal of Applied Statistics
Pages: 2890-2907
Issue: 16
Volume: 44
Year: 2017
Month: 12
X-DOI: 10.1080/02664763.2016.1266467
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1266467
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:16:p:2890-2907




Template-Type: ReDIF-Article 1.0
Author-Name: Wendy K. Tam Cho
Author-X-Name-First: Wendy K. Tam
Author-X-Name-Last: Cho
Title: Causal inferences from many experiments
Abstract: 
 The underlying statistical concept that animates empirical strategies for extracting causal inferences from observational data is that observational data may be adjusted to resemble data that might have originated from a randomized experiment. This idea has driven the literature on matching methods. We explore an un-mined idea for making causal inferences with observational data – that any given observational study may contain a large number of indistinguishably balanced matched designs. We demonstrate how the absence of a unique best solution presents an opportunity for greater information retrieval in causal inference analysis based on the principle that many solutions teach us more about a given scientific hypothesis than a single study and improves our discernment with observational studies. The implementation can be achieved by integrating the statistical theories and models within a computational optimization framework that embodies the statistical foundations and reasoning.
Journal: Journal of Applied Statistics
Pages: 2908-2922
Issue: 16
Volume: 44
Year: 2017
Month: 12
X-DOI: 10.1080/02664763.2016.1266468
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1266468
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:16:p:2908-2922




Template-Type: ReDIF-Article 1.0
Author-Name: Wenjun Xiong
Author-X-Name-First: Wenjun
Author-X-Name-Last: Xiong
Author-Name: You Su
Author-X-Name-First: You
Author-X-Name-Last: Su
Author-Name: Juan Ding
Author-X-Name-First: Juan
Author-X-Name-Last: Ding
Title: Empirical likelihood-based robust tests for genetic association analysis with quantitative traits
Abstract: 
 Genome-wide association studies (GWAS) are effective in investigating the loci related with complex diseases. For most of these studies, the genetic inheritance model is not known in advance and therefore robust tests are preferred. Empirical likelihood (EL) method is well known for its flexibility and nonparametric properties, but is rarely investigated in GWAS. In this study, we develop EL-based test statistics to detect the association of a disease and genetic loci while the genetic model is unknown. The performance of proposed tests is evaluated by simulations and compared with several existing methods. For illustration, we apply these tests to identify the single nucleotide polymorphisms associated with alkaline phosphatase level on mouse chromosome 6.
Journal: Journal of Applied Statistics
Pages: 2923-2935
Issue: 16
Volume: 44
Year: 2017
Month: 12
X-DOI: 10.1080/02664763.2016.1266469
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1266469
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:16:p:2923-2935




Template-Type: ReDIF-Article 1.0
Author-Name: Hasan Bulut
Author-X-Name-First: Hasan
Author-X-Name-Last: Bulut
Author-Name: Yüksel Öner
Author-X-Name-First: Yüksel
Author-X-Name-Last: Öner
Title: The evaluation of socio-economic development of development agency regions in Turkey using classical and robust principal component analyses
Abstract: 
 In this study, classical and robust principal component analyses are used to evaluate socioeconomic development of regions of development agencies that give service on the purpose of decreasing development difference among regions in Turkey. Due to the high differences between development levels of regions outlier problem occurs, hence robust statistical methods are used. Also, classical and robust statistical methods are used to investigate if there are any outliers in data set. In classic principal component analyse, the number of observations must be larger than the number of variables. Otherwise determinant of covariance matrix is zero. In Robust method for Principal Component Analysis (ROBPCA), a robust approach to principal component analyse in high-dimensional data, even if the number of variables is larger than the number of observations, principal components are obtained. In this paper, firstly 26 development agencies are evaluated with 19 variables by using principal component analysis based on classical and robust scatter matrices and then these 26 development agencies are evaluated with 46 variables by using the ROBPCA method.
Journal: Journal of Applied Statistics
Pages: 2936-2948
Issue: 16
Volume: 44
Year: 2017
Month: 12
X-DOI: 10.1080/02664763.2016.1267115
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1267115
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:16:p:2936-2948




Template-Type: ReDIF-Article 1.0
Author-Name: Diego I. Gallardo
Author-X-Name-First: Diego I.
Author-X-Name-Last: Gallardo
Author-Name: Heleno Bolfarine
Author-X-Name-First: Heleno
Author-X-Name-Last: Bolfarine
Author-Name: Atonio Carlos Pedroso-de-Lima
Author-X-Name-First: Atonio Carlos
Author-X-Name-Last: Pedroso-de-Lima
Title: A clustering cure rate model with application to a sealant study
Abstract: 
 In this paper, the destructive negative binomial (DNB) cure rate model with a latent activation scheme [V. Cancho, D. Bandyopadhyay, F. Louzada, and B. Yiqi, The DNB cure rate model with a latent activation scheme, Statistical Methodology 13 (2013b), pp. 48–68] is extended to the case where the observations are grouped into clusters. Parameter estimation is performed based on the restricted maximum likelihood approach and on a Bayesian approach based on Dirichlet process priors. An application to a real data set related to a sealant study in a dentistry experiment is considered to illustrate the performance of the proposed model.
Journal: Journal of Applied Statistics
Pages: 2949-2962
Issue: 16
Volume: 44
Year: 2017
Month: 12
X-DOI: 10.1080/02664763.2016.1267116
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1267116
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:16:p:2949-2962




Template-Type: ReDIF-Article 1.0
Author-Name: Felix Famoye
Author-X-Name-First: Felix
Author-X-Name-Last: Famoye
Author-Name: Carl Lee
Author-X-Name-First: Carl
Author-X-Name-Last: Lee
Title: Exponentiated-exponential geometric regression model
Abstract: 
 A regression model, based on the exponentiated-exponential geometric distribution, is defined and studied. The regression model can be applied to count data with under-dispersion or over-dispersion. Some forms of its modifications to truncated or inflated data are mentioned. Some tests to discriminate between the regression model and its competitors are discussed. Real numerical data sets are used to illustrate the applications of the regression model.
Journal: Journal of Applied Statistics
Pages: 2963-2977
Issue: 16
Volume: 44
Year: 2017
Month: 12
X-DOI: 10.1080/02664763.2016.1267117
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1267117
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:16:p:2963-2977




Template-Type: ReDIF-Article 1.0
Author-Name: Filippo Domma
Author-X-Name-First: Filippo
Author-X-Name-Last: Domma
Author-Name: Francesca Condino
Author-X-Name-First: Francesca
Author-X-Name-Last: Condino
Author-Name: Božidar V. Popović
Author-X-Name-First: Božidar V.
Author-X-Name-Last: Popović
Title: A new generalized weighted Weibull distribution with decreasing, increasing, upside-down bathtub, N-shape and M-shape hazard rate
Abstract: 
 Recently, Domma et al. [An extension of Azzalinis method, J. Comput. Appl. Math. 278 (2015), pp. 37–47] proposed an extension of Azzalini's method. This method can attract readers due to its flexibility and ease of applicability. Most of the weighted Weibull models that have been introduced are with monotonic hazard rate function. This fact limits their applicability. So, our aim is to build a new weighted Weibull distribution with monotonic and non-monotonic hazard rate function. A new weighted Weibull distribution, so-called generalized weighted Weibull (GWW) distribution, is introduced by a method exposed in Domma et al. [13]. GWW distribution possesses decreasing, increasing, upside-down bathtub, N-shape and M-shape hazard rate. Also, it is very easy to derive statistical properties of the GWW distribution. Finally, we consider application of the GWW model on a real data set, providing simulation study too.
Journal: Journal of Applied Statistics
Pages: 2978-2993
Issue: 16
Volume: 44
Year: 2017
Month: 12
X-DOI: 10.1080/02664763.2016.1267118
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1267118
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:16:p:2978-2993




Template-Type: ReDIF-Article 1.0
Author-Name: Heiko Groenitz
Author-X-Name-First: Heiko
Author-X-Name-Last: Groenitz
Title: Valid estimates for repeated randomized response methods
Abstract: 
 Surveys with sensitive characteristics (e.g. cheating in exams, fiscal evasion, social fraud, insurance fraud, discrimination, political views, financial situation) need special concepts, because normal direct questioning causes answer refusal and lies. One well-established concept is the randomized response (RR) approach. RRs protect the interviewees' privacy and facilitate their cooperation. Based on the RRs of many persons, inference is possible. A recently published article suggests two repeated RR methods. That is, each interviewee must give more than one answer. Repeated RRs are a good idea to improve the estimation efficiency of RR techniques. However, this recently published article contains serious mistakes and derives invalid estimates. For this reason, we correct these mistakes and develop valid estimates in the first part of our article. Subsequently, in the second part, we present generalized considerations that cover many more repeated RR schemes.
Journal: Journal of Applied Statistics
Pages: 2994-3010
Issue: 16
Volume: 44
Year: 2017
Month: 12
X-DOI: 10.1080/02664763.2016.1267119
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1267119
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:16:p:2994-3010




Template-Type: ReDIF-Article 1.0
Author-Name: Diego Montano
Author-X-Name-First: Diego
Author-X-Name-Last: Montano
Title: Multivariate hierarchical Bayesian models and choice of priors in the analysis of survey data
Abstract: 
 Multivariate regression models, i.e. regression models where the left-hand side of the regression equation denotes a matrix of dependent variables, have long been developed. However, the statistical analysis of empirical data is usually restricted to multivariable regression methods with only one dependent variable. Within the framework of hierarchical Bayesian methods, the present study illustrates (i) how multivariate regression models offer new possibilities of information synthesis and theory testing in survey data analysis, and (ii) how sensitive the results are to different specifications of prior distributions. To this end, a large representative survey is utilized to specify two multivariate hierarchical Bayesian regression models  $ (N = 39,280) $ (N=39,280) which are calculated under two different prior distribution specifications. The estimation procedures and their implementation in R are described, convergence and predictive power analysis of each model are presented, and the advantages and disadvantages of multivariate regression methods are discussed. In general, the results obtained under each prior specification are to some extent similar, although differences were observed regarding model complexity, efficiency and predictive power. It is concluded that these methods facilitate the development and testing of complex research hypotheses, and are promising alternatives to a more efficient data analysis of large survey data sets.
Journal: Journal of Applied Statistics
Pages: 3011-3032
Issue: 16
Volume: 44
Year: 2017
Month: 12
X-DOI: 10.1080/02664763.2016.1267120
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1267120
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:16:p:3011-3032




Template-Type: ReDIF-Article 1.0
Author-Name: Clécio S. Ferreira
Author-X-Name-First: Clécio S.
Author-X-Name-Last: Ferreira
Author-Name: Gilberto A. Paula
Author-X-Name-First: Gilberto A.
Author-X-Name-Last: Paula
Title: Estimation and diagnostic for skew-normal partially linear models
Abstract: 
 Partially linear models (PLMs) are an important tool in modelling economic and biometric data and are considered as a flexible generalization of the linear model by including a nonparametric component of some covariate into the linear predictor. Usually, the error component is assumed to follow a normal distribution. However, the theory and application (through simulation or experimentation) often generate a great amount of data sets that are skewed. The objective of this paper is to extend the PLMs allowing the errors to follow a skew-normal distribution [A. Azzalini, A class of distributions which includes the normal ones, Scand. J. Statist. 12 (1985), pp. 171–178], increasing the flexibility of the model. In particular, we develop the expectation-maximization (EM) algorithm for linear regression models and diagnostic analysis via local influence as well as generalized leverage, following [H. Zhu and S. Lee, Local influence for incomplete-data models, J. R. Stat. Soc. Ser. B 63 (2001), pp. 111–126]. A simulation study is also conducted to evaluate the efficiency of the EM algorithm. Finally, a suitable transformation is applied in a data set on ragweed pollen concentration in order to fit PLMs under asymmetric distributions. An illustrative comparison is performed between normal and skew-normal errors.
Journal: Journal of Applied Statistics
Pages: 3033-3053
Issue: 16
Volume: 44
Year: 2017
Month: 12
X-DOI: 10.1080/02664763.2016.1267124
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1267124
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:16:p:3033-3053




Template-Type: ReDIF-Article 1.0
Author-Name: Tai Vovan
Author-X-Name-First: Tai
Author-X-Name-Last: Vovan
Title: -distance and classification problem by Bayesian method
Abstract: 
 In this article we suggest a definition for the notion of L1-distance that combines probability density functions and prior probabilities. We also obtain the upper and lower bounds for this distance as well as its relation to other measures. Besides, the relationship between the proposed distance and quantities involved in classification problem by Bayesian method will be established. In practice, calculations are performed by Matlab procedures. As an illustration for applications of the obtained results, the article gives here an estimation for the ability to repay bank debt of some companies in Can Tho City, Vietnam.
Journal: Journal of Applied Statistics
Pages: 385-401
Issue: 3
Volume: 44
Year: 2017
Month: 2
X-DOI: 10.1080/02664763.2016.1174194
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1174194
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:3:p:385-401




Template-Type: ReDIF-Article 1.0
Author-Name: Tarald O. Kvålseth
Author-X-Name-First: Tarald O.
Author-X-Name-Last: Kvålseth
Title: Coefficient of variation: the second-order alternative
Abstract: 
 The coefficient of variation V introduced by Karl Pearson over 100 years ago is one of the most important and widely used moment-based summary statistics. The V does, however, have some significant limitations: (1) it becomes problematic when the data are both positive and negative, (2) it generally lacks an upper bound so that interpretations of V-values become difficult, (3) it lacks an intuitive and meaningful interpretation, (4) it is highly sensitive to outliers, and (5) it is very much affected by the mean and errors or changes in the mean. In order to overcome such limitations, this paper introduces the second-order coefficient of variation  $ {V_2} $ V2 as a simple alternative to Pearson’s V. While V is based on a ratio of second-order to first-order moments,  $ {V_2} $ V2 is a ratio of second-order moments. The  $ {V_2} $ V2 is well defined for all real-valued variables and data. The sample form of the new coefficient is also a ratio of (Euclidean) distances and takes on values between 0 and 1, making interpretations intuitively simple and meaningful. The various properties of  $ {V_2} $ V2 are discussed and compared with those of V. Statistical inference procedures are derived both for the case when the underlying random variable can reasonably be assumed to have a normal distribution and for the case when no such distribution assumption is required.
Journal: Journal of Applied Statistics
Pages: 402-415
Issue: 3
Volume: 44
Year: 2017
Month: 2
X-DOI: 10.1080/02664763.2016.1174195
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1174195
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:3:p:402-415




Template-Type: ReDIF-Article 1.0
Author-Name: Feng-shou Ko
Author-X-Name-First: Feng-shou
Author-X-Name-Last: Ko
Title: Application of trajectories from growth curve in identification of longitudinal biomarker for the multivariate survival data
Abstract: 
 In clinical studies, the researchers measure the patients' response longitudinally. In recent studies, Mixed models are used to determine effects in the individual level. In the other hand, Henderson et al. [3,4] developed a joint likelihood function which combines likelihood functions of longitudinal biomarkers and survival times. They put random effects in the longitudinal component to determine if a longitudinal biomarker is associated with time to an event. In this paper, we deal with a longitudinal biomarker as a growth curve and extend Henderson's method to determine if a longitudinal biomarker is associated with time to an event for the multivariate survival data.
Journal: Journal of Applied Statistics
Pages: 416-426
Issue: 3
Volume: 44
Year: 2017
Month: 2
X-DOI: 10.1080/02664763.2016.1174196
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1174196
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:3:p:416-426




Template-Type: ReDIF-Article 1.0
Author-Name: Panudet Saengseedam
Author-X-Name-First: Panudet
Author-X-Name-Last: Saengseedam
Author-Name: Nantachai Kantanantha
Author-X-Name-First: Nantachai
Author-X-Name-Last: Kantanantha
Title: Spatio-temporal model for crop yield forecasting
Abstract: 
 This paper proposes a linear mixed model (LMM) with spatial effects, trend, seasonality and outliers for spatio-temporal time series data. A linear trend, dummy variables for seasonality, a binary method for outliers and a multivariate conditional autoregressive (MCAR) model for spatial effects are adopted. A Bayesian method using Gibbs sampling in Markov Chain Monte Carlo is used for parameter estimation. The proposed model is applied to forecast rice and cassava yields, a spatio-temporal data type, in Thailand. The data have been extracted from the Office of Agricultural Economics, Ministry of Agriculture and Cooperatives of Thailand. The proposed model is compared with our previous model, an LMM with MCAR, and a log transformed LMM with MCAR. We found that the proposed model is the most appropriate, using the mean absolute error criterion. It fits the data very well in both the fitting part and the validation part for both rice and cassava. Therefore, it is recommended to be a primary model for forecasting these types of spatio-temporal time series data.
Journal: Journal of Applied Statistics
Pages: 427-440
Issue: 3
Volume: 44
Year: 2017
Month: 2
X-DOI: 10.1080/02664763.2016.1174197
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1174197
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:3:p:427-440




Template-Type: ReDIF-Article 1.0
Author-Name: A. G. Luwanda
Author-X-Name-First: A. G.
Author-X-Name-Last: Luwanda
Author-Name: H. G. Mwambi
Author-X-Name-First: H. G.
Author-X-Name-Last: Mwambi
Title: A nonlinear mixed-effects model for multivariate longitudinal data with partially observed outcomes with application to HIV disease dynamics
Abstract: 
 The measurable multiple bio-markers for a disease are used as indicators for studying the response variable of interest in order to monitor and model disease progression. However, it is common for subjects to drop out of the studies prematurely resulting in unbalanced data and hence complicating the inferences involving such data. In this paper we consider a case where data are unbalanced among subjects and also within a subject because for some reason only a subset of the multiple outcomes of the response variable are observed at any one occasion. We propose a nonlinear mixed-effects model for the multivariate response variable data and derive a joint likelihood function that takes into account the partial dropout of the outcomes of the response variable. We further show how the methodology can be used in the estimation of the parameters that characterise HIV disease dynamics. An approximation technique of the parameters is also given and illustrated using a routine observational HIV dataset.
Journal: Journal of Applied Statistics
Pages: 441-456
Issue: 3
Volume: 44
Year: 2017
Month: 2
X-DOI: 10.1080/02664763.2016.1177494
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1177494
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:3:p:441-456




Template-Type: ReDIF-Article 1.0
Author-Name: Lili Yu
Author-X-Name-First: Lili
Author-X-Name-Last: Yu
Author-Name: Hani Samawi
Author-X-Name-First: Hani
Author-X-Name-Last: Samawi
Author-Name: Daniel Linder
Author-X-Name-First: Daniel
Author-X-Name-Last: Linder
Author-Name: Arpita Chatterjee
Author-X-Name-First: Arpita
Author-X-Name-Last: Chatterjee
Author-Name: Yisong Huang
Author-X-Name-First: Yisong
Author-X-Name-Last: Huang
Author-Name: Robert Vogel
Author-X-Name-First: Robert
Author-X-Name-Last: Vogel
Title: On stratified bivariate ranked set sampling with optimal allocation for naïve and ratio estimators
Abstract: 
 The purpose of the current work is to introduce stratified bivariate ranked set sampling (SBVRSS) and investigate its performance for estimating the population mean using both naïve and ratio methods. The properties of the proposed estimator are derived along with the optimal allocation with respect to stratification. We conduct a simulation study to demonstrate the relative efficiency of SBVRSS as compared to stratified bivariate simple random sampling (SBVSRS) for ratio estimation. Data that consist of weights and bilirubin levels in the blood of 120 babies are used to illustrate the procedure on a real data set. Based on our simulation, SBVRSS for ratio estimation is more efficient than using SBVSRS in all cases.
Journal: Journal of Applied Statistics
Pages: 457-473
Issue: 3
Volume: 44
Year: 2017
Month: 2
X-DOI: 10.1080/02664763.2016.1177495
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1177495
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:3:p:457-473




Template-Type: ReDIF-Article 1.0
Author-Name: Xiaoshuang Zhou
Author-X-Name-First: Xiaoshuang
Author-X-Name-Last: Zhou
Author-Name: Peixin Zhao
Author-X-Name-First: Peixin
Author-X-Name-Last: Zhao
Author-Name: Xiuli Wang
Author-X-Name-First: Xiuli
Author-X-Name-Last: Wang
Title: Empirical likelihood inferences for varying coefficient partially nonlinear models
Abstract: 
 In this article, empirical likelihood inferences for the varying coefficient partially nonlinear models are investigated. An empirical log-likelihood ratio function for the unknown parameter vector in the nonlinear function part and a residual-adjusted empirical log-likelihood ratio function for the nonparametric component are proposed. The corresponding Wilks phenomena are proved and the confidence regions for parametric component and nonparametric component are constructed. Simulation studies indicate that, in terms of coverage probabilities and average areas of the confidence regions, the empirical likelihood method performs better than the normal approximation-based method. Furthermore, a real data set application is also provided to illustrate the proposed empirical likelihood estimation technique.
Journal: Journal of Applied Statistics
Pages: 474-492
Issue: 3
Volume: 44
Year: 2017
Month: 2
X-DOI: 10.1080/02664763.2016.1177496
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1177496
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:3:p:474-492




Template-Type: ReDIF-Article 1.0
Author-Name: Murat Kulahci
Author-X-Name-First: Murat
Author-X-Name-Last: Kulahci
Author-Name: John Tyssedal
Author-X-Name-First: John
Author-X-Name-Last: Tyssedal
Title: Split-plot designs for multistage experimentation
Abstract: 
 Most of today’s complex systems and processes involve several stages through which input or the raw material has to go before the final product is obtained. Also in many cases factors at different stages interact. Therefore, a holistic approach for experimentation that considers all stages at the same time will be more efficient. However, there have been only a few attempts in the literature to provide an adequate and easy-to-use approach for this problem. In this paper, we present a novel methodology for constructing two-level split-plot and multistage experiments. The methodology is based on the Kronecker product representation of orthogonal designs and can be used for any number of stages, for various numbers of subplots and for different number of subplots for each stage. The procedure is demonstrated on both regular and nonregular designs and provides the maximum number of factors that can be accommodated in each stage. Furthermore, split-plot designs for multistage experiments with good projective properties are also provided.
Journal: Journal of Applied Statistics
Pages: 493-510
Issue: 3
Volume: 44
Year: 2017
Month: 2
X-DOI: 10.1080/02664763.2016.1177497
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1177497
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:3:p:493-510




Template-Type: ReDIF-Article 1.0
Author-Name: Hamideh D. Hamedani
Author-X-Name-First: Hamideh D.
Author-X-Name-Last: Hamedani
Author-Name: Sara Sadat Moosavi
Author-X-Name-First: Sara
Author-X-Name-Last: Sadat Moosavi
Title: Dealing with big data: comparing dimension reduction and shrinkage regression methods
Abstract: 
 In the past decades, the number of variables explaining observations in different practical applications increased gradually. This has led to heavy computational tasks, despite of widely using provisional variable selection methods in data processing. Therefore, more methodological techniques have appeared to reduce the number of explanatory variables without losing much of the information. In these techniques, two distinct approaches are apparent: ‘shrinkage regression’ and ‘sufficient dimension reduction’. Surprisingly, there has not been any communication or comparison between these two methodological categories, and it is not clear when each of these two approaches are appropriate. In this paper, we fill some of this gap by first reviewing each category in brief, paying special attention to the most commonly used methods in each category. We then compare commonly used methods from both categories based on their accuracy, computation time, and their ability to select effective variables. A simulation study on the performance of the methods in each category is generated as well. The selected methods are concurrently tested on two sets of real data which allows us to recommend conditions under which one approach is more appropriate to be applied to high-dimensional data.
Journal: Journal of Applied Statistics
Pages: 511-532
Issue: 3
Volume: 44
Year: 2017
Month: 2
X-DOI: 10.1080/02664763.2016.1177498
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1177498
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:3:p:511-532




Template-Type: ReDIF-Article 1.0
Author-Name: Krystallenia Drosou
Author-X-Name-First: Krystallenia
Author-X-Name-Last: Drosou
Author-Name: Christos Koukouvinos
Author-X-Name-First: Christos
Author-X-Name-Last: Koukouvinos
Title: Proximal support vector machine techniques on medical prediction outcome
Abstract: 
 One of the major issues in medical field constitutes the correct diagnosis, including the limitation of human expertise in diagnosing the disease in a manual way. Nowadays, the use of machine learning classifiers, such as support vector machines (SVM), in medical diagnosis is increasing gradually. However, traditional classification algorithms can be limited in their performance when they are applied on highly imbalanced data sets, in which negative examples (i.e. negative to a disease) outnumber the positive examples (i.e. positive to a disease). SVM constitutes a significant improvement and its mathematical formulation allows the incorporation of different weights so as to deal with the problem of imbalanced data. In the present work an extensive study of four medical data sets is conducted using a variant of SVM, called proximal support vector machine (PSVM) proposed by Fung and Mangasarian [9]. Additionally, in order to deal with the imbalanced nature of the medical data sets we applied both a variant of SVM, referred as two-cost support vector machine and a modification of PSVM referred as modified PSVM. Both algorithms incorporate different weights one for each class examples.
Journal: Journal of Applied Statistics
Pages: 533-553
Issue: 3
Volume: 44
Year: 2017
Month: 2
X-DOI: 10.1080/02664763.2016.1177499
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1177499
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:3:p:533-553




Template-Type: ReDIF-Article 1.0
Author-Name: A. Owen
Author-X-Name-First: A.
Author-X-Name-Last: Owen
Author-Name: A. C. Smith
Author-X-Name-First: A. C.
Author-X-Name-Last: Smith
Author-Name: P. Osei-Owusu
Author-X-Name-First: P.
Author-X-Name-Last: Osei-Owusu
Author-Name: A. Harland
Author-X-Name-First: A.
Author-X-Name-Last: Harland
Author-Name: J. R. Roberts
Author-X-Name-First: J. R.
Author-X-Name-Last: Roberts
Title: Elite players’ perceptions of football playing surfaces: a mixed effects ordinal logistic regression model of players’ perceptions
Abstract: 
 The aim of this study was to determine potential explanatory factors that may be associated with different attitudes amongst the global population of elite footballers to the use of different surfaces for football. A questionnaire was used to capture elite football players’ perceptions of playing surfaces and a mixed effects ordinal logistic regression model was used to explore potential explanatory factors of players’ perceptions. In total, responses from 1129 players from 44 different countries were analysed. The majority of players expressed a strong preference for the use of Natural Turf pitches over alternatives such as Artificial Turf. The regression model, with a players’ country as a random effect, indicated that players were less favourable towards either Natural Turf or Artificial Turf where there was perceived to be greater variability in surface qualities or the surface was perceived to have less desirable properties. Player’s surface experience was also linked to their overall attitudes, with a suggestion that the quality of the Natural Turf surface players experienced dictated players’ support for Artificial Turf.
Journal: Journal of Applied Statistics
Pages: 554-570
Issue: 3
Volume: 44
Year: 2017
Month: 2
X-DOI: 10.1080/02664763.2016.1177500
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1177500
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:44:y:2017:i:3:p:554-570




Template-Type: ReDIF-Article 1.0
Author-Name: Michael Parkinson
Author-X-Name-First: Michael
Author-X-Name-Last: Parkinson
Title:  Test for the significance of the mean of a stable probability distribution with 1&lt;α≤2
Abstract: The analysis of data using a stable probability distribution with tail parameter α&lt;2 (sometimes called a Pareto–Levy distribution) seems to have been avoided in the past in part because of the lack of a significance test for the mean, even though it appears to be the correct distribution to use for describing returns in the financial markets. A z test for the significance of the mean of a stable distribution with tail parameter 1&lt;α≤2 is defined. Tables are calculated and displayed for the 5% and 1% significance levels for a range of tail and skew parameters α and β. Through the use of maximum likelihood estimates, the test becomes a practical tool even when α and β are not that accurately determined. As an example, the z test is applied to the daily closing prices for the Dow Jones Industrial average from 2 January 1940 to 19 March 2010.
Journal: Journal of Applied Statistics
Pages: 465-482
Issue: 3
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2012.740618
File-URL: http://hdl.handle.net/10.1080/02664763.2012.740618
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:3:p:465-482




Template-Type: ReDIF-Article 1.0
Author-Name: Rashid Mehmood
Author-X-Name-First: Rashid
Author-X-Name-Last: Mehmood
Author-Name: Muhammad Riaz
Author-X-Name-First: Muhammad
Author-X-Name-Last: Riaz
Author-Name: Ronald Does
Author-X-Name-First: Ronald
Author-X-Name-Last: Does
Title: Control charts for location based on different sampling schemes
Abstract: Control charts are the most important statistical process control tool for monitoring variations in a process. A number of articles are available in the literature for the X̄ control chart based on simple random sampling, ranked set sampling, median-ranked set sampling (MRSS), extreme-ranked set sampling, double-ranked set sampling, double median-ranked set sampling and median double-ranked set sampling. In this study, we highlight some limitations of the existing ranked set charting structures. Besides, we propose different runs rules-based control charting structures under a variety of sampling strategies. We evaluate the performance of the control charting structures using power curves as a performance criterion. We observe that the proposed merger of varying runs rules schemes with different sampling strategies improve significantly the detection ability of location control charting structures. More specifically, the MRSS performs the best under both single- and double-ranked set strategies with varying runs rules schemes. We also include a real-life example to explain the proposal and highlight its significance for practical data sets.
Journal: Journal of Applied Statistics
Pages: 483-494
Issue: 3
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2012.740624
File-URL: http://hdl.handle.net/10.1080/02664763.2012.740624
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:3:p:483-494




Template-Type: ReDIF-Article 1.0
Author-Name: M. Kulikova
Author-X-Name-First: M.
Author-X-Name-Last: Kulikova
Author-Name: D. Taylor
Author-X-Name-First: D.
Author-X-Name-Last: Taylor
Title: Stochastic volatility models for exchange rates and their estimation using quasi-maximum-likelihood methods: an application to the South African Rand
Abstract: This paper is concerned with the volatility modeling of a set of South African Rand (ZAR) exchange rates. We investigate the quasi-maximum-likelihood (QML) estimator based on the Kalman filter and explore how well a choice of stochastic volatility (SV) models fits the data. We note that a data set from a developing country is used. The main results are: (1) the SV model parameter estimates are in line with those reported from the analysis of high-frequency data for developed countries; (2) the SV models we considered, along with their corresponding QML estimators, fit the data well; (3) using the range return instead of the absolute return as a volatility proxy produces QML estimates that are both less biased and less variable; (4) although the log range of the ZAR exchange rates has a distribution that is quite far from normal, the corresponding QML estimator has a superior performance when compared with the log absolute return.
Journal: Journal of Applied Statistics
Pages: 495-507
Issue: 3
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2012.740791
File-URL: http://hdl.handle.net/10.1080/02664763.2012.740791
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:3:p:495-507




Template-Type: ReDIF-Article 1.0
Author-Name: Enrico Ciavolino
Author-X-Name-First: Enrico
Author-X-Name-Last: Ciavolino
Author-Name: Mariangela Nitti
Author-X-Name-First: Mariangela
Author-X-Name-Last: Nitti
Title: Using the Hybrid Two-Step estimation approach for the identification of second-order latent variable models
Abstract: The aim of this paper is to define a new approach, called Hybrid Two-Step, to estimate the parameters of a second-order latent variable (LV) model in the case of formative relationships between the first-order and the second-order LVs. In this respect, we introduce the two main approaches to the estimation of second-order constructs through the partial least squares-path modelling: the so-called Repeated Indicators approach and the Two-Step approach. Some criticisms of these methodologies are highlighted and a solution to the issue of the identification of formative second-order constructs is suggested through the adoption of a Hybrid Two-Step approach. A Monte Carlo simulation study aimed at comparing the approach proposed with the traditional ones was performed. Finally, a case study about the passenger satisfaction is presented to show the implementation of the method and to give some comparative empirical results.
Journal: Journal of Applied Statistics
Pages: 508-526
Issue: 3
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2012.745837
File-URL: http://hdl.handle.net/10.1080/02664763.2012.745837
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:3:p:508-526




Template-Type: ReDIF-Article 1.0
Author-Name: Katherine St. Clair
Author-X-Name-First: Katherine
Author-X-Name-Last: St. Clair
Author-Name: Eric Dunton
Author-X-Name-First: Eric
Author-X-Name-Last: Dunton
Author-Name: John Giudice
Author-X-Name-First: John
Author-X-Name-Last: Giudice
Title: A comparison of models using removal effort to estimate animal abundance
Abstract: This paper compares methods for modeling the probability of removal when variable amounts of removal effort are present. A hierarchical modeling framework can produce estimates of animal abundance and detection from replicated removal counts taken at different locations in a region of interest. A common method of specifying variation in detection probabilities across locations or replicates is with a logistic model that incorporates relevant detection covariates. As an alternative to this logistic model, we propose using a catch–effort (CE) model to account for heterogeneity in detection when a measure of removal effort is available for each removal count. This method models the probability of detection as a nonlinear function of removal effort and a removal probability parameter that can vary spatially. Simulation results demonstrate that the CE model can effectively estimate abundance and removal probabilities when average removal rates are large but both the CE and logistic models tend to produce biased estimates as average removal rates decrease. We also found that the CE model fits better than logistic models when estimating wild turkey abundance using harvest and hunter counts collected by the Minnesota Department of Natural Resources during the spring turkey hunting season.
Journal: Journal of Applied Statistics
Pages: 527-545
Issue: 3
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2012.748016
File-URL: http://hdl.handle.net/10.1080/02664763.2012.748016
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:3:p:527-545




Template-Type: ReDIF-Article 1.0
Author-Name: J. Rogers
Author-X-Name-First: J.
Author-X-Name-Last: Rogers
Author-Name: J. Hutton
Author-X-Name-First: J.
Author-X-Name-Last: Hutton
Title: Joint modelling of pre-randomisation event counts and multiple post-randomisation survival times with cure rates: application to data for early epilepsy and single seizures
Abstract: In this paper, we consider the analysis of recurrent event data that examines the differences between two treatments. The outcomes that are considered in the analysis are the pre-randomisation event count and post-randomisation times to first and second events with associated cure fractions. We develop methods that allow pre-randomisation counts and two post-randomisation survival times to be jointly modelled under a Poisson process framework, assuming that outcomes are predicted by (unobserved) event rates. We apply these methods to data that examine the difference between immediate and deferred treatment policies in patients presenting with single seizures or early epilepsy. We find evidence to suggest that post-randomisation seizure rates change at randomisation and following a first seizure after randomisation. We also find that there are cure rates associated with the post-randomisation times to first and second seizures. The increase in power over standard survival techniques, offered by the joint models that we propose, resulted in more precise estimates of the treatment effect and the ability to detect interactions with covariate effects.
Journal: Journal of Applied Statistics
Pages: 546-562
Issue: 3
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2012.748720
File-URL: http://hdl.handle.net/10.1080/02664763.2012.748720
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:3:p:546-562




Template-Type: ReDIF-Article 1.0
Author-Name: Inmaculada Arostegui
Author-X-Name-First: Inmaculada
Author-X-Name-Last: Arostegui
Author-Name: Vicente Núñez-Antón
Author-X-Name-First: Vicente
Author-X-Name-Last: Núñez-Antón
Author-Name: José Quintana
Author-X-Name-First: José
Author-X-Name-Last: Quintana
Title: On the recoding of continuous and bounded indexes to a binomial form: an application to quality-of-life scores
Abstract: The statistical analysis of patient-reported outcomes (PROs) as endpoints has shown to be of great practical relevance. The resulting scores or indexes from the questionnaires used to measure PROs could be treated as continuous or ordinal. The goal of this study is to propose and evaluate a recoding process of the scores, so that they can be treated as binomial outcomes and, therefore, analyzed using logistic regression with random effects. The general methodology of recoding is based on the observable values of the scores. In order to obtain an optimal recoding, the evaluation of the recoding method is tested for different values of the parameters of the binomial distribution and different probability distributions of the random effects. We illustrate, evaluate and validate the proposed method of recoding with the Short Form-36 (SF-36) Survey and real data. The optimal recoding approach is very useful and flexible. Moreover, it has a natural interpretation, not only for ordinal scores, but also for questionnaires with many dimensions and different profiles, where a common method of analysis is desired, such as the SF-36.
Journal: Journal of Applied Statistics
Pages: 563-582
Issue: 3
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2012.749845
File-URL: http://hdl.handle.net/10.1080/02664763.2012.749845
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:3:p:563-582




Template-Type: ReDIF-Article 1.0
Author-Name: Doyo Enki
Author-X-Name-First: Doyo
Author-X-Name-Last: Enki
Author-Name: Nickolay Trendafilov
Author-X-Name-First: Nickolay
Author-X-Name-Last: Trendafilov
Author-Name: Ian Jolliffe
Author-X-Name-First: Ian
Author-X-Name-Last: Jolliffe
Title: A clustering approach to interpretable principal components
Abstract: A new method for constructing interpretable principal components is proposed. The method first clusters the variables, and then interpretable (sparse) components are constructed from the correlation matrices of the clustered variables. For the first step of the method, a new weighted-variances method for clustering variables is proposed. It reflects the nature of the problem that the interpretable components should maximize the explained variance and thus provide sparse dimension reduction. An important feature of the new clustering procedure is that the optimal number of clusters (and components) can be determined in a non-subjective manner. The new method is illustrated using well-known simulated and real data sets. It clearly outperforms many existing methods for sparse principal component analysis in terms of both explained variance and sparseness.
Journal: Journal of Applied Statistics
Pages: 583-599
Issue: 3
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2012.749846
File-URL: http://hdl.handle.net/10.1080/02664763.2012.749846
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:3:p:583-599




Template-Type: ReDIF-Article 1.0
Author-Name: S. Subedi
Author-X-Name-First: S.
Author-X-Name-Last: Subedi
Author-Name: Z. Feng
Author-X-Name-First: Z.
Author-X-Name-Last: Feng
Author-Name: R. Deardon
Author-X-Name-First: R.
Author-X-Name-Last: Deardon
Author-Name: F. Schenkel
Author-X-Name-First: F.
Author-X-Name-Last: Schenkel
Title: SNP selection for predicting a quantitative trait
Abstract: Molecular markers combined with powerful statistical tools have made it possible to detect and analyze multiple loci on the genome that are responsible for the phenotypic variation in quantitative traits. The objectives of the study presented in this paper are to identify a subset of single nucleotide polymorphism (SNP) markers that are associated with a particular trait and to construct a model that can best predict the value of the trait given the genotypic information of the SNPs using a three-step strategy. In the first step, a genome-wide association test is performed to screen SNPs that are associated with the quantitative trait of interest. SNPs with p-values of less than 5% are then analyzed in the second step. In the second step, a large number of randomly selected models, each consisting of a fixed number of randomly selected SNPs, are analyzed using the least angle regression method. This step will further remove redundant SNPs due to the complicated association among SNPs. A subset of SNPs that are shown to have a significant effect on the response trait more often than by chance are considered for the third step. In the third step, two alternative methods are considered: the least angle shrinkage and selection operation and sparse partial least squares regression. For both methods, the predictive ability of the fitted model is evaluated by an independent test set. The performance of the proposed method is illustrated by the analysis of a real data set on Canadian Holstein cattle.
Journal: Journal of Applied Statistics
Pages: 600-613
Issue: 3
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2012.750282
File-URL: http://hdl.handle.net/10.1080/02664763.2012.750282
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:3:p:600-613




Template-Type: ReDIF-Article 1.0
Author-Name: Dongliang Wang
Author-X-Name-First: Dongliang
Author-X-Name-Last: Wang
Author-Name: Alan Hutson
Author-X-Name-First: Alan
Author-X-Name-Last: Hutson
Title: Smooth bootstrap-based confidence intervals for one binomial proportion and difference of two proportions
Abstract: Constructing confidence intervals (CIs) for a binomial proportion and the difference between two binomial proportions is a fundamental and well-studied problem with respect to the analysis of binary data. In this note, we propose a new bootstrap procedure to estimate the CIs by resampling from a newly developed smooth quantile function in [11] for discrete data. We perform a variety of simulation studies in order to illustrate the strong performance of our approach. The coverage probabilities of our CIs in the one-sample setting are superior than or comparable to other well-known approaches. The true utility of our new and novel approach is in the two-sample setting. For the difference of two proportions, our smooth bootstrap CIs provide better coverage probabilities almost uniformly over the interval (−1, 1), particularly in the tail region as compared than other published methods included in our simulation. We illustrate our methodology via an application to several different binary data sets.
Journal: Journal of Applied Statistics
Pages: 614-625
Issue: 3
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2012.750283
File-URL: http://hdl.handle.net/10.1080/02664763.2012.750283
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:3:p:614-625




Template-Type: ReDIF-Article 1.0
Author-Name: Verena Feirer
Author-X-Name-First: Verena
Author-X-Name-Last: Feirer
Author-Name: Herwig Friedl
Author-X-Name-First: Herwig
Author-X-Name-Last: Friedl
Author-Name: Ulrich Hirn
Author-X-Name-First: Ulrich
Author-X-Name-Last: Hirn
Title: Modelling over- and underdispersed frequencies of successful ink transmissions onto paper
Abstract: This work focuses on the statistical modelling of successful or failed ink transmission during the printing of packaging paper. The main aim is to explain the probability of successful ink transmission with the help of a regression model. But many of the applied logistic regression models show that the variabilities of various samples from the printability data set range from much smaller to much larger values than the variability assumed in a binomial model. Hence, the main part of this paper concentrates on the discussion of distribution families that are capable of handling such a wide spectrum of different variations of frequency data.
Journal: Journal of Applied Statistics
Pages: 626-643
Issue: 3
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2012.750284
File-URL: http://hdl.handle.net/10.1080/02664763.2012.750284
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:3:p:626-643




Template-Type: ReDIF-Article 1.0
Author-Name: Betül Kan
Author-X-Name-First: Betül
Author-X-Name-Last: Kan
Author-Name: Özlem Alpu
Author-X-Name-First: Özlem
Author-X-Name-Last: Alpu
Author-Name: Berna Yazıcı
Author-X-Name-First: Berna
Author-X-Name-Last: Yazıcı
Title: Robust ridge and robust Liu estimator for regression based on the LTS estimator
Abstract: In the multiple linear regression analysis, the ridge regression estimator and the Liu estimator are often used to address multicollinearity. Besides multicollinearity, outliers are also a problem in the multiple linear regression analysis. We propose new biased estimators based on the least trimmed squares (LTS) ridge estimator and the LTS Liu estimator in the case of the presence of both outliers and multicollinearity. For this purpose, a simulation study is conducted in order to see the difference between the robust ridge estimator and the robust Liu estimator in terms of their effectiveness; the mean square error. In our simulations, the behavior of the new biased estimators is examined for types of outliers: X-space outlier, Y-space outlier, and X-and Y-space outlier. The results for a number of different illustrative cases are presented. This paper also provides the results for the robust ridge regression and robust Liu estimators based on a real-life data set combining the problem of multicollinearity and outliers.
Journal: Journal of Applied Statistics
Pages: 644-655
Issue: 3
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2012.750285
File-URL: http://hdl.handle.net/10.1080/02664763.2012.750285
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:3:p:644-655




Template-Type: ReDIF-Article 1.0
Author-Name: Jacob Oleson
Author-X-Name-First: Jacob
Author-X-Name-Last: Oleson
Author-Name: Christopher Wikle
Author-X-Name-First: Christopher
Author-X-Name-Last: Wikle
Title: Predicting infectious disease outbreak risk via migratory waterfowl vectors
Abstract: The spread of an emerging infectious disease is a major public health threat. Given the uncertainties associated with vector-borne diseases, in terms of vector dynamics and disease transmission, it is critical to develop statistical models that address how and when such an infectious disease could spread throughout a region such as the USA. This paper considers a spatio-temporal statistical model for how an infectious disease could be carried into the USA by migratory waterfowl vectors during their seasonal migration and, ultimately, the risk of transmission of such a disease to domestic fowl. Modeling spatio-temporal data of this type is inherently difficult given the uncertainty associated with observations, complexity of the dynamics, high dimensionality of the underlying process, and the presence of excessive zeros. In particular, the spatio-temporal dynamics of the waterfowl migration are developed by way of a two-tiered functional temporal and spatial dimension reduction procedure that captures spatial and seasonal trends, as well as regional dynamics. Furthermore, the model relates the migration to a population of poultry farms that are known to be susceptible to such diseases, and is one of the possible avenues toward transmission to domestic poultry and humans. The result is a predictive distribution of those counties containing poultry farms that are at the greatest risk of having the infectious disease infiltrate their flocks assuming that the migratory population was infected. The model naturally fits into the hierarchical Bayesian framework.
Journal: Journal of Applied Statistics
Pages: 656-673
Issue: 3
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2012.750286
File-URL: http://hdl.handle.net/10.1080/02664763.2012.750286
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:3:p:656-673




Template-Type: ReDIF-Article 1.0
Author-Name: Sandra Melo
Author-X-Name-First: Sandra
Author-X-Name-Last: Melo
Author-Name: Oscar Melo
Author-X-Name-First: Oscar
Author-X-Name-Last: Melo
Title: Distance-based approach in univariate longitudinal data analysis
Abstract: In this paper, we propose a methodology to analyze longitudinal data through distances between pairs of observations (or individuals) with regard to the explanatory variables used to fit continuous response variables. Restricted maximum-likelihood and generalized least squares are used to estimate the parameters in the model. We applied this new approach to study the effect of gender and exposure on the deviant behavior variable with respect to tolerance for a group of youths studied over a period of 5 years. Were performed simulations where we compared our distance-based method with classic longitudinal analysis with both AR(1) and compound symmetry correlation structures. We compared them under Akaike and Bayesian information criterions, and the relative efficiency of the generalized variance of the errors of each model. We found small gains in the proposed model fit with regard to the classical methodology, particularly in small samples, regardless of variance, correlation, autocorrelation structure and number of time measurements.
Journal: Journal of Applied Statistics
Pages: 674-692
Issue: 3
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2012.750648
File-URL: http://hdl.handle.net/10.1080/02664763.2012.750648
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:3:p:674-692




Template-Type: ReDIF-Article 1.0
Author-Name: Akbar Heydarbeygie
Author-X-Name-First: Akbar
Author-X-Name-Last: Heydarbeygie
Author-Name: Nima Ahmadi
Author-X-Name-First: Nima
Author-X-Name-Last: Ahmadi
Title: Nonparametric methods for the estimation of imputation uncertainty
Abstract: It is cleared in recent researches that the raising of missing values in datasets is inevitable. Imputation of missing data is one of the several methods which have been introduced to overcome this issue. Imputation techniques are trying to answer the case of missing data by covering missing values with reasonable estimates permanently. There are a lot of benefits for these procedures rather than their drawbacks. The operation of these methods has not been clarified, which means that they provide mistrust among analytical results. One approach to evaluate the outcomes of the imputation process is estimating uncertainty in the imputed data. Nonparametric methods are appropriate to estimating the uncertainty when data are not followed by any particular distribution. This paper deals with a nonparametric method for estimation and testing the significance of the imputation uncertainty, which is based on Wilcoxon test statistic, and which could be employed for estimating the precision of the imputed values created by imputation methods. This proposed procedure could be employed to judge the possibility of the imputation process for datasets, and to evaluate the influence of proper imputation methods when they are utilized to the same dataset. This proposed approach has been compared with other nonparametric resampling methods, including bootstrap and jackknife to estimate uncertainty in the imputed data under the Bayesian bootstrap imputation method. The ideas supporting the proposed method are clarified in detail, and a simulation study, which indicates how the approach has been employed in practical situations, is illustrated.
Journal: Journal of Applied Statistics
Pages: 693-698
Issue: 3
Volume: 40
Year: 2013
X-DOI: 10.1080/02664763.2012.750649
File-URL: http://hdl.handle.net/10.1080/02664763.2012.750649
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:40:y:2013:i:3:p:693-698




Template-Type: ReDIF-Article 1.0
Author-Name: Yanbin Ma
Author-X-Name-First: Yanbin
Author-X-Name-Last: Ma
Author-Name: Wenhao Gui
Author-X-Name-First: Wenhao
Author-X-Name-Last: Gui
Title: Pivotal inference for the inverse Rayleigh distribution based on general progressively Type-II censored samples
Abstract: 
 In this paper, we consider the problem of estimating the scale parameter of the inverse Rayleigh distribution based on general progressively Type-II censored samples and progressively Type-II censored samples. The pivotal quantity method is used to derive the estimator of the scale parameter. Besides, considering that the maximum likelihood estimator is tough to obtain for this distribution, we derive an explicit estimator of the scale parameter by approximating the likelihood equation with Taylor expansion. The interval estimation is also studied based on pivotal inference. Then we conduct Monte Carlo simulations and compare the performance of different estimators. We demonstrate that the pivotal inference is simpler and more effective. The further application of the pivotal quantity method is also discussed theoretically. Finally, two real data sets are analyzed using our methods.
Journal: Journal of Applied Statistics
Pages: 771-797
Issue: 5
Volume: 46
Year: 2019
Month: 4
X-DOI: 10.1080/02664763.2018.1511773
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1511773
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:5:p:771-797




Template-Type: ReDIF-Article 1.0
Author-Name: Osvaldo Marrero
Author-X-Name-First: Osvaldo
Author-X-Name-Last: Marrero
Title: Bayesian analysis of seasonal variation when the sample size and the amplitude are small
Abstract: 
 We propose what appears to be the first Bayesian procedure for the analysis of seasonal variation when the sample size and the amplitude are small. Such data occur often in the medical sciences, where seasonality analyses and environmental considerations can help clarify disease etiologies. The method is explained in terms of a simple physico-geometric setting. We present the Bayesian version of a frequentist test that performs well. Two examples of real data illustrate the procedure's application.
Journal: Journal of Applied Statistics
Pages: 798-813
Issue: 5
Volume: 46
Year: 2019
Month: 4
X-DOI: 10.1080/02664763.2018.1514371
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1514371
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:5:p:798-813




Template-Type: ReDIF-Article 1.0
Author-Name: Sayantee Jana
Author-X-Name-First: Sayantee
Author-X-Name-Last: Jana
Author-Name: Narayanaswamy Balakrishnan
Author-X-Name-First: Narayanaswamy
Author-X-Name-Last: Balakrishnan
Author-Name: Jemila S. Hamid
Author-X-Name-First: Jemila S.
Author-X-Name-Last: Hamid
Title: Bayesian growth curve model useful for high-dimensional longitudinal data
Abstract: 
 Traditional inference on the growth curve model (GCM) requires &#x2018;small p large n&#x2019; ( $ n \gg p $ n&#x226B;p) and cannot be applied in high-dimensional scenarios, where we often encounter singularity. Several methods are proposed to tackle the singularity problem, however there are still limitations and gaps. We consider a Bayesian framework to derive a statistic for testing a linear hypothesis on the GCM. Extensive simulations are performed to investigate performance and establish optimality characteristics. We show that the test overcomes the challenge of high-dimensionality and possesses all the desirable optimality characteristics of a good test - it is unbiased, symmetric and monotone with respect to sample size and departure from the null hypotheses. The results also indicate that the test performs very well, possessing a level close to the nominal value and high power in rejecting small departures from the null. The results also show that the test overcomes limitations of a previously proposed test. We illustrated practical applications using a publicly available time course genetic data on breast cancer, where we used our test statistic for gene filtering. The genes were ranked according to the value of the test statistic and the top five genes were annotated.
Journal: Journal of Applied Statistics
Pages: 814-834
Issue: 5
Volume: 46
Year: 2019
Month: 4
X-DOI: 10.1080/02664763.2018.1517145
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1517145
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:5:p:814-834




Template-Type: ReDIF-Article 1.0
Author-Name: S. A. Abu Bakar
Author-X-Name-First: S. A.
Author-X-Name-Last: Abu Bakar
Author-Name: S. Nadarajah
Author-X-Name-First: S.
Author-X-Name-Last: Nadarajah
Title: Risk measure estimation under two component mixture models with trimmed data
Abstract: 
 Several two component mixture models from the transformed gamma and transformed beta families are developed to assess risk performance. Their common statistical properties are given and applications to real insurance loss data are shown. A new data trimming approach for parameter estimation is proposed using the maximum likelihood estimation method. Assessment with respect to Value-at-Risk and Conditional Tail Expectation risk measures are presented. Of all the models examined, the mixture of inverse transformed gamma-Burr distributions consistently provides good results in terms of goodness-of-fit and risk estimation in the context of the Danish fire loss data.
Journal: Journal of Applied Statistics
Pages: 835-852
Issue: 5
Volume: 46
Year: 2019
Month: 4
X-DOI: 10.1080/02664763.2018.1517146
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1517146
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:5:p:835-852




Template-Type: ReDIF-Article 1.0
Author-Name: Hui Nian
Author-X-Name-First: Hui
Author-X-Name-Last: Nian
Author-Name: Chang Yu
Author-X-Name-First: Chang
Author-X-Name-Last: Yu
Author-Name: Juan Ding
Author-X-Name-First: Juan
Author-X-Name-Last: Ding
Author-Name: Huiyun Wu
Author-X-Name-First: Huiyun
Author-X-Name-Last: Wu
Author-Name: William D. Dupont
Author-X-Name-First: William D.
Author-X-Name-Last: Dupont
Author-Name: Steve Brunwasser
Author-X-Name-First: Steve
Author-X-Name-Last: Brunwasser
Author-Name: Tebeb Gebretsadik
Author-X-Name-First: Tebeb
Author-X-Name-Last: Gebretsadik
Author-Name: Tina V. Hartert
Author-X-Name-First: Tina V.
Author-X-Name-Last: Hartert
Author-Name: Pingsheng Wu
Author-X-Name-First: Pingsheng
Author-X-Name-Last: Wu
Title: Performance evaluation of propensity score methods for estimating average treatment effects with multi-level treatments
Abstract: 
 The propensity score (PS) method is widely used to estimate the average treatment effect (TE) in observational studies. However, it is generally confined to the binary treatment assignment. In an extension to the settings of a multi-level treatment, Imbens proposed a generalized propensity score which is the conditional probability of receiving a particular level of the treatment given pre-treatment variables. The average TE can then be estimated by conditioning solely on the generalized PS under the assumption of weak unconfoundedness. In the present work, we adopted this approach and conducted extensive simulations to evaluate the performance of several methods using the generalized PS, including subclassification, matching, inverse probability of treatment weighting (IPTW), and covariate adjustment. Compared with other methods, IPTW had the preferred overall performance. We then applied these methods to a retrospective cohort study of 228,876 pregnant women. The impact of the exposure to different types of the antidepressant medications (no exposure, selective serotonin reuptake inhibitor (SSRI) only, non-SSRI only, and both) during pregnancy on several important infant outcomes (birth weight, gestation age, preterm labor, and respiratory distress) were assessed.
Journal: Journal of Applied Statistics
Pages: 853-873
Issue: 5
Volume: 46
Year: 2019
Month: 4
X-DOI: 10.1080/02664763.2018.1523375
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1523375
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:5:p:853-873




Template-Type: ReDIF-Article 1.0
Author-Name: Hassan S. Bakouch
Author-X-Name-First: Hassan S.
Author-X-Name-Last: Bakouch
Author-Name: Muhammad Nauman Khan
Author-X-Name-First: Muhammad Nauman
Author-X-Name-Last: Khan
Author-Name: Tassaddaq Hussain
Author-X-Name-First: Tassaddaq
Author-X-Name-Last: Hussain
Author-Name: Christophe Chesneau
Author-X-Name-First: Christophe
Author-X-Name-Last: Chesneau
Title: A power log-Dagum distribution: estimation and applications
Abstract: 
 Development and application of probability models in data analysis are of major importance for all sciences. Therefore, we introduce a new model called a power log-Dagum distribution defined on the entire real line. The model contains many new sub-models: power logistic, linear log-Dagum, linear logistic and log-Dagum distributions among them. Some properties of the model including three different estimation procedures are justified. The model exhibits various shapes for the density and hazard rate functions. Moreover, the estimation procedures are compared using simulation studies. Finally, the model with others are fitted to three data sets, and it shows a better fit than the compared distributions defined on the real line.
Journal: Journal of Applied Statistics
Pages: 874-892
Issue: 5
Volume: 46
Year: 2019
Month: 4
X-DOI: 10.1080/02664763.2018.1523376
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1523376
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:5:p:874-892




Template-Type: ReDIF-Article 1.0
Author-Name: J. Pecanka
Author-X-Name-First: J.
Author-X-Name-Last: Pecanka
Author-Name: A. W. van der Vaart
Author-X-Name-First: A. W.
Author-X-Name-Last: van der Vaart
Author-Name: M. A. Jonker
Author-X-Name-First: M. A.
Author-X-Name-Last: Jonker
Title: Modeling association between multivariate correlated outcomes and high-dimensional sparse covariates: the adaptive SVS method
Abstract: 
 The problem of modeling the relationship between a set of covariates and a multivariate response with correlated components often arises in many areas of research such as genetics, psychometrics, signal processing. In the linear regression framework, such task can be addressed using a number of existing methods. In the high-dimensional sparse setting, most of these methods rely on the idea of penalization in order to efficiently estimate the regression matrix. Examples of such methods include the lasso, the group lasso, the adaptive group lasso or the simultaneous variable selection (SVS) method. Crucially, a suitably chosen penalty also allows for an efficient exploitation of the correlation structure within the multivariate response. In this paper we introduce a novel variant of such method called the adaptive SVS, which is closely linked with the adaptive group lasso. Via a simulation study we investigate its performance in the high-dimensional sparse regression setting. We provide a comparison with a number of other popular methods under different scenarios and show that the adaptive SVS is a powerful tool for efficient recovery of signal in such setting. The methods are applied to genetic data.
Journal: Journal of Applied Statistics
Pages: 893-913
Issue: 5
Volume: 46
Year: 2019
Month: 4
X-DOI: 10.1080/02664763.2018.1523377
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1523377
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:5:p:893-913




Template-Type: ReDIF-Article 1.0
Author-Name: Jiang Du
Author-X-Name-First: Jiang
Author-X-Name-Last: Du
Author-Name: Ruiyuan Cao
Author-X-Name-First: Ruiyuan
Author-X-Name-Last: Cao
Author-Name: Eddy Kwessi
Author-X-Name-First: Eddy
Author-X-Name-Last: Kwessi
Author-Name: Zhongzhan Zhang
Author-X-Name-First: Zhongzhan
Author-X-Name-Last: Zhang
Title: Estimation for generalized partially functional linear additive regression model
Abstract: 
 In practice, it is not uncommon to encounter the situation that a discrete response is related to both a functional random variable and multiple real-value random variables whose impact on the response is nonlinear. In this paper, we consider the generalized partial functional linear additive models (GPFLAM) and present the estimation procedure. In GPFLAM, the nonparametric functions are approximated by polynomial splines and the infinite slope function is estimated based on the principal component basis function approximations. We obtain the estimator by maximizing the quasi-likelihood function. We investigate the finite sample properties of the estimation procedure via Monte Carlo simulation studies and illustrate our proposed model by a real data analysis.
Journal: Journal of Applied Statistics
Pages: 914-925
Issue: 5
Volume: 46
Year: 2019
Month: 4
X-DOI: 10.1080/02664763.2018.1523378
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1523378
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:5:p:914-925




Template-Type: ReDIF-Article 1.0
Author-Name: Yanping Qiu
Author-X-Name-First: Yanping
Author-X-Name-Last: Qiu
Author-Name: Baosheng Liang
Author-X-Name-First: Baosheng
Author-X-Name-Last: Liang
Title: Robust logistic regression of family data in the presence of missing genotypes
Abstract: 
 Large cohort studies are commonly launched to study the risk effect of genetic variants or other risk factors on a chronic disorder. In these studies, family data are often collected to provide additional information for the purpose of improving the inference results. Statistical analysis of the family data can be very challenging due to the missing observations of genotypes, incomplete records of disease occurrences in family members, and the complicated dependence attributed to the shared genetic background and environmental factors. In this article, we investigate a class of logistic models with family-shared random effects to tackle these challenges, and develop a robust regression method based on the conditional logistic technique for statistical inference. An expectation&#x2013;maximization (EM) algorithm with fast computation speed is developed to handle the missing genotypes. The proposed estimators are shown to be consistent and asymptotically normal. Additionally, a score test based on the proposed method is derived to test the genetic effect. Extensive simulation studies demonstrate that the proposed method performs well in finite samples in terms of estimate accuracy, robustness and computational speed. The proposed procedure is applied to an Alzheimer's disease study.
Journal: Journal of Applied Statistics
Pages: 926-945
Issue: 5
Volume: 46
Year: 2019
Month: 4
X-DOI: 10.1080/02664763.2018.1526890
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1526890
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:5:p:926-945




Template-Type: ReDIF-Article 1.0
Author-Name: Rand R. Wilcox
Author-X-Name-First: Rand R.
Author-X-Name-Last: Wilcox
Title: Multicolinearity and ridge regression: results on type I errors, power and heteroscedasticity
Abstract: 
 Let  $ \beta _1, \ldots , \beta _p $ &#x3B2;1,&#x2026;,&#x3B2;p be the slope parameters in a linear regression model and consider the goal of testing
 $ H_0: \beta _j=0 $ H0:&#x3B2;j=0 ( $ j=1, \ldots , p $ j=1,&#x2026;,p). A well-known concern is that multicolinearity can inflate the standard error of the least squares estimate of  $ \beta _j $ &#x3B2;j, which in turn can result in relatively low power. The paper examines heteroscedastic methods for dealing with this issue via a ridge regression estimator. A method is found that might substantially increase the probability of identifying a single slope that differs from zero. But due to the bias of the ridge estimator, it cannot reject  $ H_0: \beta _j=0 $ H0:&#x3B2;j=0 for more than one value of j. Simulations indicate that the increase in power is a function of the correlations among the dependent variables as well as the nature of the distributions generating the data.
Journal: Journal of Applied Statistics
Pages: 946-957
Issue: 5
Volume: 46
Year: 2019
Month: 4
X-DOI: 10.1080/02664763.2018.1526891
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1526891
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:5:p:946-957




Template-Type: ReDIF-Article 1.0
Author-Name: Luís G. Silva e Silva
Author-X-Name-First: Luís G.
Author-X-Name-Last: Silva e Silva
Author-Name: Renato M. Assunção
Author-X-Name-First: Renato M.
Author-X-Name-Last: Assunção
Title: COWORDS: a probabilistic model for multiple word clouds
Abstract: 
 Word clouds constitute one of the most popular statistical tools for the visual analysis of text documents because they provide users with a quick and intuitive understanding of the content. Despite their popularity for visualizing single documents, word clouds are not appropriate to compare different text documents. Independently generating word clouds for each document leads to configurations where the same word is typically located in widely different positions. This makes it very difficult to compare two or more word clouds. This paper introduces COWORDS, a new stochastic algorithm to create multiple word clouds, including one for each document. The shared words in multiple documents are placed in the same position in all clouds. Similar documents produce similar and compact clouds, making it easier to simultaneously compare and interpret several word clouds. The algorithm is based on a probability distribution in which the most probable configurations are those with a desirable visual aspect, such as a low value for the total distance between the words in all clouds. The algorithm output is a set of word clouds that are randomly selected from this probability distribution. The selection procedure uses a Markov chain Monte Carlo simulation method. We present several examples that illustrate the performance and visual results that can be obtained by our algorithm.
Journal: Journal of Applied Statistics
Pages: 2697-2717
Issue: 15
Volume: 45
Year: 2018
Month: 11
X-DOI: 10.1080/02664763.2018.1435633
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1435633
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:15:p:2697-2717




Template-Type: ReDIF-Article 1.0
Author-Name: Chen Peng
Author-X-Name-First: Chen
Author-X-Name-Last: Peng
Author-Name: Maochao Xu
Author-X-Name-First: Maochao
Author-X-Name-Last: Xu
Author-Name: Shouhuai Xu
Author-X-Name-First: Shouhuai
Author-X-Name-Last: Xu
Author-Name: Taizhong Hu
Author-X-Name-First: Taizhong
Author-X-Name-Last: Hu
Title: Modeling multivariate cybersecurity risks
Abstract: 
 Modeling cybersecurity risks is an important, yet challenging, problem. In this paper, we initiate the study of modeling multivariate cybersecurity risks. We develop the first statistical approach, which is centered at a Copula-GARCH model that uses vine copulas to model the multivariate dependence exhibited by real-world cyber attack data. We find that ignoring the due multivariate dependence causes a severe underestimation of cybersecurity risks. Both simulation and empirical studies show that the proposed approach leads to accurate predictions of multivariate cybersecurity risks.
Journal: Journal of Applied Statistics
Pages: 2718-2740
Issue: 15
Volume: 45
Year: 2018
Month: 11
X-DOI: 10.1080/02664763.2018.1436701
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1436701
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:15:p:2718-2740




Template-Type: ReDIF-Article 1.0
Author-Name: Luis Carlos Pérez-Ruiz
Author-X-Name-First: Luis Carlos
Author-X-Name-Last: Pérez-Ruiz
Author-Name: Gabriel Escarela
Author-X-Name-First: Gabriel
Author-X-Name-Last: Escarela
Title: Joint regression modeling for missing categorical covariates in generalized linear models
Abstract: 
 Missing covariates data is a common issue in generalized linear models (GLMs). A model-based procedure arising from properly specifying joint models for both the partially observed covariates and the corresponding missing indicator variables represents a sound and flexible methodology, which lends itself to maximum likelihood estimation as the likelihood function is available in computable form. In this paper, a novel model-based methodology is proposed for the regression analysis of GLMs when the partially observed covariates are categorical. Pair-copula constructions are used as graphical tools in order to facilitate the specification of the high-dimensional probability distributions of the underlying missingness components. The model parameters are estimated by maximizing the weighted log-likelihood function by using an EM algorithm. In order to compare the performance of the proposed methodology with other well-established approaches, which include complete-cases and multiple imputation, several simulation experiments of Binomial, Poisson and Normal regressions are carried out under both missing at random and non-missing at random mechanisms scenarios. The methods are illustrated by modeling data from a stage III melanoma clinical trial. The results show that the methodology is rather robust and flexible, representing a competitive alternative to traditional techniques.
Journal: Journal of Applied Statistics
Pages: 2741-2759
Issue: 15
Volume: 45
Year: 2018
Month: 11
X-DOI: 10.1080/02664763.2018.1438376
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1438376
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:15:p:2741-2759




Template-Type: ReDIF-Article 1.0
Author-Name: Mausumi Bose
Author-X-Name-First: Mausumi
Author-X-Name-Last: Bose
Author-Name: Kajal Dihidar
Author-X-Name-First: Kajal
Author-X-Name-Last: Dihidar
Title: Privacy protection measures for randomized response surveys on stigmatizing continuous variables
Abstract: 
 We consider the situation where sample surveys are to be undertaken on sensitive or stigmatizing issues. For such surveys, direct questioning methods usually lead to non-compliance or incorrect responses and so, the randomized response technique, where the responses are collected through some randomization device, is found to be useful. A majority of the literature on these techniques focus on dichotomous sensitive variables, while some techniques are also available for continuous sensitive variables. In this article, we focus on the extent of privacy protection available in sample surveys to respondents for continuous response variables. We also propose two measures of privacy protection. We demonstrate that the parameters of our randomization scheme can be so chosen as to achieve a pre-assigned level of privacy protection while at the same time yielding efficient estimates. We also show some numerical comparisons.
Journal: Journal of Applied Statistics
Pages: 2760-2772
Issue: 15
Volume: 45
Year: 2018
Month: 11
X-DOI: 10.1080/02664763.2018.1440540
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1440540
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:15:p:2760-2772




Template-Type: ReDIF-Article 1.0
Author-Name: Gregory J. Matthews
Author-X-Name-First: Gregory J.
Author-X-Name-Last: Matthews
Author-Name: Juliet K. Brophy
Author-X-Name-First: Juliet K.
Author-X-Name-Last: Brophy
Author-Name: Maxwell Luetkemeier
Author-X-Name-First: Maxwell
Author-X-Name-Last: Luetkemeier
Author-Name: Hongie Gu
Author-X-Name-First: Hongie
Author-X-Name-Last: Gu
Author-Name: George K. Thiruvathukal
Author-X-Name-First: George K.
Author-X-Name-Last: Thiruvathukal
Title: A comparison of machine learning techniques for taxonomic classification of teeth from the Family Bovidae
Abstract: 
 This study explores the performance of machine learning algorithms on the classification of fossil teeth in the Family Bovidae. Isolated bovid teeth are typically the most common fossils found in southern Africa and they often constitute the basis for paleoenvironmental reconstructions. Taxonomic identification of fossil bovid teeth, however, is often imprecise and subjective. Using modern teeth with known taxons, machine learning algorithms can be trained to classify fossils. Previous work by Brophy et al. [Quantitative morphological analysis of bovid teeth and implications for paleoenvironmental reconstruction of plovers lake, Gauteng Province, South Africa, J. Archaeol. Sci. 41 (2014), pp. 376–388] uses elliptical Fourier analysis of the form (size and shape) of the outline of the occlusal surface of each tooth as features in a linear discriminant analysis (LDA) framework. This manuscript expands on that previous work by exploring how different machine learning approaches classify the teeth and testing which technique is best for classification. In addition to LDA, four other machine learning techniques were considered (neural networks, nuclear penalized multinomial regression,random forests, and support vector machines) with support vector machines and random forests performing the best in terms of log loss and classification rate.
Journal: Journal of Applied Statistics
Pages: 2773-2787
Issue: 15
Volume: 45
Year: 2018
Month: 11
X-DOI: 10.1080/02664763.2018.1441381
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1441381
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:15:p:2773-2787




Template-Type: ReDIF-Article 1.0
Author-Name: Mina Mirshahi
Author-X-Name-First: Mina
Author-X-Name-Last: Mirshahi
Author-Name: Vahid Partovi Nia
Author-X-Name-First: Vahid
Author-X-Name-Last: Partovi Nia
Author-Name: Luc Adjengue
Author-X-Name-First: Luc
Author-X-Name-Last: Adjengue
Title: Automatic odor prediction for electronic nose
Abstract: 
 An electronic nose (e-nose), or artificial olfaction, is a device that analyzes the air to quantify odor concentration using an array of gas sensors. This equipment is a monitoring tool for industrial firms. There is a shortage of an algorithm to address odor prediction challenges in an automatic fashion. These challenges include unreliable data transfer due to sensor malfunction or missing data due to anomalies in mobile data connection. Providing such an algorithm avoids human intervention in data handling, and elevates electronic nose towards smart nose. We address these challenges by proposing a data validation and data imputation method combined with a robust odor prediction machinery. Our proposed algorithm is successfully implemented on the e-nose equipment and is serving the industry currently.
Journal: Journal of Applied Statistics
Pages: 2788-2799
Issue: 15
Volume: 45
Year: 2018
Month: 11
X-DOI: 10.1080/02664763.2018.1441382
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1441382
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:15:p:2788-2799




Template-Type: ReDIF-Article 1.0
Author-Name: Cheng Ju
Author-X-Name-First: Cheng
Author-X-Name-Last: Ju
Author-Name: Aurélien Bibaut
Author-X-Name-First: Aurélien
Author-X-Name-Last: Bibaut
Author-Name: Mark van der
Laan
Author-X-Name-First: Mark
Author-X-Name-Last: van der
Laan
Title: The relative performance of ensemble methods with deep convolutional neural networks for image classification
Abstract: 
 Artificial neural networks have been successfully applied to a variety of machine learning tasks, including image recognition, semantic segmentation, and machine translation. However, few studies fully investigated ensembles of artificial neural networks. In this work, we investigated multiple widely used ensemble methods, including unweighted averaging, majority voting, the Bayes Optimal Classifier, and the (discrete) Super Learner, for image recognition tasks, with deep neural networks as candidate algorithms. We designed several experiments, with the candidate algorithms being the same network structure with different model checkpoints within a single training process, networks with same structure but trained multiple times stochastically, and networks with different structure. In addition, we further studied the overconfidence phenomenon of the neural networks, as well as its impact on the ensemble methods. Across all of our experiments, the Super Learner achieved best performance among all the ensemble methods in this study.
Journal: Journal of Applied Statistics
Pages: 2800-2818
Issue: 15
Volume: 45
Year: 2018
Month: 11
X-DOI: 10.1080/02664763.2018.1441383
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1441383
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:15:p:2800-2818




Template-Type: ReDIF-Article 1.0
Author-Name: Patrick Aboagye-Sarfo
Author-X-Name-First: Patrick
Author-X-Name-Last: Aboagye-Sarfo
Author-Name: Qun Mai
Author-X-Name-First: Qun
Author-X-Name-Last: Mai
Title: Seasonal analysis of emergency department presentations in Western Australia, 2009/10–2014/15
Abstract: 
 Understanding the dynamic effects of seasonal variations of emergency department (ED) presentations is necessary to enhance health service planning and delivery, avoid overcrowding and meet the demand of the population. Time series analysis of seasonal trend decomposition using Loess (STL) was used to decompose and isolate the seasonal component of the ED presentations in Western Australia (WA) hospitals. Between 2009/10 and 2014/15, there were 5,652,556 ED presentations that show distinctive seasonal pattern. The overall seasonal variation was 7.0% (95% CI: 6.0–8.4%) and peaked in winter, with the highest in August. However, stratification analysis revealed that patients aged 15–64 years and those with triage 4 and 5 peaked in summer. The stratification for the most frequent conditions presented to metropolitan EDs and triaged as categories 1, 2 or 3 (most urgent conditions) shown that acute upper respiratory infection, pneumonia, viral infection, status asthmaticus and breathing abnormalities peaked in winter, whereas cellulitis, urinary tract infection, threatened abortion, intestinal infection, gastroenteritis and colitis, nausea and vomiting, and open wound of finger peaked in summer. The findings may be important in developing strategies and policies to manage ED demand in peak periods to avoid overcrowding and improve service delivery.
Journal: Journal of Applied Statistics
Pages: 2819-2830
Issue: 15
Volume: 45
Year: 2018
Month: 11
X-DOI: 10.1080/02664763.2018.1441384
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1441384
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:15:p:2819-2830




Template-Type: ReDIF-Article 1.0
Author-Name: Svenja Fischer
Author-X-Name-First: Svenja
Author-X-Name-Last: Fischer
Title: A seasonal mixed-POT model to estimate high flood quantiles from different event types and seasons
Abstract: 
 Flood events can be caused by several different meteorological circumstances. For example, heavy rain events often lead to short flood events with high peaks, whereas snowmelt normally results in events of very long duration with a high volume. Both event types have to be considered in the design of flood protection systems. Unfortunately, all these different event types are often included in annual maximum series (AMS) leading to inhomogeneous samples. Moreover, certain event types are underrepresented in the AMS. This is especially unsatisfactory if the most extreme events result from such an event type. Therefore, monthly maximum data are used to enlarge the information spectrum on the different event types. Of course, not all events can be included in the flood statistics because not every monthly maximum can be declared as a flood. To take this into account, a mixture Peak-over-threshold model is applied, with thresholds specifying flood events of several types that occur in a season of the year. This model is then extended to cover the seasonal type of the data. The applicability is shown in a German case study, where the impact of the single event types in different parts of a year is evaluated.
Journal: Journal of Applied Statistics
Pages: 2831-2847
Issue: 15
Volume: 45
Year: 2018
Month: 11
X-DOI: 10.1080/02664763.2018.1441385
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1441385
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:15:p:2831-2847




Template-Type: ReDIF-Article 1.0
Author-Name: Conceição Rocha
Author-X-Name-First: Conceição
Author-X-Name-Last: Rocha
Author-Name: Pedro Quelhas Brito
Author-X-Name-First: Pedro Quelhas
Author-X-Name-Last: Brito
Title: Profiles identification on hierarchical tree structure data sets
Abstract: 
 In this work we study a way to explore and extract more information from data sets with a hierarchical tree structure. We propose that any statistical study on this type of data should be made by group, after clustering. In this sense, the most adequate approach is to use the Mahalanobis–Wasserstein distance as a measure of similarity between the cases, to carry out clustering or unsupervised classification. This methodology allows for the clustering of cases, as well as the identification of their profiles, based on the distribution of all the variables that characterises each subject associated with each case. An application to a set of teenagers' interviews regarding their habits of communication is described. The interviewees answered several questions about the kind of contacts they had on their phone, Facebook, email or messenger as well as the frequency of communication between them. The results indicate that the methodology is adequate to cluster this kind of data sets, since it allows us to identify and characterise different profiles from the data. We compare the results obtained with this methodology with the ones obtained using the entire database, and we conclude that they may lead to different findings.
Journal: Journal of Applied Statistics
Pages: 2848-2863
Issue: 15
Volume: 45
Year: 2018
Month: 11
X-DOI: 10.1080/02664763.2018.1442423
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1442423
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:15:p:2848-2863




Template-Type: ReDIF-Article 1.0
Author-Name: Alexander Hapfelmeier
Author-X-Name-First: Alexander
Author-X-Name-Last: Hapfelmeier
Author-Name: Kurt Ulm
Author-X-Name-First: Kurt
Author-X-Name-Last: Ulm
Author-Name: Bernhard Haller
Author-X-Name-First: Bernhard
Author-X-Name-Last: Haller
Title: Subgroup identification by recursive segmentation
Abstract: 
 A new modeling approach called ‘recursive segmentation’ is proposed to support the supervised exploration and identification of subgroups or clusters. It is based on the frameworks of recursive partitioning and the Patient Rule Induction Method (PRIM). Through combining these methods, recursive segmentation aims to exploit their respective strengths while reducing their weaknesses. Consequently, recursive segmentation can be applied in a very general way, that is in any (multivariate) regression, classification or survival (time-to-event) problem, using conditional inference, evolutionary learning or the CART algorithm, with predictor variables of any scale and with missing values. Furthermore, results of a synthetic example and a benchmark application study that comprises 26 data sets suggest that recursive segmentation achieves a competitive prediction accuracy and provides more accurate definitions of subgroups by models of less complexity as compared to recursive partitioning and PRIM. An application to the German Breast Cancer Study Group data demonstrates the improved interpretability and reliability of results produced by the new approach. The method is made publicly available through the R-package rseg (http://rseg.r-forge.r-project.org/).
Journal: Journal of Applied Statistics
Pages: 2864-2887
Issue: 15
Volume: 45
Year: 2018
Month: 11
X-DOI: 10.1080/02664763.2018.1444152
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1444152
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:45:y:2018:i:15:p:2864-2887




Template-Type: ReDIF-Article 1.0
Author-Name: Hongyue Guo
Author-X-Name-First: Hongyue
Author-X-Name-Last: Guo
Author-Name: Xiaodong Liu
Author-X-Name-First: Xiaodong
Author-X-Name-Last: Liu
Author-Name: Zhubin Sun
Author-X-Name-First: Zhubin
Author-X-Name-Last: Sun
Title: Multivariate time series prediction using a hybridization of VARMA models and Bayesian networks
Abstract: 
 In this paper, a new hybrid model of vector autoregressive moving average (VARMA) models and Bayesian networks is proposed to improve the forecasting performance of multivariate time series. In the proposed model, the VARMA model, which is a popular linear model in time series forecasting, is specified to capture the linear characteristics. Then the errors of the VARMA model are clustered into some trends by K-means algorithm with Krzanowski–Lai cluster validity index determining the number of trends, and a Bayesian network is built to learn the relationship between the data and the trend of its corresponding VARMA error. Finally, the estimated values of the VARMA model are compensated by the probabilities of their corresponding VARMA errors belonging to each trend, which are obtained from the Bayesian network. Compared with VARMA models, the experimental results with a simulation study and two multivariate real-world data sets indicate that the proposed model can effectively improve the prediction performance.
Journal: Journal of Applied Statistics
Pages: 2897-2909
Issue: 16
Volume: 43
Year: 2016
Month: 12
X-DOI: 10.1080/02664763.2016.1155111
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1155111
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:16:p:2897-2909




Template-Type: ReDIF-Article 1.0
Author-Name: N. Nakhaei Rad
Author-X-Name-First: N.
Author-X-Name-Last: Nakhaei Rad
Author-Name: G.R. Mohtashami Borzadaran
Author-X-Name-First: G.R.
Author-X-Name-Last: Mohtashami Borzadaran
Author-Name: G.H. Yari
Author-X-Name-First: G.H.
Author-X-Name-Last: Yari
Title: Maximum entropy estimation of income share function from generalized Gini index
Abstract: 
 Following Sir Anthony and Atkinson who started thinking about the insensitivity of the Gini index to income shares of the lower and the upper income groups, a generalization of the classical Gini index was introduced by Kakwani, Donaldson, Weymark and Yitzhaki which is sensitive to both high and low incomes. In this paper, the maximum entropy method is used to estimate the underlying true income share function based on the limited information of the generalized Gini index about the income shares of a population's percentiles. The income share function is estimated through maximizing both the Shannon entropy and the second-order entropy. In the end, through parametric bootstrap and analyzing a real dataset, the results are compared with the estimator of the share function, which is obtained based on the total information. In contrast to the classic Gini index, the derived share function based on the generalized Gini index provides more accurate approximations for income shares of the lower and the upper percentiles.
Journal: Journal of Applied Statistics
Pages: 2910-2921
Issue: 16
Volume: 43
Year: 2016
Month: 12
X-DOI: 10.1080/02664763.2016.1155112
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1155112
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:16:p:2910-2921




Template-Type: ReDIF-Article 1.0
Author-Name: T.-F.C. Lu
Author-X-Name-First: T.-F.C.
Author-X-Name-Last: Lu
Author-Name: C.-M. Hsu
Author-X-Name-First: C.-M.
Author-X-Name-Last: Hsu
Author-Name: K.-H. Shu
Author-X-Name-First: K.-H.
Author-X-Name-Last: Shu
Author-Name: S.-C. Weng
Author-X-Name-First: S.-C.
Author-X-Name-Last: Weng
Author-Name: C.-M. Chen
Author-X-Name-First: C.-M.
Author-X-Name-Last: Chen
Title: Joint analysis of longitudinal data and competing terminal events in the presence of dependent observation times with application to chronic kidney disease
Abstract: 
 In many prospective clinical and biomedical studies, longitudinal biomarkers are repeatedly measured as health indicators to evaluate disease progression when patients are followed up over a period of time. Patient visiting times can be referred to as informative observation times if they are assumed to carry information in addition to that of the longitudinal biomarker measures alone. Irregular visiting times may reflect compliance with physician instruction, disease progression and symptom severity. When the follow-up time may be stopped by competing terminal events, it is possible that patient observation times may correlate with the competing terminal events themselves, thus making the observation times difficult to assess. To explicitly account for the impact of competing terminal events and dependent observation times on the longitudinal data analysis in the context of such complex data, we propose a joint model using latent random effects to describe the association among them. A likelihood-based approach is derived for statistical inference. Extensive simulation studies reveal that the proposed approach performs well for practical situations, and an analysis of patients with chronic kidney disease in a cohort study is presented to illustrate the proposed method.
Journal: Journal of Applied Statistics
Pages: 2922-2940
Issue: 16
Volume: 43
Year: 2016
Month: 12
X-DOI: 10.1080/02664763.2016.1155202
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1155202
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:16:p:2922-2940




Template-Type: ReDIF-Article 1.0
Author-Name: Xianhua Dai
Author-X-Name-First: Xianhua
Author-X-Name-Last: Dai
Author-Name: Wolfgang Karl Härdle
Author-X-Name-First: Wolfgang Karl
Author-X-Name-Last: Härdle
Author-Name: Keming Yu
Author-X-Name-First: Keming
Author-X-Name-Last: Yu
Title: Do maternal health problems influence child's worrying status? Evidence from the British Cohort Study
Abstract: 
 Conventional methods apply symmetric prior distributions such as a normal distribution or a Laplace distribution for regression coefficients, which may be suitable for median regression and exhibit no robustness to outliers. This work develops a quantile regression on linear panel data model without heterogeneity from a Bayesian point of view, i.e. upon a location-scale mixture representation of the asymmetric Laplace error distribution, and provides how the posterior distribution is summarized using Markov chain Monte Carlo methods. Applying this approach to the 1970 British Cohort Study (BCS) data, it finds that a different maternal health problem has different influence on child's worrying status at different quantiles. In addition, applying stochastic search variable selection for maternal health problems to the 1970 BCS data, it finds that maternal nervous breakdown, among the 25 maternal health problems, contributes most to influence the child's worrying status.
Journal: Journal of Applied Statistics
Pages: 2941-2955
Issue: 16
Volume: 43
Year: 2016
Month: 12
X-DOI: 10.1080/02664763.2016.1155203
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1155203
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:16:p:2941-2955




Template-Type: ReDIF-Article 1.0
Author-Name: F. Louzada
Author-X-Name-First: F.
Author-X-Name-Last: Louzada
Author-Name: P. H. Ferreira
Author-X-Name-First: P. H.
Author-X-Name-Last: Ferreira
Title: Modified inference function for margins for the bivariate clayton copula-based SUN Tobit Model
Abstract: 
 This paper extends the analysis of the bivariate Seemingly Unrelated Regression (SUN) Tobit model by modeling its nonlinear dependence structure through the Clayton copula. The ability to capture/model the lower tail dependence of the SUN Tobit model where some data are censored (generally, left-censored at zero) is an useful feature of the Clayton copula. We propose a modified version of the (classical) Inference Function for Margins (IFS) method by Joe and XP [H. Joe and J.J. XP, The estimation method of inference functions for margins for multivariate models, Tech. Rep. 166, Department of Statistics, University of British Columbia, 1996], which we refer to as Modified Inference Function for Margins (MIFF) method, to obtain the (point) estimates of the marginal and Clayton copula parameters. More specifically, we employ the (frequenting) data augmentation technique at the second stage of the IFS method (the first stage of the MIFF method is equivalent to the first stage of the IFS method) to generate the censored observations and then estimate the Clayton copula parameter. This process (data augmentation and copula parameter estimation) is repeated until convergence. Such modification at the second stage of the usual estimation method is justified in order to obtain continuous marginal distributions, which ensures the uniqueness of the resulting Clayton copula, as stated by Solar's [A. Solar, Fonctions de répartition à n dimensions et leurs marges, Publ. de l'Institut de Statistique de l'Université de Paris 8 (1959), pp. 229–231] theorem; and also to provide an unbiased estimate of the association parameter (the IFS method provides a biased estimate of the Clayton copula parameter in the presence of censored observations in both margins). Since the usual asymptotic approach, that is the computation of the asymptotic covariance matrix of the parameter estimates, is troublesome in this case, we also propose the use of resampling procedures (bootstrap methods, such as standard normal and percentile, by Efron and Tibshirani [B. Efron and R.J. Tibshirani, An Introduction to the Bootstrap, Chapman &amp; Hall, New York, 1993] to obtain confidence intervals for the model parameters.
Journal: Journal of Applied Statistics
Pages: 2956-2976
Issue: 16
Volume: 43
Year: 2016
Month: 12
X-DOI: 10.1080/02664763.2016.1155204
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1155204
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:16:p:2956-2976




Template-Type: ReDIF-Article 1.0
Author-Name: Pedro P. Mota
Author-X-Name-First: Pedro P.
Author-X-Name-Last: Mota
Author-Name: Manuel L. Esquível
Author-X-Name-First: Manuel L.
Author-X-Name-Last: Esquível
Title: Model selection for stock prices data
Abstract: 
 The geometric Brownian motion (GBM) is very popular in modeling the dynamics of stock prices. However, the constant volatility assumption is questionable and many models with nonconstant volatility have been developed. In the papers [7,12] the authors introduce a regime switching process where in each regime the process is driven by GBM and the change in regime is defined by the crossing of a threshold. In this paper we used Akaike's and Bayesian information criteria to show that the GBM with regimes provides a better fit than the GBM. We also perform a forecasting comparison of the models for two selected companies.
Journal: Journal of Applied Statistics
Pages: 2977-2987
Issue: 16
Volume: 43
Year: 2016
Month: 12
X-DOI: 10.1080/02664763.2016.1155205
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1155205
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:16:p:2977-2987




Template-Type: ReDIF-Article 1.0
Author-Name: Hadi Alizadeh Noughabi
Author-X-Name-First: Hadi
Author-X-Name-Last: Alizadeh Noughabi
Author-Name: Albert Vexler
Author-X-Name-First: Albert
Author-X-Name-Last: Vexler
Title: An efficient correction to the density-based empirical likelihood ratio goodness-of-fit test for the inverse Gaussian distribution
Abstract: 
 The inverse Gaussian (IG) distribution is widely used to model positively skewed data. An important issue is to develop a powerful goodness-of-fit test for the IG distribution. We propose and examine novel test statistics for testing the IG goodness of fit based on the density-based empirical likelihood (EL) ratio concept. To construct the test statistics, we use a new approach that employs a method of the minimization of the discrimination information loss estimator to minimize Kullback–Leibler type information. The proposed tests are shown to be consistent against wide classes of alternatives. We show that the density-based EL ratio tests are more powerful than the corresponding classical goodness-of-fit tests. The practical efficiency of the tests is illustrated by using real data examples.
Journal: Journal of Applied Statistics
Pages: 2988-3003
Issue: 16
Volume: 43
Year: 2016
Month: 12
X-DOI: 10.1080/02664763.2016.1156657
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1156657
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:16:p:2988-3003




Template-Type: ReDIF-Article 1.0
Author-Name: Guy Cafri
Author-X-Name-First: Guy
Author-X-Name-Last: Cafri
Title: Sparsely clustered survival data: application to the evaluation of the safety and effectiveness of medical devices
Abstract: 
 The effectiveness and safety of implantable medical devices is a critical public health concern. We consider analysis of data in which it is of interest to compare devices but some individuals may be implanted with two or more devices. Our motivating example is based on orthopedic devices, where the same individual can be implanted with as many as two devices for the same joint but on different sides of the body, referred to as bilateral cases. Different methods of analysis are considered in a simulation study and real data example, including both marginal and conditional survival models, fitting single and separate models for bilateral and non-bilateral cases, and combining estimates from these two models. The results of simulations suggest that in the context of orthopedic devices, where implants failures are rare, models fit on both bilateral and non-bilateral cases simultaneously could be quite misleading, and that combined estimates from fitting two separate models performed better under homogeneity. A real data example illustrates the issues surrounding analysis of orthopedic device data with bilateral cases. Our findings suggest that research studies of orthopedic devices should at minimum consider fitting separate models to bilateral and non-bilateral cases.
Journal: Journal of Applied Statistics
Pages: 3004-3014
Issue: 16
Volume: 43
Year: 2016
Month: 12
X-DOI: 10.1080/02664763.2016.1157143
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1157143
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:16:p:3004-3014




Template-Type: ReDIF-Article 1.0
Author-Name: Antonio Canale
Author-X-Name-First: Antonio
Author-X-Name-Last: Canale
Author-Name: Euloge Clovis Kenne Pagui
Author-X-Name-First: Euloge Clovis
Author-X-Name-Last: Kenne Pagui
Author-Name: Bruno Scarpa
Author-X-Name-First: Bruno
Author-X-Name-Last: Scarpa
Title: Bayesian modeling of university first-year students' grades after placement test
Abstract: 
 University first-year students grades are naturally correlated with the scores obtained at placement tests. Often this characteristic leads the university grades in the first exams to be asymmetrically distributed. Motivated by the analysis of grades of the basic Statistics examination of first-year students, we discuss informative priors for the shape parameter of the skew-normal model, a class of distribution which account for several degree of asymmetry. Our proposed prior leads to closed-form full-conditional posterior distributions, particularly useful in Markov Chain Monte Carlo simulation. A Gibbs sampling algorithm is discussed for the joint vector of parameters and the method is applied to a real data set from the School of Economics, University of Padua, Italy. Our analysis reveals that the correlation between the placement test and the grades of first-year students leads to a measurable positive skewness of the distribution of the university grades.
Journal: Journal of Applied Statistics
Pages: 3015-3029
Issue: 16
Volume: 43
Year: 2016
Month: 12
X-DOI: 10.1080/02664763.2016.1157144
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1157144
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:16:p:3015-3029




Template-Type: ReDIF-Article 1.0
Author-Name: María Concepción Pérez-Cárceles
Author-X-Name-First: María Concepción
Author-X-Name-Last: Pérez-Cárceles
Author-Name: Juan Cándido Gómez-Gallego
Author-X-Name-First: Juan Cándido
Author-X-Name-Last: Gómez-Gallego
Author-Name: Juan Gómez-García
Author-X-Name-First: Juan
Author-X-Name-Last: Gómez-García
Title: Distribution of cost inefficiency in stochastic frontier approach: evidence from Spanish banking
Abstract: 
 The aim of the paper is to demonstrate the appropriateness of an a priori analysis to determine the distributional assumption of the inefficiency term in a stochastic frontier model. To this end, theoretical distributions of estimated inefficiency were obtained when the inefficiency term is assumed to be distributed as a half normal and an exponential in a cost frontier model. Comparisons of such theoretical distributions with the respective cost inefficiency estimators using the goodness of fit test allow selecting the most appropriate distributional assumption. The application on three data sets of Spanish banking system in 2009 demonstrated the relevance of the research question. First, the results of estimated cost inefficiency with a half normal assumption are larger than with an exponential distribution significantly. Besides, half normal assumption was rejected and exponential was not rejected as the most appropriate distribution of inefficiency term in Spanish banking data set. However, the adjustment of saving banks data had been better with the former distribution than the latter. In the case of banks, any distribution results appropriate. To sum up, this work demonstrate that the distributional assumption on inefficiency term in Stochastic Frontier Approach must be established in a justified way, as it can significantly bias the results of estimated inefficiency and therefore, influences improving policies and strategies in the Spanish banking sector.
Journal: Journal of Applied Statistics
Pages: 3030-3041
Issue: 16
Volume: 43
Year: 2016
Month: 12
X-DOI: 10.1080/02664763.2016.1161737
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1161737
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:16:p:3030-3041




Template-Type: ReDIF-Article 1.0
Author-Name: The Editors
Title: Corrigendum
Journal: Journal of Applied Statistics
Pages: 3042-3042
Issue: 16
Volume: 43
Year: 2016
Month: 12
X-DOI: 10.1080/02664763.2016.1226041
File-URL: http://hdl.handle.net/10.1080/02664763.2016.1226041
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:43:y:2016:i:16:p:3042-3042




Template-Type: ReDIF-Article 1.0
Author-Name: Harlan Campbell
Author-X-Name-First: Harlan
Author-X-Name-Last: Campbell
Title: Is it even rainier in North Vancouver? A non-parametric rank-based test for semicontinuous longitudinal data
Abstract: 
 When the outcome of interest is semicontinuous and collected longitudinally, efficient testing can be difficult. Daily rainfall data is an excellent example which we use to illustrate the various challenges. Even under the simplest scenario, the popular ‘two-part model’, which uses correlated random-effects to account for both the semicontinuous and longitudinal characteristics of the data, often requires prohibitively intensive numerical integration and difficult interpretation. Reducing data to binary (truncating continuous positive values to equal one), while relatively straightforward, leads to a potentially substantial loss in power. We propose an alternative: using a non-parametric rank test recently proposed for joint longitudinal survival data. We investigate the potential benefits of such a test for the analysis of semicontinuous longitudinal data with regards to power and computational feasibility.
Journal: Journal of Applied Statistics
Pages: 1155-1176
Issue: 7
Volume: 46
Year: 2019
Month: 5
X-DOI: 10.1080/02664763.2018.1536878
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1536878
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:7:p:1155-1176




Template-Type: ReDIF-Article 1.0
Author-Name: Steven E. Kozlowski
Author-X-Name-First: Steven E.
Author-X-Name-Last: Kozlowski
Author-Name: Thaddeus Sim
Author-X-Name-First: Thaddeus
Author-X-Name-Last: Sim
Title: Predicting recessions using trends in the yield spread
Abstract: 
 The yield spread, measured as the difference between long- and short-term interest rates, is widely regarded as one of the strongest predictors of economic recessions. In this paper, we propose an enhanced recession prediction model that incorporates trends in the value of the yield spread. We expect our model to generate stronger recession signals because a steadily declining value of the yield spread typically indicates growing pessimism associated with the reduced future business activity. We capture trends in the yield spread by considering both the level of the yield spread at a lag of 12 months as well as its value at each of the previous two quarters leading up to the forecast origin, and we evaluate its predictive abilities using both logit and artificial neural network models. Our results indicate that models incorporating information from the time series of the yield spread correctly predict future recession periods much better than models only considering the spread value as of the forecast origin. Furthermore, the results are strongest for our artificial neural network model and logistic regression model that includes interaction terms, which we confirm using both a blocked cross-validation technique as well as an expanding estimation window approach.
Journal: Journal of Applied Statistics
Pages: 1323-1335
Issue: 7
Volume: 46
Year: 2019
Month: 5
X-DOI: 10.1080/02664763.2018.1537364
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1537364
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:7:p:1323-1335




Template-Type: ReDIF-Article 1.0
Author-Name: Jaeoh Kim
Author-X-Name-First: Jaeoh
Author-X-Name-Last: Kim
Author-Name: HyungJun Cho
Author-X-Name-First: HyungJun
Author-X-Name-Last: Cho
Title: Seemingly unrelated regression tree
Abstract: 
 Nonparametric seemingly unrelated regression provides a powerful alternative to parametric seemingly unrelated regression for relaxing the linearity assumption. The existing methods are limited, particularly with sharp changes in the relationship between the predictor variables and the corresponding response variable. We propose a new nonparametric method for seemingly unrelated regression, which adopts a tree-structured regression framework, has satisfiable prediction accuracy and interpretability, no restriction on the inclusion of categorical variables, and is less vulnerable to the curse of dimensionality. Moreover, an important feature is constructing a unified tree-structured model for multivariate data, even though the predictor variables corresponding to the response variable are entirely different. This unified model can offer revelatory insights such as underlying economic meaning. We propose the key factors of tree-structured regression, which are an impurity function detecting complex nonlinear relationships between the predictor variables and the response variable, split rule selection with negligible selection bias, and tree size determination solving underfitting and overfitting problems. We demonstrate our proposed method using simulated data and illustrate it using data from the Korea stock exchange sector indices.
Journal: Journal of Applied Statistics
Pages: 1177-1195
Issue: 7
Volume: 46
Year: 2019
Month: 5
X-DOI: 10.1080/02664763.2018.1538327
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1538327
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:7:p:1177-1195




Template-Type: ReDIF-Article 1.0
Author-Name: T. Kirschstein
Author-X-Name-First: T.
Author-X-Name-Last: Kirschstein
Author-Name: Steffen Liebscher
Author-X-Name-First: Steffen
Author-X-Name-Last: Liebscher
Title: Assessing the market values of soccer players – a robust analysis of data from German 1. and 2. Bundesliga
Abstract: 
 The paper examines to what extent a player's market value depends on his skills. Therefore, a data set covering 28 performance measures and the market values of about 493 players from 1. and 2. German Bundesliga is analysed. Applying robust analysis techniques, we are able to robustly estimate market values of soccer players. The results show (1) that there are significantly underrated and overrated players and (2) that a player's affiliation to a certain team may contribute to his market value. We conclude that a club's reputation affects the market values of its players and that star players are in tendency overrated.
Journal: Journal of Applied Statistics
Pages: 1336-1349
Issue: 7
Volume: 46
Year: 2019
Month: 5
X-DOI: 10.1080/02664763.2018.1540689
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1540689
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:7:p:1336-1349




Template-Type: ReDIF-Article 1.0
Author-Name: Mohammad Vali Ahmadi
Author-X-Name-First: Mohammad Vali
Author-X-Name-Last: Ahmadi
Author-Name: Mahdi Doostparast
Author-X-Name-First: Mahdi
Author-X-Name-Last: Doostparast
Title: Pareto analysis for the lifetime performance index of products on the basis of progressively first-failure-censored batches under balanced symmetric and asymmetric loss functions
Abstract: 
 One of the most important topics in manufacturing industries is the evaluation of performance lifetimes of products. Based on a given lifetime performance index, this paper deals with evaluating the performance of a process subject to a given lower specification limit. We confine ourselves to the progressively first-failure-censored data coming from a common Pareto distribution. With both the Bayesian and the non-Bayesian approaches being investigated here, we pay more attention to Bayesian estimators under balanced type loss functions. The results are presented under the balanced versions of two well-known loss functions, namely the squared error loss and the Varian's linear-exponential (LINEX) loss. Moreover, based on the Bayesian and the non-Bayesian approaches, the problem of testing hypotheses on the lifetime performance index is studied. Also, a simulation study is performed to assess the obtained results. Finally, two illustrative examples are given.
Journal: Journal of Applied Statistics
Pages: 1196-1227
Issue: 7
Volume: 46
Year: 2019
Month: 5
X-DOI: 10.1080/02664763.2018.1541170
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1541170
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:7:p:1196-1227




Template-Type: ReDIF-Article 1.0
Author-Name: Yilin Li
Author-X-Name-First: Yilin
Author-X-Name-Last: Li
Author-Name: Ming Zheng
Author-X-Name-First: Ming
Author-X-Name-Last: Zheng
Author-Name: Wen Yu
Author-X-Name-First: Wen
Author-X-Name-Last: Yu
Title: A semiparametric type II Tobit model for time-to-event data with application to a merger and acquisition study
Abstract: 
 Merger and acquisition is an important corporate strategy. We collect recent merger and acquisition data for companies on the China A-share stock market to explore the relationship between corporate ownership structure and speed of merger success. When studying merger success, selection bias occurs if only completed mergers are analyzed. There is also a censoring problem when duration time is used to measure the speed. In this article, for time-to-event outcomes, we propose a semiparametric version of the type II Tobit model that can simultaneously handle selection bias and right censoring. The proposed model can also easily incorporate time-dependent covariates. A nonparametric maximum likelihood estimator is proposed. The resulting estimators are shown to be consistent, asymptotically normal, and semiparametrically efficient. Some Monte Carlo studies are carried out to assess the finite-sample performance of the proposed approach. Using the proposed model, we find that higher power balance of a company is associated with faster merger success.
Journal: Journal of Applied Statistics
Pages: 1228-1245
Issue: 7
Volume: 46
Year: 2019
Month: 5
X-DOI: 10.1080/02664763.2018.1541969
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1541969
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:7:p:1228-1245




Template-Type: ReDIF-Article 1.0
Author-Name: Ram C. Kafle
Author-X-Name-First: Ram C.
Author-X-Name-Last: Kafle
Author-Name: Keshav P. Pokhrel
Author-X-Name-First: Keshav P.
Author-X-Name-Last: Pokhrel
Author-Name: Netra Khanal
Author-X-Name-First: Netra
Author-X-Name-Last: Khanal
Author-Name: Chris P. Tsokos
Author-X-Name-First: Chris P.
Author-X-Name-Last: Tsokos
Title: Differential equation model of carbon dioxide emission using functional linear regression
Abstract: 
 Carbon dioxide is one of the major contributors to Global Warming. In the present study, we develop a differential equation to model the carbon dioxide emission data in the atmosphere using functional linear regression approach. In the proposed method, a differential operator is defined as data smoother and we use the penalized least square fitting criteria to smooth the data. The profile error sum of squares is optimized to estimate the differential operators using functional regression. The solution of the developed differential equation estimates and predicts the rate of change of carbon dioxide in the atmosphere at a particular time. We apply the proposed model to fit the emission of carbon dioxide data in the continental United States. Numerical simulations of a number of test cases depict a satisfactory agreement with real data.
Journal: Journal of Applied Statistics
Pages: 1246-1259
Issue: 7
Volume: 46
Year: 2019
Month: 5
X-DOI: 10.1080/02664763.2018.1542667
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1542667
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:7:p:1246-1259




Template-Type: ReDIF-Article 1.0
Author-Name: Antonio Punzo
Author-X-Name-First: Antonio
Author-X-Name-Last: Punzo
Title: A new look at the inverse Gaussian distribution with applications to insurance and economic data
Abstract: 
 Insurance and economic data are often positive, and we need to take into account this peculiarity in choosing a statistical model for their distribution. An example is the inverse Gaussian (IG), which is one of the most famous and considered distributions with positive support. With the aim of increasing the use of the IG distribution on insurance and economic data, we propose a convenient mode-based parameterization yielding the reparametrized IG (rIG) distribution; it allows/simplifies the use of the IG distribution in various branches of statistics, and we give some examples. In nonparametric statistics, we define a smoother based on rIG kernels. By construction, the estimator is well-defined and does not allocate probability mass to unrealistic negative values. We adopt likelihood cross-validation to select the smoothing parameter. In robust statistics, we propose the contaminated IG distribution, a heavy-tailed generalization of the rIG distribution to accommodate mild outliers. Finally, for model-based clustering and semiparametric density estimation, we present finite mixtures of rIG distributions. We use the EM algorithm to obtain maximum likelihood estimates of the parameters of the mixture and contaminated models. We use insurance data about bodily injury claims, and economic data about incomes of Italian households, to illustrate the models.
Journal: Journal of Applied Statistics
Pages: 1260-1287
Issue: 7
Volume: 46
Year: 2019
Month: 5
X-DOI: 10.1080/02664763.2018.1542668
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1542668
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:7:p:1260-1287




Template-Type: ReDIF-Article 1.0
Author-Name: A. M. Elsawah
Author-X-Name-First: A. M.
Author-X-Name-Last: Elsawah
Author-Name: Kai-Tai Fang
Author-X-Name-First: Kai-Tai
Author-X-Name-Last: Fang
Title: A catalog of optimal foldover plans for constructing U-uniform minimum aberration four-level combined designs
Abstract: 
 The foldover plan is a transformation map for adding a foldover design to the initial design, thus resulting in a combined design which can be used for breaking the links between aliased effects. This paper discusses the optimality of foldover plans for four-level designs via the most common criteria: the generalized word-length pattern (GWLP), Lee discrepancy (LD), wrap-around discrepancy (WD), centered discrepancy (CD) and mixture discrepancy (MD). We prove that the LD, WD and GWLP are equivalent for: any initial design and the corresponding foldover designs; any foldover design and its complementary foldover design; and any combined design and its complementary combined design. However, these interesting properties do not necessarily take place in the cases of the CD and MD. New analytical expressions and lower bounds of these discrepancies are given for initial and combined designs, which can be used as benchmarks for constructing uniform designs. For illustration of the usage of our theoretical results, a catalog of optimal foldover plans for constructing U-uniform minimum aberration four-level combined designs that involve  $ 2\leq m\leq 10 $ 2≤m≤10 factors with  $ 8\leq n\leq 52 $ 8≤n≤52 run is tabulated, which can be used for investigating either qualitative or quantitative factors.
Journal: Journal of Applied Statistics
Pages: 1288-1322
Issue: 7
Volume: 46
Year: 2019
Month: 5
X-DOI: 10.1080/02664763.2018.1545013
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1545013
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:7:p:1288-1322




Template-Type: ReDIF-Article 1.0
Author-Name: Shi-Fang Qiu
Author-X-Name-First: Shi-Fang
Author-X-Name-Last: Qiu
Author-Name: Li-Xuan Guo
Author-X-Name-First: Li-Xuan
Author-X-Name-Last: Guo
Author-Name: G. Y. Zou
Author-X-Name-First: G. Y.
Author-X-Name-Last: Zou
Author-Name: Dan Yu
Author-X-Name-First: Dan
Author-X-Name-Last: Yu
Title: Tests for homogeneity of risk differences in stratified design with correlated bilateral data
Abstract: 
 Correlated bilateral data arise from stratified studies involving paired body organs in a subject. When it is desirable to conduct inference on the scale of risk difference, one needs first to assess the assumption of homogeneity in risk differences across strata. For testing homogeneity of risk differences, we herein propose eight methods derived respectively from weighted-least-squares (WLS), the Mantel-Haenszel (MH) estimator, the WLS method in combination with inverse hyperbolic tangent transformation, and the test statistics based on their log-transformation, the modified Score test statistic and Likelihood ratio test statistic. Simulation results showed that four of the tests perform well in general, with the tests based on the WLS method and inverse hyperbolic tangent transformation always performing satisfactorily even under small sample size designs. The methods are illustrated with a dataset.
Journal: Journal of Applied Statistics
Pages: 2491-2513
Issue: 14
Volume: 46
Year: 2019
Month: 10
X-DOI: 10.1080/02664763.2019.1601162
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1601162
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:14:p:2491-2513




Template-Type: ReDIF-Article 1.0
Author-Name: Zhi Liu
Author-X-Name-First: Zhi
Author-X-Name-Last: Liu
Author-Name: Tie Zhang
Author-X-Name-First: Tie
Author-X-Name-Last: Zhang
Title: A second-order fuzzy time series model for stock price analysis
Abstract: 
 It is difficult to model stock market because of its uncertainty. Many methods have been introduced to tackle these difficulties, in which fuzzy time series has shown its advantages in dealing with fuzzy and uncertainty data. In recent years, many researchers have applied the fuzzy time series to analyze and forecast the stock price, and how to improve the accuracy of forecasting has attracted many researchers. In this paper, the data are first preprocessed and a new way to divide the universe of discourse is given, after which the data are fuzzified applying the triangular membership function, then three-layer back propagation (BP) neural network is established. Finally, the generalized inverse fuzzy number formula is applied to defuzzify the relation obtained with the prediction results. The proposed method is applied to predict the stock price of State Bank of India (SBI) and Dow-Jones Industrial Average (DJIA). The experimental results show that the proposed method can greatly improve the accuracy of forecasting. Furthermore, the proposed method is not sensitive to its parameters.
Journal: Journal of Applied Statistics
Pages: 2514-2526
Issue: 14
Volume: 46
Year: 2019
Month: 10
X-DOI: 10.1080/02664763.2019.1601163
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1601163
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:14:p:2514-2526




Template-Type: ReDIF-Article 1.0
Author-Name: Juliana Cobre
Author-X-Name-First: Juliana
Author-X-Name-Last: Cobre
Author-Name: Fabiana Arca Cruz Tortorelli
Author-X-Name-First: Fabiana Arca Cruz
Author-X-Name-Last: Tortorelli
Author-Name: Sandra Cristina de Oliveira
Author-X-Name-First: Sandra Cristina
Author-X-Name-Last: de Oliveira
Title: Modelling two types of heterogeneity in the analysis of student success
Abstract: 
 Student dropout is a worldwide problem, leading private and public universities in developed and underdeveloped countries to study the subject carefully or, as has recently been done, to analyse what drives student success. On this matter, different approaches are used to obtain useful information for decision-making. We propose a model that considers the enrolment date to the dropout or graduation date and also covariates to measure student success rates, to identify what the academic and non-academic factors are, and how they drive the student success. Our proposal assumes that there is one part of the population who is not at risk of dropping out, and that the part of the population at risk is heterogeneous, that is, we assume two types of heterogeneity. We highlight two advantages of our model: one is to identify the period of higher risk to dropout due to considering the academic survival time and the second is due to the inclusion of covariates that enable us to identify the characteristics linked to dropout. In this research, we also demonstrate the identifiability of the model and describe the estimation procedures. To exemplify the applicability of the approach, we use two real datasets.
Journal: Journal of Applied Statistics
Pages: 2527-2539
Issue: 14
Volume: 46
Year: 2019
Month: 10
X-DOI: 10.1080/02664763.2019.1601164
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1601164
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:14:p:2527-2539




Template-Type: ReDIF-Article 1.0
Author-Name: Soeun Kim
Author-X-Name-First: Soeun
Author-X-Name-Last: Kim
Author-Name: Woojoo Lee
Author-X-Name-First: Woojoo
Author-X-Name-Last: Lee
Title: Discovering hidden statistical issues through individual-level models in ecological studies
Abstract: 
 In ecological studies, individual inference is made based on results from ecological models. Interpretation of the results requires caution since ecological analysis on group level may not hold in the individual level within the groups, leading to ecological fallacy. Using an ecological regression example for analyzing voting behaviors, we highlight that the explicit use of individual-level models is crucial in understanding the results of ecological studies. In particular, we clarify three relevant statistical issues for each individual-level models: assessment of the uncertainty of parameter estimates obtained from a wrong model, the use of shrinkage estimation method for simultaneous estimation of many parameters, and the necessity of sensitivity analysis rather than adhering to one seemingly most compelling assumption.
Journal: Journal of Applied Statistics
Pages: 2540-2552
Issue: 14
Volume: 46
Year: 2019
Month: 10
X-DOI: 10.1080/02664763.2019.1601687
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1601687
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:14:p:2540-2552




Template-Type: ReDIF-Article 1.0
Author-Name: Il Do Ha
Author-X-Name-First: Il Do
Author-X-Name-Last: Ha
Author-Name: Jong-Min Kim
Author-X-Name-First: Jong-Min
Author-X-Name-Last: Kim
Author-Name: Takeshi Emura
Author-X-Name-First: Takeshi
Author-X-Name-Last: Emura
Title: Profile likelihood approaches for semiparametric copula and frailty models for clustered survival data
Abstract: 
 In clustered survival data, the dependence among individual survival times within a cluster has usually been described using copula models and frailty models. In this paper we propose a profile likelihood approach for semiparametric copula models with different cluster sizes. We also propose a likelihood ratio method based on profile likelihood for testing the absence of association parameter (i.e. test of independence) under the copula models, leading to the boundary problem of the parameter space. For this purpose, we show via simulation study that the proposed likelihood ratio method using an asymptotic chi-square mixture distribution performs well as sample size increases. We compare the behaviors of the two models using the profile likelihood approach under a semiparametric setting. The proposed method is demonstrated using two well-known data sets.
Journal: Journal of Applied Statistics
Pages: 2553-2571
Issue: 14
Volume: 46
Year: 2019
Month: 10
X-DOI: 10.1080/02664763.2019.1601688
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1601688
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:14:p:2553-2571




Template-Type: ReDIF-Article 1.0
Author-Name: Louis Ehwerhemuepha
Author-X-Name-First: Louis
Author-X-Name-Last: Ehwerhemuepha
Author-Name: Heng Sok
Author-X-Name-First: Heng
Author-X-Name-Last: Sok
Author-Name: Cyril Rakovski
Author-X-Name-First: Cyril
Author-X-Name-Last: Rakovski
Title: A more powerful unconditional exact test of homogeneity for 2 × c contingency table analysis
Abstract: 
 The classical unconditional exact p-value test can be used to compare two multinomial distributions with small samples. This general hypothesis requires parameter estimation under the null which makes the test severely conservative. Similar property has been observed for Fisher's exact test with Barnard and Boschloo providing distinct adjustments that produce more powerful testing approaches. In this study, we develop a novel adjustment for the conservativeness of the unconditional multinomial exact p-value test that produces nominal type I error rate and increased power in comparison to all alternative approaches. We used a large simulation study to empirically estimate the 5th percentiles of the distributions of the p-values of the exact test over a range of scenarios and implemented a regression model to predict the values for two-sample multinomial settings. Our results show that the new test is uniformly more powerful than Fisher's, Barnard's, and Boschloo's tests with gains in power as large as several hundred percent in certain scenarios. Lastly, we provide a real-life data example where the unadjusted unconditional exact test wrongly fails to reject the null hypothesis and the corrected unconditional exact test rejects the null appropriately.
Journal: Journal of Applied Statistics
Pages: 2572-2582
Issue: 14
Volume: 46
Year: 2019
Month: 10
X-DOI: 10.1080/02664763.2019.1601689
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1601689
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:14:p:2572-2582




Template-Type: ReDIF-Article 1.0
Author-Name: F. S. Zaidi
Author-X-Name-First: F. S.
Author-X-Name-Last: Zaidi
Author-Name: P. Castagliola
Author-X-Name-First: P.
Author-X-Name-Last: Castagliola
Author-Name: K. P. Tran
Author-X-Name-First: K. P.
Author-X-Name-Last: Tran
Author-Name: M. B. C. Khoo
Author-X-Name-First: M. B. C.
Author-X-Name-Last: Khoo
Title: Performance of the hotelling T2 control chart for compositional data in the presence of measurement errors
Abstract: 
 In statistical process monitoring, the presence of measurement errors is known to impact the performance of control charts. This paper makes an attempt to investigate the performance of the Hotelling CoDa (Compositional Data)  $T^2 $T2 control chart in the presence of measurement errors. A linearly covariate measurement error model for CoDa is introduced to study the influence of the measurement device parameters ( $\sigma _{{\rm M}} $σM and b) and the number of independent observations m on the performance of the Hotelling CoDa  $T^2 $T2 control chart in the in- and out-of-control cases. A realistic illustrative example based on the production of muesli is used to illustrate the estimation of the measurement device parameters and the in-control process parameters, as well as to demonstrate the ability of this control chart to efficiently detect changes in the muesli composition.
Journal: Journal of Applied Statistics
Pages: 2583-2602
Issue: 14
Volume: 46
Year: 2019
Month: 10
X-DOI: 10.1080/02664763.2019.1605339
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1605339
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:14:p:2583-2602




Template-Type: ReDIF-Article 1.0
Author-Name: Chenlong Li
Author-X-Name-First: Chenlong
Author-X-Name-Last: Li
Author-Name: Zhanjie Song
Author-X-Name-First: Zhanjie
Author-X-Name-Last: Song
Author-Name: Wenjun Wang
Author-X-Name-First: Wenjun
Author-X-Name-Last: Wang
Author-Name: Xu (Sunny) Wang
Author-X-Name-First: Xu (Sunny)
Author-X-Name-Last: Wang
Title: Mining periodic patterns and cascading bursts phenomenon in individual e-mail communication
Abstract: 
 Quantitative understanding of human activity is very important as many social and economic trends are driven by human actions. We propose a novel stochastic process, the Multi-state Markov Cascading Non-homogeneous Poisson Process (M2CNPP), to analyze human e-mail communication involving both periodic patterns and bursts phenomenon. The model parameters are estimated using the Generalized Expectation Maximization (GEM) algorithm while the hidden states are treated as missing values. The empirical results demonstrate that the proposed model adequately captures the major temporal cascading features as well as the periodic patterns in e-mail communication.
Journal: Journal of Applied Statistics
Pages: 2603-2626
Issue: 14
Volume: 46
Year: 2019
Month: 10
X-DOI: 10.1080/02664763.2019.1605340
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1605340
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:14:p:2603-2626




Template-Type: ReDIF-Article 1.0
Author-Name: Fekadu L. Bayisa
Author-X-Name-First: Fekadu L.
Author-X-Name-Last: Bayisa
Author-Name: Jun Yu
Author-X-Name-First: Jun
Author-X-Name-Last: Yu
Title: Model-based computed tomography image estimation: partitioning approach
Abstract: 
 There is a growing interest to get a fully MR based radiotherapy. The most important development needed is to obtain improved bone tissue estimation. The existing model-based methods perform poorly on bone tissues. This paper was aimed at obtaining improved bone tissue estimation. Skew-Gaussian mixture model and Gaussian mixture model were proposed to investigate CT image estimation from MR images by partitioning the data into two major tissue types. The performance of the proposed models was evaluated using the leave-one-out cross-validation method on real data. In comparison with the existing model-based approaches, the model-based partitioning approach outperformed in bone tissue estimation, especially in dense bone tissue estimation.
Journal: Journal of Applied Statistics
Pages: 2627-2648
Issue: 14
Volume: 46
Year: 2019
Month: 10
X-DOI: 10.1080/02664763.2019.1606169
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1606169
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:14:p:2627-2648




Template-Type: ReDIF-Article 1.0
Author-Name: Hikaru Hasegawa
Author-X-Name-First: Hikaru
Author-X-Name-Last: Hasegawa
Author-Name: Kazuhiro Ueda
Author-X-Name-First: Kazuhiro
Author-X-Name-Last: Ueda
Title: Time use of married couples: Bayesian approach
Abstract: 
 The living hours data of individuals' time spent on daily activities are compositional and include many zeros because individuals do not pursue all activities every day. Thus, we should exercise caution in using such data for empirical analyses. The Bayesian method offers several advantages in analyzing compositional data. In this study, we analyze the time allocation of Japanese married couples using the Bayesian model. Based on the Bayes factors, we compare models that consider and do not consider the correlations between married couples' time use data. The model that considers the correlation shows superior performance. We show that the Bayesian method can adequately take into account the correlations of wives' and husbands' living hours, facilitating the calculation of partial effects that their activities' variables have on living hours. The partial effects of the model that considers the correlations between the couples' time use are easily calculated from the posterior results.
Journal: Journal of Applied Statistics
Pages: 2649-2665
Issue: 14
Volume: 46
Year: 2019
Month: 10
X-DOI: 10.1080/02664763.2019.1607833
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1607833
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:14:p:2649-2665




Template-Type: ReDIF-Article 1.0
Author-Name: Minkyung Oh
Author-X-Name-First: Minkyung
Author-X-Name-Last: Oh
Author-Name: Jungwon Mun
Author-X-Name-First: Jungwon
Author-X-Name-Last: Mun
Title: Diagnostics for repeated measurements in generalized linear mixed effects models
Abstract: 
 As there is an extensive body of research on diagnostics in regression models, various outlier detection methods have been developed. These methods have been extended to mixed effects models and generalized linear models, but there exist intrinsic drawbacks and limitations. This paper presents two-dimensional plots to identify discordant subjects and observations in generalized linear mixed effects models, displaying discordance in two directions. The sTudentized Residual Sum of Squares is not an extension of any regression tools but a new approach designed to efficiently reflect the characteristics of repeated measures. And this noteworthy clustering of outliers is identified in the plot. Applications to real-life examples are presented to illustrate the favorable/beneficial performance of the new tool.
Journal: Journal of Applied Statistics
Pages: 2666-2676
Issue: 14
Volume: 46
Year: 2019
Month: 10
X-DOI: 10.1080/02664763.2019.1608427
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1608427
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:14:p:2666-2676




Template-Type: ReDIF-Article 1.0
Author-Name: Van Cuong Nguyen
Author-X-Name-First: Van Cuong
Author-X-Name-Last: Nguyen
Author-Name: Chi Tim Ng
Author-X-Name-First: Chi Tim
Author-X-Name-Last: Ng
Title: Variable selection under multicollinearity using modified log penalty
Abstract: 
 To handle the multicollinearity issues in the regression analysis, a class of ‘strictly concave penalty function’ is described in this paper. As an example, a new penalty function called ‘modified log penalty’ is introduced. The penalized estimator based on strictly concave penalties enjoys the oracle property under certain regularity conditions discussed in the literature. In the multicollinearity cases where such conditions are not applicable, the behaviors of the strictly concave penalties are discussed through examples involving strongly correlated covariates. Real data examples and simulation studies are provided to show the finite-sample performance of the modified log penalty in terms of prediction error under scenarios exhibiting multicollinearity.
Journal: Journal of Applied Statistics
Pages: 201-230
Issue: 2
Volume: 47
Year: 2020
Month: 1
X-DOI: 10.1080/02664763.2019.1637829
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1637829
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:2:p:201-230




Template-Type: ReDIF-Article 1.0
Author-Name: Bing Wang
Author-X-Name-First: Bing
Author-X-Name-Last: Wang
Author-Name: Xiaoguang Wang
Author-X-Name-First: Xiaoguang
Author-X-Name-Last: Wang
Author-Name: Lixin Song
Author-X-Name-First: Lixin
Author-X-Name-Last: Song
Title: Estimation in the single change-point hazard function for interval-censored data with a cure fraction
Abstract: 
 In reliability or survival analysis, the hazard function plays a significant part for it can display the instantaneous failure rate at any time point. In practice, the abrupt change in hazard function at an unknown time point may occur after a maintenance activity or major operation. Under these circumstances, identifying the change point and estimating the size of the change are meaningful. In this paper, we assume that the hazard function is piecewise constant with a single jump at an unknown time. We propose the single change-point model for interval-censored survival data with a cure fraction. Estimation methods for the proposed model are investigated, and large-sample properties of the estimators are established. Simulation studies are carried out to evaluate the performance of the estimating method. The liver cancer data and breast cancer data are analyzed as the applications.
Journal: Journal of Applied Statistics
Pages: 231-247
Issue: 2
Volume: 47
Year: 2020
Month: 1
X-DOI: 10.1080/02664763.2019.1635571
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1635571
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:2:p:231-247




Template-Type: ReDIF-Article 1.0
Author-Name: Gebrenegus Ghilagaber
Author-X-Name-First: Gebrenegus
Author-X-Name-Last: Ghilagaber
Author-Name: Parfait Munezero
Author-X-Name-First: Parfait
Author-X-Name-Last: Munezero
Title: Bayesian change-point modelling of the effects of 3-points-for-a-win rule in football
Abstract: 
 We examine the effects of the 3-points-for-a-win (3pfaw) rule in the football world. Data that form the basis of our analyses come from seven leagues around the world (Albania, Brazil, England, Germany, Poland, Romania, and Scotland) and consist of mean goals and proportions of decided matches over a period of about six years before- and about seven years after the introduction of the rule in the respective leagues. Bayesian change-point analyses and Shiryaev-Roberts tests show that the rule had no effects on the mean goals but, indeed, had increasing effects on the proportions of decided matches in most of the leagues studied. This, in turn, implies that while the rule has given teams the incentive to aim at winning matches, such aim was not achieved by scoring excess goals. Instead, it was achieved by scoring enough goals in order to win and, at the same time, defending enough in order not to lose. Our results are in accordance with recent findings on comparing the values of attack and defense - that, in top-level football, not conceding a goal is more valuable than scoring a single goal.
Journal: Journal of Applied Statistics
Pages: 248-264
Issue: 2
Volume: 47
Year: 2020
Month: 1
X-DOI: 10.1080/02664763.2019.1635572
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1635572
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:2:p:248-264




Template-Type: ReDIF-Article 1.0
Author-Name: Artur J. Lemonte
Author-X-Name-First: Artur J.
Author-X-Name-Last: Lemonte
Author-Name: Germán Moreno-Arenas
Author-X-Name-First: Germán
Author-X-Name-Last: Moreno-Arenas
Author-Name: Fredy Castellares
Author-X-Name-First: Fredy
Author-X-Name-Last: Castellares
Title: Zero-inflated Bell regression models for count data
Abstract: 
 By starting from the one-parameter Bell distribution proposed recently in the statistic literature, we introduce the zero-inflated Bell family of distributions. Additionally, on the basis of the proposed zero-inflated distribution, a novel zero-inflated regression model is proposed, which is quite simple and may be an interesting alternative to usual zero-inflated regression models for count data. We consider a frequentist approach to perform inferences, and the maximum likelihood method is employed to estimate the zero-inflated Bell regression parameters. Monte Carlo simulations indicate that the maximum likelihood method is quite effective to estimate the zero-inflated Bell regression parameters. We also propose the Pearson residuals for the new zero-inflated regression model to assess departures from model assumptions. Additionally, the global and local influence methods are discussed. In particular, the normal curvature for studying local influence is derived under case weighting perturbation scheme. Finally, an application to the count of infected blood cells is considered to illustrate the usefulness of the zero-inflated Bell regression model in practice. The results suggest that the new zero-inflated Bell regression is more appropriate to model these count data than other familiar zero-inflated (or not) regression models commonly used in practice.
Journal: Journal of Applied Statistics
Pages: 265-286
Issue: 2
Volume: 47
Year: 2020
Month: 1
X-DOI: 10.1080/02664763.2019.1636940
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1636940
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:2:p:265-286




Template-Type: ReDIF-Article 1.0
Author-Name: Eghbal Zandkarimi
Author-X-Name-First: Eghbal
Author-X-Name-Last: Zandkarimi
Author-Name: Abbas Moghimbeigi
Author-X-Name-First: Abbas
Author-X-Name-Last: Moghimbeigi
Author-Name: Hossein Mahjub
Author-X-Name-First: Hossein
Author-X-Name-Last: Mahjub
Author-Name: Reza Majdzadeh
Author-X-Name-First: Reza
Author-X-Name-Last: Majdzadeh
Title: Robust inference in the multilevel zero-inflated negative binomial model
Abstract: 
 A popular way to model correlated count data with excess zeros and over-dispersion simultaneously is by means of the multilevel zero-inflated negative binomial (MZINB) distribution. Due to the complexity of the likelihood of these models, numerical methods such as the EM algorithm are used to estimate parameters. On the other hand, in the presence of outliers or when mixture components are poorly separated, the likelihood-based methods can become unstable. To overcome this challenge, we extend the robust expectation-solution (RES) approach for building a robust estimator of the regression parameters in the MZINB model. This approach achieves robustness by applying robust estimating equations in the S-step instead of estimating equations in the M-step of the EM algorithm. The robust estimation equation in the logistic component only weighs the design matrix (X) and reduces the effect of the leverage points, but in the negative binomial component, the influence of deviations on the response (Y) and design matrix (X) are bound separately. Simulation studies under various settings show that the RES algorithm gives us consistent estimates with smaller biases than the EM algorithm under contaminations. The RES algorithm applies to the data of the DMFT index and the fertility rate data.
Journal: Journal of Applied Statistics
Pages: 287-305
Issue: 2
Volume: 47
Year: 2020
Month: 1
X-DOI: 10.1080/02664763.2019.1636942
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1636942
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:2:p:287-305




Template-Type: ReDIF-Article 1.0
Author-Name: Pedro L. Ramos
Author-X-Name-First: Pedro L.
Author-X-Name-Last: Ramos
Author-Name: Dipak K. Dey
Author-X-Name-First: Dipak K.
Author-X-Name-Last: Dey
Author-Name: Francisco Louzada
Author-X-Name-First: Francisco
Author-X-Name-Last: Louzada
Author-Name: Victor H. Lachos
Author-X-Name-First: Victor H.
Author-X-Name-Last: Lachos
Title: An extended poisson family of life distribution: a unified approach in competitive and complementary risks
Abstract: 
 In this paper, we introduce a new approach to generate flexible parametric families of distributions. These models arise on competitive and complementary risks scenario, in which the lifetime associated with a particular risk is not observable; rather, we observe only the minimum/maximum lifetime value among all risks. The latent variables have a zero-truncated Poisson distribution. For the proposed family of distribution, the extra shape parameter has an important physical interpretation in the competing and complementary risks scenario. The mathematical properties and inferential procedures are discussed. The proposed approach is applied in some existing distributions in which it is fully illustrated by an important data set.
Journal: Journal of Applied Statistics
Pages: 306-322
Issue: 2
Volume: 47
Year: 2020
Month: 1
X-DOI: 10.1080/02664763.2019.1644488
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1644488
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:2:p:306-322




Template-Type: ReDIF-Article 1.0
Author-Name: Mengta Yang
Author-X-Name-First: Mengta
Author-X-Name-Last: Yang
Author-Name: Reza Modarres
Author-X-Name-First: Reza
Author-X-Name-Last: Modarres
Author-Name: Lingzhe Guo
Author-X-Name-First: Lingzhe
Author-X-Name-Last: Guo
Title: Depth functions and mutidimensional medians on minimal spanning trees
Abstract: 
 Based on the minimal spanning tree (MST) of the observed data set, the paper introduces new notions of data depth and medians for multivariate data. The MST of a data set of size n is the MST of the complete weighted undirected graph on n vertices, where the edge weights are the pairwise distances of the data points. We study several properties of the MST-based depth functions. We consider the corresponding multidimensional medians, investigate their robustness and computational complexity. An example illustrates the use of the MST-based depth functions.
Journal: Journal of Applied Statistics
Pages: 323-336
Issue: 2
Volume: 47
Year: 2020
Month: 1
X-DOI: 10.1080/02664763.2019.1636939
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1636939
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:2:p:323-336




Template-Type: ReDIF-Article 1.0
Author-Name: Brent Burch
Author-X-Name-First: Brent
Author-X-Name-Last: Burch
Author-Name: Jesse Egbert
Author-X-Name-First: Jesse
Author-X-Name-Last: Egbert
Title: Zero-inflated beta distribution applied to word frequency and lexical dispersion in corpus linguistics
Abstract: 
 Corpus linguistics is the study of language as expressed in a body of texts or documents. The relative frequency of a word within a text and the dispersion of the word across the collection of texts provide information about the word's prominence and diffusion, respectively. In practice, people tend to use a relatively small number of words in a language's inventory of words and thus a large number of words in the lexicon are rarely employed. The zero-inflated beta distribution enables one to model the relative frequency of a word in a text since some texts may not even contain the word under study. In this paper, the expectation of a word's prominence and dispersion are defined under the zero-inflated beta model. Estimates of a word's prominence and dispersion are computed for words in the British National Corpus 1994 (BNC), a 100 million word collection of written and spoken language of a wide range of British English. The relationship between a word's prominence and dispersion is discussed as well as measures that are functions of both prominence and dispersion.
Journal: Journal of Applied Statistics
Pages: 337-353
Issue: 2
Volume: 47
Year: 2020
Month: 1
X-DOI: 10.1080/02664763.2019.1636941
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1636941
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:2:p:337-353




Template-Type: ReDIF-Article 1.0
Author-Name: M. El-Morshedy
Author-X-Name-First: M.
Author-X-Name-Last: El-Morshedy
Author-Name: M. S. Eliwa
Author-X-Name-First: M. S.
Author-X-Name-Last: Eliwa
Author-Name: H. Nagy
Author-X-Name-First: H.
Author-X-Name-Last: Nagy
Title: A new two-parameter exponentiated discrete Lindley distribution: properties, estimation and applications
Abstract: 
 This paper introduces a new two-parameter exponentiated discrete Lindley distribution. A wide range of its structural properties are investigated. This includes the shape of the probability mass function, hazard rate function, moments, skewness, kurtosis, stress–strength reliability, mean residual lifetime, mean past lifetime, order statistics and L-moment statistics. The hazard rate function can be increasing, decreasing, decreasing–increasing–decreasing, increasing–decreasing–increasing, unimodal, bathtub, and J-shaped depending on its parameters values. Two methods are used herein to estimate the model parameters, namely, the maximum likelihood, and the proportion. A detailed simulation study is carried out to examine the bias and mean square error of maximum likelihood and proportion estimators. The flexibility of the proposed model is explained by using four distinctive data sets. It can serve as an alternative model to other lifetime distributions in the existing statistical literature for modeling positive real data in many areas.
Journal: Journal of Applied Statistics
Pages: 354-375
Issue: 2
Volume: 47
Year: 2020
Month: 1
X-DOI: 10.1080/02664763.2019.1638893
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1638893
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:2:p:354-375




Template-Type: ReDIF-Article 1.0
Author-Name: Soumita Modak
Author-X-Name-First: Soumita
Author-X-Name-Last: Modak
Author-Name: Tanuka Chattopadhyay
Author-X-Name-First: Tanuka
Author-X-Name-Last: Chattopadhyay
Author-Name: Asis Kumar Chattopadhyay
Author-X-Name-First: Asis Kumar
Author-X-Name-Last: Chattopadhyay
Title: Unsupervised classification of eclipsing binary light curves through k-medoids clustering
Abstract: 
 This paper proposes k-medoids clustering method to reveal the distinct groups of 1318 variable stars in the Galaxy based on their light curves, where each light curve represents the graph of brightness of the star against time. To overcome the deficiencies of subjective traditional classification, we separate the stars more scientifically according to their geometrical configuration and show that our approach outperforms the existing classification schemes in astronomy. It results in two optimum groups of eclipsing binaries corresponding to bright, massive systems and fainter, less massive systems.
Journal: Journal of Applied Statistics
Pages: 376-392
Issue: 2
Volume: 47
Year: 2020
Month: 1
X-DOI: 10.1080/02664763.2019.1635574
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1635574
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:2:p:376-392




Template-Type: ReDIF-Article 1.0
Author-Name: Hafida Goual
Author-X-Name-First: Hafida
Author-X-Name-Last: Goual
Author-Name: Haitham M. Yousof
Author-X-Name-First: Haitham M.
Author-X-Name-Last: Yousof
Title: Validation of Burr XII inverse Rayleigh model via a modified chi-squared goodness-of-fit test
Abstract: 
 In this work, we propose a new three parameter distribution called the Burr XII inverse Rayleigh model, this model is a generalization of the inverse Rayleigh distribution using the Burr XII family introduced by Cordeiro et al. [The burr XII system of densities: properties, regression model and applications. J. Stat. Comput. Simul. 88 (2018), pp. 432–456]. After studying the statistical characterization of this model, we construct a modified chi-squared goodness-of-fit test based on the Nikulin–Rao–Robson statistic in the presence of two cases: censored and complete data. We describe the theory and the mechanism of the  $Y_{n}^{2} $Yn2 statistic test which can be used in survival and reliability data analysis. We use the maximum likelihood estimators based on initial non grouped data. Then, we conduct numerical simulations to reinforce the results. For showing the applicability of our model in various fields, we illustrate it and the proposed test by applications to two real data sets for complete data case and two other data sets in the presence of right censored.
Journal: Journal of Applied Statistics
Pages: 393-423
Issue: 3
Volume: 47
Year: 2020
Month: 2
X-DOI: 10.1080/02664763.2019.1639642
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1639642
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:3:p:393-423




Template-Type: ReDIF-Article 1.0
Author-Name: Luciana Dalla Valle
Author-X-Name-First: Luciana
Author-X-Name-Last: Dalla Valle
Author-Name: Fabrizio Leisen
Author-X-Name-First: Fabrizio
Author-X-Name-Last: Leisen
Author-Name: Luca Rossini
Author-X-Name-First: Luca
Author-X-Name-Last: Rossini
Author-Name: Weixuan Zhu
Author-X-Name-First: Weixuan
Author-X-Name-Last: Zhu
Title: Bayesian analysis of immigration in Europe with generalized logistic regression
Abstract: 
 The number of immigrants moving to and settling in Europe has increased over the past decade, making migration one of the most topical and pressing issues in European politics. It is without a doubt that immigration has multiple impacts, in terms of economy, society and culture, on the European Union. It is fundamental to policy-makers to correctly evaluate people's attitudes towards immigration when designing integration policies. Of critical interest is to properly discriminate between subjects who are favourable towards immigration from those who are against it. Public opinions on migration are typically coded as binary responses in surveys. However, traditional methods, such as the standard logistic regression, may suffer from computational issues and are often not able to accurately model survey information. In this paper we propose an efficient Bayesian approach for modelling binary response data based on the generalized logistic regression. We show how the proposed approach provides an increased flexibility compared to traditional methods, due to its ability to capture heavy and light tails. The power of our methodology is tested through simulation studies and is illustrated using European Social Survey data on immigration collected in different European countries in 2016–2017.
Journal: Journal of Applied Statistics
Pages: 424-438
Issue: 3
Volume: 47
Year: 2020
Month: 2
X-DOI: 10.1080/02664763.2019.1642310
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1642310
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:3:p:424-438




Template-Type: ReDIF-Article 1.0
Author-Name: Debanjan Mitra
Author-X-Name-First: Debanjan
Author-X-Name-Last: Mitra
Author-Name: Ujjwal Das
Author-X-Name-First: Ujjwal
Author-X-Name-Last: Das
Author-Name: Kalyan Das
Author-X-Name-First: Kalyan
Author-X-Name-Last: Das
Title: Analysis of interval-censored competing risks data under missing causes
Abstract: 
 In this article, interval-censored competing risks data are analyzed when some of the causes of failure are missing. The vertical modeling approach has been proposed here. This approach utilizes the data to extract information to the maximum possible extent especially when some causes of failure are missing. The maximum likelihood estimates of the model parameters are obtained. The asymptotic confidence intervals for the model parameters are constructed using approaches based on observed Fisher information matrix, and parametric bootstrap. A simulation study is considered in detail to assess the performance of the point and interval estimators. It is observed that the proposed analysis performs better than the complete case analysis. This establishes the fact that the our methodology is an extremely useful technique for interval-censored competing risks data when some of the causes of failure are missing. Such analysis seems to be quite useful for smaller sample sizes where complete case analysis may have a significant impact on the inferential procedures. Through Monte Carlo simulations, the effect of a possible model misspecification is also assessed on the basis of the cumulative incidence function. For illustration purposes, three datasets are analyzed and in all cases the conclusion appears to be quite realistic.
Journal: Journal of Applied Statistics
Pages: 439-459
Issue: 3
Volume: 47
Year: 2020
Month: 2
X-DOI: 10.1080/02664763.2019.1642309
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1642309
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:3:p:439-459




Template-Type: ReDIF-Article 1.0
Author-Name: XinYing Chew
Author-X-Name-First: XinYing
Author-X-Name-Last: Chew
Author-Name: Khai Wah Khaw
Author-X-Name-First: Khai Wah
Author-X-Name-Last: Khaw
Author-Name: Wai Chung Yeong
Author-X-Name-First: Wai Chung
Author-X-Name-Last: Yeong
Title: The efficiency of run rules schemes for the multivariate coefficient of variation: a Markov chain approach
Abstract: 
 Control charts are one of the important tools to monitor quality. The coefficient of variation (CV) is a common measure of dispersion in many real-life applications. Recently, CV control charts are proposed to monitor processes which do not have a constant mean and a standard deviation which changes with the mean. These processes cannot be monitored by standard control charts which monitor the mean and/or standard deviation. This research proposes the monitoring of the multivariate coefficient of variation (MCV) by means of run rules (RR MCV) control charts, which is not available in the existing literature. The design of these charts is obtained using a Markov-chain approach. The proposed charts are simple to implement. The performance of the RR MCV and Shewhart MCV (SH MCV) charts are compared in terms of the average run length (ARL) and the expected average run length (EARL). An example is illustrated based on a real dataset. The findings revealed that the performance of the proposed charts surpasses the SH MCV chart for detecting small and moderate MCV shifts.
Journal: Journal of Applied Statistics
Pages: 460-480
Issue: 3
Volume: 47
Year: 2020
Month: 2
X-DOI: 10.1080/02664763.2019.1643296
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1643296
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:3:p:460-480




Template-Type: ReDIF-Article 1.0
Author-Name: J. Derek Tucker
Author-X-Name-First: J. Derek
Author-X-Name-Last: Tucker
Author-Name: John R. Lewis
Author-X-Name-First: John R.
Author-X-Name-Last: Lewis
Author-Name: Caleb King
Author-X-Name-First: Caleb
Author-X-Name-Last: King
Author-Name: Sebastian Kurtek
Author-X-Name-First: Sebastian
Author-X-Name-Last: Kurtek
Title: A geometric approach for computing tolerance bounds for elastic functional data
Abstract: 
 We develop a method for constructing tolerance bounds for functional data with random warping variability. In particular, we define a generative, probabilistic model for the amplitude and phase components of such observations, which parsimoniously characterizes variability in the baseline data. Based on the proposed model, we define two different types of tolerance bounds that are able to measure both types of variability, and as a result, identify when the data has gone beyond the bounds of amplitude and/or phase. The first functional tolerance bounds are computed via a bootstrap procedure on the geometric space of amplitude and phase functions. The second functional tolerance bounds utilize functional Principal Component Analysis to construct a tolerance factor. This work is motivated by two main applications: process control and disease monitoring. The problem of statistical analysis and modeling of functional data in process control is important in determining when a production has moved beyond a baseline. Similarly, in biomedical applications, doctors use long, approximately periodic signals (such as the electrocardiogram) to diagnose and monitor diseases. In this context, it is desirable to identify abnormalities in these signals. We additionally consider a simulated example to assess our approach and compare it to two existing methods.
Journal: Journal of Applied Statistics
Pages: 481-505
Issue: 3
Volume: 47
Year: 2020
Month: 2
X-DOI: 10.1080/02664763.2019.1645818
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1645818
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:3:p:481-505




Template-Type: ReDIF-Article 1.0
Author-Name: C. Satheesh Kumar
Author-X-Name-First: C.
Author-X-Name-Last: Satheesh Kumar
Author-Name: Rakhi Ramachandran
Author-X-Name-First: Rakhi
Author-X-Name-Last: Ramachandran
Title: On some aspects of a zero-inflated overdispersed model and its applications
Abstract: 
 Count data with excess zeros are so common in several areas of scientific research. In particular, the zero-inflated version of count data models has been used for modelling data sets with excessive number of zeros. In this regard, zero-inflated Poisson distribution has received much attention in the literature. Through this paper, we propose a generalized class of zero-inflated Poisson distribution namely ‘zero-inflated Hermite distribution (ZIHD)’, which can be considered as a more flexible class of zero-inflated Poisson-type distribution suitable for tackling overdispersed data sets. Here we investigate several important properties of the ZIHD along with a discussion on certain inference aspects of the model. Certain test procedures for checking zero-inflation have also been developed and these tests have been investigated by using simulation studies. Further, two real life data applications are given for illustrating the usefulness of the model.
Journal: Journal of Applied Statistics
Pages: 506-523
Issue: 3
Volume: 47
Year: 2020
Month: 2
X-DOI: 10.1080/02664763.2019.1645098
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1645098
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:3:p:506-523




Template-Type: ReDIF-Article 1.0
Author-Name: Xueying Wang
Author-X-Name-First: Xueying
Author-X-Name-Last: Wang
Author-Name: Chunxiao Zhou
Author-X-Name-First: Chunxiao
Author-X-Name-Last: Zhou
Author-Name: Kepher Makambi
Author-X-Name-First: Kepher
Author-X-Name-Last: Makambi
Author-Name: Ao Yuan
Author-X-Name-First: Ao
Author-X-Name-Last: Yuan
Author-Name: Jaeil Ahn
Author-X-Name-First: Jaeil
Author-X-Name-Last: Ahn
Title: Analysis of batched service time data using Gaussian and semi-parametric kernel models
Abstract: 
 Batched data is a type of data where each observed data value is the sum of a number of grouped (batched) latent ones obtained under different conditions. Batched data arises in various practical backgrounds and is often found in social studies and management sector. The analysis of such data is analytically challenging due to its structural complexity. In this article, we describe how to analyze batched service time data, estimate the mean and variance of each batch that are latent. We in particular focus on the situation when the observed total time includes an unknown proportion of non-service time. To address this problem, we propose a Gaussian model for efficiency as well as a semi-parametric kernel density model for robustness. We evaluate the performance of both proposed methods through simulation studies and then applied our methods to analyze a batched data.
Journal: Journal of Applied Statistics
Pages: 524-540
Issue: 3
Volume: 47
Year: 2020
Month: 2
X-DOI: 10.1080/02664763.2019.1645820
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1645820
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:3:p:524-540




Template-Type: ReDIF-Article 1.0
Author-Name: Navdeep Singh
Author-X-Name-First: Navdeep
Author-X-Name-Last: Singh
Author-Name: Parminder Singh
Author-X-Name-First: Parminder
Author-X-Name-Last: Singh
Title: A closed testing procedure for comparison between successive variances
Abstract: 
 For a sequence of  $k $k independent normally distributed random samples from  $k $k populations with different means and variances, it is common to know which successive populations differ significantly with respect to a parameter. In this article, a stepwise test procedure is proposed for simultaneously testing the difference between successive normal populations with respect to the variance. The proposed procedure controls the family-wise error rate strongly. Critical constants, obtained numerically, are tabulated for the use of the proposed procedure. An extension of the proposed procedure for detecting the difference between scale parameters of successive two-parameter exponential populations is given. A Monte Carlo simulation study of the power comparison of the proposed procedure with a single-step procedure revealed that the proposed stepwise procedure performs better than the single-step procedure. Finally, a numerical example is also given to illustrate the advantage of the proposed procedure in comparison to the single-step procedure.
Journal: Journal of Applied Statistics
Pages: 541-553
Issue: 3
Volume: 47
Year: 2020
Month: 2
X-DOI: 10.1080/02664763.2019.1645819
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1645819
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:3:p:541-553




Template-Type: ReDIF-Article 1.0
Author-Name: Stefania Capecchi
Author-X-Name-First: Stefania
Author-X-Name-Last: Capecchi
Author-Name: Maurizio Curtarelli
Author-X-Name-First: Maurizio
Author-X-Name-Last: Curtarelli
Title: A mixture model to assess perception of discrimination on grounds of sexual orientation for policy considerations
Abstract: 
 The paper explores the relationships between subjective and contextual covariates as determinants of discrimination perception in a large sample survey. A modelling approach is implemented to detect the perception of discrimination on the ground of sexual orientation through expressed ratings, in a cross-country perspective. Exploiting the capabilities of a mixture model, which interprets ordinal evaluations as the combined results of two latent components, the cognitive process of selection among discrete ordered alternatives is discussed in terms of attractiveness towards the item and uncertainty in the response pattern. Gender, age and political orientation turn out to be significant variables at the individual level. Furthermore, the ILGA country score and education level, as contextual variables, exert prominent effects at country level. Empirical evidence from the Special Eurobarometer 2015 on Opinions as well as a simulation experiment support the usefulness of the approach.
Journal: Journal of Applied Statistics
Pages: 554-567
Issue: 3
Volume: 47
Year: 2020
Month: 2
X-DOI: 10.1080/02664763.2019.1639643
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1639643
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:3:p:554-567




Template-Type: ReDIF-Article 1.0
Author-Name: Lili Zhang
Author-X-Name-First: Lili
Author-X-Name-Last: Zhang
Author-Name: Herman Ray
Author-X-Name-First: Herman
Author-X-Name-Last: Ray
Author-Name: Jennifer Priestley
Author-X-Name-First: Jennifer
Author-X-Name-Last: Priestley
Author-Name: Soon Tan
Author-X-Name-First: Soon
Author-X-Name-Last: Tan
Title: A descriptive study of variable discretization and cost-sensitive logistic regression on imbalanced credit data
Abstract: 
 Training classification models on imbalanced data tends to result in bias towards the majority class. In this paper, we demonstrate how variable discretization and cost-sensitive logistic regression help mitigate this bias on an imbalanced credit scoring dataset, and further show the application of the variable discretization technique on the data from other domains, demonstrating its potential as a generic technique for classifying imbalanced data beyond credit socring. The performance measurements include ROC curves, Area under ROC Curve (AUC), Type I Error, Type II Error, accuracy, and F1 score. The results show that proper variable discretization and cost-sensitive logistic regression with the best class weights can reduce the model bias and/or variance. From the perspective of the algorithm, cost-sensitive logistic regression is beneficial for increasing the value of predictors even if they are not in their optimized forms while maintaining monotonicity. From the perspective of predictors, the variable discretization performs better than cost-sensitive logistic regression, provides more reasonable coefficient estimates for predictors which have nonlinear relationships against their empirical logit, and is robust to penalty weights on misclassifications of events and non-events determined by their apriori proportions.
Journal: Journal of Applied Statistics
Pages: 568-581
Issue: 3
Volume: 47
Year: 2020
Month: 2
X-DOI: 10.1080/02664763.2019.1643829
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1643829
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:3:p:568-581




Template-Type: ReDIF-Article 1.0
Author-Name: Jouchi Nakajima
Author-X-Name-First: Jouchi
Author-X-Name-Last: Nakajima
Title: Skew selection for factor stochastic volatility models
Abstract: 
 This paper proposes factor stochastic volatility models with skew error distributions. The generalized hyperbolic skew t-distribution is employed for common-factor processes and idiosyncratic shocks. Using a Bayesian sparsity modeling strategy for the skewness parameter provides a parsimonious skew structure for possibly high-dimensional stochastic volatility models. Analyses of daily stock returns are provided. Empirical results show that the skewness is important for common-factor processes but less for idiosyncratic shocks. The sparse skew structure improves prediction and portfolio performance.
Journal: Journal of Applied Statistics
Pages: 582-601
Issue: 4
Volume: 47
Year: 2020
Month: 3
X-DOI: 10.1080/02664763.2019.1646227
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1646227
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:4:p:582-601




Template-Type: ReDIF-Article 1.0
Author-Name: Zhihuang Yang
Author-X-Name-First: Zhihuang
Author-X-Name-Last: Yang
Author-Name: Jiahua Chen
Author-X-Name-First: Jiahua
Author-X-Name-Last: Chen
Title: Small area mean estimation after effect clustering
Abstract: 
 Providing reliable estimates of subpopulation/area parameters has attracted increased attention due to their importance in applications such as policymaking. Due to low or even no samples from some areas, we must adopt indirect model approaches. Existing indirect small area estimation methods often assume that a single nested error regression model is suitable for all the small areas. In particular, the effects of the auxiliary variables are either fixed or have a single attraction center. In some applications, it can be more appropriate to cluster the small areas so that the effects of the auxiliary variables are fixed but have multiple centers in the nested error regression model. In this paper, we examine an extended nested error regression model in which the auxiliary variables have mixed effects with multiple centers. We use a penalty approach to identify these centers and estimate the model parameters simultaneously. We then propose two new small area mean estimators and construct estimators of their mean square errors. Simulations based on artificial and realistic finite populations show that the new estimators can be efficient. Furthermore, the confidence intervals based on the new methods have accurate coverage probabilities. We illustrate the proposed methods with the Survey of Labour and Income Dynamics conducted in Canada.
Journal: Journal of Applied Statistics
Pages: 602-623
Issue: 4
Volume: 47
Year: 2020
Month: 3
X-DOI: 10.1080/02664763.2019.1648390
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1648390
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:4:p:602-623




Template-Type: ReDIF-Article 1.0
Author-Name: Narayanaswamy Balakrishnan
Author-X-Name-First: Narayanaswamy
Author-X-Name-Last: Balakrishnan
Author-Name: Chengwei Qin
Author-X-Name-First: Chengwei
Author-X-Name-Last: Qin
Title: Nonparametric optimal designs for degradation tests
Abstract: 
 This paper discusses the optimal design of a degradation test within the nonparametric framework by assuming the underlying process to be an empirical Lévy process with heterogeneity. The optimization of the experiment relies on the design variables including the sample size, the measurement frequency and total operation time. It is necessary to investigate the effect of the design variables on the estimation of first passage time (FPT). Subject to total experimental cost not exceeding a pre-specified budget, the values of design variables are determined such that the bootstrap mean square error of the  $100p $100pth percentile of the FPT distribution is minimized.
Journal: Journal of Applied Statistics
Pages: 624-641
Issue: 4
Volume: 47
Year: 2020
Month: 3
X-DOI: 10.1080/02664763.2019.1648392
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1648392
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:4:p:624-641




Template-Type: ReDIF-Article 1.0
Author-Name: Mian Huang
Author-X-Name-First: Mian
Author-X-Name-Last: Huang
Author-Name: Shangbing Yu
Author-X-Name-First: Shangbing
Author-X-Name-Last: Yu
Title: A new procedure for resampled portfolio with shrinkaged covariance matrix
Abstract: 
 Dealing with estimation error is an important issue when we implement the mean–variance paradigm for portfolio construction. To tackle the problem, two approaches are proposed in literature, the portfolio resampling technique introduced by Michuad and the well-known shrinkaged covariance matrix method. There are certain evidences on the advantages of shrinkaged covariance over portfolio resampling, however, it is unclear whether a combination of the two approaches could produce a better performance compared with using shrinkaged covariance alone. In this paper, we propose a new algorithm to integrated linear or nonlinear shrinkage estimation with resampled portfolio to achieve a further improvement. Our method are demonstrated via extensive simulation and application in active portfolio management process.
Journal: Journal of Applied Statistics
Pages: 642-652
Issue: 4
Volume: 47
Year: 2020
Month: 3
X-DOI: 10.1080/02664763.2019.1648394
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1648394
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:4:p:642-652




Template-Type: ReDIF-Article 1.0
Author-Name: Marco Marozzi
Author-X-Name-First: Marco
Author-X-Name-Last: Marozzi
Author-Name: Amitava Mukherjee
Author-X-Name-First: Amitava
Author-X-Name-Last: Mukherjee
Author-Name: Jan Kalina
Author-X-Name-First: Jan
Author-X-Name-Last: Kalina
Title: Interpoint distance tests for high-dimensional comparison studies
Abstract: 
 Modern data collection techniques allow to analyze a very large number of endpoints. In biomedical research, for example, expressions of thousands of genes are commonly measured only on a small number of subjects. In these situations, traditional methods for comparison studies are not applicable. Moreover, the assumption of normal distribution is often questionable for high-dimensional data, and some variables may be at the same time highly correlated with others. Hypothesis tests based on interpoint distances are very appealing for studies involving the comparison of means, because they do not assume data to come from normally distributed populations and comprise tests that are distribution free, unbiased, consistent, and computationally feasible, even if the number of endpoints is much larger than the number of subjects. New tests based on interpoint distances are proposed for multivariate studies involving simultaneous comparison of means and variability, or the whole distribution shapes. The tests are shown to perform well in terms of power, when the endpoints have complex dependence relations, such as in genomic and metabolomic studies. A practical application to a genetic cardiovascular case-control study is discussed.
Journal: Journal of Applied Statistics
Pages: 653-665
Issue: 4
Volume: 47
Year: 2020
Month: 3
X-DOI: 10.1080/02664763.2019.1649374
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1649374
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:4:p:653-665




Template-Type: ReDIF-Article 1.0
Author-Name: Mehdi Soleymani
Author-X-Name-First: Mehdi
Author-X-Name-Last: Soleymani
Author-Name: Paul S. F. Yip
Author-X-Name-First: Paul S. F.
Author-X-Name-Last: Yip
Title: Assessing the impact of the economic crises in 1997 and 2008 on suicides in Hong Kong, Taiwan and South Korea using a strata-bootstrap algorithm
Abstract: 
 The Asian economic crises of 1997 and the 2008 Global Financial Crisis (GFC) had far-reaching impacts on Asian and other global economies. Turmoil in the banking and finance sectors led to downturns in stock markets, resulting in bankruptcies, house repossessions and high unemployment. These crises have been shown to be correlated with a deterioration in mental health and an increase in suicides, and it is important to understand the implication of these impacts and how such recessions affect the health of affected populations. With the benefit of hindsight, did lessons learned from the negative effects of the 1997 Asian economic recession impact the aftermath of the 2008 GFC in Asian countries? Utilising a framework based on a simple strata-bootstrap algorithm using daily data – where available – we investigate the trend in suicide rates over time in three different populations (Hong Kong, Taiwan and South Korea), and examine whether there were any changes in the pattern of suicide rates in each country subsequent to both the 1997 Asian and 2008. We find that each country responded differently to each of the crises and the suicide rates for certain age-gender specific groups in each country were more affected.
Journal: Journal of Applied Statistics
Pages: 666-684
Issue: 4
Volume: 47
Year: 2020
Month: 3
X-DOI: 10.1080/02664763.2019.1650008
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1650008
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:4:p:666-684




Template-Type: ReDIF-Article 1.0
Author-Name: Amjad D. Al-Nasser
Author-X-Name-First: Amjad D.
Author-X-Name-Last: Al-Nasser
Author-Name: Mohammed Obeidat
Author-X-Name-First: Mohammed
Author-X-Name-Last: Obeidat
Title: Acceptance sampling plans from truncated life test based on Tsallis q-exponential distribution
Abstract: 
 In this study, we propose a new single acceptance sampling plan from truncated life test assuming that the quality characteristics follow the Tsallis q-exponential distribution. The proposed plan is given, and then we derived the operation characteristics function and calculate the optimal sample size and producer’'s risk for some given parameter values to measure the performance of this plan. Also, a comparative study with other sampling plan is discussed to show the benefit of the proposed plan, and real data analysis is given to illustrate the applicability of the proposed plan in the industry.
Journal: Journal of Applied Statistics
Pages: 685-697
Issue: 4
Volume: 47
Year: 2020
Month: 3
X-DOI: 10.1080/02664763.2019.1650254
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1650254
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:4:p:685-697




Template-Type: ReDIF-Article 1.0
Author-Name: Christoph Behrens
Author-X-Name-First: Christoph
Author-X-Name-Last: Behrens
Author-Name: Christian Pierdzioch
Author-X-Name-First: Christian
Author-X-Name-Last: Pierdzioch
Author-Name: Marian Risse
Author-X-Name-First: Marian
Author-X-Name-Last: Risse
Title: Do German economic research institutes publish efficient growth and inflation forecasts? A Bayesian analysis
Abstract: 
 We use Bayesian additive regression trees to reexamine the efficiency of growth and inflation forecasts for Germany. To this end, we use forecasts of four leading German economic research institutes for the sample period from 1970 to 2016. We reject the strong form of forecast efficiency and find evidence against the weak form of forecast efficiency for longer-term growth and longer-term inflation forecasts. We cannot reject weak efficiency of short-term growth and inflation forecasts and of forecasts disaggregated at the institute level. We find that Bayesian additive regression trees perform significantly better than a standard linear efficiency-regression model in terms of forecast accuracy.
Journal: Journal of Applied Statistics
Pages: 698-723
Issue: 4
Volume: 47
Year: 2020
Month: 3
X-DOI: 10.1080/02664763.2019.1652253
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1652253
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:4:p:698-723




Template-Type: ReDIF-Article 1.0
Author-Name: Guanfu Liu
Author-X-Name-First: Guanfu
Author-X-Name-Last: Liu
Author-Name: Yuejiao Fu
Author-X-Name-First: Yuejiao
Author-X-Name-Last: Fu
Author-Name: Jianjun Zhang
Author-X-Name-First: Jianjun
Author-X-Name-Last: Zhang
Author-Name: Xiaolong Pu
Author-X-Name-First: Xiaolong
Author-X-Name-Last: Pu
Author-Name: Boying Wang
Author-X-Name-First: Boying
Author-X-Name-Last: Wang
Title: EM-test for homogeneity in a two-sample problem with a mixture structure
Abstract: 
 In many applications such as case-control studies with contaminated controls, or the test of a treatment effect in the presence of nonresponders in biological experiments or clinical trials, a two-sample problem with one of the samples having a mixture structure often arises. Due to the importance and wide applications of scale mixtures and location mixtures, we consider in this paper the case that the component densities differ only in scale parameters and the case that the component densities differ only in location parameters, and further construct an EM-test for the two-sample problem under each case. We show that both the EM-tests possess a chi-squared null limiting distribution. The local power analysis and sample size calculations are also investigated. Finally, the simulation studies and real data analysis demonstrate that the proposed EM-tests have better performance than the existing methods.
Journal: Journal of Applied Statistics
Pages: 724-738
Issue: 4
Volume: 47
Year: 2020
Month: 3
X-DOI: 10.1080/02664763.2019.1652254
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1652254
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:4:p:724-738




Template-Type: ReDIF-Article 1.0
Author-Name: Nurul Syafiah Abd Naeeim
Author-X-Name-First: Nurul Syafiah
Author-X-Name-Last: Abd Naeeim
Author-Name: Nuzlinda Abdul Rahman
Author-X-Name-First: Nuzlinda
Author-X-Name-Last: Abdul Rahman
Author-Name: Fatin Afiqah Muhammad Fahimi
Author-X-Name-First: Fatin Afiqah
Author-X-Name-Last: Muhammad Fahimi
Title: A spatial–temporal study of dengue in Peninsular Malaysia for the year 2017 in two different space–time model
Abstract: 
 Spatio-temporal disease mapping models give a great worth in epidemiology, especially in describing the pattern of disease incidence across geographical space and time. This paper analyses the spatial and temporal variability of dengue disease rates based on generalized linear mixed models. For spatio-temporal study, the models incorporate spatially correlated random effects as well as temporal effects. In this study, two different spatial random effects are applied and compared. The first model is based on Leroux spatial model, while the second model is based on the stochastic partial differential equation approach. For the temporal effects, both models follow an autoregressive model of first-order model. The models are fitted within a hierarchical Bayesian framework with integrated nested Laplace approximation methodology. The main objective of this study is to compare both spatio-temporal models in terms of their ability in representing the disease phenomenon. The models are applied to weekly dengue fever data in Peninsular Malaysia reported to the Ministry of Health Malaysia in the year 2017 according to the district level.
Journal: Journal of Applied Statistics
Pages: 739-756
Issue: 4
Volume: 47
Year: 2020
Month: 3
X-DOI: 10.1080/02664763.2019.1648391
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1648391
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:4:p:739-756




Template-Type: ReDIF-Article 1.0
Author-Name: Hansapani Rodrigo
Author-X-Name-First: Hansapani
Author-X-Name-Last: Rodrigo
Author-Name: Chris Tsokos
Author-X-Name-First: Chris
Author-X-Name-Last: Tsokos
Title: Bayesian modelling of nonlinear Poisson regression with artificial neural networks
Abstract: 
 Modelling and prediction of count and rate responses have substantial usage in many fields, including health, finance, social, etc. Conventionally, linear Poisson regression models have been widely used to model these responses. However, the linearity assumption of the systematic component of linear Poisson regression models restricts their capability of handling complex data patterns. In this regard, it is important to develop nonlinear Poisson regression models to capture the inherent variability within the count data. In this study, we introduce a probabilistically driven nonlinear Poisson regression model with Bayesian artificial neural networks (ANN) to model count and rate data. This new nonlinear Poisson regression model developed with Bayesian ANN provides higher prediction accuracies over traditional Poisson or negative binomial regression models as revealed in our simulation and real data studies.
Journal: Journal of Applied Statistics
Pages: 757-774
Issue: 5
Volume: 47
Year: 2020
Month: 4
X-DOI: 10.1080/02664763.2019.1653268
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1653268
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:5:p:757-774




Template-Type: ReDIF-Article 1.0
Author-Name: Naha J. Farhat
Author-X-Name-First: Naha J.
Author-X-Name-Last: Farhat
Author-Name: Edward L. Boone
Author-X-Name-First: Edward L.
Author-X-Name-Last: Boone
Author-Name: David J. Edwards
Author-X-Name-First: David J.
Author-X-Name-Last: Edwards
Title: A new method for determining the benchmark dose tolerable region and endpoint probabilities for toxicology experiments
Abstract: 
 The increase of exposure to toxic materials and hazardous chemicals is a major concern due to the adverse effect on human health. Among the major concerns of toxicologists is to determine acceptable levels of exposure to hazardous substances. Current approaches often evaluate each endpoint and stressor individually. We propose a novel method to simultaneously determine the Benchmark Dose Tolerable Region (BMDTR) for multiple endpoint and multiple stressor studies by adopting a Bayesian approach. A main concern while assessing the combined toxicological effect of a chemical mixture is the anticipated type of the combined action (i.e. synergistic or antagonistic); thus it was essential to account for interaction effects to handle this situation, imposing more challenges due to the non-linearity of the tolerable region. The proposed method will be evaluated using two approaches, the first one using the estimated value of the posterior median and the second approach using all MCMC samples from the posterior distribution. Furthermore, we propose a new method to determine the endpoint probabilities for each endpoint, which reflects the importance of each endpoint in helping determining the boundaries of the benchmark dose tolerable region (BMDTR).
Journal: Journal of Applied Statistics
Pages: 775-803
Issue: 5
Volume: 47
Year: 2020
Month: 4
X-DOI: 10.1080/02664763.2019.1654985
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1654985
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:5:p:775-803




Template-Type: ReDIF-Article 1.0
Author-Name: Mohadeseh Alsadat Farzammehr
Author-X-Name-First: Mohadeseh Alsadat
Author-X-Name-Last: Farzammehr
Author-Name: Mohammad Reza Zadkarami
Author-X-Name-First: Mohammad Reza
Author-X-Name-Last: Zadkarami
Author-Name: Geoffrey J. McLachlan
Author-X-Name-First: Geoffrey J.
Author-X-Name-Last: McLachlan
Author-Name: Sharon X. Lee
Author-X-Name-First: Sharon X.
Author-X-Name-Last: Lee
Title: Skew-normal Bayesian spatial heterogeneity panel data models
Abstract: 
 This paper proposes a new regression model for the analysis of spatial panel data in the case of spatial heterogeneity and non-normality. In empirical economic research, the normality of error components is a routine assumption for the models with continuous responses. However, such an assumption may not be appropriate in many applications. This work relaxes the normality assumption by using a multivariate skew-normal distribution, which includes the normal distribution as a special case. The methodology is illustrated through a simulation study and application to insurance and gasoline demand data sets. In these analyses, a simple Bayesian framework that implements a Markov chain Monte Carlo algorithm is derived for parameter estimation and inference.
Journal: Journal of Applied Statistics
Pages: 804-826
Issue: 5
Volume: 47
Year: 2020
Month: 4
X-DOI: 10.1080/02664763.2019.1657812
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1657812
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:5:p:804-826




Template-Type: ReDIF-Article 1.0
Author-Name: Yang Yang
Author-X-Name-First: Yang
Author-X-Name-Last: Yang
Author-Name: Jiwei Zhao
Author-X-Name-First: Jiwei
Author-X-Name-Last: Zhao
Author-Name: Gregory Wilding
Author-X-Name-First: Gregory
Author-X-Name-Last: Wilding
Author-Name: Melissa Kluczynski
Author-X-Name-First: Melissa
Author-X-Name-Last: Kluczynski
Author-Name: Leslie Bisson
Author-X-Name-First: Leslie
Author-X-Name-Last: Bisson
Title: Stability enhanced variable selection for a semiparametric model with flexible missingness mechanism and its application to the ChAMP study
Abstract: 
 This paper is motivated by the analytical challenges we encounter when analyzing the ChAMP (Chondral Lesions And Meniscus Procedures) study, a randomized controlled trial to compare debridement to observation of chondral lesions in arthroscopic knee surgery. The main outcome, WOMAC (Western Ontario and McMaster Universities Osteoarthritis Index) pain score, is derived from the patient's responses to the questionnaire collected in the study. The major goal is to identify potentially important variables that contribute to this outcome. In this paper, the model of interest is a semiparametric model for the pain score. To address the missing data issue, we adopt a flexible missingness mechanism that is much more versatile in practice than a single parametric model. Then we propose a pairwise conditional likelihood approach to estimate the unknown parameter in the semiparametric model without the need of modeling its nonparametric counterpart nor the missingness mechanism. For variable selection, we apply a regularization approach with a variety of stability enhanced tuning parameter selection methods. We conduct comprehensive simulation studies to evaluate the performance of the proposed method. We also apply the proposed method to the ChAMP study to demonstrate its usefulness.
Journal: Journal of Applied Statistics
Pages: 827-843
Issue: 5
Volume: 47
Year: 2020
Month: 4
X-DOI: 10.1080/02664763.2019.1658727
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1658727
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:5:p:827-843




Template-Type: ReDIF-Article 1.0
Author-Name: Alexandra M. Espinosa
Author-X-Name-First: Alexandra M.
Author-X-Name-Last: Espinosa
Author-Name: Luís Horna
Author-X-Name-First: Luís
Author-X-Name-Last: Horna
Title: The statistical properties of the threshold model and the feedback leadership condition
Abstract: 
 This paper analyses the statistical properties of the threshold model of collective actions. We establish the conditions of the threshold model under which the sequence of decisions is exchangeable. Then, if the probability of participation is a continuous and strictly increasing function of the willingness to participate, then the unique urn representation of the threshold model is the Pólya urn. In such a case, the sequence of participation rates converges almost surely to a random variable Z that has a beta distribution. Also, we show the critical mass and self-sustaining participation rates are fixed points of the distribution function. Further, we discuss the non-exchangeable model of collective actions, and we define the feedback leadership condition. We apply these results to characterise the process of the adoption of hybrid corn in Iowa, and the diffusion of the Internet for different levels of income.
Journal: Journal of Applied Statistics
Pages: 844-864
Issue: 5
Volume: 47
Year: 2020
Month: 4
X-DOI: 10.1080/02664763.2019.1658728
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1658728
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:5:p:844-864




Template-Type: ReDIF-Article 1.0
Author-Name: Hassan Piriaei
Author-X-Name-First: Hassan
Author-X-Name-Last: Piriaei
Author-Name: Gholamhossein Yari
Author-X-Name-First: Gholamhossein
Author-X-Name-Last: Yari
Author-Name: Rahman Farnoosh
Author-X-Name-First: Rahman
Author-X-Name-Last: Farnoosh
Title: On E-Bayesian estimations for the cumulative hazard rate and mean residual life under generalized inverted exponential distribution and type-II censoring
Abstract: 
 Estimation of reliability and hazard rate is necessary in many applications. To this aim, different methods of estimation have been employed. Each method suffers from its own problems such as complexity of calculations, high risk and so on. Toward this end, this study employed a new method, E-Bayesian, for estimating the parametric functions of the Generalized Inverted Exponential distribution, which is one of the most noticeable distributions in lifetime studies. Relations are derived under a squared error loss function, type-II censoring and a conjugate prior. E-Bayesian estimations are obtained based on different priors of the hyperparameters to investigate the influence of different prior distributions on these estimations. The asymptotic behaviors of E-Bayesian estimations and relations among them have been investigated. Finally, a comparison among the maximum likelihood, Bayes and E-Bayesian estimations in different sample sizes are made, using a real data and the Monte Carlo simulation. Simulations show that the new presented method is more efficient than previous methods and is also easy to operate. Also, some comparisons among the results of Generalized Inverted Exponential distribution, Exponential distribution and Generalized Exponential distribution are provided.
Journal: Journal of Applied Statistics
Pages: 865-889
Issue: 5
Volume: 47
Year: 2020
Month: 4
X-DOI: 10.1080/02664763.2019.1661359
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1661359
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:5:p:865-889




Template-Type: ReDIF-Article 1.0
Author-Name: Oludare Ariyo
Author-X-Name-First: Oludare
Author-X-Name-Last: Ariyo
Author-Name: Adrian Quintero
Author-X-Name-First: Adrian
Author-X-Name-Last: Quintero
Author-Name: Johanna Muñoz
Author-X-Name-First: Johanna
Author-X-Name-Last: Muñoz
Author-Name: Geert Verbeke
Author-X-Name-First: Geert
Author-X-Name-Last: Verbeke
Author-Name: Emmanuel Lesaffre
Author-X-Name-First: Emmanuel
Author-X-Name-Last: Lesaffre
Title: Bayesian model selection in linear mixed models for longitudinal data
Abstract: 
 Linear mixed models (LMMs) are popular to analyze repeated measurements with a Gaussian response. For longitudinal studies, the LMMs consist of a fixed part expressing the effect of covariates on the mean evolution in time and a random part expressing the variation of the individual curves around the mean curve. Selecting the appropriate fixed and random effect parts is an important modeling exercise. In a Bayesian framework, there is little agreement on the appropriate selection criteria. This paper compares the performance of the deviance information criterion (DIC), the pseudo-Bayes factor and the widely applicable information criterion (WAIC) in LMMs, with an extension to LMMs with skew-normal distributions. We focus on the comparison between the conditional criteria (given random effects) versus the marginal criteria (averaged over random effects). In spite of theoretical arguments, there is not much enthusiasm among applied statisticians to make use of the marginal criteria. We show in an extensive simulation study that the three marginal criteria are superior in choosing the appropriate longitudinal model. In addition, the marginal criteria selected most appropriate model for growth curves of Nigerian chicken. A self-written R function can be combined with standard Bayesian software packages to obtain the marginal selection criteria.
Journal: Journal of Applied Statistics
Pages: 890-913
Issue: 5
Volume: 47
Year: 2020
Month: 4
X-DOI: 10.1080/02664763.2019.1657814
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1657814
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:5:p:890-913




Template-Type: ReDIF-Article 1.0
Author-Name: Ju-Hyun Seo
Author-X-Name-First: Ju-Hyun
Author-X-Name-Last: Seo
Author-Name: Hyun-Ji Kim
Author-X-Name-First: Hyun-Ji
Author-X-Name-Last: Kim
Author-Name: Jea-Young Lee
Author-X-Name-First: Jea-Young
Author-X-Name-Last: Lee
Title: Nomogram construction to predict dyslipidemia based on a logistic regression analysis
Abstract: 
 Dyslipidemia is a chronic disease requiring continuous management and is a well-known risk factor for cardiovascular diseases as well as hypertension and diabetes. However, no studies have so far visualized and predicted the probability of dyslipidemia. Hence, this study proposes a nomogram based on a logistic regression model that can visualize its risk factors and predict the probability of developing dyslipidemia. Twelve risk factors for dyslipidemia are identified through a chi-squared test. We then conduct a logistic regression analysis with two interaction variables to obtain a model and build a nomogram for dyslipidemia. Finally, we verify the constructed nomogram using a receiver operation characteristic curve and calibration plot.
Journal: Journal of Applied Statistics
Pages: 914-926
Issue: 5
Volume: 47
Year: 2020
Month: 4
X-DOI: 10.1080/02664763.2019.1660760
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1660760
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:5:p:914-926




Template-Type: ReDIF-Article 1.0
Author-Name: Pablo Juan Verdoy
Author-X-Name-First: Pablo Juan
Author-X-Name-Last: Verdoy
Title: Spatio-temporal hierarchical Bayesian analysis of wildfires with Stochastic Partial Differential Equations. A case study from Valencian Community (Spain)
Abstract: 
 The spatio-temporal study of wildfires has two complex elements that are the computational efficiency and longtime processing. Modelling the spatial variability of a wildfire could be performed in different ways, and an important issue is the computational facilities that the new methodological techniques afford us. The Markov random fields methods have made possible to build risk maps, but for many forest managers, it is more advantageous to know the size of the fire and its location. In the first part of this work, Stochastic Partial Differential Equation with Integrated Nested Laplace Approximation is utilised to model the size of the forest fires observed in the Valencian Community (Spain) and so it does the inclusion of the time effect, and the study of the emergency calls. The most crucial element in this paper is the inclusion of the improved meshes for the spatial effect and the time, these are, 2d (locations) and 1d (time) respectively. The advantage of the use of spatio-temporal meshes is described with the inclusion of Bayesian methodology in all the scenarios.
Journal: Journal of Applied Statistics
Pages: 927-946
Issue: 5
Volume: 47
Year: 2020
Month: 4
X-DOI: 10.1080/02664763.2019.1661360
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1661360
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:5:p:927-946




Template-Type: ReDIF-Article 1.0
Author-Name: Saralees Nadarajah
Author-X-Name-First: Saralees
Author-X-Name-Last: Nadarajah
Author-Name: Stephen Chan
Author-X-Name-First: Stephen
Author-X-Name-Last: Chan
Title: On moments of the unit Lindley distribution
Abstract: 
 Mazucheli et al. [On the one parameter unit-Lindley distribution and its associated regression model for proportion data. J. Appl. Stat. 46 (2019), pp. 700–714] introduced the unit Lindley distribution. The expressions given for the moments and incomplete moments are either not correct or not in closed form. We derive closed form expressions moments and incomplete moments. The expressions are elementary except for the exponential integral.
Journal: Journal of Applied Statistics
Pages: 947-949
Issue: 5
Volume: 47
Year: 2020
Month: 4
X-DOI: 10.1080/02664763.2019.1675607
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1675607
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:5:p:947-949




Template-Type: ReDIF-Article 1.0
Author-Name: J. Mazucheli
Author-X-Name-First: J.
Author-X-Name-Last: Mazucheli
Author-Name: A. F. B. Menezes
Author-X-Name-First: A. F. B.
Author-X-Name-Last: Menezes
Author-Name: S. Chakraborty
Author-X-Name-First: S.
Author-X-Name-Last: Chakraborty
Title: Discussion comments on ‘On moments of the unit Lindley distribution’
Abstract: 
 In a note about the paper titled ‘On the one parameter unit-Lindley distribution and its associated regression model for proportion data’, Mazucheli et al. [J. Appl. Stat. 46 (2019), pp. 700–714] and Nadarajah and Chan [On moments of the unit Lindley distribution, J. Appl. Stat. (under review)] claim that ‘The expressions given for the moments and incomplete moments are not correct and not in closed form’. While we agree that they are not in closed form and observe a typo in the expressions for  $\mu _{k}' $μk′ and  $T_k(t) $Tk(t),  $k=1,\ldots $k=1,…, the expressions for  $\mu _{1}' $μ1′,  $\mu _{2}' $μ2′,  $\mu _{3}' $μ3′ and  $\mu _{4}' $μ4′ are, however, entirely correct.
Journal: Journal of Applied Statistics
Pages: 950-953
Issue: 5
Volume: 47
Year: 2020
Month: 4
X-DOI: 10.1080/02664763.2019.1675609
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1675609
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:5:p:950-953




Template-Type: ReDIF-Article 1.0
Author-Name: M. Templ
Author-X-Name-First: M.
Author-X-Name-Last: Templ
Author-Name: J. Gussenbauer
Author-X-Name-First: J.
Author-X-Name-Last: Gussenbauer
Author-Name: P. Filzmoser
Author-X-Name-First: P.
Author-X-Name-Last: Filzmoser
Title: Evaluation of robust outlier detection methods for zero-inflated complex data
Abstract: 
 Outlier detection can be seen as a pre-processing step for locating data points in a data sample, which do not conform to the majority of observations. Various techniques and methods for outlier detection can be found in the literature dealing with different types of data. However, many data sets are inflated by true zeros and, in addition, some components/variables might be of compositional nature. Important examples of such data sets are the Structural Earnings Survey, the Structural Business Statistics, the European Statistics on Income and Living Conditions, tax data or – as in this contribution – household expenditure data which are used, for example, to estimate the Purchase Power Parity of a country.In this work, robust univariate and multivariate outlier detection methods are compared by a complex simulation study that considers various challenges included in data sets, namely structural (true) zeros, missing values, and compositional variables. These circumstances make it difficult or impossible to flag true outliers and influential observations by well-known outlier detection methods.Our aim is to assess the performance of outlier detection methods in terms of their effectiveness to identify outliers when applied to challenging data sets such as the household expenditures data surveyed all over the world. Moreover, different methods are evaluated through a close-to-reality simulation study. Differences in performance of univariate and multivariate robust techniques for outlier detection and their shortcomings are reported. We found that robust multivariate methods outperform robust univariate methods. The best performing methods in finding the outliers and in providing a low false discovery rate were found to be the generalized S estimators (GSE), the BACON-EEM algorithm and a compositional method (CoDa-Cov). In addition, these methods performed also best when the outliers are imputed based on the corresponding outlier detection method and indicators are estimated from the data sets.
Journal: Journal of Applied Statistics
Pages: 1144-1167
Issue: 7
Volume: 47
Year: 2020
Month: 5
X-DOI: 10.1080/02664763.2019.1671961
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1671961
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:7:p:1144-1167




Template-Type: ReDIF-Article 1.0
Author-Name: Sha Li
Author-X-Name-First: Sha
Author-X-Name-Last: Li
Author-Name: Daniel A. Griffith
Author-X-Name-First: Daniel A.
Author-X-Name-Last: Griffith
Author-Name: Hong Shu
Author-X-Name-First: Hong
Author-X-Name-Last: Shu
Title: Temperature prediction based on a space–time regression-kriging model
Abstract: 
 Many phenomena exist in the space–time domain, often with a low data sampling rate and sparsely distributed network of observed points. Therefore, spatio-temporal interpolation with high accuracy is necessary. In this paper, a space–time regression-kriging model was introduced and applied to monthly average temperature data. First, a time series decomposition was applied for each station, and a multiple linear regression model was used to fit space–time trends. Second, a valid nonseparable spatio-temporal variogram function was utilized to describe similarities of the residuals in space–time. Finally, space–time kriging was applied to predict monthly air temperature. Jackknife techniques were used to predict the monthly temperature at all stations, with correlation coefficients between predictions and observed data very close to 1. Moreover, to evaluate the advantages of space–time kriging, pure time forecasting also was executed employing an autoregressive integrated moving average (ARIMA) model. The results of these two methods show that both mean absolute error (MAE) and root-mean-square error (RMSE) of space–time prediction are much lower than those of the pure time forecasting. The estimated temperature curves for stations also show that the former present a conspicuous improvement in interpolation accuracy when compared with the latter.
Journal: Journal of Applied Statistics
Pages: 1168-1190
Issue: 7
Volume: 47
Year: 2020
Month: 5
X-DOI: 10.1080/02664763.2019.1671962
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1671962
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:7:p:1168-1190




Template-Type: ReDIF-Article 1.0
Author-Name: Antoine Rolland
Author-X-Name-First: Antoine
Author-X-Name-Last: Rolland
Author-Name: Jairo Cugliari
Author-X-Name-First: Jairo
Author-X-Name-Last: Cugliari
Title: Sensitivity index to measure dependence on parameters for rankings and top-k rankings
Abstract: 
 In a multivariate framework, ranking a data set can be done by using an aggregation function in order to obtain a global score for each individual, and then by using these scores to rank the individuals. The choice of the aggregation function (e.g. a weighted sum) and the choice of the parameters of the function (e.g. the weights) may have a great influence on the obtained ranking. We introduce in this communication a ratio index that can quantify the sensitivity of the data set ranking up to a change of weights. This index is investigated in the general case and in the restricted case of top-k rankings. We also illustrate the interest to use such an index to analyse ranked data sets.
Journal: Journal of Applied Statistics
Pages: 1191-1207
Issue: 7
Volume: 47
Year: 2020
Month: 5
X-DOI: 10.1080/02664763.2019.1671963
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1671963
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:7:p:1191-1207




Template-Type: ReDIF-Article 1.0
Author-Name: Deepesh Bhati
Author-X-Name-First: Deepesh
Author-X-Name-Last: Bhati
Author-Name: Sudheesh K. Kattumannil
Author-X-Name-First: Sudheesh K.
Author-X-Name-Last: Kattumannil
Title: Jackknife empirical likelihood test for testing one-sided Lévy distribution
Abstract: 
 Based on U-empirical process we construct a jackknife empirical likelihood-based test for testing one-sided Lévy distribution. Adjusted jackknife empirical likelihood test also developed. The simulation study shows that the proposed tests have very good power for various alternatives. Finally, we illustrate the test procedure using three real data sets.
Journal: Journal of Applied Statistics
Pages: 1208-1219
Issue: 7
Volume: 47
Year: 2020
Month: 5
X-DOI: 10.1080/02664763.2019.1672630
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1672630
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:7:p:1208-1219




Template-Type: ReDIF-Article 1.0
Author-Name: Qiaohui Lin
Author-X-Name-First: Qiaohui
Author-X-Name-Last: Lin
Author-Name: Brenda Betancourt
Author-X-Name-First: Brenda
Author-X-Name-Last: Betancourt
Author-Name: Benjamin A. Goldstein
Author-X-Name-First: Benjamin A.
Author-X-Name-Last: Goldstein
Author-Name: Rebecca C. Steorts
Author-X-Name-First: Rebecca C.
Author-X-Name-Last: Steorts
Title: Prediction of appointment no-shows using electronic health records
Abstract: 
 Appointment no-shows have a negative impact on patient health and have caused substantial loss in resources and revenue for health care systems. Intervention strategies to reduce no-show rates can be more effective if targeted to the subpopulations of patients with higher risk of not showing to their appointments. We use electronic health records (EHR) from a large medical center to predict no-show patients based on demographic and health care features. We apply sparse Bayesian modeling approaches based on Lasso and automatic relevance determination to predict and identify the most relevant risk factors of no-show patients at a provider level.
Journal: Journal of Applied Statistics
Pages: 1220-1234
Issue: 7
Volume: 47
Year: 2020
Month: 5
X-DOI: 10.1080/02664763.2019.1672631
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1672631
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:7:p:1220-1234




Template-Type: ReDIF-Article 1.0
Author-Name: Samira F. Abushilah
Author-X-Name-First: Samira F.
Author-X-Name-Last: Abushilah
Author-Name: Charles C. Taylor
Author-X-Name-First: Charles C.
Author-X-Name-Last: Taylor
Author-Name: Arief Gusnanto
Author-X-Name-First: Arief
Author-X-Name-Last: Gusnanto
Title: Geometry-based distance for clustering amino acids
Abstract: 
 Clustering amino acids is one of the most challenging problems in functional and structural prediction of protein. Previous studies have proposed clusters based on measurements of physical and biochemical characteristics of the amino acids such as volume, area, hydrophilicity, polarity, hydrogen bonding, shape, and charge. These characteristics, although important, are less directly related to the protein structure compared to geometrical characteristics such as dihedral angles between amino acids. We propose using the p-value from a test of equality of dihedral-angle distributions as the basis of a distance measure for the clustering. In this novel approach, an energy test is modified to deal with bivariate angular data and the p-value is obtained via a permutation method. The results indicate that the clusters of amino acids have sensible interpretation where Glycine, Proline, and Asparagine each forms a distinct cluster. A simulation study suggests that this approach has good working characteristics to cluster amino acids.
Journal: Journal of Applied Statistics
Pages: 1235-1250
Issue: 7
Volume: 47
Year: 2020
Month: 5
X-DOI: 10.1080/02664763.2019.1673324
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1673324
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:7:p:1235-1250




Template-Type: ReDIF-Article 1.0
Author-Name: Satoru Shinoda
Author-X-Name-First: Satoru
Author-X-Name-Last: Shinoda
Author-Name: Kouji Yamamoto
Author-X-Name-First: Kouji
Author-X-Name-Last: Yamamoto
Author-Name: Kouji Tahata
Author-X-Name-First: Kouji
Author-X-Name-Last: Tahata
Author-Name: Sadao Tomizawa
Author-X-Name-First: Sadao
Author-X-Name-Last: Tomizawa
Title: A measure of asymmetry for ordinal square contingency tables with an application to modified LANZA score data
Abstract: 
 In clinical researches, various clinical scales with ordered categories are used to evaluate the efficacy and safety/toxicity of treatments. Such the clinical scales are sometimes summarized on the transition between the baseline and the study end point as a square contingency table. Also, clinical scales may be reclassified into three groups. However, the cutpoints can be varied depending on clinical researches or clinicians. Hence, this paper proposes a measure which is expressed by using same weights for collapsed tables and which can see the directionality for two kinds of asymmetries. Also, this paper shows an application of the proposed measure to clinical data, and that the proposed measure is a useful statistical method for analyzing ordered categorical data.
Journal: Journal of Applied Statistics
Pages: 1251-1260
Issue: 7
Volume: 47
Year: 2020
Month: 5
X-DOI: 10.1080/02664763.2019.1673325
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1673325
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:7:p:1251-1260




Template-Type: ReDIF-Article 1.0
Author-Name: Daniel Fernández
Author-X-Name-First: Daniel
Author-X-Name-Last: Fernández
Author-Name: Ivy Liu
Author-X-Name-First: Ivy
Author-X-Name-Last: Liu
Author-Name: Roy Costilla
Author-X-Name-First: Roy
Author-X-Name-Last: Costilla
Author-Name: Peter Yongqi Gu
Author-X-Name-First: Peter Yongqi
Author-X-Name-Last: Gu
Title: Assigning scores for ordered categorical responses
Abstract: 
 Deciding on the best statistical method to apply when the response variable is ordinal is essential because the way the categories are ordered in the data is relevant as it could change the results of the analysis. Although the models for continuous variables have similarities to those for ordinal variables, this paper presents the advantages of the use of the ordering information on the outcomes with methods developed for modeling ordinal data such as the ordered stereotype model. The novelty of this article lies in showing the dangers of assigning equally spaced scores to ordered response categories in statistical analysis, which are illustrated with a simulation study and a case study. We propose a new way to use the score parameters, which incorporates the fitted spacing dictated by the data. Additionally, this article uses score parameter estimates in the ordered stereotype model to propose a new measure to calculate continuous medians in the raw data: the adjusted c-median. It benefits the general audience who can easily understand the median as a summary statistic. Supplementary materials for this article are available online.
Journal: Journal of Applied Statistics
Pages: 1261-1281
Issue: 7
Volume: 47
Year: 2020
Month: 5
X-DOI: 10.1080/02664763.2019.1674790
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1674790
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:7:p:1261-1281




Template-Type: ReDIF-Article 1.0
Author-Name: Haruka Yamashita
Author-X-Name-First: Haruka
Author-X-Name-Last: Yamashita
Author-Name: Yoshinobu Kawahara
Author-X-Name-First: Yoshinobu
Author-X-Name-Last: Kawahara
Title: Principal points analysis via p-median problem for binary data
Abstract: 
 Analysis with principal points is a useful statistical tool for summarizing large data. In this paper, we propose a subgradient-based algorithm to calculate a set of principal points for multivariate binary data by the formulating it as a p-median problem. This enables us to find a globally optimal set of principal points or an ε-optimal solution in the middle of the calculation by combining an upper bound found using the greedy method. This algorithm is an iterative procedure where each iteration can be calculated in an efficient manner. We investigate the applicability of the proposed framework with questionnaire data and arXiv co-authors data.
Journal: Journal of Applied Statistics
Pages: 1282-1297
Issue: 7
Volume: 47
Year: 2020
Month: 5
X-DOI: 10.1080/02664763.2019.1675605
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1675605
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:7:p:1282-1297




Template-Type: ReDIF-Article 1.0
Author-Name: Adrian Dobra
Author-X-Name-First: Adrian
Author-X-Name-Last: Dobra
Author-Name: Katherine Buhikire
Author-X-Name-First: Katherine
Author-X-Name-Last: Buhikire
Author-Name: Joachim G. Voss
Author-X-Name-First: Joachim G.
Author-X-Name-Last: Voss
Title: Identifying mediating variables with graphical models: an application to the study of causal pathways in people living with HIV
Abstract: 
 We empirically demonstrate that graphical models can be a valuable tool in the identification of mediating variables in causal pathways. We make use of graphical models to elucidate the causal pathway through which the treatment influences the levels of fatigue and weakness in people living with HIV (PLHIV) based on a secondary analysis of a categorical dataset collected in a behavioral clinical trial: is weakness a mediator for the treatment and fatigue, or is fatigue a mediator for the treatment and weakness? Causal mediation analysis could not offer any definite answers to these questions.
Journal: Journal of Applied Statistics
Pages: 1298-1314
Issue: 7
Volume: 47
Year: 2020
Month: 5
X-DOI: 10.1080/02664763.2019.1669543
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1669543
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:7:p:1298-1314




Template-Type: ReDIF-Article 1.0
Author-Name: Aisha Betoko
Author-X-Name-First: Aisha
Author-X-Name-Last: Betoko
Author-Name: Chike Nwabuo
Author-X-Name-First: Chike
Author-X-Name-Last: Nwabuo
Author-Name: Bharath Ambale Venkatesh
Author-X-Name-First: Bharath
Author-X-Name-Last: Ambale Venkatesh
Author-Name: Erin P. Ricketts
Author-X-Name-First: Erin P.
Author-X-Name-Last: Ricketts
Author-Name: Sejong Bae
Author-X-Name-First: Sejong
Author-X-Name-Last: Bae
Author-Name: Colin Wu
Author-X-Name-First: Colin
Author-X-Name-Last: Wu
Author-Name: Samuel S. Gidding
Author-X-Name-First: Samuel S.
Author-X-Name-Last: Gidding
Author-Name: Kiang Liu
Author-X-Name-First: Kiang
Author-X-Name-Last: Liu
Author-Name: João A. C. Lima
Author-X-Name-First: João A. C.
Author-X-Name-Last: Lima
Author-Name: Christopher Cox
Author-X-Name-First: Christopher
Author-X-Name-Last: Cox
Title: Application of measurement error models to correct for systematic differences among readers and vendors in echocardiography measurements: the CARDIA study
Abstract: 
 We illustrate the application of linear measurement error models to calibrate echocardiography measurements acquired 20 years apart in the CARDIA study. Of 4242 echocardiograms acquired at Year-5 (1990–1991), 36% were reread 20 years later. Left ventricular (LV) mass and 8 other measurements were assessed. A machine reproducibility study including 96 additional patients also compared Year-5 and Year-25 equipment. A linear measurement error model was developed to calibrate the original Year-5 measurements, incorporating the additional Year-5 reread and machine reproducibility study data, and adjusting for differences among readers and machines. Median (quartiles) of original Year-5 LV mass was 144.4 (117.6, 174.2) g before and 129.9 (103.8, 158.6) g, after calibration. The correlation between original and calibrated LV mass was 0.989 (95% confidence interval: 0.988, 0.990). The original and calibrated measurements had similar distributions. Additional comparisons of original and calibrated data supported the use of the model. We conclude that systematic differences among readers and machines have been accounted for, and that the calibrated Year-5 measurements can be used in future longitudinal comparisons. It is hoped that this paper will encourage the wider application of measurement error models.
Journal: Journal of Applied Statistics
Pages: 1315-1324
Issue: 7
Volume: 47
Year: 2020
Month: 5
X-DOI: 10.1080/02664763.2019.1686133
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1686133
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:7:p:1315-1324




Template-Type: ReDIF-Article 1.0
Author-Name: The Editors
Title: Correction
Journal: Journal of Applied Statistics
Pages: i-i
Issue: 7
Volume: 47
Year: 2020
Month: 5
X-DOI: 10.1080/02664763.2019.1706803
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1706803
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:7:p:i-i




Template-Type: ReDIF-Article 1.0
Author-Name: J. Mazucheli
Author-X-Name-First: J.
Author-X-Name-Last: Mazucheli
Author-Name: A. F. B. Menezes
Author-X-Name-First: A. F. B.
Author-X-Name-Last: Menezes
Author-Name: L. B. Fernandes
Author-X-Name-First: L. B.
Author-X-Name-Last: Fernandes
Author-Name: R. P. de Oliveira
Author-X-Name-First: R. P.
Author-X-Name-Last: de Oliveira
Author-Name: M. E. Ghitany
Author-X-Name-First: M. E.
Author-X-Name-Last: Ghitany
Title: The unit-Weibull distribution as an alternative to the Kumaraswamy distribution for the modeling of quantiles conditional on covariates
Abstract: 
 The Beta distribution is the standard model for quantifying the influence of covariates on the mean of a response variable on the unit interval. However, this well-known distribution is no longer useful when we are interested in quantifying the influence of such covariates on the quantiles of the response variable. Unlike Beta, the Kumaraswamy distribution has a closed-form expression for its quantile and can be useful for the modeling of quantiles in the absence/presence of covariates. As an alternative to the Kumaraswamy distribution for the modeling of quantiles, in this paper the unit-Weibull distribution was considered. This distribution was obtained by the transformation of a random variable with Weibull distribution. The same transformation applied to a random variable with Exponentiated Exponential distribution generates the Kumaraswamy distribution. The suitability of our proposal was demonstrated to model quantiles, conditional on covariates, with two simulated examples and three real applications with datasets from health, accounting and social science. For such data sets, the obtained fits of the proposed regression model were compared with those provided by the Beta and Kumaraswamy regression models.
Journal: Journal of Applied Statistics
Pages: 954-974
Issue: 6
Volume: 47
Year: 2020
Month: 4
X-DOI: 10.1080/02664763.2019.1657813
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1657813
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:6:p:954-974




Template-Type: ReDIF-Article 1.0
Author-Name: Sanku Dey
Author-X-Name-First: Sanku
Author-X-Name-Last: Dey
Author-Name: Mazen Nassar
Author-X-Name-First: Mazen
Author-X-Name-Last: Nassar
Title: Classical methods of estimation on constant stress accelerated life tests under exponentiated Lindley distribution
Abstract: 
 Accelerated life testing is adopted in several fields to obtain adequate failure time data of test units in a much shorter time than testing at normal operating conditions. The lifetime of a product at constant level of stress is assumed to have an exponentiated Lindley distribution. In this paper, besides maximum likelihood method, eight other frequentist methods of estimation, namely, method of least square estimation, method of weighted least square estimation, method of maximum product of spacing estimation, method of minimum spacing absolute distance estimation, method of minimum spacing absolute-log distance estimation, method of Cramér-von-Mises estimation, method of Anderson-Darling estimation and Right-tail Anderson-Darling estimation are considered to estimate the parameters of the exponentiated Lindley distribution under constant stress accelerated life testing. Moreover, shape parameter and the reliability function under usual conditions are estimated based on aforementioned methods of estimation. To evaluate the performance of the proposed methods, a simulation study is carried out. The performances of the estimators have been compared in terms of their mean squared error using small, medium and large sample sizes. As an illustration, the model and the proposed methods are applied to two accelerated life test data sets.
Journal: Journal of Applied Statistics
Pages: 975-996
Issue: 6
Volume: 47
Year: 2020
Month: 4
X-DOI: 10.1080/02664763.2019.1661361
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1661361
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:6:p:975-996




Template-Type: ReDIF-Article 1.0
Author-Name: Sungtaek Son
Author-X-Name-First: Sungtaek
Author-X-Name-Last: Son
Author-Name: Cheolwoo Park
Author-X-Name-First: Cheolwoo
Author-X-Name-Last: Park
Author-Name: Yongho Jeon
Author-X-Name-First: Yongho
Author-X-Name-Last: Jeon
Title: Sparse graphical models via calibrated concave convex procedure with application to fMRI data
Abstract: 
 This paper proposes a calibrated concave convex procedure (calibrated CCCP) for high-dimensional graphical model selection. The calibrated CCCP approach for the smoothly clipped absolute deviation (SCAD) penalty is known to be path-consistent with probability converging to one in linear regression models. We implement the calibrated CCCP method with the SCAD penalty for the graphical model selection. We use a quadratic objective function for undirected Gaussian graphical models and adopt the SCAD penalty for sparse estimation. For the tuning procedure, we propose to use columnwise tuning on the quadratic objective function adjusted for test data. In a simulation study, we compare the performance of the proposed method with two existing graphical model estimators for high-dimensional data in terms of matrix error norms and support recovery rate. We also compare the bias and the variance of the estimated matrices. Then, we apply the method to functional magnetic resonance imaging (fMRI) data of an attention deficit hyperactivity disorders (ADHD) patient.
Journal: Journal of Applied Statistics
Pages: 997-1016
Issue: 6
Volume: 47
Year: 2020
Month: 4
X-DOI: 10.1080/02664763.2019.1663158
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1663158
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:6:p:997-1016




Template-Type: ReDIF-Article 1.0
Author-Name: Xiaoning Kang
Author-X-Name-First: Xiaoning
Author-X-Name-Last: Kang
Author-Name: Chaoping Xie
Author-X-Name-First: Chaoping
Author-X-Name-Last: Xie
Author-Name: Mingqiu Wang
Author-X-Name-First: Mingqiu
Author-X-Name-Last: Wang
Title: A Cholesky-based estimation for large-dimensional covariance matrices
Abstract: 
 This paper develops a new method to estimate a large-dimensional covariance matrix when the variables have no natural ordering among themselves. The modified Cholesky decomposition technique is used to provide a set of estimates of the covariance matrix under multiple orderings of variables. The proposed estimator is in the form of a linear combination of these available estimates and the identity matrix. It is positive definite and applicable in large dimensions. The merits of the proposed estimator are demonstrated through the numerical study and a real data example by comparison with several existing methods.
Journal: Journal of Applied Statistics
Pages: 1017-1030
Issue: 6
Volume: 47
Year: 2020
Month: 4
X-DOI: 10.1080/02664763.2019.1664424
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1664424
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:6:p:1017-1030




Template-Type: ReDIF-Article 1.0
Author-Name: P. Mantalos
Author-X-Name-First: P.
Author-X-Name-Last: Mantalos
Author-Name: A. Karagrigoriou
Author-X-Name-First: A.
Author-X-Name-Last: Karagrigoriou
Author-Name: L. Střelec
Author-X-Name-First: L.
Author-X-Name-Last: Střelec
Author-Name: P. Jordanova
Author-X-Name-First: P.
Author-X-Name-Last: Jordanova
Author-Name: P. Hermann
Author-X-Name-First: P.
Author-X-Name-Last: Hermann
Author-Name: J. Kiseľák
Author-X-Name-First: J.
Author-X-Name-Last: Kiseľák
Author-Name: J. Hudák
Author-X-Name-First: J.
Author-X-Name-Last: Hudák
Author-Name: M. Stehlík
Author-X-Name-First: M.
Author-X-Name-Last: Stehlík
Title: On improved volatility modelling by fitting skewness in ARCH models
Abstract: 
 We study ARCH/GARCH effects under possible deviation from normality. Since skewness is the principal cause for deviations from normality in many practical applications, e.g. finance, we study in particular skewness. We propose robust tests for normality both for NoVaS and modified NoVaS transformed and original data. Such an approach is not applicable for EGARCH, but applicable for GARCH-GJR models. A novel test procedure is proposed for the skewness in autoregressive conditional volatility models. The power of the tests is investigated with various underlying models. Applications with financial data show the applicability and the capabilities of the proposed testing procedure.
Journal: Journal of Applied Statistics
Pages: 1031-1063
Issue: 6
Volume: 47
Year: 2020
Month: 4
X-DOI: 10.1080/02664763.2019.1671323
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1671323
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:6:p:1031-1063




Template-Type: ReDIF-Article 1.0
Author-Name: Jinyu Li
Author-X-Name-First: Jinyu
Author-X-Name-Last: Li
Author-Name: Yutong Lai
Author-X-Name-First: Yutong
Author-X-Name-Last: Lai
Author-Name: Chi Zhang
Author-X-Name-First: Chi
Author-X-Name-Last: Zhang
Author-Name: Qi Zhang
Author-X-Name-First: Qi
Author-X-Name-Last: Zhang
Title: TGCnA: temporal gene coexpression network analysis using a low-rank plus sparse framework
Abstract: 
 Various gene network models with distinct physical nature have been widely used in biological studies. For temporal transcriptomic studies, the current dynamic models either ignore the temporal variation in the network structure or fail to scale up to a large number of genes due to severe computational bottlenecks and sample size limitation. Although the correlation-based gene networks are computationally affordable, they have limitations after being applied to gene expression time-course data. We proposed Temporal Gene Coexpression Network Analysis (TGCnA) framework for the transcriptomic time-course data. The mathematical nature of TGCnA is the joint modeling of multiple covariance matrices across time points using a ‘low-rank plus sparse’ framework, in which the network similarity across time points is explicitly modeled in the low-rank component. We demonstrated the advantage of TGCnA in covariance matrix estimation and gene module discovery using both simulation data and real transcriptomic data. The code is available at https://github.com/QiZhangStat/TGCnA.
Journal: Journal of Applied Statistics
Pages: 1064-1083
Issue: 6
Volume: 47
Year: 2020
Month: 4
X-DOI: 10.1080/02664763.2019.1667311
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1667311
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:6:p:1064-1083




Template-Type: ReDIF-Article 1.0
Author-Name: Shuvashree Mondal
Author-X-Name-First: Shuvashree
Author-X-Name-Last: Mondal
Author-Name: Debasis Kundu
Author-X-Name-First: Debasis
Author-X-Name-Last: Kundu
Title: A bivariate inverse Weibull distribution and its application in complementary risks model
Abstract: 
 In reliability and survival analysis the inverse Weibull distribution has been used quite extensively as a heavy tailed distribution with a non-monotone hazard function. Recently a bivariate inverse Weibull (BIW) distribution has been introduced in the literature, where the marginals have inverse Weibull distributions and it has a singular component. Due to this reason this model cannot be used when there are no ties in the data. In this paper we have introduced an absolutely continuous bivariate inverse Weibull (ACBIW) distribution omitting the singular component from the BIW distribution. A natural application of this model can be seen in the analysis of dependent complementary risks data. We discuss different properties of this model and also address the inferential issues both from the classical and Bayesian approaches. In the classical approach, the maximum likelihood estimators cannot be obtained explicitly and we propose to use the expectation maximization algorithm based on the missing value principle. In the Bayesian analysis, we use a very flexible prior on the unknown model parameters and obtain the Bayes estimates and the associated credible intervals using importance sampling technique. Simulation experiments are performed to see the effectiveness of the proposed methods and two data sets have been analyzed to see how the proposed methods and the model work in practice.
Journal: Journal of Applied Statistics
Pages: 1084-1108
Issue: 6
Volume: 47
Year: 2020
Month: 4
X-DOI: 10.1080/02664763.2019.1669542
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1669542
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:6:p:1084-1108




Template-Type: ReDIF-Article 1.0
Author-Name: Konstantinos Gkillas
Author-X-Name-First: Konstantinos
Author-X-Name-Last: Gkillas
Author-Name: Rangan Gupta
Author-X-Name-First: Rangan
Author-X-Name-Last: Gupta
Author-Name: Chi Keung Marco Lau
Author-X-Name-First: Chi Keung Marco
Author-X-Name-Last: Lau
Author-Name: Muhammad Tahir Suleman
Author-X-Name-First: Muhammad Tahir
Author-X-Name-Last: Suleman
Title: Jumps beyond the realms of cricket: India's performance in One Day Internationals and stock market movements
Abstract: 
 We examine the impact of the Indian cricket team's performance in one-day international cricket matches on return, realized volatility and jumps of the Indian stock market, based on intraday data covering the period of 30th October, 2006 to 31st March, 2017. Using a nonparametric causality-in-quantiles test, we were able to detect evidence of predictability from wins or losses for primarily volatility and jumps, especially over the lower-quantiles of the conditional distributions, with losses having stronger predictability than wins. However, the impact on the stock return is weak and restricted towards the upper end of the conditional distribution.
Journal: Journal of Applied Statistics
Pages: 1109-1127
Issue: 6
Volume: 47
Year: 2020
Month: 4
X-DOI: 10.1080/02664763.2019.1663157
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1663157
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:6:p:1109-1127




Template-Type: ReDIF-Article 1.0
Author-Name: Hossein Hassani
Author-X-Name-First: Hossein
Author-X-Name-Last: Hassani
Author-Name: Mohammad Reza Yeganegi
Author-X-Name-First: Mohammad Reza
Author-X-Name-Last: Yeganegi
Author-Name: Juncal Cuñado
Author-X-Name-First: Juncal
Author-X-Name-Last: Cuñado
Author-Name: Rangan Gupta
Author-X-Name-First: Rangan
Author-X-Name-Last: Gupta
Title: Forecasting interest rate volatility of the United Kingdom: evidence from over 150 years of data
Abstract: 
 This study examines the very short, short, medium and long-term forecasting ability of different univariate GARCH models of United Kingdom (UK)'s interest rate volatility, using a long span monthly data from May 1836 to June 2018. The main results show the relevance of considering alternative error distributions to the normal distribution when estimating GARCH-type models. Thus, we obtain that the Asymmetric Power ARCH (A-PARCH) models with skew generalized error distribution are the most accurate models when forecasting UK interest rates, while for the short, medium and long-term term forecasting horizons, GARCH models with generalized error distribution for the error term are the most accurate models in forecasting UK's interest rates.
Journal: Journal of Applied Statistics
Pages: 1128-1143
Issue: 6
Volume: 47
Year: 2020
Month: 4
X-DOI: 10.1080/02664763.2019.1666093
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1666093
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:6:p:1128-1143




Template-Type: ReDIF-Article 1.0
Author-Name: The Editors
Title: Correction
Journal: Journal of Applied Statistics
Pages: i-ii
Issue: 6
Volume: 47
Year: 2020
Month: 4
X-DOI: 10.1080/02664763.2019.1689732
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1689732
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:6:p:i-ii




Template-Type: ReDIF-Article 1.0
Author-Name: Mark Thackham
Author-X-Name-First: Mark
Author-X-Name-Last: Thackham
Author-Name: Jun Ma
Author-X-Name-First: Jun
Author-X-Name-Last: Ma
Title: On maximum likelihood estimation of the semi-parametric Cox model with time-varying covariates
Abstract: 
 Including time-varying covariates is a popular extension to the Cox model and a suitable approach for dealing with non-proportional hazards. However, partial likelihood (PL) estimation of this model has three shortcomings: (i) estimated regression coefficients can be less accurate in small samples with heavy censoring; (ii) the baseline hazard is not directly estimated and (iii) a covariance matrix for both the regression coefficients and the baseline hazard is not easily produced.We address these by developing a maximum likelihood (ML) approach to jointly estimate regression coefficients and baseline hazard using a constrained optimisation ensuring the latter's non-negativity. We demonstrate asymptotic properties of these estimates and show via simulation their increased accuracy compared to PL estimates in small samples and show our method produces smoother baseline hazard estimates than the Breslow estimator.Finally, we apply our method to two examples, including an important real-world financial example to estimate time to default for retail home loans. We demonstrate using our ML estimate for the baseline hazard can give much clearer corroboratory evidence of the ‘humped hazard’, whereby the risk of loan default rises to a peak and then later falls.
Journal: Journal of Applied Statistics
Pages: 1511-1528
Issue: 9
Volume: 47
Year: 2020
Month: 6
X-DOI: 10.1080/02664763.2019.1681946
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1681946
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:9:p:1511-1528




Template-Type: ReDIF-Article 1.0
Author-Name: P. Martínez-Camblor
Author-X-Name-First: P.
Author-X-Name-Last: Martínez-Camblor
Author-Name: S. Pérez-Fernández
Author-X-Name-First: S.
Author-X-Name-Last: Pérez-Fernández
Author-Name: S. Díaz-Coto
Author-X-Name-First: S.
Author-X-Name-Last: Díaz-Coto
Title: The role of the p-value in the multitesting problem
Abstract: 
 Modern science frequently involves the analysis of large amount of quantitative information and the simultaneous testing of thousands or even hundreds of thousands null hypotheses. In this context, sometimes, naive deductions derived from the statistical reports substitute the rational thinking. The reproducibility crisis is a direct consequence of the misleading statistical conclusions. In this paper, the authors revisit some of the controversies on the implications derived from the statistical hypothesis testing. They focus on the role of the p-value on the massive multitesting problem and the loss of its standard probabilistic interpretation. The analogy between the hypothesis tests and the usual diagnostic process (both involve a decision-making) is used to point out some limitations in the probabilistic p-value interpretation and to introduce the receiver-operating characteristic, ROC, curve as a useful tool in the large-scale multitesting context. The analysis of the well-known Hedenfalk data illustrates the problem.
Journal: Journal of Applied Statistics
Pages: 1529-1542
Issue: 9
Volume: 47
Year: 2020
Month: 6
X-DOI: 10.1080/02664763.2019.1682128
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1682128
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:9:p:1529-1542




Template-Type: ReDIF-Article 1.0
Author-Name: Sukhdev Singh
Author-X-Name-First: Sukhdev
Author-X-Name-Last: Singh
Author-Name: Sanku Dey
Author-X-Name-First: Sanku
Author-X-Name-Last: Dey
Author-Name: Devendra Kumar
Author-X-Name-First: Devendra
Author-X-Name-Last: Kumar
Title: Statistical inference based on generalized Lindley record values
Abstract: 
 This paper addresses the problems of frequentist and Bayesian estimation for the unknown parameters of generalized Lindley distribution based on lower record values. We first derive the exact explicit expressions for the single and product moments of lower record values, and then use these results to compute the means, variances and covariance between two lower record values. We next obtain the maximum likelihood estimators and associated asymptotic confidence intervals. Furthermore, we obtain Bayes estimators under the assumption of gamma priors on both the shape and the scale parameters of the generalized Lindley distribution, and associated the highest posterior density interval estimates. The Bayesian estimation is studied with respect to both symmetric (squared error) and asymmetric (linear-exponential (LINEX)) loss functions. Finally, we compute Bayesian predictive estimates and predictive interval estimates for the future record values. To illustrate the findings, one real data set is analyzed, and Monte Carlo simulations are performed to compare the performances of the proposed methods of estimation and prediction.
Journal: Journal of Applied Statistics
Pages: 1543-1561
Issue: 9
Volume: 47
Year: 2020
Month: 6
X-DOI: 10.1080/02664763.2019.1683153
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1683153
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:9:p:1543-1561




Template-Type: ReDIF-Article 1.0
Author-Name: Ana C. Guedes
Author-X-Name-First: Ana C.
Author-X-Name-Last: Guedes
Author-Name: Francisco Cribari-Neto
Author-X-Name-First: Francisco
Author-X-Name-Last: Cribari-Neto
Author-Name: Patrícia L. Espinheira
Author-X-Name-First: Patrícia L.
Author-X-Name-Last: Espinheira
Title: Modified likelihood ratio tests for unit gamma regressions
Abstract: 
 Regression analyses are commonly performed with doubly limited continuous dependent variables; for instance, when modeling the behavior of rates, proportions and income concentration indices. Several models are available in the literature for use with such variables, one of them being the unit gamma regression model. In all such models, parameter estimation is typically performed using the maximum likelihood method and testing inferences on the model's parameters are usually based on the likelihood ratio test. Such a test can, however, deliver quite imprecise inferences when the sample size is small. In this paper, we propose two modified likelihood ratio test statistics for use with the unit gamma regressions that deliver much more accurate inferences when the number of data points in small. Numerical (i.e. simulation) evidence is presented for both fixed dispersion and varying dispersion models, and also for tests that involve nonnested models. We also present and discuss two empirical applications.
Journal: Journal of Applied Statistics
Pages: 1562-1586
Issue: 9
Volume: 47
Year: 2020
Month: 6
X-DOI: 10.1080/02664763.2019.1683152
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1683152
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:9:p:1562-1586




Template-Type: ReDIF-Article 1.0
Author-Name: Bo Chang
Author-X-Name-First: Bo
Author-X-Name-Last: Chang
Author-Name: Harry Joe
Author-X-Name-First: Harry
Author-X-Name-Last: Joe
Title: Copula diagnostics for asymmetries and conditional dependence
Abstract: 
 Vine copulas are constructed from a sequence of trees to represent dependence and conditional dependence, and a set of bivariate copulas that are applied to univariate distributions in tree 1 and to conditional univariate distributions in subsequent trees. Diagnostic methods based on measures of dependence and tail asymmetry are proposed to guide the choice of parametric bivariate copula families assigned to the edges of the trees in the vine and to assess whether a copula is constant over the conditioning value(s) for trees 2 and higher. The measures are conditional measures applied to bivariate conditional distributions in trees 2 and higher. If the diagnostic methods suggest the existence of reflection asymmetry, permutation asymmetry and possible asymmetric tail dependence, then three- or four-parameter bivariate copula families might be needed. Moreover, if the conditional dependence measures or asymmetry measures in trees 2 and up are not constant over the conditioning value(s), then non-constant copulas should be considered. We illustrate the use of the diagnostic methods for a gamma factor model and two real datasets. The examples show that better models are attained by using asymmetric and non-constant copulas under the guidance of the diagnostic tools.
Journal: Journal of Applied Statistics
Pages: 1587-1615
Issue: 9
Volume: 47
Year: 2020
Month: 6
X-DOI: 10.1080/02664763.2019.1685080
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1685080
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:9:p:1587-1615




Template-Type: ReDIF-Article 1.0
Author-Name: Peter W. Marcy
Author-X-Name-First: Peter W.
Author-X-Name-Last: Marcy
Author-Name: Scott A. Vander Wiel
Author-X-Name-First: Scott A.
Author-X-Name-Last: Vander Wiel
Author-Name: Curtis B. Storlie
Author-X-Name-First: Curtis B.
Author-X-Name-Last: Storlie
Author-Name: Veronica Livescu
Author-X-Name-First: Veronica
Author-X-Name-Last: Livescu
Author-Name: Curt A. Bronkhorst
Author-X-Name-First: Curt A.
Author-X-Name-Last: Bronkhorst
Title: Modeling material stress using integrated Gaussian Markov random fields
Abstract: 
 The equations of a physical constitutive model for material stress within tantalum grains were solved numerically using a tetrahedrally meshed volume. The resulting output included a scalar vonMises stress for each of the more than 94,000 tetrahedra within the finite element discretization. In this paper, we define an intricate statistical model for the spatial field of vonMises stress which uses the given grain geometry in a fundamental way. Our model relates the three-dimensional field to integrals of latent stochastic processes defined on the vertices of the one- and two-dimensional grain boundaries. An intuitive neighborhood structure of the said boundary nodes suggested the use of a latent Gaussian Markov random field (GMRF). However, despite the potential for computational gains afforded by GMRFs, the integral nature of our model and the sheer number of data points pose substantial challenges for a full Bayesian analysis. To overcome these problems and encourage efficient exploration of the posterior distribution, a number of techniques are now combined: parallel computing, sparse matrix methods, and a modification of a block update strategy within the sampling routine. In addition, we use an auxiliary variables approach to accommodate the presence of outliers in the data.
Journal: Journal of Applied Statistics
Pages: 1616-1636
Issue: 9
Volume: 47
Year: 2020
Month: 6
X-DOI: 10.1080/02664763.2019.1686131
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1686131
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:9:p:1616-1636




Template-Type: ReDIF-Article 1.0
Author-Name: Lakshmi Kanta Patra
Author-X-Name-First: Lakshmi Kanta
Author-X-Name-Last: Patra
Title: On the improved estimation of a function of the scale parameter of an exponential distribution based on doubly censored sample
Abstract: 
 In the present article, we have studied the estimation of entropy, that is, a function of scale parameter  $\ln \sigma $ln⁡σ of an exponential distribution based on doubly censored sample when the location parameter is restricted to positive real line. The estimation problem is studied under a general class of bowl-shaped non monotone location invariant loss functions. It is established that the best affine equivariant estimator (BAEE) is inadmissible by deriving an improved estimator. This estimator is non-smooth. Further, we have obtained a smooth improved estimator. A class of estimators is considered and sufficient conditions are derived under which these estimators improve upon the BAEE. In particular, using these results we have obtained the improved estimators for the squared error and the linex loss functions. Finally, we have compared the risk performance of the proposed estimators numerically. One data analysis has been performed for illustrative purposes.
Journal: Journal of Applied Statistics
Pages: 1637-1651
Issue: 9
Volume: 47
Year: 2020
Month: 6
X-DOI: 10.1080/02664763.2019.1688261
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1688261
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:9:p:1637-1651




Template-Type: ReDIF-Article 1.0
Author-Name: Abdul Haq
Author-X-Name-First: Abdul
Author-X-Name-Last: Haq
Author-Name: Michael B. C. Khoo
Author-X-Name-First: Michael B. C.
Author-X-Name-Last: Khoo
Title: Multivariate process dispersion monitoring without subgrouping
Abstract: 
 The memory-type adaptive and non-adaptive control charts are among the best control charts for detecting small-to-moderate changes in the process parameter(s). In this paper, we propose the Crosier CUSUM (CCUSUM), EWMA, adaptive CCUSUM (ACCUSUM) and adaptive EWMA (AEWMA) charts for efficiently monitoring the changes in the covariance matrix of a multivariate normal process without subgrouping. Using extensive Monte Carlo simulations, the length characteristics of these control charts are computed. It turns out that the ACCUSUM and AEWMA charts perform uniformly and substantially better than the CCUSUM and EWMA charts when detecting a range of shift sizes in the covariance matrix. Moreover, the AEWMA chart outperforms the ACCUSUM chart. A real dataset is used to explain the implementation of the proposed control charts.
Journal: Journal of Applied Statistics
Pages: 1652-1675
Issue: 9
Volume: 47
Year: 2020
Month: 6
X-DOI: 10.1080/02664763.2019.1688262
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1688262
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:9:p:1652-1675




Template-Type: ReDIF-Article 1.0
Author-Name: Tsai-Yu Lin
Author-X-Name-First: Tsai-Yu
Author-X-Name-Last: Lin
Author-Name: Chen-Tuo Liao
Author-X-Name-First: Chen-Tuo
Author-X-Name-Last: Liao
Author-Name: Yen-Ya Liao
Author-X-Name-First: Yen-Ya
Author-X-Name-Last: Liao
Title: Nonconformance probability of an air quality index
Abstract: 
 Air pollution is one of the most important global environmental issues. Taiwan Environmental Protection Agency (EPA) is currently using an Air Quality Index (AQI) to measure and monitor its national air quality. The main objective of this study is to assess the air quality per hour every month in Taichung City of Taiwan from 2014 to 2016 based on the nonconformance probability of the AQI index. The nonconformance probability is defined as the probability that a characteristic of interest falls outside of an acceptance region. A lower confidence bound for the nonconformance probability is applied to test whether the AQI index value exceeds a warning threshold, and then the government could issue warnings according to the decision made by such statistical inference. An unbalanced two-way random effects model is presented for fitting the AQI index values. We evaluate three different lower confidence bound construction methods, including a t-based, an adjusted t-based and a generalized pivotal quantity (GPQ) based methods, through a detailed simulation study. Finally, a hybrid method of the t-based and the adjusted t-based estimators is recommended for practical use.
Journal: Journal of Applied Statistics
Pages: 1676-1689
Issue: 9
Volume: 47
Year: 2020
Month: 6
X-DOI: 10.1080/02664763.2019.1690639
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1690639
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:9:p:1676-1689




Template-Type: ReDIF-Article 1.0
Author-Name: Clécio da Silva Ferreira
Author-X-Name-First: Clécio
Author-X-Name-Last: da Silva Ferreira
Author-Name: Víctor H. Lachos
Author-X-Name-First: Víctor H.
Author-X-Name-Last: Lachos
Author-Name: Aldo M. Garay
Author-X-Name-First: Aldo M.
Author-X-Name-Last: Garay
Title: Inference and diagnostics for heteroscedastic nonlinear regression models under skew scale mixtures of normal distributions
Abstract: 
 The heteroscedastic nonlinear regression model (HNLM) is an important tool in data modeling. In this paper we propose a HNLM considering skew scale mixtures of normal (SSMN) distributions, which allows fitting asymmetric and heavy-tailed data simultaneously. Maximum likelihood (ML) estimation is performed via the expectation-maximization (EM) algorithm. The observed information matrix is derived analytically to account for standard errors. In addition, diagnostic analysis is developed using case-deletion measures and the local influence approach. A simulation study is developed to verify the empirical distribution of the likelihood ratio statistic, the power of the homogeneity of variances test and a study for misspecification of the structure function. The method proposed is also illustrated by analyzing a real dataset.
Journal: Journal of Applied Statistics
Pages: 1690-1719
Issue: 9
Volume: 47
Year: 2020
Month: 6
X-DOI: 10.1080/02664763.2019.1691158
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1691158
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:9:p:1690-1719




Template-Type: ReDIF-Article 1.0
Author-Name: Giovanni Maria Merola
Author-X-Name-First: Giovanni
Author-X-Name-Last: Maria Merola
Title: SIMPCA: a framework for rotating and sparsifying principal components
Abstract: 
 We propose an algorithmic framework for computing sparse components from rotated principal components. This methodology, called SIMPCA, is useful to replace the unreliable practice of ignoring small coefficients of rotated components when interpreting them. The algorithm computes genuinely sparse components by projecting rotated principal components onto subsets of variables. The so simplified components are highly correlated with the corresponding components. By choosing different simplification strategies different sparse solutions can be obtained which can be used to compare alternative interpretations of the principal components. We give some examples of how effective simplified solutions can be achieved with SIMPCA using some publicly available data sets.
Journal: Journal of Applied Statistics
Pages: 1325-1353
Issue: 8
Volume: 47
Year: 2020
Month: 6
X-DOI: 10.1080/02664763.2019.1676404
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1676404
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:8:p:1325-1353




Template-Type: ReDIF-Article 1.0
Author-Name: El Houcine Hibbah
Author-X-Name-First: El Houcine
Author-X-Name-Last: Hibbah
Author-Name: Hamid El Maroufy
Author-X-Name-First: Hamid
Author-X-Name-Last: El Maroufy
Author-Name: Christiane Fuchs
Author-X-Name-First: Christiane
Author-X-Name-Last: Fuchs
Author-Name: Taib Ziad
Author-X-Name-First: Taib
Author-X-Name-Last: Ziad
Title: An MCMC computational approach for a continuous time state-dependent regime switching diffusion process
Abstract: 
 State-dependent regime switching diffusion processes or hybrid switching diffusion (HSD) processes are hard to simulate with classical methods which leads us to adopt a Markov chain Monte Carlo (MCMC) Bayesian approach very convenient to estimate complicated models such as the HSD one. In the HSD, the diffusion component is dependent on the switching discrete hidden regimes and the transition rates of the regime switching are dependent on the diffusion observations. Since in reality phenomena are only observed in discrete times, data imputation is called for to create more observations so as to have good approximations for the density of the diffusion process. Three categories of entities will be computed in a Bayesian context: The latent imputed observations, the regime switching states, and the parameters of the models. The latent imputed data is updated at random time intervals in block using a Metropolis Hastings algorithm. The switching states are computed by an adaptation of a forward filtering backward smoothing algorithm to the HSD model. The parameters are estimated after prior specifications and conditional posterior densities formulation using Gibbs sampler or Metropolis Hastings algorithm.
Journal: Journal of Applied Statistics
Pages: 1354-1374
Issue: 8
Volume: 47
Year: 2020
Month: 6
X-DOI: 10.1080/02664763.2019.1677573
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1677573
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:8:p:1354-1374




Template-Type: ReDIF-Article 1.0
Author-Name: Shi-Fang Qiu
Author-X-Name-First: Shi-Fang
Author-X-Name-Last: Qiu
Author-Name: Jie He
Author-X-Name-First: Jie
Author-X-Name-Last: He
Author-Name: Ji-Ran Tao
Author-X-Name-First: Ji-Ran
Author-X-Name-Last: Tao
Author-Name: Man-Lai Tang
Author-X-Name-First: Man-Lai
Author-X-Name-Last: Tang
Author-Name: Wai-Yin Poon
Author-X-Name-First: Wai-Yin
Author-X-Name-Last: Poon
Title: Comparison of disease prevalence in two populations under double-sampling scheme with two fallible classifiers
Abstract: 
 A disease prevalence can be estimated by classifying subjects according to whether they have the disease. When gold-standard tests are too expensive to be applied to all subjects, partially validated data can be obtained by double-sampling in which all individuals are classified by a fallible classifier, and some of individuals are validated by the gold-standard classifier. However, it could happen in practice that such infallible classifier does not available. In this article, we consider two models in which both classifiers are fallible and propose four asymptotic test procedures for comparing disease prevalence in two groups. Corresponding sample size formulae and validated ratio given the total sample sizes are also derived and evaluated. Simulation results show that (i) Score test performs well and the corresponding sample size formula is also accurate in terms of the empirical power and size in two models; (ii) the Wald test based on the variance estimator with parameters estimated under the null hypothesis outperforms the others even under small sample sizes in Model II, and the sample size estimated by this test is also accurate; (iii) the estimated validated ratios based on all tests are accurate. The malarial data are used to illustrate the proposed methodologies.
Journal: Journal of Applied Statistics
Pages: 1375-1401
Issue: 8
Volume: 47
Year: 2020
Month: 6
X-DOI: 10.1080/02664763.2019.1679727
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1679727
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:8:p:1375-1401




Template-Type: ReDIF-Article 1.0
Author-Name: Chandrakant Lodhi
Author-X-Name-First: Chandrakant
Author-X-Name-Last: Lodhi
Author-Name: Yogesh Mani Tripathi
Author-X-Name-First: Yogesh Mani
Author-X-Name-Last: Tripathi
Title: Inference on a progressive type I interval-censored truncated normal distribution
Abstract: 
 In this paper, we consider the problem of making statistical inference for a truncated normal distribution under progressive type I interval censoring. We obtain maximum likelihood estimators of unknown parameters using the expectation-maximization algorithm and in sequel, we also compute corresponding midpoint estimates of parameters. Estimation based on the probability plot method is also considered. Asymptotic confidence intervals of unknown parameters are constructed based on the observed Fisher information matrix. We obtain Bayes estimators of parameters with respect to informative and non-informative prior distributions under squared error and linex loss functions. We compute these estimates using the importance sampling procedure. The highest posterior density intervals of unknown parameters are constructed as well. We present a Monte Carlo simulation study to compare the performance of proposed point and interval estimators. Analysis of a real data set is also performed for illustration purposes. Finally, inspection times and optimal censoring plans based on the expected Fisher information matrix are discussed.
Journal: Journal of Applied Statistics
Pages: 1402-1422
Issue: 8
Volume: 47
Year: 2020
Month: 6
X-DOI: 10.1080/02664763.2019.1679096
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1679096
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:8:p:1402-1422




Template-Type: ReDIF-Article 1.0
Author-Name: Abdelfattah Mustafa
Author-X-Name-First: Abdelfattah
Author-X-Name-Last: Mustafa
Title: Improving the bridge structure by using linear failure rate distribution
Abstract: 
 In this paper, a system of five components is studied; one of these components is a bridge network component. Each of these components has a non-constant failure rate. The system components have linear failure rate lifetime distribution. The given system is improved by using three methods: reduction, warm standby with perfect switch and warm standby with imperfect switch. The reliability equivalence factors of the bridge structure system are obtained. The γ-fractiles are obtained to compare the original system with these improved systems. Finally, we present numerical results to show the difference between these methods.
Journal: Journal of Applied Statistics
Pages: 1423-1438
Issue: 8
Volume: 47
Year: 2020
Month: 6
X-DOI: 10.1080/02664763.2019.1679098
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1679098
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:8:p:1423-1438




Template-Type: ReDIF-Article 1.0
Author-Name: Gavin Q. Collins
Author-X-Name-First: Gavin Q.
Author-X-Name-Last: Collins
Author-Name: Matthew J. Heaton
Author-X-Name-First: Matthew J.
Author-X-Name-Last: Heaton
Author-Name: Leiqiu Hu
Author-X-Name-First: Leiqiu
Author-X-Name-Last: Hu
Title: Physically constrained spatiotemporal modeling: generating clear-sky constructions of land surface temperature from sparse, remotely sensed satellite data
Abstract: 
 Satellite remote-sensing is used to collect important atmospheric and geophysical data at various spatial resolutions, providing insight into spatiotemporal surface and climate variability globally. These observations are often plagued with missing spatial and temporal information of Earth's surface due to (1) cloud cover at the time of a satellite passing and (2) infrequent passing of polar-orbiting satellites. While many methods are available to model missing data in space and time, in the case of land surface temperature (LST) from thermal infrared remote sensing, these approaches generally ignore the temporal pattern called the ‘diurnal cycle’ which physically constrains temperatures to peak in the early afternoon and reach a minimum at sunrise. In order to infill an LST dataset, we parameterize the diurnal cycle into a functional form with unknown spatiotemporal parameters. Using multiresolution spatial basis functions, we estimate these parameters from sparse satellite observations to reconstruct an LST field with continuous spatial and temporal distributions. These estimations may then be used to better inform scientists of spatiotemporal thermal patterns over relatively complex domains. The methodology is demonstrated using data collected by MODIS on NASA's Aqua and Terra satellites over both Houston, TX and Phoenix, AZ USA.
Journal: Journal of Applied Statistics
Pages: 1439-1459
Issue: 8
Volume: 47
Year: 2020
Month: 6
X-DOI: 10.1080/02664763.2019.1681384
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1681384
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:8:p:1439-1459




Template-Type: ReDIF-Article 1.0
Author-Name: Mohammad Samsul Alam
Author-X-Name-First: Mohammad Samsul
Author-X-Name-Last: Alam
Author-Name: Sangita Paul
Author-X-Name-First: Sangita
Author-X-Name-Last: Paul
Title: A comparative analysis of clustering algorithms to identify the homogeneous rainfall gauge stations of Bangladesh
Abstract: 
 Dealing with individual rainfall station is time consuming as well as prone to more variation. It seems reasonable and advantageous to deal with a group of homogeneous stations rather than an individual station. Such groups can be identified using clustering algorithms, techniques used in the multivariate data analysis. Particularly, in this study, covering both hard and soft clustering approaches, three clustering algorithms namely Agglomerative hierarchical, K-means clustering and Fuzzy C-means methods are chosen due to their popularity. These algorithms are applied over precipitation data recorded by the Bangladesh Meteorology Department, and a comparison among the algorithms is made. Annual and seasonal precipitations from 1977 to 2012 recorded in 30 stations are used in this study. Optimal numbers of clusters in the four precipitation series are determined using the Gap statistic for K-means clustering and using the extended Gap statistic for Fuzzy C-means clustering, and are found as 3, 1, 3 and 2 for annual, pre-monsoon, monsoon and post-monsoon, respectively. This study investigates the clustering methods in terms of the similarity, members and homogeneity, among the clusters formed. The clusters are also characterized to see how they are distributed. Moreover, in terms of cluster homogeneity, Fuzzy C-means algorithm outperforms the other clustering methods.
Journal: Journal of Applied Statistics
Pages: 1460-1481
Issue: 8
Volume: 47
Year: 2020
Month: 6
X-DOI: 10.1080/02664763.2019.1675606
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1675606
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:8:p:1460-1481




Template-Type: ReDIF-Article 1.0
Author-Name: Muhammad Naveed
Author-X-Name-First: Muhammad
Author-X-Name-Last: Naveed
Author-Name: Muhammad Azam
Author-X-Name-First: Muhammad
Author-X-Name-Last: Azam
Author-Name: Nasrullah Khan
Author-X-Name-First: Nasrullah
Author-X-Name-Last: Khan
Author-Name: Muhammad Aslam
Author-X-Name-First: Muhammad
Author-X-Name-Last: Aslam
Title: Designing a control chart of extended EWMA statistic based on multiple dependent state sampling
Abstract: 
 In this paper, we presented a memory type control chart (CC) based on multiple dependent state (MDS) sampling to pinpoint the slight variation in the process mean for the quality trait of normal distribution (ND). Two pairs of control limits denominated as internal and external control limits are derived using under control mean and variance. The essential steps are taken to get the value of average run length (ARL) for stable and disturb process. Various tables of ARLs are erected using different smoothing constants, shifts and MDS parameter. Comparisons are established to assess the effectiveness of initiated CC with the various existing CC in term of ARL. It has been ascertained that offered CC manifest the best performance in searching out the diminutive changes in the process mean. Two examples, one is based on simulation study and other is related to real-life data, have been discussed for its practical purpose.
Journal: Journal of Applied Statistics
Pages: 1482-1492
Issue: 8
Volume: 47
Year: 2020
Month: 6
X-DOI: 10.1080/02664763.2019.1676405
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1676405
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:8:p:1482-1492




Template-Type: ReDIF-Article 1.0
Author-Name: Mei Li
Author-X-Name-First: Mei
Author-X-Name-Last: Li
Author-Name: Houbao Xu
Author-X-Name-First: Houbao
Author-X-Name-Last: Xu
Title: Reliability analysis for gap null gate based on model comparison criterion
Abstract: 
 The length of the gap is the key factor affecting its reliability. Based on the mechanism of the gap null gate, this paper regards the two endpoint thresholds of the gap length as bivariate random variables and establishes successful response models. Score test statistic is presented to test the correlation coefficient. The DIC criterion is also provided to compare the models. With the experimental data of the gap null gate, we build Probit model and Logit model as the successful response models, and prove that the correlation coefficients in the both models can be regarded as 0. By comparing the DIC value, we find that the Probit model is more suitable to describe the distribution of the endpoint thresholds of the reliability window. Finally, both the point estimation and interval estimation results of the reliability window are given to illustrate the feasibility of the method shown in the paper.
Journal: Journal of Applied Statistics
Pages: 1493-1509
Issue: 8
Volume: 47
Year: 2020
Month: 6
X-DOI: 10.1080/02664763.2019.1680615
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1680615
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:8:p:1493-1509




Template-Type: ReDIF-Article 1.0
Author-Name: Ana J. Righetto
Author-X-Name-First: Ana J.
Author-X-Name-Last: Righetto
Author-Name: Thiago G. Ramires
Author-X-Name-First: Thiago G.
Author-X-Name-Last: Ramires
Author-Name: Luiz R. Nakamura
Author-X-Name-First: Luiz R.
Author-X-Name-Last: Nakamura
Author-Name: Pedro L. D. B. Castanho
Author-X-Name-First: Pedro L. D. B.
Author-X-Name-Last: Castanho
Author-Name: Christel Faes
Author-X-Name-First: Christel
Author-X-Name-Last: Faes
Author-Name: Taciana V. Savian
Author-X-Name-First: Taciana V.
Author-X-Name-Last: Savian
Title: Predicting weed invasion in a sugarcane cultivar using multispectral image
Abstract: 
 The cultivation of sugar cane has been gaining great focus in several countries due to its diversity of use. The modernization of agriculture has allowed high productivity, which is affected by the invasion of weeds. With sustainable agriculture, the use of herbicides has been increasingly avoided in society, requiring more effective weed control methods. In this paper, we propose a statistical model capable of identifying the invasion of weeds in the field, using four color spectra as regressor variables obtained by a multispectral camera mounted on an unmanned aerial vehicle. With the exact identification of the weed infestation, it is possible to carry out the management in the field with herbicide applications in the exact places, thus avoiding the increase of the cost of production or even dispensing with the use of herbicides, effecting the mechanical removal of them. Results show that in the experimental field, it was possible to reduce herbicide spraying by 57%.
Journal: Journal of Applied Statistics
Pages: 1-12
Issue: 1
Volume: 46
Year: 2019
Month: 1
X-DOI: 10.1080/02664763.2018.1450362
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1450362
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:1:p:1-12




Template-Type: ReDIF-Article 1.0
Author-Name: Jeongyoun Ahn
Author-X-Name-First: Jeongyoun
Author-X-Name-Last: Ahn
Author-Name: Myung Hee Lee
Author-X-Name-First: Myung Hee
Author-X-Name-Last: Lee
Author-Name: Jung Ae Lee
Author-X-Name-First: Jung Ae
Author-X-Name-Last: Lee
Title: Distance-based outlier detection for high dimension, low sample size data
Abstract: 
 Despite the popularity of high dimension, low sample size data analysis, there has not been enough attention to the sample integrity issue, in particular, a possibility of outliers in the data. A new outlier detection procedure for data with much larger dimensionality than the sample size is presented. The proposed method is motivated by asymptotic properties of high-dimensional distance measures. Empirical studies suggest that high-dimensional outlier detection is more likely to suffer from a swamping effect rather than a masking effect, thus yields more false positives than false negatives. We compare the proposed approaches with existing methods using simulated data from various population settings. A real data example is presented with a consideration on the implication of found outliers.
Journal: Journal of Applied Statistics
Pages: 13-29
Issue: 1
Volume: 46
Year: 2019
Month: 1
X-DOI: 10.1080/02664763.2018.1452901
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1452901
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:1:p:13-29




Template-Type: ReDIF-Article 1.0
Author-Name: Riten Mitra
Author-X-Name-First: Riten
Author-X-Name-Last: Mitra
Author-Name: Ryan Gill
Author-X-Name-First: Ryan
Author-X-Name-Last: Gill
Author-Name: Sinjini Sikdar
Author-X-Name-First: Sinjini
Author-X-Name-Last: Sikdar
Author-Name: Susmita Datta
Author-X-Name-First: Susmita
Author-X-Name-Last: Datta
Title: Bayesian hierarchical model for protein identifications
Abstract: 
 In proteomics, identification of proteins from complex mixtures of proteins extracted from biological samples is an important problem. Among the experimental technologies, mass spectrometry (MS) is the most popular one. Protein identification from MS data typically relies on a ‘two-step’ procedure of identifying the peptide first followed by the separate protein identification procedure next. In this setup, the interdependence of peptides and proteins is neglected resulting in relatively inaccurate protein identification. In this article, we propose a Markov chain Monte Carlo based Bayesian hierarchical model, a first of its kind in protein identification, which integrates the two steps and performs joint analysis of proteins and peptides using posterior probabilities. We remove the assumption of independence of proteins by using clustering group priors to the proteins based on the assumption that proteins sharing the same biological pathway are likely to be present or absent together and are correlated. The complete conditionals of the proposed joint model being tractable, we propose and implement a Gibbs sampling scheme for full posterior inference that provides the estimation and statistical uncertainties of all relevant parameters. The model has better operational characteristics compared to two existing ‘one-step’ procedures on a range of simulation settings as well as on two well-studied datasets.
Journal: Journal of Applied Statistics
Pages: 30-46
Issue: 1
Volume: 46
Year: 2019
Month: 1
X-DOI: 10.1080/02664763.2018.1454893
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1454893
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:1:p:30-46




Template-Type: ReDIF-Article 1.0
Author-Name: Antoine Godichon-Baggioni
Author-X-Name-First: Antoine
Author-X-Name-Last: Godichon-Baggioni
Author-Name: Cathy Maugis-Rabusseau
Author-X-Name-First: Cathy
Author-X-Name-Last: Maugis-Rabusseau
Author-Name: Andrea Rau
Author-X-Name-First: Andrea
Author-X-Name-Last: Rau
Title: Clustering transformed compositional data using K-means, with applications in gene expression and bicycle sharing system data
Abstract: 
 Although there is no shortage of clustering algorithms proposed in the literature, the question of the most relevant strategy for clustering compositional data (i.e. data whose rows belong to the simplex) remains largely unexplored in cases where the observed value is equal or close to zero for one or more samples. This work is motivated by the analysis of two applications, both focused on the categorization of compositional profiles: (1) identifying groups of co-expressed genes from high-throughput RNA sequencing data, in which a given gene may be completely silent in one or more experimental conditions; and (2) finding patterns in the usage of stations over the course of one week in the Velib' bicycle sharing system in Paris, France. For both of these applications, we make use of appropriately chosen data transformations, including the Centered Log Ratio and a novel extension called the Log Centered Log Ratio, in conjunction with the K-means algorithm. We use a non-asymptotic penalized criterion, whose penalty is calibrated with the slope heuristics, to select the number of clusters. Finally, we illustrate the performance of this clustering strategy, which is implemented in the Bioconductor package coseq, on both the gene expression and bicycle sharing system data.
Journal: Journal of Applied Statistics
Pages: 47-65
Issue: 1
Volume: 46
Year: 2019
Month: 1
X-DOI: 10.1080/02664763.2018.1454894
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1454894
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:1:p:47-65




Template-Type: ReDIF-Article 1.0
Author-Name: Sezen Cekic
Author-X-Name-First: Sezen
Author-X-Name-Last: Cekic
Author-Name: Didier Grandjean
Author-X-Name-First: Didier
Author-X-Name-Last: Grandjean
Author-Name: Olivier Renaud
Author-X-Name-First: Olivier
Author-X-Name-Last: Renaud
Title: Multiscale Bayesian state-space model for Granger causality analysis of brain signal
Abstract: 
 Modelling time-varying and frequency-specific relationships between two brain signals is becoming an essential methodological tool to answer theoretical questions in experimental neuroscience. In this article, we propose to estimate a frequency Granger causality statistic that may vary in time in order to evaluate the functional connections between two brain regions during a task. We use for that purpose an adaptive Kalman filter type of estimator of a linear Gaussian vector autoregressive model with coefficients evolving over time. The estimation procedure is achieved through variational Bayesian approximation and is extended for multiple trials. This Bayesian State Space (BSS) model provides a dynamical Granger-causality statistic that is quite natural. We propose to extend the BSS model to include the à trous Haar decomposition. This wavelet-based forecasting method is based on a multiscale resolution decomposition of the signal using the redundant à trous wavelet transform and allows us to capture short- and long-range dependencies between signals. Equally importantly it allows us to derive the desired dynamical and frequency-specific Granger-causality statistic. The application of these models to intracranial local field potential data recorded during a psychological experimental task shows the complex frequency-based cross-talk between amygdala and medial orbito-frontal cortex.
Journal: Journal of Applied Statistics
Pages: 66-84
Issue: 1
Volume: 46
Year: 2019
Month: 1
X-DOI: 10.1080/02664763.2018.1455814
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1455814
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:1:p:66-84




Template-Type: ReDIF-Article 1.0
Author-Name: Frosso S. Makri
Author-X-Name-First: Frosso S.
Author-X-Name-Last: Makri
Author-Name: Zaharias M. Psillakis
Author-X-Name-First: Zaharias M.
Author-X-Name-Last: Psillakis
Author-Name: Anastasios N. Arapis
Author-X-Name-First: Anastasios N.
Author-X-Name-Last: Arapis
Title: On the concentration of runs of ones of length exceeding a threshold in a Markov chain
Abstract: 
 Consider a homogeneous two state (failure-success or zero-one) Markov chain of first order. The paper deals with the position and the length of the shortest segment of the first n,  $ n\geq 5 $ n≥5, trials of the chain in which all runs of ones of length greater than or equal to a fixed number are concentrated. Accordingly, we define random variables denoting the starting/ending position of the first/last such runs in the chain as well as the implied distance between them. The paper provides exact closed form expressions for the probability mass function of these random variables given that the number of the considered runs in the chain is at least two. An application concerning DNA sequences is discussed. It is accompanied by numerics which exemplify further the theoretical results.
Journal: Journal of Applied Statistics
Pages: 85-100
Issue: 1
Volume: 46
Year: 2019
Month: 1
X-DOI: 10.1080/02664763.2018.1455815
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1455815
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:1:p:85-100




Template-Type: ReDIF-Article 1.0
Author-Name: Marcelo Bourguignon
Author-X-Name-First: Marcelo
Author-X-Name-Last: Bourguignon
Author-Name: Josemar Rodrigues
Author-X-Name-First: Josemar
Author-X-Name-Last: Rodrigues
Author-Name: Manoel Santos-Neto
Author-X-Name-First: Manoel
Author-X-Name-Last: Santos-Neto
Title: Extended Poisson INAR(1) processes with equidispersion, underdispersion and overdispersion
Abstract: 
 Real count data time series often show the phenomenon of the underdispersion and overdispersion. In this paper, we develop two extensions of the first-order integer-valued autoregressive process with Poisson innovations, based on binomial thinning, for modeling integer-valued time series with equidispersion, underdispersion, and overdispersion. The main properties of the models are derived. The methods of conditional maximum likelihood, Yule–Walker, and conditional least squares are used for estimating the parameters, and their asymptotic properties are established. We also use a test based on our processes for checking if the count time series considered is overdispersed or underdispersed. The proposed models are fitted to time series of the weekly number of syphilis cases and monthly counts of family violence illustrating its capabilities in challenging the overdispersed and underdispersed count data.
Journal: Journal of Applied Statistics
Pages: 101-118
Issue: 1
Volume: 46
Year: 2019
Month: 1
X-DOI: 10.1080/02664763.2018.1458216
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1458216
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:1:p:101-118




Template-Type: ReDIF-Article 1.0
Author-Name: Bernard Bercu
Author-X-Name-First: Bernard
Author-X-Name-Last: Bercu
Author-Name: Sami Capderou
Author-X-Name-First: Sami
Author-X-Name-Last: Capderou
Author-Name: Gilles Durrieu
Author-X-Name-First: Gilles
Author-X-Name-Last: Durrieu
Title: A nonparametric statistical procedure for the detection of marine pollution
Abstract: 
 This paper is devoted to the estimation of the derivative of the regression function in fixed-design nonparametric regression. We establish the almost sure convergence as well as the asymptotic normality of our estimate. We also provide concentration inequalities which are useful for small sample sizes. Numerical experiments on simulated data show that our nonparametric statistical procedure performs very well. We also illustrate our approach on high-frequency environmental data for the study of marine pollution.
Journal: Journal of Applied Statistics
Pages: 119-140
Issue: 1
Volume: 46
Year: 2019
Month: 1
X-DOI: 10.1080/02664763.2018.1458824
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1458824
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:1:p:119-140




Template-Type: ReDIF-Article 1.0
Author-Name: Yolanda M. Gómez
Author-X-Name-First: Yolanda M.
Author-X-Name-Last: Gómez
Author-Name: Diego I. Gallardo
Author-X-Name-First: Diego I.
Author-X-Name-Last: Gallardo
Author-Name: Yuri Iriarte
Author-X-Name-First: Yuri
Author-X-Name-Last: Iriarte
Author-Name: Heleno Bolfarine
Author-X-Name-First: Heleno
Author-X-Name-Last: Bolfarine
Title: The Rayleigh–Lindley model: properties and applications
Abstract: 
 In this paper, the Rayleigh–Lindley (RL) distribution is introduced, obtained by compounding the Rayleigh and Lindley discrete distributions, where the compounding procedure follows an approach similar to the one previously studied by Adamidis and Loukas in some other contexts. The resulting distribution is a two-parameter model, which is competitive with other parsimonious models such as the gamma and Weibull distributions. We study some properties of this new model such as the moments and the mean residual life. The estimation was approached via EM algorithm. The behavior of these estimators was studied in finite samples through a simulation study. Finally, we report two real data illustrations in order to show the performance of the proposed model versus other common two-parameter models in the literature. The main conclusion is that the model proposed can be a valid alternative to other competing models well established in the literature.
Journal: Journal of Applied Statistics
Pages: 141-163
Issue: 1
Volume: 46
Year: 2019
Month: 1
X-DOI: 10.1080/02664763.2018.1458825
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1458825
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:1:p:141-163




Template-Type: ReDIF-Article 1.0
Author-Name: The Editors
Title: Corrigendum
Journal: Journal of Applied Statistics
Pages: 185-186
Issue: 1
Volume: 46
Year: 2019
Month: 1
X-DOI: 10.1080/02664763.2018.1466855
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1466855
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:1:p:185-186




Template-Type: ReDIF-Article 1.0
Author-Name: Byron C. Jaeger
Author-X-Name-First: Byron C.
Author-X-Name-Last: Jaeger
Author-Name: Lloyd J. Edwards
Author-X-Name-First: Lloyd J.
Author-X-Name-Last: Edwards
Author-Name: Matthew J. Gurka
Author-X-Name-First: Matthew J.
Author-X-Name-Last: Gurka
Title: An R2 statistic for covariance model selection in the linear mixed model
Abstract: 
 The linear mixed model, sometimes referred to as the multi-level model, is one of the most widely used tools for analyses involving clustered data. Various definitions of  $ R^2 $ R2 have been proposed for the linear mixed model, but several limitations prevail. Presently, there is no method to compute  $ R^2 $ R2 for the linear mixed model that accommodates an interpretation based on variance partitioning, a method to quantify uncertainty and produce confidence limits for the  $ R^2 $ R2 statistic, and a capacity to use the  $ R^2 $ R2 statistic to conduct covariance model selection in a manner similar to information criteria. In this article, we introduce such an  $ R^2 $ R2 statistic. The proposed  $ R^2 $ R2 measures the proportion of generalized variance explained by fixed effects in the linear mixed model. Simulated and real longitudinal data are used to illustrate the statistical properties of the proposed  $ R^2 $ R2 and its capacity to be applied to covariance model selection.
Journal: Journal of Applied Statistics
Pages: 164-184
Issue: 1
Volume: 46
Year: 2019
Month: 1
X-DOI: 10.1080/02664763.2018.1466869
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1466869
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:1:p:164-184




Template-Type: ReDIF-Article 1.0
Author-Name: G. N. Singh
Author-X-Name-First: G. N.
Author-X-Name-Last: Singh
Author-Name: S. Suman
Author-X-Name-First: S.
Author-X-Name-Last: Suman
Title: A modified two-stage randomized response model for estimating the proportion of stigmatized attribute
Abstract: 
 The survey related to stigmatized characteristics leads to the non-response problem if it is conducted according to classical (direct) methods, especially, developed for non-sensitive issues; therefore, it needs to be applied appropriate survey methodology to get a reliable response from respondents in incriminating issues. Randomized response model is one of the most recent methods which is attracting the attention of survey practitioners to deal with the problems of non-response because it protects the privacy of individuals in order to acquire the truthful response. The present work proposes a new two-stage randomized response model to get rid of misleading response or non-response due to the stigmatized nature of attribute under the study. The proposed randomized response model results in the unbiased estimator of population proportion possessing the sensitive attribute. The properties of the resultant estimator have been studied and empirical comparisons are performed to show its dominance over existing estimators. Suitable recommendations have been put forward to the survey practitioners.
Journal: Journal of Applied Statistics
Pages: 958-978
Issue: 6
Volume: 46
Year: 2019
Month: 4
X-DOI: 10.1080/02664763.2018.1529150
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1529150
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:6:p:958-978




Template-Type: ReDIF-Article 1.0
Author-Name: Meiling Hao
Author-X-Name-First: Meiling
Author-X-Name-Last: Hao
Author-Name: Yuanyuan Lin
Author-X-Name-First: Yuanyuan
Author-X-Name-Last: Lin
Author-Name: Xianhui Liu
Author-X-Name-First: Xianhui
Author-X-Name-Last: Liu
Author-Name: Wenlu Tang
Author-X-Name-First: Wenlu
Author-X-Name-Last: Tang
Title: Robust feature screening for high-dimensional survival data
Abstract: 
 Ultra-high dimensional data arise in many fields of modern science, such as medical science, economics, genomics and imaging processing, and pose unprecedented challenge for statistical analysis. With such rapid-growth size of scientific data in various disciplines, feature screening becomes a primary step to reduce the high dimensionality to a moderate scale that can be handled by the existing penalized methods. In this paper, we introduce a simple and robust feature screening method without any model assumption to tackle high dimensional censored data. The proposed method is model-free and hence applicable to a general class of survival models. The sure screening and ranking consistency properties without any finite moment condition of the predictors and the response are established. The computation of the proposed method is rather straightforward. Finite sample performance of the newly proposed method is examined via extensive simulation studies. An application is illustrated with the gene association study of the mantle cell lymphoma.
Journal: Journal of Applied Statistics
Pages: 979-994
Issue: 6
Volume: 46
Year: 2019
Month: 4
X-DOI: 10.1080/02664763.2018.1529151
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1529151
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:6:p:979-994




Template-Type: ReDIF-Article 1.0
Author-Name: Massimiliano Bonamente
Author-X-Name-First: Massimiliano
Author-X-Name-Last: Bonamente
Title: Probability models of chance fluctuations in spectra of astronomical sources with applications to X-ray absorption lines
Abstract: 
 The search for faint emission or absorption lines in astronomical spectra has received considerable attention in recent years, especially in the X-ray wavelength range. These features usually appear as a deficit or excess of counts in a single-resolution element of the detector, and as such they are referred to as unresolved fluctuations. The general problem under investigation is the probability of occurrence of chance fluctuations. A quantitative answer is necessary to determine whether detected fluctuations are a real (astronomical, in this case) signal, or if they can be attributed to chance. This application note provides a new comprehensive method to answer this question as function of the instrument's resolution, the wavelength coverage of the spectrum, the number of fluctuations of interest, and the confidence level chosen. The method is based on the binomial distribution and addresses also the probability of multiple simultaneous fluctuations. A critical aspect of the model is the a priori knowledge of the location of possible fluctuations, which significantly affects the probability of detection. In fact, a wider wavelength range for the ‘blind’ search of possible fluctuations results in a larger number of ‘tries’ for the detection of a fluctuation, lowering the overall significance of a specific feature. The method is illustrated with a case study and examples using X-ray data.
Journal: Journal of Applied Statistics
Pages: 1129-1154
Issue: 6
Volume: 46
Year: 2019
Month: 4
X-DOI: 10.1080/02664763.2018.1531976
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1531976
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:6:p:1129-1154




Template-Type: ReDIF-Article 1.0
Author-Name: P. Bountzis
Author-X-Name-First: P.
Author-X-Name-Last: Bountzis
Author-Name: E. Papadimitriou
Author-X-Name-First: E.
Author-X-Name-Last: Papadimitriou
Author-Name: G. Tsaklidis
Author-X-Name-First: G.
Author-X-Name-Last: Tsaklidis
Title: Estimating the earthquake occurrence rates in Corinth Gulf (Greece) through Markovian arrival process modeling
Abstract: 
 The Markovian Arrival Process (MAP) is applied as a candidate model to describe the time-varying earthquake activity in Corinth Gulf, Greece. To the best of our knowledge, this is the first attempt to study the earthquake temporal evolution with the specific class of MAPs. A complete catalogue is used for the earthquake temporal distribution investigation, along with data sets of different magnitude cutoffs. The study area is divided into its western and eastern subareas, and possible variations in the earthquake occurrence times were sought. Hidden states of MAPs correspond to different levels of seismicity, and hence various numbers of states are examined. Akaike and Bayes information criteria are implemented for identifying the best model, and comparison to the most known and broadly accepted theoretical interevent time distributions is provided. In all cases, the fitted MAPs with phase type distributed intearrival times outperform the models with other distributions. Important indicators of the underlying Markov process are computed, and the earthquake frequency is approximated by the counting process. The analysis demonstrates high index of burstiness for the earthquake generation in the eastern part, i.e. long quiescent periods alternate with short ones of intense seismic activity.
Journal: Journal of Applied Statistics
Pages: 995-1020
Issue: 6
Volume: 46
Year: 2019
Month: 4
X-DOI: 10.1080/02664763.2018.1531977
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1531977
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:6:p:995-1020




Template-Type: ReDIF-Article 1.0
Author-Name: Alejandra Tapia
Author-X-Name-First: Alejandra
Author-X-Name-Last: Tapia
Author-Name: Viviana Giampaoli
Author-X-Name-First: Viviana
Author-X-Name-Last: Giampaoli
Author-Name: Maria del Pilar Diaz
Author-X-Name-First: Maria del Pilar
Author-X-Name-Last: Diaz
Author-Name: Victor Leiva
Author-X-Name-First: Victor
Author-X-Name-Last: Leiva
Title: Sensitivity analysis of longitudinal count responses: a local influence approach and application to medical data
Abstract: 
 Longitudinal count responses are often analyzed with a Poisson mixed model. However, under overdispersion, these responses are better described by a negative binomial mixed model. Estimators of the corresponding parameters are usually obtained by the maximum likelihood method. To investigate the stability of these maximum likelihood estimators, we propose a methodology of sensitivity analysis using local influence. As count responses are discrete, we are unable to perturb them with the standard scheme used in local influence. Then, we consider an appropriate perturbation for the means of these responses. The proposed methodology is useful in different applications, but particularly when medical data are analyzed, because the removal of influential cases can change the statistical results and then the medical decision. We study the performance of the methodology by using Monte Carlo simulation and applied it to real medical data related to epilepsy and headache. All of these numerical studies show the good performance and potential of the proposed methodology.
Journal: Journal of Applied Statistics
Pages: 1021-1042
Issue: 6
Volume: 46
Year: 2019
Month: 4
X-DOI: 10.1080/02664763.2018.1531978
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1531978
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:6:p:1021-1042




Template-Type: ReDIF-Article 1.0
Author-Name: Marie V. Ozanne
Author-X-Name-First: Marie V.
Author-X-Name-Last: Ozanne
Author-Name: Grant D. Brown
Author-X-Name-First: Grant D.
Author-X-Name-Last: Brown
Author-Name: Jacob J. Oleson
Author-X-Name-First: Jacob J.
Author-X-Name-Last: Oleson
Author-Name: Iraci D. Lima
Author-X-Name-First: Iraci D.
Author-X-Name-Last: Lima
Author-Name: Jose W. Queiroz
Author-X-Name-First: Jose W.
Author-X-Name-Last: Queiroz
Author-Name: Selma M. B. Jeronimo
Author-X-Name-First: Selma M. B.
Author-X-Name-Last: Jeronimo
Author-Name: Christine A. Petersen
Author-X-Name-First: Christine A.
Author-X-Name-Last: Petersen
Author-Name: Mary E. Wilson
Author-X-Name-First: Mary E.
Author-X-Name-Last: Wilson
Title: Bayesian compartmental model for an infectious disease with dynamic states of infection
Abstract: 
 Population-level proportions of individuals that fall at different points in the spectrum [of disease severity], from asymptomatic infection to severe disease, are often difficult to observe, but estimating these quantities can provide information about the nature and severity of the disease in a particular population. Logistic and multinomial regression techniques are often applied to infectious disease modeling of large populations and are suited to identifying variables associated with a particular disease or disease state. However, they are less appropriate for estimating infection state prevalence over time because they do not naturally accommodate known disease dynamics like duration of time an individual is infectious, heterogeneity in the risk of acquiring infection, and patterns of seasonality. We propose a Bayesian compartmental model to estimate latent infection state prevalence over time that easily incorporates known disease dynamics. We demonstrate how and why a stochastic compartmental model is a better approach for determining infection state proportions than multinomial regression is by using a novel method for estimating Bayes factors for models with high-dimensional parameter spaces. We provide an example using visceral leishmaniasis in Brazil and present an empirically-adjusted reproductive number for the infection.
Journal: Journal of Applied Statistics
Pages: 1043-1065
Issue: 6
Volume: 46
Year: 2019
Month: 4
X-DOI: 10.1080/02664763.2018.1531979
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1531979
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:6:p:1043-1065




Template-Type: ReDIF-Article 1.0
Author-Name: Ahad Malekzadeh
Author-X-Name-First: Ahad
Author-X-Name-Last: Malekzadeh
Author-Name: Mahmood Kharrati-Kopaei
Author-X-Name-First: Mahmood
Author-X-Name-Last: Kharrati-Kopaei
Title: Inferences on the common mean of several heterogeneous log-normal distributions
Abstract: 
 We consider the problem of making inferences on the common mean of several heterogeneous log-normal populations. We apply the parametric bootstrap (PB) approach and the method of variance estimate recovery (MOVER) to construct confidence intervals for the log-normal common mean. We then compare the performances of the proposed confidence intervals with the existing confidence intervals via an extensive simulation study. Simulation results show that our proposed MOVER and PB confidence intervals can be recommended generally for different sample sizes and number of populations.
Journal: Journal of Applied Statistics
Pages: 1066-1083
Issue: 6
Volume: 46
Year: 2019
Month: 4
X-DOI: 10.1080/02664763.2018.1531980
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1531980
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:6:p:1066-1083




Template-Type: ReDIF-Article 1.0
Author-Name: Artur J. Lemonte
Author-X-Name-First: Artur J.
Author-X-Name-Last: Lemonte
Author-Name: Germán Moreno–Arenas
Author-X-Name-First: Germán
Author-X-Name-Last: Moreno–Arenas
Title: On a multivariate regression model for rates and proportions
Abstract: 
 The paper by Johnson [Systems of frequency curves generated by the methods of translation, Biometrika 36 (2014), pp. 149–176] has introduced a very interesting univariate distribution with bounded support which is known in the statistical literature as the  $ S_B $ SB class of distributions. In this paper we generalize this class of univariate distributions to the multivariate case whose marginals are  $ S_B $ SB distributions. On the basis of the multivariate distribution introduced, we propose a multivariate regression model for dealing with multivariate response variables which are vectors of rates or proportions. We consider a frequentist approach to perform inferences, and the maximum likelihood method is employed to estimate the model parameters. Monte Carlo simulation results reveal that the maximum likelihood method can be used effectively in estimating the model parameters. An application to real data is presented to show the usefulness of the multivariate regression model in practice.
Journal: Journal of Applied Statistics
Pages: 1084-1106
Issue: 6
Volume: 46
Year: 2019
Month: 4
X-DOI: 10.1080/02664763.2018.1534945
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1534945
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:6:p:1084-1106




Template-Type: ReDIF-Article 1.0
Author-Name: A. T. Walden
Author-X-Name-First: A. T.
Author-X-Name-Last: Walden
Author-Name: L. Zhuang
Author-X-Name-First: L.
Author-X-Name-Last: Zhuang
Title: Constructing brain connectivity group graphs from EEG time series
Abstract: 
 Graphical analysis of complex brain networks is a fundamental area of modern neuroscience. Functional connectivity is important since many neurological and psychiatric disorders, including schizophrenia, are described as ‘dys-connectivity’ syndromes. Using electroencephalogram time series collected on each of a group of 15 individuals with a common medical diagnosis of positive syndrome schizophrenia we seek to build a single, representative, brain functional connectivity group graph. Disparity/distance measures between spectral matrices are identified and used to define the normalized graph Laplacian enabling clustering of the spectral matrices for detecting ‘outlying’ individuals. Two such individuals are identified. For each remaining individual, we derive a test for each edge in the connectivity graph based on average estimated partial coherence over frequencies, and associated p-values are found. For each edge these are used in a multiple hypothesis test across individuals and the proportion rejecting the hypothesis of no edge is used to construct a connectivity group graph. This study provides a framework for integrating results on multiple individuals into a single overall connectivity structure.
Journal: Journal of Applied Statistics
Pages: 1107-1128
Issue: 6
Volume: 46
Year: 2019
Month: 4
X-DOI: 10.1080/02664763.2018.1536198
File-URL: http://hdl.handle.net/10.1080/02664763.2018.1536198
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:46:y:2019:i:6:p:1107-1128




Template-Type: ReDIF-Article 1.0
Author-Name: Davide Vidotto
Author-X-Name-First: Davide
Author-X-Name-Last: Vidotto
Author-Name: Jeroen K. Vermunt
Author-X-Name-First: Jeroen K.
Author-X-Name-Last: Vermunt
Author-Name: Katrijn Van Deun
Author-X-Name-First: Katrijn
Author-X-Name-Last: Van Deun
Title: Multiple imputation of longitudinal categorical data through bayesian mixture latent Markov models
Abstract: 
 Standard latent class modeling has recently been shown to provide a flexible tool for the multiple imputation (MI) of missing categorical covariates in cross-sectional studies. This article introduces an analogous tool for longitudinal studies: MI using Bayesian mixture Latent Markov (BMLM) models. Besides retaining the benefits of latent class models, i.e. respecting the (categorical) measurement scale of the variables and preserving possibly complex relationships between variables within a measurement occasion, the Markov dependence structure of the proposed BMLM model allows capturing lagged dependencies between adjacent time points, while the time-constant mixture structure allows capturing dependencies across all time points, as well as retrieving associations between time-varying and time-constant variables. The performance of the BMLM model for MI is evaluated by means of a simulation study and an empirical experiment, in which it is compared with complete case analysis and MICE. Results show good performance of the proposed method in retrieving the parameters of the analysis model. In contrast, competing methods could provide correct estimates only for some aspects of the data.
Journal: Journal of Applied Statistics
Pages: 1720-1738
Issue: 10
Volume: 47
Year: 2020
Month: 7
X-DOI: 10.1080/02664763.2019.1692794
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1692794
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:10:p:1720-1738




Template-Type: ReDIF-Article 1.0
Author-Name: Seongoh Park
Author-X-Name-First: Seongoh
Author-X-Name-Last: Park
Author-Name: Johan Lim
Author-X-Name-First: Johan
Author-X-Name-Last: Lim
Author-Name: Hyejeong Choi
Author-X-Name-First: Hyejeong
Author-X-Name-Last: Choi
Author-Name: Minjung Kwak
Author-X-Name-First: Minjung
Author-X-Name-Last: Kwak
Title: Clustering of longitudinal interval-valued data via mixture distribution under covariance separability
Abstract: 
 We consider the clustering of repeatedly measured ‘min-max’ type interval-valued data. We read the data as matrix variate data and assume the covariance matrix is separable for the model-based clustering (M-clustering). The use of a separable covariance matrix introduces several advantages in M-clustering, which include fewer samples required for a valid procedure. In addition, the numerical study shows that this structured matrix allows us to find the correct number of clusters more accurately compared to other commonly assumed covariance matrices. We apply the M-clustering with various covariance structures to clustering the longitudinal blood pressure data from the National Heart, Lung, and Blood Institute Growth and Health Study (NGHS).
Journal: Journal of Applied Statistics
Pages: 1739-1756
Issue: 10
Volume: 47
Year: 2020
Month: 7
X-DOI: 10.1080/02664763.2019.1692795
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1692795
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:10:p:1739-1756




Template-Type: ReDIF-Article 1.0
Author-Name: Xuehua Hu
Author-X-Name-First: Xuehua
Author-X-Name-Last: Hu
Author-Name: Wenhao Gui
Author-X-Name-First: Wenhao
Author-X-Name-Last: Gui
Title: Assessing the lifetime performance index with Lomax distribution based on progressive type I interval censored sample
Abstract: 
 In manufacturing industry, the lifetime performance index  $C_L $CL is applied to evaluate the larger-the-better quality features of products. It can quickly show whether the lifetime performance of products meets the desired level. In this article, first we obtain the maximum likelihood estimator of  $C_L $CL with two unknown parameters in the Lomax distribution on the basis of progressive type I interval censored sample. With the MLE we proposed, some asymptotic confidence intervals of  $C_L $CL are discussed by using the delta method. Furthermore, the MLE of  $C_L $CL is used to establish the hypothesis test procedure under a given lower specification limit L. In addition, we also conduct a hypothesis test procedure when the scale parameter in the Lomax distribution is given. Finally, we illustrate the proposed inspection procedures through a real example. The testing procedure algorithms presented in this paper are efficient and easy to implement.
Journal: Journal of Applied Statistics
Pages: 1757-1775
Issue: 10
Volume: 47
Year: 2020
Month: 7
X-DOI: 10.1080/02664763.2019.1693523
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1693523
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:10:p:1757-1775




Template-Type: ReDIF-Article 1.0
Author-Name: R. A. Moral
Author-X-Name-First: R. A.
Author-X-Name-Last: Moral
Author-Name: J. Hinde
Author-X-Name-First: J.
Author-X-Name-Last: Hinde
Author-Name: E. M. M. Ortega
Author-X-Name-First: E. M. M.
Author-X-Name-Last: Ortega
Author-Name: C. G. B. Demétrio
Author-X-Name-First: C. G. B.
Author-X-Name-Last: Demétrio
Author-Name: W. A. C. Godoy
Author-X-Name-First: W. A. C.
Author-X-Name-Last: Godoy
Title: Location-scale mixed models and goodness-of-fit assessment applied to insect ecology
Abstract: 
 Survival models have been extensively used to analyse time-until-event data. There is a range of extended models that incorporate different aspects, such as overdispersion/frailty, mixtures, and flexible response functions through semi-parametric models. In this work, we show how a useful tool to assess goodness-of-fit, the half-normal plot of residuals with a simulated envelope, implemented in the hnp package in R, can be used on a location-scale modelling context. We fitted a range of survival models to time-until-event data, where the event was an insect predator attacking a larva in a biological control experiment. We started with the Weibull model and then fitted the exponentiated-Weibull location-scale model with regressors both for the location and scale parameters. We performed variable selection for each model and, by producing half-normal plots with simulated envelopes for the deviance residuals of the model fits, we found that the exponentiated-Weibull fitted the data better. We then included a random effect in the exponentiated-Weibull model to accommodate correlated observations. Finally, we discuss possible implications of the results found in the case study.
Journal: Journal of Applied Statistics
Pages: 1776-1793
Issue: 10
Volume: 47
Year: 2020
Month: 7
X-DOI: 10.1080/02664763.2019.1693522
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1693522
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:10:p:1776-1793




Template-Type: ReDIF-Article 1.0
Author-Name: Brice M. Nguelifack
Author-X-Name-First: Brice M.
Author-X-Name-Last: Nguelifack
Author-Name: Isabelle Kemajou-Brown
Author-X-Name-First: Isabelle
Author-X-Name-Last: Kemajou-Brown
Title: Robust signed-rank estimation and variable selection for semi-parametric additive partial linear models
Abstract: 
 A fully nonparametric model may not perform well or when the researcher wants to use a parametric model but the functional form with respect to a subset of the regressors or the density of the errors is not known. This becomes even more challenging when the data contain gross outliers or unusual observations. However, in practice the true covariates are not known in advance, nor is the smoothness of the functional form. A robust model selection approach through which we can choose the relevant covariates components and estimate the smoothing function may represent an appealing tool to the solution. A weighted signed-rank estimation and variable selection under the adaptive lasso for semi-parametric partial additive models is considered in this paper. B-spline is used to estimate the unknown additive nonparametric function. It is shown that despite using B-spline to estimate the unknown additive nonparametric function, the proposed estimator has an oracle property. The robustness of the weighted signed-rank approach for data with heavy-tail, contaminated errors, and data containing high-leverage points are validated via finite sample simulations. A practical application to an economic study is provided using an updated Canadian household gasoline consumption data.
Journal: Journal of Applied Statistics
Pages: 1794-1819
Issue: 10
Volume: 47
Year: 2020
Month: 7
X-DOI: 10.1080/02664763.2019.1695759
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1695759
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:10:p:1794-1819




Template-Type: ReDIF-Article 1.0
Author-Name: Min Wang
Author-X-Name-First: Min
Author-X-Name-Last: Wang
Author-Name: Fang Chen
Author-X-Name-First: Fang
Author-X-Name-Last: Chen
Author-Name: Tao Lu
Author-X-Name-First: Tao
Author-X-Name-Last: Lu
Author-Name: Jianping Dong
Author-X-Name-First: Jianping
Author-X-Name-Last: Dong
Title: Bayesian t-tests for correlations and partial correlations
Abstract: 
 In this paper, we develop Bayes factor based testing procedures for the presence of a correlation or a partial correlation. The proposed Bayesian tests are obtained by restricting the class of the alternative hypotheses to maximize the probability of rejecting the null hypothesis when the Bayes factor is larger than a specified threshold. It turns out that they depend simply on the frequentist t-statistics with the associated critical values and can thus be easily calculated by using a spreadsheet in Excel and in fact by just adding one more step after one has performed the frequentist correlation tests. In addition, they are able to yield an identical decision with the frequentist paradigm, provided that the evidence threshold of the Bayesian tests is determined by the significance level of the frequentist paradigm. We illustrate the performance of the proposed procedures through simulated and real-data examples.
Journal: Journal of Applied Statistics
Pages: 1820-1832
Issue: 10
Volume: 47
Year: 2020
Month: 7
X-DOI: 10.1080/02664763.2019.1695760
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1695760
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:10:p:1820-1832




Template-Type: ReDIF-Article 1.0
Author-Name: Gustavo H. A. Pereira
Author-X-Name-First: Gustavo H. A.
Author-X-Name-Last: Pereira
Author-Name: Juliana Scudilio
Author-X-Name-First: Juliana
Author-X-Name-Last: Scudilio
Author-Name: Manoel Santos-Neto
Author-X-Name-First: Manoel
Author-X-Name-Last: Santos-Neto
Author-Name: Denise A. Botter
Author-X-Name-First: Denise A.
Author-X-Name-Last: Botter
Author-Name: Mônica C. Sandoval
Author-X-Name-First: Mônica C.
Author-X-Name-Last: Sandoval
Title: A class of residuals for outlier identification in zero adjusted regression models
Abstract: 
 Zero adjusted regression models are used to fit variables that are discrete at zero and continuous at some interval of the positive real numbers. Diagnostic analysis in these models is usually performed using the randomized quantile residual, which is useful for checking the overall adequacy of a zero adjusted regression model. However, it may fail to identify some outliers. In this work, we introduce a class of residuals for outlier identification in zero adjusted regression models. Monte Carlo simulation studies and two applications suggest that one of the residuals of the class introduced here has good properties and detects outliers that are not identified by the randomized quantile residual.
Journal: Journal of Applied Statistics
Pages: 1833-1847
Issue: 10
Volume: 47
Year: 2020
Month: 7
X-DOI: 10.1080/02664763.2019.1696759
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1696759
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:10:p:1833-1847




Template-Type: ReDIF-Article 1.0
Author-Name: Ibrahim Alkhairy
Author-X-Name-First: Ibrahim
Author-X-Name-Last: Alkhairy
Author-Name: Samantha Low-Choy
Author-X-Name-First: Samantha
Author-X-Name-Last: Low-Choy
Author-Name: Justine Murray
Author-X-Name-First: Justine
Author-X-Name-Last: Murray
Author-Name: Junhu Wang
Author-X-Name-First: Junhu
Author-X-Name-Last: Wang
Author-Name: Anthony Pettitt
Author-X-Name-First: Anthony
Author-X-Name-Last: Pettitt
Title: Quantifying conditional probability tables in Bayesian networks: Bayesian regression for scenario-based encoding of elicited expert assessments on feral pig habitat
Abstract: 
 Bayesian networks are now widespread for modelling uncertain knowledge. They graph probabilistic relationships, which are quantified using conditional probability tables (CPTs). When empirical data are unavailable, experts may specify CPTs. Here we propose novel methodology for quantifying CPTs: a Bayesian statistical approach to both elicitation and encoding of expert-specified probabilities, in a way that acknowledges their uncertainty. We illustrate this new approach using a case study describing habitat most at risk from feral pigs. For complicated CPTs, it is difficult to elicit all scenarios (CPT entries). Like the CPT Calculator software program, we ask about a few scenarios (e.g. under a one-factor-at-a-time design) to reduce the experts' workload. Unlike CPT Calculator, we adopt a global rather than local regression to ‘fill out’ CPT entries. Unlike other methods for scenario-based elicitation for regression, we capture uncertainty about each probability in a sequence that explicitly controls biases and enhances interpretation. Furthermore, to utilize all elicited information, we introduce Bayesian rather than Classical generalised linear modelling (GLM). For large CPTs (e.g. >3 levels per parent) we show Bayesian GLM supports richer inference, particularly on interactions, even with few scenarios, providing more information regarding accuracy of encoding.
Journal: Journal of Applied Statistics
Pages: 1848-1884
Issue: 10
Volume: 47
Year: 2020
Month: 7
X-DOI: 10.1080/02664763.2019.1697651
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1697651
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:10:p:1848-1884




Template-Type: ReDIF-Article 1.0
Author-Name: Hadi Alizadeh Noughabi
Author-X-Name-First: Hadi
Author-X-Name-Last: Alizadeh Noughabi
Author-Name: Jalil Jarrahiferiz
Author-X-Name-First: Jalil
Author-X-Name-Last: Jarrahiferiz
Title: Tests of fit for the Gumbel distribution: EDF-based tests against entropy-based tests
Abstract: 
 In this article, we propose some tests of fit based on sample entropy for the composite Gumbel (Extreme Value) hypothesis. The proposed test statistics are constructed using different entropy estimates. Through a Monte Carlo simulation, critical values of the test statistics for various sample sizes are obtained. Since the tests based on the empirical distribution function (EDF) are commonly used in practice, the power values of the entropy-based tests with those of the EDF tests are compared against various alternatives and different sample sizes. Finally, two real data sets are modeled by the Gumbel distribution.
Journal: Journal of Applied Statistics
Pages: 1885-1900
Issue: 10
Volume: 47
Year: 2020
Month: 7
X-DOI: 10.1080/02664763.2019.1698522
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1698522
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:10:p:1885-1900




Template-Type: ReDIF-Article 1.0
Author-Name: Seyed Ehsan Saffari
Author-X-Name-First: Seyed Ehsan
Author-X-Name-Last: Saffari
Author-Name: John Carson Allen
Author-X-Name-First: John Carson
Author-X-Name-Last: Allen
Title: Bivariate negative binomial regression model with excess zeros and right censoring: an application to Indonesian data
Abstract: 
 We propose a bivariate hurdle negative binomial (BHNB) regression model with right censoring to model correlated bivariate count data with excess zeros and few extreme observations. The parameters of the BHNB regression model are obtained using maximum likelihood with conjugate gradient optimization. The proposed model is applied to actual survey data where the bivariate outcome is number of days missed from primary activities and number of days spent in bed due to illness during the 4-week period preceding the inquiry date. We compared the right censored BHNB model to the right censored bivariate negative binomial (BNB) model. A simulation study is conducted to discuss some properties of the BHNB model. Our proposed model demonstrated superior performance in goodness-of-fit of estimated frequencies.
Journal: Journal of Applied Statistics
Pages: 1901-1914
Issue: 10
Volume: 47
Year: 2020
Month: 7
X-DOI: 10.1080/02664763.2019.1695761
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1695761
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:10:p:1901-1914




Template-Type: ReDIF-Article 1.0
Author-Name: Jörn Schulz
Author-X-Name-First: Jörn
Author-X-Name-Last: Schulz
Author-Name: Jan Terje Kvaløy
Author-X-Name-First: Jan Terje
Author-X-Name-Last: Kvaløy
Author-Name: Kjersti Engan
Author-X-Name-First: Kjersti
Author-X-Name-Last: Engan
Author-Name: Trygve Eftestøl
Author-X-Name-First: Trygve
Author-X-Name-Last: Eftestøl
Author-Name: Samwel Jatosh
Author-X-Name-First: Samwel
Author-X-Name-Last: Jatosh
Author-Name: Hussein Kidanto
Author-X-Name-First: Hussein
Author-X-Name-Last: Kidanto
Author-Name: Hege Ersdal
Author-X-Name-First: Hege
Author-X-Name-Last: Ersdal
Title: State transition modeling of complex monitored health data
Abstract: 
 This article considers the analysis of complex monitored health data, where often one or several signals are reflecting the current health status that can be represented by a finite number of states, in addition to a set of covariates. In particular, we consider a novel application of a non-parametric state intensity regression method in order to study time-dependent effects of covariates on the state transition intensities. The method can handle baseline, time varying as well as dynamic covariates. Because of the non-parametric nature, the method can handle different data types and challenges under minimal assumptions. If the signal that is reflecting the current health status is of continuous nature, we propose the application of a weighted median and a hysteresis filter as data pre-processing steps in order to facilitate robust analysis. In intensity regression, covariates can be aggregated by a suitable functional form over a time history window. We propose to study the estimated cumulative regression parameters for different choices of the time history window in order to investigate short- and long-term effects of the given covariates. The proposed framework is discussed and applied to resuscitation data of newborns collected in Tanzania.
Journal: Journal of Applied Statistics
Pages: 1915-1935
Issue: 11
Volume: 47
Year: 2020
Month: 8
X-DOI: 10.1080/02664763.2019.1698523
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1698523
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:11:p:1915-1935




Template-Type: ReDIF-Article 1.0
Author-Name: Yuantao Zhang
Author-X-Name-First: Yuantao
Author-X-Name-Last: Zhang
Author-Name: Yue Kuen Kwok
Author-X-Name-First: Yue
Author-X-Name-Last: Kuen Kwok
Title: Saddlepoint approximations to tail expectations under non-Gaussian base distributions: option pricing applications
Abstract: 
 The saddlepoint approximation formulas provide versatile tools for analytic approximation of the tail expectation of a random variable by approximating the complex Laplace integral of the tail expectation expressed in terms of the cumulant generating function of the random variable. We generalize the saddlepoint approximation formulas for calculating tail expectations from the usual Gaussian base distribution to an arbitrary base distribution. Specific discussion is presented on the criteria of choosing the base distribution that fits better the underlying distribution. Numerical performance and comparison of accuracy are made among different saddlepoint approximation formulas. Improved accuracy of the saddlepoint approximations to tail expectations is revealed when proper base distributions are chosen. We also demonstrate enhanced accuracy of the generalized saddlepoint approximation formulas under non-Gaussian base distributions in pricing European options on continuous integrated variance under the Heston stochastic volatility model.
Journal: Journal of Applied Statistics
Pages: 1936-1956
Issue: 11
Volume: 47
Year: 2020
Month: 8
X-DOI: 10.1080/02664763.2019.1703915
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1703915
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:11:p:1936-1956




Template-Type: ReDIF-Article 1.0
Author-Name: Junhyeon Kwon
Author-X-Name-First: Junhyeon
Author-X-Name-Last: Kwon
Author-Name: Hee-Seok Oh
Author-X-Name-First: Hee-Seok
Author-X-Name-Last: Oh
Author-Name: Yaeji Lim
Author-X-Name-First: Yaeji
Author-X-Name-Last: Lim
Title: Dynamic principal component analysis with missing values
Abstract: 
 Dynamic principal component analysis (DPCA), also known as frequency domain principal component analysis, has been developed by Brillinger [Time Series: Data Analysis and Theory, Vol. 36, SIAM, 1981] to decompose multivariate time-series data into a few principal component series. A primary advantage of DPCA is its capability of extracting essential components from the data by reflecting the serial dependence of them. It is also used to estimate the common component in a dynamic factor model, which is frequently used in econometrics. However, its beneficial property cannot be utilized when missing values are present, which should not be simply ignored when estimating the spectral density matrix in the DPCA procedure. Based on a novel combination of conventional DPCA and self-consistency concept, we propose a DPCA method when missing values are present. We demonstrate the advantage of the proposed method over some existing imputation methods through the Monte Carlo experiments and real data analysis.
Journal: Journal of Applied Statistics
Pages: 1957-1969
Issue: 11
Volume: 47
Year: 2020
Month: 8
X-DOI: 10.1080/02664763.2019.1699910
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1699910
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:11:p:1957-1969




Template-Type: ReDIF-Article 1.0
Author-Name: Stephan Schlüter
Author-X-Name-First: Stephan
Author-X-Name-Last: Schlüter
Author-Name: Milena Kresoja
Author-X-Name-First: Milena
Author-X-Name-Last: Kresoja
Title: Two preprocessing algorithms for climate time series
Abstract: 
 We propose two preprocessing algorithms suitable for climate time series. The first algorithm detects outliers based on an autoregressive cost update mechanism. The second one is based on the wavelet transform, a method from pattern recognition. In order to benchmark the algorithms' performance we compare them to existing methods based on a synthetic data set. Eventually, for exemplary purposes, the proposed methods are applied to a data set of high-frequent temperature measurements from Novi Sad, Serbia. The results show that both methods together form a powerful tool for signal preprocessing: In case of solitary outliers the autoregressive cost update mechanism prevails, whereas the wavelet-based mechanism is the method of choice in the presence of multiple consecutive outliers.
Journal: Journal of Applied Statistics
Pages: 1970-1989
Issue: 11
Volume: 47
Year: 2020
Month: 8
X-DOI: 10.1080/02664763.2019.1701637
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1701637
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:11:p:1970-1989




Template-Type: ReDIF-Article 1.0
Author-Name: Catalina B. García
Author-X-Name-First: Catalina B.
Author-X-Name-Last: García
Author-Name: Román Salmerón
Author-X-Name-First: Román
Author-X-Name-Last: Salmerón
Author-Name: Claudia García
Author-X-Name-First: Claudia
Author-X-Name-Last: García
Author-Name: José García
Author-X-Name-First: José
Author-X-Name-Last: García
Title: Residualization: justification, properties and application
Abstract: 
 Although it is usual to find collinearity in econometric models, it is commonly disregarded. An extended solution is to eliminate the variable causing the problem but, in some cases, this decision can affect the goal of the research. Alternatively, residualization not only allows mitigation of collinearity, but it also provides an alternative interpretation of the coefficients isolating the effect of the residualized variable. This paper fully develops the residualization procedure and justifies its application not only for dealing with multicollinearity but also for separating the individual effects of the regressor variables. This contribution is illustrated by two econometric models with financial and ecological data, although it can also be extended to many different fields.
Journal: Journal of Applied Statistics
Pages: 1990-2010
Issue: 11
Volume: 47
Year: 2020
Month: 8
X-DOI: 10.1080/02664763.2019.1701638
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1701638
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:11:p:1990-2010




Template-Type: ReDIF-Article 1.0
Author-Name: Jan Graffelman
Author-X-Name-First: Jan
Author-X-Name-Last: Graffelman
Title: Goodness-of-fit filtering in classical metric multidimensional scaling with large datasets
Abstract: 
 Metric multidimensional scaling (MDS) is a widely used multivariate method with applications in almost all scientific disciplines. Eigenvalues obtained in the analysis are usually reported in order to calculate the overall goodness-of-fit of the distance matrix. In this paper, we refine MDS goodness-of-fit calculations, proposing additional point and pairwise goodness-of-fit statistics that can be used to filter poorly represented observations in MDS maps. The proposed statistics are especially relevant for large data sets that contain outliers, with typically many poorly fitted observations, and are helpful for improving MDS output and emphasizing the most important features of the dataset. Several goodness-of-fit statistics are considered, and both Euclidean and non-Euclidean distance matrices are considered. Some examples with data from demographic, genetic and geographic studies are shown.
Journal: Journal of Applied Statistics
Pages: 2011-2024
Issue: 11
Volume: 47
Year: 2020
Month: 8
X-DOI: 10.1080/02664763.2019.1702929
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1702929
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:11:p:2011-2024




Template-Type: ReDIF-Article 1.0
Author-Name: Fulya Gokalp Yavuz
Author-X-Name-First: Fulya
Author-X-Name-Last: Gokalp Yavuz
Author-Name: Olcay Arslan
Author-X-Name-First: Olcay
Author-X-Name-Last: Arslan
Title: Variable selection in elliptical linear mixed model
Abstract: 
 Variable selection in elliptical Linear Mixed Models (LMMs) with a shrinkage penalty function (SPF) is the main scope of this study. SPFs are applied for parameter estimation and variable selection simultaneously. The smoothly clipped absolute deviation penalty (SCAD) is one of the SPFs and it is adapted into the elliptical LMM in this study. The proposed idea is highly applicable to a variety of models which are set up with different distributions such as normal, student-t, Pearson VII, power exponential and so on. Simulation studies and real data example with one of the elliptical distributions show that if the variable selection is also a concern, it is worthwhile to carry on the variable selection and the parameter estimation simultaneously in the elliptical LMM.
Journal: Journal of Applied Statistics
Pages: 2025-2043
Issue: 11
Volume: 47
Year: 2020
Month: 8
X-DOI: 10.1080/02664763.2019.1702928
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1702928
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:11:p:2025-2043




Template-Type: ReDIF-Article 1.0
Author-Name: Massimiliano Bonamente
Author-X-Name-First: Massimiliano
Author-X-Name-Last: Bonamente
Title: Distribution of the C statistic with applications to the sample mean of Poisson data
Abstract: 
 The  ${C} $C statistic, also known as the Cash statistic, is often used in astronomy for the analysis of low-count Poisson data. The main advantage of this statistic, compared to the more commonly used  $\chi ^2 $χ2 statistic, is its applicability without the need to combine data points. This feature has made the  ${C} $C statistic a very useful method to analyze Poisson data that have small (or even null) counts in each resolution element. One of the challenges of the  ${C} $C statistic is that its probability distribution, under the null hypothesis that the data follow a parent model, is not known exactly. This paper presents an effort towards improving our understanding of the  ${C} $C statistic by studying (a) the distribution of  ${C} $C statistic for a fully specified model, (b) the distribution of Cmin resulting from a maximum-likelihood fit to a simple one-parameter constant model, i.e. a model that represents the sample mean of N Poisson measurements, and (c) the distribution of the associated  $\Delta C $ΔC statistic that is used for parameter estimation. The results confirm the expectation that, in the high-count limit, both  ${C} $C statistic and Cmin have the same mean and variance as a  $\chi ^2 $χ2 statistic with same number of degrees of freedom. It is also found that, in the low-count regime, the expectation of the  ${C} $C statistic and Cmin can be substantially lower than for a  $\chi ^2 $χ2 distribution. The paper makes use of recent X-ray observations of the astronomical source PG 1116+215 to illustrate the application of the  ${C} $C statistic to Poisson data.
Journal: Journal of Applied Statistics
Pages: 2044-2065
Issue: 11
Volume: 47
Year: 2020
Month: 8
X-DOI: 10.1080/02664763.2019.1704703
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1704703
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:11:p:2044-2065




Template-Type: ReDIF-Article 1.0
Author-Name: Nadja Klein
Author-X-Name-First: Nadja
Author-X-Name-Last: Klein
Author-Name: Andrew Entwistle
Author-X-Name-First: Andrew
Author-X-Name-Last: Entwistle
Author-Name: Albert Rosenberger
Author-X-Name-First: Albert
Author-X-Name-Last: Rosenberger
Author-Name: Thomas Kneib
Author-X-Name-First: Thomas
Author-X-Name-Last: Kneib
Author-Name: Heike Bickeböller
Author-X-Name-First: Heike
Author-X-Name-Last: Bickeböller
Title: Candidate-gene association analysis for a continuous phenotype with a spike at zero using parent-offspring trios
Abstract: 
 In this paper, we propose the class of generalized additive models for location, scale and shape in a test for the association of genetic markers with non-normally distributed phenotypes comprising a spike at zero. The resulting statistical test is a generalization of the quantitative transmission disequilibrium test with mating type indicator, which was originally designed for normally distributed quantitative traits and parent-offspring data. As a motivational example, we consider coronary artery calcification (CAC), which can accurately be identified by electron beam tomography. In the investigated regions, individuals will have a continuous measure of the extent of calcium found or they will be calcium-free. Hence, the resulting distribution is a mixed discrete-continuous distribution with spike at zero. We carry out parent-offspring simulations motivated by such CAC measurement values in a screening population to study statistical properties of the proposed test for genetic association. Furthermore, we apply the approach to data of the Genetic Analysis Workshop 16 that are based on real genotype and family data of the Framingham Heart Study, and test the association of selected genetic markers with simulated coronary artery calcification.
Journal: Journal of Applied Statistics
Pages: 2066-2080
Issue: 11
Volume: 47
Year: 2020
Month: 8
X-DOI: 10.1080/02664763.2019.1704226
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1704226
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:11:p:2066-2080




Template-Type: ReDIF-Article 1.0
Author-Name: Christopher J. Cappelli
Author-X-Name-First: Christopher J.
Author-X-Name-Last: Cappelli
Author-Name: Audrey J. Leroux
Author-X-Name-First: Audrey J.
Author-X-Name-Last: Leroux
Author-Name: Congying Sun
Author-X-Name-First: Congying
Author-X-Name-Last: Sun
Title: A new way for handling mobility in longitudinal data
Abstract: 
 In the social sciences, applied researchers often face a statistical dilemma when multilevel data is structured such that lower-level units are not purely clustered within higher-level units. To aid applied researchers in appropriately analyzing such data structures, this study proposes a multiple membership growth curve model (MM-GCM). The MM-GCM offers some advantages to other similar modeling approaches, including greater flexibility in modeling the intercept at the time-point most desired for interpretation. A real longitudinal dataset from the field of education with a multiple membership structure, where some students changed schools over time, was used to demonstrate the application of the MM-GCM. Baseline and conditional MM-GCMs are presented, and parameter estimates were compared with two other common approaches to handling such data structures – the final school-GCM that ignores mobile students by only modeling the final school attended and the delete-GCM that deletes mobile students. Additionally, a simulation study was conducted to further assess the impact of ignoring mobility on parameter estimates. The results indicate that ignoring mobility results in substantial bias in model estimates, especially for cluster-level coefficients and variance components.
Journal: Journal of Applied Statistics
Pages: 2081-2096
Issue: 11
Volume: 47
Year: 2020
Month: 8
X-DOI: 10.1080/02664763.2019.1704224
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1704224
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:11:p:2081-2096




Template-Type: ReDIF-Article 1.0
Author-Name: Mustafa Ç. Korkmaz
Author-X-Name-First: Mustafa Ç.
Author-X-Name-Last: Korkmaz
Title: A new heavy-tailed distribution defined on the bounded interval: the logit slash distribution and its application
Abstract: 
 This paper proposes a new heavy-tailed and alternative slash type distribution on a bounded interval via a relation of a slash random variable with respect to the standard logistic function to model the real data set with skewed and high kurtosis which includes the outlier observation. Some basic statistical properties of the newly defined distribution are studied. We derive the maximum likelihood, least-square, and weighted least-square estimations of its parameters. We assess the performance of the estimators of these estimation methods by the simulation study. Moreover, an application to real data demonstrates that the proposed distribution can provide a better fit than well-known bounded distributions in the literature when the skewed data set with high kurtosis contains the outlier observations.
Journal: Journal of Applied Statistics
Pages: 2097-2119
Issue: 12
Volume: 47
Year: 2020
Month: 9
X-DOI: 10.1080/02664763.2019.1704701
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1704701
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:12:p:2097-2119




Template-Type: ReDIF-Article 1.0
Author-Name: Rodolfo Metulini
Author-X-Name-First: Rodolfo
Author-X-Name-Last: Metulini
Author-Name: Mael Le Carre
Author-X-Name-First: Mael
Author-X-Name-Last: Le Carre
Title: Measuring sport performances under pressure by classification trees with application to basketball shooting
Abstract: 
 Measuring players' performance in team sports is fundamental since managers need to evaluate players with respect to the ability to score during crucial moments of the game. Using Classification and Regression Trees (CART) and play-by-play basketball data, we estimate the probabilities to score the shot with respect to a selection of game covariates related to game pressure. We use scoring probabilities to develop a player-specific shooting performance index that takes into account for the difficulty associated to score different types of shots. By applying this procedure to a large sample of 2016–2017 Basketball Champions League (BCL) and 2017–2018 National Basketball Association (NBA) games, we compare the factors affecting shooting performance in Europe and in the United States and we evaluate a selection of players in terms of the proposed shooting performance index with the final aim of providing useful guidelines for the team strategy.
Journal: Journal of Applied Statistics
Pages: 2120-2135
Issue: 12
Volume: 47
Year: 2020
Month: 9
X-DOI: 10.1080/02664763.2019.1704702
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1704702
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:12:p:2120-2135




Template-Type: ReDIF-Article 1.0
Author-Name: Zainab Abbasi Ganji
Author-X-Name-First: Zainab
Author-X-Name-Last: Abbasi Ganji
Author-Name: Bahram Sadeghpour Gildeh
Author-X-Name-First: Bahram
Author-X-Name-Last: Sadeghpour Gildeh
Title: Fuzzy process capability indices for simple linear profile
Abstract: 
 Process capability indices are numerical tools that quantify how well a process can meet customer requirements, specifications or engineering tolerances. Fuzzy logic is incorporated to deal imprecise, incomplete data along with uncertainty. This paper develops two fuzzy methods for measuring the process capability in simple linear profiles for the circumstances in which lower and upper specification limits are imprecise. To guide practitioners, numerical example is provided.
Journal: Journal of Applied Statistics
Pages: 2136-2158
Issue: 12
Volume: 47
Year: 2020
Month: 9
X-DOI: 10.1080/02664763.2019.1704225
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1704225
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:12:p:2136-2158




Template-Type: ReDIF-Article 1.0
Author-Name: E. M. Hashimoto
Author-X-Name-First: E. M.
Author-X-Name-Last: Hashimoto
Author-Name: E. M. M. Ortega
Author-X-Name-First: E. M. M.
Author-X-Name-Last: Ortega
Author-Name: G. M. Cordeiro
Author-X-Name-First: G. M.
Author-X-Name-Last: Cordeiro
Author-Name: A. K. Suzuki
Author-X-Name-First: A. K.
Author-X-Name-Last: Suzuki
Author-Name: M. W. Kattan
Author-X-Name-First: M. W.
Author-X-Name-Last: Kattan
Title: The multinomial logistic regression model for predicting the discharge status after liver transplantation: estimation and diagnostics analysis
Abstract: 
 The multinomial logistic regression model (MLRM) can be interpreted as a natural extension of the binomial model with logit link function to situations where the response variable can have three or more possible outcomes. In addition, when the categories of the response variable are nominal, the MLRM can be expressed in terms of two or more logistic models and analyzed in both frequentist and Bayesian approaches. However, few discussions about post modeling in categorical data models are found in the literature, and they mainly use Bayesian inference. The objective of this work is to present classic and Bayesian diagnostic measures for categorical data models. These measures are applied to a dataset (status) of patients undergoing kidney transplantation.
Journal: Journal of Applied Statistics
Pages: 2159-2177
Issue: 12
Volume: 47
Year: 2020
Month: 9
X-DOI: 10.1080/02664763.2019.1706725
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1706725
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:12:p:2159-2177




Template-Type: ReDIF-Article 1.0
Author-Name: Xiaodong Fan
Author-X-Name-First: Xiaodong
Author-X-Name-Last: Fan
Author-Name: Shi-shun Zhao
Author-X-Name-First: Shi-shun
Author-X-Name-Last: Zhao
Author-Name: Qingchun Zhang
Author-X-Name-First: Qingchun
Author-X-Name-Last: Zhang
Author-Name: Jianguo Sun
Author-X-Name-First: Jianguo
Author-X-Name-Last: Sun
Title: Nonparametric tests for stratified additive hazards model based on current status data
Abstract: 
 Stratified regression models are commonly employed when study subjects may come from possibly different strata such as different medical centers, and for the situation, one common question of interest is to test the existence of the stratum effect. To address this, there exists some literature on the testing of the stratum effects under the framework of the proportional hazards model when one observes right-censored data or interval-censored data. In this paper, we consider the situation under the additive hazards model when one faces current status data, for which there does not seem to exist an established test procedure. The asymptotic distributions of the proposed test procedure are provided. Also a simulation study is performed to evaluate the performance of the proposed method and indicates that it works well for practical situations. The approach is applied to a set of real current status data from a tumorigenicity study.
Journal: Journal of Applied Statistics
Pages: 2178-2191
Issue: 12
Volume: 47
Year: 2020
Month: 9
X-DOI: 10.1080/02664763.2019.1707515
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1707515
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:12:p:2178-2191




Template-Type: ReDIF-Article 1.0
Author-Name: Elif Akça
Author-X-Name-First: Elif
Author-X-Name-Last: Akça
Author-Name: Ceylan Yozgatlıgil
Author-X-Name-First: Ceylan
Author-X-Name-Last: Yozgatlıgil
Title: Mutual information model selection algorithm for time series
Abstract: 
 Time series model selection has been widely studied in recent years. It is of importance to select the best model among candidate models proposed for a series in terms of explaining the procedure that governs the series and providing the most accurate forecast for the future observations. In this study, it is aimed to create an algorithm for order selection in Box–Jenkins models that combines penalized natural logarithm of mutual information among the original series and predictions coming from each candidate. The penalization is achieved by subtracting the number of parameters in each candidate and empirical information the data provide.Simulation studies under various scenarios and applications on real data sets imply that our algorithm offers a promising and satisfactory alternative to its counterparts.
Journal: Journal of Applied Statistics
Pages: 2192-2207
Issue: 12
Volume: 47
Year: 2020
Month: 9
X-DOI: 10.1080/02664763.2019.1707516
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1707516
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:12:p:2192-2207




Template-Type: ReDIF-Article 1.0
Author-Name: E. Gómez–Déniz
Author-X-Name-First: E.
Author-X-Name-Last: Gómez–Déniz
Author-Name: D. I. Gallardo
Author-X-Name-First: D. I.
Author-X-Name-Last: Gallardo
Author-Name: H. W. Gómez
Author-X-Name-First: H. W.
Author-X-Name-Last: Gómez
Title: Quasi-binomial zero-inflated regression model suitable for variables with bounded support
Abstract: 
 In recent years, a variety of regression models, including zero-inflated and hurdle versions, have been proposed to explain the case of a dependent variable with respect to exogenous covariates. Apart from the classical Poisson, negative binomial and generalised Poisson distributions, many proposals have appeared in the statistical literature, perhaps in response to the new possibilities offered by advanced software that now enables researchers to implement numerous special functions in a relatively simple way. However, we believe that a significant research gap remains, since very little attention has been paid to the quasi-binomial distribution, which was first proposed over fifty years ago. We believe this distribution might constitute a valid alternative to existing regression models, in situations in which the variable has bounded support. Therefore, in this paper we present a zero-inflated regression model based on the quasi-binomial distribution, taking into account the moments and maximum likelihood estimators, and perform a score test to compare the zero-inflated quasi-binomial distribution with the zero-inflated binomial distribution, and the zero-inflated model with the homogeneous model (the model in which covariates are not considered). This analysis is illustrated with two data sets that are well known in the statistical literature and which contain a large number of zeros.
Journal: Journal of Applied Statistics
Pages: 2208-2229
Issue: 12
Volume: 47
Year: 2020
Month: 9
X-DOI: 10.1080/02664763.2019.1707517
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1707517
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:12:p:2208-2229




Template-Type: ReDIF-Article 1.0
Author-Name: Ioannis S. Triantafyllou
Author-X-Name-First: Ioannis S.
Author-X-Name-Last: Triantafyllou
Author-Name: Nikolaos I. Panayiotou
Author-X-Name-First: Nikolaos I.
Author-X-Name-Last: Panayiotou
Title: Distribution-free monitoring schemes based on order statistics: a general approach
Abstract: 
 In this article, we establish a new class of distribution-free Shewhart-type monitoring schemes based on order statistics. The setup of the proposed family of nonparametric control charts is presented in detail. Specific monitoring schemes, already introduced in the literature, are confirmed to be members of the new class. In addition, a new nonparametric monitoring scheme that belongs to the class is established, while explicit formulae for its basic characteristics are reached. The numerical study carried out reveals that the proposed scheme achieves adversarial in-control and out-of-control performance.
Journal: Journal of Applied Statistics
Pages: 2230-2257
Issue: 12
Volume: 47
Year: 2020
Month: 9
X-DOI: 10.1080/02664763.2019.1707518
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1707518
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:12:p:2230-2257




Template-Type: ReDIF-Article 1.0
Author-Name: Muhammad Qasim
Author-X-Name-First: Muhammad
Author-X-Name-Last: Qasim
Author-Name: B. M. G. Kibria
Author-X-Name-First: B. M. G.
Author-X-Name-Last: Kibria
Author-Name: Kristofer Månsson
Author-X-Name-First: Kristofer
Author-X-Name-Last: Månsson
Author-Name: Pär Sjölander
Author-X-Name-First: Pär
Author-X-Name-Last: Sjölander
Title: A new Poisson Liu Regression Estimator: method and application
Abstract: 
 This paper considers the estimation of parameters for the Poisson regression model in the presence of high, but imperfect multicollinearity. To mitigate this problem, we suggest using the Poisson Liu Regression Estimator (PLRE) and propose some new approaches to estimate this shrinkage parameter. The small sample statistical properties of these estimators are systematically scrutinized using Monte Carlo simulations. To evaluate the performance of these estimators, we assess the Mean Square Errors (MSE) and the Mean Absolute Percentage Errors (MAPE). The simulation results clearly illustrate the benefit of the methods of estimating these types of shrinkage parameters in finite samples. Finally, we illustrate the empirical relevance of our newly proposed methods using an empirically relevant application. Thus, in summary, via simulations of empirically relevant parameter values, and by a standard empirical application, it is clearly demonstrated that our technique exhibits more precise estimators, compared to traditional techniques – at least when multicollinearity exist among the regressors.
Journal: Journal of Applied Statistics
Pages: 2258-2271
Issue: 12
Volume: 47
Year: 2020
Month: 9
X-DOI: 10.1080/02664763.2019.1707485
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1707485
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:12:p:2258-2271




Template-Type: ReDIF-Article 1.0
Author-Name: Leili Tapak
Author-X-Name-First: Leili
Author-X-Name-Last: Tapak
Author-Name: Omid Hamidi
Author-X-Name-First: Omid
Author-X-Name-Last: Hamidi
Author-Name: Payam Amini
Author-X-Name-First: Payam
Author-X-Name-Last: Amini
Author-Name: Geert Verbeke
Author-X-Name-First: Geert
Author-X-Name-Last: Verbeke
Title: Random effect exponentiated-exponential geometric model for clustered/longitudinal zero-inflated count data
Abstract: 
 For count responses, there are situations in biomedical and sociological applications in which extra zeroes occur. Modeling correlated (e.g. repeated measures and clustered) zero-inflated count data includes special challenges because the correlation between measurements for a subject or a cluster needs to be taken into account. Moreover, zero-inflated count data are often faced with over/under dispersion problem. In this paper, we propose a random effect model for repeated measurements or clustered data with over/under dispersed response called random effect zero-inflated exponentiated-exponential geometric regression model. The proposed method was illustrated through real examples. The performance of the model and asymptotical properties of the estimations were investigated using simulation studies.
Journal: Journal of Applied Statistics
Pages: 2272-2288
Issue: 12
Volume: 47
Year: 2020
Month: 9
X-DOI: 10.1080/02664763.2019.1706726
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1706726
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:12:p:2272-2288




Template-Type: ReDIF-Article 1.0
Author-Name: M. Stehlík
Author-X-Name-First: M.
Author-X-Name-Last: Stehlík
Author-Name: L. M. Grilo
Author-X-Name-First: L. M.
Author-X-Name-Last: Grilo
Author-Name: P. K. Jordanova
Author-X-Name-First: P. K.
Author-X-Name-Last: Jordanova
Title: Editorial to special issue V WCDANM 2018
Journal: Journal of Applied Statistics
Pages: 2289-2298
Issue: 13-15
Volume: 47
Year: 2020
Month: 11
X-DOI: 10.1080/02664763.2020.1818489
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1818489
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:13-15:p:2289-2298




Template-Type: ReDIF-Article 1.0
Author-Name: Barry C. Arnold
Author-X-Name-First: Barry C.
Author-X-Name-Last: Arnold
Author-Name: Matthew A. Arvanitis
Author-X-Name-First: Matthew A.
Author-X-Name-Last: Arvanitis
Title: On bivariate pseudo-exponential distributions
Abstract: 
 A bivariate conditionally specified distribution is one in which the dependence relationship between the two random variables is accomplished by defining the distribution of one of the random variables, given the other. One such conditionally specified model is called the pseudo-exponential distribution, where both the marginal distribution of one and the conditional distribution of the other, given the first, are exponential. In this paper, a variation of this conditioning regime is introduced, and its characteristics are contrasted with the original. An example is used to demonstrate the applicability of the new model. Per-capita Gross Domestic Product (GDP) is a measure of a nation's total annual production of goods and services, divided by its population. Two variations of both the original and the new conditioning regime are applied to GDP and infant mortality data across nations and territories. Possible generalizations are considered.
Journal: Journal of Applied Statistics
Pages: 2299-2311
Issue: 13-15
Volume: 47
Year: 2020
Month: 11
X-DOI: 10.1080/02664763.2019.1686132
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1686132
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:13-15:p:2299-2311




Template-Type: ReDIF-Article 1.0
Author-Name: Ryan A. Peterson
Author-X-Name-First: Ryan A.
Author-X-Name-Last: Peterson
Author-Name: Joseph E. Cavanaugh
Author-X-Name-First: Joseph E.
Author-X-Name-Last: Cavanaugh
Title: Ordered quantile normalization: a semiparametric transformation built for the cross-validation era
Abstract: 
 Normalization transformations have recently experienced a resurgence in popularity in the era of machine learning, particularly in data preprocessing. However, the classical methods that can be adapted to cross-validation are not always effective. We introduce Ordered Quantile (ORQ) normalization, a one-to-one transformation that is designed to consistently and effectively transform a vector of arbitrary distribution into a vector that follows a normal (Gaussian) distribution. In the absence of ties, ORQ normalization is guaranteed to produce normally distributed transformed data. Once trained, an ORQ transformation can be readily and effectively applied to new data. We compare the effectiveness of the ORQ technique with other popular normalization methods in a simulation study where the true data generating distributions are known. We find that ORQ normalization is the only method that works consistently and effectively, regardless of the underlying distribution. We also explore the use of repeated cross-validation to identify the best normalizing transformation when the true underlying distribution is unknown. We apply our technique and other normalization methods via the bestNormalize R package on a car pricing data set. We built bestNormalize to evaluate the normalization efficacy of many candidate transformations; the package is freely available via the Comprehensive R Archive Network.
Journal: Journal of Applied Statistics
Pages: 2312-2327
Issue: 13-15
Volume: 47
Year: 2020
Month: 11
X-DOI: 10.1080/02664763.2019.1630372
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1630372
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:13-15:p:2312-2327




Template-Type: ReDIF-Article 1.0
Author-Name: Salvatore D. Tomarchio
Author-X-Name-First: Salvatore D.
Author-X-Name-Last: Tomarchio
Author-Name: Antonio Punzo
Author-X-Name-First: Antonio
Author-X-Name-Last: Punzo
Title: Dichotomous unimodal compound models: application to the distribution of insurance losses
Abstract: 
 A correct modelization of the insurance losses distribution is crucial in the insurance industry. This distribution is generally highly positively skewed, unimodal hump-shaped, and with a heavy right tail. Compound models are a profitable way to accommodate situations in which some of the probability masses are shifted to the tails of the distribution. Therefore, in this work, a general approach to compound unimodal hump-shaped distributions with a mixing dichotomous distribution is introduced. A 2-parameter unimodal hump-shaped distribution, defined on a positive support, is considered and reparametrized with respect to the mode and to another parameter related to the distribution variability. The compound is performed by scaling the latter parameter by means of a dichotomous mixing distribution that governs the tail behavior of the resulting model. The proposed model can also allow for automatic detection of typical and atypical losses via a simple procedure based on maximum a posteriori probabilities. Unimodal gamma and log-normal are considered as examples of unimodal hump-shaped distributions. The resulting models are firstly evaluated in a sensitivity study and then fitted to two real insurance loss datasets, along with several well-known competitors. Likelihood-based information criteria and risk measures are used to compare the models.
Journal: Journal of Applied Statistics
Pages: 2328-2353
Issue: 13-15
Volume: 47
Year: 2020
Month: 11
X-DOI: 10.1080/02664763.2020.1789076
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1789076
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:13-15:p:2328-2353




Template-Type: ReDIF-Article 1.0
Author-Name: Vlad Stefan Barbu
Author-X-Name-First: Vlad Stefan
Author-X-Name-Last: Barbu
Author-Name: Alex Karagrigoriou
Author-X-Name-First: Alex
Author-X-Name-Last: Karagrigoriou
Author-Name: Andreas Makrides
Author-X-Name-First: Andreas
Author-X-Name-Last: Makrides
Title: Statistical inference for a general class of distributions with time-varying parameters
Abstract: 
 In this article we are interested in a general class of distributions for independent not necessarily identically distributed random variables, closed under minima, that includes a number of discrete and continuous distributions like the Geometric, Exponential, Weibull or Pareto. The main parameter involved in this class of distributions is assumed to be time varying with several possible modeling options. This is of particular interest in reliability and survival analysis for describing the time to event or failure. The maximum likelihood estimation of the parameters is addressed and the asymptotic properties of the estimators are discussed. We provide real and simulated examples and we explore the accuracy of the estimating procedure as well as the performance of classical model selection criteria in choosing the correct model among a number of competing models for the time-varying parameters of interest.
Journal: Journal of Applied Statistics
Pages: 2354-2373
Issue: 13-15
Volume: 47
Year: 2020
Month: 11
X-DOI: 10.1080/02664763.2020.1763271
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1763271
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:13-15:p:2354-2373




Template-Type: ReDIF-Article 1.0
Author-Name: Patrícia Antunes
Author-X-Name-First: Patrícia
Author-X-Name-Last: Antunes
Author-Name: Sandra S. Ferreira
Author-X-Name-First: Sandra S.
Author-X-Name-Last: Ferreira
Author-Name: Dário Ferreira
Author-X-Name-First: Dário
Author-X-Name-Last: Ferreira
Author-Name: Célia Nunes
Author-X-Name-First: Célia
Author-X-Name-Last: Nunes
Author-Name: João Tiago Mexia
Author-X-Name-First: João
Author-X-Name-Last: Tiago Mexia
Title: Estimation in additive models and ANOVA-like applications
Abstract: 
 A well-known property of cumulant generating function is used to estimate the first four order cumulants, using least-squares estimators. In the case of additive models, empirical best linear unbiased predictors are also obtained. Pairs of independent and identically distributed models associated with the treatments of a base design are used to obtain unbiased estimators for the fourth-order cumulants. An application to real data is presented, showing the good behaviour of the least-squares estimators and the great flexibility of our approach.
Journal: Journal of Applied Statistics
Pages: 2374-2383
Issue: 13-15
Volume: 47
Year: 2020
Month: 11
X-DOI: 10.1080/02664763.2020.1723501
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1723501
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:13-15:p:2374-2383




Template-Type: ReDIF-Article 1.0
Author-Name: Knute D. Carter
Author-X-Name-First: Knute D.
Author-X-Name-Last: Carter
Author-Name: Joseph E. Cavanaugh
Author-X-Name-First: Joseph E.
Author-X-Name-Last: Cavanaugh
Title: Best-subset model selection based on multitudinal assessments of likelihood improvements
Abstract: 
 A common model selection approach is to select the best model, according to some criterion, from among the collection of models defined by all possible subsets of the explanatory variables. Identifying an optimal subset has proven to be a challenging problem, both statistically and computationally. Our model selection procedure allows the researcher to nominate, a priori, the probability at which models containing false or spurious variables will be selected from among all possible subsets. The procedure determines whether inclusion of each candidate variable results in a sufficiently improved fitting term – and is hence named the SIFT procedure. Two variants are proposed: a naive method based on a set of restrictive assumptions and an empirical permutation-based method. Properties of these methods are investigated within the standard linear modeling framework and performance is evaluated against other model selection techniques. The SIFT procedure behaves as designed – asymptotically selecting variables that characterize the underlying data generating mechanism, while limiting selection of spurious variables to the desired level. The SIFT methodology offers researchers a promising new approach to model selection, providing the ability to control the probability of selecting a model that includes spurious variables to a level based on the context of the application.
Journal: Journal of Applied Statistics
Pages: 2384-2420
Issue: 13-15
Volume: 47
Year: 2020
Month: 11
X-DOI: 10.1080/02664763.2019.1645097
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1645097
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:13-15:p:2384-2420




Template-Type: ReDIF-Article 1.0
Author-Name: C. Santos
Author-X-Name-First: C.
Author-X-Name-Last: Santos
Author-Name: C. Nunes
Author-X-Name-First: C.
Author-X-Name-Last: Nunes
Author-Name: C. Dias
Author-X-Name-First: C.
Author-X-Name-Last: Dias
Author-Name: J.T. Mexia
Author-X-Name-First: J.T.
Author-X-Name-Last: Mexia
Title: Models with commutative orthogonal block structure: a general condition for commutativity
Abstract: 
 A linear mixed model whose variance-covariance matrix is a linear combination of known pairwise orthogonal projection matrices that add to the identity matrix, is a model with orthogonal block structure (OBS). OBS have estimators with good behavior for estimable vectors and variance components, moreover it may be interesting that the least squares estimators give the best linear unbiased estimators, for estimable vectors. We can achieve it, requiring commutativity between the orthogonal projection matrix, on the space spanned by the mean vector, and the orthogonal projection matrices involved in the expression of the variance-covariance matrix. This commutativity condition defines a more restrict class of OBS, named COBS (model with commutative orthogonal block structure). With this work we aim to present a commutativity condition, resorting to a special class of matrices, named U-matrices.
Journal: Journal of Applied Statistics
Pages: 2421-2430
Issue: 13-15
Volume: 47
Year: 2020
Month: 11
X-DOI: 10.1080/02664763.2020.1765322
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1765322
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:13-15:p:2421-2430




Template-Type: ReDIF-Article 1.0
Author-Name: Nancy Flournoy
Author-X-Name-First: Nancy
Author-X-Name-Last: Flournoy
Author-Name: Assaf P. Oron
Author-X-Name-First: Assaf P.
Author-X-Name-Last: Oron
Title: Bias induced by adaptive dose-finding designs
Abstract: 
 There is a long literature on bias in maximum likelihood estimators. Here we demonstrate that adaptive dose-finding procedures (such as Continual Reassessment Methods, Up-and-Down and Interval Designs) themselves induce bias. In particular, with Bernoulli responses and dose assignments that depend on prior responses, we provide an explicit formula for the bias of observed response rates. We illustrate the patterns of bias for designs that aim to concentrate dose allocations around a target dose, which represents a specific quantile of a cumulative response-threshold distribution. For such designs, bias tends to be positive above the target dose and negative below it. To our knowledge, this property of dose-finding designs has not previously been recognized by design developers. We discuss the implications of this bias and suggest a simple shrinkage mitigation formula that improves estimation at doses away from the target.
Journal: Journal of Applied Statistics
Pages: 2431-2442
Issue: 13-15
Volume: 47
Year: 2020
Month: 11
X-DOI: 10.1080/02664763.2019.1649375
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1649375
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:13-15:p:2431-2442




Template-Type: ReDIF-Article 1.0
Author-Name: M. Jabbari Nooghabi
Author-X-Name-First: M.
Author-X-Name-Last: Jabbari Nooghabi
Title: Process capability indices in normal distribution with the presence of outliers
Abstract: 
 Process capability indices (PCIs) are useful measures to evaluate the performance and capability of a process when it is under control. Assuming the specification variable is distributed from a normal population, several PCIs are derived by the researchers. Also, many scientists have worked on these indices when data are contaminated with outliers as well as in the homogenous case. But, in almost all studies, they evaluated the effect of outliers on the PCIs nonparametrical and used robust methods. Here, the parametric model of outliers is considered and introduced the PCIs based on the outliers model. Therefore, these indices are estimated based on the maximum-likelihood and moment estimator of the unknown parameters of the normal distribution contaminated by outliers. Finally, the performances of these measures as well as their parametric and nonparametric estimators are discussed by using simulation studies and several numerical examples. It has been seen that parametric estimation has better performances than a nonparametric method.
Journal: Journal of Applied Statistics
Pages: 2443-2478
Issue: 13-15
Volume: 47
Year: 2020
Month: 11
X-DOI: 10.1080/02664763.2020.1796934
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1796934
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:13-15:p:2443-2478




Template-Type: ReDIF-Article 1.0
Author-Name: Aboubacar Y. Touré
Author-X-Name-First: Aboubacar Y.
Author-X-Name-Last: Touré
Author-Name: Simplice Dossou-Gbété
Author-X-Name-First: Simplice
Author-X-Name-Last: Dossou-Gbété
Author-Name: Célestin C. Kokonendji
Author-X-Name-First: Célestin C.
Author-X-Name-Last: Kokonendji
Title: Asymptotic normality of the test statistics for the unified relative dispersion and relative variation indexes
Abstract: 
 Dispersion indexes with respect to the Poisson and binomial distributions are widely used to assess the conformity of the underlying distribution from an observed sample of the count with one or the other of these theoretical distributions. Recently, the exponential variation index has been proposed as an extension to nonnegative continuous data. This paper aims to gather to study the unified definition of these indexes with respect to the relative variability of a nonnegative natural exponential family of distributions through its variance function. We establish the strong consistency of the plug-in estimators of the indexes as well as their asymptotic normalities. Since the exact distributions of the estimators are not available in closed form, we consider the test of hypothesis relying on these estimators as test statistics with their asymptotic distributions. Simulation studies globally suggest good behaviours of these tests of hypothesis procedures. Applicable examples are analysed, including the lesser-known references such as negative binomial and inverse Gaussian, and improving the very usual case of the Poisson dispersion index. Concluding remarks are made with suggestions of possible extensions.
Journal: Journal of Applied Statistics
Pages: 2479-2491
Issue: 13-15
Volume: 47
Year: 2020
Month: 11
X-DOI: 10.1080/02664763.2020.1779193
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1779193
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:13-15:p:2479-2491




Template-Type: ReDIF-Article 1.0
Author-Name: Essam A. Ahmed
Author-X-Name-First: Essam A.
Author-X-Name-Last: Ahmed
Author-Name: Ziyad Ali Alhussain
Author-X-Name-First: Ziyad
Author-X-Name-Last: Ali Alhussain
Author-Name: Mukhtar M. Salah
Author-X-Name-First: Mukhtar M.
Author-X-Name-Last: Salah
Author-Name: Hanan Haj Ahmed
Author-X-Name-First: Hanan
Author-X-Name-Last: Haj Ahmed
Author-Name: M. S. Eliwa
Author-X-Name-First: M. S.
Author-X-Name-Last: Eliwa
Title: Inference of progressively type-II censored competing risks data from Chen distribution with an application
Abstract: 
 In this paper, the estimation of unknown parameters of Chen distribution is considered under progressive Type-II censoring in the presence of competing failure causes. It is assumed that the latent causes of failures have independent Chen distributions with the common shape parameter, but different scale parameters. From a frequentist perspective, the maximum likelihood estimate of parameters via expectation–maximization (EM) algorithm is obtained. Also, the expected Fisher information matrix based on the missing information principle is computed. By using the obtained expected Fisher information matrix of the MLEs, asymptotic 95% confidence intervals for the parameters are constructed. We also apply the bootstrap methods (Bootstrap-p and Bootstrap-t) to construct confidence intervals. From Bayesian aspect, the Bayes estimates of the unknown parameters are computed by applying the Markov chain Monte Carlo (MCMC) procedure, the average length and coverage rate of credible intervals are also carried out. The Bayes inference is based on the squared error, LINEX, and general entropy loss functions. The performance of point estimators and confidence intervals is evaluated by a simulation study. Finally, a real-life example is considered for illustrative purposes.
Journal: Journal of Applied Statistics
Pages: 2492-2524
Issue: 13-15
Volume: 47
Year: 2020
Month: 11
X-DOI: 10.1080/02664763.2020.1815670
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1815670
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:13-15:p:2492-2524




Template-Type: ReDIF-Article 1.0
Author-Name: Lisandro J. Fermín
Author-X-Name-First: Lisandro J.
Author-X-Name-Last: Fermín
Author-Name: Jacques Lévy-Véhel
Author-X-Name-First: Jacques
Author-X-Name-Last: Lévy-Véhel
Title: Variability and singularity arising from a Piecewise-Deterministic Markov Process applied to model poor patient compliance in the multi-IV case
Abstract: 
 We propose a Piecewise-Deterministic Markov Process (PDMP) to model the drug concentration in the case of multiple intravenous-bolus (multi-IV) doses and poor patient adherence situation: the scheduled time and doses of drug administration are not respected by the patient, the drug administration considers switching regime with random drug intake times. We study the randomness of drug concentration and derive probability results on the stochastic dynamics using the PDMP theory, focusing on two aspects of practical relevance: the variability of the concentration and the regularity of its stationary probability distribution. The main result show as the regularity of the concentration is governed by a parameter, which quantifies in a precise way the situations where drug intake times are too scarce concerning the elimination rate. Our approach is novel for the study of the regularity of the stationary distribution in PDMP models. This article extends the results given in [J. Lévy-Véhel and P.E. Lévy-Véhel, Variability and singularity arising from poor compliance in a pharmacodynamical model I: The multi-IV case, J. Pharmacokinet. Pharmacodyn. 40 (2013), pp. 15–39], by considering more realistic irregular dosing schedules. The computations permit precise assessment of the effect of various significant parameters such as the mean rate of intake, the elimination rate, and the mean dose. They quantify how much poor adherence will affect the regimen. Our results help to understand the consequences of poor adherence.
Journal: Journal of Applied Statistics
Pages: 2525-2545
Issue: 13-15
Volume: 47
Year: 2020
Month: 11
X-DOI: 10.1080/02664763.2019.1711030
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1711030
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:13-15:p:2525-2545




Template-Type: ReDIF-Article 1.0
Author-Name: Isabel Silva
Author-X-Name-First: Isabel
Author-X-Name-Last: Silva
Author-Name: Maria Eduarda Silva
Author-X-Name-First: Maria
Author-X-Name-Last: Eduarda Silva
Author-Name: Cristina Torres
Author-X-Name-First: Cristina
Author-X-Name-Last: Torres
Title: Inference for bivariate integer-valued moving average models based on binomial thinning operation
Abstract: 
 Time series of (small) counts are common in practice and appear in a wide variety of fields. In the last three decades, several models that explicitly account for the discreteness of the data have been proposed in the literature. However, for multivariate time series of counts several difficulties arise and the literature is not so detailed. This work considers Bivariate INteger-valued Moving Average, BINMA, models based on the binomial thinning operation. The main probabilistic and statistical properties of BINMA models are studied. Two parametric cases are analysed, one with the cross-correlation generated through a Bivariate Poisson innovation process and another with a Bivariate Negative Binomial innovation process. Moreover, parameter estimation is carried out by the Generalized Method of Moments. The performance of the model is illustrated with synthetic data as well as with real datasets.
Journal: Journal of Applied Statistics
Pages: 2546-2564
Issue: 13-15
Volume: 47
Year: 2020
Month: 11
X-DOI: 10.1080/02664763.2020.1747411
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1747411
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:13-15:p:2546-2564




Template-Type: ReDIF-Article 1.0
Author-Name: Michael Cullan
Author-X-Name-First: Michael
Author-X-Name-Last: Cullan
Author-Name: Scott Lidgard
Author-X-Name-First: Scott
Author-X-Name-Last: Lidgard
Author-Name: Beckett Sterner
Author-X-Name-First: Beckett
Author-X-Name-Last: Sterner
Title: Controlling the error probabilities of model selection information criteria using bootstrapping
Abstract: 
 The Akaike Information Criterion (AIC) and related information criteria are powerful and increasingly popular tools for comparing multiple, non-nested models without the specification of a null model. However, existing procedures for information-theoretic model selection do not provide explicit and uniform control over error rates for the choice between models, a key feature of classical hypothesis testing. We show how to extend notions of Type-I and Type-II error to more than two models without requiring a null. We then present the Error Control for Information Criteria (ECIC) method, a bootstrap approach to controlling Type-I error using Difference of Goodness of Fit (DGOF) distributions. We apply ECIC to empirical and simulated data in time series and regression contexts to illustrate its value for parametric Neyman–Pearson classification. An R package implementing the bootstrap method is publicly available.
Journal: Journal of Applied Statistics
Pages: 2565-2581
Issue: 13-15
Volume: 47
Year: 2020
Month: 11
X-DOI: 10.1080/02664763.2019.1701636
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1701636
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:13-15:p:2565-2581




Template-Type: ReDIF-Article 1.0
Author-Name: Benjamin Riedle
Author-X-Name-First: Benjamin
Author-X-Name-Last: Riedle
Author-Name: Andrew A. Neath
Author-X-Name-First: Andrew A.
Author-X-Name-Last: Neath
Author-Name: Joseph E. Cavanaugh
Author-X-Name-First: Joseph E.
Author-X-Name-Last: Cavanaugh
Title: Reconceptualizing the p-value from a likelihood ratio test: a probabilistic pairwise comparison of models based on Kullback-Leibler discrepancy measures
Abstract: 
 Discrepancy measures are often employed in problems involving the selection and assessment of statistical models. A discrepancy gauges the separation between a fitted candidate model and the underlying generating model. In this work, we consider pairwise comparisons of fitted models based on a probabilistic evaluation of the ordering of the constituent discrepancies. An estimator of the probability is derived using the bootstrap. In the framework of hypothesis testing, nested models are often compared on the basis of the p-value. Specifically, the simpler null model is favored unless the p-value is sufficiently small, in which case the null model is rejected and the more general alternative model is retained. Using suitably defined discrepancy measures, we mathematically show that, in general settings, the likelihood ratio test p-value is approximated by the bootstrapped discrepancy comparison probability (BDCP). We argue that the connection between the p-value and the BDCP leads to potentially new insights regarding the utility and limitations of the p-value. The BDCP framework also facilitates discrepancy-based inferences in settings beyond the limited confines of nested model hypothesis testing.
Journal: Journal of Applied Statistics
Pages: 2582-2609
Issue: 13-15
Volume: 47
Year: 2020
Month: 11
X-DOI: 10.1080/02664763.2020.1754360
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1754360
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:13-15:p:2582-2609




Template-Type: ReDIF-Article 1.0
Author-Name: Ya. Yu. Nikitin
Author-X-Name-First: Ya. Yu.
Author-X-Name-Last: Nikitin
Author-Name: I. A. Ragozin
Author-X-Name-First: I. A.
Author-X-Name-Last: Ragozin
Title: Goodness-of-fit tests for the logistic location family
Abstract: 
 We construct two U-empirical tests for the logistic location family which are based on appropriate characterization of this family using independent exponential shifts. We study the limiting distributions and local Bahadur efficiency of corresponding test statistics under close alternatives. It turns out that the present tests are considerably more efficient than the recently proposed similar tests based on another characterization. The efficiency calculations are accompanied by the simulation of power for new tests together with the previous ones.Both efficiency and power turn out to be very high. Finally we consider the application of our tests to real data example.
Journal: Journal of Applied Statistics
Pages: 2610-2622
Issue: 13-15
Volume: 47
Year: 2020
Month: 11
X-DOI: 10.1080/02664763.2020.1761952
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1761952
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:13-15:p:2610-2622




Template-Type: ReDIF-Article 1.0
Author-Name: M. Li
Author-X-Name-First: M.
Author-X-Name-Last: Li
Author-Name: Q. Guo
Author-X-Name-First: Q.
Author-X-Name-Last: Guo
Author-Name: W. J. Zhai
Author-X-Name-First: W. J.
Author-X-Name-Last: Zhai
Author-Name: B. Z. Chen
Author-X-Name-First: B. Z.
Author-X-Name-Last: Chen
Title: The linearized alternating direction method of multipliers for low-rank and fused LASSO matrix regression model
Abstract: 
 Datasets with matrix and vector form are increasingly popular in modern scientific fields. Based on structures of datasets, matrix and vector coefficients need to be estimated. At present, the matrix regression models were proposed, and they mainly focused on the matrix without vector variables. In order to fully explore complex structures of datasets, we propose a novel matrix regression model which combines fused LASSO and nuclear norm penalty, which can deal with the data containing matrix and vector variables meanwhile. Our main work is to design an efficient algorithm to solve the proposed low-rank and fused LASSO matrix regression model. Following the existing idea, we design the linearized alternating direction method of multipliers and establish its global convergence. Finally, we carry out numerical experiments to demonstrate the efficiency of our method. Especially, we apply our model to two real datasets, i.e. the signal shapes and the trip time prediction from partial trajectories.
Journal: Journal of Applied Statistics
Pages: 2623-2640
Issue: 13-15
Volume: 47
Year: 2020
Month: 11
X-DOI: 10.1080/02664763.2020.1742296
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1742296
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:13-15:p:2623-2640




Template-Type: ReDIF-Article 1.0
Author-Name: Célia Nunes
Author-X-Name-First: Célia
Author-X-Name-Last: Nunes
Author-Name: Elsa Moreira
Author-X-Name-First: Elsa
Author-X-Name-Last: Moreira
Author-Name: Sandra S. Ferreira
Author-X-Name-First: Sandra S.
Author-X-Name-Last: Ferreira
Author-Name: Dário Ferreira
Author-X-Name-First: Dário
Author-X-Name-Last: Ferreira
Author-Name: João T. Mexia
Author-X-Name-First: João T.
Author-X-Name-Last: Mexia
Title: Considering the sample sizes as truncated Poisson random variables in mixed effects models
Abstract: 
 When applying analysis of variance, the sample sizes may not be previously known, so it is more appropriate to consider them as realizations of random variables. A motivating example is the collection of observations during a fixed time span in a study comparing, for example, several pathologies of patients arriving at a hospital. This paper extends the theory of analysis of variance to those situations considering mixed effects models. We will assume that the occurrences of observations correspond to a counting process and the sample dimensions have Poisson distribution. The proposed approach is applied to a study of cancer patients.
Journal: Journal of Applied Statistics
Pages: 2641-2657
Issue: 13-15
Volume: 47
Year: 2020
Month: 11
X-DOI: 10.1080/02664763.2019.1641188
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1641188
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:13-15:p:2641-2657




Template-Type: ReDIF-Article 1.0
Author-Name: Mario Giacomazzo
Author-X-Name-First: Mario
Author-X-Name-Last: Giacomazzo
Author-Name: Yiannis Kamarianakis
Author-X-Name-First: Yiannis
Author-X-Name-Last: Kamarianakis
Title: Bayesian estimation of subset threshold autoregressions: short-term forecasting of traffic occupancy
Abstract: 
 Traffic management authorities in metropolitan areas use real-time systems that analyze high-frequency measurements from fixed sensors, to perform short-term forecasting and incident detection for various locations of a road network. Published research over the last 20 years focused primarily on modeling and forecasting of traffic volumes and speeds. Traffic occupancy approximates vehicular density through the percentage of time a sensor detects a vehicle within a pre-specified time interval. It exhibits weekly periodic patterns and heteroskedasticity and has been used as a metric for characterizing traffic regimes (e.g. free flow, congestion). This article presents a Bayesian three-step model building procedure for parsimonious estimation of Threshold-Autoregressive (TAR) models, designed for location- day- and horizon-specific forecasting of traffic occupancy. In the first step, multiple regime TAR models reformulated as high-dimensional linear regressions are estimated using Bayesian horseshoe priors. Next, significant regimes are identified through a forward selection algorithm based on Kullback-Leibler (KL) distances between the posterior predictive distribution of the full reference model and TAR models with fewer regimes. Given the regimes, the forward selection algorithm can be implemented again to select significant autoregressive terms. In addition to forecasting, the proposed specification and model-building scheme, may assist in determining location-specific congestion thresholds and associations between traffic dynamics observed in different regions of a network. Empirical results applied to data from a traffic forecasting competition, illustrate the efficacy of the proposed procedures in obtaining interpretable models and in producing satisfactory point and density forecasts at multiple horizons.
Journal: Journal of Applied Statistics
Pages: 2658-2689
Issue: 13-15
Volume: 47
Year: 2020
Month: 11
X-DOI: 10.1080/02664763.2020.1801606
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1801606
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:13-15:p:2658-2689




Template-Type: ReDIF-Article 1.0
Author-Name: Jaime Arrué
Author-X-Name-First: Jaime
Author-X-Name-Last: Arrué
Author-Name: Reinaldo B. Arellano-Valle
Author-X-Name-First: Reinaldo B.
Author-X-Name-Last: Arellano-Valle
Author-Name: Héctor W. Gómez
Author-X-Name-First: Héctor W.
Author-X-Name-Last: Gómez
Author-Name: Víctor Leiva
Author-X-Name-First: Víctor
Author-X-Name-Last: Leiva
Title: On a new type of Birnbaum-Saunders models and its inference and application to fatigue data
Abstract: 
 The Birnbaum-Saunders distribution is a widely studied model with diverse applications. Its origins are in the modeling of lifetimes associated with material fatigue. By using a motivating example, we show that, even when lifetime data related to fatigue are modeled, the Birnbaum-Saunders distribution can be unsuitable to fit these data in the distribution tails. Based on the nice properties of the Birnbaum-Saunders model, in this work, we use a modified skew-normal distribution to construct such a model. This allows us to obtain flexibility in skewness and kurtosis, which is controlled by a shape parameter. We provide a mathematical characterization of this new type of Birnbaum-Saunders distribution and then its statistical characterization is derived by using the maximum-likelihood method, including the associated information matrices. In order to improve the inferential performance, we correct the bias of the corresponding estimators, which is supported by a simulation study. To conclude our investigation, we retake the motivating example based on fatigue life data to show the good agreement between the new type of Birnbaum-Saunders distribution proposed in this work and the data, reporting its potential applications.
Journal: Journal of Applied Statistics
Pages: 2690-2710
Issue: 13-15
Volume: 47
Year: 2020
Month: 11
X-DOI: 10.1080/02664763.2019.1668365
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1668365
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:13-15:p:2690-2710




Template-Type: ReDIF-Article 1.0
Author-Name: Rafael Romero
Author-X-Name-First: Rafael
Author-X-Name-Last: Romero
Author-Name: Jose M. Pavía
Author-X-Name-First: Jose M.
Author-X-Name-Last: Pavía
Author-Name: Jorge Martín
Author-X-Name-First: Jorge
Author-X-Name-Last: Martín
Author-Name: Gerardo Romero
Author-X-Name-First: Gerardo
Author-X-Name-Last: Romero
Title: Assessing uncertainty of voter transitions estimated from aggregated data. Application to the 2017 French presidential election
Abstract: 
 Inferring electoral individual behaviour from aggregated data is a very active research area, with ramifications in sociology and political science. A new approach based on linear programming is proposed to estimate voter transitions among parties (or candidates) between two elections. Compared to other linear and quadratic programming models previously published, our approach presents two important innovations. Firstly, it explicitly deals with new entries and exits in the election census without assuming unrealistic hypotheses, enabling a reasonable estimation of vote behaviour of young electors voting for the first time. Secondly, by exploiting the information contained in the model residuals, we develop a procedure to assess the uncertainty in the estimates. This significantly distinguishes our model from other published mathematical programming methods. The method is illustrated estimating the vote transfer matrix between the first and second rounds of the 2017 French presidential election and measuring its level of uncertainty. Likewise, compared to the most current alternatives based on ecological regression, our approach is considerably simpler and faster, and has provided reasonable results in all the actual elections to which it has been applied. Interested scholars can easily use our procedure with the aid of the R-function provided in the Supplemental Material.
Journal: Journal of Applied Statistics
Pages: 2711-2736
Issue: 13-15
Volume: 47
Year: 2020
Month: 11
X-DOI: 10.1080/02664763.2020.1804842
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1804842
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:13-15:p:2711-2736




Template-Type: ReDIF-Article 1.0
Author-Name: Sandra Oliveira
Author-X-Name-First: Sandra
Author-X-Name-Last: Oliveira
Author-Name: Célia Nunes
Author-X-Name-First: Célia
Author-X-Name-Last: Nunes
Author-Name: Elsa Moreira
Author-X-Name-First: Elsa
Author-X-Name-Last: Moreira
Author-Name: Miguel Fonseca
Author-X-Name-First: Miguel
Author-X-Name-Last: Fonseca
Author-Name: João T. Mexia
Author-X-Name-First: João T.
Author-X-Name-Last: Mexia
Title: Balanced prime basis factorial fixed effects model with random number of observations
Abstract: 
 Factorial designs are in general more efficient for experiments that involve the study of the effects of two or more factors. In this paper we consider a  $p^U $pU factorial model with U factors, each one having a p prime number of levels. We consider a balanced (r replicates per treatment) prime factorial with fixed effects. Our goal is to extend these models to the case where it is not possible to known in advance the number of treatments replicates, r. In these situations is more appropriate to consider r as a realization of a random variable R, which will be assumed to be geometrically distributed. The proposed approach is illustrated through an application considering simulated data.
Journal: Journal of Applied Statistics
Pages: 2737-2748
Issue: 13-15
Volume: 47
Year: 2020
Month: 11
X-DOI: 10.1080/02664763.2019.1679097
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1679097
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:13-15:p:2737-2748




Template-Type: ReDIF-Article 1.0
Author-Name: Viktor Witkovský
Author-X-Name-First: Viktor
Author-X-Name-Last: Witkovský
Title: Computing the exact distribution of the Bartlett's test statistic by numerical inversion of its characteristic function
Abstract: 
 Application of the exact statistical inference frequently leads to non-standard probability distributions of the considered estimators or test statistics. The exact distributions of many estimators and test statistics can be specified by their characteristic functions, as is the case for the null distribution of the Bartlett's test statistic. However, analytical inversion of the characteristic function, if possible, frequently leads to complicated expressions for computing the distribution function and the corresponding quantiles. An efficient alternative is the well-known method based on numerical inversion of the characteristic functions, which is, however, ignored in popular statistical software packages. In this paper, we present the explicit characteristic function of the corrected Bartlett's test statistic together with the computationally fast and efficient implementation of the approach based on numerical inversion of this characteristic function, suggested for evaluating the exact null distribution used for testing homogeneity of variances in several normal populations, with possibly unequal sample sizes.
Journal: Journal of Applied Statistics
Pages: 2749-2764
Issue: 13-15
Volume: 47
Year: 2020
Month: 11
X-DOI: 10.1080/02664763.2019.1675608
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1675608
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:13-15:p:2749-2764




Template-Type: ReDIF-Article 1.0
Author-Name: F. J. Marques
Author-X-Name-First: F. J.
Author-X-Name-Last: Marques
Author-Name: C. A. Coelho
Author-X-Name-First: C. A.
Author-X-Name-Last: Coelho
Title: Testing simultaneously different covariance block diagonal structures – the multi-sample case
Abstract: 
 In this work a likelihood ratio test which allows to test simultaneously if, several covariance matrices are equal and block diagonal with different specific structures in the diagonal blocks, is developed. The distribution of the likelihood ratio statistic is studied and the expression of its hth null moment are derived. In order to make the test useful in practical terms, near-exact approximations are developed for the likelihood ratio statistic. A practical application to real data set together with numerical studies and simulations are provided in order illustrate the applicability of the test and also to assess the precision of the near-exact approximations developed.
Journal: Journal of Applied Statistics
Pages: 2765-2784
Issue: 13-15
Volume: 47
Year: 2020
Month: 11
X-DOI: 10.1080/02664763.2020.1712590
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1712590
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:13-15:p:2765-2784




Template-Type: ReDIF-Article 1.0
Author-Name: Najme Sharifipanah
Author-X-Name-First: Najme
Author-X-Name-Last: Sharifipanah
Author-Name: Rahim Chinipardaz
Author-X-Name-First: Rahim
Author-X-Name-Last: Chinipardaz
Author-Name: Gholam Ali Parham
Author-X-Name-First: Gholam Ali
Author-X-Name-Last: Parham
Title: A new class of weighted bimodal distribution with application to gamma-ray burst duration data
Abstract: 
 Gamma-ray bursts (GRBs) have been confidently identified thus far and are prescribed to different physical scenarios, black hole mergers, and collapse of massive stars. The distribution of GRBs duration, which is one of the main characteristics of GRBs, is bimodal. Hence, many authors have used mixtures of distribution models to fit them, which suffers serious estimation problems either from classical or Bayesian approaches. Therefore, in this article we introduced a more flexible class of weighted bimodal distribution, called alpha two-piece skew normal (ATPSN), for modeling GRBs duration data set. Some of the main probabilistic and inferential properties of the distribution are discussed and a simulation study is carried out to illustrate the performance of the MLEs.
Journal: Journal of Applied Statistics
Pages: 2785-2807
Issue: 13-15
Volume: 47
Year: 2020
Month: 11
X-DOI: 10.1080/02664763.2020.1815669
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1815669
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:13-15:p:2785-2807




Template-Type: ReDIF-Article 1.0
Author-Name: P. Economou
Author-X-Name-First: P.
Author-X-Name-Last: Economou
Author-Name: G. Tzavelas
Author-X-Name-First: G.
Author-X-Name-Last: Tzavelas
Author-Name: A. Batsidis
Author-X-Name-First: A.
Author-X-Name-Last: Batsidis
Title: Robust inference under r-size-biased sampling without replacement from finite population
Abstract: 
 The case of size-biased sampling of known order from a finite population without replacement is considered. The behavior of such a sampling scheme is studied with respect to the sampling fraction. Based on a simulation study, it is concluded that such a sample cannot be treated either as a random sample from the parent distribution or as a random sample from the corresponding r-size weighted distribution and as the sampling fraction increases, the biasness in the sample decreases resulting in a transition from an r-size-biased sample to a random sample. A modified version of a likelihood-free method is adopted for making statistical inference for the unknown population parameters, as well as for the size of the population when it is unknown. A simulation study, which takes under consideration the sampling fraction, demonstrates that the proposed method presents better and more robust behavior compared to the approaches, which treat the r-size-biased sample either as a random sample from the parent distribution or as a random sample from the corresponding r-size weighted distribution. Finally, a numerical example which motivates this study illustrates our results.
Journal: Journal of Applied Statistics
Pages: 2808-2824
Issue: 13-15
Volume: 47
Year: 2020
Month: 11
X-DOI: 10.1080/02664763.2019.1711031
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1711031
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:13-15:p:2808-2824




Template-Type: ReDIF-Article 1.0
Author-Name: Helena Penalva
Author-X-Name-First: Helena
Author-X-Name-Last: Penalva
Author-Name: M. Ivette Gomes
Author-X-Name-First: M. Ivette
Author-X-Name-Last: Gomes
Author-Name: Frederico Caeiro
Author-X-Name-First: Frederico
Author-X-Name-Last: Caeiro
Author-Name: M. Manuela Neves
Author-X-Name-First: M. Manuela
Author-X-Name-Last: Neves
Title: Lehmer's mean-of-order-p extreme value index estimation: a simulation study and applications
Abstract: 
 The main objective of extreme value theory is essentially the estimation of quantities related to extreme events. One of its main issues has been the estimation of the extreme value index (EVI), a parameter directly related to the tail weight of the distribution. Here we deal with the semi-parametric estimation of the EVI, for heavy tails. A recent class of EVI-estimators, based on the Lehmer's mean-of-order p (L $_p $p), which generalizes the arithmetic mean, is considered. An asymptotic comparison at optimal levels performed in previous works has revealed the competitiveness of this class of EVI-estimators. A large-scale Monte-Carlo simulation study for finite simulated samples has been here performed, showing the behaviour of L $_p $p, as a function of p. A bootstrap adaptive choice of  $(k,p) $(k,p), where k is the number of upper order statistics used in the estimation, and a second algorithm based on a stability criterion are computationally studied and applied to simulated and real data.
Journal: Journal of Applied Statistics
Pages: 2825-2845
Issue: 13-15
Volume: 47
Year: 2020
Month: 11
X-DOI: 10.1080/02664763.2019.1694871
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1694871
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:13-15:p:2825-2845




Template-Type: ReDIF-Article 1.0
Author-Name: D. Prata Gomes
Author-X-Name-First: D. Prata
Author-X-Name-Last: Gomes
Author-Name: M. Manuela Neves
Author-X-Name-First: M. Manuela
Author-X-Name-Last: Neves
Title: Extremal index blocks estimator: the threshold and the block size choice
Abstract: 
 The main objective of Statistics of Extremes is the estimation of probabilities of rare events. When extending the analysis of the limiting behaviour of the extreme values from independent and identically distributed sequences to stationary sequences a key parameter appears, the extremal index θ, whose accurate estimation is not easy. Here we focus on the estimation of θ using blocks estimators, that can be constructed by using disjoint or sliding blocks. The asymptotic properties for both procedures were studied and compared but both blocks estimators require the choice of a threshold and a block length. Some criteria have appeared for the choice of those nuisance quantities but some research is still needed. We will show how the threshold and the block size choices can affect the estimates. However the main objective of this work is to revisit another estimation procedure that only depends on the block length, although some conditions on the underlying process need to be verified. The associated estimator presents nice asymptotic properties, and for finite samples is here illustrated a stability criterion for choosing the block length and then obtaining the θ estimate. A large simulation study has been performed and an application to daily mean flow discharge rate in the hydrometric station of Fragas da Torre in river Paiva, data collected from 1 October 1946 to 30 April 2012 is done.
Journal: Journal of Applied Statistics
Pages: 2846-2861
Issue: 13-15
Volume: 47
Year: 2020
Month: 11
X-DOI: 10.1080/02664763.2020.1720626
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1720626
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:13-15:p:2846-2861




Template-Type: ReDIF-Article 1.0
Author-Name: A. Tchorbadjieff
Author-X-Name-First: A.
Author-X-Name-Last: Tchorbadjieff
Author-Name: P. Mayster
Author-X-Name-First: P.
Author-X-Name-Last: Mayster
Title: Models induced from critical birth–death process with random initial conditions
Abstract: 
 In this work, we study a linear birth–death process starting from random initial conditions. First, we consider these initial conditions as a random number of particles following different standard probabilistic distributions – Negative-Binomial and its closest Geometric, Poisson or Pólya–Aeppli distributions. It is proved analytically and numerically that in these cases the random number of particles alive at any positive time follows the same probability law like the initial condition, but with different parameters depending on time. The random initial conditions cannot change the critical parameter of branching mechanism, but they impact the extinction probability. Finally, the numerical model is extended to an application for studying branching processes with more complex initial conditions. This is demonstrated with a linear birth–death process initialised with Pólya urn sampling scheme. The obtained preliminary results for particle distribution show close relation to Pólya–Aeppli distribution.
Journal: Journal of Applied Statistics
Pages: 2862-2878
Issue: 13-15
Volume: 47
Year: 2020
Month: 11
X-DOI: 10.1080/02664763.2020.1732309
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1732309
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:13-15:p:2862-2878




Template-Type: ReDIF-Article 1.0
Author-Name: Eliana Costa e Silva
Author-X-Name-First: Eliana
Author-X-Name-Last: Costa e Silva
Author-Name: Isabel Cristina Lopes
Author-X-Name-First: Isabel Cristina
Author-X-Name-Last: Lopes
Author-Name: Aldina Correia
Author-X-Name-First: Aldina
Author-X-Name-Last: Correia
Author-Name: Susana Faria
Author-X-Name-First: Susana
Author-X-Name-Last: Faria
Title: A logistic regression model for consumer default risk
Abstract: 
 In this study, a logistic regression model is applied to credit scoring data from a given Portuguese financial institution to evaluate the default risk of consumer loans. It was found that the risk of default increases with the loan spread, loan term and age of the customer, but decreases if the customer owns more credit cards. Clients receiving the salary in the same banking institution of the loan have less chances of default than clients receiving their salary in another institution. We also found that clients in the lowest income tax echelon have more propensity to default. The model predicted default correctly in 89.79% of the cases.
Journal: Journal of Applied Statistics
Pages: 2879-2894
Issue: 13-15
Volume: 47
Year: 2020
Month: 11
X-DOI: 10.1080/02664763.2020.1759030
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1759030
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:13-15:p:2879-2894




Template-Type: ReDIF-Article 1.0
Author-Name: Olusola Samuel Makinde
Author-X-Name-First: Olusola
Author-X-Name-Last: Samuel Makinde
Title: On rank distribution classifiers for high-dimensional data
Abstract: 
 Spatial sign and rank-based methods have been studied in the recent literature, especially when the dimension is smaller than the sample size. In this paper, a classification method based on the distribution of rank functions for high-dimensional data is considered with extension to functional data. The method is fully nonparametric in nature. The performance of the classification method is illustrated in comparison with some other classifiers using simulated and real data sets. Supporting code in R are provided for computational implementation of the classification method that will be of use to others.
Journal: Journal of Applied Statistics
Pages: 2895-2911
Issue: 13-15
Volume: 47
Year: 2020
Month: 11
X-DOI: 10.1080/02664763.2020.1768227
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1768227
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:13-15:p:2895-2911




Template-Type: ReDIF-Article 1.0
Author-Name: A. N. Oliveira
Author-X-Name-First: A. N.
Author-X-Name-Last: Oliveira
Author-Name: R. Menezes
Author-X-Name-First: R.
Author-X-Name-Last: Menezes
Author-Name: S. Faria
Author-X-Name-First: S.
Author-X-Name-Last: Faria
Author-Name: P. Afonso
Author-X-Name-First: P.
Author-X-Name-Last: Afonso
Title: Mixed-effects modelling for crossed and nested data: an analysis of dengue fever in the state of Goiás, Brazil
Abstract: 
 Dengue fever is a viral disease transmitted by the mosquito Aedes aegypti. In order to avoid epidemics and deaths, this transmitting vector must be controlled. This work assembles, for the first time, data from multiple governmental bodies describing the number of dengue cases reported, and meteorological conditions in 20 cities in the Goiás state, Brazil, from 2008 to 2015. We then apply generalised linear mixed modelling to this novel data set to model dengue occurrences in this state, where the tropical climate favours the proliferation of the main transmitting vector of this disease. The number of reported dengue cases is estimated using meteorological variables as fixed effects, and city and year data are included in the model as random effects. The proposed models can cope with complex data structures, such as nested data, while taking into account the particularities of each year dependent on the city under analysis. The results confirm that precipitation, minimum temperature, and relative air humidity contribute to the increase of dengue cases number, while year and city location are determining factors. Public policies, based on these new results, together with joint actions involving local populations, are essential to combat the vector transmitting dengue and avoid epidemics.
Journal: Journal of Applied Statistics
Pages: 2912-2926
Issue: 13-15
Volume: 47
Year: 2020
Month: 11
X-DOI: 10.1080/02664763.2020.1736528
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1736528
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:13-15:p:2912-2926




Template-Type: ReDIF-Article 1.0
Author-Name: Isabel Silva
Author-X-Name-First: Isabel
Author-X-Name-Last: Silva
Author-Name: Hugo Alonso
Author-X-Name-First: Hugo
Author-X-Name-Last: Alonso
Title: New developments in the forecasting of monthly overnight stays in the North Region of Portugal
Abstract: 
 The Tourism sector is of strategic importance to the North Region of Portugal and is growing. Forecasting monthly overnight stays in this region is, therefore, a relevant problem. In this paper, we analyze data more recent than those considered in previous studies and use them to develop and compare several forecasting models and methods. We conclude that the best results are achieved by models based on a non-parametric approach not considered so far for these data, the singular spectrum analysis.
Journal: Journal of Applied Statistics
Pages: 2927-2940
Issue: 13-15
Volume: 47
Year: 2020
Month: 11
X-DOI: 10.1080/02664763.2020.1795812
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1795812
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:13-15:p:2927-2940




Template-Type: ReDIF-Article 1.0
Author-Name: Junhui Yin
Author-X-Name-First: Junhui
Author-X-Name-Last: Yin
Author-Name: Liucang Wu
Author-X-Name-First: Liucang
Author-X-Name-Last: Wu
Author-Name: Lin Dai
Author-X-Name-First: Lin
Author-X-Name-Last: Dai
Title: Variable selection in finite mixture of regression models using the skew-normal distribution
Abstract: 
 Variable selection in finite mixture of regression (FMR) models is frequently used in statistical modeling. The majority of applications of variable selection in FMR models use a normal distribution for regression error. Such assumptions are unsuitable for a set of data containing a group or groups of observations with asymmetric behavior. In this paper, we introduce a variable selection procedure for FMR models using the skew-normal distribution. With appropriate choice of the tuning parameters, we establish the theoretical properties of our procedure, including consistency in variable selection and the oracle property in estimation. To estimate the parameters of the model, a modified EM algorithm for numerical computations is developed. The methodology is illustrated through numerical experiments and a real data example.
Journal: Journal of Applied Statistics
Pages: 2941-2960
Issue: 16
Volume: 47
Year: 2020
Month: 12
X-DOI: 10.1080/02664763.2019.1709051
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1709051
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:16:p:2941-2960




Template-Type: ReDIF-Article 1.0
Author-Name: Zs. T. Kosztyán
Author-X-Name-First: Zs. T.
Author-X-Name-Last: Kosztyán
Author-Name: É. Orbán-Mihálykó
Author-X-Name-First: É.
Author-X-Name-Last: Orbán-Mihálykó
Author-Name: Cs. Mihálykó
Author-X-Name-First: Cs.
Author-X-Name-Last: Mihálykó
Author-Name: V. V. Csányi
Author-X-Name-First: V. V.
Author-X-Name-Last: Csányi
Author-Name: A. Telcs
Author-X-Name-First: A.
Author-X-Name-Last: Telcs
Title: Analyzing and clustering students' application preferences in higher education
Abstract: 
 We present a framework based on a higher education application preference list that allows a different type of flexible aggregation and, hence, the analysis and clustering of application data. Preference lists are converted into scores. The proposed approach is demonstrated in the context of higher education applications in Hungary over the period of 2006–2015. Our method reveals that efforts to leverage center-periphery differences do not fulfill expectations. Furthermore, the student's top preference is very hard to influence, and recruiters may build their strategy on information about the first and second choices.
Journal: Journal of Applied Statistics
Pages: 2961-2983
Issue: 16
Volume: 47
Year: 2020
Month: 12
X-DOI: 10.1080/02664763.2019.1709052
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1709052
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:16:p:2961-2983




Template-Type: ReDIF-Article 1.0
Author-Name: J. van Doorn
Author-X-Name-First: J.
Author-X-Name-Last: van Doorn
Author-Name: A. Ly
Author-X-Name-First: A.
Author-X-Name-Last: Ly
Author-Name: M. Marsman
Author-X-Name-First: M.
Author-X-Name-Last: Marsman
Author-Name: E.-J. Wagenmakers
Author-X-Name-First: E.-J.
Author-X-Name-Last: Wagenmakers
Title: Bayesian rank-based hypothesis testing for the rank sum test, the signed rank test, and Spearman's ρ
Abstract: 
 Bayesian inference for rank-order problems is frustrated by the absence of an explicit likelihood function. This hurdle can be overcome by assuming a latent normal representation that is consistent with the ordinal information in the data: the observed ranks are conceptualized as an impoverished reflection of an underlying continuous scale, and inference concerns the parameters that govern the latent representation. We apply this generic data-augmentation method to obtain Bayes factors for three popular rank-based tests: the rank sum test, the signed rank test, and Spearman's  $\rho _s $ρs.
Journal: Journal of Applied Statistics
Pages: 2984-3006
Issue: 16
Volume: 47
Year: 2020
Month: 12
X-DOI: 10.1080/02664763.2019.1709053
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1709053
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:16:p:2984-3006




Template-Type: ReDIF-Article 1.0
Author-Name: Farzane Hashemi
Author-X-Name-First: Farzane
Author-X-Name-Last: Hashemi
Author-Name: Mehrdad Naderi
Author-X-Name-First: Mehrdad
Author-X-Name-Last: Naderi
Author-Name: Ahad Jamalizadeh
Author-X-Name-First: Ahad
Author-X-Name-Last: Jamalizadeh
Author-Name: Tsung-I Lin
Author-X-Name-First: Tsung-I
Author-X-Name-Last: Lin
Title: A skew factor analysis model based on the normal mean–variance mixture of Birnbaum–Saunders distribution
Abstract: 
 This paper presents a robust extension of factor analysis model by assuming the multivariate normal mean–variance mixture of Birnbaum–Saunders distribution for the unobservable factors and errors. A computationally analytical EM-based algorithm is developed to find maximum likelihood estimates of the parameters. The asymptotic standard errors of parameter estimates are derived under an information-based paradigm. Numerical merits of the proposed methodology are illustrated using both simulated and real datasets.
Journal: Journal of Applied Statistics
Pages: 3007-3029
Issue: 16
Volume: 47
Year: 2020
Month: 12
X-DOI: 10.1080/02664763.2019.1709054
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1709054
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:16:p:3007-3029




Template-Type: ReDIF-Article 1.0
Author-Name: Mohammad Mahdi Maghami
Author-X-Name-First: Mohammad Mahdi
Author-X-Name-Last: Maghami
Author-Name: Mohammad Bahrami
Author-X-Name-First: Mohammad
Author-X-Name-Last: Bahrami
Author-Name: Farkhondeh Alsadat Sajadi
Author-X-Name-First: Farkhondeh Alsadat
Author-X-Name-Last: Sajadi
Title: On bias reduction estimators of skew-normal and skew-t distributions
Abstract: 
 A particular concerns of researchers in statistical inference is bias in parameters estimation. Maximum likelihood estimators are often biased and for small sample size, the first order bias of them can be large and so it may influence the efficiency of the estimator. There are different methods for reduction of this bias. In this paper, we proposed a modified maximum likelihood estimator for the shape parameter of two popular skew distributions, namely skew-normal and skew-t, by offering a new method. We show that this estimator has lower asymptotic bias than the maximum likelihood estimator and is more efficient than those based on the existing methods.
Journal: Journal of Applied Statistics
Pages: 3030-3052
Issue: 16
Volume: 47
Year: 2020
Month: 12
X-DOI: 10.1080/02664763.2019.1710114
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1710114
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:47:y:2020:i:16:p:3030-3052




Template-Type: ReDIF-Article 1.0
Author-Name: Jie Chen
Author-X-Name-First: Jie
Author-X-Name-Last: Chen
Title: Editorial
Journal: Journal of Applied Statistics
Pages: 1-3
Issue: 1
Volume: 48
Year: 2021
Month: 1
X-DOI: 10.1080/02664763.2020.1830613
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1830613
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:1:p:1-3




Template-Type: ReDIF-Article 1.0
Author-Name: Muhammad Shujaat Nawaz
Author-X-Name-First: Muhammad Shujaat
Author-X-Name-Last: Nawaz
Author-Name: Muhammad Azam
Author-X-Name-First: Muhammad
Author-X-Name-Last: Azam
Author-Name: Muhammad Aslam
Author-X-Name-First: Muhammad
Author-X-Name-Last: Aslam
Title: EWMA and DEWMA repetitive control charts under non-normal processes
Abstract: 
 In this paper, we present a repetitive sampling method to construct control charts using exponentially weighted moving averages (EWMA) and double exponentially weighted moving averages (DEWMA) to monitor shift in the process. For non-normal processes, t-distribution with various degrees of freedom (i.e.  $\textrm{df} = 4, 10, 20, 40, 50 $df=4,10,20,40,50) is used as symmetric distribution, gamma distribution with unit scale parameter and various shape parameters (i.e.  $0.5, 1, 2, 3, 4 $0.5,1,2,3,4) is used as positively skewed distribution and Weibull distribution with unit scale parameter and various shape parameters (i.e. 10 and 20) is used as negatively skewed distribution. We use Monte Carlo simulations to check whether the process is out of control. We use average run length as a tool to find the ability of proposed control charts to identify a shift earlier in a process, as compared to other control charts currently used to monitor the same type of process. The proposed control charts are applied to two real datasets.
Journal: Journal of Applied Statistics
Pages: 4-40
Issue: 1
Volume: 48
Year: 2021
Month: 1
X-DOI: 10.1080/02664763.2019.1709809
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1709809
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:1:p:4-40




Template-Type: ReDIF-Article 1.0
Author-Name: Xu Zhang
Author-X-Name-First: Xu
Author-X-Name-Last: Zhang
Author-Name: Sean Barnes
Author-X-Name-First: Sean
Author-X-Name-Last: Barnes
Author-Name: Bruce Golden
Author-X-Name-First: Bruce
Author-X-Name-Last: Golden
Author-Name: Paul Smith
Author-X-Name-First: Paul
Author-X-Name-Last: Smith
Title: A continuous-time Markov model for estimating readmission risk for hospital inpatients
Abstract: 
 Research concerning hospital readmissions has mostly focused on statistical and machine learning models that attempt to predict this unfortunate outcome for individual patients. These models are useful in certain settings, but their performance in many cases is insufficient for implementation in practice, and the dynamics of how readmission risk changes over time is often ignored. Our objective is to develop a model for aggregated readmission risk over time – using a continuous-time Markov chain – beginning at the point of discharge. We derive point and interval estimators for readmission risk, and find the asymptotic distributions for these probabilities. Finally, we validate our derived estimators using simulation, and apply our methods to estimate readmission risk over time using discharge and readmission data for surgical patients.
Journal: Journal of Applied Statistics
Pages: 41-60
Issue: 1
Volume: 48
Year: 2021
Month: 1
X-DOI: 10.1080/02664763.2019.1709810
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1709810
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:1:p:41-60




Template-Type: ReDIF-Article 1.0
Author-Name: Rohan D. Koshti
Author-X-Name-First: Rohan D.
Author-X-Name-Last: Koshti
Author-Name: Kirtee K. Kamalja
Author-X-Name-First: Kirtee K.
Author-X-Name-Last: Kamalja
Title: Parameter estimation of Cambanis-type bivariate uniform distribution with Ranked Set Sampling
Abstract: 
 The concept of ranked set sampling (RSS) is applicable whenever ranking on a set of sampling units can be done easily using a judgment method or based on an auxiliary variable. In this paper, we consider a study variable  $Y $Y correlated with the auxiliary variable  $X $X and use it to rank the sampling units. Further  $({X,Y} ) $(X,Y) is assumed to have Cambanis-type bivariate uniform (CTBU) distribution. We obtain an unbiased estimator of a scale parameter associated with the study variable  $Y $Y based on different RSS schemes. We perform the efficiency comparison of the proposed estimators numerically. We present the trends in the efficiency performance of estimators under various RSS schemes with respect to parameters through line and surface plots. Further, we develop a Matlab function to simulate data from CTBU distribution and present the performance of proposed estimators through a simulation study. The results developed are implemented to real-life data also.
Journal: Journal of Applied Statistics
Pages: 61-83
Issue: 1
Volume: 48
Year: 2021
Month: 1
X-DOI: 10.1080/02664763.2019.1709808
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1709808
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:1:p:61-83




Template-Type: ReDIF-Article 1.0
Author-Name: Philippe Gagnon
Author-X-Name-First: Philippe
Author-X-Name-Last: Gagnon
Author-Name: Mylène Bédard
Author-X-Name-First: Mylène
Author-X-Name-Last: Bédard
Author-Name: Alain Desgagné
Author-X-Name-First: Alain
Author-X-Name-Last: Desgagné
Title: An automatic robust Bayesian approach to principal component regression
Abstract: 
 Principal component regression uses principal components (PCs) as regressors. It is particularly useful in prediction settings with high-dimensional covariates. The existing literature treating of Bayesian approaches is relatively sparse. We introduce a Bayesian approach that is robust to outliers in both the dependent variable and the covariates. Outliers can be thought of as observations that are not in line with the general trend. The proposed approach automatically penalises these observations so that their impact on the posterior gradually vanishes as they move further and further away from the general trend, corresponding to a concept in Bayesian statistics called whole robustness. The predictions produced are thus consistent with the bulk of the data. The approach also exploits the geometry of PCs to efficiently identify those that are significant. Individual predictions obtained from the resulting models are consolidated according to model-averaging mechanisms to account for model uncertainty. The approach is evaluated on real data and compared to its nonrobust Bayesian counterpart, the traditional frequentist approach and a commonly employed robust frequentist method. Detailed guidelines to automate the entire statistical procedure are provided. All required code is made available, see ArXiv:1711.06341.
Journal: Journal of Applied Statistics
Pages: 84-104
Issue: 1
Volume: 48
Year: 2021
Month: 1
X-DOI: 10.1080/02664763.2019.1710478
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1710478
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:1:p:84-104




Template-Type: ReDIF-Article 1.0
Author-Name: Zhihang Dong
Author-X-Name-First: Zhihang
Author-X-Name-Last: Dong
Author-Name: Yen-Chi Chen
Author-X-Name-First: Yen-Chi
Author-X-Name-Last: Chen
Author-Name: Adrian Dobra
Author-X-Name-First: Adrian
Author-X-Name-Last: Dobra
Title: A statistical framework for measuring the temporal stability of human mobility patterns
Abstract: 
 Despite the growing popularity of human mobility studies that collect GPS location data, the problem of determining the minimum required length of GPS monitoring has not been addressed in the current statistical literature. In this paper, we tackle this problem by laying out a theoretical framework for assessing the temporal stability of human mobility based on GPS location data. We define several measures of the temporal dynamics of human spatiotemporal trajectories based on the average velocity process, and on activity distributions in a spatial observation window. We demonstrate the use of our methods with data that comprise the GPS locations of 185 individuals over the course of 18 months. Our empirical results suggest that GPS monitoring should be performed over periods of time that are significantly longer than what has been previously suggested. Furthermore, we argue that GPS study designs should take into account demographic groups.
Journal: Journal of Applied Statistics
Pages: 105-123
Issue: 1
Volume: 48
Year: 2021
Month: 1
X-DOI: 10.1080/02664763.2019.1711363
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1711363
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:1:p:105-123




Template-Type: ReDIF-Article 1.0
Author-Name: Christophe Chesneau
Author-X-Name-First: Christophe
Author-X-Name-Last: Chesneau
Author-Name: Hassan S. Bakouch
Author-X-Name-First: Hassan S.
Author-X-Name-Last: Bakouch
Author-Name: Tassaddaq Hussain
Author-X-Name-First: Tassaddaq
Author-X-Name-Last: Hussain
Author-Name: Bilal A. Para
Author-X-Name-First: Bilal A.
Author-X-Name-Last: Para
Title: The cosine geometric distribution with count data modeling
Abstract: 
 In this paper, a new two-parameter discrete distribution is introduced. It belongs to the family of the weighted geometric distribution (GD), with the feature of using a particular trigonometric weight. This configuration adds an oscillating property to the former GD which can be helpful in analyzing the data with over-dispersion, as developed in this study. First, we present the basic statistical properties of the new distribution, including the cumulative distribution function, hazard rate function and moment generating function. Estimation of the related model parameters is investigated using the maximum likelihood method. A simulation study is performed to illustrate the convergence of the estimators. Applications to two practical datasets are given to show that the new model performs at least as well as some competitors.
Journal: Journal of Applied Statistics
Pages: 124-137
Issue: 1
Volume: 48
Year: 2021
Month: 1
X-DOI: 10.1080/02664763.2019.1711364
File-URL: http://hdl.handle.net/10.1080/02664763.2019.1711364
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:1:p:124-137




Template-Type: ReDIF-Article 1.0
Author-Name: Muhammad Aslam
Author-X-Name-First: Muhammad
Author-X-Name-Last: Aslam
Author-Name: Muhammad Ali Raza
Author-X-Name-First: Muhammad Ali
Author-X-Name-Last: Raza
Author-Name: Rehan Ahmad Khan Sherwani
Author-X-Name-First: Rehan Ahmad Khan
Author-X-Name-Last: Sherwani
Author-Name: Muhammad Farooq
Author-X-Name-First: Muhammad
Author-X-Name-Last: Farooq
Author-Name: Jun Yong Jeong
Author-X-Name-First: Jun Yong
Author-X-Name-Last: Jeong
Author-Name: Chi-Hyuck Jun
Author-X-Name-First: Chi-Hyuck
Author-X-Name-Last: Jun
Title: A mixed control chart for monitoring failure times under accelerated hybrid censoring
Abstract: 
 In an accelerated hybrid censoring scheme several stress factors can be accelerated to make the products to respond to fail more quickly than under normal operating conditions. In such situations, the control charts available in the literature cover the attribute characteristics only to monitor the performance of the process over time. This study extends the idea by proposing an optimal mixed attribute-variable control chart for Weibull distribution under an accelerated hybrid censoring scheme keeping the advantages of both attribute and variable control charts. It first monitors the number of defectives under accelerated conditions and switches to the variable control chart to investigate the mean failure times when the process stability is dubious. The performance of the proposed chart is evaluated by using run-length characteristics, and the optimality of the design parameter is achieved by minimizing the out-of-control average run length. The simulation study depicted better performance of the proposed control chart than the traditional charts in detecting shifts in the process. A real-life application is also included.
Journal: Journal of Applied Statistics
Pages: 138-153
Issue: 1
Volume: 48
Year: 2021
Month: 1
X-DOI: 10.1080/02664763.2020.1713060
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1713060
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:1:p:138-153




Template-Type: ReDIF-Article 1.0
Author-Name: Chen Feng
Author-X-Name-First: Chen
Author-X-Name-Last: Feng
Author-Name: Paul Griffin
Author-X-Name-First: Paul
Author-X-Name-Last: Griffin
Author-Name: Shravan Kethireddy
Author-X-Name-First: Shravan
Author-X-Name-Last: Kethireddy
Author-Name: Yajun Mei
Author-X-Name-First: Yajun
Author-X-Name-Last: Mei
Title: A boosting inspired personalized threshold method for sepsis screening
Abstract: 
 Sepsis is one of the biggest risks to patient safety, with a natural mortality rate between 25% and 50%. It is difficult to diagnose, and no validated standard for diagnosis currently exists. A commonly used scoring criteria is the quick sequential organ failure assessment (qSOFA). It demonstrates very low specificity in ICU populations, however. We develop a method to personalize thresholds in qSOFA that incorporates easily to measure patient baseline characteristics. We compare the personalized threshold method to qSOFA, five previously published methods that obtain an optimal constant threshold for a single biomarker, and to the machine learning algorithms based on logistic regression and AdaBoosting using patient data in the MIMIC-III database. The personalized threshold method achieves higher accuracy than qSOFA and the five published methods and has comparable performance to machine learning methods. Personalized thresholds, however, are much easier to adopt in real-life monitoring than machine learning methods as they are computed once for a patient and used in the same way as qSOFA, whereas the machine learning methods are hard to implement and interpret.
Journal: Journal of Applied Statistics
Pages: 154-175
Issue: 1
Volume: 48
Year: 2021
Month: 1
X-DOI: 10.1080/02664763.2020.1716695
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1716695
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:1:p:154-175




Template-Type: ReDIF-Article 1.0
Author-Name: Jan van den Broek
Author-X-Name-First: Jan
Author-X-Name-Last: van den Broek
Title: Modelling the reproductive power function
Abstract: 
 This paper discusses methods of estimating the reproductive power and the accompanying survival function of communicable events, e.g. infectious disease transmission. The early stage of an outbreak can be described by the infectiousness of the outbreak process, but in later stages of the outbreak, this is complicated by factors such as changing contact patterns and the impact of control measures. It is important to take these factors into account in order to get a good, if approximate, model for an outbreak process. This paper proposes a non-homogeneous birth process and regression model for the reproductive power function, similar to models in discrete survival analysis. A baseline reproductive power function gives a description of the outbreak when covariates are at their baseline values. As an illustration these methods are applied to an avian influenza (H5N1) outbreak among poultry in Thailand.
Journal: Journal of Applied Statistics
Pages: 176-190
Issue: 1
Volume: 48
Year: 2021
Month: 1
X-DOI: 10.1080/02664763.2020.1716696
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1716696
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:1:p:176-190




Template-Type: ReDIF-Article 1.0
Author-Name: Hossein Hassani
Author-X-Name-First: Hossein
Author-X-Name-Last: Hassani
Author-Name: Mansi Ghodsi
Author-X-Name-First: Mansi
Author-X-Name-Last: Ghodsi
Author-Name: Xu Huang
Author-X-Name-First: Xu
Author-X-Name-Last: Huang
Author-Name: Emmanuel Sirimal Silva
Author-X-Name-First: Emmanuel Sirimal
Author-X-Name-Last: Silva
Title: Is there a causal relationship between oil prices and tourist arrivals?
Abstract: 
 This application note investigates the causal relationship between oil price and tourist arrivals to further explain the impact of oil price volatility on tourism-related economic activities. The analysis itself considers the time domain, frequency domain and information theory domain perspectives. Data relating to US and nine European countries are exploited in this paper with causality tests which include time domain, frequency domain, and Convergent Cross Mapping (CCM). The CCM approach is nonparametric and therefore not restricted by assumptions. We contribute to existing research through the successful and introductory application of an advanced method, and via the uncovering of significant causal links from oil prices to tourist arrivals.
Journal: Journal of Applied Statistics
Pages: 191-202
Issue: 1
Volume: 48
Year: 2021
Month: 1
X-DOI: 10.1080/02664763.2020.1720625
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1720625
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:1:p:191-202




Template-Type: ReDIF-Article 1.0
Author-Name: Patrick Borges
Author-X-Name-First: Patrick
Author-X-Name-Last: Borges
Title: Estimating the turning point of the log-logistic hazard function in the presence of long-term survivors with an application for uterine cervical cancer data
Abstract: 
 The hazard function plays an important role in cancer patient survival studies, as it quantifies the instantaneous risk of death of a patient at any given time. Often in cancer clinical trials, unimodal hazard functions are observed, and it is of interest to detect (estimate) the turning point (mode) of hazard function, as this may be an important measure in patient treatment strategies with cancer. Moreover, when patient cure is a possibility, estimating cure rates at different stages of cancer, in addition to their proportions, may provide a better summary of the effects of stages on survival rates. Therefore, the main objective of this paper is to consider the problem of estimating the mode of hazard function of patients at different stages of cervical cancer in the presence of long-term survivors. To this end, a mixture cure rate model is proposed using the log-logistic distribution. The model is conveniently parameterized through the mode of the hazard function, in which cancer stages can affect both the cured fraction and the mode. In addition, we discuss aspects of model inference through the maximum likelihood estimation method. A Monte Carlo simulation study assesses the coverage probability of asymptotic confidence intervals.
Journal: Journal of Applied Statistics
Pages: 203-213
Issue: 2
Volume: 48
Year: 2021
Month: 1
X-DOI: 10.1080/02664763.2020.1720627
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1720627
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:2:p:203-213




Template-Type: ReDIF-Article 1.0
Author-Name: J. de Sousa
Author-X-Name-First: J.
Author-X-Name-Last: de Sousa
Author-Name: K. Hron
Author-X-Name-First: K.
Author-X-Name-Last: Hron
Author-Name: K. Fačevicová
Author-X-Name-First: K.
Author-X-Name-Last: Fačevicová
Author-Name: P. Filzmoser
Author-X-Name-First: P.
Author-X-Name-Last: Filzmoser
Title: Robust principal component analysis for compositional tables
Abstract: 
 A data table arranged according to two factors can often be considered a compositional table. An example is the number of unemployed people, split according to gender and age classes. Analyzed as compositions, the relevant information consists of ratios between different cells of such a table. This is particularly useful when analyzing several compositional tables jointly, where the absolute numbers are in very different ranges, e.g. if unemployment data are considered from different countries. Within the framework of the logratio methodology, compositional tables can be decomposed into independent and interactive parts, and orthonormal coordinates can be assigned to these parts. However, these coordinates usually require some prior knowledge about the data, and they are not easy to handle for exploring the relationships between the given factors. Here we propose a special choice of coordinates with direct relation to centered logratio (clr) coefficients, which are particularly useful for an interpretation in terms of the original cells of the tables. With these coordinates, robust principal component analysis (rPCA) is performed for dimension reduction, allowing to investigate relationships between the factors. The link between orthonormal coordinates and clr coefficients enables to apply rPCA, which would otherwise suffer from the singularity of clr coefficients.
Journal: Journal of Applied Statistics
Pages: 214-233
Issue: 2
Volume: 48
Year: 2021
Month: 1
X-DOI: 10.1080/02664763.2020.1722078
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1722078
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:2:p:214-233




Template-Type: ReDIF-Article 1.0
Author-Name: Yunlu Jiang
Author-X-Name-First: Yunlu
Author-X-Name-Last: Jiang
Author-Name: Yan Wang
Author-X-Name-First: Yan
Author-X-Name-Last: Wang
Author-Name: Jiantao Zhang
Author-X-Name-First: Jiantao
Author-X-Name-Last: Zhang
Author-Name: Baojian Xie
Author-X-Name-First: Baojian
Author-X-Name-Last: Xie
Author-Name: Jibiao Liao
Author-X-Name-First: Jibiao
Author-X-Name-Last: Liao
Author-Name: Wenhui Liao
Author-X-Name-First: Wenhui
Author-X-Name-Last: Liao
Title: Outlier detection and robust variable selection via the penalized weighted LAD-LASSO method
Abstract: 
 This paper studies the outlier detection and robust variable selection problem in the linear regression model. The penalized weighted least absolute deviation (PWLAD) regression estimation method and the adaptive least absolute shrinkage and selection operator (LASSO) are combined to simultaneously achieve outlier detection, and robust variable selection. An iterative algorithm is proposed to solve the proposed optimization problem. Monte Carlo studies are evaluated the finite-sample performance of the proposed methods. The results indicate that the finite sample performance of the proposed methods performs better than that of the existing methods when there are leverage points or outliers in the response variable or explanatory variables. Finally, we apply the proposed methodology to analyze two real datasets.
Journal: Journal of Applied Statistics
Pages: 234-246
Issue: 2
Volume: 48
Year: 2021
Month: 1
X-DOI: 10.1080/02664763.2020.1722079
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1722079
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:2:p:234-246




Template-Type: ReDIF-Article 1.0
Author-Name: Sajid Ali
Author-X-Name-First: Sajid
Author-X-Name-Last: Ali
Title: Monitoring time and magnitude based on the renewal reward process with a random failure threshold
Abstract: 
 Control charts are effective tools to distinguish between special and natural variations and have applications in medical and business industries besides manufacturing industry. Due to the advancement of modern technology, we often deal with the high-quality products, where the traditional process monitoring techniques have certain drawbacks. This article presents a control chart for jointly monitoring time and magnitude based on the renewal reward process. In particular, the focus of this study is to model magnitudes by threshold exceedance. More specifically, assuming a random failure threshold, two cases for magnitude are considered: (i) magnitude is cumulative over time and, (ii) magnitude is non-cumulative or independent over time. A comparative study to show the effectiveness of the proposal is also a part of this study.
Journal: Journal of Applied Statistics
Pages: 247-284
Issue: 2
Volume: 48
Year: 2021
Month: 1
X-DOI: 10.1080/02664763.2020.1723502
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1723502
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:2:p:247-284




Template-Type: ReDIF-Article 1.0
Author-Name: Fayyaz Bahari
Author-X-Name-First: Fayyaz
Author-X-Name-Last: Bahari
Author-Name: Safar Parsi
Author-X-Name-First: Safar
Author-X-Name-Last: Parsi
Author-Name: Mojtaba Ganjali
Author-X-Name-First: Mojtaba
Author-X-Name-Last: Ganjali
Title: Reliability of a soccer player based on the bivariate Rayleigh distribution with right censored and ignorable missing data
Abstract: 
 In this paper, we study the performance of a soccer player based on analysing an incomplete data set. To achieve this aim, we fit the bivariate Rayleigh distribution to the soccer dataset by the maximum likelihood method. In this way, the missing data and right censoring problems, that usually happen in such studies, are considered. Our aim is to inference about the performance of a soccer player by considering the stress and strength components. The first goal of the player of interest in a match is assumed as the stress component and the second goal of the match is assumed as the strength component. We propose some methods to overcome incomplete data problem and we use these methods to inference about the performance of a soccer player.
Journal: Journal of Applied Statistics
Pages: 285-300
Issue: 2
Volume: 48
Year: 2021
Month: 1
X-DOI: 10.1080/02664763.2020.1723504
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1723504
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:2:p:285-300




Template-Type: ReDIF-Article 1.0
Author-Name: Andrea Gabrio
Author-X-Name-First: Andrea
Author-X-Name-Last: Gabrio
Title: Bayesian hierarchical models for the prediction of volleyball results
Abstract: 
 Statistical modelling of sports data has become more and more popular in the recent years and different types of models have been proposed to achieve a variety of objectives: from identifying the key characteristics which lead a team to win or lose to predicting the outcome of a game or the team rankings in national leagues. Although not as popular as football or basketball, volleyball is a team sport with both national and international level competitions in almost every country. However, there is almost no study investigating the prediction of volleyball game outcomes and team rankings in national leagues. We propose a Bayesian hierarchical model for the prediction of the rankings of volleyball national teams, which also allows to estimate the results of each match in the league. We consider two alternative model specifications of different complexity which are validated using data from the women's volleyball Italian Serie A1 2017–2018 season.
Journal: Journal of Applied Statistics
Pages: 301-321
Issue: 2
Volume: 48
Year: 2021
Month: 1
X-DOI: 10.1080/02664763.2020.1723506
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1723506
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:2:p:301-321




Template-Type: ReDIF-Article 1.0
Author-Name: Riccardo D'Alberto
Author-X-Name-First: Riccardo
Author-X-Name-Last: D'Alberto
Author-Name: Meri Raggi
Author-X-Name-First: Meri
Author-X-Name-Last: Raggi
Title: How much reliable are the integrated ‘live’ data? A validation strategy proposal for the non-parametric micro statistical matching
Abstract: 
 The integration of different data sources is a widely discussed topic among both the researchers and the Official Statistics. Integrating data helps to contain costs and time required by new data collections. The non-parametric micro Statistical Matching (SM) allows to integrate ‘live’ data resorting only to the observed information, potentially avoiding the misspecification bias and speeding the computational effort. Despite these pros, the assessment of the integration goodness when we use this method is not robust. Moreover, several applications comply with some commonly accepted practices which recommend e.g. to use the biggest data set as donor. We propose a validation strategy to assess the integration goodness. We apply it to investigate these practices and to explore how different combinations of the SM techniques and distance functions perform in terms of the reliability of the synthetic (complete) data set generated. The validation strategy takes advantage of the relation existing among the variables pre-and-post the integration. The results show that ‘the biggest, the best’ rule must not be considered mandatory anymore. Indeed, the integration goodness increases in relation to the variability of the matching variables rather than with respect to the dimensionality ratio between the recipient and the donor data set.
Journal: Journal of Applied Statistics
Pages: 322-348
Issue: 2
Volume: 48
Year: 2021
Month: 1
X-DOI: 10.1080/02664763.2020.1724272
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1724272
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:2:p:322-348




Template-Type: ReDIF-Article 1.0
Author-Name: Julio Cezar Souza Vasconcelos
Author-X-Name-First: Julio Cezar Souza
Author-X-Name-Last: Vasconcelos
Author-Name: Gauss Moutinho Cordeiro
Author-X-Name-First: Gauss Moutinho
Author-X-Name-Last: Cordeiro
Author-Name: Edwin Moises Marcos Ortega
Author-X-Name-First: Edwin Moises Marcos
Author-X-Name-Last: Ortega
Author-Name: Édila Maria de Rezende
Author-X-Name-First: Édila Maria de
Author-X-Name-Last: Rezende
Title: A new regression model for bimodal data and applications in agriculture
Abstract: 
 We define the odd log-logistic exponential Gaussian regression with two systematic components, which extends the heteroscedastic Gaussian regression and it is suitable for bimodal data quite common in the agriculture area. We estimate the parameters by the method of maximum likelihood. Some simulations indicate that the maximum-likelihood estimators are accurate. The model assumptions are checked through case deletion and quantile residuals. The usefulness of the new regression model is illustrated by means of three real data sets in different areas of agriculture, where the data present bimodality.
Journal: Journal of Applied Statistics
Pages: 349-372
Issue: 2
Volume: 48
Year: 2021
Month: 1
X-DOI: 10.1080/02664763.2020.1723503
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1723503
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:2:p:349-372




Template-Type: ReDIF-Article 1.0
Author-Name: Rui Martins
Author-X-Name-First: Rui
Author-X-Name-Last: Martins
Author-Name: Jorge Caldeira
Author-X-Name-First: Jorge
Author-X-Name-Last: Caldeira
Author-Name: Inês Lopes
Author-X-Name-First: Inês
Author-X-Name-Last: Lopes
Author-Name: José João Mendes
Author-X-Name-First: José
Author-X-Name-Last: João Mendes
Title: Improving teeth aesthetics using a spatially shared-parameters model for independent regular lattices
Abstract: 
 An important feature in dentistry is teeth gloss. During an intervention, the doctor applies a resin and a polishing to achieve the lowest roughness and the highest gloss possible. This work aims to evaluate the effect of four polishing protocols in teeth surface roughness and gloss when combined with two different resins and eventually indicate the best combination (treatment). An atomic force microscope is used for measuring the in vitro roughness of a dental surface surrogate. We consider a shared parameters approach for linking the information carried by those two correlated variables. The model fitted to the gloss considers some features of the roughness, namely the information conveyed by a set of spatial structured random effects, specific to each treatment, and the within treatment variance, which allows interpreting how the heterogeneity and the variability of the surface roughness impacts a tooth gloss. The statistical model here developed is an alternative to the “traditional” two-way ANOVA used in dentistry journals. The results, using the recent R-NIMBLE package in R, show that variability characteristics of the surface's roughness are central for explaining differences among the gloss achieved after each treatment and not just the mean roughness of that surface.
Journal: Journal of Applied Statistics
Pages: 373-392
Issue: 2
Volume: 48
Year: 2021
Month: 1
X-DOI: 10.1080/02664763.2020.1724273
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1724273
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:2:p:373-392




Template-Type: ReDIF-Article 1.0
Author-Name: Ji Hwan Cha
Author-X-Name-First: Ji Hwan
Author-X-Name-Last: Cha
Author-Name: F. G. Badía
Author-X-Name-First: F. G.
Author-X-Name-Last: Badía
Title: Variables acceptance reliability sampling plan for items subject to inverse Gaussian degradation process
Abstract: 
 Until now, in the literature, a variety of acceptance reliability sampling plans have been developed based on different life test plans. In most of the reliability sampling plans, the decision procedures to accept or reject the corresponding lot are developed based on the lifetimes of the items observed on tests, or the number of failures observed during a pre-specified testing time. However, frequently, the items are subject to degradation phenomena and, in these cases, the observed degradation level of the item can be used as a decision statistic. In this paper, we develop a variables acceptance sampling plan based on the information on the degradation process of the items, assuming that the degradation process follows the inverse Gaussian process. It is shown that the developed sampling plan improves the reliability performance of the items conditional on the acceptance in the test and that the lifetimes of items after the reliability sampling test are stochastically larger than those before the test. A study comparing the proposed degradation-based sampling plan with the conventional sampling plan which is based on a life test is also performed.
Journal: Journal of Applied Statistics
Pages: 393-409
Issue: 3
Volume: 48
Year: 2021
Month: 2
X-DOI: 10.1080/02664763.2020.1723505
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1723505
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:3:p:393-409




Template-Type: ReDIF-Article 1.0
Author-Name: Xia Wang
Author-X-Name-First: Xia
Author-X-Name-Last: Wang
Author-Name: Joseph Pancras
Author-X-Name-First: Joseph
Author-X-Name-Last: Pancras
Author-Name: Dipak K. Dey
Author-X-Name-First: Dipak K.
Author-X-Name-Last: Dey
Title: Investigating emergent nested geographic structure in consumer purchases: a Bayesian dynamic multi-scale spatiotemporal modeling approach
Abstract: 
 Spatial modeling of consumer response data has gained increased interest recently in the marketing literature. In this paper, we extend the (spatial) multi-scale model by incorporating both spatial and temporal dimensions in the dynamic multi-scale spatiotemporal modeling approach. Our empirical application with a US company’s catalog purchase data for the period 1997–2001 reveals a nested geographic market structure that spans geopolitical boundaries such as state borders. This structure identifies spatial clusters of consumers who exhibit similar spatiotemporal behavior, thus pointing to the importance of emergent geographic structure, emergent nested structure and dynamic patterns in multi-resolution methods. The multi-scale model also has better performance in estimation and prediction compared with several spatial and spatiotemporal models and uses a scalable and computationally efficient Markov chain Monte Carlo method that makes it suitable for analyzing large spatiotemporal consumer purchase datasets.
Journal: Journal of Applied Statistics
Pages: 410-433
Issue: 3
Volume: 48
Year: 2021
Month: 2
X-DOI: 10.1080/02664763.2020.1725810
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1725810
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:3:p:410-433




Template-Type: ReDIF-Article 1.0
Author-Name: Shu Wu
Author-X-Name-First: Shu
Author-X-Name-Last: Wu
Author-Name: Philippe Castagliola
Author-X-Name-First: Philippe
Author-X-Name-Last: Castagliola
Author-Name: Giovanni Celano
Author-X-Name-First: Giovanni
Author-X-Name-Last: Celano
Title: A distribution-free EWMA control chart for monitoring time-between-events-and-amplitude data
Abstract: 
 Many control charts have been developed for the simultaneous monitoring of the time interval T between successive occurrences of an event E and its magnitude X. All these TBEA (Time Between Events and Amplitude) control charts assume a known distribution for the random variables T and X. But, in practice, as it is rather difficult to know their actual distributions, proposing a distribution free approach could be a way to overcome this ‘distribution choice’ dilemma. For this reason, we propose in this paper a distribution free upper-sided EWMA (Exponentially Weighted Moving Average) type control chart, for simultaneously monitoring the time interval T and the magnitude X of an event. In order to investigate the performance of this control chart and obtain its run length properties, we also develop a specific method called ‘continuousify’ which, coupled with a classical Markov chain technique, allows to obtain reliable and replicable results. A numerical comparison shows that our distribution-free EWMA TBEA chart performs as the parametric Shewhart TBEA chart, but without the need to pre-specify any distribution. An illustrative example obtained from a French forest fire database is also provided to show the implementation of the proposed EWMA TBEA control chart.
Journal: Journal of Applied Statistics
Pages: 434-454
Issue: 3
Volume: 48
Year: 2021
Month: 2
X-DOI: 10.1080/02664763.2020.1729347
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1729347
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:3:p:434-454




Template-Type: ReDIF-Article 1.0
Author-Name: Cheng Zhang
Author-X-Name-First: Cheng
Author-X-Name-Last: Zhang
Author-Name: Tapan K. Nayak
Author-X-Name-First: Tapan K.
Author-X-Name-Last: Nayak
Title: Post-randomization for controlling identification risk in releasing microdata from general surveys
Abstract: 
 Before releasing survey data, statistical agencies usually perturb the original data to keep each survey unit's information confidential. One significant concern in releasing survey microdata is identity disclosure, which occurs when an intruder correctly identifies the records of a survey unit by matching the values of some key (or pseudo-identifying) variables. We examine a recently developed post-randomization method for a strict control of identification risks in releasing survey microdata. While that procedure well preserves the observed frequencies and hence statistical estimates in case of simple random sampling, we show that in general surveys, it may induce considerable bias in commonly used survey-weighted estimators. We propose a modified procedure that better preserves weighted estimates. The procedure is illustrated and empirically assessed with an application to a publicly available US Census Bureau data set.
Journal: Journal of Applied Statistics
Pages: 455-470
Issue: 3
Volume: 48
Year: 2021
Month: 2
X-DOI: 10.1080/02664763.2020.1732310
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1732310
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:3:p:455-470




Template-Type: ReDIF-Article 1.0
Author-Name: Alejandro Rodriguez
Author-X-Name-First: Alejandro
Author-X-Name-Last: Rodriguez
Author-Name: Gabriel Pino
Author-X-Name-First: Gabriel
Author-X-Name-Last: Pino
Author-Name: Rodrigo Herrera
Author-X-Name-First: Rodrigo
Author-X-Name-Last: Herrera
Title: A non-parametric statistic for testing conditional heteroscedasticity for unobserved component models
Abstract: 
 When prediction intervals are constructed using unobserved component models (UCM), problems can arise due to the possible existence of components that may or may not be conditionally heteroscedastic. Accurate coverage depends on correctly identifying the source of the heteroscedasticity. Different proposals for testing heteroscedasticity have been applied to UCM; however, in most cases, these procedures are unable to identify the heteroscedastic component correctly. The main issue is that test statistics are affected by the presence of serial correlation, causing the distribution of the statistic under conditional homoscedasticity to remain unknown. We propose a nonparametric statistic for testing heteroscedasticity based on the well-known Wilcoxon's rank statistic. We study the asymptotic validation of the statistic and examine bootstrap procedures for approximating its finite sample distribution. Simulation results show an improvement in the size of the homoscedasticity tests and a power that is clearly comparable with the best alternative in the literature. We also apply the test on real inflation data. Looking for the presence of a conditionally heteroscedastic effect on the error terms, we arrive at conclusions that almost all cases are different than those given by the alternative test statistics presented in the literature.
Journal: Journal of Applied Statistics
Pages: 471-497
Issue: 3
Volume: 48
Year: 2021
Month: 2
X-DOI: 10.1080/02664763.2020.1732885
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1732885
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:3:p:471-497




Template-Type: ReDIF-Article 1.0
Author-Name: Nedka Dechkova Nikiforova
Author-X-Name-First: Nedka Dechkova
Author-X-Name-Last: Nikiforova
Author-Name: Rossella Berni
Author-X-Name-First: Rossella
Author-X-Name-Last: Berni
Author-Name: Gabriele Arcidiacono
Author-X-Name-First: Gabriele
Author-X-Name-Last: Arcidiacono
Author-Name: Luciano Cantone
Author-X-Name-First: Luciano
Author-X-Name-Last: Cantone
Author-Name: Pierpaolo Placidoli
Author-X-Name-First: Pierpaolo
Author-X-Name-Last: Placidoli
Title: Latin hypercube designs based on strong orthogonal arrays and Kriging modelling to improve the payload distribution of trains
Abstract: 
 Nowadays, computer experiments are used increasingly more to solve complex engineering and technological issues. Computer experiments are analysed through suitable metamodels acting as statistical interpolators of the simulated input-output data: Kriging is the most appropriate and widely used one. We optimise the braking performance of freight trains through computer experiments and Kriging modelling by focussing on the payload distribution along the train, so as to reduce the effects of in-train forces among wagons during a train emergency braking. One contribution of this manuscript is that to improve the freight train efficiency in terms of braking performance, we consider that the train is composed of several train sections with each one characterised by its own overall payload. A suitable Latin hypercube design is planned for the computer experiment that achieves excellent space-filling properties with a relatively low number of experimental runs. Kriging models with anisotropic covariance function are subsequently applied to assess which is the best payload distribution capable of reducting the in-train forces according to the specific train-set arrangement considered. The results are very satisfactory and confirm that our approach represents a valid method to be successfully applied by interested Railway Undertakings.
Journal: Journal of Applied Statistics
Pages: 498-516
Issue: 3
Volume: 48
Year: 2021
Month: 2
X-DOI: 10.1080/02664763.2020.1733943
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1733943
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:3:p:498-516




Template-Type: ReDIF-Article 1.0
Author-Name: Farrukh Javed
Author-X-Name-First: Farrukh
Author-X-Name-Last: Javed
Author-Name: Stepan Mazur
Author-X-Name-First: Stepan
Author-X-Name-Last: Mazur
Author-Name: Edward Ngailo
Author-X-Name-First: Edward
Author-X-Name-Last: Ngailo
Title: Higher order moments of the estimated tangency portfolio weights
Abstract: 
 In this paper, we consider the estimated weights of the tangency portfolio. We derive analytical expressions for the higher order non-central and central moments of these weights when the returns are assumed to be independently and multivariate normally distributed. Moreover, the expressions for mean, variance, skewness and kurtosis of the estimated weights are obtained in closed forms. Later, we complement our results with a simulation study where data from the multivariate normal and t-distributions are simulated, and the first four moments of estimated weights are computed by using the Monte Carlo experiment. It is noteworthy to mention that the distributional assumption of returns is found to be important, especially for the first two moments. Finally, through an empirical illustration utilizing returns of four financial indices listed in NASDAQ stock exchange, we observe the presence of time dynamics in higher moments.
Journal: Journal of Applied Statistics
Pages: 517-535
Issue: 3
Volume: 48
Year: 2021
Month: 2
X-DOI: 10.1080/02664763.2020.1736523
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1736523
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:3:p:517-535




Template-Type: ReDIF-Article 1.0
Author-Name: Sancharee Basak
Author-X-Name-First: Sancharee
Author-X-Name-Last: Basak
Author-Name: Ayanendranath Basu
Author-X-Name-First: Ayanendranath
Author-X-Name-Last: Basu
Author-Name: M. C. Jones
Author-X-Name-First: M. C.
Author-X-Name-Last: Jones
Title: On the ‘optimal’ density power divergence tuning parameter
Abstract: 
 The density power divergence, indexed by a single tuning parameter α, has proved to be a very useful tool in minimum distance inference. The family of density power divergences provides a generalized estimation scheme which includes likelihood-based procedures (represented by choice  $\alpha = 0 $α=0 for the tuning parameter) as a special case. However, under data contamination, this scheme provides several more stable choices for model fitting and analysis (provided by positive values for the tuning parameter α). As larger values of α necessarily lead to a drop in model efficiency, determining the optimal value of α to provide the best compromise between model-efficiency and stability against data contamination in any real situation is a major challenge. In this paper, we provide a refinement of an existing technique with the aim of eliminating the dependence of the procedure on an initial pilot estimator. Numerical evidence is provided to demonstrate the very good performance of the method. Our technique has a general flavour, and we expect that similar tuning parameter selection algorithms will work well for other M-estimators, or any robust procedure that depends on the choice of a tuning parameter.
Journal: Journal of Applied Statistics
Pages: 536-556
Issue: 3
Volume: 48
Year: 2021
Month: 2
X-DOI: 10.1080/02664763.2020.1736524
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1736524
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:3:p:536-556




Template-Type: ReDIF-Article 1.0
Author-Name: Julio Cezar Souza Vasconcelos
Author-X-Name-First: Julio Cezar
Author-X-Name-Last: Souza Vasconcelos
Author-Name: Cristian Villegas
Author-X-Name-First: Cristian
Author-X-Name-Last: Villegas
Title: Generalized symmetrical partial linear model
Abstract: 
 In this work, we propose a new model called generalized symmetrical partial linear model, based on the theory of generalized linear models and symmetrical distributions. In our model the response variable follows a symmetrical distribution such a normal, Student-t, power exponential, among others. Following the context of generalized linear models we consider replacing the traditional linear predictors by the more general predictors in whose case one covariate is related with the response variable in a non-parametric fashion, that we do not specified the parametric function. As an example, we could imagine a regression model in which the intercept term is believed to vary in time or geographical location. The backfitting algorithm is used for estimating the parameters of the proposed model. We perform a simulation study for assessing the behavior of the penalized maximum likelihood estimators. We use the quantile residuals for checking the assumption of the model. Finally, we analyzed real data set related with pH rivers in Ireland.
Journal: Journal of Applied Statistics
Pages: 557-572
Issue: 3
Volume: 48
Year: 2021
Month: 2
X-DOI: 10.1080/02664763.2020.1726301
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1726301
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:3:p:557-572




Template-Type: ReDIF-Article 1.0
Author-Name: Tomson Ogwang
Author-X-Name-First: Tomson
Author-X-Name-Last: Ogwang
Author-Name: Danny I. Cho
Author-X-Name-First: Danny I.
Author-X-Name-Last: Cho
Title: Olympic rankings based on objective weighting schemes
Abstract: 
 In this paper, we propose an objective principal components weighting scheme for all-time Winter Olympic gold, silver and bronze medals based solely on the number of medals won. Our results suggest that the approximately equal weights be assigned (or the total medal counts be used regardless of color) if all of the three medal types are retained for ranking purposes. When the proposed methodology is tested against five alternative weighting schemes that have been suggested in the literature using the results for the 2010 Vancouver Winter Olympics, we find a significant agreement in the country rankings. Furthermore, our implementation of principal components variable reduction strategy results in the identification of silver as the best representative medal count for parsimonious Winter Olympics rankings.
Journal: Journal of Applied Statistics
Pages: 573-582
Issue: 3
Volume: 48
Year: 2021
Month: 2
X-DOI: 10.1080/02664763.2020.1736525
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1736525
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:3:p:573-582




Template-Type: ReDIF-Article 1.0
Author-Name: Han Lin Shang
Author-X-Name-First: Han Lin
Author-X-Name-Last: Shang
Title: Bayesian bandwidth estimation and semi-metric selection for a functional partial linear model with unknown error density
Abstract: 
 This study examines the optimal selections of bandwidth and semi-metric for a functional partial linear model. Our proposed method begins by estimating the unknown error density using a kernel density estimator of residuals, where the regression function, consisting of parametric and nonparametric components, can be estimated by functional principal component and functional Nadayara-Watson estimators. The estimation accuracy of the regression function and error density crucially depends on the optimal estimations of bandwidth and semi-metric. A Bayesian method is utilized to simultaneously estimate the bandwidths in the regression function and kernel error density by minimizing the Kullback-Leibler divergence. For estimating the regression function and error density, a series of simulation studies demonstrate that the functional partial linear model gives improved estimation and forecast accuracies compared with the functional principal component regression and functional nonparametric regression. Using a spectroscopy dataset, the functional partial linear model yields better forecast accuracy than some commonly used functional regression models. As a by-product of the Bayesian method, a pointwise prediction interval can be obtained, and marginal likelihood can be used to select the optimal semi-metric.
Journal: Journal of Applied Statistics
Pages: 583-604
Issue: 4
Volume: 48
Year: 2021
Month: 3
X-DOI: 10.1080/02664763.2020.1736527
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1736527
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:4:p:583-604




Template-Type: ReDIF-Article 1.0
Author-Name: Özlem Kaymaz
Author-X-Name-First: Özlem
Author-X-Name-Last: Kaymaz
Author-Name: Khaled Alqahtani
Author-X-Name-First: Khaled
Author-X-Name-Last: Alqahtani
Author-Name: Henry M. Wood
Author-X-Name-First: Henry M.
Author-X-Name-Last: Wood
Author-Name: Arief Gusnanto
Author-X-Name-First: Arief
Author-X-Name-Last: Gusnanto
Title: Prediction of tumour pathological subtype from genomic profile using sparse logistic regression with random effects
Abstract: 
 The purpose of this study is to highlight the application of sparse logistic regression models in dealing with prediction of tumour pathological subtypes based on lung cancer patients' genomic information. We consider sparse logistic regression models to deal with the high dimensionality and correlation between genomic regions. In a hierarchical likelihood (HL) method, it is assumed that the random effects follow a normal distribution and its variance is assumed to follow a gamma distribution. This formulation considers ridge and lasso penalties as special cases. We extend the HL penalty to include a ridge penalty (called ‘HLnet’) in a similar principle of the elastic net penalty, which is constructed from lasso penalty. The results indicate that the HL penalty creates more sparse estimates than lasso penalty with comparable prediction performance, while HLnet and elastic net penalties have the best prediction performance in real data. We illustrate the methods in a lung cancer study.
Journal: Journal of Applied Statistics
Pages: 605-622
Issue: 4
Volume: 48
Year: 2021
Month: 3
X-DOI: 10.1080/02664763.2020.1738358
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1738358
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:4:p:605-622




Template-Type: ReDIF-Article 1.0
Author-Name: Ayan Pal
Author-X-Name-First: Ayan
Author-X-Name-Last: Pal
Author-Name: Sharmishtha Mitra
Author-X-Name-First: Sharmishtha
Author-X-Name-Last: Mitra
Author-Name: Debasis Kundu
Author-X-Name-First: Debasis
Author-X-Name-Last: Kundu
Title: Order restricted classical inference of a Weibull multiple step-stress model
Abstract: 
 In this paper, a multiple step-stress model is designed and analyzed when the data are Type-I censored. Lifetime distributions of the experimental units at each stress level are assumed to follow a two-parameter Weibull distribution. Further, distributions under each of the stress levels are connected through a tampered failure-rate based model. In a step-stress experiment, as the stress level increases, the load on the experimental units increases and hence the mean lifetime is expected to be shortened. Taking this into account, the aim of this paper is to develop the order restricted inference of the model parameters of a multiple step-stress model based on the frequentist approach. An extensive simulation study has been carried out and two real data sets have been analyzed for illustrative purposes.
Journal: Journal of Applied Statistics
Pages: 623-645
Issue: 4
Volume: 48
Year: 2021
Month: 3
X-DOI: 10.1080/02664763.2020.1736526
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1736526
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:4:p:623-645




Template-Type: ReDIF-Article 1.0
Author-Name: Jamil Ownuk
Author-X-Name-First: Jamil
Author-X-Name-Last: Ownuk
Author-Name: Hossein Baghishani
Author-X-Name-First: Hossein
Author-X-Name-Last: Baghishani
Author-Name: Ahmad Nezakati
Author-X-Name-First: Ahmad
Author-X-Name-Last: Nezakati
Title: Heavy or semi-heavy tail, that is the question
Abstract: 
 While there has been considerable research on the analysis of extreme values and outliers by using heavy-tailed distributions, little is known about the semi-heavy-tailed behaviors of data when there are a few suspicious outliers. To address the situation where data are skewed possessing semi-heavy tails, we introduce two new skewed distribution families of the hyperbolic secant with exciting properties. We extend the semi-heavy-tailedness property of data to a linear regression model. In particular, we investigate the asymptotic properties of the ML estimators of the regression parameters when the error term has a semi-heavy-tailed distribution. We conduct simulation studies comparing the ML estimators of the regression parameters under various assumptions for the distribution of the error term. We also provide three real examples to show the priority of the semi-heavy-tailedness of the error term comparing to heavy-tailedness. Online supplementary materials for this article are available. All the new proposed models in this work are implemented by the shs R package, which can be found on the GitHub webpage.
Journal: Journal of Applied Statistics
Pages: 646-668
Issue: 4
Volume: 48
Year: 2021
Month: 3
X-DOI: 10.1080/02664763.2020.1738360
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1738360
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:4:p:646-668




Template-Type: ReDIF-Article 1.0
Author-Name: Guangbao Guo
Author-X-Name-First: Guangbao
Author-X-Name-Last: Guo
Title: Taylor quasi-likelihood for limited generalized linear models
Abstract: 
 It is a major research topic of limited generalized linear models, namely, generalized linear models with limited dependent variables. The models are developed in many research fields. However, quasi-likelihood estimation of the models is an unresolved issue, due to including limited dependent variables. We propose a novel quasi-likelihood, called Taylor quasi-likelihood, to handle with the unified estimation problem of the limited models. It is based on Taylor expansion of distribution function or likelihood function. We also extend the likelihood to a generalized version and an adaptive version and propose a distributed procedure to obtain the likelihood estimator. In low-dimensional setting, we give selection criteria for the proposed method and make arguments for the consistency and asymptotic normality of the estimator. In high-dimensional setting, we discuss feature selection and oracle properties of the proposed method. Simulation results confirm the advantages of the proposed method.
Journal: Journal of Applied Statistics
Pages: 669-692
Issue: 4
Volume: 48
Year: 2021
Month: 3
X-DOI: 10.1080/02664763.2020.1743650
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1743650
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:4:p:669-692




Template-Type: ReDIF-Article 1.0
Author-Name: Uchenna C. Nduka
Author-X-Name-First: Uchenna C.
Author-X-Name-Last: Nduka
Author-Name: Tobias E. Ugah
Author-X-Name-First: Tobias E.
Author-X-Name-Last: Ugah
Author-Name: Chinyeaka H. Izunobi
Author-X-Name-First: Chinyeaka H.
Author-X-Name-Last: Izunobi
Title: Robust estimation using multivariate t innovations for vector autoregressive models via ECM algorithm
Abstract: 
 This paper considers the vector autoregressive model of order p, VAR(p), with multivariate t error distributions, the latter being more prevalent in real life than the usual multivariate normal distribution. It is believed that the maximum-likelihood equations for the multivariate t distribution have convergence problem, hence we develop estimation procedures for VAR(p) model using the normal mean–variance mixture representation of multivariate t distribution. The procedure relies on the computational ease available in Expectation Maximization-based algorithms. The estimators obtained are explicit functions of sample observations and therefore are easy to compute. Extensive simulation experiments show that the estimators have negligible bias and are considerably more efficient than an existing method that uses the least-squares error approach. It is shown that the proposed estimators are robust to plausible deviations from an assumed distribution and hence are more advantageous when compared with the other estimator. One real-life example is given for illustration purposes.
Journal: Journal of Applied Statistics
Pages: 693-711
Issue: 4
Volume: 48
Year: 2021
Month: 3
X-DOI: 10.1080/02664763.2020.1742297
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1742297
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:4:p:693-711




Template-Type: ReDIF-Article 1.0
Author-Name: Subrata Chakraborty
Author-X-Name-First: Subrata
Author-X-Name-Last: Chakraborty
Author-Name: Dhrubajyoti Chakravarty
Author-X-Name-First: Dhrubajyoti
Author-X-Name-Last: Chakravarty
Author-Name: Josmar Mazucheli
Author-X-Name-First: Josmar
Author-X-Name-Last: Mazucheli
Author-Name: Wesley Bertoli
Author-X-Name-First: Wesley
Author-X-Name-Last: Bertoli
Title: A discrete analog of Gumbel distribution: properties, parameter estimation and applications
Abstract: 
 A discrete version of the Gumbel distribution (Type-I Extreme Value distribution) has been derived by using the general approach of discretization of a continuous distribution. Important distributional and reliability properties have been explored. It has been shown that depending on the choice of parameters the proposed distribution can be positively or negatively skewed; possess long-tail(s). Log-concavity of the distribution and consequent results have been established. Estimation of parameters by method of maximum likelihood, method of moments, and method of proportions has been discussed. A method of checking model adequacy and regression type estimation based on empirical survival function has also been examined. A simulation study has been carried out to compare and check the efficacy of the three methods of estimations. The distribution has been applied to model three real count data sets from diverse application area namely, survival times in number of days, maximum annual floods data from Brazil and goal differences in English premier league, and the results show the relevance of the proposed distribution.
Journal: Journal of Applied Statistics
Pages: 712-737
Issue: 4
Volume: 48
Year: 2021
Month: 3
X-DOI: 10.1080/02664763.2020.1744538
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1744538
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:4:p:712-737




Template-Type: ReDIF-Article 1.0
Author-Name: Yang-Jin Kim
Author-X-Name-First: Yang-Jin
Author-X-Name-Last: Kim
Title: Joint model for bivariate zero-inflated recurrent event data with terminal events
Abstract: 
 Bivariate recurrent event data are observed when subjects are at risk of experiencing two different type of recurrent events. In this paper, our interest is to suggest statistical model when there is a substantial portion of subjects not experiencing recurrent events but having a terminal event. In a context of recurrent event data, zero events can be related with either the risk free group or a terminal event. For simultaneously reflecting both a zero inflation and a terminal event in a context of bivariate recurrent event data, a joint model is implemented with bivariate frailty effects. Simulation studies are performed to evaluate the suggested models. Infection data from AML (acute myeloid leukemia) patients are analyzed as an application.
Journal: Journal of Applied Statistics
Pages: 738-749
Issue: 4
Volume: 48
Year: 2021
Month: 3
X-DOI: 10.1080/02664763.2020.1744539
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1744539
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:4:p:738-749




Template-Type: ReDIF-Article 1.0
Author-Name: Qingzhao Yu
Author-X-Name-First: Qingzhao
Author-X-Name-Last: Yu
Author-Name: Bin Li
Author-X-Name-First: Bin
Author-X-Name-Last: Li
Title: A multivariate multiple third-variable effect analysis with an application to explore racial and ethnic disparities in obesity
Abstract: 
 Third-Variable effect refers to the intervening effect from a third variable (called mediators or confounders) to the observed relationship between an exposure and an outcome. The general multiple third-variable effect analysis method (TVEA) allows consideration of multiple mediators/confounders (MC) simultaneously and the use of linear and nonlinear predictive models for estimating MC effects. Previous studies have found that compared with non-Hispanic White population, Blacks and Hispanic Whites suffered disproportionally more with obesity and related chronic diseases. In this paper, we extend the general TVEA to deal with multivariate/multi-categorical predictors and multivariate response variables. We designed algorithms and an R package for this extension and applied MMA on the NHANES data to identify MCs and quantify the indirect effect of each MC in explaining both racial and ethnic disparities in obesity and the body mass index (BMI) simultaneously. We considered a number of socio-demographic variables, individual factors, and environmental variables as potential MCs and found that some of the ethnic/racial differences in obesity and BMI were explained by the included variables.
Journal: Journal of Applied Statistics
Pages: 750-764
Issue: 4
Volume: 48
Year: 2021
Month: 3
X-DOI: 10.1080/02664763.2020.1738359
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1738359
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:4:p:750-764




Template-Type: ReDIF-Article 1.0
Author-Name: Elham Tabrizi
Author-X-Name-First: Elham
Author-X-Name-Last: Tabrizi
Author-Name: Ehsan Bahrami Samani
Author-X-Name-First: Ehsan
Author-X-Name-Last: Bahrami Samani
Author-Name: Mojtaba Ganjali
Author-X-Name-First: Mojtaba
Author-X-Name-Last: Ganjali
Title: General location multivariate latent variable models for mixed correlated bounded continuous, ordinal, and nominal responses with non-ignorable missing data
Abstract: 
 Using a multivariate latent variable approach, this article proposes some new general models to analyze the correlated bounded continuous and categorical (nominal or/and ordinal) responses with and without non-ignorable missing values. First, we discuss regression methods for jointly analyzing continuous, nominal, and ordinal responses that we motivated by analyzing data from studies of toxicity development. Second, using the beta and Dirichlet distributions, we extend the models so that some bounded continuous responses are replaced for continuous responses. The joint distribution of the bounded continuous, nominal and ordinal variables is decomposed into a marginal multinomial distribution for the nominal variable and a conditional multivariate joint distribution for the bounded continuous and ordinal variables given the nominal variable. We estimate the regression parameters under the new general location models using the maximum-likelihood method. Sensitivity analysis is also performed to study the influence of small perturbations of the parameters of the missing mechanisms of the model on the maximal normal curvature. The proposed models are applied to two data sets: BMI, Steatosis and Osteoporosis data and Tehran household expenditure budgets.
Journal: Journal of Applied Statistics
Pages: 765-785
Issue: 5
Volume: 48
Year: 2021
Month: 4
X-DOI: 10.1080/02664763.2020.1745765
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1745765
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:5:p:765-785




Template-Type: ReDIF-Article 1.0
Author-Name: Mohammed Alqawba
Author-X-Name-First: Mohammed
Author-X-Name-Last: Alqawba
Author-Name: Norou Diawara
Author-X-Name-First: Norou
Author-X-Name-Last: Diawara
Title: Copula-based Markov zero-inflated count time series models with application
Abstract: 
 Count time series data with excess zeros are observed in several applied disciplines. When these zero-inflated counts are sequentially recorded, they might result in serial dependence. Ignoring the zero-inflation and the serial dependence might produce inaccurate results. In this paper, Markov zero-inflated count time series models based on a joint distribution on consecutive observations are proposed. The joint distribution function of the consecutive observations is constructed through copula functions. First- and second-order Markov chains are considered with the univariate margins of zero-inflated Poisson (ZIP), zero-inflated negative binomial (ZINB), or zero-inflated Conway–Maxwell–Poisson (ZICMP) distributions. Under the Markov models, bivariate copula functions such as the bivariate Gaussian, Frank, and Gumbel are chosen to construct a bivariate distribution of two consecutive observations. Moreover, the trivariate Gaussian and max-infinitely divisible copula functions are considered to build the joint distribution of three consecutive observations. Likelihood-based inference is performed and asymptotic properties are studied. To evaluate the estimation method and the asymptotic results, simulated examples are studied. The proposed class of models are applied to sandstorm counts example. The results suggest that the proposed models have some advantages over some of the models in the literature for modeling zero-inflated count time series data.
Journal: Journal of Applied Statistics
Pages: 786-803
Issue: 5
Volume: 48
Year: 2021
Month: 4
X-DOI: 10.1080/02664763.2020.1748581
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1748581
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:5:p:786-803




Template-Type: ReDIF-Article 1.0
Author-Name: António Casimiro Puindi
Author-X-Name-First: António Casimiro
Author-X-Name-Last: Puindi
Author-Name: Maria Eduarda Silva
Author-X-Name-First: Maria Eduarda
Author-X-Name-Last: Silva
Title: Dynamic structural models with covariates for short-term forecasting of time series with complex seasonal patterns
Abstract: 
 This work presents a framework of dynamic structural models with covariates for short-term forecasting of time series with complex seasonal patterns. The framework is based on the multiple sources of randomness formulation. A noise model is formulated to allow the incorporation of randomness into the seasonal component and to propagate this same randomness in the coefficients of the variant trigonometric terms over time. A unique, recursive and systematic computational procedure based on the maximum likelihood estimation under the hypothesis of Gaussian errors is introduced. The referred procedure combines the Kalman filter with recursive adjustment of the covariance matrices and the selection method of harmonics number in the trigonometric terms. A key feature of this method is that it allows estimating not only the states of the system but also allows obtaining the standard errors of the estimated parameters and the prediction intervals. In addition, this work also presents a non-parametric bootstrap approach to improve the forecasting method based on Kalman filter recursions. The proposed framework is empirically explored with two real time series.
Journal: Journal of Applied Statistics
Pages: 804-826
Issue: 5
Volume: 48
Year: 2021
Month: 4
X-DOI: 10.1080/02664763.2020.1748178
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1748178
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:5:p:804-826




Template-Type: ReDIF-Article 1.0
Author-Name: Paravee Maneejuk
Author-X-Name-First: Paravee
Author-X-Name-Last: Maneejuk
Author-Name: Woraphon Yamaka
Author-X-Name-First: Woraphon
Author-X-Name-Last: Yamaka
Title: Significance test for linear regression: how to test without P-values?
Abstract: 
 The discussion on the use and misuse of p-values in 2016 by the American Statistician Association was a timely assertion that statistical concept should be properly used in science. Some researchers, especially the economists, who adopt significance testing and p-values to report their results, may felt confused by the statement, leading to misinterpretations of the statement. In this study, we aim to re-examine the accuracy of the p-value and introduce an alternative way for testing the hypothesis. We conduct a simulation study to investigate the reliability of the p-value. Apart from investigating the performance of p-value, we also introduce some existing approaches, Minimum Bayes Factors and Belief functions, for replacing p-value. Results from the simulation study confirm unreliable p-value in some cases and that our proposed approaches seem to be useful as the substituted tool in the statistical inference. Moreover, our results show that the plausibility approach is more accurate for making decisions about the null hypothesis than the traditionally used p-values when the null hypothesis is true. However, the MBFs of Edwards et al. [Bayesian statistical inference for psychological research. Psychol. Rev. 70(3) (1963), pp. 193–242]; Vovk [A logic of probability, with application to the foundations of statistics. J. Royal Statistical Soc. Series B (Methodological) 55 (1993), pp. 317–351] and Sellke et al. [Calibration of p values for testing precise null hypotheses. Am. Stat. 55(1) (2001), pp. 62–71] provide more reliable results compared to all other methods when the null hypothesis is false.
Journal: Journal of Applied Statistics
Pages: 827-845
Issue: 5
Volume: 48
Year: 2021
Month: 4
X-DOI: 10.1080/02664763.2020.1748180
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1748180
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:5:p:827-845




Template-Type: ReDIF-Article 1.0
Author-Name: Mingyue Du
Author-X-Name-First: Mingyue
Author-X-Name-Last: Du
Author-Name: Qingning Zhou
Author-X-Name-First: Qingning
Author-X-Name-Last: Zhou
Author-Name: Shishun Zhao
Author-X-Name-First: Shishun
Author-X-Name-Last: Zhao
Author-Name: Jianguo Sun
Author-X-Name-First: Jianguo
Author-X-Name-Last: Sun
Title: Regression analysis of case-cohort studies in the presence of dependent interval censoring
Abstract: 
 The case-cohort design is widely used as a means of reducing the cost in large cohort studies, especially when the disease rate is low and covariate measurements may be expensive, and has been discussed by many authors. In this paper, we discuss regression analysis of case-cohort studies that produce interval-censored failure time with dependent censoring, a situation for which there does not seem to exist an established approach. For inference, a sieve inverse probability weighting estimation procedure is developed with the use of Bernstein polynomials to approximate the unknown baseline cumulative hazard functions. The proposed estimators are shown to be consistent and the asymptotic normality of the resulting regression parameter estimators is established. A simulation study is conducted to assess the finite sample properties of the proposed approach and indicates that it works well in practical situations. The proposed method is applied to an HIV/AIDS case-cohort study that motivated this investigation.
Journal: Journal of Applied Statistics
Pages: 846-865
Issue: 5
Volume: 48
Year: 2021
Month: 4
X-DOI: 10.1080/02664763.2020.1752633
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1752633
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:5:p:846-865




Template-Type: ReDIF-Article 1.0
Author-Name: Yoonsuh Jung
Author-X-Name-First: Yoonsuh
Author-X-Name-Last: Jung
Author-Name: Steven N. MacEachern
Author-X-Name-First: Steven N.
Author-X-Name-Last: MacEachern
Author-Name: Hang Joon Kim
Author-X-Name-First: Hang
Author-X-Name-Last: Joon Kim
Title: Modified check loss for efficient estimation via model selection in quantile regression
Abstract: 
 The check loss function is used to define quantile regression. In cross-validation, it is also employed as a validation function when the true distribution is unknown. However, our empirical study indicates that validation with the check loss often leads to overfitting the data. In this work, we suggest a modified or L2-adjusted check loss which rounds the sharp corner in the middle of check loss. This has the effect of guarding against overfitting to some extent. The adjustment is devised to shrink to zero as sample size grows. Through various simulation settings of linear and nonlinear regressions, the improvement due to modification of the check loss by quadratic adjustment is examined empirically.
Journal: Journal of Applied Statistics
Pages: 866-886
Issue: 5
Volume: 48
Year: 2021
Month: 4
X-DOI: 10.1080/02664763.2020.1753023
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1753023
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:5:p:866-886




Template-Type: ReDIF-Article 1.0
Author-Name: Mingjing Chen
Author-X-Name-First: Mingjing
Author-X-Name-Last: Chen
Author-Name: Xiangyong Tan
Author-X-Name-First: Xiangyong
Author-X-Name-Last: Tan
Author-Name: Jian Wu
Author-X-Name-First: Jian
Author-X-Name-Last: Wu
Title: Time varying factor models with possibly strongly correlated noises
Abstract: 
 In factor models, noises are often assumed to be weakly correlated; otherwise, separation of factors from noises becomes difficult, if not impossible. This paper will address this problem. We utilize an econometric idea, the so called common correlated effects (CCE) to estimate time varying factor models. We first cross sectionally average the covariates and then project the responses to the space spanned by the averaged covariates. By doing so, noises are diminished while factors are distinguished. The advantages of our new estimators are two folds. First, the convergence rates of estimated factors and loadings are independent of cross sectional dimension. Second, our new estimators are robust to the correlation of noises. Hence our new estimators can, on one hand, separate market factors for the stock data set used in this paper even if noises exhibit strong correlations within industries due to industry-specific factors and on the other hand, avoid inappropriately absorbing industry-specific factors into market factors.
Journal: Journal of Applied Statistics
Pages: 887-906
Issue: 5
Volume: 48
Year: 2021
Month: 4
X-DOI: 10.1080/02664763.2020.1753024
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1753024
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:5:p:887-906




Template-Type: ReDIF-Article 1.0
Author-Name: Magda Carvalho Pires
Author-X-Name-First: Magda Carvalho
Author-X-Name-Last: Pires
Author-Name: Enrico Antônio Colosimo
Author-X-Name-First: Enrico Antônio
Author-X-Name-Last: Colosimo
Author-Name: Guilherme Augusto Veloso
Author-X-Name-First: Guilherme Augusto
Author-X-Name-Last: Veloso
Author-Name: Raquel de Souza Borges Ferreira
Author-X-Name-First: Raquel de Souza Borges
Author-X-Name-Last: Ferreira
Title: Interval-censored data with misclassification: a Bayesian approach
Abstract: 
 Survival data involving silent events are often subject to interval censoring (the event is known to occur within a time interval) and classification errors if a test with no perfect sensitivity and specificity is applied. Considering the nature of this data plays an important role in estimating the time distribution until the occurrence of the event. In this context, we incorporate validation subsets into the parametric proportional hazard model, and show that this additional data, combined with Bayesian inference, compensate the lack of knowledge about test sensitivity and specificity improving the parameter estimates. The proposed model is evaluated through simulation studies, and Bayesian analysis is conducted within a Gibbs sampling procedure. The posterior estimates obtained under validation subset models present lower bias and standard deviation compared to the scenario with no validation subset or the model that assumes perfect sensitivity and specificity. Finally, we illustrate the usefulness of the new methodology with an analysis of real data about HIV acquisition in female sex workers that have been discussed in the literature.
Journal: Journal of Applied Statistics
Pages: 907-923
Issue: 5
Volume: 48
Year: 2021
Month: 4
X-DOI: 10.1080/02664763.2020.1753025
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1753025
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:5:p:907-923




Template-Type: ReDIF-Article 1.0
Author-Name: Özge Kuran
Author-X-Name-First: Özge
Author-X-Name-Last: Kuran
Author-Name: M. Revan Özkale
Author-X-Name-First: M. Revan
Author-X-Name-Last: Özkale
Title: Improvement of mixed predictors in linear mixed models
Abstract: 
 In this paper, we introduce stochastic-restricted Liu predictors which will be defined by combining in a special way the two approaches followed in obtaining the mixed predictors and the Liu predictors in the linear mixed models. Superiorities of the linear combination of the new predictor to the Liu and mixed predictors are done in the sense of mean square error matrix criterion. Finally, numerical examples and a simulation study are done to illustrate the findings. In numerical examples, we took some arbitrary observations from the data as the prior information since we did not have historical data or additional information about the data sets. The results show that this case does the new estimator gain efficiency over the constituent estimators and provide accurate estimation and prediction of the data.
Journal: Journal of Applied Statistics
Pages: 924-942
Issue: 5
Volume: 48
Year: 2021
Month: 4
X-DOI: 10.1080/02664763.2020.1833182
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1833182
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:5:p:924-942




Template-Type: ReDIF-Article 1.0
Author-Name: Šárka Večeřová
Author-X-Name-First: Šárka
Author-X-Name-Last: Večeřová
Author-Name: Arnošt Komárek
Author-X-Name-First: Arnošt
Author-X-Name-Last: Komárek
Author-Name: Luk Bruyneel
Author-X-Name-First: Luk
Author-X-Name-Last: Bruyneel
Author-Name: Emmanuel Lesaffre
Author-X-Name-First: Emmanuel
Author-X-Name-Last: Lesaffre
Title: Identifying influential observations in a Bayesian multi-level mediation model
Abstract: 
 Increasingly complex models are being fit to data these days. This is especially the case for Bayesian modelling making use of Markov chain Monte Carlo methods. Tailored model diagnostics are usually lacking behind. This is also the case for Bayesian mediation models. In this paper, we developed a method for the detection of influential observations for a popular mediation model and its extensions in a Bayesian context. Detection of influential observations is based on the case-deletion principle. Importance sampling with weights which take advantage of the dependence structure in hierarchical models is utilized in order to identify the part of the model which is influenced most. We make use of the variance of log importance sampling weights as the measure of influence. It is demonstrated that this approach is useful when interest lies in the impact of individual observations on a subset of model parameters. The method is illustrated on a three-level data set from the field of nursing research, which was previously used to fit a mediation model of patient satisfaction with care. We focused on influential cases on both the second and the third level of the data.
Journal: Journal of Applied Statistics
Pages: 943-960
Issue: 5
Volume: 48
Year: 2021
Month: 4
X-DOI: 10.1080/02664763.2020.1748179
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1748179
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:5:p:943-960




Template-Type: ReDIF-Article 1.0
Author-Name: Xu Guo
Author-X-Name-First: Xu
Author-X-Name-Last: Guo
Author-Name: Yan Wang
Author-X-Name-First: Yan
Author-X-Name-Last: Wang
Author-Name: Niwen Zhou
Author-X-Name-First: Niwen
Author-X-Name-Last: Zhou
Author-Name: Xuehu Zhu
Author-X-Name-First: Xuehu
Author-X-Name-Last: Zhu
Title: Optimal weighted two-sample t-test with partially paired data in a unified framework
Abstract: 
 In this paper, we provide a unified framework for two-sample t-test with partially paired data. We show that many existing two-sample t-tests with partially paired data can be viewed as special members in our unified framework. Some shortcomings of these t-tests are discussed. We also propose the asymptotically optimal weighted linear combination of the test statistics comparing all four paired and unpaired data sets. Simulation studies are used to illustrate the performance of our proposed asymptotically optimal weighted combinations of test statistics and compare with some existing methods. It is found that our proposed test statistic is generally more powerful. Three real data sets about CD4 count, DNA extraction concentrations, and the quality of sleep are also analyzed by using our newly introduced test statistic.
Journal: Journal of Applied Statistics
Pages: 961-976
Issue: 6
Volume: 48
Year: 2021
Month: 4
X-DOI: 10.1080/02664763.2020.1753027
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1753027
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:6:p:961-976




Template-Type: ReDIF-Article 1.0
Author-Name: Nusrat Harun
Author-X-Name-First: Nusrat
Author-X-Name-Last: Harun
Author-Name: Bo Cai
Author-X-Name-First: Bo
Author-X-Name-Last: Cai
Author-Name: Yu Shen
Author-X-Name-First: Yu
Author-X-Name-Last: Shen
Title: A Bayesian semiparametric method for analyzing length-biased data
Abstract: 
 Survival data obtained from prevalent cohort study designs are often subject to length-biased sampling. Frequentist methods including estimating equation approaches, as well as full likelihood methods, are available for assessing covariate effects on survival from such data. Bayesian methods allow a perspective of probability interpretation for the parameters of interest, and may easily provide the predictive distribution for future observations while incorporating weak prior knowledge on the baseline hazard function. There is lack of Bayesian methods for analyzing length-biased data. In this paper, we propose Bayesian methods for analyzing length-biased data under a proportional hazards model. The prior distribution for the cumulative hazard function is specified semiparametrically using I-Splines. Bayesian conditional and full likelihood approaches are developed for analyzing simulated and real data.
Journal: Journal of Applied Statistics
Pages: 977-992
Issue: 6
Volume: 48
Year: 2021
Month: 04
X-DOI: 10.1080/02664763.2020.1753028
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1753028
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:6:p:977-992




Template-Type: ReDIF-Article 1.0
Author-Name: Yu Zhang
Author-X-Name-First: Yu
Author-X-Name-Last: Zhang
Author-Name: Xiangzhong Fang
Author-X-Name-First: Xiangzhong
Author-X-Name-Last: Fang
Title: On the lengths of t-based confidence intervals
Abstract: 
 Confidence interval is a basic type of interval estimation in statistics. When dealing with samples from a normal population with the unknown mean and the variance, the traditional method to construct t-based confidence intervals for the mean parameter is to treat the n sampled units as n groups and build the intervals. Here we propose a generalized method. We first divide them into several equal-sized groups and then calculate the confidence intervals with the mean values of these groups. If we define “better” in terms of the expected length of the confidence interval, then the first method is better because the expected length of the confidence interval obtained from the first method is shorter. We prove this intuition theoretically. We also specify when the elements in each group are correlated, the first method is invalid, while the second can give us correct results in terms of the coverage probability. We illustrate this with analytical expressions. In practice, when the data set is extremely large and distributed in several data centers, the second method is a good tool to get confidence intervals, in both independent and correlated cases. Some simulations and real data analyses are presented to verify our theoretical results.
Journal: Journal of Applied Statistics
Pages: 993-1008
Issue: 6
Volume: 48
Year: 2021
Month: 4
X-DOI: 10.1080/02664763.2020.1754357
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1754357
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:6:p:993-1008




Template-Type: ReDIF-Article 1.0
Author-Name: Milind A. Phadnis
Author-X-Name-First: Milind A.
Author-X-Name-Last: Phadnis
Author-Name: Matthew S. Mayo
Author-X-Name-First: Matthew S.
Author-X-Name-Last: Mayo
Title: Sample size calculations for noninferiority trials for time-to-event data using the concept of proportional time
Abstract: 
 Noninferiority trials intend to show that a new treatment is ‘not worse' than a standard-of-care active control and can be used as an alternative when it is likely to cause fewer side effects compared to the active control. In the case of time-to-event endpoints, existing methods of sample size calculation are done either assuming proportional hazards between the two study arms, or assuming exponentially distributed lifetimes. In scenarios where these assumptions are not true, there are few reliable methods for calculating the sample sizes for a time-to-event noninferiority trial. Additionally, the choice of the non-inferiority margin is obtained either from a meta-analysis of prior studies, or strongly justifiable ‘expert opinion', or from a ‘well conducted' definitive large-sample study. Thus, when historical data do not support the traditional assumptions, it would not be appropriate to use these methods to design a noninferiority trial. For such scenarios, an alternate method of sample size calculation based on the assumption of Proportional Time is proposed. This method utilizes the generalized gamma ratio distribution to perform the sample size calculations. A practical example is discussed, followed by insights on choice of the non-inferiority margin, and the indirect testing of superiority of treatment compared to placebo.
Journal: Journal of Applied Statistics
Pages: 1009-1032
Issue: 6
Volume: 48
Year: 2021
Month: 4
X-DOI: 10.1080/02664763.2020.1753026
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1753026
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:6:p:1009-1032




Template-Type: ReDIF-Article 1.0
Author-Name: Zhensheng Huang
Author-X-Name-First: Zhensheng
Author-X-Name-Last: Huang
Author-Name: Wen Lou
Author-X-Name-First: Wen
Author-X-Name-Last: Lou
Title: Statistical inferences for single-index models with measurement errors
Abstract: 
 An important factor in house prices is its location. However, measurement errors arise frequently in the process of observing variables such as the latitude and longitude of the house. The single-index models with measurement errors are used to study the relationship between house location and house price. We obtain the estimators by a SIMEX method based on the local linear method and the estimating equation. To test the significance of the index coefficient and the linearity of the link function, we establish the generalized likelihood ratio (GLR) tests for the models. We demonstrate that the asymptotic null distributions of the established GLR tests follow 
 $\chi ^{2} $χ2-distributions which are independent of nuisance parameters or functions. Finally, two simulated examples and a real estate valuation data set are given to illustrate the effect of GLR tests.
Journal: Journal of Applied Statistics
Pages: 1033-1052
Issue: 6
Volume: 48
Year: 2021
Month: 4
X-DOI: 10.1080/02664763.2020.1754358
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1754358
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:6:p:1033-1052




Template-Type: ReDIF-Article 1.0
Author-Name: Alex Paynter
Author-X-Name-First: Alex
Author-X-Name-Last: Paynter
Author-Name: Amy D. Willis
Author-X-Name-First: Amy D.
Author-X-Name-Last: Willis
Title: Tuning parameter selection for a penalized estimator of species richness
Abstract: 
 Our goal is to estimate the true number of classes in a population, called the species richness. We consider the case where multiple frequency count tables have been collected from a homogeneous population and investigate a penalized maximum likelihood estimator under a negative binomial model. Because high probabilities of unobserved classes increase the variance of species richness estimates, our method penalizes the probability of a class being unobserved. Tuning the penalization parameter is challenging because the true species richness is never known, and so we propose and validate four novel methods for tuning the penalization parameter. We illustrate and contrast the performance of the proposed methods by estimating the strain-level microbial diversity of Lake Champlain over three consecutive years, and global human host-associated species-level microbial richness.
Journal: Journal of Applied Statistics
Pages: 1053-1070
Issue: 6
Volume: 48
Year: 2021
Month: 4
X-DOI: 10.1080/02664763.2020.1754359
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1754359
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:6:p:1053-1070




Template-Type: ReDIF-Article 1.0
Author-Name: Vahid Nekoukhou
Author-X-Name-First: Vahid
Author-X-Name-Last: Nekoukhou
Author-Name: Ashkan Khalifeh
Author-X-Name-First: Ashkan
Author-X-Name-Last: Khalifeh
Author-Name: Hamid Bidram
Author-X-Name-First: Hamid
Author-X-Name-Last: Bidram
Title: A bivariate discrete inverse resilience family of distributions with resilience marginals
Abstract: 
 In this paper, a new bivariate discrete generalized exponential distribution, whose marginals are discrete generalized exponential distributions, is studied. It is observed that the proposed bivariate distribution is a flexible distribution whose cumulative distribution function has an analytical structure. In addition, a new bivariate geometric distribution can be obtained as a special case. We study different properties of this distribution and propose estimation of its parameters. We will see that the maximum of the variables involved in the proposed bivariate distribution defines some new classes of univariate discrete distributions, which are interesting in their own sake, and can be used to analyze some Reliability systems whose components are positive dependent. Some important futures of this new univariate family of discrete distributions are also studied in details. In addition, a general class of bivariate discrete distributions, whose marginals are exponentiated discrete distributions, is introduced. Moreover, the analysis of two real bivariate data sets is performed to indicate the effectiveness of the proposed models. Finally, we conclude the paper.
Journal: Journal of Applied Statistics
Pages: 1071-1090
Issue: 6
Volume: 48
Year: 2021
Month: 4
X-DOI: 10.1080/02664763.2020.1755618
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1755618
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:6:p:1071-1090




Template-Type: ReDIF-Article 1.0
Author-Name: Fang Xia
Author-X-Name-First: Fang
Author-X-Name-Last: Xia
Author-Name: Stephen L. George
Author-X-Name-First: Stephen L.
Author-X-Name-Last: George
Author-Name: Jing Ning
Author-X-Name-First: Jing
Author-X-Name-Last: Ning
Author-Name: Liang Li
Author-X-Name-First: Liang
Author-X-Name-Last: Li
Author-Name: Xuelin Huang
Author-X-Name-First: Xuelin
Author-X-Name-Last: Huang
Title: A signature enrichment design with Bayesian adaptive randomization
Abstract: 
 Clinical trials in the era of precision cancer medicine aim to identify and validate biomarker signatures which can guide the assignment of individually optimal treatments to patients. In this article, we propose a group sequential randomized phase II design, which updates the biomarker signature as the trial goes on, utilizes enrichment strategies for patient selection, and uses Bayesian response-adaptive randomization for treatment assignment. To evaluate the performance of the new design, in addition to the commonly considered criteria of Type I error and power, we propose four new criteria measuring the benefits and losses for individuals both inside and outside of the clinical trial. Compared with designs with equal randomization, the proposed design gives trial participants a better chance to receive their personalized optimal treatments and thus results in a higher response rate on the trial. This design increases the chance to discover a successful new drug by an adaptive enrichment strategy, i.e. identification and selective enrollment of a subset of patients who are sensitive to the experimental therapies. Simulation studies demonstrate these advantages of the proposed design. It is illustrated by an example based on an actual clinical trial in non-small-cell lung cancer.
Journal: Journal of Applied Statistics
Pages: 1091-1110
Issue: 6
Volume: 48
Year: 2021
Month: 4
X-DOI: 10.1080/02664763.2020.1757048
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1757048
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:6:p:1091-1110




Template-Type: ReDIF-Article 1.0
Author-Name: Erfan Ghasemi
Author-X-Name-First: Erfan
Author-X-Name-Last: Ghasemi
Author-Name: Alireza Akbarzadeh Baghban
Author-X-Name-First: Alireza
Author-X-Name-Last: Akbarzadeh Baghban
Author-Name: Farid Zayeri
Author-X-Name-First: Farid
Author-X-Name-Last: Zayeri
Author-Name: Asma Pourhoseingholi
Author-X-Name-First: Asma
Author-X-Name-Last: Pourhoseingholi
Author-Name: Seyed Mohammadreza Safavi
Author-X-Name-First: Seyed Mohammadreza
Author-X-Name-Last: Safavi
Title: A doubly-inflated Poisson regression for correlated count data
Abstract: 
 Count data have emerged in many applied research areas. In recent years, there has been a considerable interest in models for count data. In modelling such data, it is common to face a large frequency of zeroes. The data are regarded as zero-inflated when the frequency of observed zeroes is larger than what is expected from a theoretical distribution such as Poisson distribution, as a standard model for analysing count data. Data analysis, using the simple Poisson model, may lead to over-dispersion. Several classes of different mixture models were proposed for handling zero-inflated data. But they do not apply to cases when inflated counts happen at some other points, in addition to zero. In these cases, a doubly-inflated Poisson model has been suggested which only be used for cross-sectional data and cannot consider correlations between observations. However, correlated count data have a large application, especially in the health and medical fields. The present study aims to introduce a Doubly-Inflated Poisson models with random effect for correlated doubly-inflated data. Then, the best performance of the proposed method is shown via different simulation scenarios. Finally, the proposed model is applied to a dental study.
Journal: Journal of Applied Statistics
Pages: 1111-1127
Issue: 6
Volume: 48
Year: 2021
Month: 4
X-DOI: 10.1080/02664763.2020.1757049
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1757049
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:6:p:1111-1127




Template-Type: ReDIF-Article 1.0
Author-Name: Rhoda Nandai Muse
Author-X-Name-First: Rhoda Nandai
Author-X-Name-Last: Muse
Author-Name: Satheesh Aradhyula
Author-X-Name-First: Satheesh
Author-X-Name-Last: Aradhyula
Title: Correlated discrete and continuous outcomes with endogeneity and lagged effects: past season yield impact on improved corn seed adoption
Abstract: 
 Farmers in Sub-Saharan Africa have lower agricultural technology adoption rates compared to the rest of the world. It is believed that the past season yield affects a farmer's capacity to take on the riskier improved seed variety; but this effect has not been studied. We quantify the effect of past season yield on improved corn seed use in future seasons while addressing the impact of the seed variety on yield. We develop a maximum likelihood method that addresses the fact that farmers self-select into a technology resulting in its effect on yield being endogenous. The method is unique since it models both lagged and endogenous effects in correlated discrete and continuous outcomes simultaneously. Due to the prescence of the lagged effect in a three year dataset, we also propose a solution to the initial conditions problem and demonstrate with simulations its effectiveness. We used survey longitudinal data collected from Kenyan corn farmers for three years. Our results show that higher past season yield increased the likelihood of adoption in future seasons. The simulation and empirical studies indicate that ignoring the self selection of improved seed use biases the results; we obtain a different sign in the covariance.
Journal: Journal of Applied Statistics
Pages: 1128-1153
Issue: 6
Volume: 48
Year: 2021
Month: 4
X-DOI: 10.1080/02664763.2020.1757050
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1757050
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:6:p:1128-1153




Template-Type: ReDIF-Article 1.0
Author-Name: Mahdi Teimouri
Author-X-Name-First: Mahdi
Author-X-Name-Last: Teimouri
Title: EM algorithm for mixture of skew-normal distributions fitted to grouped data
Abstract: 
 Grouped data are frequently used in several fields of study. In this work, we use the expectation-maximization (EM) algorithm for fitting the skew-normal (SN) mixture model to the grouped data. Implementing the EM algorithm requires computing the one-dimensional integrals for each group or class. Our simulation study and real data analyses reveal that the EM algorithm not only always converges but also can be implemented in just a few seconds even when the number of components is large, contrary to the Bayesian paradigm that is computationally expensive. The accuracy of the EM algorithm and superiority of the SN mixture model over the traditional normal mixture model in modelling grouped data are demonstrated through the simulation and three real data illustrations. For implementing the EM algorithm, we use the package called ForestFit developed for R environment available at https://cran.r-project.org/web/packages/ForestFit/index.html.
Journal: Journal of Applied Statistics
Pages: 1154-1179
Issue: 7
Volume: 48
Year: 2021
Month: 5
X-DOI: 10.1080/02664763.2020.1759032
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1759032
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:7:p:1154-1179




Template-Type: ReDIF-Article 1.0
Author-Name: Ingo Klein
Author-X-Name-First: Ingo
Author-X-Name-Last: Klein
Author-Name: Monika Doll
Author-X-Name-First: Monika
Author-X-Name-Last: Doll
Title: Tests on asymmetry for ordered categorical variables
Abstract: 
 Skewness is a well-established statistical concept for continuous and, to a lesser extent, for discrete quantitative statistical variables. However, for ordered categorical variables, limited literature concerning skewness exists, although this type of variables is common for behavioral, educational, and social sciences. Suitable measures of skewness for ordered categorical variables have to be invariant with respect to the group of strictly increasing, continuous transformations. Therefore, they have to depend on the corresponding maximal-invariants. Based on these maximal-invariants, we propose a new class of skewness functionals, show that members of this class preserve a suitable ordering of skewness and derive the asymptotic distribution of the corresponding skewness statistic. Finally, we show the good power behavior of the corresponding skewness tests and illustrate these tests by applying real data examples.
Journal: Journal of Applied Statistics
Pages: 1180-1198
Issue: 7
Volume: 48
Year: 2021
Month: 5
X-DOI: 10.1080/02664763.2020.1757045
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1757045
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:7:p:1180-1198




Template-Type: ReDIF-Article 1.0
Author-Name: T. Šimková
Author-X-Name-First: T.
Author-X-Name-Last: Šimková
Title: Confidence intervals based on L-moments for quantiles of the GP and GEV distributions with application to market-opening asset prices data
Abstract: 
 In a ground-breaking paper published in 1990 by the Journal of the Royal Statistical Society, J.R.M. Hosking defined the L-moment of a random variable as an expectation of certain linear combinations of order statistics. L-moments are an alternative to conventional moments and recently they have been used often in inferential statistics. L-moments have several advantages over the conventional moments, including robustness to the the presence of outliers, which may lead to more accurate estimates in some cases as the characteristics of distributions. In this contribution, asymptotic theory and L-moments are used to derive confidence intervals of the population parameters and quantiles of the three-parametric generalized Pareto and extreme-value distributions. Computer simulations are performed to determine the performance of confidence intervals for the population quantiles based on L-moments and to compare them to those obtained by traditional estimation techniques. The results obtained show that they perform well in comparison to the moments and maximum likelihood methods when the interest is in higher quantiles, or even best. L-moments are especially recommended when the tail of the distribution is rather heavier and the sample size is small. The derived intervals are applied to real economic data, and specifically to market-opening asset prices.
Journal: Journal of Applied Statistics
Pages: 1199-1226
Issue: 7
Volume: 48
Year: 2021
Month: 5
X-DOI: 10.1080/02664763.2020.1757046
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1757046
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:7:p:1199-1226




Template-Type: ReDIF-Article 1.0
Author-Name: Harsh Tripathi
Author-X-Name-First: Harsh
Author-X-Name-Last: Tripathi
Author-Name: Sanku Dey
Author-X-Name-First: Sanku
Author-X-Name-Last: Dey
Author-Name: Mahendra Saha
Author-X-Name-First: Mahendra
Author-X-Name-Last: Saha
Title: Double and group acceptance sampling plan for truncated life test based on inverse log-logistic distribution
Abstract: 
 This paper introduces a double and group acceptance sampling plans based on time truncated lifetimes when the lifetime of an item follows the inverse log-logistic (ILL) distribution with known shape parameter. The operating characteristic function and average sample number (ASN) values of the double acceptance sampling inspection plan are provided. The values of the minimum number of groups and operating characteristic function for various quality levels are obtained for a group acceptance sampling inspection plan. A comparative study between single acceptance sampling inspection plan and double acceptance sampling inspection plan is carried out in terms of sample size. One simulated example and four real-life examples are discussed to show the applicability of the proposed double and group acceptance sampling inspection plans for ILL distributed quality parameters.
Journal: Journal of Applied Statistics
Pages: 1227-1242
Issue: 7
Volume: 48
Year: 2021
Month: 5
X-DOI: 10.1080/02664763.2020.1759031
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1759031
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:7:p:1227-1242




Template-Type: ReDIF-Article 1.0
Author-Name: Sandile Charles Shongwe
Author-X-Name-First: Sandile Charles
Author-X-Name-Last: Shongwe
Author-Name: Jean-Claude Malela-Majika
Author-X-Name-First: Jean-Claude
Author-X-Name-Last: Malela-Majika
Author-Name: Philippe Castagliola
Author-X-Name-First: Philippe
Author-X-Name-Last: Castagliola
Title: A combined mixed-s-skip sampling strategy to reduce the effect of autocorrelation on the X̄ scheme with and without measurement errors
Abstract: 
 In order to reduce the effect of autocorrelation on the 
 $\bar{X} $X¯ monitoring scheme, a new sampling strategy is proposed to form rational subgroup samples of size n. It requires sampling to be done such that: (i) observations from two consecutive samples are merged, and (ii) some consecutive observations are skipped before sampling. This technique which is a generalized version of the mixed samples strategy is shown to yield a better reduction of the negative effect of autocorrelation when monitoring the mean of processes with and without measurement errors. For processes subjected to a combined effect of autocorrelation and measurement errors, the proposed sampling technique, together with multiple measurement strategy, yields an uniformly better zero-state run-length performance than its two main existing competitors for any autocorrelation level. However, in steady-state mode, it yields the best performance only when the monitoring process is subject to a high level of autocorrelation, for any given level of measurement errors. A real life example is used to illustrate the implementation of the proposed sampling strategy.
Journal: Journal of Applied Statistics
Pages: 1243-1268
Issue: 7
Volume: 48
Year: 2021
Month: 05
X-DOI: 10.1080/02664763.2020.1759033
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1759033
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:7:p:1243-1268




Template-Type: ReDIF-Article 1.0
Author-Name: Stefano Nasini
Author-X-Name-First: Stefano
Author-X-Name-Last: Nasini
Author-Name: Victor Martínez-de-Albéniz
Author-X-Name-First: Victor
Author-X-Name-Last: Martínez-de-Albéniz
Title: Pairwise influences in dynamic choice: network-based model and application
Abstract: 
 In this paper, we study the problem of network discovery and influence propagation, and propose an integrated approach for the analysis of lead-lag synchronization in multiple choices. Network models for the processes by which decisions propagate through social interaction have been studied before, but only a few consider unknown structures of interacting agents. In fact, while individual choices are typically observed, inferring individual influences – who influences who – from sequences of dynamic choices requires strong modeling assumptions on the cross-section dependencies of the observed panels. We propose a class of parametric models which extends the vector autoregression to the case of pairwise influences between individual choices over multiple items and supports the analysis of influence propagation. After uncovering a collection of theoretical properties (conditional moments, parameter sensitivity, identifiability and estimation), we provide an economic application to music broadcasting, where a set of songs are diffused over radio stations; we infer station-to-station influences based on the proposed methodology and assess the propagation effect of initial launching stations to maximize songs diffusion. Both on the theoretical and empirical sides, the proposed approach connects fields which are traditionally treated as separated areas: the problem of network discovery and the one of influence propagation.
Journal: Journal of Applied Statistics
Pages: 1269-1302
Issue: 7
Volume: 48
Year: 2021
Month: 5
X-DOI: 10.1080/02664763.2020.1761948
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1761948
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:7:p:1269-1302




Template-Type: ReDIF-Article 1.0
Author-Name: Pejman Bordbar
Author-X-Name-First: Pejman
Author-X-Name-Last: Bordbar
Author-Name: Sodeif Ahadpour
Author-X-Name-First: Sodeif
Author-X-Name-Last: Ahadpour
Title: Type-I intermittency from Markov binary block visibility graph perspective
Abstract: 
 In this work, the type-I intermittency is studied from the optimized Markov binary visibility graphs perspective. We consider a local Poincaré map such as the logistic map that is a simple model for exhibiting this type of intermittency. To consider the acceptance gate as 
 $G \ll 0.01 $G≪0.01, we show that the transition between laminar and non-laminar zones in type-I intermittency takes distinct phases and regions. According to their behavioral characteristics, we call them as pure, switching, threshold, trapping, and transforming phases for the laminar zone and initial, terminal reinjection, and chaotic burst regions for non-laminar zone. We investigate their properties based on statistical tools such as the maximum and the mean length of the laminar zone and also length distributions of the laminar zone. For further investigation, we study degree distribution of the complex network generated by type-I intermittency time series and finally, predict various behaviors of phases and regions by proposed theoretical degree distributions.
Journal: Journal of Applied Statistics
Pages: 1303-1318
Issue: 7
Volume: 48
Year: 2021
Month: 5
X-DOI: 10.1080/02664763.2020.1761949
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1761949
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:7:p:1303-1318




Template-Type: ReDIF-Article 1.0
Author-Name: Vasileios Alevizakos
Author-X-Name-First: Vasileios
Author-X-Name-Last: Alevizakos
Author-Name: Christos Koukouvinos
Author-X-Name-First: Christos
Author-X-Name-Last: Koukouvinos
Title: Monitoring of zero-inflated binomial processes with a DEWMA control chart
Abstract: 
 Control charts are widely used for monitoring quality characteristics of high-yield processes. In such processes where a large number of zero observations exists in count data, the zero-inflated binomial (ZIB) models are more appropriate than the ordinary binomial models. In ZIB models, random shocks occur with probability θ, and upon the occurrence of random shocks, the number of non-conforming items in a sample of size n follows the binomial distribution with proportion p. In the present article, we study in more detail the exponentially weighted moving average control chart based on ZIB distribution (ZIB-EWMA) and we also propose a new control chart based on the double exponentially weighted moving average statistic for monitoring ZIB data (ZIB-DEWMA). The two control charts are studied in detecting upward shifts in θ or p individually, as well as in both parameters simultaneously. Through a simulation study, we compare the performance of the proposed chart with the ZIB-Shewhart, ZIB-EWMA and ZIB-CUSUM charts. Finally, an illustrative example is also presented to display the practical application of the ZIB charts.
Journal: Journal of Applied Statistics
Pages: 1319-1338
Issue: 7
Volume: 48
Year: 2021
Month: 5
X-DOI: 10.1080/02664763.2020.1761950
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1761950
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:7:p:1319-1338




Template-Type: ReDIF-Article 1.0
Author-Name: Daniel K. Sewell
Author-X-Name-First: Daniel K.
Author-X-Name-Last: Sewell
Author-Name: Journey Penney
Author-X-Name-First: Journey
Author-X-Name-Last: Penney
Author-Name: Melissa Jay
Author-X-Name-First: Melissa
Author-X-Name-Last: Jay
Author-Name: Ying Zhang
Author-X-Name-First: Ying
Author-X-Name-Last: Zhang
Author-Name: Jane S. Paulsen
Author-X-Name-First: Jane S.
Author-X-Name-Last: Paulsen
Title: Predicting an optimal composite outcome variable for Huntington's disease clinical trials
Abstract: 
 While there is no known cure for Huntington's disease (HD), there are early-phase clinical trials aimed at altering disease progression patterns. There is, however, no obvious single outcome for these trials to evaluate treatment efficacy. Currently used outcomes are, while reasonable, not optimal in any sense. In this paper we derive a method for constructing a composite variable via a linear combination of clinical measures. Our composite variable optimizes the signal-to-noise ratio (SNR) within the context of a longitudinal study design. We also demonstrate how to induce sparsity using a soft-approximation of an 
 $L_1 $L1 penalty on the coefficients of the composite variable. We applied our method to data from the TRACK-HD study, a longitudinal study aimed at establishing good outcome measures for HD, and found that compared to the existing composite measurement our composite variable provides a larger SNR and allows clinical trials with smaller sample sizes to achieve equivalent power.
Journal: Journal of Applied Statistics
Pages: 1339-1348
Issue: 7
Volume: 48
Year: 2021
Month: 5
X-DOI: 10.1080/02664763.2020.1759034
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1759034
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:7:p:1339-1348




Template-Type: ReDIF-Article 1.0
Author-Name: The Editors
Title: Correction
Journal: Journal of Applied Statistics
Pages: i-i
Issue: 7
Volume: 48
Year: 2021
Month: 05
X-DOI: 10.1080/02664763.2020.1807783
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1807783
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:7:p:i-i




Template-Type: ReDIF-Article 1.0
Author-Name: Bojuan Barbara Zhao
Author-X-Name-First: Bojuan Barbara
Author-X-Name-Last: Zhao
Author-Name: Ruijuan Su
Author-X-Name-First: Ruijuan
Author-X-Name-Last: Su
Title: Determinants of the heavily right-tailed residential housing price in Tianjin
Abstract: 
 The housing price in Tianjin, one of the typical monocentric cities of China, exhibits a heavily right-tailed distribution even after the logarithm transformation of the price, which might lead to a biased estimation of the parameters under normal distribution assumption. Therefore, the extended Cox proportional hazards regression model and the generalized concept of relative risk are used to identify factors associated with the housing price. The analysis shows that the implementation dates of the macro regulation policies were related to the price changing trends. Qualities of public elementary and secondary schools were significantly associated with the housing price, and the associations between the structure and neighborhood characteristics and the housing price were influenced by the distance of the residential property to downtown Tianjin.
Journal: Journal of Applied Statistics
Pages: 1457-1474
Issue: 8
Volume: 48
Year: 2021
Month: 06
X-DOI: 10.1080/02664763.2020.1840534
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1840534
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:8:p:1457-1474




Template-Type: ReDIF-Article 1.0
Author-Name: Yang Liu
Author-X-Name-First: Yang
Author-X-Name-Last: Liu
Author-Name: JunJia Zhu
Author-X-Name-First: JunJia
Author-X-Name-Last: Zhu
Author-Name: Dennis K. J. Lin
Author-X-Name-First: Dennis K. J.
Author-X-Name-Last: Lin
Title: A generalized likelihood ratio test for monitoring profile data
Abstract: 
 Profile data emerges when the quality of a product or process is characterized by a functional relationship among (input and output) variables. In this paper, we focus on the case where each profile has one response variable Y, one explanatory variable x, and the functional relationship between these two variables can be rather arbitrary. The basic concept can be applied to a much wider case, however. We propose a general method based on the Generalized Likelihood Ratio Test (GLRT) for monitoring of profile data. The proposed method uses nonparametric regression to estimate the on-line profiles and thus does not require any functional form for the profiles. Both Shewhart-type and EWMA-type control charts are considered. The average run length (ARL) performance of the proposed method is studied. It is shown that the proposed GLRT-based control chart can efficiently detect both location and dispersion shifts of the on-line profiles from the baseline profile. An upper control limit (UCL) corresponding to a desired in-control ARL value is constructed.
Journal: Journal of Applied Statistics
Pages: 1402-1415
Issue: 8
Volume: 48
Year: 2021
Month: 06
X-DOI: 10.1080/02664763.2021.1880555
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1880555
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:8:p:1402-1415




Template-Type: ReDIF-Article 1.0
Author-Name: Antai Wang
Author-X-Name-First: Antai
Author-X-Name-Last: Wang
Author-Name: Xieyang Jia
Author-X-Name-First: Xieyang
Author-X-Name-Last: Jia
Author-Name: Zhezhen Jin
Author-X-Name-First: Zhezhen
Author-X-Name-Last: Jin
Title: Estimation of the cumulative baseline hazard function for dependently right-censored failure time data
Abstract: 
 In this paper, we study the properties of a special class of frailty models when the frailty is common to several failure times. The models are closely linked to Archimedean copula models. We establish a useful formula for cumulative baseline hazard functions and develop a new estimator for cumulative baseline hazard functions in bivariate frailty regression models. Based on our proposed estimator, we present a graphical model checking procedure. We fit a leukemia data set using our model and end our paper with some discussions.
Journal: Journal of Applied Statistics
Pages: 1416-1428
Issue: 8
Volume: 48
Year: 2021
Month: 06
X-DOI: 10.1080/02664763.2020.1795818
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1795818
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:8:p:1416-1428




Template-Type: ReDIF-Article 1.0
Author-Name: Liang Zhu
Author-X-Name-First: Liang
Author-X-Name-Last: Zhu
Author-Name: Xingwei Tong
Author-X-Name-First: Xingwei
Author-X-Name-Last: Tong
Author-Name: Dingjiao Cai
Author-X-Name-First: Dingjiao
Author-X-Name-Last: Cai
Author-Name: Yimei Li
Author-X-Name-First: Yimei
Author-X-Name-Last: Li
Author-Name: Ryan Sun
Author-X-Name-First: Ryan
Author-X-Name-Last: Sun
Author-Name: Deo K. Srivastava
Author-X-Name-First: Deo K.
Author-X-Name-Last: Srivastava
Author-Name: Melissa M. Hudson
Author-X-Name-First: Melissa M.
Author-X-Name-Last: Hudson
Title: Maximum likelihood estimation for the proportional odds model with mixed interval-censored failure time data
Abstract: 
 This article discusses regression analysis of mixed interval-censored failure time data. Such data frequently occur across a variety of settings, including clinical trials, epidemiologic investigations, and many other biomedical studies with a follow-up component. For example, mixed failure times are commonly found in the two largest studies of long-term survivorship after childhood cancer, the datasets that motivated this work. However, most existing methods for failure time data consider only right-censored or only interval-censored failure times, not the more general case where times may be mixed. Additionally, among regression models developed for mixed interval-censored failure times, the proportional hazards formulation is generally assumed. It is well-known that the proportional hazards model may be inappropriate in certain situations, and alternatives are needed to analyze mixed failure time data in such cases. To fill this need, we develop a maximum likelihood estimation procedure for the proportional odds regression model with mixed interval-censored data. We show that the resulting estimators are consistent and asymptotically Gaussian. An extensive simulation study is performed to assess the finite-sample properties of the method, and this investigation indicates that the proposed method works well for many practical situations. We then apply our approach to examine the impact of age at cranial radiation therapy on risk of growth hormone deficiency in long-term survivors of childhood cancer.
Journal: Journal of Applied Statistics
Pages: 1496-1512
Issue: 8
Volume: 48
Year: 2021
Month: 06
X-DOI: 10.1080/02664763.2020.1789077
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1789077
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:8:p:1496-1512




Template-Type: ReDIF-Article 1.0
Author-Name: Myeonggyun Lee
Author-X-Name-First: Myeonggyun
Author-X-Name-Last: Lee
Author-Name: Anne Zeleniuch-Jacquotte
Author-X-Name-First: Anne
Author-X-Name-Last: Zeleniuch-Jacquotte
Author-Name: Mengling Liu
Author-X-Name-First: Mengling
Author-X-Name-Last: Liu
Title: Empirical evaluation of sub-cohort sampling designs for risk prediction modeling
Abstract: 
 Sub-cohort sampling designs, such as nested case-control (NCC) and case-cohort (CC) studies, have been widely used to estimate biomarker-disease associations because of their cost effectiveness. These designs have been well studied and shown to maintain relatively high efficiency compared to full-cohort designs, but their performance of building risk prediction models has been less studied. Moreover, sub-cohort sampling designs often use matching (or stratifying) to further control for confounders or to reduce measurement error. Their predictive performance depends on both the design and matching procedures. Based on a dataset from the NYU Women's Health Study (NYUWHS), we performed Monte Carlo simulations to systematically evaluate risk prediction performance under NCC, CC, and full-cohort studies. Our simulations demonstrate that sub-cohort sampling designs can have predictive accuracy (i.e. discrimination and calibration) similar to that of the full-cohort design, but could be sensitive to the matching procedure used. Our results suggest that researchers can have the option of performing NCC and CC studies with huge potential benefits in cost and resources, but need to pay particular attention to the matching procedure when developing a risk prediction model in biomarker studies.
Journal: Journal of Applied Statistics
Pages: 1374-1401
Issue: 8
Volume: 48
Year: 2021
Month: 06
X-DOI: 10.1080/02664763.2020.1861225
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1861225
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:8:p:1374-1401




Template-Type: ReDIF-Article 1.0
Author-Name: Zhezhen Jin
Author-X-Name-First: Zhezhen
Author-X-Name-Last: Jin
Author-Name: Jianguo Sun
Author-X-Name-First: Jianguo
Author-X-Name-Last: Sun
Title: Editorial to special issue Frontiers of Data Analysis
Journal: Journal of Applied Statistics
Pages: 1349-1351
Issue: 8
Volume: 48
Year: 2021
Month: 06
X-DOI: 10.1080/02664763.2021.1922853
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1922853
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:8:p:1349-1351




Template-Type: ReDIF-Article 1.0
Author-Name: Chunjie Wang
Author-X-Name-First: Chunjie
Author-X-Name-Last: Wang
Author-Name: Jingjing Jiang
Author-X-Name-First: Jingjing
Author-X-Name-Last: Jiang
Author-Name: Linlin Luo
Author-X-Name-First: Linlin
Author-X-Name-Last: Luo
Author-Name: Shuying Wang
Author-X-Name-First: Shuying
Author-X-Name-Last: Wang
Title: Bayesian analysis of the Box-Cox transformation model based on left-truncated and right-censored data
Abstract: 
 In this paper, we discuss the inference problem about the Box-Cox transformation model when one faces left-truncated and right-censored data, which often occur in studies, for example, involving the cross-sectional sampling scheme. It is well-known that the Box-Cox transformation model includes many commonly used models as special cases such as the proportional hazards model and the additive hazards model. For inference, a Bayesian estimation approach is proposed and in the method, the piecewise function is used to approximate the baseline hazards function. Also the conditional marginal prior, whose marginal part is free of any constraints, is employed to deal with many computational challenges caused by the constraints on the parameters, and a MCMC sampling procedure is developed. A simulation study is conducted to assess the finite sample performance of the proposed method and indicates that it works well for practical situations. We apply the approach to a set of data arising from a retirement center.
Journal: Journal of Applied Statistics
Pages: 1429-1441
Issue: 8
Volume: 48
Year: 2021
Month: 06
X-DOI: 10.1080/02664763.2020.1784854
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1784854
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:8:p:1429-1441




Template-Type: ReDIF-Article 1.0
Author-Name: Xinwei He
Author-X-Name-First: Xinwei
Author-X-Name-Last: He
Author-Name: Xiaoqiang Sun
Author-X-Name-First: Xiaoqiang
Author-X-Name-Last: Sun
Author-Name: Yongzhao Shao
Author-X-Name-First: Yongzhao
Author-X-Name-Last: Shao
Title: Network-based survival analysis to discover target genes for developing cancer immunotherapies and predicting patient survival
Abstract: 
 Recently, cancer immunotherapies have been life-savers; however, only a fraction of treated patients have durable responses. Consequently, statistical methods that enable the discovery of target genes for developing new treatments and predicting the patient survival are of importance. This paper introduced a network-based survival analysis method and applied it to identify candidate genes as possible targets for developing new treatments. RNA-seq data from a mouse study was used to select differentially expressed genes, which were then translated to those in humans. We constructed a gene network and identified gene clusters using a training set of 310 human gliomas. Then we conducted gene set enrichment analysis to select the gene clusters with significant biological function. A penalized Cox model was built to identify a small set of candidate genes to predict survival. An independent set of 690 human glioma samples was used to evaluate a predictive accuracy of the survival model. The areas under time-dependent ROC curves in both the training and validation sets are more than 90%, indicating a strong association between selected genes and patient survival. Consequently, potential biomedical interventions targeting these genes might be able to alter their expressions and prolong patient survival.
Journal: Journal of Applied Statistics
Pages: 1352-1373
Issue: 8
Volume: 48
Year: 2021
Month: 06
X-DOI: 10.1080/02664763.2020.1812543
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1812543
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:8:p:1352-1373




Template-Type: ReDIF-Article 1.0
Author-Name: Jaehong Yu
Author-X-Name-First: Jaehong
Author-X-Name-Last: Yu
Author-Name: Hua Zhong
Author-X-Name-First: Hua
Author-X-Name-Last: Zhong
Title: Time varying mixed effects model with fused lasso regularization
Abstract: 
 The associations between covariates and the outcomes often vary over time, regardless of whether the covariate is time-varying or time-invariant. For example, we hypothesize that the impact of chronic diseases, such as diabetes and heart disease, on people’s physical functions differ with aging. However, the age-varying effect would be missed if one models the covariate simply as a time-invariant covariate (yes/no) with a time-constant coefficient. We propose a fused lasso-based time-varying linear mixed effect (FTLME) model and an efficient two-stage parameter estimation algorithm to estimate the longitudinal trajectories of fixed-effect coefficients. Simulation studies are presented to demonstrate the efficacy of the method and its computational efficiency in estimating smooth time-varying effects in high dimensional settings. A real data example on the Health and Retirement Study (HRS) analysis is used to demonstrate the practical usage of our method to infer age-varying impact of chronic disease on older people’s physical functions.
Journal: Journal of Applied Statistics
Pages: 1513-1526
Issue: 8
Volume: 48
Year: 2021
Month: 06
X-DOI: 10.1080/02664763.2020.1791805
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1791805
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:8:p:1513-1526




Template-Type: ReDIF-Article 1.0
Author-Name: Yuna Zhao
Author-X-Name-First: Yuna
Author-X-Name-Last: Zhao
Author-Name: Dennis K. J. Lin
Author-X-Name-First: Dennis K. J.
Author-X-Name-Last: Lin
Author-Name: Min-Qian Liu
Author-X-Name-First: Min-Qian
Author-X-Name-Last: Liu
Title: Designs for order-of-addition experiments
Abstract: 
 The order-of-addition experiment aims at determining the optimal order of adding components such that the response of interest is optimized. Order of addition has been widely involved in many areas, including bio-chemistry, food science, nutritional science, pharmaceutical science, etc. However, such an important study is rather primitive in statistical literature. In this paper, a thorough study on pair-wise ordering designs for order of addition is provided. The recursive relation between two successive full pair-wise ordering designs is developed. Based on this recursive relation, the full pair-wise ordering design can be obtained without evaluating all the orders of components. The value of the D-efficiency for the full pair-wise ordering model is then derived. It provides a benchmark for choosing the fractional pair-wise ordering designs. To overcome the unaffordability of the full pair-wise ordering design, a new class of minimal-point pair-wise ordering designs is proposed. A job scheduling problem as well as simulation studies are conducted to illustrate the performance of the pair-wise ordering designs for determining the optimal orders. It is shown that the proposed designs are very efficient in determining the optimal order of addition.
Journal: Journal of Applied Statistics
Pages: 1475-1495
Issue: 8
Volume: 48
Year: 2021
Month: 06
X-DOI: 10.1080/02664763.2020.1801607
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1801607
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:8:p:1475-1495




Template-Type: ReDIF-Article 1.0
Author-Name: Zhengyu Yang
Author-X-Name-First: Zhengyu
Author-X-Name-Last: Yang
Author-Name: Guo-Liang Tian
Author-X-Name-First: Guo-Liang
Author-X-Name-Last: Tian
Author-Name: Xiaobin Liu
Author-X-Name-First: Xiaobin
Author-X-Name-Last: Liu
Author-Name: Chang-Xing Ma
Author-X-Name-First: Chang-Xing
Author-X-Name-Last: Ma
Title: Simultaneous confidence interval construction for many-to-one comparisons of proportion differences based on correlated paired data
Abstract: 
 In some medical researches such as ophthalmological, orthopaedic and otolaryngologic studies, it is often of interest to compare multiple groups with a control using data collected from paired organs of patients. The major difficulty in performing the data analysis is to adjust the multiplicity between the comparison of multiple groups, and the correlation within the same patient's paired organs. In this article, we construct asymptotic simultaneous confidence intervals (SCIs) for many-to-one comparisons of proportion differences adjusting for multiplicity and the correlation. The coverage probabilities and widths of the proposed CIs are evaluated by Monte Carlo simulation studies. The methods are illustrated by a real data example.
Journal: Journal of Applied Statistics
Pages: 1442-1456
Issue: 8
Volume: 48
Year: 2021
Month: 06
X-DOI: 10.1080/02664763.2020.1795815
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1795815
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:8:p:1442-1456




Template-Type: ReDIF-Article 1.0
Author-Name: Yunfei Ye
Author-X-Name-First: Yunfei
Author-X-Name-Last: Ye
Author-Name: Dong Han
Author-X-Name-First: Dong
Author-X-Name-Last: Han
Title: An optimal control chart for finite matrix sequences at some unknown change point
Abstract: 
 We present a new measure for evaluating the performance of control charts to detect abrupt changes of finite matrix sequences. The objective is to minimize the probability that the control chart fails to raise the alarm at unknown change point time for a given in-control average run length. We construct and prove the optimal control chart with dynamic control limits in different pre- and post-change distributions. We validate the optimality of the proposed chart by conducting exhaustive experiments on both simulation study and real-world data.
Journal: Journal of Applied Statistics
Pages: 1628-1643
Issue: 9
Volume: 48
Year: 2021
Month: 7
X-DOI: 10.1080/02664763.2020.1772208
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1772208
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:9:p:1628-1643




Template-Type: ReDIF-Article 1.0
Author-Name: Maurizio Carpita
Author-X-Name-First: Maurizio
Author-X-Name-Last: Carpita
Author-Name: Silvia Golia
Author-X-Name-First: Silvia
Author-X-Name-Last: Golia
Title: Discovering associations between players' performance indicators and matches' results in the European Soccer Leagues
Abstract: 
 The application of data mining techniques and statistical analysis to the sports field has received increasing attention in the last decade. One of the most famous sports in the world is soccer, and the present work deals with it, using data from the 2009/2010 season to the 2015/2016 season from nine European leagues extracted from the Kaggle European Soccer database. Overall performance indicators of the four roles in a soccer team (forward, midfielder, defender and goalkeeper) for home and away teams are used to investigate the relationships between them and the results of matches, and to predict the wins of the home team. The model used to answer both these demands is the Bayesian Network. This study shows that this model can be very useful for mining the relations between players' performance indicators and for improving knowledge of the game strategies applied by coaches in different leagues. Moreover, it is shown that the ability to predict match results of the proposed Bayesian Network is roughly the same as that of the Naive Bayes model.
Journal: Journal of Applied Statistics
Pages: 1696-1711
Issue: 9
Volume: 48
Year: 2021
Month: 7
X-DOI: 10.1080/02664763.2020.1772210
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1772210
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:9:p:1696-1711




Template-Type: ReDIF-Article 1.0
Author-Name: Camillo Cammarota
Author-X-Name-First: Camillo
Author-X-Name-Last: Cammarota
Author-Name: Alessandro Pinto
Author-X-Name-First: Alessandro
Author-X-Name-Last: Pinto
Title: Variable selection and importance in presence of high collinearity: an application to the prediction of lean body mass from multi-frequency bioelectrical impedance
Abstract: 
 In prediction problems both response and covariates may have high correlation with a second group of influential regressors, that can be considered as background variables. An important challenge is to perform variable selection and importance assessment among the covariates in the presence of these variables. A clinical example is the prediction of the lean body mass (response) from bioimpedance (covariates), where anthropometric measures play the role of background variables. We introduce a reduced dataset in which the variables are defined as the residuals with respect to the background, and perform variable selection and importance assessment both in linear and random forest models. Using a clinical dataset of multi-frequency bioimpedance, we show the effectiveness of this method to select the most relevant predictors of the lean body mass beyond anthropometry.
Journal: Journal of Applied Statistics
Pages: 1644-1658
Issue: 9
Volume: 48
Year: 2021
Month: 7
X-DOI: 10.1080/02664763.2020.1763930
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1763930
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:9:p:1644-1658




Template-Type: ReDIF-Article 1.0
Author-Name: M. I. Alheety
Author-X-Name-First: M. I.
Author-X-Name-Last: Alheety
Author-Name: Kristofer Månsson
Author-X-Name-First: Kristofer
Author-X-Name-Last: Månsson
Author-Name: B. M. Golam Kibria
Author-X-Name-First: B. M.
Author-X-Name-Last: Golam Kibria
Title: A new kind of stochastic restricted biased estimator for logistic regression model
Abstract: 
 In the logistic regression model, the variance of the maximum likelihood estimator is inflated and unstable when the multicollinearity exists in the data. There are several methods available in literature to overcome this problem. We propose a new stochastic restricted biased estimator. We study the statistical properties of the proposed estimator and compare its performance with some existing estimators in the sense of scalar mean squared criterion. An example and a simulation study are provided to illustrate the performance of the proposed estimator.
Journal: Journal of Applied Statistics
Pages: 1559-1578
Issue: 9
Volume: 48
Year: 2021
Month: 7
X-DOI: 10.1080/02664763.2020.1769576
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1769576
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:9:p:1559-1578




Template-Type: ReDIF-Article 1.0
Author-Name: Kai-Sheng Song
Author-X-Name-First: Kai-Sheng
Author-X-Name-Last: Song
Title: Simultaneous statistical modelling of excess zeros, over/underdispersion, and multimodality with applications in hotel industry
Abstract: 
 We propose zero-inflated statistical models based on the generalized Hermite distribution for simultaneously modelling of excess zeros, over/underdispersion, and multimodality. These new models are parsimonious yet remarkably flexible allowing the covariates to be introduced directly through the mean, dispersion, and zero-inflated parameters. To accommodate the interval inequality constraint for the dispersion parameter, we present a new link function for the covariate-dependent dispersion regression model. We derive score tests for zero inflation in both covariate-free and covariate-dependent models. Both the score test and the likelihood-ratio test are conducted to examine the validity of zero inflation. The score test provides a useful tool when computing the likelihood-ratio statistic proves to be difficult. We analyse several hotel booking cancellation datasets extracted from two recently published real datasets from a resort hotel and a city hotel. These extracted cancellation datasets reveal complex features of excess zeros, over/underdispersion, and multimodality simultaneously making them difficult to analyse with existing approaches. The application of the proposed methods to the cancellation datasets illustrates the usefulness and flexibility of the models.
Journal: Journal of Applied Statistics
Pages: 1603-1627
Issue: 9
Volume: 48
Year: 2021
Month: 7
X-DOI: 10.1080/02664763.2020.1769577
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1769577
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:9:p:1603-1627




Template-Type: ReDIF-Article 1.0
Author-Name: Anne Helby Petersen
Author-X-Name-First: Anne Helby
Author-X-Name-Last: Petersen
Author-Name: Bo Markussen
Author-X-Name-First: Bo
Author-X-Name-Last: Markussen
Author-Name: Karl Bang Christensen
Author-X-Name-First: Karl Bang
Author-X-Name-Last: Christensen
Title: Exploratory data structure comparisons: three new visual tools based on principal component analysis
Abstract: 
 Datasets are sometimes divided into distinct subsets, e.g. due to multi-center sampling, or to variations in instruments, questionnaire item ordering or mode of administration, and the data analyst then needs to assess whether a joint analysis is meaningful. The Principal Component Analysis-based Data Structure Comparisons (PCADSC) tools are three new non-parametric, visual diagnostic tools for investigating differences in structure for two subsets of a dataset through covariance matrix comparisons by use of principal component analysis. The PCADCS tools are demonstrated in a data example using European Social Survey data on psychological well-being in three countries, Denmark, Sweden, and Bulgaria. The data structures are found to be different in Denmark and Bulgaria, and thus a comparison of for example mean psychological well-being scores is not meaningful. However, when comparing Denmark and Sweden, very similar data structures, and thus comparable concepts of well-being, are found. Therefore, inter-country comparisons are warranted for these countries.
Journal: Journal of Applied Statistics
Pages: 1675-1695
Issue: 9
Volume: 48
Year: 2021
Month: 7
X-DOI: 10.1080/02664763.2020.1773772
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1773772
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:9:p:1675-1695




Template-Type: ReDIF-Article 1.0
Author-Name: Satya Prakash Singh
Author-X-Name-First: Satya Prakash
Author-X-Name-Last: Singh
Author-Name: Pradeep Yadav
Author-X-Name-First: Pradeep
Author-X-Name-Last: Yadav
Title: Optimal allocation of subjects in a matched pair cluster-randomized trial with fixed number of heterogeneous clusters
Abstract: 
 In cluster-randomized trials, investigators randomize clusters of individuals such as households, medical practices, schools or classrooms despite the unit of interest are the individuals. It results in the loss of efficiency in terms of the estimation of the unknown parameters as well as the power of the test for testing the treatment effects. To recoup this efficiency loss, some studies pair similar clusters and randomize treatment within pairs. However, the clusters within a treatment arm might be heterogeneous in nature. In this article, we propose a locally optimal design that accounts the clusters heterogeneity and optimally allocates the subjects within each cluster. To address the dependency of design on the unknown parameters, we also discuss Bayesian optimal designs. Performances of proposed designs are investigated numerically through some data examples.
Journal: Journal of Applied Statistics
Pages: 1527-1540
Issue: 9
Volume: 48
Year: 2021
Month: 07
X-DOI: 10.1080/02664763.2020.1779195
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1779195
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:9:p:1527-1540




Template-Type: ReDIF-Article 1.0
Author-Name: Shin-Fu Tsai
Author-X-Name-First: Shin-Fu
Author-X-Name-Last: Tsai
Author-Name: Tse-Le Huang
Author-X-Name-First: Tse-Le
Author-X-Name-Last: Huang
Title: Confidence limits for conformance proportions in normal mixture models
Abstract: 
 Conformance proportions are important numerical indices for quality assessments. When the population is characterized by a normal mixture model, estimating conformance proportions can be a practical issue. To account for the inherent structure of normal mixture models, universal and individual conformance proportions are first defined for the purpose of evaluating the overall population and specific subpopulations of interest, respectively. On the basis of generalized fiducial quantities, a systematic method is then proposed in this paper to obtain confidence limits for the two classes of conformance proportions. The simulation results demonstrate that the proposed method can maintain the empirical coverage rate sufficiently close to the nominal level. In addition, two examples are given to illustrate the proposed method.
Journal: Journal of Applied Statistics
Pages: 1579-1602
Issue: 9
Volume: 48
Year: 2021
Month: 7
X-DOI: 10.1080/02664763.2020.1769578
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1769578
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:9:p:1579-1602




Template-Type: ReDIF-Article 1.0
Author-Name: J. P. Burgard
Author-X-Name-First: J. P.
Author-X-Name-Last: Burgard
Author-Name: J. Krause
Author-X-Name-First: J.
Author-X-Name-Last: Krause
Author-Name: R. Münnich
Author-X-Name-First: R.
Author-X-Name-Last: Münnich
Title: An elastic net penalized small area model combining unit- and area-level data for regional hypertension prevalence estimation
Abstract: 
 Hypertension is a highly prevalent cardiovascular disease. It marks a considerable cost factor to many national health systems. Despite its prevalence, regional disease distributions are often unknown and must be estimated from survey data. However, health surveys frequently lack in regional observations due to limited resources. Obtained prevalence estimates suffer from unacceptably large sampling variances and are not reliable. Small area estimation solves this problem by linking auxiliary data from multiple regions in suitable regression models. Typically, either unit- or area-level observations are considered for this purpose. But with respect to hypertension, both levels should be used. Hypertension has characteristic comorbidities and is strongly related to lifestyle features, which are unit-level information. It is also correlated with socioeconomic indicators that are usually measured on the area-level. But the level combination is challenging as it requires multi-level model parameter estimation from small samples. We use a multi-level small area model with level-specific penalization to overcome this issue. Model parameter estimation is performed via stochastic coordinate gradient descent. A jackknife estimator of the mean squared error is presented. The methodology is applied to combine health survey data and administrative records to estimate regional hypertension prevalence in Germany.
Journal: Journal of Applied Statistics
Pages: 1659-1674
Issue: 9
Volume: 48
Year: 2021
Month: 7
X-DOI: 10.1080/02664763.2020.1765323
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1765323
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:9:p:1659-1674




Template-Type: ReDIF-Article 1.0
Author-Name: Ngan Hoang-Nguyen-Thuy
Author-X-Name-First: Ngan
Author-X-Name-Last: Hoang-Nguyen-Thuy
Author-Name: K. Krishnamoorthy
Author-X-Name-First: K.
Author-X-Name-Last: Krishnamoorthy
Title: Estimation of the probability content in a specified interval using fiducial approach
Abstract: 
 Statistical methods for constructing confidence intervals for the probability content in a specified interval are proposed. Exact and approximate solutions based on the fiducial approach are described when the measurements on the variable of interest can be modelled by a location-scale (or log-location-scale) distribution. Methods are described for the normal, Weibull, two-parameter exponential and two-parameter Rayleigh distributions. For each case, the solutions are evaluated for their merits. Three examples, where it is desired to estimate the percentages of engineering products meet the specification limits, are provided to illustrate the methods.
Journal: Journal of Applied Statistics
Pages: 1541-1558
Issue: 9
Volume: 48
Year: 2021
Month: 7
X-DOI: 10.1080/02664763.2020.1768228
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1768228
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:9:p:1541-1558




Template-Type: ReDIF-Article 1.0
Author-Name: Thomas J. Zhou
Author-X-Name-First: Thomas J.
Author-X-Name-Last: Zhou
Author-Name: Sughra Raza
Author-X-Name-First: Sughra
Author-X-Name-Last: Raza
Author-Name: Kerrie P. Nelson
Author-X-Name-First: Kerrie P.
Author-X-Name-Last: Nelson
Title: Methods of assessing categorical agreement between correlated screening tests in clinical studies
Abstract: 
 Advances in breast imaging and other screening tests have prompted studies to evaluate and compare the consistency between experts' ratings of existing with new screening tests. In clinical settings, medical experts make subjective assessments of screening test results such as mammograms. Consistency between experts' ratings is evaluated by measures of inter-rater agreement or association. However, conventional measures, such as Cohen's and Fleiss' kappas, are unable to be applied or may perform poorly when studies consist of many experts, unbalanced data, or dependencies between experts' ratings exist. Here we assess the performance of existing approaches including recently developed summary measures for assessing the agreement between experts' binary and ordinal ratings when patients undergo two screening procedures. Methods to assess consistency between repeated measurements by the same experts are also described. We present applications to three large-scale clinical screening studies. Properties of these agreement measures are illustrated via simulation studies. Generally, a model-based approach provides several advantages over alternative methods including the ability to flexibly incorporate various measurement scales (i.e. binary or ordinal), large numbers of experts and patients, sparse data, and robustness to prevalence of underlying disease.
Journal: Journal of Applied Statistics
Pages: 1861-1881
Issue: 10
Volume: 48
Year: 2021
Month: 07
X-DOI: 10.1080/02664763.2020.1777394
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1777394
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:10:p:1861-1881




Template-Type: ReDIF-Article 1.0
Author-Name: Qazi J. Azhad
Author-X-Name-First: Qazi J.
Author-X-Name-Last: Azhad
Author-Name: Mohd. Arshad
Author-X-Name-First: Mohd.
Author-X-Name-Last: Arshad
Author-Name: Amit Kumar Misra
Author-X-Name-First: Amit Kumar
Author-X-Name-Last: Misra
Title: Estimation of common location parameter of several heterogeneous exponential populations based on generalized order statistics
Abstract: 
 In this article, several independent populations following exponential distribution with common location parameter and unknown and unequal scale parameters are considered. From these populations, several independent samples of generalized order statistics (gos) are drawn. Under the setup of gos, the problem of estimation of common location parameter is discussed and various estimators of common location parameter are derived. The authors obtained maximum likelihood estimator (MLE), modified MLE and uniformly minimum variance unbiased estimator of common location parameter. Furthermore, under scaled-squared error loss function, a general inadmissibility result of invariant estimator is proposed. The derived results are further reduced for upper record values which is a special case of gos. Finally, simulation study and real life example are reported to show the performances of various competing estimators in terms of percentage risk improvement.
Journal: Journal of Applied Statistics
Pages: 1798-1815
Issue: 10
Volume: 48
Year: 2021
Month: 07
X-DOI: 10.1080/02664763.2020.1777395
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1777395
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:10:p:1798-1815




Template-Type: ReDIF-Article 1.0
Author-Name: Joseph Wu
Author-X-Name-First: Joseph
Author-X-Name-Last: Wu
Author-Name: Mayetri Gupta
Author-X-Name-First: Mayetri
Author-X-Name-Last: Gupta
Author-Name: Amira I. Hussein
Author-X-Name-First: Amira I.
Author-X-Name-Last: Hussein
Author-Name: Louis Gerstenfeld
Author-X-Name-First: Louis
Author-X-Name-Last: Gerstenfeld
Title: Bayesian modeling of factorial time-course data with applications to a bone aging gene expression study
Abstract: 
 Many scientific studies, especially in the biomedical sciences, generate data measured simultaneously over a multitude of units, over a period of time, and under different conditions or combinations of factors. Often, an important question of interest asked relates to which units behave similarly under different conditions, but measuring the variation over time complicates the analysis significantly. In this article we address such a problem arising from a gene expression study relating to bone aging, and develop a Bayesian statistical method that can simultaneously detect and uncover signals on three levels within such data: factorial, longitudinal, and transcriptional. Our model framework considers both cluster and time-point-specific parameters and these parameters uniquely determine the shapes of the temporal gene expression profiles, allowing the discovery and characterization of latent gene clusters based on similar underlying biological mechanisms. Our methodology was successfully applied to discover transcriptional networks in a microarray data set comparing the transcriptomic changes that occurred during bone aging in male and female mice expressing one or both copies of the bromodomain (Brd2) gene, a transcriptional regulator which exhibits an age-dependent sex-linked bone loss phenotype.
Journal: Journal of Applied Statistics
Pages: 1730-1754
Issue: 10
Volume: 48
Year: 2021
Month: 7
X-DOI: 10.1080/02664763.2020.1772733
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1772733
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:10:p:1730-1754




Template-Type: ReDIF-Article 1.0
Author-Name: Richard Puurbalanta
Author-X-Name-First: Richard
Author-X-Name-Last: Puurbalanta
Title: A Clipped Gaussian Geo-Classification model for poverty mapping
Abstract: 
 The importance of discrete spatial models cannot be overemphasized, especially when measuring living standards. The battery of measurements is generally categorical with nearer geo-referenced observations featuring stronger dependencies. This study presents a Clipped Gaussian Geo-Classification (CGG-C) model for spatially-dependent ordered data, and compares its performance with existing methods to classify household poverty using Ghana living standards survey (GLSS 6) data. Bayesian inference was performed on data sampled by MCMC. Model evaluation was based on measures of classification and prediction accuracy. Spatial associations, given some household features, were quantified, and a poverty classification map for Ghana was developed. Overall, the results of estimation showed that many of the statistically significant covariates were generally strongly related with the ordered response variable. Households at specific locations tended to uniformly experience specific levels of poverty, thus, providing an empirical spatial character of poverty in Ghana. A comparative analysis of validation results showed that the CGG-C model (with 14.2% misclassification rate) outperformed the Cumulative Probit (CP) model with misclassification rate of 17.4%. This approach to poverty analysis is relevant for policy design and the implementation of cost-effective programmes to reduce category and site-specific poverty incidence, and monitor changes in both category and geographical trends thereof.
Journal: Journal of Applied Statistics
Pages: 1882-1895
Issue: 10
Volume: 48
Year: 2021
Month: 07
X-DOI: 10.1080/02664763.2020.1779191
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1779191
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:10:p:1882-1895




Template-Type: ReDIF-Article 1.0
Author-Name: H. Karamikabir
Author-X-Name-First: H.
Author-X-Name-Last: Karamikabir
Author-Name: M. Afshari
Author-X-Name-First: M.
Author-X-Name-Last: Afshari
Author-Name: F. Lak
Author-X-Name-First: F.
Author-X-Name-Last: Lak
Title: Wavelet threshold based on Stein's unbiased risk estimators of restricted location parameter in multivariate normal
Abstract: 
 In this paper, the problem of estimating the mean vector under non-negative constraints on location vector of the multivariate normal distribution is investigated. The value of the wavelet threshold based on Stein's unbiased risk estimators is calculated for the shrinkage estimator in restricted parameter space. We suppose that covariance matrix is unknown and we find the dominant class of shrinkage estimators under Balance loss function. The performance evaluation of the proposed class of estimators is checked through a simulation study by using risk and average mean square error values.
Journal: Journal of Applied Statistics
Pages: 1712-1729
Issue: 10
Volume: 48
Year: 2021
Month: 07
X-DOI: 10.1080/02664763.2020.1772209
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1772209
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:10:p:1712-1729




Template-Type: ReDIF-Article 1.0
Author-Name: Soyoung Park
Author-X-Name-First: Soyoung
Author-X-Name-Last: Park
Author-Name: Alicia Carriquiry
Author-X-Name-First: Alicia
Author-X-Name-Last: Carriquiry
Title: Quantifying the similarity of 2D images using edge pixels: an application to the forensic comparison of footwear impressions
Abstract: 
 We propose a novel method to quantify the similarity between an impression (Q) from an unknown source and a test impression (K) from a known source. Using the property of geometrical congruence in the impressions, the degree of correspondence is quantified using ideas from graph theory and maximum clique (MC). The algorithm uses the x and y coordinates of the edges in the images as the data. We focus on local areas in Q and the corresponding regions in K and extract features for comparison. Using pairs of images with known origin, we train a random forest to classify pairs into mates and non-mates. We collected impressions from 60 pairs of shoes of the same brand and model, worn over six months. Using a different set of very similar shoes, we evaluated the performance of the algorithm in terms of the accuracy with which it correctly classified images into source classes. Using classification error rates and ROC curves, we compare the proposed method to other algorithms in the literature and show that for these data, our method shows good classification performance relative to other methods. The algorithm can be implemented with the R package shoeprintr.
Journal: Journal of Applied Statistics
Pages: 1833-1860
Issue: 10
Volume: 48
Year: 2021
Month: 07
X-DOI: 10.1080/02664763.2020.1779194
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1779194
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:10:p:1833-1860




Template-Type: ReDIF-Article 1.0
Author-Name: Lahiru Wickramasinghe
Author-X-Name-First: Lahiru
Author-X-Name-Last: Wickramasinghe
Author-Name: Alexandre Leblanc
Author-X-Name-First: Alexandre
Author-X-Name-Last: Leblanc
Author-Name: Saman Muthukumarana
Author-X-Name-First: Saman
Author-X-Name-Last: Muthukumarana
Title: Model-based estimation of baseball batting metrics
Abstract: 
 We introduce an approach to model the batting outcomes of baseball batters based on the weighted likelihood approach and make use of our methodology to estimate commonly used baseball batting metrics. The weighted likelihood allows the sharing of relevant information among players. Specifically, this allows the inference on each batter to make use of the batting data from all other players in the league and, in the process, allows for improved inference. MAMSE (Minimum Averaged Mean Squared Error) weights are used as the likelihood weights. For comparison, we implemented a semi-parametric Bayesian approach based on the Dirichlet process, which enables the borrowing of information across batters while providing a natural clustering mechanism. We demonstrate and compare these approaches using 2018 Major League Baseball (MLB) batters data.
Journal: Journal of Applied Statistics
Pages: 1775-1797
Issue: 10
Volume: 48
Year: 2021
Month: 07
X-DOI: 10.1080/02664763.2020.1775792
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1775792
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:10:p:1775-1797




Template-Type: ReDIF-Article 1.0
Author-Name: Jing Zhang
Author-X-Name-First: Jing
Author-X-Name-Last: Zhang
Author-Name: Yanyan Liu
Author-X-Name-First: Yanyan
Author-X-Name-Last: Liu
Title: Model-free slice screening for ultrahigh-dimensional survival data
Abstract: 
 For ultrahigh-dimensional data, independent feature screening has been demonstrated both theoretically and empirically to be an effective dimension reduction method with low computational demanding. Motivated by the Buckley–James method to accommodate censoring, we propose a fused Kolmogorov–Smirnov filter to screen out the irrelevant dependent variables for ultrahigh-dimensional survival data. The proposed model-free screening method can work with many types of covariates (e.g. continuous, discrete and categorical variables) and is shown to enjoy the sure independent screening property under mild regularity conditions without requiring any moment conditions on covariates. In particular, the proposed procedure can still be powerful when covariates are strongly dependent on each other. We further develop an iterative algorithm to enhance the performance of our method while dealing with the practical situations where some covariates may be marginally unrelated but jointly related to the response. We conduct extensive simulations to evaluate the finite-sample performance of the proposed method, showing that it has favourable exhibition over the existing typical methods. As an illustration, we apply the proposed method to the diffuse large-B-cell lymphoma study.
Journal: Journal of Applied Statistics
Pages: 1755-1774
Issue: 10
Volume: 48
Year: 2021
Month: 7
X-DOI: 10.1080/02664763.2020.1772734
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1772734
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:10:p:1755-1774




Template-Type: ReDIF-Article 1.0
Author-Name: Opeoluwa F. Oyedele
Author-X-Name-First: Opeoluwa F.
Author-X-Name-Last: Oyedele
Title: Extension of biplot methodology to multivariate regression analysis
Abstract: 
 At the core of multivariate statistics is the investigation of relationships between different sets of variables. More precisely, the inter-variable relationships and the causal relationships. The latter is a regression problem, where one set of variables is referred to as the response variables and the other set of variables as the predictor variables. In this situation, the effect of the predictors on the response variables is revealed through the regression coefficients. Results from the resulting regression analysis can be viewed graphically using the biplot. The consequential biplot provides a single graphical representation of the samples together with the predictor variables and response variables. In addition, their effect in terms of the regression coefficients can be visualized, although sub-optimally, in the said biplot.
Journal: Journal of Applied Statistics
Pages: 1816-1832
Issue: 10
Volume: 48
Year: 2021
Month: 07
X-DOI: 10.1080/02664763.2020.1779192
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1779192
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:10:p:1816-1832




Template-Type: ReDIF-Article 1.0
Author-Name: Muhammad Ijaz
Author-X-Name-First: Muhammad
Author-X-Name-Last: Ijaz
Author-Name: Wali Khan Mashwani
Author-X-Name-First: Wali Khan
Author-X-Name-Last: Mashwani
Author-Name: Atilla Göktaş
Author-X-Name-First: Atilla
Author-X-Name-Last: Göktaş
Author-Name: Yuksel Akay Unvan
Author-X-Name-First: Yuksel Akay
Author-X-Name-Last: Unvan
Title: RETRACTED ARTICLE: A novel alpha power transformed exponential distribution with real-life applications
Abstract: 
 We, the Editor-in-Chief and Publisher of Journal of Applied Statistics have retracted the following article, which was due to appear in a special issue:Muhammad Ijaz, Wali Khan Mashwani, Atilla Göktaş & Yuksel Akay Unvan (2021): A novel alpha power transformed exponential distribution with real-life applications, Journal of Applied Statistics. DOI: 10.1080/02664763.2020.1870673.The Editor-in-Chief and the Publisher are cognisant of clear evidence that the findings presented are unreliable. The probability distribution is only valid if α > 1 and numerous mathematical properties in Section 2 have been shown to be incorrect. This has then impacted at least two figures in the article.We are further cognisant that the article contained a number of similarities to previously published papers where some of the findings had been published without proper cross-referencing including:Gupta, R.D. and Kundu, D. (2001), Exponentiated Exponential Family: An Alternative to Gamma and Weibull Distributions. Biom. J., 43: 117–130. https://doi.org/10.1002/1521-4036(200102)43:1<117::AID-BIMJ117>3.0.CO;2-R.We have been informed in our decision-making by our corrections and editorial policies and the Committee on Publication Ethics (COPE) guidelines on retractions.The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as ‘Retracted’.The Editor-in-Chief and the Publisher would like to thank the anonymous reader/s for their comments which alerted JAS to these major errors in the first instance.
Journal: Journal of Applied Statistics
Pages: I-XVI
Issue: 11
Volume: 48
Year: 2021
Month: 08
X-DOI: 10.1080/02664763.2020.1870673
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1870673
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:11:p:I-XVI




Template-Type: ReDIF-Article 1.0
Author-Name: Sugnet Gardner-Lubbe
Author-X-Name-First: Sugnet
Author-X-Name-Last: Gardner-Lubbe
Title: Linear discriminant analysis for multiple functional data analysis
Abstract: 
 In multivariate data analysis, Fisher linear discriminant analysis is useful to optimally separate two classes of observations by finding a linear combination of p variables. Functional data analysis deals with the analysis of continuous functions and thus can be seen as a generalisation of multivariate analysis where the dimension of the analysis space p strives to infinity. Several authors propose methods to perform discriminant analysis in this infinite dimensional space. Here, the methodology is introduced to perform discriminant analysis, not on single infinite dimensional functions, but to find a linear combination of p infinite dimensional continuous functions, providing a set of continuous canonical functions which are optimally separated in the canonical space.
Journal: Journal of Applied Statistics
Pages: 1917-1933
Issue: 11
Volume: 48
Year: 2021
Month: 08
X-DOI: 10.1080/02664763.2020.1780569
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1780569
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:11:p:1917-1933




Template-Type: ReDIF-Article 1.0
Author-Name: Fernando de Souza Bastos
Author-X-Name-First: Fernando de Souza
Author-X-Name-Last: Bastos
Author-Name: Wagner Barreto-Souza
Author-X-Name-First: Wagner
Author-X-Name-Last: Barreto-Souza
Title: Birnbaum–Saunders sample selection model
Abstract: 
 The sample selection bias problem occurs when the outcome of interest is only observed according to some selection rule, where there is a dependence structure between the outcome and the selection rule. In a pioneering work, J. Heckman proposed a sample selection model based on a bivariate normal distribution for dealing with this problem. Due to the non-robustness of the normal distribution, many alternatives have been introduced in the literature by assuming extensions of the normal distribution like the Student-t and skew-normal models. One common limitation of the existent sample selection models is that they require a transformation of the outcome of interest, which is common 
 $\mathbb {R}^+ $R+-valued, such as income and wage. With this, data are analyzed on a non-original scale which complicates the interpretation of the parameters. In this paper, we propose a sample selection model based on the bivariate Birnbaum–Saunders distribution, which has the same number of parameters that the classical Heckman model. Further, our associated outcome equation is 
 $\mathbb R^+ $R+-valued. We discuss estimation by maximum likelihood and present some Monte Carlo simulation studies. An empirical application to the ambulatory expenditures data from the 2001 Medical Expenditure Panel Survey is presented.
Journal: Journal of Applied Statistics
Pages: 1896-1916
Issue: 11
Volume: 48
Year: 2021
Month: 08
X-DOI: 10.1080/02664763.2020.1780570
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1780570
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:11:p:1896-1916




Template-Type: ReDIF-Article 1.0
Author-Name: Ana R. S. Silva
Author-X-Name-First: Ana R. S.
Author-X-Name-Last: Silva
Author-Name: Caio L. N. Azevedo
Author-X-Name-First: Caio L. N.
Author-X-Name-Last: Azevedo
Author-Name: Jorge L. Bazán
Author-X-Name-First: Jorge L.
Author-X-Name-Last: Bazán
Author-Name: Juvêncio S. Nobre
Author-X-Name-First: Juvêncio S.
Author-X-Name-Last: Nobre
Title: Augmented-limited regression models with an application to the study of the risk perceived using continuous scales
Abstract: 
 Studies of risk perceived using continuous scales of [0,100] were recently introduced in psychometrics, which can be transformed to the unit interval, but the presence of zeros or ones are commonly observed. Motivated by this, we introduce a full inferential set of tools that allows for augmented and limited data modeling. We considered parameter estimation, residual analysis, influence diagnostic and model selection for zero-and/or-one augmented beta rectangular (ZOABR) regression models and their particular nested models, which is based on a new parameterization of the beta rectangular distribution. Different from other alternatives, we performed maximum-likelihood estimation using a combination of the EM algorithm (for the continuous part) and Fisher scoring algorithm (for the discrete part). Also, we perform an additional step, by considering other link functions, besides the usual logistic link, for modeling the response mean. By considering randomized quantile residuals, (local) influence diagnostics and model selection tools, we identified that the ZOABR regression model is the best one. We also conducted extensive simulations studies, which indicate that all developed tools work properly. Finally, we discuss the use of this type of models to treat psychometric data. It is worthwhile to mention that applications of the developed methods go beyond to Psychometric data. Indeed, they can be useful when the response variable in bounded, including or not the respective limits.
Journal: Journal of Applied Statistics
Pages: 1998-2021
Issue: 11
Volume: 48
Year: 2021
Month: 08
X-DOI: 10.1080/02664763.2020.1783518
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1783518
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:11:p:1998-2021




Template-Type: ReDIF-Article 1.0
Author-Name: Xingli Yang
Author-X-Name-First: Xingli
Author-X-Name-Last: Yang
Author-Name: Yu Wang
Author-X-Name-First: Yu
Author-X-Name-Last: Wang
Author-Name: Wennan Yan
Author-X-Name-First: Wennan
Author-X-Name-Last: Yan
Author-Name: Jihong Li
Author-X-Name-First: Jihong
Author-X-Name-Last: Li
Title: Variance estimation based on blocked 3×2 cross-validation in high-dimensional linear regression
Abstract: 
 In high-dimensional linear regression, the dimension of variables is always greater than the sample size. In this situation, the traditional variance estimation technique based on ordinary least squares constantly exhibits a high bias even under sparsity assumption. One of the major reasons is the high spurious correlation between unobserved realized noise and several predictors. To alleviate this problem, a refitted cross-validation (RCV) method has been proposed in the literature. However, for a complicated model, the RCV exhibits a lower probability that the selected model includes the true model in case of finite samples. This phenomenon may easily result in a large bias of variance estimation. Thus, a model selection method based on the ranks of the frequency of occurrences in six votes from a blocked 3×2 cross-validation is proposed in this study. The proposed method has a considerably larger probability of including the true model in practice than the RCV method. The variance estimation obtained using the model selected by the proposed method also shows a lower bias and a smaller variance. Furthermore, theoretical analysis proves the asymptotic normality property of the proposed variance estimation.
Journal: Journal of Applied Statistics
Pages: 1934-1947
Issue: 11
Volume: 48
Year: 2021
Month: 08
X-DOI: 10.1080/02664763.2020.1780571
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1780571
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:11:p:1934-1947




Template-Type: ReDIF-Article 1.0
Author-Name: Zequn Sun
Author-X-Name-First: Zequn
Author-X-Name-Last: Sun
Author-Name: Thomas J. Fisher
Author-X-Name-First: Thomas J.
Author-X-Name-Last: Fisher
Title: Testing for correlation between two time series using a parametric bootstrap
Abstract: 
 We study the problem of determining if two time series are correlated in the mean and variance. Several test statistics, originally designed for determining the correlation between two mean processes or goodness-of-fit testing, are explored and formally introduced for determining cross-correlation in variance. Simulations demonstrate the theoretical asymptotic distribution can be ineffective in finite samples. Parametric bootstrapping is shown to be an effective tool in such an enterprise. A large simulation study is provided demonstrating the efficacy of the bootstrapping method. Lastly, an empirical example explores a correlation between the Standard & Poor's 500 index and the Euro/US dollar exchange rate while also demonstrating a level of robustness for the proposed method.
Journal: Journal of Applied Statistics
Pages: 2042-2063
Issue: 11
Volume: 48
Year: 2021
Month: 08
X-DOI: 10.1080/02664763.2020.1783519
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1783519
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:11:p:2042-2063




Template-Type: ReDIF-Article 1.0
Author-Name: Corrine F. Elliott
Author-X-Name-First: Corrine F.
Author-X-Name-Last: Elliott
Author-Name: Joshua W. Lambert
Author-X-Name-First: Joshua W.
Author-X-Name-Last: Lambert
Author-Name: Arnold J. Stromberg
Author-X-Name-First: Arnold J.
Author-X-Name-Last: Stromberg
Author-Name: Pei Wang
Author-X-Name-First: Pei
Author-X-Name-Last: Wang
Author-Name: Ting Zeng
Author-X-Name-First: Ting
Author-X-Name-Last: Zeng
Author-Name: Katherine L. Thompson
Author-X-Name-First: Katherine L.
Author-X-Name-Last: Thompson
Title: Feasibility as a mechanism for model identification and validation
Abstract: 
 As new technologies permit the generation of hitherto unprecedented volumes of data (e.g. genome-wide association study data), researchers struggle to keep up with the added complexity and time commitment required for its analysis. For this reason, model selection commonly relies on machine learning and data-reduction techniques, which tend to afford models with obscure interpretations. Even in cases with straightforward explanatory variables, the so-called ‘best’ model produced by a given model-selection technique may fail to capture information of vital importance to the domain-specific questions at hand. Herein we propose a new concept for model selection, feasibility, for use in identifying multiple models that are in some sense optimal and may unite to provide a wider range of information relevant to the topic of interest, including (but not limited to) interaction terms. We further provide an R package and associated Shiny Applications for use in identifying or validating feasible models, the performance of which we demonstrate on both simulated and real-life data.
Journal: Journal of Applied Statistics
Pages: 2022-2041
Issue: 11
Volume: 48
Year: 2021
Month: 08
X-DOI: 10.1080/02664763.2020.1783522
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1783522
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:11:p:2022-2041




Template-Type: ReDIF-Article 1.0
Author-Name: M. S. Eliwa
Author-X-Name-First: M. S.
Author-X-Name-Last: Eliwa
Author-Name: M. El-Morshedy
Author-X-Name-First: M.
Author-X-Name-Last: El-Morshedy
Author-Name: Sajid Ali
Author-X-Name-First: Sajid
Author-X-Name-Last: Ali
Title: Exponentiated odd Chen-G family of distributions: statistical properties, Bayesian and non-Bayesian estimation with applications
Abstract: 
 In this paper, a new flexible generator of distributions is proposed. Some of its fundamental properties including quantile, skewness, kurtosis, hazard rate function, moments, mean deviations, mean time to failure, mean time between failure, availability and reliability function of consecutive linear and circular systems are studied. The hazard rate function can be increasing, decreasing, unimodal-bathtub, unimodal, bathtub, J and inverse J-shaped depending on its parameters values. After introducing the general class, two special models of the new family are discussed in detail. Maximum likelihood and Bayesian methods are used to estimate the model parameters. A detailed simulation study is carried out to examine the bias and mean square error of maximum likelihood and Bayesian estimators. We also illustrate the importance of the new family by means of two distinctive real data sets. It can serve as an alternative model to other lifetime distributions in the existing statistical literature for modeling positive and negative real data in many areas.
Journal: Journal of Applied Statistics
Pages: 1948-1974
Issue: 11
Volume: 48
Year: 2021
Month: 08
X-DOI: 10.1080/02664763.2020.1783520
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1783520
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:11:p:1948-1974




Template-Type: ReDIF-Article 1.0
Author-Name: The Editors
Title: Statement of Retraction: A novel alpha power transformed exponential distribution with real-life applications
Journal: Journal of Applied Statistics
Pages: 2064-2064
Issue: 11
Volume: 48
Year: 2021
Month: 08
X-DOI: 10.1080/02664763.2021.1955521
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1955521
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:11:p:2064-2064




Template-Type: ReDIF-Article 1.0
Author-Name: Masoumeh Shirozhan
Author-X-Name-First: Masoumeh
Author-X-Name-Last: Shirozhan
Author-Name: Mehrnaz Mohammadpour
Author-X-Name-First: Mehrnaz
Author-X-Name-Last: Mohammadpour
Title: A dependent counting INAR model with serially dependent innovation
Abstract: 
 To provide a more flexible model of count data, we extend the first-order integer-valued autoregressive model with serially dependent innovations based on the dependent thinning operator. This model is appropriate for modelling the number of dependent random events affecting each other when the number of new cases depend on the previous count through a linear functional relationship. Several statistical properties of the model are determined, parameters are estimated by some methods and their properties are studied via simulations. This study was carried out to investigate the efficiency of the new model by two real count data sets, the number of contagious diseases and robbery.
Journal: Journal of Applied Statistics
Pages: 1975-1997
Issue: 11
Volume: 48
Year: 2021
Month: 08
X-DOI: 10.1080/02664763.2020.1783521
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1783521
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:11:p:1975-1997




Template-Type: ReDIF-Article 1.0
Author-Name: Katherine Davies
Author-X-Name-First: Katherine
Author-X-Name-Last: Davies
Author-Name: Suvra Pal
Author-X-Name-First: Suvra
Author-X-Name-Last: Pal
Author-Name: Joynob A. Siddiqua
Author-X-Name-First: Joynob A.
Author-X-Name-Last: Siddiqua
Title: Stochastic EM algorithm for generalized exponential cure rate model and an empirical study
Abstract: 
 In this paper, we consider two well-known parametric long-term survival models, namely, the Bernoulli cure rate model and the promotion time (or Poisson) cure rate model. Assuming the long-term survival probability to depend on a set of risk factors, the main contribution is in the development of the stochastic expectation maximization (SEM) algorithm to determine the maximum likelihood estimates of the model parameters. We carry out a detailed simulation study to demonstrate the performance of the proposed SEM algorithm. For this purpose, we assume the lifetimes due to each competing cause to follow a two-parameter generalized exponential distribution. We also compare the results obtained from the SEM algorithm with those obtained from the well-known expectation maximization (EM) algorithm. Furthermore, we investigate a simplified estimation procedure for both SEM and EM algorithms that allow the objective function to be maximized to split into simpler functions with lower dimensions with respect to model parameters. Moreover, we present examples where the EM algorithm fails to converge but the SEM algorithm still works. For illustrative purposes, we analyze a breast cancer survival data. Finally, we use a graphical method to assess the goodness-of-fit of the model with generalized exponential lifetimes.
Journal: Journal of Applied Statistics
Pages: 2112-2135
Issue: 12
Volume: 48
Year: 2021
Month: 09
X-DOI: 10.1080/02664763.2020.1786676
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1786676
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:12:p:2112-2135




Template-Type: ReDIF-Article 1.0
Author-Name: Piotr Sulewski
Author-X-Name-First: Piotr
Author-X-Name-Last: Sulewski
Title: Equal-bin-width histogram versus equal-bin-count histogram
Abstract: 
 The histogram has all its bin widths equal to some non-random number arbitrary set by an analyst (EBWH). In the result, particular bin counts are random variables. This paper presents also a histogram that is constructed in a converse manner. Bin counts are all equal to some non-random number arbitrary set by an analyst (EBCH). In the result, particular bin widths are random variables. The first goal of the paper is a choose of constant bin width (of bin numbers k) in the EBWH, which maximize the similarity measure in the Monte Carlo simulation. The second goal is a choose of constant bin count in the EBCH, which maximize the similarity measure in the Monte Carlo simulation. The third goal is to present similarity measures between empirical and theoretical data. The fourth goal is the comparative analysis of two histogram methods by means of the frequency formula. The first additional goal is a tip how to proceed in EBCH when modulo(n,k)≠0. The second additional goal is the software in the form of a Mathcad file with the implementation of EBWH and EBCH.
Journal: Journal of Applied Statistics
Pages: 2092-2111
Issue: 12
Volume: 48
Year: 2021
Month: 09
X-DOI: 10.1080/02664763.2020.1784853
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1784853
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:12:p:2092-2111




Template-Type: ReDIF-Article 1.0
Author-Name: Yousri Slaoui
Author-X-Name-First: Yousri
Author-X-Name-Last: Slaoui
Title: Two new nonparametric kernel distribution estimators based on a transformation of the data
Abstract: 
 In this paper, we propose two kernel distribution estimators based on a data transformation. We study the properties of these estimators and we compare them with two conventional estimators. It appears that with an appropriate choice of the parameters of the two proposed estimators, the convergence rate of two estimators will be faster than that of the two conventional estimators and the Mean Integrated Square Error will be smaller than the two conventional estimators. We corroborate these theoretical results through simulations as well as a real data set.
Journal: Journal of Applied Statistics
Pages: 2065-2091
Issue: 12
Volume: 48
Year: 2021
Month: 09
X-DOI: 10.1080/02664763.2020.1786675
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1786675
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:12:p:2065-2091




Template-Type: ReDIF-Article 1.0
Author-Name: Phuong Hanh Tran
Author-X-Name-First: Phuong Hanh
Author-X-Name-Last: Tran
Author-Name: Cédric Heuchenne
Author-X-Name-First: Cédric
Author-X-Name-Last: Heuchenne
Author-Name: Huu Du Nguyen
Author-X-Name-First: Huu Du
Author-X-Name-Last: Nguyen
Author-Name: Hélène Marie
Author-X-Name-First: Hélène
Author-X-Name-Last: Marie
Title: Monitoring coefficient of variation using one-sided run rules control charts in the presence of measurement errors
Abstract: 
 We investigate, in this paper, the effect of the measurement error (ME) on the performance of Run Rules control charts monitoring the coefficient of variation (CV) squared. The previous Run Rules CV chart in the literature is improved slightly by monitoring the CV squared using two one-sided Run Rules charts instead of monitoring the CV itself using a two-sided chart. The numerical results show that this improvement gives better performance in detecting process shifts. Moreover, we will show through simulation that the precision and accuracy errors do have a negative effect on the performance of the proposed Run Rules charts. We also find out that taking multiple measurements per item is not an effective way to reduce these negative effects. The proposed Run Rules control charts can be applied in the anomaly detection area.
Journal: Journal of Applied Statistics
Pages: 2178-2204
Issue: 12
Volume: 48
Year: 2021
Month: 09
X-DOI: 10.1080/02664763.2020.1787356
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1787356
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:12:p:2178-2204




Template-Type: ReDIF-Article 1.0
Author-Name: Q.F. Xu
Author-X-Name-First: Q.F.
Author-X-Name-Last: Xu
Author-Name: X.H. Ding
Author-X-Name-First: X.H.
Author-X-Name-Last: Ding
Author-Name: C.X. Jiang
Author-X-Name-First: C.X.
Author-X-Name-Last: Jiang
Author-Name: K.M. Yu
Author-X-Name-First: K.M.
Author-X-Name-Last: Yu
Author-Name: L. Shi
Author-X-Name-First: L.
Author-X-Name-Last: Shi
Title: An elastic-net penalized expectile regression with applications
Abstract: 
 To perform variable selection in expectile regression, we introduce the elastic-net penalty into expectile regression and propose an elastic-net penalized expectile regression (ER-EN) model. We then adopt the semismooth Newton coordinate descent (SNCD) algorithm to solve the proposed ER-EN model in high-dimensional settings. The advantages of ER-EN model are illustrated via extensive Monte Carlo simulations. The numerical results show that the ER-EN model outperforms the elastic-net penalized least squares regression (LSR-EN), the elastic-net penalized Huber regression (HR-EN), the elastic-net penalized quantile regression (QR-EN) and conventional expectile regression (ER) in terms of variable selection and predictive ability, especially for asymmetric distributions. We also apply the ER-EN model to two real-world applications: relative location of CT slices on the axial axis and metabolism of tacrolimus (Tac) drug. Empirical results also demonstrate the superiority of the ER-EN model.
Journal: Journal of Applied Statistics
Pages: 2205-2230
Issue: 12
Volume: 48
Year: 2021
Month: 09
X-DOI: 10.1080/02664763.2020.1787355
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1787355
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:12:p:2205-2230




Template-Type: ReDIF-Article 1.0
Author-Name: Sukru Acitas
Author-X-Name-First: Sukru
Author-X-Name-Last: Acitas
Author-Name: Ismail Yenilmez
Author-X-Name-First: Ismail
Author-X-Name-Last: Yenilmez
Author-Name: Birdal Senoglu
Author-X-Name-First: Birdal
Author-X-Name-Last: Senoglu
Author-Name: Yeliz Mert Kantar
Author-X-Name-First: Yeliz Mert
Author-X-Name-Last: Kantar
Title: Modified maximum likelihood estimator under the Jones and Faddy's skew t-error distribution for censored regression model
Abstract: 
 It is well-known that classical Tobit estimator of the parameters of the censored regression (CR) model is inefficient in case of non-normal error terms. In this paper, we propose to use the modified maximum likelihood (MML) estimator under the Jones and Faddy's skew t-error distribution, which covers a wide range of skew and symmetric distributions, for the CR model. The MML estimators, providing an alternative to the Tobit estimator, are explicitly expressed and they are asymptotically equivalent to the maximum likelihood estimator. A simulation study is conducted to compare the efficiencies of the MML estimators with the classical estimators such as the ordinary least squares, Tobit, censored least absolute deviations and symmetrically trimmed least squares estimators. The results of the simulation study show that the MML estimators work well among the others with respect to the root mean square error criterion for the CR model. A real life example is also provided to show the suitability of the MML methodology.
Journal: Journal of Applied Statistics
Pages: 2136-2151
Issue: 12
Volume: 48
Year: 2021
Month: 09
X-DOI: 10.1080/02664763.2020.1786673
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1786673
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:12:p:2136-2151




Template-Type: ReDIF-Article 1.0
Author-Name: Chong Sheng
Author-X-Name-First: Chong
Author-X-Name-Last: Sheng
Author-Name: Matthias Hwai Yong Tan
Author-X-Name-First: Matthias Hwai Yong
Author-X-Name-Last: Tan
Author-Name: Lu Zou
Author-X-Name-First: Lu
Author-X-Name-Last: Zou
Title: Maximum expected entropy transformed Latin hypercube designs
Abstract: 
 Existing projection designs (e.g. maximum projection designs) attempt to achieve good space-filling properties in all projections. However, when using a Gaussian process (GP), model-based design criteria such as the entropy criterion is more appropriate. We employ the entropy criterion averaged over a set of projections, called expected entropy criterion (EEC), to generate projection designs. We show that maximum EEC designs are invariant to monotonic transformations of the response, i.e. they are optimal for a wide class of stochastic process models. We also demonstrate that transformation of each column of a Latin hypercube design (LHD) based on a monotonic function can substantially improve the EEC. Two types of input transformations are considered: a quantile function of a symmetric Beta distribution chosen to optimize the EEC, and a nonparametric transformation corresponding to the quantile function of a symmetric density chosen to optimize the EEC. Numerical studies show that the proposed transformations of the LHD are efficient and effective for building robust maximum EEC designs. These designs give projections with markedly higher entropies and lower maximum prediction variances (MPV's) at the cost of small increases in average prediction variances (APV's) compared to state-of-the-art space-filling designs over wide ranges of covariance parameter values.
Journal: Journal of Applied Statistics
Pages: 2152-2177
Issue: 12
Volume: 48
Year: 2021
Month: 09
X-DOI: 10.1080/02664763.2020.1786674
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1786674
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:12:p:2152-2177




Template-Type: ReDIF-Article 1.0
Author-Name: Atakan Gerger
Author-X-Name-First: Atakan
Author-X-Name-Last: Gerger
Author-Name: Ali Riza Firuzan
Author-X-Name-First: Ali Riza
Author-X-Name-Last: Firuzan
Title: Taguchi based Case study in the automotive industry: nonconformity decreasing with use of Six Sigma methodology
Abstract: 
 In this study, we applied a conceptual Six Sigma/design of experiment hybrid framework that aims to integrate the Taguchi method and Six Sigma for process improvement in a complex industrial environment. In this context, the Six Sigma methodology was employed on a company operating within the automotive industry to improve a manufacturing process which caused a customer complaint within the company. Studies employing the Taguchi experiment design usually focus on a single variable and neglect the effects of adjustments on remaining quality characteristics. In this study, a multi-response Taguchi design of experiment was preferred, and all of the quality characteristics were taken into account. In our study, define, measure, analysis, improve and control phases were used to reduce the nonconformity rate from 23.940 percent (baseline) to 0.049 percent. As a result of implementing Six Sigma, the sigma level increased from 2.21 (baseline) to 4.80.
Journal: Journal of Applied Statistics
Pages: 2889-2905
Issue: 13-15
Volume: 48
Year: 2021
Month: 11
X-DOI: 10.1080/02664763.2020.1837086
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1837086
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:13-15:p:2889-2905




Template-Type: ReDIF-Article 1.0
Author-Name: Necla Gündüz
Author-X-Name-First: Necla
Author-X-Name-Last: Gündüz
Author-Name: Celal Aydın
Author-X-Name-First: Celal
Author-X-Name-Last: Aydın
Title: Optimal bandwidth estimators of kernel density functionals for contaminated data
Abstract: 
 In this study, we provide simulation-based exploration and characterization of the two most crucial kernel density functionals that play a central role in kernel density estimation, considering the probability density functions that are members of the location-scale family. Kernel density functional estimates are known to rely on the choice of preliminary bandwidth. Normal-scale estimators are commonly used to obtain preliminary bandwidth estimates, with the assumption that the data come from normal distribution. Here, we present an alternative approach, called the Cauchy-scale estimators, to obtain preliminary bandwidth estimates. In this approach, data are assumed to come from a Cauchy distribution. Furthermore, analysis results related to the sampling distribution of bandwidth estimators based on the normal- and Cauchy-scale approaches are presented. As a case study, we provide a comprehensive characterization of different contamination levels with a simulation study constructed for the random samples from normal distributions with various parameters and various contamination levels. The proposed preliminary bandwidth selection shows lower variance in both mixture and contaminated data in our simulations. Besides, functional bandwidth presents results similar to the simulation results in the applications we made on the real data set.
Journal: Journal of Applied Statistics
Pages: 2239-2258
Issue: 13-15
Volume: 48
Year: 2021
Month: 11
X-DOI: 10.1080/02664763.2021.1944999
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1944999
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:13-15:p:2239-2258




Template-Type: ReDIF-Article 1.0
Author-Name: Pakize Taylan
Author-X-Name-First: Pakize
Author-X-Name-Last: Taylan
Author-Name: Fatma Yerlikaya-Özkurt
Author-X-Name-First: Fatma
Author-X-Name-Last: Yerlikaya-Özkurt
Author-Name: Burcu Bilgiç Uçak
Author-X-Name-First: Burcu
Author-X-Name-Last: Bilgiç Uçak
Author-Name: Gerhard-Wilhelm Weber
Author-X-Name-First: Gerhard-Wilhelm
Author-X-Name-Last: Weber
Title: A new outlier detection method based on convex optimization: application to diagnosis of Parkinson’s disease
Abstract: 
 Neuroscience is a combination of different scientific disciplines which investigate the nervous system for understanding of the biological basis. Recently, applications to the diagnosis of neurodegenerative diseases like Parkinson’s disease have become very promising by considering different statistical regression models. However, well-known statistical regression models may give misleading results for the diagnosis of the neurodegenerative diseases when experimental data contain outlier observations that lie an abnormal distance from the other observation. The main achievements of this study consist of a novel mathematics-supported approach beside statistical regression models to identify and treat the outlier observations without direct elimination for a great and emerging challenge in humankind, such as neurodegenerative diseases. By this approach, a new method named as CMTMSOM is proposed with the contributions of the powerful convex and continuous optimization techniques referred to as conic quadratic programing. This method, based on the mean-shift outlier regression model, is developed by combining robustness of M-estimation and stability of Tikhonov regularization. We apply our method and other parametric models on Parkinson telemonitoring dataset which is a real-world dataset in Neuroscience. Then, we compare these methods by using well-known method-free performance measures. The results indicate that the CMTMSOM method performs better than current parametric models.
Journal: Journal of Applied Statistics
Pages: 2421-2440
Issue: 13-15
Volume: 48
Year: 2021
Month: 11
X-DOI: 10.1080/02664763.2020.1864815
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1864815
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:13-15:p:2421-2440




Template-Type: ReDIF-Article 1.0
Author-Name: Asuman Yılmaz
Author-X-Name-First: Asuman
Author-X-Name-Last: Yılmaz
Author-Name: Mahmut Kara
Author-X-Name-First: Mahmut
Author-X-Name-Last: Kara
Author-Name: Onur Özdemir
Author-X-Name-First: Onur
Author-X-Name-Last: Özdemir
Title: Comparison of different estimation methods for extreme value distribution
Abstract: 
 The extreme value distribution was developed for modeling extreme-order statistics or extreme events. In this study, we discuss the distribution of the largest extreme. The main objective of this paper is to determine the best estimators of the unknown parameters of the extreme value distribution. Thus, both classical and Bayesian methods are used. The classical estimation methods under consideration are maximum likelihood estimators, moment’s estimators, least squares estimators, and weighted least squares estimators, percentile estimators, the ordinary least squares estimators, best linear unbiased estimators, L-moments estimators, trimmed L-moments estimators, and Bain and Engelhardt estimators. We also propose new estimators for the unknown parameters. Bayesian estimators of the parameters are derived by using Lindley’s approximation and Markov Chain Monte Carlo methods. The asymptotic confidence intervals are considered by using maximum likelihood estimators. The Bayesian credible intervals are also obtained by using Gibbs sampling. The performances of these estimation methods are compared with respect to their biases and mean square errors through a simulation study. The maximum daily flood discharge (annual) data sets of the Meriç River and Feather River are analyzed at the end of the study for a better understanding of the methods presented in this paper.
Journal: Journal of Applied Statistics
Pages: 2259-2284
Issue: 13-15
Volume: 48
Year: 2021
Month: 11
X-DOI: 10.1080/02664763.2021.1940109
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1940109
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:13-15:p:2259-2284




Template-Type: ReDIF-Article 1.0
Author-Name: Esmat Jamshidi Eini
Author-X-Name-First: Esmat Jamshidi
Author-X-Name-Last: Eini
Author-Name: Hamid Khaloozadeh
Author-X-Name-First: Hamid
Author-X-Name-Last: Khaloozadeh
Title: Tail conditional moment for generalized skew-elliptical distributions
Abstract: 
 Substantial changes in the financial markets and insurance companies have needed the development of the structure of the risk benchmark, which is the challenge addressed in this paper. We propose a theorem that expands the tail conditional moment (TCM) measure from elliptical distributions to wider classes of skew-elliptical distributions. This family of distributions is suitable for modeling asymmetric phenomena. We obtain the analytical formula for the 
 $ {n^{\textrm{th}}} $ nth TCM for skew-elliptical distributions to help well to figure out the risk behavior along the tail of loss distributions. We derive four significant results and generalize the tail conditional skewness (TCS) and the tail conditional kurtosis (TCK) measures for generalized skew-elliptical distributions, which are used to determine the skewness and the kurtosis in the tail of loss distributions. The proposed TCM measure has been applied to well-known families of generalized skew-elliptical distributions. We also provide a practical example of a portfolio problem by calculating the proposed TCM measure for the weighted sum of generalized skew-elliptical distributions.
Journal: Journal of Applied Statistics
Pages: 2285-2305
Issue: 13-15
Volume: 48
Year: 2021
Month: 11
X-DOI: 10.1080/02664763.2021.1896687
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1896687
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:13-15:p:2285-2305




Template-Type: ReDIF-Article 1.0
Author-Name: O. Ozan Evkaya
Author-X-Name-First: O.
Author-X-Name-Last: Ozan Evkaya
Author-Name: Fatma Sevinç Kurnaz
Author-X-Name-First: Fatma
Author-X-Name-Last: Sevinç Kurnaz
Title: Forecasting drought using neural network approaches with transformed time series data
Abstract: 
 Drought is one of the important and costliest disaster all over the world. With the accelerated progress of climate change, its frequency of occurrence and negative impacts are rapidly increasing. It is crucial to initiate and sustain an early warning system to monitor and predict the possible impacts of future droughts. Recently, with the rise of data driven models, various case studies are conducted by using Machine Learning algorithms instead of using pure statistical approaches. The main goal of this paper is to conduct a drought forecasting study for a weather station located in Marmara Region. For that purpose, firstly, widely used univariate drought index, Standardized Precipitation Index is calculated for Bursa station. Thereafter, both the historical information retrieved from time series data and its wavelet transformation are considered to investigate Nonlinear Auto-Regressive and Nonlinear Auto-Regressive with External Input (NARX) type Neural Network (NN) models. According to a pool of Goodness-of-Fit (GOF) tests, the forecasting performance of the models with various number of hidden neurons are compared. The recent findings of the study showed that considering the data with its wavelet transformation under (NARX-NN) has benefits to increase the capacity of forecasting the drought index.
Journal: Journal of Applied Statistics
Pages: 2591-2606
Issue: 13-15
Volume: 48
Year: 2021
Month: 11
X-DOI: 10.1080/02664763.2020.1867829
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1867829
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:13-15:p:2591-2606




Template-Type: ReDIF-Article 1.0
Author-Name: Berna Yazici
Author-X-Name-First: Berna
Author-X-Name-Last: Yazici
Author-Name: Mustafa Cavus
Author-X-Name-First: Mustafa
Author-X-Name-Last: Cavus
Title: A comparative study of computation approaches of the generalized F-test
Abstract: 
 The Generalized F-test is derived based on the Generalized P-value Method to test the equality of normally distributed group means under unequal variances. There are two approaches to compute the p-value of the GF test, based on beta and chi-squared random numbers. From prior art in the literature, it appears that the two computation approaches of the Generalized tests are equivalent. In this study, the equivalence of these approaches is investigated in an extensive Monte-Carlo simulation study in terms of Type I error probability and penalized power. It is found that the equivalence of the computation approaches is not quite correct and that there is a difference between their conclusion, and researchers should decide which one is powerful than the others according to the structure of data, such as sample size, and the number of groups. Also, real data examples are given to show the opposite decisions of the computation approaches.
Journal: Journal of Applied Statistics
Pages: 2906-2919
Issue: 13-15
Volume: 48
Year: 2021
Month: 11
X-DOI: 10.1080/02664763.2021.1939660
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1939660
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:13-15:p:2906-2919




Template-Type: ReDIF-Article 1.0
Author-Name: M. Esra Atukalp
Author-X-Name-First: M. Esra
Author-X-Name-Last: Atukalp
Title: Determining the relationship between stock return and financial performance: an analysis on Turkish deposit banks
Abstract: 
 Banks play a very important role in financial markets due to their intermediary function. The availability of financing to businesses and individuals, the prevalence of branches throughout the country as well as the preference status at the collection point as a result of the habits of savings holders, have made deposit banks more active among other financial institutions. Since the banking system affects the whole economy, their performance and their performance evaluation become important. Performance measurement can be defined as one of the most important issues in the financial field. In this study, the relationship between stock return and financial performance of Turkish deposit banks was examined via CRITIC method, TOPSIS method and Spearman’s rank correlation analysis for 2014–2018 periods. According to the results of the analysis, there is no statistically significant correlation between the stock return ranking and financial performance rankings of deposit banks in Turkey.
Journal: Journal of Applied Statistics
Pages: 2643-2657
Issue: 13-15
Volume: 48
Year: 2021
Month: 11
X-DOI: 10.1080/02664763.2020.1849056
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1849056
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:13-15:p:2643-2657




Template-Type: ReDIF-Article 1.0
Author-Name: Ding-Geng Chen
Author-X-Name-First: Ding-Geng
Author-X-Name-Last: Chen
Author-Name: Haipeng Gao
Author-X-Name-First: Haipeng
Author-X-Name-Last: Gao
Author-Name: Chuanshu Ji
Author-X-Name-First: Chuanshu
Author-X-Name-Last: Ji
Author-Name: Xinguang Chen
Author-X-Name-First: Xinguang
Author-X-Name-Last: Chen
Title: Stochastic cusp catastrophe model and its Bayesian computations
Abstract: 
 This paper revitalizes the investigation of the classical cusp catastrophe model in catastrophe theory and tackles the unsolved statistical inference problem concerning stochastic cusp differential equation. This model is challenging because its associated transition density hence the likelihood function is analytically intractable. We propose a novel Bayesian approach combining Hamiltonian Monte Carlo with two likelihood approximation methods, namely, Euler approximation and Hermite expansion. We validate this novel approach through a series of simulation studies. We further demonstrate potential application of this novel approach using the real USD/EUR exchange rate.
Journal: Journal of Applied Statistics
Pages: 2714-2733
Issue: 13-15
Volume: 48
Year: 2021
Month: 11
X-DOI: 10.1080/02664763.2021.1922993
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1922993
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:13-15:p:2714-2733




Template-Type: ReDIF-Article 1.0
Author-Name: Atacan Erdiş
Author-X-Name-First: Atacan
Author-X-Name-Last: Erdiş
Author-Name: M. Akif Bakir
Author-X-Name-First: M.
Author-X-Name-Last: Akif Bakir
Author-Name: Muhammed I. Jaiteh
Author-X-Name-First: Muhammed
Author-X-Name-Last: I. Jaiteh
Title: A method for detection of Mode-Mixing problem
Abstract: 
 Classical Empirical Mode Decomposition (EMD) is a data-driven method used to analyze non-linear and non-stationary time series data. Besides being an adaptable method by its nature, EMD assumes that every data consists of oscillations of the intrinsic mode functions (IMF). EMD also requires the condition that IMFs which represent the characteristic structures in the data should show only a unique sub-characteristic of the data. However, in some cases, depending on the way the sub-characteristics which make up a sophisticated data coexist, the IMFs are able to be not unique. This is called the mode-mixing problem. Although there are many studies and successful methods (such as EEMD, CEEMDAN) for eliminating the mode-mixing problem, a limited number of studies exist on determining the presence of the aforementioned problem. In this study, a method for the determination of the mode-mixing problem is proposed. In the suggested method, the Itakura–Saito distance, which is a measurement of the similarity of stationary signals and based on Fourier spectrums, is modified by applying Kaiser filter onto short-time signals. The performance of the method is tested via various applications with simulated and real data, and the results show successful detection of the mode-mixing if it exists in time series.
Journal: Journal of Applied Statistics
Pages: 2847-2863
Issue: 13-15
Volume: 48
Year: 2021
Month: 11
X-DOI: 10.1080/02664763.2021.1908969
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1908969
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:13-15:p:2847-2863




Template-Type: ReDIF-Article 1.0
Author-Name: Naoya Yamaguchi
Author-X-Name-First: Naoya
Author-X-Name-Last: Yamaguchi
Author-Name: Yuka Yamaguchi
Author-X-Name-First: Yuka
Author-X-Name-Last: Yamaguchi
Author-Name: Ryuei Nishii
Author-X-Name-First: Ryuei
Author-X-Name-Last: Nishii
Title: Minimizing the expected value of the asymmetric loss function and an inequality for the variance of the loss
Abstract: 
 The coefficients of regression are usually estimated for minimization problems with asymmetric loss functions. In this paper, we rather correct predictions so that the prediction error follows a generalized Gaussian distribution. In our method, we not only minimize the expected value of the asymmetric loss, but also lower the variance of the loss. Predictions usually have errors. Therefore, it is necessary to use predictions in consideration of these errors. Our approach takes into account prediction errors. Furthermore, even if we do not understand the prediction method, which is a possible circumstance in, e.g. deep learning, we can use our method if we know the prediction error distribution and asymmetric loss function. Our method can be applied to procurement of electricity from electricity markets.
Journal: Journal of Applied Statistics
Pages: 2348-2368
Issue: 13-15
Volume: 48
Year: 2021
Month: 11
X-DOI: 10.1080/02664763.2020.1761951
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1761951
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:13-15:p:2348-2368




Template-Type: ReDIF-Article 1.0
Author-Name: Bükre Yıldırım Külekci
Author-X-Name-First: Bükre
Author-X-Name-Last: Yıldırım Külekci
Author-Name: A. Sevtap Selcuk-Kestel
Author-X-Name-First: A. Sevtap
Author-X-Name-Last: Selcuk-Kestel
Title: Assessment of longevity risk: credibility approach
Abstract: 
 To correctly measure the effect of mortality rates on the stability of insurance and pension provider's financial risk, longevity risk should be considered. This paper aims to investigate the future mortality and longevity risk with different age structures for different countries. Lee–Carter mortality model is used on the historical census data to forecast future mortality rates. Turkey, Germany, and Japan are chosen concerning their expected life and population distributions. Then, the longevity risk on a hypothetical portfolio is assessed based on static and dynamic mortality table approaches. To determine the impact of longevity risk, which is retrieved using a stochastic mortality model, a pension insurance product is taken into account. The net single premium for an annuity is quantified under the proposed set up for the selected countries. Additionally, the credibility approach is proposed to establish a reliable estimate for the annuity net single premium.
Journal: Journal of Applied Statistics
Pages: 2695-2713
Issue: 13-15
Volume: 48
Year: 2021
Month: 11
X-DOI: 10.1080/02664763.2021.1922613
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1922613
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:13-15:p:2695-2713




Template-Type: ReDIF-Article 1.0
Author-Name: Adnan Karaibrahimoglu
Author-X-Name-First: Adnan
Author-X-Name-Last: Karaibrahimoglu
Author-Name: Seren Ayhan
Author-X-Name-First: Seren
Author-X-Name-Last: Ayhan
Author-Name: Mustafa Karaagac
Author-X-Name-First: Mustafa
Author-X-Name-Last: Karaagac
Author-Name: Mehmet Artac
Author-X-Name-First: Mehmet
Author-X-Name-Last: Artac
Title: Circular analyses of dates on patients with gastric carcinoma
Abstract: 
 Dates have great importance in cancer diseases. However, the date variables themselves are not analyzed. This study aims to evaluate the descriptive statistics of diagnosis, operation, and last examination dates in gastric carcinoma patients by circular analysis methods. Totally 502 gastric carcinoma patients were enrolled in the study. The mean month of diagnosis date was found in nearly November (∼10.86) for females and May (∼5.17) for male patients. The mean month of operation date was found March (∼3.24) for females, and July & August (∼7.79) for males. The mean month of the last examination date was found as February & March (∼2.61) for females, and May (∼4.85) for males. Moreover, the mean day of the week for diagnosis date was found Thursday (∼5.50) for both female and male patients. The fitting of distributions of all variables was checked, also, according to von Mises, Rayleigh, and Kuiper’s tests. When the days and months were analyzed by classical descriptive statistics, the results were obtained completely different from the circular analyses results. Therefore, the dates and times should be analyzed in certain diseases to give an idea for physicians.
Journal: Journal of Applied Statistics
Pages: 2931-2943
Issue: 13-15
Volume: 48
Year: 2021
Month: 11
X-DOI: 10.1080/02664763.2021.1977259
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1977259
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:13-15:p:2931-2943




Template-Type: ReDIF-Article 1.0
Author-Name: Meral Ebegil
Author-X-Name-First: Meral
Author-X-Name-Last: Ebegil
Author-Name: Yaprak Arzu Özdemir
Author-X-Name-First: Yaprak Arzu
Author-X-Name-Last: Özdemir
Author-Name: Fikri Gökpinar
Author-X-Name-First: Fikri
Author-X-Name-Last: Gökpinar
Title: Some Shrinkage estimators based on median ranked set sampling
Abstract: 
 In this study, some shrinkage estimators using a median ranked set sample in the presence of multicollinearity were studied. Initially, we constructed the multiple regression model using median ranked set sampling. We also adapted the Ridge and Liu-type estimators to these multiple regression model. To investigate the efficiency of these estimators, a simulation study was performed for a different number of explanatory variables, sample sizes, correlation coefficients, and error variances in perfect and imperfect ranking cases. In addition, these estimators were compared with other estimators that are based on ranked set sample using simulation study. It is shown that when the collinearity is moderate, Ridge estimator using median ranked set sample performs better than other estimators and when the collinearity increases, Liu-type estimator using median ranked set sample gets better than all other estimators do. When the collinearity is smaller than 0.95, ridge estimator based on median ranked set sample is more efficient than Liu-type estimator based on same sample. However, this threshold increases as the sample size increases and the number of explanatory variables decreases. In addition, real data example is presented to illustrate how collinearity affects the estimators under median ranked set sampling and ranked set sampling.
Journal: Journal of Applied Statistics
Pages: 2473-2498
Issue: 13-15
Volume: 48
Year: 2021
Month: 11
X-DOI: 10.1080/02664763.2021.1895088
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1895088
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:13-15:p:2473-2498




Template-Type: ReDIF-Article 1.0
Author-Name: Cenker Burak Metin
Author-X-Name-First: Cenker Burak
Author-X-Name-Last: Metin
Author-Name: Sinem Tuğba Şahin Tekin
Author-X-Name-First: Sinem Tuğba
Author-X-Name-Last: Şahin Tekin
Author-Name: Yaprak Arzu Özdemir
Author-X-Name-First: Yaprak Arzu
Author-X-Name-Last: Özdemir
Title: Restricted calibration and weight trimming approaches for estimation of the population total in business statistics
Abstract: 
 Some adjustments are made to design weights to reduce the negative effects of non-response and out-of-scope problems. The calibration approach is a weighting process that agrees with the known population values by using auxiliary information. In this study, alternative calibration approaches and weight trimming process that can be used in large data sets with extreme weights and different correlation structures were analysed. In addition, the effect of the correlation structure of auxiliary variables on the efficiency of the calibration estimators was investigated by a simulation study. The 2017 Annual Industry and Service Statistics data were used in the simulation study and it was seen that restricted calibration estimators were more efficient than the generalized regression estimator in estimating the variables with a high variance such as turnover. Especially in small sample fractions, we recommend the application of restricted calibration estimators, as they are more efficient than the weight trimming in solving the negative and less than one weights problem encountered after the calibration process.
Journal: Journal of Applied Statistics
Pages: 2658-2672
Issue: 13-15
Volume: 48
Year: 2021
Month: 11
X-DOI: 10.1080/02664763.2020.1869703
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1869703
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:13-15:p:2658-2672




Template-Type: ReDIF-Article 1.0
Author-Name: Berivan Çakmak
Author-X-Name-First: Berivan
Author-X-Name-Last: Çakmak
Author-Name: Fatma Zehra Doğru
Author-X-Name-First: Fatma Zehra
Author-X-Name-Last: Doğru
Title: Optimal B-robust estimators for the parameters of the power Lindley distribution
Abstract: 
 Parameters of a distribution are generally estimated by using the classical methods such as maximum likelihood (ML) and least squares (LS) estimation. However, these classical methods are very sensitive to outliers. This study, therefore, proposes the application of the optimal B-robust (OBR) estimation method, which is resistant to outliers, to estimate the parameters of power Lindley (PL) distribution. We also provide a simulation study and a real data example to compare the performance of the OBR estimators with the performances of the ML, LS, and the regression M estimators.
Journal: Journal of Applied Statistics
Pages: 2369-2388
Issue: 13-15
Volume: 48
Year: 2021
Month: 11
X-DOI: 10.1080/02664763.2020.1854201
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1854201
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:13-15:p:2369-2388




Template-Type: ReDIF-Article 1.0
Author-Name: H. A. Rusyda
Author-X-Name-First: H. A.
Author-X-Name-Last: Rusyda
Author-Name: L. Noviyanti
Author-X-Name-First: L.
Author-X-Name-Last: Noviyanti
Author-Name: A. Z. Soleh
Author-X-Name-First: A. Z.
Author-X-Name-Last: Soleh
Author-Name: A. Chadidjah
Author-X-Name-First: A.
Author-X-Name-Last: Chadidjah
Author-Name: F. Indrayatna
Author-X-Name-First: F.
Author-X-Name-Last: Indrayatna
Title: The design of multiple crop insurance in Indonesia based on revenue risk using the copula model approach
Abstract: 
 It is important for Indonesia as a country with agricultural bases to develop crop insurance. Until now, Indonesia has not had any insurance for horticultural crops other than for corn. This paper discusses horticultural multicrop insurance products based on revenue risk that can be triggered by low prices, low yields, or a combination of both. In designing multicrop insurance products, it is important to model the variability of revenue risk through the implementation of copula toward crop yield and price and to estimate indemnity of the revenue-based multicrop insurance. The analysis employed Gumbel and Clayton copulas to model the dependency structure between crop yield and price of multicrops. Each marginal variable was modeled by using the ARIMA model. The results showed that multicrop revenue insurance tends to reduce the price of agricultural insurance in Indonesia, and thus this program has the potential to have good acceptance in agricultural insurance.
Journal: Journal of Applied Statistics
Pages: 2920-2930
Issue: 13-15
Volume: 48
Year: 2021
Month: 11
X-DOI: 10.1080/02664763.2021.1897089
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1897089
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:13-15:p:2920-2930




Template-Type: ReDIF-Article 1.0
Author-Name: Asiye Nur Yildirim
Author-X-Name-First: Asiye Nur
Author-X-Name-Last: Yildirim
Author-Name: Eren Bas
Author-X-Name-First: Eren
Author-X-Name-Last: Bas
Author-Name: Erol Egrioglu
Author-X-Name-First: Erol
Author-X-Name-Last: Egrioglu
Title: Threshold single multiplicative neuron artificial neural networks for non-linear time series forecasting
Abstract: 
 Single multiplicative neuron artificial neural networks have different importance than many other artificial neural networks because they do not have complex architecture problem, too many parameters and they need more computation time to use. In single multiplicative neuron artificial neural network, it is assumed that there is a one data generation process for time series. Many time series need an assumption that they have two data generation process or more. Based on this idea, the threshold model structure can be employed in a single multiplicative neuron model artificial neural network for taking into considering data generation processes problem. In this study, a new artificial neural network type is proposed and it is called a threshold single multiplicative neuron artificial neural network. It is assumed that time series have two data generation processes according to the architecture of single multiplicative neuron artificial neural network. Training algorithms are proposed based on harmony search algorithm and particle swarm optimization for threshold single multiplicative neuron artificial neural network. The proposed method is tested by various time series data sets and compared with well-known forecasting methods by considering different error measures. Finally, the performance of the proposed method is evaluated by a simulation study.
Journal: Journal of Applied Statistics
Pages: 2809-2825
Issue: 13-15
Volume: 48
Year: 2021
Month: 11
X-DOI: 10.1080/02664763.2020.1869702
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1869702
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:13-15:p:2809-2825




Template-Type: ReDIF-Article 1.0
Author-Name: Huseyin Guler
Author-X-Name-First: Huseyin
Author-X-Name-Last: Guler
Author-Name: Ebru Ozgur Guler
Author-X-Name-First: Ebru Ozgur
Author-X-Name-Last: Guler
Title: Mixed Lasso estimator for stochastic restricted regression models
Abstract: 
 Parameters of a linear regression model can be estimated with the help of traditional methods like generalized least squares and mixed estimator. However, recent developments increased the importance of big data sets, which have much more predictors than observations where some predictors have no impact on the dependent variable. The estimation and model selection problem of big datasets can be solved using the least absolute shrinkage and selection operator (Lasso). However, to the authors’ knowledge, there is no study that incorporates stochastic restrictions, within a Lasso framework. In this paper, we propose a Mixed Lasso (M-Lasso) estimator that incorporates stochastic linear restrictions to big data sets for selecting the true model and estimating parameters simultaneously. We conduct a simulation study to compare the performance of M-Lasso with existing estimators based on mean squared error 
 $ (\textrm{mse}) $ (mse) and model selection performance. Results show that M-Lasso is superior in terms of 
 $ \textrm{mse} $ mse and it generally dominates compared estimators according to the model selection criteria. We employ M-Lasso to estimate parameters of a widely analysed production function under stochastic restrictions raised from economic theory. Our results show that M-Lasso can provide reasonable and more precise estimates of model parameters that are in line with the economic theory.
Journal: Journal of Applied Statistics
Pages: 2795-2808
Issue: 13-15
Volume: 48
Year: 2021
Month: 11
X-DOI: 10.1080/02664763.2021.1922614
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1922614
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:13-15:p:2795-2808




Template-Type: ReDIF-Article 1.0
Author-Name: O. Ozan Evkaya
Author-X-Name-First: O.
Author-X-Name-Last: Ozan Evkaya
Author-Name: Ceylan Yozgatlıgil
Author-X-Name-First: Ceylan
Author-X-Name-Last: Yozgatlıgil
Author-Name: A. Sevtap Selcuk-Kestel
Author-X-Name-First: A.
Author-X-Name-Last: Sevtap Selcuk-Kestel
Title: CD-vine model for capturing complex dependence
Abstract: 
 Copula based finite mixture models allow us to capture the dependence between random variables more flexibly. Although bivariate case of finite mixture models has been commonly studied, limited efforts have been spent on finite mixture of vines. Instead of using classical mixture models, it is possible to incorporate C-vines into the D-vine model (CD-vine) to understand both the dependence among the variables over different time points. The aim of this study is to create a CD-vine mixture model expressing the dependencies between variables in temporal order. To achieve this, cumulative distribution function values generated within the time components are tied together with D-vine probabilistically. With this approach, dependence structure between variables at each time point is explained by C-vine and the dependence among the time points is captured by the D-vine model. The performance of the proposed CD-vine model is validated using simulated data and applied on four stock market indices.
Journal: Journal of Applied Statistics
Pages: 2406-2420
Issue: 13-15
Volume: 48
Year: 2021
Month: 11
X-DOI: 10.1080/02664763.2020.1834519
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1834519
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:13-15:p:2406-2420




Template-Type: ReDIF-Article 1.0
Author-Name: Beyza Cetin
Author-X-Name-First: Beyza
Author-X-Name-Last: Cetin
Author-Name: Idil Yavuz
Author-X-Name-First: Idil
Author-X-Name-Last: Yavuz
Title: Comparison of forecast accuracy of Ata and exponential smoothing
Abstract: 
 Forecasting is a crucial step in almost all scientific research and is essential in many areas of industrial, commercial, clinical and economic activity. There are many forecasting methods in the literature; but exponential smoothing stands out due to its simplicity and accuracy. Despite the facts that exponential smoothing is widely used and has been in the literature for a long time, it suffers from some problems that potentially affect the model's forecast accuracy. An alternative forecasting framework, called Ata, was recently proposed to overcome these problems and to provide improved forecasts. In this study, the forecast accuracy of Ata and exponential smoothing will be compared among data sets with no or linear trend. The results of this study are obtained using simulated data sets with different sample sizes, variances. Forecast errors are compared within both short and long term forecasting horizons. The results show that the proposed approach outperforms exponential smoothing for both types of time series data when forecasting the near and distant future. The methods are implemented on the U.S. annualized monthly interest rates for services data and their forecasting performance are also compared for this data set.
Journal: Journal of Applied Statistics
Pages: 2580-2590
Issue: 13-15
Volume: 48
Year: 2021
Month: 11
X-DOI: 10.1080/02664763.2020.1803813
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1803813
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:13-15:p:2580-2590




Template-Type: ReDIF-Article 1.0
Author-Name: Julide Yildirim
Author-X-Name-First: Julide
Author-X-Name-Last: Yildirim
Author-Name: Barış Alpaslan
Author-X-Name-First: Barış
Author-X-Name-Last: Alpaslan
Author-Name: Erdener Emin Eker
Author-X-Name-First: Erdener Emin
Author-X-Name-Last: Eker
Title: The role of social capital in environmental protection efforts: evidence from Turkey
Abstract: 
 The existing literature has recognized the role and importance of social capital in natural resource management. Several studies provide empirical evidence that higher levels of social capital may positively affect individuals' behavior towards natural resources management. This study is therefore an attempt to investigate the environmental quality impacts of social capital and central government expenditures on environmental protection, taking spatial dimension into account from 2009 to 2017 for Turkey. A general-to-specific approach has been adopted where spatial variations in the relationships have been examined with a dynamic spatial Durbin model, using the panel data at NUTS3 level. The empirical results do not support the validity of an environmental Kuznets curve, rather a U-shaped environmental Kuznets curve is validated, which exhibits spatial dependence. Estimation results show that industrial production has detrimental effects on the environment, while social capital improves it. The central government expenditures on environmental protection are effective in the abatement of pollution, and its effectiveness is enhanced when social capital is controlled. In addition to spatial spillover effects, our results show the presence of strong path dependency; that is, there is certain pollution inertia. Moreover, environmental protection policies would be more effective if social capital levels are improved.
Journal: Journal of Applied Statistics
Pages: 2626-2642
Issue: 13-15
Volume: 48
Year: 2021
Month: 11
X-DOI: 10.1080/02664763.2020.1843609
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1843609
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:13-15:p:2626-2642




Template-Type: ReDIF-Article 1.0
Author-Name: Timothy E. O’Brien
Author-X-Name-First: Timothy E.
Author-X-Name-Last: O’Brien
Author-Name: Jack Silcox
Author-X-Name-First: Jack
Author-X-Name-Last: Silcox
Title: Efficient experimental design for dose response modelling
Abstract: 
 The logit binomial logistic dose response model is commonly used in applied research to model binary outcomes as a function of the dose or concentration of a substance. This model is easily tailored to assess the relative potency of two substances. Consequently, in instances where two such dose response curves are parallel so one substance can be viewed as a dilution of the other, the degree of that dilution is captured in the relative potency model parameter. It is incumbent that experimental researchers working in fields including biomedicine, environmental science, toxicology and applied sciences choose efficient experimental designs to run their studies to both fit their dose response curves and to garner important information regarding drug or substance potency. This article provides far-reaching practical design strategies for dose response model fitting and estimation of relative potency using key illustrations. These results are subsequently extended here to handle situations where the assessment of parallelism and the proper dose-scale are also of interest. Conclusions and recommended strategies are supported by both theoretical and simulation results.
Journal: Journal of Applied Statistics
Pages: 2864-2888
Issue: 13-15
Volume: 48
Year: 2021
Month: 11
X-DOI: 10.1080/02664763.2021.1880556
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1880556
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:13-15:p:2864-2888




Template-Type: ReDIF-Article 1.0
Author-Name: Paul P. Momtaz
Author-X-Name-First: Paul P.
Author-X-Name-Last: Momtaz
Title: Econometric models of duration data in entrepreneurship with an application to start-ups' time-to-funding by venture capitalists (VCs)
Abstract: 
 Because time is a key determinant of entrepreneurial decision making, time-to-event models are ubiquitous in entrepreneurship. Widespread econometric misconception, however, may cause complicated biases in existing studies. The reason is spurious duration dependency, a complicated form of endogeneity caused by unobserved heterogeneity, which is particularly pronounced in entrepreneurship data. This article discusses the endogeneity problem and methods to ‘debias’ time-to-event models in entrepreneurship. Simulations and empirical evidence indicate that only the frailty approach yields consistently unbiased parameter estimates. An application to start-up firms' time-to-funding shows that other methods lead to dramatic biases. Therefore, this article advocates a paradigm shift in the modeling of time variables in entrepreneurship.
Journal: Journal of Applied Statistics
Pages: 2673-2694
Issue: 13-15
Volume: 48
Year: 2021
Month: 11
X-DOI: 10.1080/02664763.2021.1896686
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1896686
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:13-15:p:2673-2694




Template-Type: ReDIF-Article 1.0
Author-Name: Luis A. Gil-Alana
Author-X-Name-First: Luis A.
Author-X-Name-Last: Gil-Alana
Author-Name: OlaOluwa S. Yaya
Author-X-Name-First: OlaOluwa S.
Author-X-Name-Last: Yaya
Title: Testing fractional unit roots with non-linear smooth break approximations using Fourier functions
Abstract: 
 In this paper, we present a testing procedure for fractional orders of integration in the context of non-linear terms approximated by Fourier functions. The test statistic has an asymptotic standard normal distribution and several Monte Carlo experiments conducted in the paper show that it performs well in finite samples. Various applications using real life time series, such as US unemployment rates, US GNP and Purchasing Power Parity (PPP) of G7 countries are presented at the end of the paper.
Journal: Journal of Applied Statistics
Pages: 2542-2559
Issue: 13-15
Volume: 48
Year: 2021
Month: 11
X-DOI: 10.1080/02664763.2020.1757047
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1757047
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:13-15:p:2542-2559




Template-Type: ReDIF-Article 1.0
Author-Name: Mehmet Ali Balcı
Author-X-Name-First: Mehmet Ali
Author-X-Name-Last: Balcı
Author-Name: Ömer Akgüller
Author-X-Name-First: Ömer
Author-X-Name-Last: Akgüller
Author-Name: Serdar Can Güzel
Author-X-Name-First: Serdar
Author-X-Name-Last: Can Güzel
Title: Hierarchies in communities of UK stock market from the perspective of Brexit
Abstract: 
 Nowadays, increase of analyzing stock markets as complex systems lead graph theory to play a key role. For instance, detecting graph communities is an important task in the analysis of stocks, and as planar maximally filtered graphs let us to get important information for the topology of the market. In this study, we first obtain correlation network representation of UK's leading stock market network by using a novel threshold method. Then, we determine vertex clusters by using modularity and analyze clusters in planar maximally filtered graph substructures. Our analyze include a new measure called weighted Gini index for measuring the sparsity. The main goal of this paper is to study the hierarchical evolution of the market communities throughout the Brexit referendum, which is known as the stress period for the stock market. Hence, the overall sample is divided into two sub-periods of pre-referendum, and post-referendum to obtain communities and hierarchical structures. Our results indicate that financial companies are leading elements of the clusters. Moreover, the significant changes within the network topologies are observed for insurance, consumer goods, consumer services, mining, and technology sectors whereas oil and gas and health care sectors have not been affected by Brexit stress.
Journal: Journal of Applied Statistics
Pages: 2607-2625
Issue: 13-15
Volume: 48
Year: 2021
Month: 11
X-DOI: 10.1080/02664763.2020.1796942
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1796942
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:13-15:p:2607-2625




Template-Type: ReDIF-Article 1.0
Author-Name: Fatma Yerlikaya-Özkurt
Author-X-Name-First: Fatma
Author-X-Name-Last: Yerlikaya-Özkurt
Author-Name: Pakize Taylan
Author-X-Name-First: Pakize
Author-X-Name-Last: Taylan
Author-Name: Müjgan Tez
Author-X-Name-First: Müjgan
Author-X-Name-Last: Tez
Title: Estimation in the partially nonlinear model by continuous optimization
Abstract: 
 A useful model for data analysis is the partially nonlinear model where response variable is represented as the sum of a nonparametric and a parametric component. In this study, we propose a new procedure for estimating the parameters in the partially nonlinear models. Therefore, we consider penalized profile nonlinear least square problem where nonparametric components are expressed as a B-spline basis function, and then estimation problem is expressed in terms of conic quadratic programming which is a continuous optimization problem and solved interior point method. An application study is conducted to evaluate the performance of the proposed method by considering some well-known performance measures. The results are compared against parametric nonlinear model.
Journal: Journal of Applied Statistics
Pages: 2826-2846
Issue: 13-15
Volume: 48
Year: 2021
Month: 11
X-DOI: 10.1080/02664763.2020.1864816
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1864816
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:13-15:p:2826-2846




Template-Type: ReDIF-Article 1.0
Author-Name: Necla Gündüz
Author-X-Name-First: Necla
Author-X-Name-Last: Gündüz
Author-Name: Ernest Fokoué
Author-X-Name-First: Ernest
Author-X-Name-Last: Fokoué
Title: Understanding students' evaluations of professors using non-negative matrix factorization
Abstract: 
 In this paper, we use Nonnegative Matrix Factorization (NMF) and several other state of the art statistical machine learning techniques to provide an in-depth study of university professor evaluations by their students. We specifically use the Kullback–Leibler divergence as our loss function in keeping with the type of the data and extract revealing patterns consistent with the educational objectives underlying the questionnaire design. In particular, the application of our techniques to a dataset gathered at Gazi University in Turkey reveals compelling patterns such as the strong association between the student's seriousness and dedication (measured by attendance) and the kind of scores they tend to assign to the courses and the corresponding professors. Insights emerging from our study suggest that more aspects of students' evaluations should be explored at greater depths.
Journal: Journal of Applied Statistics
Pages: 2961-2981
Issue: 13-15
Volume: 48
Year: 2021
Month: 11
X-DOI: 10.1080/02664763.2021.1991288
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1991288
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:13-15:p:2961-2981




Template-Type: ReDIF-Article 1.0
Author-Name: J. M. Muñoz-Pichardo
Author-X-Name-First: J. M.
Author-X-Name-Last: Muñoz-Pichardo
Author-Name: R. Pino-Mejías
Author-X-Name-First: R.
Author-X-Name-Last: Pino-Mejías
Author-Name: J. García-Heras
Author-X-Name-First: J.
Author-X-Name-Last: García-Heras
Author-Name: F. Ruiz-Muñoz
Author-X-Name-First: F.
Author-X-Name-Last: Ruiz-Muñoz
Author-Name: M. Luz González-Regalado
Author-X-Name-First: M.
Author-X-Name-Last: Luz González-Regalado
Title: A multivariate Poisson regression model for count data
Abstract: 
 We propose a new technique for the study of multivariate count data. The proposed model is applied to the study of the number of individuals several fossil species found in a set of geographical observation points. First, we are proposing a multivariate model based on the Poisson distributions, which allows positive and negative correlations between the components. We are extending the log-linear Poisson model in the multivariate case through the conditional distributions. For this model, we obtain the maximum likelihood estimates and compute several goodness of fit statistics. Finally we illustrate the application of the proposed method over data sets: various simulated data sets and a count data set of various fossil species.
Journal: Journal of Applied Statistics
Pages: 2525-2541
Issue: 13-15
Volume: 48
Year: 2021
Month: 11
X-DOI: 10.1080/02664763.2021.1877637
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1877637
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:13-15:p:2525-2541




Template-Type: ReDIF-Article 1.0
Author-Name: Fatma Başoğlu Kabran
Author-X-Name-First: Fatma
Author-X-Name-Last: Başoğlu Kabran
Author-Name: Kamil Demirberk Ünlü
Author-X-Name-First: Kamil Demirberk
Author-X-Name-Last: Ünlü
Title: A two-step machine learning approach to predict S&P 500 bubbles
Abstract: 
 In this paper, we are interested in predicting the bubbles in the S&P 500 stock market with a two-step machine learning approach that employs a real-time bubble detection test and support vector machine (SVM). SVM as a nonparametric binary classification technique is already a widely used method in financial time series forecasting. In the literature, a bubble is often defined as a situation where the asset price exceeds its fundamental value. As one of the early warning signals, prediction of bubbles is vital for policymakers and regulators who are responsible to take preemptive measures against the future crises. Therefore, many attempts have been made to understand the main factors in bubble formation and to predict them in their earlier phases. Our analysis consists of two steps. The first step is to identify the bubbles in the S&P 500 index using a widely recognized right-tailed unit root test. Then, SVM is employed to predict the bubbles by macroeconomic indicators. Also, we compare SVM with different supervised learning algorithms by using k-fold cross-validation. The experimental results show that the proposed approach with high predictive power could be a favourable alternative in bubble prediction.
Journal: Journal of Applied Statistics
Pages: 2776-2794
Issue: 13-15
Volume: 48
Year: 2021
Month: 11
X-DOI: 10.1080/02664763.2020.1823947
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1823947
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:13-15:p:2776-2794




Template-Type: ReDIF-Article 1.0
Author-Name: Hatice Tul Kubra Akdur
Author-X-Name-First: Hatice Tul Kubra
Author-X-Name-Last: Akdur
Title: Unit-Lindley mixed-effect model for proportion data
Abstract: 
 Recently, unit-Lindley distribution and its associated regression models have been developed as an alternative to Beta regression model for which continuous outcome in the unit interval 
 $(0, 1) $(0,1). Proportion data usually occur in clinical trials, economics and social studies with hierarchical structures. In this study, unit-Lindley mixed-effect model is proposed and the appropriate likelihood analysis methods for parameter estimation are investigated. In the case of clustered or longitudinal proportion data in mixed-effect models, the full-likelihood function does not have a closed form. Parameter estimations of unit-Lindley mixed-effect model are obtained with Laplace and adaptive Gaussian quadrature approximation methods in this study. We analyzed a dataset on the proportion of households with insufficient water supply and sewage with some sociodemographic variables in the cities of Brazil by using unit-Lindley mixed-effect model including a random intercept as federative states of Brazil. Analysis results indicate that the proposed unit-Lindley mixed-effect model provides better fit than unit-Lindley regression model and beta mixed model. Also, in the simulation study the accuracy of the estimates of approximation methods are evaluated and compared via Monte Carlo simulation study in terms of bias and mean square error.
Journal: Journal of Applied Statistics
Pages: 2389-2405
Issue: 13-15
Volume: 48
Year: 2021
Month: 11
X-DOI: 10.1080/02664763.2020.1823946
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1823946
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:13-15:p:2389-2405




Template-Type: ReDIF-Article 1.0
Author-Name: Nuriye Sancar
Author-X-Name-First: Nuriye
Author-X-Name-Last: Sancar
Author-Name: Deniz Inan
Author-X-Name-First: Deniz
Author-X-Name-Last: Inan
Title: A new alternative estimation method for Liu-type logistic estimator via particle swarm optimization: an application to data of collapse of Turkish commercial banks during the Asian financial crisis
Abstract: 
 In the existence of multicollinearity problem in the logistic model, some important problems may occur in the analysis of the model, such as unstable maximum likelihood estimator with very high standard errors, false inferences. The Liu-type logistic estimator was proposed as two-parameter estimator to overcome multicollinearity problem in the logistic model. In the existing previous studies, the (k, d) pair in this shrinkage estimator is estimated by two-phase methods. However, since the different estimators can be utilized in the estimation of d, optimal choice of the (k, d) pair provided using the two-phase approaches is not guaranteed to overcome multicollinearity. In this article, a new alternative method based on particle swarm optimization is suggested to estimate (k, d) pair in Liu-type logistic estimator, simultaneously. For this purpose, an objective function that eliminates the multicollinearity problem, provides minimization of the bias of the model and improvement of the model’s predictive performance, is developed. Monte Carlo simulation study is conducted to show the performance of the proposed method by comparing it with existing methods. The performance of the proposed method is also demonstrated by the real dataset which is related to the collapse of commercial banks in Turkey during Asian financial crisis.
Journal: Journal of Applied Statistics
Pages: 2499-2514
Issue: 13-15
Volume: 48
Year: 2021
Month: 11
X-DOI: 10.1080/02664763.2020.1837085
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1837085
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:13-15:p:2499-2514




Template-Type: ReDIF-Article 1.0
Author-Name: Atila Göktaş
Author-X-Name-First: Atila
Author-X-Name-Last: Göktaş
Author-Name: Özge Akkuş
Author-X-Name-First: Özge
Author-X-Name-Last: Akkuş
Author-Name: Aykut Kuvat
Author-X-Name-First: Aykut
Author-X-Name-Last: Kuvat
Title: A new robust ridge parameter estimator based on search method for linear regression model
Abstract: 
 A large and wide variety of ridge parameter estimators proposed for linear regression models exist in the literature. Actually proposing new ridge parameter estimator lately proving its efficiency on few cases seems endless. However, so far there is no ridge parameter estimator that can serve best for any sample size or any degree of collinearity among regressors. In this study we propose a new robust ridge parameter estimator that serves best for any case assuring that is free of sample size, number of regressors and degree of collinearity. This is in fact realized by choosing three best from enormous number of ridge parameter estimators performing well in different cases in developing the new ridge parameter estimator in a way of search method providing the smallest mean square error values of regression parameters. After that a simulation study is conducted to show that the proposed parameter is robust. In conclusion, it is found that this ridge parameter estimator is promising in any case. Moreover, a recent data set is used as an example for illustration to show that the proposed ridge parameter estimator is performing better.
Journal: Journal of Applied Statistics
Pages: 2457-2472
Issue: 13-15
Volume: 48
Year: 2021
Month: 11
X-DOI: 10.1080/02664763.2020.1803814
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1803814
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:13-15:p:2457-2472




Template-Type: ReDIF-Article 1.0
Author-Name: G. Deliorman
Author-X-Name-First: G.
Author-X-Name-Last: Deliorman
Author-Name: D. Inan
Author-X-Name-First: D.
Author-X-Name-Last: Inan
Title: Binary particle swarm optimization as a detection tool for influential subsets in linear regression
Abstract: 
 An influential observation is any point that has a huge effect on the coefficients of a regression line fitting the data. The presence of such observations in the data set reduces the sensitivity and validity of the statistical analysis. In the literature there are many methods used for identifying influential observations. However, many of those methods are highly influenced by masking and swamping effects and require distributional assumptions. Especially in the presence of influential subsets most of these methods are insufficient to detect these observations. This study aims to develop a new diagnostic tool for identifying influential observations using the meta-heuristic binary particle swarm optimization algorithm. This proposed approach does not require any distributional assumptions and also not affected by masking and swamping effects as the known methods. The performance of the proposed method is analyzed via simulations and real data set applications.
Journal: Journal of Applied Statistics
Pages: 2441-2456
Issue: 13-15
Volume: 48
Year: 2021
Month: 11
X-DOI: 10.1080/02664763.2020.1779196
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1779196
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:13-15:p:2441-2456




Template-Type: ReDIF-Article 1.0
Author-Name: Adrien Ehrhardt
Author-X-Name-First: Adrien
Author-X-Name-Last: Ehrhardt
Author-Name: Christophe Biernacki
Author-X-Name-First: Christophe
Author-X-Name-Last: Biernacki
Author-Name: Vincent Vandewalle
Author-X-Name-First: Vincent
Author-X-Name-Last: Vandewalle
Author-Name: Philippe Heinrich
Author-X-Name-First: Philippe
Author-X-Name-Last: Heinrich
Author-Name: Sébastien Beben
Author-X-Name-First: Sébastien
Author-X-Name-Last: Beben
Title: Reject inference methods in credit scoring
Abstract: 
 The granting process is based on the probability that the applicant will refund his/her loan given his/her characteristics. This probability, also called score, is learnt based on a dataset in which rejected applicants are excluded. Thus, the population on which the score is used is different from the learning population. Many “reject inference” methods try to exploit the data available from the rejected applicants in the learning process. However, most of these methods are empirical and lack of formalization of their assumptions, and of their expected theoretical properties. We formalize such hidden assumptions in a general missing data setting for some of the most common reject inference methods. It reveals that hidden modelling is mostly incomplete, thus prohibiting to compare existing methods within the general model selection mechanism (except by financing “non-fundable” applicants). So, we assess performance of the methods on both simulated data and real data (from CACF, a major European loan issuer). Unsurprisingly, no method seems uniformly dominant. Both these theoretical and empirical results not only reinforce the idea to carefully use the classical reject inference methods but also to invest in future research works for designing model-based reject inference methods (without financing “non-fundable” applicants).
Journal: Journal of Applied Statistics
Pages: 2734-2754
Issue: 13-15
Volume: 48
Year: 2021
Month: 11
X-DOI: 10.1080/02664763.2021.1929090
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1929090
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:13-15:p:2734-2754




Template-Type: ReDIF-Article 1.0
Author-Name: Burak A. Erog̃lu
Author-X-Name-First: Burak A.
Author-X-Name-Last: Erog̃lu
Author-Name: Selim Yıldırım
Author-X-Name-First: Selim
Author-X-Name-Last: Yıldırım
Title: On the performance of the variance ratio unit root tests with flexible Fourier form
Abstract: 
 This article introduces a new unit root test that combines the variance ratio framework with the Flexible Fourier Form under the generalized least squares detrending mechanism. The advantage of the proposed method against its alternatives can be listed as: (1) it suggests a non-parametric procedure that does not require any parametric or semi-parametric model to remove serial correlation in the innovation process; (2) it can reasonably adapt itself to deal with the multiple structural breaks with various functional specifications. In the simulation exercises, we show that the proposed method exhibits satisfactory performance in the size and size-adjusted power analysis.
Journal: Journal of Applied Statistics
Pages: 2560-2579
Issue: 13-15
Volume: 48
Year: 2021
Month: 11
X-DOI: 10.1080/02664763.2020.1796939
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1796939
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:13-15:p:2560-2579




Template-Type: ReDIF-Article 1.0
Author-Name: Atif Evren
Author-X-Name-First: Atif
Author-X-Name-Last: Evren
Author-Name: Elif Tuna
Author-X-Name-First: Elif
Author-X-Name-Last: Tuna
Author-Name: Erhan Ustaoglu
Author-X-Name-First: Erhan
Author-X-Name-Last: Ustaoglu
Author-Name: Busra Sahin
Author-X-Name-First: Busra
Author-X-Name-Last: Sahin
Title: Some dominance indices to determine market concentration
Abstract: 
 This study intends to provide a new insight into the concentration and dominance indices as the concerns grow about the increasing concentration in the markets around the world. Most of the studies attempting to measure concentration or dominance in a market employ the popular concentration/dominance indices like Herfindahl–Hirschmann, Hannah–Kay, Rosenbluth–Hall–Tidemann and Concentration ratio. On the other hand, measures of qualitative variation are closely related to entropy, diversity and concentration/dominance measures. In this study, two normalized dominance measures that can be derived from the work of Wilcox on qualitative variation are proposed. The limiting distributions of these normalized dominance measures are formulated. By some simulations, asymptotic behaviors of these indices are analyzed under some assumptions about the market structure. In the end, by an application on the Turkish car sales in 2019, it is determined that the values of dominance indices vary in a considerably large range. Thus one of the dominance indices is determined to have the advantage of having less error in estimation, less sensitivity to smaller market shares, and less sampling variability.
Journal: Journal of Applied Statistics
Pages: 2755-2775
Issue: 13-15
Volume: 48
Year: 2021
Month: 11
X-DOI: 10.1080/02664763.2021.1963421
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1963421
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:13-15:p:2755-2775




Template-Type: ReDIF-Article 1.0
Author-Name: Hao Chen
Author-X-Name-First: Hao
Author-X-Name-Last: Chen
Author-Name: Minguang Zhang
Author-X-Name-First: Minguang
Author-X-Name-Last: Zhang
Author-Name: Lanshan Han
Author-X-Name-First: Lanshan
Author-X-Name-Last: Han
Author-Name: Alvin Lim
Author-X-Name-First: Alvin
Author-X-Name-Last: Lim
Title: Hierarchical marketing mix models with sign constraints
Abstract: 
 Marketing mix models (MMMs) are statistical models for measuring the effectiveness of various marketing activities such as promotion, media advertisement, etc. In this research, we propose a comprehensive marketing mix model that captures the hierarchical structure and the carryover, shape and scale effects of certain marketing activities, as well as sign restrictions on certain coefficients that are consistent with common business sense. In contrast to commonly adopted approaches in practice, which estimate parameters in a multi-stage process, the proposed approach estimates all the unknown parameters simultaneously using a constrained maximum likelihood approach and a Hamiltonian Monte Carlo algorithm. We present results on real datasets to illustrate the use of the proposed solution algorithms.
Journal: Journal of Applied Statistics
Pages: 2944-2960
Issue: 13-15
Volume: 48
Year: 2021
Month: 11
X-DOI: 10.1080/02664763.2021.1946020
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1946020
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:13-15:p:2944-2960




Template-Type: ReDIF-Article 1.0
Author-Name: Emrah Altun
Author-X-Name-First: Emrah
Author-X-Name-Last: Altun
Title: The Lomax regression model with residual analysis: an application to insurance data
Abstract: 
 In this paper, we introduce a new regression model, called Lomax regression model, as an alternative to the gamma regression model. The maximum-likelihood method is used to estimate the unknown parameters of the proposed model, and the finite sample performance of the maximum-likelihood estimation method is evaluated by means of the Monte-Carlo simulation study. The randomized quantile residuals are used to check the adequacy of the fitted model. The insurance data are analyzed to demonstrate the usefulness of the proposed regression model against the gamma regression model.
Journal: Journal of Applied Statistics
Pages: 2515-2524
Issue: 13-15
Volume: 48
Year: 2021
Month: 11
X-DOI: 10.1080/02664763.2020.1834515
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1834515
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:13-15:p:2515-2524




Template-Type: ReDIF-Article 1.0
Author-Name: Barry C. Arnold
Author-X-Name-First: Barry C.
Author-X-Name-Last: Arnold
Author-Name: B. G. Manjunath
Author-X-Name-First: B. G.
Author-X-Name-Last: Manjunath
Title: Statistical inference for distributions with one Poisson conditional
Abstract: 
 It will be recalled that the classical bivariate normal distributions have normal marginals and normal conditionals. It is natural to ask whether a similar phenomenon can be encountered involving Poisson marginals and conditionals. However, it is known, from research on conditionally specified models, that Poisson marginals will be encountered, together with both conditionals being of the Poisson form, only in the case in which the variables are independent. In order to have a flexible dependent bivariate model with some Poisson components, in the present article, we will be focusing on bivariate distributions with one marginal and the other family of conditionals being of the Poisson form. Such distributions are called Pseudo-Poisson distributions. We discuss distributional features of such models, explore inferential aspects and include an example of applications of the Pseudo-Poisson model to sets of over-dispersed data.
Journal: Journal of Applied Statistics
Pages: 2306-2325
Issue: 13-15
Volume: 48
Year: 2021
Month: 11
X-DOI: 10.1080/02664763.2021.1928017
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1928017
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:13-15:p:2306-2325




Template-Type: ReDIF-Article 1.0
Author-Name: Özlem Türkşen
Author-X-Name-First: Özlem
Author-X-Name-Last: Türkşen
Title: A novel perspective for parameter estimation of seemingly unrelated nonlinear regression
Abstract: 
 Nonlinear regression is commonly used as a modeling tool to get a functional form between inputs and response variables when the inputs and the responses have a nonlinear relationship. It should be better to compose the predicted nonlinear models with considering correlation between the responses for multi-response data sets. For this purpose, seemingly unrelated nonlinear regression (SUNR) have been widely used in the literature. The parameter estimation procedure of the SUNR is based on nonlinear least squares (NLS) method, based on L2-norm. However, it is possible to use different norms for parameter estimation process. The novelty of this study is presenting the applicability of least absolute deviation (LAD) method, defined in L1-norm, with the NLS method simultaneously for obtaining parameter estimates of the SUNR model in a multi objective perspective. In this study, the proposed multi-objective SUNR model is called MO-SUNR. The optimization of the MO-SUNR model is achieved by using soft computing methods. Two data set examples are given for application purposes of the MO-SUNR model. It is seen from the results that the MO-SUNR provides many alternatively usable compromise parameter estimates through the simultaneous evaluation of the LAD and the NLS methods.
Journal: Journal of Applied Statistics
Pages: 2326-2347
Issue: 13-15
Volume: 48
Year: 2021
Month: 11
X-DOI: 10.1080/02664763.2021.1877638
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1877638
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:13-15:p:2326-2347




Template-Type: ReDIF-Article 1.0
Author-Name: A. Göktaş
Author-X-Name-First: A.
Author-X-Name-Last: Göktaş
Author-Name: Ö. Akkuş
Author-X-Name-First: Ö.
Author-X-Name-Last: Akkuş
Title: Editorial to the special issue: Recent statistical methods for data analysis, applied economics, business & finance
Journal: Journal of Applied Statistics
Pages: 2231-2238
Issue: 13-15
Volume: 48
Year: 2021
Month: 11
X-DOI: 10.1080/02664763.2021.1991180
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1991180
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:13-15:p:2231-2238




Template-Type: ReDIF-Article 1.0
Author-Name: Indranil Ghosh
Author-X-Name-First: Indranil
Author-X-Name-Last: Ghosh
Author-Name: Filipe Marques
Author-X-Name-First: Filipe
Author-X-Name-Last: Marques
Author-Name: Subrata Chakraborty
Author-X-Name-First: Subrata
Author-X-Name-Last: Chakraborty
Title: A new bivariate Poisson distribution via conditional specification: properties and applications
Abstract: 
 In this article, we discuss a bivariate Poisson distribution whose conditionals are univariate Poisson distributions and the marginals are not Poisson which exhibits negative correlation. Some useful structural properties of this distribution namely marginals, moments, generating functions, stochastic ordering are investigated. Simple proofs of negative correlation, marginal over-dispersion, distribution of sum and conditional given the sum are also derived. The distribution is shown to be a member of the multi-parameter exponential family and some natural but useful consequences are also outlined. Parameter estimation with maximum likelihood is implemented. Copula-based simulation experiments are carried out using Bivariate Normal and the Farlie–Gumbel–Morgenstern copulas to assess how the model behaves in dealing with the situation. Finally, the distribution is fitted to seven bivariate count data sets with an inherent negative correlation to illustrate suitability.
Journal: Journal of Applied Statistics
Pages: 3025-3047
Issue: 16
Volume: 48
Year: 2021
Month: 12
X-DOI: 10.1080/02664763.2020.1793307
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1793307
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:16:p:3025-3047




Template-Type: ReDIF-Article 1.0
Author-Name: Daniel C. F. Guzmán
Author-X-Name-First: Daniel C. F.
Author-X-Name-Last: Guzmán
Author-Name: Clécio S. Ferreira
Author-X-Name-First: Clécio S.
Author-X-Name-Last: Ferreira
Author-Name: Camila B. Zeller
Author-X-Name-First: Camila B.
Author-X-Name-Last: Zeller
Title: Linear censored regression models with skew scale mixtures of normal distributions
Abstract: 
 A special source of difficulty in the statistical analysis is the possibility that some subjects may not have a complete observation of the response variable. Such incomplete observation of the response variable is called censoring. Censorship can occur for a variety of reasons, including limitations of measurement equipment, design of the experiment, and non-occurrence of the event of interest until the end of the study. In the presence of censoring, the dependence of the response variable on the explanatory variables can be explored through regression analysis. In this paper, we propose to examine the censorship problem in context of the class of asymmetric, i.e., we have proposed a linear regression model with censored responses based on skew scale mixtures of normal distributions. We develop a Monte Carlo EM (MCEM) algorithm to perform maximum likelihood inference of the parameters in the proposed linear censored regression models with skew scale mixtures of normal distributions. The MCEM algorithm has been discussed with an emphasis on the skew-normal, skew Student-t-normal, skew-slash and skew-contaminated normal distributions. To examine the performance of the proposed method, we present some simulation studies and analyze a real dataset.
Journal: Journal of Applied Statistics
Pages: 3060-3085
Issue: 16
Volume: 48
Year: 2021
Month: 12
X-DOI: 10.1080/02664763.2020.1795814
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1795814
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:16:p:3060-3085




Template-Type: ReDIF-Article 1.0
Author-Name: Tao Chen
Author-X-Name-First: Tao
Author-X-Name-Last: Chen
Author-Name: Qingliang Fan
Author-X-Name-First: Qingliang
Author-X-Name-Last: Fan
Author-Name: Kai Liu
Author-X-Name-First: Kai
Author-X-Name-Last: Liu
Author-Name: Lingshan Le
Author-X-Name-First: Lingshan
Author-X-Name-Last: Le
Title: Identifying key factors in momentum in basketball games
Abstract: 
 Momentum as elaborated under a recent novel definition has been shown quantitatively to have a significant impact on basketball game outcomes. This paper makes two contributions to the analytical literature on sports momentum: (1) two aspects of the new definition are operationalized so that its practicality becomes evident; and (2) through a dimension-reduction technique (elastic net), key factors associated with momentum are identified. Both technical variables such as field goals, assists, rebounds, etc. and environmental variables such as the spectator attendance rate and player salary dispersion are considered, and the potential for useful real-time analyzes is illustrated.
Journal: Journal of Applied Statistics
Pages: 3116-3129
Issue: 16
Volume: 48
Year: 2021
Month: 12
X-DOI: 10.1080/02664763.2020.1795819
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1795819
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:16:p:3116-3129




Template-Type: ReDIF-Article 1.0
Author-Name: Vitor Capdeville
Author-X-Name-First: Vitor
Author-X-Name-Last: Capdeville
Author-Name: Kelly C. M. Gonçalves
Author-X-Name-First: Kelly C. M.
Author-X-Name-Last: Gonçalves
Author-Name: João B. M. Pereira
Author-X-Name-First: João B. M.
Author-X-Name-Last: Pereira
Title: Bayesian factor models for multivariate categorical data obtained from questionnaires
Abstract: 
 Factor analysis is a flexible technique for assessment of multivariate dependence and codependence. Besides being an exploratory tool used to reduce the dimensionality of multivariate data, it allows estimation of common factors that often have an interesting theoretical interpretation in real problems. However, standard factor analysis is only applicable when the variables are scaled, which is often inappropriate, for example, in data obtained from questionnaires in the field of psychology, where the variables are often categorical. In this framework, we propose a factor model for the analysis of multivariate ordered and non-ordered polychotomous data. The inference procedure is done under the Bayesian approach via Markov chain Monte Carlo methods. Two Monte Carlo simulation studies are presented to investigate the performance of this approach in terms of estimation bias, precision and assessment of the number of factors. We also illustrate the proposed method to analyze participants' responses to the Motivational State Questionnaire dataset, developed to study emotions in laboratory and field settings.
Journal: Journal of Applied Statistics
Pages: 3150-3173
Issue: 16
Volume: 48
Year: 2021
Month: 12
X-DOI: 10.1080/02664763.2020.1796935
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1796935
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:16:p:3150-3173




Template-Type: ReDIF-Article 1.0
Author-Name: The Editors
Title: Correction
Journal: Journal of Applied Statistics
Pages: 3253-3254
Issue: 16
Volume: 48
Year: 2021
Month: 12
X-DOI: 10.1080/02664763.2021.1972659
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1972659
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:16:p:3253-3254




Template-Type: ReDIF-Article 1.0
Author-Name: Hassan S. Bakouch
Author-X-Name-First: Hassan S.
Author-X-Name-Last: Bakouch
Author-Name: Meitner Cadena
Author-X-Name-First: Meitner
Author-X-Name-Last: Cadena
Author-Name: Christophe Chesneau
Author-X-Name-First: Christophe
Author-X-Name-Last: Chesneau
Title: A new class of skew distributions with climate data analysis
Abstract: 
 In this paper, we develop a new general class of skew distributions with flexibility properties on the tails. Moreover, such class can provide heavy and light tails. Some of its mathematical properties are studied, including the quantile function, the moments, the moment generating function and the mean of deviations. New skew distributions are derived and used to construct new models capturing asymmetry inherent to data. The estimation of the class parameters is investigated by the method of maximum likelihood and the performance of the estimators is assessed by a simulation study. Applications of the proposed distribution are explored for two climate data sets. The first data set concerns the annual heat wave index and the second data set involves temperature and precipitation measures from the meteorological station located at Schiphol, Netherlands. Data fitting results show that our models perform better than the competitors.
Journal: Journal of Applied Statistics
Pages: 3002-3024
Issue: 16
Volume: 48
Year: 2021
Month: 12
X-DOI: 10.1080/02664763.2020.1791804
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1791804
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:16:p:3002-3024




Template-Type: ReDIF-Article 1.0
Author-Name: Renata Rojas Guerra
Author-X-Name-First: Renata Rojas
Author-X-Name-Last: Guerra
Author-Name: Fernando A. Peña-Ramírez
Author-X-Name-First: Fernando A.
Author-X-Name-Last: Peña-Ramírez
Author-Name: Marcelo Bourguignon
Author-X-Name-First: Marcelo
Author-X-Name-Last: Bourguignon
Title: The unit extended Weibull families of distributions and its applications
Abstract: 
 In this paper, two new general families of distributions supported on the unit interval are introduced. The proposed families include several known models as special cases and define at least twenty (each one) new special models. Since the list of well-being indicators may include several double bounded random variables, the applicability for modeling those is the major practical motivation for introducing the distributions on those families. We propose a parametrization of the new families in terms of the median and develop a shiny application to provide interactive density shape illustrations for some special cases. Various properties of the introduced families are studied. Some special models in the new families are discussed. In particular, the complementary unit Weibull distribution is studied in some detail. The method of maximum likelihood for estimating the model parameters is discussed. An extensive Monte Carlo experiment is conducted to evaluate the performances of these estimators in finite samples. Applications to the literacy rate in Brazilian and Colombian municipalities illustrate the usefulness of the two new families for modeling well-being indicators.
Journal: Journal of Applied Statistics
Pages: 3174-3192
Issue: 16
Volume: 48
Year: 2021
Month: 12
X-DOI: 10.1080/02664763.2020.1796936
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1796936
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:16:p:3174-3192




Template-Type: ReDIF-Article 1.0
Author-Name: Julio López-Laborda
Author-X-Name-First: Julio
Author-X-Name-Last: López-Laborda
Author-Name: Carmen Marín-González
Author-X-Name-First: Carmen
Author-X-Name-Last: Marín-González
Author-Name: Jorge Onrubia-Fernández
Author-X-Name-First: Jorge
Author-X-Name-Last: Onrubia-Fernández
Title: Estimating Engel curves: a new way to improve the SILC-HBS matching process using GLM methods
Abstract: 
 Microdata are required to evaluate the distributive impact of the taxation system as a whole (direct and indirect taxes) on individuals or households. However, in European Union countries this information is usually distributed into two separate surveys: the Household Budget Surveys (HBS), including total household expenditure and its composition, and EU Statistics on Income and Living Conditions (EU-SILC), including detailed information about households' income and direct (but not indirect) taxes paid. We present a parametric statistical matching procedure to merge both surveys. For the first stage of matching, we propose estimating total household expenditure in HBS (Engel curves) using a GLM estimator, instead of the traditionally used OLS method. It is a better alternative, insofar as it can deal with the heteroskedasticity problem of the OLS estimates, while making it unnecessary to retransform the regressors estimated in logarithms. To evaluate these advantages of the GLM estimator, we conducted a computational Monte Carlo simulation. In addition, when an error term is added to the deterministic imputation of expenditure in the EU-SILC, we propose replacing the usual Normal distribution of the error with a Chi-square type, which allows a better approximation to the original expenditures variance in the HBS. An empirical analysis is provided using Spanish surveys for years 2012–2016. In addition, we extend the empirical analysis to the rest of the European Union countries, using the surveys provided by Eurostat (EU-SILC, 2011; HBS, 2010).
Journal: Journal of Applied Statistics
Pages: 3233-3250
Issue: 16
Volume: 48
Year: 2021
Month: 12
X-DOI: 10.1080/02664763.2020.1796933
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1796933
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:16:p:3233-3250




Template-Type: ReDIF-Article 1.0
Author-Name: Sungsu Kim
Author-X-Name-First: Sungsu
Author-X-Name-Last: Kim
Author-Name: Ashis SenGupta
Author-X-Name-First: Ashis
Author-X-Name-Last: SenGupta
Title: Multimodal exponential families of circular distributions with application to daily peak hours of PM2.5 level in a large city
Abstract: 
 In this paper, we propose two multimodal circular distributions which are suitable for modeling circular data sets with two or more modes. Both distributions belong to the regular exponential family of distributions and are considered as extensions of the von Mises distribution. Hence, they possess the highly desirable properties, such as the existence of non-trivial sufficient statistics and optimal inferences for their parameters. Fine particulates (PM2.5) are generally emitted from activities such as industrial and residential combustion and from vehicle exhaust. We illustrate the utility of our proposed models using a real data set consisting of fine particulates (PM2.5) pollutant levels in Houston region during Fall season in 2019. Our results provide a strong evidence that its diurnal pattern exhibits four modes; two peaks during morning and evening rush hours and two peaks in between.
Journal: Journal of Applied Statistics
Pages: 3193-3207
Issue: 16
Volume: 48
Year: 2021
Month: 12
X-DOI: 10.1080/02664763.2020.1796938
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1796938
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:16:p:3193-3207




Template-Type: ReDIF-Article 1.0
Author-Name: Thomas J. Smith
Author-X-Name-First: Thomas J.
Author-X-Name-Last: Smith
Author-Name: David A. Walker
Author-X-Name-First: David A.
Author-X-Name-Last: Walker
Author-Name: Cornelius M. McKenna
Author-X-Name-First: Cornelius M.
Author-X-Name-Last: McKenna
Title: A coefficient of discrimination for use with nominal and ordinal regression models
Abstract: 
 This study introduces a coefficient of discrimination for use with nominal and ordinal regression models. Computation of the coefficient is demonstrated with data from the Pew Research Center’s 25th Anniversary of the Web Omnibus Survey pertaining to cell/home phone ownership, where the coefficient of discrimination indicates that respondent age and gender increased the probability of a correct versus incorrect classification by 13.9%. Additionally, the coefficient is compared to existing coefficients.
Journal: Journal of Applied Statistics
Pages: 3208-3219
Issue: 16
Volume: 48
Year: 2021
Month: 12
X-DOI: 10.1080/02664763.2020.1796940
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1796940
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:16:p:3208-3219




Template-Type: ReDIF-Article 1.0
Author-Name: E. P. Sreedevi
Author-X-Name-First: E. P.
Author-X-Name-Last: Sreedevi
Author-Name: P. G. Sankaran
Author-X-Name-First: P. G.
Author-X-Name-Last: Sankaran
Title: Nonparametric inference for panel count data with competing risks
Abstract: 
 In survival and reliability studies, panel count data arise when we investigate a recurrent event process and each study subject is observed only at discrete time points. If recurrent events of several types are possible, we obtain panel count data with competing risks. Such data arise frequently from transversal studies on recurrent events in demography, epidemiology and reliability experiments where the individuals cannot be observed continuously. In the present paper, we propose an isotonic regression estimator for the cause specific mean function of the underlying recurrent event process of a competing risks panel count data. Further, a nonparametric test is proposed to compare the cause specific mean functions of the panel count competing risks data. Asymptotic properties of the proposed estimator and test statistic are studied. A simulation study is conducted to assess the finite sample behaviour of the proposed estimator and test statistic. Finally, the procedures developed are applied to a real data arising from skin cancer chemo prevention trial.
Journal: Journal of Applied Statistics
Pages: 3102-3115
Issue: 16
Volume: 48
Year: 2021
Month: 12
X-DOI: 10.1080/02664763.2020.1795816
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1795816
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:16:p:3102-3115




Template-Type: ReDIF-Article 1.0
Author-Name: Pallab Kumar Ghosh
Author-X-Name-First: Pallab Kumar
Author-X-Name-Last: Ghosh
Title: Box–Cox power transformation unconditional quantile regressions with an application on wage inequality
Abstract: 
 This study proposes a semi-parametric estimation method, Box–Cox power transformation unconditional quantile regression, to estimate the impact of changes in the distribution of the explanatory variables on the unconditional quantile of the outcome variable. The proposed method consists of running a nonlinear regression of the recentered influence function (RIF) of the outcome variable on the explanatory variables. We also show the asymptotic properties of the proposed estimator and apply the estimation method to address an existing puzzle in labor economics–why the 50th/10th percentile wage gap has been falling in the USA since the late 1980s. Our results show that declining unionization can explain approximately 10% of the decline in the 50/10 wage gap in 1990–2000 and 23% in 2000–2010.
Journal: Journal of Applied Statistics
Pages: 3086-3101
Issue: 16
Volume: 48
Year: 2021
Month: 12
X-DOI: 10.1080/02664763.2020.1795817
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1795817
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:16:p:3086-3101




Template-Type: ReDIF-Article 1.0
Author-Name: Fernando Ferraz do Nascimento
Author-X-Name-First: Fernando Ferraz
Author-X-Name-Last: do Nascimento
Author-Name: Andreson Almeida Azevedo
Author-X-Name-First: Andreson Almeida
Author-X-Name-Last: Azevedo
Author-Name: Valmaria Rocha da Silva Ferraz
Author-X-Name-First: Valmaria Rocha da Silva
Author-X-Name-Last: Ferraz
Title: Regression models to dependence for exceedance
Abstract: 
 Extreme Value Theory (EVT) aims to study the tails of probability distributions in order to measure and quantify extreme events of maximum and minimum. In river flow data, an extreme level of a river may be related to the level of a neighboring river that flows into it. In this type of data, it is very common for flooding of a location to have been caused by a very large flow from an affluent river that is tens or hundreds of kilometers from this location. In this sense, an interesting approach is to consider a conditional model for the estimation of a multivariate model. Inspired by this idea, we propose a Bayesian model to describe the dependence of exceedance between rivers, where we considered a conditionally independent structure. In this model, the dependence between rivers is captured by modeling the excess marginally of one river as a consequence of linear functions of the other rivers. The results showed that there is a strong and positive connection between excesses in one river caused by the excesses of the other rivers.
Journal: Journal of Applied Statistics
Pages: 3048-3059
Issue: 16
Volume: 48
Year: 2021
Month: 12
X-DOI: 10.1080/02664763.2020.1795088
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1795088
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:16:p:3048-3059




Template-Type: ReDIF-Article 1.0
Author-Name: Jochen Kruppa
Author-X-Name-First: Jochen
Author-X-Name-Last: Kruppa
Author-Name: Ludwig Hothorn
Author-X-Name-First: Ludwig
Author-X-Name-Last: Hothorn
Title: A comparison study on modeling of clustered and overdispersed count data for multiple comparisons
Abstract: 
 Data collected in various scientific fields are count data. One way to analyze such data is to compare the individual levels of the factor treatment using multiple comparisons. However, the measured individuals are often clustered – e.g. according to litter or rearing. This must be considered when estimating the parameters by a repeated measurement model. In addition, ignoring the overdispersion to which count data is prone leads to an increase of the type one error rate. We carry out simulation studies using several different data settings and compare different multiple contrast tests with parameter estimates from generalized estimation equations and generalized linear mixed models in order to observe coverage and rejection probabilities. We generate overdispersed, clustered count data in small samples as can be observed in many biological settings. We have found that the generalized estimation equations outperform generalized linear mixed models if the variance-sandwich estimator is correctly specified. Furthermore, generalized linear mixed models show problems with the convergence rate under certain data settings, but there are model implementations with lower implications exists. Finally, we use an example of genetic data to demonstrate the application of the multiple contrast test and the problems of ignoring strong overdispersion.
Journal: Journal of Applied Statistics
Pages: 3220-3232
Issue: 16
Volume: 48
Year: 2021
Month: 12
X-DOI: 10.1080/02664763.2020.1788518
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1788518
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:16:p:3220-3232




Template-Type: ReDIF-Article 1.0
Author-Name: Reza Azimi
Author-X-Name-First: Reza
Author-X-Name-Last: Azimi
Author-Name: Mahdy Esmailian
Author-X-Name-First: Mahdy
Author-X-Name-Last: Esmailian
Title: Correction to: The Weibull Fréchet distribution and its applications
Abstract: 
 Afify et al. [The Weibull Fréchet distribution and its applications, J. Appl. Stat., 43 (2016), pp. 2608–2626] defined and studied a new four-parameter lifetime model called the Weibull Fréchet distribution. They made some mistakes in presenting the log-likelihood function and the components of score vector. In this note, we will correct them.
Journal: Journal of Applied Statistics
Pages: 3251-3252
Issue: 16
Volume: 48
Year: 2021
Month: 12
X-DOI: 10.1080/02664763.2021.1973388
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1973388
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:16:p:3251-3252




Template-Type: ReDIF-Article 1.0
Author-Name: Ricardo Puziol de Oliveira
Author-X-Name-First: Ricardo Puziol
Author-X-Name-Last: de Oliveira
Author-Name: Jorge Alberto Achcar
Author-X-Name-First: Jorge Alberto
Author-X-Name-Last: Achcar
Title: Accurate estimation for extra-Poisson variability assuming random effect models
Abstract: 
 In this study, the components of extra-Poisson variability are estimated assuming random effect models under a Bayesian approach. A standard existing methodology to estimate extra-Poisson variability assumes a negative binomial distribution. The obtained results show that using the proposed random effect model it is possible to get more accurate estimates for the extra-Poisson variability components when compared to the use of a negative binomial distribution where it is possible to estimate only one component of extra-Poisson variability. Some illustrative examples are introduced considering real data sets.
Journal: Journal of Applied Statistics
Pages: 2982-3001
Issue: 16
Volume: 48
Year: 2021
Month: 12
X-DOI: 10.1080/02664763.2020.1789075
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1789075
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:16:p:2982-3001




Template-Type: ReDIF-Article 1.0
Author-Name: Jiajia Chen
Author-X-Name-First: Jiajia
Author-X-Name-Last: Chen
Author-Name: Xiaoqin Zhang
Author-X-Name-First: Xiaoqin
Author-X-Name-Last: Zhang
Author-Name: Karel Hron
Author-X-Name-First: Karel
Author-X-Name-Last: Hron
Title: Partial least squares regression with compositional response variables and covariates
Abstract: 
 The common approach for regression analysis with compositional variables is to express compositions in log-ratio coordinates (coefficients) and then perform standard statistical processing in real space. Similar to working in real space, the problem is that the standard least squares regression fails when the number of parts of all compositional covariates is higher than the number of observations. The aim of this study is to analyze in detail the partial least squares (PLS) regression which can deal with this problem. In this paper, we focus on the PLS regression between more than one compositional response variable and more than one compositional covariate. First, we give the PLS regression model with log-ratio coordinates of compositional variables, then we express the PLS model directly in the simplex. We also prove that the PLS model is invariant under the change of coordinate system, such as the ilr coordinates with a different contrast matrix or the clr coefficients. Moreover, we give the estimation and inference for parameters in PLS model. Finally, the PLS model with clr coefficients is used to analyze the relationship between the chemical metabolites of Astragali Radix and the plasma metabolites of rat after giving Astragali Radix.
Journal: Journal of Applied Statistics
Pages: 3130-3149
Issue: 16
Volume: 48
Year: 2021
Month: 12
X-DOI: 10.1080/02664763.2020.1795813
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1795813
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:48:y:2021:i:16:p:3130-3149




Template-Type: ReDIF-Article 1.0
Author-Name: Nabakumar Jana
Author-X-Name-First: Nabakumar
Author-X-Name-Last: Jana
Author-Name: Samadrita Bera
Author-X-Name-First: Samadrita
Author-X-Name-Last: Bera
Title: Estimation of parameters of inverse Weibull distribution and application to multi-component stress-strength model
Abstract: 
 The problem of estimation of the parameters of two-parameter inverse Weibull distributions has been considered. We establish existence and uniqueness of the maximum likelihood estimators of the scale and shape parameters. We derive Bayes estimators of the parameters under the entropy loss function. Hierarchical Bayes estimator, equivariant estimator and a class of minimax estimators are derived when shape parameter is known. Ordered Bayes estimators using information about second population are also derived. We investigate the reliability of multi-component stress-strength model using classical and Bayesian approaches. Risk comparison of the classical and Bayes estimators is done using Monte Carlo simulations. Applications of the proposed estimators are shown using real data sets.
Journal: Journal of Applied Statistics
Pages: 169-194
Issue: 1
Volume: 49
Year: 2022
Month: 01
X-DOI: 10.1080/02664763.2020.1803815
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1803815
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:1:p:169-194




Template-Type: ReDIF-Article 1.0
Author-Name: María Dolores Esteban
Author-X-Name-First: María Dolores
Author-X-Name-Last: Esteban
Author-Name: María José Lombardía
Author-X-Name-First: María José
Author-X-Name-Last: Lombardía
Author-Name: Esther López-Vizcaíno
Author-X-Name-First: Esther
Author-X-Name-Last: López-Vizcaíno
Author-Name: Domingo Morales
Author-X-Name-First: Domingo
Author-X-Name-Last: Morales
Author-Name: Agustín Pérez
Author-X-Name-First: Agustín
Author-X-Name-Last: Pérez
Title: Small area estimation of expenditure means and ratios under a unit-level bivariate linear mixed model
Abstract: 
 Under a unit-level bivariate linear mixed model, this paper introduces small area predictors of expenditure means and ratios, and derives approximations and estimators of the corresponding mean squared errors. For the considered model, the REML estimation method is implemented. Several simulation experiments, designed to analyze the behavior of the introduced fitting algorithm, predictors and mean squared error estimators, are carried out. An application to real data from the Spanish household budget survey illustrates the behavior of the proposed statistical methodology. The target is the estimation of means of food and non-food household annual expenditures and of ratios of food household expenditures by Spanish provinces.
Journal: Journal of Applied Statistics
Pages: 143-168
Issue: 1
Volume: 49
Year: 2022
Month: 01
X-DOI: 10.1080/02664763.2020.1803809
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1803809
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:1:p:143-168




Template-Type: ReDIF-Article 1.0
Author-Name: F. Prataviera
Author-X-Name-First: F.
Author-X-Name-Last: Prataviera
Author-Name: A. M. Batista
Author-X-Name-First: A. M.
Author-X-Name-Last: Batista
Author-Name: P. L. Libardi
Author-X-Name-First: P. L.
Author-X-Name-Last: Libardi
Author-Name: G. M. Cordeiro
Author-X-Name-First: G. M.
Author-X-Name-Last: Cordeiro
Author-Name: E. M. M. Ortega
Author-X-Name-First: E. M. M.
Author-X-Name-Last: Ortega
Title: Joint regression modeling of location and scale parameters of the skew t distribution with application in soil chemistry data
Abstract: 
 In regression model applications, the errors may frequently present a symmetric shape. In such cases, the normal and Student t distributions are commonly used. In this paper, we shall be concerned only to model heavy-tailed, skewed errors and absence of variance homogeneity with two regression structures based on the skew t distribution. We consider a classic analysis for the parameters of the proposed model. We perform a diagnostic analysis based on global influence and quantile residuals. For different parameter settings and sample sizes, various simulation results are obtained and compared to evaluate the performance of the skew t regression. Further, we illustrate the usefulness of the new regression by means of a real data set (amount of potassium in different soil areas) from a study carried out at the Department of Soil Science of the Luiz de Queiroz School of Agriculture, University of São Paulo.
Journal: Journal of Applied Statistics
Pages: 195-213
Issue: 1
Volume: 49
Year: 2022
Month: 01
X-DOI: 10.1080/02664763.2020.1801608
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1801608
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:1:p:195-213




Template-Type: ReDIF-Article 1.0
Author-Name: Shiva S. Dibaj
Author-X-Name-First: Shiva S.
Author-X-Name-Last: Dibaj
Author-Name: Alan D. Hutson
Author-X-Name-First: Alan D.
Author-X-Name-Last: Hutson
Author-Name: Graham W. Warren
Author-X-Name-First: Graham W.
Author-X-Name-Last: Warren
Author-Name: Gregory E. Wilding
Author-X-Name-First: Gregory E.
Author-X-Name-Last: Wilding
Title: Exact unconditional inference for analyzing contingency tables in finite populations
Abstract: 
 With recent developments in computer power the application of exact inferential methods has become more feasible which has resulted in increasing popularity of these approaches. However, there is a lack of such methodology for populations with more complex structure, such as finite populations. When a small sample is drawn from a finite population, the number of individuals with a specific characteristic of interest follows hypergeometric distribution. In order to test for the comparison of two proportions in finite populations we develop an exact unconditional test. We utilize the information gained from the sample to restrict our search for the maximum p-value. Our proposed test has power equal to its competitors while maintains the pre-specified nominal significance level.
Journal: Journal of Applied Statistics
Pages: 86-97
Issue: 1
Volume: 49
Year: 2022
Month: 01
X-DOI: 10.1080/02664763.2020.1798363
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1798363
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:1:p:86-97




Template-Type: ReDIF-Article 1.0
Author-Name: Cindy Feng
Author-X-Name-First: Cindy
Author-X-Name-Last: Feng
Title: Zero-inflated models for adjusting varying exposures: a cautionary note on the pitfalls of using offset
Abstract: 
 Zero-inflated count data are frequently encountered in public health and epidemiology research. Two-parts model is often used to model the excessive zeros, which are a mixture of two components: a point mass at zero and a count distribution, such as a Poisson distribution. When the rate of events per unit exposure is of interest, offset is commonly used to account for the varying extent of exposure, which is essentially a predictor whose regression coefficient is fixed at one. Such an assumption of exposure effect is, however, quite restrictive for many practical problems. Further, for zero-inflated models, offset is often only included in the count component of the model. However, the probability of excessive zero component could also be affected by the amount of ‘exposure’. We, therefore, proposed incorporating the varying exposure as a covariate rather than an offset term in both the probability of excessive zeros and conditional counts components of the zero-inflated model. A real example is used to illustrate the usage of the proposed methods, and simulation studies are conducted to assess the performance of the proposed methods for a broad variety of situations.
Journal: Journal of Applied Statistics
Pages: 1-23
Issue: 1
Volume: 49
Year: 2022
Month: 01
X-DOI: 10.1080/02664763.2020.1796943
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1796943
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:1:p:1-23




Template-Type: ReDIF-Article 1.0
Author-Name: Haiqiang Ma
Author-X-Name-First: Haiqiang
Author-X-Name-Last: Ma
Author-Name: Jin Yang
Author-X-Name-First: Jin
Author-X-Name-Last: Yang
Author-Name: Sheng Xu
Author-X-Name-First: Sheng
Author-X-Name-Last: Xu
Author-Name: Chao Liu
Author-X-Name-First: Chao
Author-X-Name-Last: Liu
Author-Name: Qinyi Zhang
Author-X-Name-First: Qinyi
Author-X-Name-Last: Zhang
Title: Combination of multiple functional markers to improve diagnostic accuracy
Abstract: 
 Combination of multiple biomarkers to improve diagnostic accuracy is meaningful for practitioners and clinicians, and are attractive to lots of researchers. Nowadays, with development of modern techniques, functional markers such as curves or images, play an important role in diagnosis. There exists rich literature developing combination methods for continuous scalar markers. Unfortunately, only sporadic works have studied how functional markers affect diagnosis in the literature. Moreover, no publication can be found to do combination of multiple functional markers to improve the diagnostic accuracy. It is impossible to apply scalar combination methods to the multiple functional markers directly because of infinite dimensionality of functional markers. In this article, we propose a one-dimension scalar feature motivated by square loss distance, as an alternative of the original functional curve in the sense that, it can retain information to the most extent. The square loss distance is defined as the function of projection scores generated from functional principal component decomposition. Then existing variety of scalar combination methods can be applied to scalar features of functional markers after dimension reduction to improve the diagnostic accuracy. Area under the receiver operating characteristic curve and Youden index are used to assess performances of various methods in numerical studies. We also analyzed the high- or low- hospital admissions due to respiratory diseases between 2010 and 2017 in Hong Kong by combining weather conditions and media information, which are regarded as functional markers. Finally, we provide an R function for convenient application.
Journal: Journal of Applied Statistics
Pages: 44-63
Issue: 1
Volume: 49
Year: 2022
Month: 01
X-DOI: 10.1080/02664763.2020.1796945
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1796945
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:1:p:44-63




Template-Type: ReDIF-Article 1.0
Author-Name: D. Scaldelai
Author-X-Name-First: D.
Author-X-Name-Last: Scaldelai
Author-Name: L. C. Matioli
Author-X-Name-First: L. C.
Author-X-Name-Last: Matioli
Author-Name: S. R. Santos
Author-X-Name-First: S. R.
Author-X-Name-Last: Santos
Author-Name: M. Kleina
Author-X-Name-First: M.
Author-X-Name-Last: Kleina
Title: MulticlusterKDE: a new algorithm for clustering based on multivariate kernel density estimation
Abstract: 
 In this paper, we propose the MulticlusterKDE algorithm applied to classify elements of a database into categories based on their similarity. MulticlusterKDE is centered on the multiple optimization of the kernel density estimator function with multivariate Gaussian kernel. One of the main features of the proposed algorithm is that the number of clusters is an optional input parameter. Furthermore, it is very simple, easy to implement, well defined and stops at a finite number of steps and it always converges regardless of the data set. We illustrate our findings by implementing the algorithm in R software. The results indicate that the MulticlusterKDE algorithm is competitive when compared to K-means, K-medoids, CLARA, DBSCAN and PdfCluster algorithms. Features such as simplicity and efficiency make the proposed algorithm an attractive and promising research field that can be used as basis for its improvement and also for the development of new density-based clustering algorithms.
Journal: Journal of Applied Statistics
Pages: 98-121
Issue: 1
Volume: 49
Year: 2022
Month: 01
X-DOI: 10.1080/02664763.2020.1799958
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1799958
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:1:p:98-121




Template-Type: ReDIF-Article 1.0
Author-Name: Dariush Najarzadeh
Author-X-Name-First: Dariush
Author-X-Name-Last: Najarzadeh
Title: Conservative confidence intervals on multiple correlation coefficient for high-dimensional elliptical data using random projection methodology
Abstract: 
 So called multiple correlation coefficient (MCC) is a measure of linear relationship between a given variable and set of covariates. In the multiple correlation and regression analysis, it is common practice to construct a confidence interval for the population MCC. In high-dimensional data settings, by which the data dimension p is much larger than the sample size n, due to the singularity of the sample covariance matrix, the classical confidence intervals for the MCC are no longer useable. For high-dimensional elliptical data, some (conservative) confidence intervals for the population MCC are presented using the random projection methodology. To evaluate and compare the performance of the proposed confidence intervals, some simulations are conducted in terms of the coverage probability and average interval length. Experimental validation of the proposed intervals is carried out on two real gene expression datasets.
Journal: Journal of Applied Statistics
Pages: 64-85
Issue: 1
Volume: 49
Year: 2022
Month: 01
X-DOI: 10.1080/02664763.2020.1796937
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1796937
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:1:p:64-85




Template-Type: ReDIF-Article 1.0
Author-Name: M. Carmen Pardo
Author-X-Name-First: M.
Author-X-Name-Last: Carmen Pardo
Author-Name: Ying Lu
Author-X-Name-First: Ying
Author-X-Name-Last: Lu
Author-Name: Alba M. Franco-Pereira
Author-X-Name-First: Alba M.
Author-X-Name-Last: Franco-Pereira
Title: Extensions of empirical likelihood and chi-squared-based tests for ordered alternatives
Abstract: 
 Several methods for comparing k populations have been proposed in the literature. These methods assess the same null hypothesis of equal distributions but differ in the alternative hypothesis they consider. We focus on two important alternative hypotheses: monotone and umbrella ordering. Two new families of test statistics are proposed, including two known tests, as well as two new powerful tests under monotone ordering. Furthermore, these families are adapted for testing umbrella ordering. We compare some members of the families with respect to power and Type I errors under different simulation scenarios. Finally, the methods are illustrated in several applications to real data.
Journal: Journal of Applied Statistics
Pages: 24-43
Issue: 1
Volume: 49
Year: 2022
Month: 01
X-DOI: 10.1080/02664763.2020.1796944
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1796944
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:1:p:24-43




Template-Type: ReDIF-Article 1.0
Author-Name: J. C. S. Vasconcelos
Author-X-Name-First: J. C. S.
Author-X-Name-Last: Vasconcelos
Author-Name: G. M. Cordeiro
Author-X-Name-First: G. M.
Author-X-Name-Last: Cordeiro
Author-Name: E. M. M. Ortega
Author-X-Name-First: E. M. M.
Author-X-Name-Last: Ortega
Title: The semiparametric regression model for bimodal data with different penalized smoothers applied to climatology, ethanol and air quality data
Abstract: 
 Semiparametric regressions can be used to model data when covariables and the response variable have a nonlinear relationship. In this work, we propose three flexible regression models for bimodal data called the additive, additive partial and semiparametric regressions, basing on the odd log-logistic generalized inverse Gaussian distribution under three types of penalized smoothers, where the main idea is not to confront the three forms of smoothings but to show the versatility of the distribution with three types of penalized smoothers. We present several Monte Carlo simulations carried out for different configurations of the parameters and some sample sizes to verify the precision of the penalized maximum-likelihood estimators. The usefulness of the proposed regressions is proved empirically through three applications to climatology, ethanol and air quality data.
Journal: Journal of Applied Statistics
Pages: 248-267
Issue: 1
Volume: 49
Year: 2022
Month: 01
X-DOI: 10.1080/02664763.2020.1803812
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1803812
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:1:p:248-267




Template-Type: ReDIF-Article 1.0
Author-Name: Liang Wang
Author-X-Name-First: Liang
Author-X-Name-Last: Wang
Author-Name: Ke Wu
Author-X-Name-First: Ke
Author-X-Name-Last: Wu
Author-Name: Yogesh Mani Tripathi
Author-X-Name-First: Yogesh Mani
Author-X-Name-Last: Tripathi
Author-Name: Chandrakant Lodhi
Author-X-Name-First: Chandrakant
Author-X-Name-Last: Lodhi
Title: Reliability analysis of multicomponent stress–strength reliability from a bathtub-shaped distribution
Abstract: 
 In this paper, inference for a multicomponent stress–strength model is studied. When latent strength and stress random variables follow a bathtub-shaped distribution and the failure times are Type-II censored, the maximum likelihood estimate of the multicomponent stress–strength reliability (MSR) is established when there are common strength and stress parameters. Approximate confidence interval is also constructed by using the asymptotic distribution theory and delta method. Furthermore, another alternative generalized point and confidence interval estimators for the MSR are constructed based on pivotal quantities. Moreover, the likelihood and the pivotal quantities-based estimates for the MSR are also provided under unequal strength and stress parameter case. To compare the equivalence of the stress and strength parameters, the likelihood ratio test for hypothesis of interest is also provided. Finally, simulation studies and a real data example are given for illustration.
Journal: Journal of Applied Statistics
Pages: 122-142
Issue: 1
Volume: 49
Year: 2022
Month: 01
X-DOI: 10.1080/02664763.2020.1803808
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1803808
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:1:p:122-142




Template-Type: ReDIF-Article 1.0
Author-Name: Han Yu
Author-X-Name-First: Han
Author-X-Name-Last: Yu
Author-Name: Rachael Hageman Blair
Author-X-Name-First: Rachael
Author-X-Name-Last: Hageman Blair
Title: Scalable module detection for attributed networks with applications to breast cancer
Abstract: 
 The objective of network module detection is to identify groups of nodes within a network structure that are tightly connected. Nodes in a network often have attributes (aka metadata) associated with them. It is often desirable to identify groups of nodes that are tightly connected in the network structure, but also have strong similarity in their attributes. Utilizing attribute information in module detection is a major challenge because it requires bridging the structural network with attribute data. A Weighted Fast Greedy (WFG) algorithm for attribute-based module detection is proposed. WFG utilizes logistic regression to bridge the structural and attribute spaces. The logistic function naturally emphasizes associations between attributes and network structure accordingly, and can be easily interpreted. A breast cancer application is presented that connects a protein–protein interaction network gene expression data and a survival outcome. This application demonstrates the importance of embedding attribute information into the community detection framework on a breast cancer dataset. Five modules were significant for survival and they contained known pathways and markers for cancer, including cell cycle, p53 pathway, BRCA1, BRCA2, and AURKB, among others. Whereas, neither the gene expression data nor the network structure alone gave rise to these cancer biomarkers and signatures.
Journal: Journal of Applied Statistics
Pages: 230-247
Issue: 1
Volume: 49
Year: 2022
Month: 01
X-DOI: 10.1080/02664763.2020.1803811
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1803811
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:1:p:230-247




Template-Type: ReDIF-Article 1.0
Author-Name: Mariangela Sciandra
Author-X-Name-First: Mariangela
Author-X-Name-Last: Sciandra
Author-Name: Irene Carola Spera
Author-X-Name-First: Irene Carola
Author-X-Name-Last: Spera
Title: A model-based approach to Spotify data analysis: a Beta GLMM
Abstract: 
 Digital music distribution is increasingly powered by automated mechanisms that continuously capture, sort and analyze large amounts of Web-based data. This paper deals with the management of songs audio features from a statistical point of view. In particular, it explores the data catching mechanisms enabled by Spotify Web API and suggests statistical tools for the analysis of these data. Special attention is devoted to songs popularity and a Beta model, including random effects, is proposed in order to give the first answer to questions like: which are the determinants of popularity? The identification of a model able to describe this relationship, the determination within the set of characteristics of those considered most important in making a song popular is a very interesting topic for those who aim to predict the success of new products.
Journal: Journal of Applied Statistics
Pages: 214-229
Issue: 1
Volume: 49
Year: 2022
Month: 01
X-DOI: 10.1080/02664763.2020.1803810
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1803810
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:1:p:214-229




Template-Type: ReDIF-Article 1.0
Author-Name: José L. Jiménez
Author-X-Name-First: José L.
Author-X-Name-Last: Jiménez
Title: Quantifying treatment differences in confirmatory trials under non-proportional hazards
Abstract: 
 Proportional hazards are a common assumption when designing confirmatory clinical trials in oncology. With the emergence of immunotherapy and novel targeted therapies, departure from the proportional hazard assumption is not rare in nowadays clinical research. Under non-proportional hazards, the hazard ratio does not have a straightforward clinical interpretation, and the log-rank test is no longer the most powerful statistical test even though it is still valid. Nevertheless, the log-rank test and the hazard ratio are still the primary analysis tools, and traditional approaches such as sample size increase are still proposed to account for the impact of non-proportional hazards. The weighed log-rank test and the test based on the restricted mean survival time (RMST) are receiving a lot of attention as a potential alternative to the log-rank test. We conduct a simulation study comparing the performance and operating characteristics of the log-rank test, the weighted log-rank test and the test based on the RMST, including a treatment effect estimation, under different non-proportional hazards patterns. Results show that, under non-proportional hazards, the hazard ratio and weighted hazard ratio have no straightforward clinical interpretation whereas the RMST ratio can be interpreted regardless of the proportional hazards assumption. In terms of power, the RMST achieves a similar performance when compared to the log-rank test.
Journal: Journal of Applied Statistics
Pages: 466-484
Issue: 2
Volume: 49
Year: 2022
Month: 01
X-DOI: 10.1080/02664763.2020.1815673
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1815673
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:2:p:466-484




Template-Type: ReDIF-Article 1.0
Author-Name: Hongwei Tan
Author-X-Name-First: Hongwei
Author-X-Name-Last: Tan
Author-Name: Guodong Wang
Author-X-Name-First: Guodong
Author-X-Name-Last: Wang
Author-Name: Wendong Wang
Author-X-Name-First: Wendong
Author-X-Name-Last: Wang
Author-Name: Zili Zhang
Author-X-Name-First: Zili
Author-X-Name-Last: Zhang
Title: Feature selection based on distance correlation: a filter algorithm
Abstract: 
 Feature selection (FS) is one of the most powerful techniques to cope with the curse of dimensionality. In the study, a new filter approach to feature selection based on distance correlation is presented (DCFS, for short), which keeps the model-free advantage without any pre-specified parameters. Our method consists of two steps: hard step (forward selection) and soft step (backward selection). In the hard step, two types of associations, between univariate feature and the classes and between group feature and the classes, are involved to pick out the most relevant features with respect to the target classes. Due to the strict screening condition in the first step, some of the useful features are likely removed. Therefore, in the soft step, a feature-relationship gain (like feature score) based on the distance correlation is introduced, which is concerned with five kinds of associations. We sort the feature gain values and implement the backward selection procedure until the errors stop declining. The simulation results show that our method becomes more competitive on several datasets compared with some of the representative feature selection methods based on several classification models.
Journal: Journal of Applied Statistics
Pages: 411-426
Issue: 2
Volume: 49
Year: 2022
Month: 01
X-DOI: 10.1080/02664763.2020.1815672
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1815672
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:2:p:411-426




Template-Type: ReDIF-Article 1.0
Author-Name: Chandima N. P. G. Arachchige
Author-X-Name-First: Chandima N. P. G.
Author-X-Name-Last: Arachchige
Author-Name: Luke A. Prendergast
Author-X-Name-First: Luke A.
Author-X-Name-Last: Prendergast
Author-Name: Robert G. Staudte
Author-X-Name-First: Robert G.
Author-X-Name-Last: Staudte
Title: Robust analogs to the coefficient of variation
Abstract: 
 The coefficient of variation (CV) is commonly used to measure relative dispersion. However, since it is based on the sample mean and standard deviation, outliers can adversely affect it. Additionally, for skewed distributions the mean and standard deviation may be difficult to interpret and, consequently, that may also be the case for the 
 ${\rm CV} $CV. Here we investigate the extent to which quantile-based measures of relative dispersion can provide appropriate summary information as an alternative to the CV. In particular, we investigate two measures, the first being the interquartile range (in lieu of the standard deviation), divided by the median (in lieu of the mean), and the second being the median absolute deviation, divided by the median, as robust estimators of relative dispersion. In addition to comparing the influence functions of the competing estimators and their asymptotic biases and variances, we compare interval estimators using simulation studies to assess coverage.
Journal: Journal of Applied Statistics
Pages: 268-290
Issue: 2
Volume: 49
Year: 2022
Month: 01
X-DOI: 10.1080/02664763.2020.1808599
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1808599
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:2:p:268-290




Template-Type: ReDIF-Article 1.0
Author-Name: Xiaoxue Li
Author-X-Name-First: Xiaoxue
Author-X-Name-Last: Li
Author-Name: Stewart J. Anderson
Author-X-Name-First: Stewart J.
Author-X-Name-Last: Anderson
Author-Name: Saul Shiffman
Author-X-Name-First: Saul
Author-X-Name-Last: Shiffman
Author-Name: Bo Zhang
Author-X-Name-First: Bo
Author-X-Name-Last: Zhang
Title: Time-varying coefficient cumulative gap time models for intensive longitudinal ecological momentary assessment data with missingness
Abstract: 
 Ecological momentary assessment (EMA) studies investigate intensive repeated observations of the current behavior and experiences of subjects in real time. In particular, such studies aim to minimize recall bias and maximize ecological validity, thereby strengthening the investigation and inference of microprocesses that influence behavior in real-world contexts by gathering intensive information on the temporal patterning of behavior of study subjects. Throughout this paper, we focus on the data analysis of an EMA study that examined behavior of intermittent smokers (ITS). Specifically, we sought to explore the pattern of clustered smoking behavior of ITS, or smoking ‘bouts’, as well as the covariates that predict such smoking behavior. To do this, in this paper we introduce a framework for characterizing the temporal behavior of ITS via the functions of event gap time to distinguish the smoking bouts. We used the time-varying coefficient models for the cumulative log gap time and to characterize the temporal patterns of smoking behavior, while simultaneously adjusting for behavioral covariates, and incorporated the inverse probability weighting into the models to accommodate missing data. Simulation studies showed that irrespective of whether missing by design or missing at random, the model was able to reliably determine prespecified time-varying functional forms of a given covariate coefficient, provided the the within-subject level was small.
Journal: Journal of Applied Statistics
Pages: 498-521
Issue: 2
Volume: 49
Year: 2022
Month: 01
X-DOI: 10.1080/02664763.2020.1815676
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1815676
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:2:p:498-521




Template-Type: ReDIF-Article 1.0
Author-Name: Mohammad Vali Ahmadi
Author-X-Name-First: Mohammad Vali
Author-X-Name-Last: Ahmadi
Author-Name: Mahdi Doostparast
Author-X-Name-First: Mahdi
Author-X-Name-Last: Doostparast
Title: Predictive analysis for joint progressive censoring plans: a Bayesian approach
Abstract: 
 Comparative lifetime experiments are of particular importance in production processes when one wishes to determine the relative merits of several competing products with regard to their reliability. This paper confines itself to the data obtained by running a joint progressive Type-II censoring plan on samples in a combined manner. The problem of Bayesian predicting failure times of surviving units is discussed in details when parent populations are exponential. Two real data sets are analyzed in order to illustrate all the inferential procedures developed here. When destructive experiments under a censoring scheme finished, the researchers are usually interested to estimate remaining lifetimes of surviving units for sequel experiments. Findings of this paper are useful for these purposes specially when samples are non-homogeneous such as those taken from industrial storages.
Journal: Journal of Applied Statistics
Pages: 394-410
Issue: 2
Volume: 49
Year: 2022
Month: 01
X-DOI: 10.1080/02664763.2020.1815671
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1815671
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:2:p:394-410




Template-Type: ReDIF-Article 1.0
Author-Name: C. Manté
Author-X-Name-First: C.
Author-X-Name-Last: Manté
Author-Name: S. Cornu
Author-X-Name-First: S.
Author-X-Name-Last: Cornu
Author-Name: D. Borschneck
Author-X-Name-First: D.
Author-X-Name-Last: Borschneck
Author-Name: C. Mocuta
Author-X-Name-First: C.
Author-X-Name-Last: Mocuta
Author-Name: R. van den Bogaert
Author-X-Name-First: R.
Author-X-Name-Last: van den Bogaert
Title: Detecting the Guttman effect with the help of ordinal correspondence analysis in synchrotron X-ray diffraction data analysis
Abstract: 
 We propose a method for detecting a Guttman effect in a complete disjunctive table 
 $\mathbf{U} $U with Q questions. Since such an investigation is a nonsense when the Q variables are independent, we reuse a previous unpublished work about the chi-squared independence test for Burt's tables. Then, we introduce a two-steps method consisting in plugging the first singular vector from a preliminary Correspondence Analysis (CA) of 
 $\mathbf{U} $U as a score x into a subsequent singly-ordered Ordinal Correspondence Analysis (OCA) of 
 $\mathbf{U} $U. OCA mainly consists in completing x by a sequence of orthogonal polynomials superseding the classical factors of CA. As a consequence, in presence of a pure Guttman effect, we should in principle have that the second singular vector coincide with the polynomial of degree 2, etc. The hybrid decomposition of the Pearson chi-squared statistics (resulting from OCA) used in association with permutation tests makes possible to reveal such relationships, i.e. the presence of a Guttman effect in the structure of 
 $\mathbf{U} $U, and to determine its degree - with an accuracy depending on the signal to noise ratio. The proposed method is successively tested on artificial data (more or less noisy), a well-known benchmark, and synchrotron X-ray diffraction data of soil samples.
Journal: Journal of Applied Statistics
Pages: 291-316
Issue: 2
Volume: 49
Year: 2022
Month: 01
X-DOI: 10.1080/02664763.2020.1810644
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1810644
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:2:p:291-316




Template-Type: ReDIF-Article 1.0
Author-Name: Lingzhe Guo
Author-X-Name-First: Lingzhe
Author-X-Name-Last: Guo
Author-Name: Reza Modarres
Author-X-Name-First: Reza
Author-X-Name-Last: Modarres
Title: Two multivariate online change detection models
Abstract: 
 Online change point detection methods monitor changes in the distribution of a data stream. This article discusses two non-parametric online change detection methods based on the energy statistics and Mahalanobis depth. To apply the energy statistic, we use sliding-window algorithm with efficient training and updating procedures. For Mahalanobis depth, we propose an algorithm to train the threshold with desired protective ability against false alarms and discuss factors that have an influence on the threshold. Numerical studies evaluate and compare the performance of the proposed models with three existing methods to detect changes in the mean and variability of a data stream. The methods are applied to detecting changes in the flowing volume of the Mississippi River.
Journal: Journal of Applied Statistics
Pages: 427-448
Issue: 2
Volume: 49
Year: 2022
Month: 01
X-DOI: 10.1080/02664763.2020.1815674
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1815674
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:2:p:427-448




Template-Type: ReDIF-Article 1.0
Author-Name: Kostas Loumponias
Author-X-Name-First: Kostas
Author-X-Name-Last: Loumponias
Author-Name: George Tsaklidis
Author-X-Name-First: George
Author-X-Name-Last: Tsaklidis
Title: Kalman filtering with censored measurements
Abstract: 
 This paper concerns Kalman filtering when the measurements of the process are censored. The censored measurements are addressed by the Tobit model of Type I and are one-dimensional with two censoring limits, while the (hidden) state vectors are multidimensional. For this model, Bayesian estimates for the state vectors are provided through a recursive algorithm of Kalman filtering type. Experiments are presented to illustrate the effectiveness and applicability of the algorithm. The experiments show that the proposed method outperforms other filtering methodologies in minimizing the computational cost as well as the overall Root Mean Square Error (RMSE) for synthetic and real data sets.
Journal: Journal of Applied Statistics
Pages: 317-335
Issue: 2
Volume: 49
Year: 2022
Month: 01
X-DOI: 10.1080/02664763.2020.1810645
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1810645
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:2:p:317-335




Template-Type: ReDIF-Article 1.0
Author-Name: Paulo C. Marques F.
Author-X-Name-First: Paulo
Author-X-Name-Last: C. Marques F.
Author-Name: Helton Graziadei
Author-X-Name-First: Helton
Author-X-Name-Last: Graziadei
Author-Name: Hedibert F. Lopes
Author-X-Name-First: Hedibert F.
Author-X-Name-Last: Lopes
Title: Bayesian generalizations of the integer-valued autoregressive model
Abstract: 
 We develop two Bayesian generalizations of the Poisson integer-valued autoregressive model. The AdINAR(1) model accounts for overdispersed data by means of an innovation process whose marginal distributions are finite mixtures, while the DP-INAR(1) model is a hierarchical extension involving a Dirichlet process, which is capable of modeling a latent pattern of heterogeneity in the distribution of the innovations rates. The probabilistic forecasting capabilities of both models are put to test in the analysis of crime data in Pittsburgh, with favorable results.
Journal: Journal of Applied Statistics
Pages: 336-356
Issue: 2
Volume: 49
Year: 2022
Month: 01
X-DOI: 10.1080/02664763.2020.1812544
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1812544
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:2:p:336-356




Template-Type: ReDIF-Article 1.0
Author-Name: Luai Al-Labadi
Author-X-Name-First: Luai
Author-X-Name-Last: Al-Labadi
Author-Name: Sean Berry
Author-X-Name-First: Sean
Author-X-Name-Last: Berry
Title: Bayesian estimation of extropy and goodness of fit tests
Abstract: 
 Extropy, a complementary dual of entropy, is considered in this paper. A Bayesian approach based on the Dirichlet process is proposed for the estimation of extropy. A goodness of fit test is also developed. Many theoretical properties of the procedure are derived. Several examples are discussed to illustrate the approach.
Journal: Journal of Applied Statistics
Pages: 357-370
Issue: 2
Volume: 49
Year: 2022
Month: 01
X-DOI: 10.1080/02664763.2020.1812545
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1812545
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:2:p:357-370




Template-Type: ReDIF-Article 1.0
Author-Name: Vikas Kumar Sharma
Author-X-Name-First: Vikas Kumar
Author-X-Name-Last: Sharma
Author-Name: Sudhanshu V. Singh
Author-X-Name-First: Sudhanshu V.
Author-X-Name-Last: Singh
Author-Name: Komal Shekhawat
Author-X-Name-First: Komal
Author-X-Name-Last: Shekhawat
Title: Exponentiated Teissier distribution with increasing, decreasing and bathtub hazard functions
Abstract: 
 This article introduces a two-parameter exponentiated Teissier distribution. It is the main advantage of the distribution to have increasing, decreasing and bathtub shapes for its hazard rate function. The expressions of the ordinary moments, identifiability, quantiles, moments of order statistics, mean residual life function and entropy measure are derived. The skewness and kurtosis of the distribution are explored using the quantiles. In order to study two independent random variables, stress–strength reliability and stochastic orderings are discussed. Estimators based on likelihood, least squares, weighted least squares and product spacings are constructed for estimating the unknown parameters of the distribution. An algorithm is presented for random sample generation from the distribution. Simulation experiments are conducted to compare the performances of the considered estimators of the parameters and percentiles. Three sets of real data are fitted by using the proposed distribution over the competing distributions.
Journal: Journal of Applied Statistics
Pages: 371-393
Issue: 2
Volume: 49
Year: 2022
Month: 01
X-DOI: 10.1080/02664763.2020.1813694
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1813694
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:2:p:371-393




Template-Type: ReDIF-Article 1.0
Author-Name: Caitlin Ward
Author-X-Name-First: Caitlin
Author-X-Name-Last: Ward
Author-Name: Jacob Oleson
Author-X-Name-First: Jacob
Author-X-Name-Last: Oleson
Author-Name: J. Bruce Tomblin
Author-X-Name-First: J. Bruce
Author-X-Name-Last: Tomblin
Author-Name: Elizabeth Walker
Author-X-Name-First: Elizabeth
Author-X-Name-Last: Walker
Title: Modeling population and subject-specific growth in a latent trait measured by multiple instruments over time using a hierarchical Bayesian framework
Abstract: 
 Psychometric growth curve modeling techniques are used to describe a person’s latent ability and how that ability changes over time based on a specific measurement instrument. However, the same instrument cannot always be used over a period of time to measure that latent ability. This is often the case when measuring traits longitudinally in children. Reasons may be that over time some measurement tools that were difficult for young children become too easy as they age resulting in floor effects or ceiling effects or both. We propose a Bayesian hierarchical model for such a scenario. Within the Bayesian model we combine information from multiple instruments used at different age ranges and having different scoring schemes to examine growth in latent ability over time. The model includes between-subject variance and within-subject variance and does not require linking item specific difficulty between the measurement tools. The model’s utility is demonstrated on a study of language ability in children from ages one to ten who are hard of hearing where measurement tool specific growth and subject-specific growth are shown in addition to a group level latent growth curve comparing the hard of hearing children to children with normal hearing.
Journal: Journal of Applied Statistics
Pages: 449-465
Issue: 2
Volume: 49
Year: 2022
Month: 01
X-DOI: 10.1080/02664763.2020.1817346
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1817346
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:2:p:449-465




Template-Type: ReDIF-Article 1.0
Author-Name: Mikkel Slot Nielsen
Author-X-Name-First: Mikkel Slot
Author-X-Name-Last: Nielsen
Author-Name: Victor Rohde
Author-X-Name-First: Victor
Author-X-Name-Last: Rohde
Title: A surrogate model for estimating extreme tower loads on wind turbines based on random forest proximities
Abstract: 
 In the present paper, we present a surrogate model, which can be used to estimate extreme tower loads on a wind turbine from a number of signals and a suitable simulation tool. Due to the requirements of the International Electrotechnical Commission (IEC) Standard 61400-1, assessing extreme tower loads on wind turbines constitutes a key component of the design phase. The proposed model imputes tower loads by matching observed signals with simulated quantities using proximities induced by random forests. In this way, the algorithm's adaptability to high-dimensional and sparse settings is exploited without using regression-based surrogate loads (which may display misleading probabilistic characteristics). Finally, the model is applied to estimate tower loads on an operating wind turbine from data on its operational statistics.
Journal: Journal of Applied Statistics
Pages: 485-497
Issue: 2
Volume: 49
Year: 2022
Month: 01
X-DOI: 10.1080/02664763.2020.1815675
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1815675
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:2:p:485-497




Template-Type: ReDIF-Article 1.0
Author-Name: Tasnime Hamdeni
Author-X-Name-First: Tasnime
Author-X-Name-Last: Hamdeni
Author-Name: Soufiane Gasmi
Author-X-Name-First: Soufiane
Author-X-Name-Last: Gasmi
Title: A proportional-hazards model for survival analysis and long-term survivors modeling: application to amyotrophic lateral sclerosis data
Abstract: 
 The majority of survival data are affected by explanatory variables. We develop a new regression model for survival data analysis. As an alternative to standard mixture models, another model is proposed to describe the eventual presence of a surviving fraction. The proposed models are based on the Marshall–Olkin extended generalized Gompertz distribution. A maximum-likelihood inference is presented in the presence of covariates and a censorship phenomenon. Explanatory variables are incorporated into the model through proportional-hazards to evaluate the effect of risk factors on overall survival under different assumptions. Parametric, semi-parametric, and non-parametric methods are applied to survival analysis of patients treated for amyotrophic lateral sclerosis. Interesting results about riluzole use and other treatment effects on patients' survival have been obtained.
Journal: Journal of Applied Statistics
Pages: 694-708
Issue: 3
Volume: 49
Year: 2022
Month: 02
X-DOI: 10.1080/02664763.2020.1830954
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1830954
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:3:p:694-708




Template-Type: ReDIF-Article 1.0
Author-Name: Xinyan Qin
Author-X-Name-First: Xinyan
Author-X-Name-Last: Qin
Author-Name: Jiao Yu
Author-X-Name-First: Jiao
Author-X-Name-Last: Yu
Author-Name: Wenhao Gui
Author-X-Name-First: Wenhao
Author-X-Name-Last: Gui
Title: Goodness-of-fit test for exponentiality based on spacings for general progressive Type-II censored data
Abstract: 
 There have been numerous tests proposed to determine whether or not the exponential model is suitable for a given data set. In this article, we propose a new test statistic based on spacings to test whether the general progressive Type-II censored samples are from exponential distribution. The null distribution of the test statistic is discussed and it could be approximated by the standard normal distribution. Meanwhile, we propose an approximate method for calculating the expectation and variance of samples under null hypothesis and corresponding power function is also given. Then, a simulation study is conducted. We calculate the approximation of the power based on normality and compare the results with those obtained by Monte Carlo simulation under different alternatives with distinct types of hazard function. Results of simulation study disclose that the power properties of this statistic by using Monte Carlo simulation are better for the alternatives with monotone increasing hazard function, and otherwise, normal approximation simulation results are relatively better. Finally, two illustrative examples are presented.
Journal: Journal of Applied Statistics
Pages: 599-620
Issue: 3
Volume: 49
Year: 2022
Month: 02
X-DOI: 10.1080/02664763.2020.1821613
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1821613
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:3:p:599-620




Template-Type: ReDIF-Article 1.0
Author-Name: B. Bastien
Author-X-Name-First: B.
Author-X-Name-Last: Bastien
Author-Name: T. Boukhobza
Author-X-Name-First: T.
Author-X-Name-Last: Boukhobza
Author-Name: H. Dumond
Author-X-Name-First: H.
Author-X-Name-Last: Dumond
Author-Name: A. Gégout-Petit
Author-X-Name-First: A.
Author-X-Name-Last: Gégout-Petit
Author-Name: A. Muller-Gueudin
Author-X-Name-First: A.
Author-X-Name-Last: Muller-Gueudin
Author-Name: C. Thiébaut
Author-X-Name-First: C.
Author-X-Name-Last: Thiébaut
Title: A statistical methodology to select covariates in high-dimensional data under dependence. Application to the classification of genetic profiles in oncology
Abstract: 
 We propose a new methodology for selecting and ranking covariates associated with a variable of interest in a context of high-dimensional data under dependence but few observations. The methodology successively intertwines the clustering of covariates, decorrelation of covariates using Factor Latent Analysis, selection using aggregation of adapted methods and finally ranking. A simulation study shows the interest of the decorrelation inside the different clusters of covariates. We first apply our method to transcriptomic data of 37 patients with advanced non-small-cell lung cancer who have received chemotherapy, to select the transcriptomic covariates that explain the survival outcome of the treatment. Secondly, we apply our method to 79 breast tumor samples to define patient profiles for a new metastatic biomarker and associated gene network in order to personalize the treatments.
Journal: Journal of Applied Statistics
Pages: 764-781
Issue: 3
Volume: 49
Year: 2022
Month: 02
X-DOI: 10.1080/02664763.2020.1837083
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1837083
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:3:p:764-781




Template-Type: ReDIF-Article 1.0
Author-Name: Kalanka. P. Jayalath
Author-X-Name-First: Kalanka. P.
Author-X-Name-Last: Jayalath
Author-Name: Raj S. Chhikara
Author-X-Name-First: Raj S.
Author-X-Name-Last: Chhikara
Title: Survival analysis for the inverse Gaussian distribution with the Gibbs sampler
Abstract: 
 This paper describes a comprehensive survival analysis for the inverse Gaussian distribution employing Bayesian and Fiducial approaches. It focuses on making inferences on the inverse Gaussian (IG) parameters μ and λ and the average remaining time of censored units. A flexible Gibbs sampling approach applicable in the presence of censoring is discussed and illustrations with Type II, progressive Type II, and random rightly censored observations are included. The analyses are performed using both simulated IG data and empirical data examples. Further, the bootstrap comparisons are made between the Bayesian and Fiducial estimates. It is concluded that the shape parameter (
 $\phi =\lambda /\mu $ϕ=λ/μ) of the inverse Gaussian distribution has the most impact on the two analyses, Bayesian vs. Fiducial, and so does the size of censoring in data to a lesser extent. Overall, both these approaches are effective in estimating IG parameters and the average remaining lifetime. The suggested Gibbs sampler allowed a great deal of flexibility in implementation for all types of censoring considered.
Journal: Journal of Applied Statistics
Pages: 656-675
Issue: 3
Volume: 49
Year: 2022
Month: 02
X-DOI: 10.1080/02664763.2020.1828314
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1828314
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:3:p:656-675




Template-Type: ReDIF-Article 1.0
Author-Name: A. Baíllo
Author-X-Name-First: A.
Author-X-Name-Last: Baíllo
Author-Name: J. E. Chacón
Author-X-Name-First: J. E.
Author-X-Name-Last: Chacón
Title: A new selection criterion for statistical home range estimation
Abstract: 
 The home range of an animal describes the geographic area where this individual spends most of the time while doing its usual activities. From a statistical viewpoint, the problem of home range estimation can be considered as a set estimation one. In the ecological literature, there are a variety of home range estimators. We address the open question of choosing the ‘best’ home range from a collection of them constructed on the same sample. We introduce the penalized overestimation ratio, a numerical index to rank the estimated home ranges. The key idea is to balance the excess area covered by the estimator (with respect to the sample) and a shape descriptor measuring the over-adjustment of the home range to the data. To our knowledge, apart from computing the home range area, our ranking procedure is the first one both applicable to real data and to any type of home range estimator. Further, optimization of the selection index provides a way to select the tuning parameters of nonparametric home ranges. For illustration purposes, we apply our selection proposal to a dataset of a Mongolian wolf and we carry out a simulation study.
Journal: Journal of Applied Statistics
Pages: 722-737
Issue: 3
Volume: 49
Year: 2022
Month: 02
X-DOI: 10.1080/02664763.2020.1822302
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1822302
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:3:p:722-737




Template-Type: ReDIF-Article 1.0
Author-Name: Shafiqah Azman
Author-X-Name-First: Shafiqah
Author-X-Name-Last: Azman
Author-Name: Dharini Pathmanathan
Author-X-Name-First: Dharini
Author-X-Name-Last: Pathmanathan
Title: The GLM framework of the Lee–Carter model: a multi-country study
Abstract: 
 The Lee–Carter model is a well-known model in modeling mortality. We aim to compare three probability models (Poisson, negative binomial and binomial) based on the Generalized Linear Model (GLM) framework of the Lee–Carter model. These models are applied to mortality data for 10 selected countries (Japan, United States, United Kingdom, Australia, Sweden, Spain, Belgium, Canada, Netherlands and Bulgaria) and the fit of these models is assessed using the deviance statistics and standardized residuals against fitted value plot. Among these three models, the negative binomial Lee–Carter model gave the best fit based on the deviance statistics and estimates of the log of deaths.
Journal: Journal of Applied Statistics
Pages: 752-763
Issue: 3
Volume: 49
Year: 2022
Month: 02
X-DOI: 10.1080/02664763.2020.1833183
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1833183
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:3:p:752-763




Template-Type: ReDIF-Article 1.0
Author-Name: Junhyung Park
Author-X-Name-First: Junhyung
Author-X-Name-Last: Park
Author-Name: Adam W. Chaffee
Author-X-Name-First: Adam W.
Author-X-Name-Last: Chaffee
Author-Name: Ryan J. Harrigan
Author-X-Name-First: Ryan J.
Author-X-Name-Last: Harrigan
Author-Name: Frederic Paik Schoenberg
Author-X-Name-First: Frederic Paik
Author-X-Name-Last: Schoenberg
Title: A non-parametric Hawkes model of the spread of Ebola in west Africa
Abstract: 
 Recently developed methods for the non-parametric estimation of Hawkes point process models facilitate their application for describing and forecasting the spread of epidemic diseases. We use data from the 2014 Ebola outbreak in West Africa to evaluate how well a simple Hawkes point process model can forecast the spread of Ebola virus in Guinea, Sierra Leone, and Liberia. For comparison, SEIR models that fit previously to the same data are evaluated using identical metrics. To test the predictive power of each of the models, we simulate the ability to make near real-time predictions during an actual outbreak by using the first 75% of the data for estimation and the subsequent 25% of the data for evaluation. Forecasts generated from Hawkes models more accurately describe the spread of Ebola in each of the three countries investigated and result in a 38% reduction in RMSE for weekly case estimation across all countries when compared to SEIR models (total RMSE of 59.8 cases/week using SEIR compared to 37.1 for Hawkes). We demonstrate that the improved fit from Hawkes modeling cannot be attributed to overfitting and evaluate the advantages and disadvantages of Hawkes models in general for forecasting the spread of epidemic diseases.
Journal: Journal of Applied Statistics
Pages: 621-637
Issue: 3
Volume: 49
Year: 2022
Month: 02
X-DOI: 10.1080/02664763.2020.1825646
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1825646
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:3:p:621-637




Template-Type: ReDIF-Article 1.0
Author-Name: Massimiliano Bonamente
Author-X-Name-First: Massimiliano
Author-X-Name-Last: Bonamente
Author-Name: David Spence
Author-X-Name-First: David
Author-X-Name-Last: Spence
Title: A semi-analytical solution to the maximum-likelihood fit of Poisson data to a linear model using the Cash statistic
Abstract: 
 The Cash statistic, also known as the 
 ${C} $C statistic, is commonly used for the analysis of low-count Poisson data, including data with null counts for certain values of the independent variable. The use of this statistic is especially attractive for low-count data that cannot be combined, or re-binned, without loss of resolution. This paper presents a new maximum-likelihood solution for the best-fit parameters of a linear model using the Poisson-based Cash statistic. The solution presented in this paper provides a new and simple method to measure the best-fit parameters of a linear model for any Poisson-based data, including data with null counts. In particular, the method enforces the requirement that the best-fit linear model be non-negative throughout the support of the independent variable. The method is summarized in a simple algorithm to fit Poisson counting data of any size and counting rate with a linear model, by-passing entirely the use of the traditional 
 $\chi ^2 $χ2 statistic.
Journal: Journal of Applied Statistics
Pages: 522-552
Issue: 3
Volume: 49
Year: 2022
Month: 02
X-DOI: 10.1080/02664763.2020.1820960
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1820960
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:3:p:522-552




Template-Type: ReDIF-Article 1.0
Author-Name: T. Baghfalaki
Author-X-Name-First: T.
Author-X-Name-Last: Baghfalaki
Author-Name: M. Ganjali
Author-X-Name-First: M.
Author-X-Name-Last: Ganjali
Author-Name: A. Kabir
Author-X-Name-First: A.
Author-X-Name-Last: Kabir
Author-Name: A. Pazouki
Author-X-Name-First: A.
Author-X-Name-Last: Pazouki
Title: A Bayesian shared parameter model for joint modeling of longitudinal continuous and binary outcomes
Abstract: 
 Joint modeling of associated mixed biomarkers in longitudinal studies leads to a better clinical decision by improving the efficiency of parameter estimates. In many clinical studies, the observed time for two biomarkers may not be equivalent and one of the longitudinal responses may have recorded in a longer time than the other one. In addition, the response variables may have different missing patterns. In this paper, we propose a new joint model of associated continuous and binary responses by accounting different missing patterns for two longitudinal outcomes. A conditional model for joint modeling of the two responses is used and two shared random effects models are considered for intermittent missingness of two responses. A Bayesian approach using Markov Chain Monte Carlo (MCMC) is adopted for parameter estimation and model implementation. The validation and performance of the proposed model are investigated using some simulation studies. The proposed model is also applied for analyzing a real data set of bariatric surgery.
Journal: Journal of Applied Statistics
Pages: 638-655
Issue: 3
Volume: 49
Year: 2022
Month: 02
X-DOI: 10.1080/02664763.2020.1822303
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1822303
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:3:p:638-655




Template-Type: ReDIF-Article 1.0
Author-Name: Man-Suk Oh
Author-X-Name-First: Man-Suk
Author-X-Name-Last: Oh
Author-Name: Chee Kyung Park
Author-X-Name-First: Chee Kyung
Author-X-Name-Last: Park
Title: Regional source apportionment of PM2.5 in Seoul using Bayesian multivariate receptor model
Abstract: 
 Seoul, the capital city of Korea with over 10 million residents, has been experiencing serious air pollution problems. Previous studies on source apportionment of PM2.5 in Seoul are based on measurements of chemical compositions of PM2.5 from a single monitoring site. In this paper, we analyse PM2.5 concentration data collected from multiple sites in 24 districts of Seoul and estimate regional source profiles using Bayesian multivariate receptor model. The regional source profiles provide information for the identification of major PM2.5 sources as well as the regions relatively more seriously affected by each source than other regions. These regional characteristics relevant to PM2.5 can help establish effective, customised, region-specific PM2.5 control strategies for each region rather than general strategies that apply to every region of Seoul.
Journal: Journal of Applied Statistics
Pages: 738-751
Issue: 3
Volume: 49
Year: 2022
Month: 02
X-DOI: 10.1080/02664763.2020.1822305
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1822305
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:3:p:738-751




Template-Type: ReDIF-Article 1.0
Author-Name: T. Senga Kiessé
Author-X-Name-First: T.
Author-X-Name-Last: Senga Kiessé
Author-Name: Etienne Rivot
Author-X-Name-First: Etienne
Author-X-Name-Last: Rivot
Author-Name: Christophe Jaeger
Author-X-Name-First: Christophe
Author-X-Name-Last: Jaeger
Author-Name: Joël Aubin
Author-X-Name-First: Joël
Author-X-Name-Last: Aubin
Title: Bayesian inference in based-kernel regression: comparison of count data of condition factor of fish in pond systems
Abstract: 
 The discrete kernel-based regression approach generally provides pointwise estimates of count data that do not account for uncertainty about both parameters and resulting estimates. This work aims to provide probabilistic kernel estimates of count regression function by using Bayesian approach and then allows for a readily quantification of uncertainty. Bayesian approach enables to incorporate prior knowledge of parameters used in discrete kernel-based regression. An application was proposed on count data of condition factor of fish (K) provided from an experimental project that analyzed various pond management strategies. The probabilistic distribution of estimates were contrasted by discrete kernels, as a support to theoretical results on the performance of kernels. More practically, Bayesian credibility intervals of K-estimates were evaluated to compare pond management strategies. Thus, similarities were found between performances of semi-intensive and coupled fishponds, with formulated feed, in comparison with extensive fishponds, without formulated feed. In particular, the fish development was less predictable in extensive fishpond, dependent on natural resources, than in the two other fishponds, supplied in formulated feed.
Journal: Journal of Applied Statistics
Pages: 676-693
Issue: 3
Volume: 49
Year: 2022
Month: 02
X-DOI: 10.1080/02664763.2020.1830953
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1830953
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:3:p:676-693




Template-Type: ReDIF-Article 1.0
Author-Name: Maria Anastasopoulou
Author-X-Name-First: Maria
Author-X-Name-Last: Anastasopoulou
Author-Name: Athanasios C. Rakitzis
Author-X-Name-First: Athanasios C.
Author-X-Name-Last: Rakitzis
Title: EWMA control charts for monitoring correlated counts with finite range
Abstract: 
 In this work, we develop and study upper and lower one-sided EWMA control charts for monitoring correlated counts with finite range. Often in practice, data of that kind can be adequately described by a first-order binomial or beta-binomial autoregressive model. Especially, when there is evidence that data demonstrate extra-binomial variation, the latter model is preferable than the former. The proposed charts can be used for detecting upward or downward shifts in process mean level. Practical guidelines concerning the statistical design of the proposed charts are given, while the effect of the extra-binomial variation is investigated as well. Comparisons with existing control charting procedures are also provided. Finally, an illustrative real-data example is also given.
Journal: Journal of Applied Statistics
Pages: 553-573
Issue: 3
Volume: 49
Year: 2022
Month: 02
X-DOI: 10.1080/02664763.2020.1820959
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1820959
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:3:p:553-573




Template-Type: ReDIF-Article 1.0
Author-Name: Qingguo Tang
Author-X-Name-First: Qingguo
Author-X-Name-Last: Tang
Author-Name: Rohana J. Karunamuni
Author-X-Name-First: Rohana J.
Author-X-Name-Last: Karunamuni
Author-Name: Boxiao Liu
Author-X-Name-First: Boxiao
Author-X-Name-Last: Liu
Title: Regularized robust estimation in binary regression models
Abstract: 
 In this paper, we investigate robust parameter estimation and variable selection for binary regression models with grouped data. We investigate estimation procedures based on the minimum-distance approach. In particular, we employ minimum Hellinger and minimum symmetric chi-squared distances criteria and propose regularized minimum-distance estimators. These estimators appear to possess a certain degree of automatic robustness against model misspecification and/or for potential outliers. We show that the proposed non-penalized and penalized minimum-distance estimators are efficient under the model and simultaneously have excellent robustness properties. We study their asymptotic properties such as consistency, asymptotic normality and oracle properties. Using Monte Carlo studies, we examine the small-sample and robustness properties of the proposed estimators and compare them with traditional likelihood estimators. We also study two real-data applications to illustrate our methods. The numerical studies indicate the satisfactory finite-sample performance of our procedures.
Journal: Journal of Applied Statistics
Pages: 574-598
Issue: 3
Volume: 49
Year: 2022
Month: 02
X-DOI: 10.1080/02664763.2020.1822304
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1822304
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:3:p:574-598




Template-Type: ReDIF-Article 1.0
Author-Name: Ayyub Sheikhi
Author-X-Name-First: Ayyub
Author-X-Name-Last: Sheikhi
Author-Name: Fatemeh Bahador
Author-X-Name-First: Fatemeh
Author-X-Name-Last: Bahador
Author-Name: Mohammad Arashi
Author-X-Name-First: Mohammad
Author-X-Name-Last: Arashi
Title: On a generalization of the test of endogeneity in a two stage least squares estimation
Abstract: 
 In situations that the predictors are correlated with the error term, we propose a bridge estimator in the two-stage least squares estimation. We apply this estimator to overcome the multicollinearity and sparsity of the explanatory variables, when the endogeneity problem is present.The proposed estimator was applied to modify the Durbin-Wu-Hausman (DWH) test of endogeneity in the presence of multicollinearity. To compare our modified test with the existing DWH for detection of an endogenous problem in multi-collinear data, some numerical assessments are carried out. The numerical results showed that the proposed estimators and the suggested test perform better for the multi-collinear data. Finally, a genetical data set is applied for illustration the our results by estimating the coefficients parameters in the presence of endogeneity and multicollinearity.
Journal: Journal of Applied Statistics
Pages: 709-721
Issue: 3
Volume: 49
Year: 2022
Month: 02
X-DOI: 10.1080/02664763.2020.1837084
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1837084
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:3:p:709-721




Template-Type: ReDIF-Article 1.0
Author-Name: The Editors
Title: Book Reviews
Journal: Journal of Applied Statistics
Pages: 639-643
Issue: 5
Volume: 34
Year: 2007
Month: 7
X-DOI: 10.1080/02664760701390652
File-URL: http://hdl.handle.net/10.1080/02664760701390652
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:34:y:2007:i:5:p:639-643




Template-Type: ReDIF-Article 1.0
Author-Name: Anurag Pathak
Author-X-Name-First: Anurag
Author-X-Name-Last: Pathak
Author-Name: Manoj Kumar
Author-X-Name-First: Manoj
Author-X-Name-Last: Kumar
Author-Name: Sanjay Kumar Singh
Author-X-Name-First: Sanjay Kumar
Author-X-Name-Last: Singh
Author-Name: Umesh Singh
Author-X-Name-First: Umesh
Author-X-Name-Last: Singh
Title: Bayesian inference: Weibull Poisson model for censored data using the expectation–maximization algorithm and its application to bladder cancer data
Abstract: 
 This article focuses on the parameter estimation of experimental items/units from Weibull Poisson Model under progressive type-II censoring with binomial removals (PT-II CBRs). The expectation–maximization algorithm has been used for maximum likelihood estimators (MLEs). The MLEs and Bayes estimators have been obtained under symmetric and asymmetric loss functions. Performance of competitive estimators have been studied through their simulated risks. One sample Bayes prediction and expected experiment time have also been studied. Furthermore, through real bladder cancer data set, suitability of considered model and proposed methodology have been illustrated.
Journal: Journal of Applied Statistics
Pages: 926-948
Issue: 4
Volume: 49
Year: 2022
Month: 03
X-DOI: 10.1080/02664763.2020.1845626
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1845626
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:4:p:926-948




Template-Type: ReDIF-Article 1.0
Author-Name: Zijian Fang
Author-X-Name-First: Zijian
Author-X-Name-Last: Fang
Author-Name: Peng Zhao
Author-X-Name-First: Peng
Author-X-Name-Last: Zhao
Author-Name: Maochao Xu
Author-X-Name-First: Maochao
Author-X-Name-Last: Xu
Author-Name: Shouhuai Xu
Author-X-Name-First: Shouhuai
Author-X-Name-Last: Xu
Author-Name: Taizhong Hu
Author-X-Name-First: Taizhong
Author-X-Name-Last: Hu
Author-Name: Xing Fang
Author-X-Name-First: Xing
Author-X-Name-Last: Fang
Title: Statistical modeling of computer malware propagation dynamics in cyberspace
Abstract: 
 Modeling cyber threats, such as the computer malicious software (malware) propagation dynamics in cyberspace, is an important research problem because models can deepen our understanding of dynamical cyber threats. In this paper, we study the statistical modeling of the macro-level evolution of dynamical cyber attacks. Specifically, we propose a Bayesian structural time series approach for modeling the computer malware propagation dynamics in cyberspace. Our model not only possesses the parsimony property (i.e. using few model parameters) but also can provide the predictive distribution of the dynamics by accommodating uncertainty. Our simulation study shows that the proposed model can fit and predict the computer malware propagation dynamics accurately, without requiring to know the information about the underlying attack-defense interaction mechanism and the underlying network topology. We use the model to study the propagation of two particular kinds of computer malware, namely the Conficker and Code Red worms, and show that our model has very satisfactory fitting and prediction accuracies.
Journal: Journal of Applied Statistics
Pages: 858-883
Issue: 4
Volume: 49
Year: 2022
Month: 03
X-DOI: 10.1080/02664763.2020.1845621
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1845621
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:4:p:858-883




Template-Type: ReDIF-Article 1.0
Author-Name: Seungchul Baek
Author-X-Name-First: Seungchul
Author-X-Name-Last: Baek
Author-Name: Yewon Kim
Author-X-Name-First: Yewon
Author-X-Name-Last: Kim
Author-Name: Junyong Park
Author-X-Name-First: Junyong
Author-X-Name-Last: Park
Author-Name: Jong Soo Lee
Author-X-Name-First: Jong Soo
Author-X-Name-Last: Lee
Title: Revisit to functional data analysis of sleeping energy expenditure
Abstract: 
 In this paper, we consider the classification problem of functional data including the sleeping energy expenditure (SEE) data, focusing on functional classification. Many existing classification rules are not effective in distinguishing the two classes of SEE data, because the trajectories of each observation have very different patterns for each class. It is often observed that some aspect of data such as the variability of paths is helpful in classification of functional data. To reflect this issue, we introduce a variable measuring the length of path in functional data and then propose a logistic model with fused lasso that considers the behavior of fluctuation of path as well as local correlations within each path. Our proposed model shows a significant improvement over some models used in the existing literature on the classification accuracy rate of functional data such as SEE data. We carry out simulation studies to show the finite sample performance and the gain that it makes in comparison with fused lasso without considering path length. With two more real datasets studied in some existing literature, we demonstrate that the new model achieves better or similar accuracy rate than the best accuracy rates reported in those studies.
Journal: Journal of Applied Statistics
Pages: 988-1002
Issue: 4
Volume: 49
Year: 2022
Month: 03
X-DOI: 10.1080/02664763.2020.1838457
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1838457
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:4:p:988-1002




Template-Type: ReDIF-Article 1.0
Author-Name: Armin Hatefi
Author-X-Name-First: Armin
Author-X-Name-Last: Hatefi
Author-Name: Amirhossein Alvandi
Author-X-Name-First: Amirhossein
Author-X-Name-Last: Alvandi
Title: Efficient estimators with categorical ranked set samples: estimation procedures for osteoporosis
Abstract: 
 Ranked set sampling (RSS) design as a cost-effective sampling is a powerful tool in situations where measuring the variable of interest is costly and time-consuming; however, ranking information about sampling units can be obtained easily through inexpensive and easy to measure characteristics at little or no cost. In this paper, we study RSS data for analysis of an ordinal population. First, we compare the problem of non-representative extreme samples under RSS and commonly-used simple random sampling. Using RSS data with tie information, we propose non-parametric and maximum likelihood estimators for population parameters. Through extensive numerical studies, we investigate the effect of various factors including ranking ability, tie generating mechanisms, the number of categories and population setting on the performance of the estimators. Finally, we apply the proposed methods to the bone disorder data to estimate the proportions of patients with osteopenia and osteoporosis status.
Journal: Journal of Applied Statistics
Pages: 803-818
Issue: 4
Volume: 49
Year: 2022
Month: 03
X-DOI: 10.1080/02664763.2020.1841742
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1841742
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:4:p:803-818




Template-Type: ReDIF-Article 1.0
Author-Name: María José Presno
Author-X-Name-First: María José
Author-X-Name-Last: Presno
Author-Name: Manuel Landajo
Author-X-Name-First: Manuel
Author-X-Name-Last: Landajo
Author-Name: Paula Fernandez-Gonzalez
Author-X-Name-First: Paula
Author-X-Name-Last: Fernandez-Gonzalez
Title: Nonparametric panel stationarity testing with an application to crude oil production
Abstract: 
 A nonparametric panel stationarity test is proposed which offers the advantage of not requiring prior specification of the trend function for each of the series in the panel. A bootstrap implementation of the test is outlined and its finite sample performance is analyzed via Monte Carlo simulations. An application is also included where the proposed test is used to analyze the stochastic properties of monthly crude oil production for a panel of 20 -both OPEC and non-OPEC- countries from 1973 to 2015. Our analysis detects strong evidence of non-stationarity, both globally and group-wise. Results have implications for the effectiveness of government intervention and stabilization policies.
Journal: Journal of Applied Statistics
Pages: 1033-1048
Issue: 4
Volume: 49
Year: 2022
Month: 03
X-DOI: 10.1080/02664763.2020.1846691
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1846691
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:4:p:1033-1048




Template-Type: ReDIF-Article 1.0
Author-Name: Giovanni Boscaino
Author-X-Name-First: Giovanni
Author-X-Name-Last: Boscaino
Author-Name: Gianluca Sottile
Author-X-Name-First: Gianluca
Author-X-Name-Last: Sottile
Author-Name: Giada Adelfio
Author-X-Name-First: Giada
Author-X-Name-Last: Adelfio
Title: Migration and students' performance: detecting geographical differences following a curves clustering approach
Abstract: 
 Students' migration mobility is the new form of migration: students migrate to improve their skills and become more valued for the job market. The data regard the migration of Italian Bachelors who enrolled at Master Degree level, moving typically from poor to rich areas. This paper investigates the migration and other possible determinants on the Master Degree students' performance. The Clustering of Effects approach for Quantile Regression Coefficients Modelling has been used to cluster the effects of some variables on the students' performance for three Italian macro-areas. Results show evidence of similarity between Southern and Centre students, with respect to the Northern ones.
Journal: Journal of Applied Statistics
Pages: 1018-1032
Issue: 4
Volume: 49
Year: 2022
Month: 03
X-DOI: 10.1080/02664763.2020.1845624
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1845624
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:4:p:1018-1032




Template-Type: ReDIF-Article 1.0
Author-Name: Hande Konşuk Ünlü
Author-X-Name-First: Hande Konşuk
Author-X-Name-Last: Ünlü
Author-Name: Derek S. Young
Author-X-Name-First: Derek S.
Author-X-Name-Last: Young
Author-Name: Ayten Yiğiter
Author-X-Name-First: Ayten
Author-X-Name-Last: Yiğiter
Author-Name: L. Hilal Özcebe
Author-X-Name-First: L.
Author-X-Name-Last: Hilal Özcebe
Title: A mixture model with Poisson and zero-truncated Poisson components to analyze road traffic accidents in Turkey
Abstract: 
 The analysis of traffic accident data is crucial to address numerous concerns, such as understanding contributing factors in an accident's chain-of-events, identifying hotspots, and informing policy decisions about road safety management. The majority of statistical models employed for analyzing traffic accident data are logically count regression models (commonly Poisson regression) since a count – like the number of accidents – is used as the response. However, features of the observed data frequently do not make the Poisson distribution a tenable assumption. For example, observed data rarely demonstrate an equal mean and variance and often times possess excess zeros. Sometimes, data may have heterogeneous structure consisting of a mixture of populations, rather than a single population. In such data analyses, mixtures-of-Poisson-regression models can be used. In this study, the number of injuries resulting from casualties of traffic accidents registered by the General Directorate of Security (Turkey, 2005–2014) are modeled using a novel mixture distribution with two components: a Poisson and zero-truncated-Poisson distribution. Such a model differs from existing mixture models in literature where the components are either all Poisson distributions or all zero-truncated Poisson distributions. The proposed model is compared with the Poisson regression model via simulation and in the analysis of the traffic data.
Journal: Journal of Applied Statistics
Pages: 1003-1017
Issue: 4
Volume: 49
Year: 2022
Month: 03
X-DOI: 10.1080/02664763.2020.1843610
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1843610
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:4:p:1003-1017




Template-Type: ReDIF-Article 1.0
Author-Name: David P. M. Scollnik
Author-X-Name-First: David P. M.
Author-X-Name-Last: Scollnik
Title: Bayesian analyses of an exponential-Poisson and related zero augmented type models
Abstract: 
 We consider several alternatives to the continuous exponential-Poisson distribution in order to accommodate the occurrence of zeros. Three of these are modifications of the exponential-Poisson model. One of these remains a fully continuous model. The other models we consider are all semi-continuous models, each with a discrete point mass at zero and a continuous density on the positive values. All of the models are applied to two environmental data sets concerning precipitation, and their Bayesian analyses using MCMC are discussed. This discussion covers convergence of the MCMC simulations and model selection procedures and considerations.
Journal: Journal of Applied Statistics
Pages: 949-967
Issue: 4
Volume: 49
Year: 2022
Month: 03
X-DOI: 10.1080/02664763.2020.1846692
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1846692
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:4:p:949-967




Template-Type: ReDIF-Article 1.0
Author-Name: Alexis Zavez
Author-X-Name-First: Alexis
Author-X-Name-Last: Zavez
Author-Name: Emeir M. McSorley
Author-X-Name-First: Emeir M.
Author-X-Name-Last: McSorley
Author-Name: Alison J. Yeates
Author-X-Name-First: Alison J.
Author-X-Name-Last: Yeates
Author-Name: Sally W. Thurston
Author-X-Name-First: Sally W.
Author-X-Name-Last: Thurston
Title: Modeling the effects of multiple exposures with unknown group memberships: a Bayesian latent variable approach
Abstract: 
 We propose a Bayesian latent variable model to allow estimation of the covariate-adjusted relationships between an outcome and a small number of latent exposure variables, using data from multiple observed exposures. Each latent variable is assumed to be represented by multiple exposures, where membership of the observed exposures to latent groups is unknown. Our model assumes that one measured exposure variable can be considered as a sentinel marker for each latent variable, while membership of the other measured exposures is estimated using MCMC sampling based on a classical measurement error model framework. We illustrate our model using data on multiple cytokines and birth weight from the Seychelles Child Development Study, and evaluate the performance of our model in a simulation study. Classification of cytokines into Th1 and Th2 cytokine classes in the Seychelles study revealed some differences from standard Th1/Th2 classifications. In simulations, our model correctly classified measured exposures into latent groups, and estimated model parameters with little bias and with coverage that was similar to the oracle model.
Journal: Journal of Applied Statistics
Pages: 831-857
Issue: 4
Volume: 49
Year: 2022
Month: 03
X-DOI: 10.1080/02664763.2020.1843611
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1843611
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:4:p:831-857




Template-Type: ReDIF-Article 1.0
Author-Name: George O. Agogo
Author-X-Name-First: George O.
Author-X-Name-Last: Agogo
Author-Name: Alexander K. Muoka
Author-X-Name-First: Alexander K.
Author-X-Name-Last: Muoka
Title: A three-part regression calibration to handle excess zeroes, skewness and heteroscedasticity in adjusting for measurement error in dietary intake data
Abstract: 
 Exposure measurement error (ME) biases exposure-outcome associations. Calibration dietary intake data used in the regression calibration (RC) response to adjust for ME are usually right-skewed, heteroscedastic and with excess zeroes. We proposed three-part RC models to handle these distributional complexities simultaneously, while correcting for ME in fish intake. We applied data from the National Health and Nutrition Examination Survey (NHANES), where long-term intake was measured with food frequency questionnaire (FFQ) in the main study and short-term intake with 24-hour recall (24HR) in the calibration study. In the three-part RC models, never consumers were modelled using two approaches: a zero distribution (Three-part RC-het-det), and logistic distribution (Three-part RC-het-prob); heteroscedasticity using an exponential distribution and right-skewness using generalized gamma distribution. The proposed models were compared with two-part RC model that ignores never consumers, and with methods that estimate intakes using FFQ and 24HR. The models were evaluated in a simulation study. With NHANES data, mean increase in the mercury level (in 
 $ \mu \mathrm{g/L} $ μg/L) was 1.20 using FFQ-method, 0.4 using 24HR-method, 1.87 using two-part RC and 2.02 using three-part RC-het-prob method. The three-part RC estimated the association with the least bias in the simulation study.
Journal: Journal of Applied Statistics
Pages: 884-901
Issue: 4
Volume: 49
Year: 2022
Month: 03
X-DOI: 10.1080/02664763.2020.1845622
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1845622
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:4:p:884-901




Template-Type: ReDIF-Article 1.0
Author-Name: Peihan Xiong
Author-X-Name-First: Peihan
Author-X-Name-Last: Xiong
Author-Name: Weiwei Zhuang
Author-X-Name-First: Weiwei
Author-X-Name-Last: Zhuang
Author-Name: Guoxin Qiu
Author-X-Name-First: Guoxin
Author-X-Name-Last: Qiu
Title: Testing exponentiality based on the extropy of record values
Abstract: 
 In this paper, we first present a characterization of exponential distribution based on the extropy of record values and next introduce a goodness-of-fit test for exponentiality. Monte Carlo simulation is used to compute the critical values of our proposed test for different sample sizes and significance levels. To show the advantage of the proposed test, we adopt 58 competitor tests and compute the adjusted power against different alternatives with distinct types of hazard function. The power results show that our proposed test has superior adjusted power if the alternatives have increasing failure rates or bathtub decreasing-increasing failure rates, especially when the sample size is small. Finally, three real examples are used to illustrate the applicability and robustness of our proposed test by monitoring the p-values of the tests.
Journal: Journal of Applied Statistics
Pages: 782-802
Issue: 4
Volume: 49
Year: 2022
Month: 03
X-DOI: 10.1080/02664763.2020.1840535
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1840535
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:4:p:782-802




Template-Type: ReDIF-Article 1.0
Author-Name: Yuexia Zhang
Author-X-Name-First: Yuexia
Author-X-Name-Last: Zhang
Author-Name: Guoyou Qin
Author-X-Name-First: Guoyou
Author-X-Name-Last: Qin
Author-Name: Zhongyi Zhu
Author-X-Name-First: Zhongyi
Author-X-Name-Last: Zhu
Author-Name: Bo Fu
Author-X-Name-First: Bo
Author-X-Name-Last: Fu
Title: Robust estimation of models for longitudinal data with dropouts and outliers
Abstract: 
 Missing data and outliers usually arise in longitudinal studies. Ignoring the effects of missing data and outliers will make the classical generalized estimating equation approach invalid. The longitudinal cohort study of rheumatoid arthritis patients was designed to investigate whether the Health Assessment Questionnaire score was associated with baseline covariates and changed with time. There exist dropouts and outliers in the data. In order to analyze the data, we develop a robust estimating equation approach. To deal with the responses missing at random, we extend a doubly robust method. To achieve robustness against outliers, we utilize an outlier robust method, which corrects the bias induced by outliers through centralizing the covariate matrix in the estimating equation. The doubly robust method for dropouts is easy to combine with the outlier robust method. The proposed method has the property of robustness in the sense that the proposed estimator is not only doubly robust against model misspecification for dropouts when there is no outlier in the data, but also robust against outliers. Consistency and asymptotic normality of the proposed estimator are established under regularity conditions. A comprehensive simulation study and real data analysis demonstrate that the proposed estimator does have the property of robustness.
Journal: Journal of Applied Statistics
Pages: 902-925
Issue: 4
Volume: 49
Year: 2022
Month: 03
X-DOI: 10.1080/02664763.2020.1845623
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1845623
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:4:p:902-925




Template-Type: ReDIF-Article 1.0
Author-Name: Usha Govindarajulu
Author-X-Name-First: Usha
Author-X-Name-Last: Govindarajulu
Author-Name: Thaddeus Tarpey
Author-X-Name-First: Thaddeus
Author-X-Name-Last: Tarpey
Title: Optimal partitioning for the proportional hazards model
Abstract: 
 This paper discusses methods for clustering a continuous covariate in a survival analysis model. The advantages of using a categorical covariate defined from discretizing a continuous covariate (via clustering) is (i) enhanced interpretability of the covariate's impact on survival and (ii) relaxing model assumptions that are usually required for survival models, such as the proportional hazards model. Simulations and an example are provided to illustrate the methods.
Journal: Journal of Applied Statistics
Pages: 968-987
Issue: 4
Volume: 49
Year: 2022
Month: 03
X-DOI: 10.1080/02664763.2020.1846690
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1846690
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:4:p:968-987




Template-Type: ReDIF-Article 1.0
Author-Name: Lukáš Malec
Author-X-Name-First: Lukáš
Author-X-Name-Last: Malec
Title: On the rank-deficient canonical correlation technique solved by analytic spectral decomposition
Abstract: 
 Regularization is a well-known and used statistical approach covering individual points or limit approximations. In this study, the canonical correlation analysis (CCA) process of the paths is discussed with partial least squares (PLS) as the other boundary covering transformation to a symmetric eigenvalue (or singular value) problem dependent on a parameter. Two regularizations of the original criterion in the parameterization domain are compared, i.e. using projection and by identity matrix. We discuss the existence and uniqueness of the analytic path for eigenvalues and corresponding elements of eigenvectors. Specifically, canonical analysis is applied to an ill-conditioned case of singular within-sets input matrices encompassing tourism accommodation data.
Journal: Journal of Applied Statistics
Pages: 819-830
Issue: 4
Volume: 49
Year: 2022
Month: 03
X-DOI: 10.1080/02664763.2020.1843608
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1843608
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:4:p:819-830




Template-Type: ReDIF-Article 1.0
Author-Name: N. Murugeswari
Author-X-Name-First: N.
Author-X-Name-Last: Murugeswari
Author-Name: P. Jeyadurga
Author-X-Name-First: P.
Author-X-Name-Last: Jeyadurga
Author-Name: S. Balamurali
Author-X-Name-First: S.
Author-X-Name-Last: Balamurali
Title: Optimal designing of two-level skip-lot sampling reinspection plan
Abstract: 
 Skip-lot sampling plan is often applied in industries for reducing the cost and effort of the inspection of the product having excellent quality history. Consequence of skip-lot sampling plans is to reduce the cost of inspection so which are more attractive in economical aspect. In this paper, we develop a sampling plan by incorporating the idea of resampling in two-level skip lot sampling plan and the new plan is designated as SkSP-2L.1-R. This paper presents the Markov chain formulation of the proposed plan along with the derivation of performance measures of the plan. We also provide the designing methodology to determine the optimal parameters of the SkSP-2L.1-R plan so as to minimize the average sample number by using two points on the operating characteristic curve approach. By contemplating various combinations of producer and consumer quality levels along with respective risks, a table is constructed to determine the optimal parameters. An industrial application of the proposed SkSP-2L.1-R plan is discussed. The SkSP-2L.1-R with single sampling plan as a reference plan is compared with the conventional single sampling plan, SkSP-2 plan and SkSP-2-R plan and proved that the proposed SkSP-2L.1-R plan outperforms these plans.
Journal: Journal of Applied Statistics
Pages: 1086-1104
Issue: 5
Volume: 49
Year: 2022
Month: 04
X-DOI: 10.1080/02664763.2020.1849059
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1849059
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:5:p:1086-1104




Template-Type: ReDIF-Article 1.0
Author-Name: Ufuk Beyaztas
Author-X-Name-First: Ufuk
Author-X-Name-Last: Beyaztas
Author-Name: Han Lin Shang
Author-X-Name-First: Han Lin
Author-X-Name-Last: Shang
Title: Robust bootstrap prediction intervals for univariate and multivariate autoregressive time series models
Abstract: 
 The bootstrap procedure has emerged as a general framework to construct prediction intervals for future observations in autoregressive time series models. Such models with outlying data points are standard in real data applications, especially in the field of econometrics. These outlying data points tend to produce high forecast errors, which reduce the forecasting performances of the existing bootstrap prediction intervals calculated based on non-robust estimators. In the univariate and multivariate autoregressive time series, we propose a robust bootstrap algorithm for constructing prediction intervals and forecast regions. The proposed procedure is based on the weighted likelihood estimates and weighted residuals. Its finite sample properties are examined via a series of Monte Carlo studies and two empirical data examples.
Journal: Journal of Applied Statistics
Pages: 1179-1202
Issue: 5
Volume: 49
Year: 2022
Month: 04
X-DOI: 10.1080/02664763.2020.1856351
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1856351
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:5:p:1179-1202




Template-Type: ReDIF-Article 1.0
Author-Name: Kuangnan Fang
Author-X-Name-First: Kuangnan
Author-X-Name-Last: Fang
Author-Name: Peng Wang
Author-X-Name-First: Peng
Author-X-Name-Last: Wang
Author-Name: Xiaochen Zhang
Author-X-Name-First: Xiaochen
Author-X-Name-Last: Zhang
Author-Name: Qingzhao Zhang
Author-X-Name-First: Qingzhao
Author-X-Name-Last: Zhang
Title: Structured sparse support vector machine with ordered features
Abstract: 
 In the application of high-dimensional data classification, several attempts have been made to achieve variable selection by replacing the 
 $ \ell _{2} $ ℓ2-penalty with other penalties for the support vector machine (SVM). However, these high-dimensional SVM methods usually do not take into account the special structure among covariates (features). In this article, we consider a classification problem, where the covariates are ordered in some meaningful way, and the number of covariates p can be much larger than the sample size n. We propose a structured sparse SVM to tackle this type of problems, which combines the non-convex penalty and cubic spline estimation procedure (i.e. penalizing second-order derivatives of the coefficients) to the SVM. From a theoretical point of view, the proposed method satisfies the local oracle property. Simulations show that the method works effectively both in feature selection and classification accuracy. A real application is conducted to illustrate the benefits of the method.
Journal: Journal of Applied Statistics
Pages: 1105-1120
Issue: 5
Volume: 49
Year: 2022
Month: 04
X-DOI: 10.1080/02664763.2020.1849053
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1849053
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:5:p:1105-1120




Template-Type: ReDIF-Article 1.0
Author-Name: Alexey Koloydenko
Author-X-Name-First: Alexey
Author-X-Name-Last: Koloydenko
Author-Name: Kristi Kuljus
Author-X-Name-First: Kristi
Author-X-Name-Last: Kuljus
Author-Name: Jüri Lember
Author-X-Name-First: Jüri
Author-X-Name-Last: Lember
Title: MAP segmentation in Bayesian hidden Markov models: a case study
Abstract: 
 We consider the problem of estimating the maximum posterior probability (MAP) state sequence for a finite state and finite emission alphabet hidden Markov model (HMM) in the Bayesian setup, where both emission and transition matrices have Dirichlet priors. We study a training set consisting of thousands of protein alignment pairs. The training data is used to set the prior hyperparameters for Bayesian MAP segmentation. Since the Viterbi algorithm is not applicable any more, there is no simple procedure to find the MAP path, and several iterative algorithms are considered and compared. The main goal of the paper is to test the Bayesian setup against the frequentist one, where the parameters of HMM are estimated using the training data.
Journal: Journal of Applied Statistics
Pages: 1203-1234
Issue: 5
Volume: 49
Year: 2022
Month: 04
X-DOI: 10.1080/02664763.2020.1858273
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1858273
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:5:p:1203-1234




Template-Type: ReDIF-Article 1.0
Author-Name: Yonghui Liu
Author-X-Name-First: Yonghui
Author-X-Name-Last: Liu
Author-Name: Chaoxuan Mao
Author-X-Name-First: Chaoxuan
Author-X-Name-Last: Mao
Author-Name: Víctor Leiva
Author-X-Name-First: Víctor
Author-X-Name-Last: Leiva
Author-Name: Shuangzhe Liu
Author-X-Name-First: Shuangzhe
Author-X-Name-Last: Liu
Author-Name: Waldemiro A. Silva Neto
Author-X-Name-First: Waldemiro A.
Author-X-Name-Last: Silva Neto
Title: Asymmetric autoregressive models: statistical aspects and a financial application under COVID-19 pandemic
Abstract: 
 In the present study, we provide a motivating example with a financial application under COVID-19 pandemic to investigate autoregressive (AR) modeling and its diagnostics based on asymmetric distributions. The objectives of this work are: (i) to formulate asymmetric AR models and their estimation and diagnostics; (ii) to assess the performance of the parameters estimators and of the local influence technique for these models; and (iii) to provide a tool to show how data following an asymmetric distribution under an AR structure should be analyzed. We take the advantages of the stochastic representation of the skew-normal distribution to estimate the parameters of the corresponding AR model efficiently with the expectation-maximization algorithm. Diagnostic analytics are conducted by using the local influence technique with four perturbation schemes. By employing Monte Carlo simulations, we evaluate the statistical behavior of the corresponding estimators and of the local influence technique. An illustration with financial data updated until 2020, analyzed using the methodology introduced in the present work, is presented as an example of effective applications, from where it is possible to explain atypical cases from the COVID-19 pandemic.
Journal: Journal of Applied Statistics
Pages: 1323-1347
Issue: 5
Volume: 49
Year: 2022
Month: 04
X-DOI: 10.1080/02664763.2021.1913103
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1913103
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:5:p:1323-1347




Template-Type: ReDIF-Article 1.0
Author-Name: Zahra Barkhordar
Author-X-Name-First: Zahra
Author-X-Name-Last: Barkhordar
Author-Name: Mohsen Maleki
Author-X-Name-First: Mohsen
Author-X-Name-Last: Maleki
Author-Name: Zahra Khodadadi
Author-X-Name-First: Zahra
Author-X-Name-Last: Khodadadi
Author-Name: Darren Wraith
Author-X-Name-First: Darren
Author-X-Name-Last: Wraith
Author-Name: Farajollah Negahdari
Author-X-Name-First: Farajollah
Author-X-Name-Last: Negahdari
Title: A Bayesian approach on the two-piece scale mixtures of normal homoscedastic nonlinear regression models
Abstract: 
 In this application note paper, we propose and examine the performance of a Bayesian approach for a homoscedastic nonlinear regression (NLR) model assuming errors with two-piece scale mixtures of normal (TP-SMN) distributions. The TP-SMN is a large family of distributions, covering both symmetrical/ asymmetrical distributions as well as light/heavy tailed distributions, and provides an alternative to another well-known family of distributions, called scale mixtures of skew-normal distributions. The proposed family and Bayesian approach provides considerable flexibility and advantages for NLR modelling in different practical settings. We examine the performance of the approach using simulated and real data.
Journal: Journal of Applied Statistics
Pages: 1305-1322
Issue: 5
Volume: 49
Year: 2022
Month: 04
X-DOI: 10.1080/02664763.2020.1854203
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1854203
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:5:p:1305-1322




Template-Type: ReDIF-Article 1.0
Author-Name: Xiaojuan Hao
Author-X-Name-First: Xiaojuan
Author-X-Name-Last: Hao
Author-Name: Kent M. Eskridge
Author-X-Name-First: Kent M.
Author-X-Name-Last: Eskridge
Author-Name: Dong Wang
Author-X-Name-First: Dong
Author-X-Name-Last: Wang
Title: Variational Bayesian inference for association over phylogenetic trees for microorganisms
Abstract: 
 With the advance of next generation sequencing technologies, researchers now routinely obtain a collection of microbial sequences with complex phylogenetic relationships. It is often of interest to analyze the association between certain environmental factors and characteristics of the microbial collection. Though methods have been developed to test for association between the microbial composition with environmental factors as well as between coevolving traits, a flexible model that can provide a comprehensive picture of the relationship between microbial community characteristics and environmental variables will be tremendously beneficial. We developed a Bayesian approach for association analysis while incorporating the phylogenetic structure to account for the dependence between observations. To overcome the computational difficulty related to the phylogenetic tree, a variational algorithm was developed to evaluate the posterior distribution. As the posterior distribution can be readily obtained for parameters of interest and any derived variables, the association relationship can be examined comprehensively. With two application examples, we demonstrated that the Bayesian approach can uncover nuanced details of the microbial assemblage with regard to the environmental factor. The proposed Bayesian approach and variational algorithm can be extended for other problems involving dependence over tree-like structures.
Journal: Journal of Applied Statistics
Pages: 1140-1153
Issue: 5
Volume: 49
Year: 2022
Month: 04
X-DOI: 10.1080/02664763.2020.1854200
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1854200
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:5:p:1140-1153




Template-Type: ReDIF-Article 1.0
Author-Name: T. H. A. Nguyen
Author-X-Name-First: T. H. A.
Author-X-Name-Last: Nguyen
Author-Name: T. Laurent
Author-X-Name-First: T.
Author-X-Name-Last: Laurent
Author-Name: C. Thomas-Agnan
Author-X-Name-First: C.
Author-X-Name-Last: Thomas-Agnan
Author-Name: A. Ruiz-Gazen
Author-X-Name-First: A.
Author-X-Name-Last: Ruiz-Gazen
Title: Analyzing the impacts of socio-economic factors on French departmental elections with CoDa methods
Abstract: 
 The vote shares by party on a given subdivision of a territory form a vector called composition (mathematically, a vector belonging to a simplex). It is interesting to model these shares and study the impact of the characteristics of the territorial units on the outcome of the elections. In the political economy literature, few regression models are adapted to the case of more than two political parties. In the statistical literature, there are regression models adapted to share vectors including Compositional Data (CoDa) models, but also Dirichlet models, and others. Our goal is to discuss and illustrate the use CoDa regression models for political economy models for more than two parties. The models are fitted on French electoral data of the 2015 departmental elections.
Journal: Journal of Applied Statistics
Pages: 1235-1251
Issue: 5
Volume: 49
Year: 2022
Month: 04
X-DOI: 10.1080/02664763.2020.1858274
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1858274
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:5:p:1235-1251




Template-Type: ReDIF-Article 1.0
Author-Name: M.B. Seitshiro
Author-X-Name-First: M.B.
Author-X-Name-Last: Seitshiro
Author-Name: H.P. Mashele
Author-X-Name-First: H.P.
Author-X-Name-Last: Mashele
Title: Quantification of model risk that is caused by model misspecification
Abstract: 
 In this paper, we suggest a technique to quantify model risk, particularly model misspecification for binary response regression problems found in financial risk management, such as in credit risk modelling. We choose the probability of default model as one instance of many other credit risk models that may be misspecified in a financial institution. By way of illustrating the model misspecification for probability of default, we carry out quantification of two specific statistical predictive response techniques, namely the binary logistic regression and complementary log–log. The maximum likelihood estimation technique is employed for parameter estimation. The statistical inference, precisely the goodness of fit and model performance measurements, are assessed. Using the simulation dataset and Taiwan credit card default dataset, our finding reveals that with the same sample size and very small simulation iterations, the two techniques produce similar goodness-of-fit results but completely different performance measures. However, when the iterations increase, the binary logistic regression technique for balanced dataset reveals prominent goodness of fit and performance measures as opposed to the complementary log–log technique for both simulated and real datasets.
Journal: Journal of Applied Statistics
Pages: 1065-1085
Issue: 5
Volume: 49
Year: 2022
Month: 04
X-DOI: 10.1080/02664763.2020.1849055
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1849055
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:5:p:1065-1085




Template-Type: ReDIF-Article 1.0
Author-Name: Thomas Xavier
Author-X-Name-First: Thomas
Author-X-Name-Last: Xavier
Author-Name: Joby K. Jose
Author-X-Name-First: Joby K.
Author-X-Name-Last: Jose
Title: Stress–strength reliability estimation involving paired observation with ties using bivariate exponentiated half-logistic model
Abstract: 
 This paper deals with the problem of maximum likelihood and Bayesian estimation of stress–strength reliability involving paired observation with ties using bivariate exponentiated half-logistic distribution. This problem is of importance because in some real applications the strength of the component is highly dependent on the stress experienced by it. A bivariate extension of exponentiated half-logistic is discussed and an expression for the stress–strength reliability is obtained. This model is also useful to analyse data having the unusual feature of having a number of pairs with tied scores, even when the scores are continuous. The maximum likelihood estimate and interval estimate of the stress–strength reliability has been developed. The Bayes estimates of the stress–strength reliability under squared error loss function are obtained using importance sampling technique. Simulation studies are conducted to evaluate the performance of maximum likelihood and Bayes estimates. Two real-life data sets are analysed to numerically illustrate the usefulness of the developed method.
Journal: Journal of Applied Statistics
Pages: 1049-1064
Issue: 5
Volume: 49
Year: 2022
Month: 04
X-DOI: 10.1080/02664763.2020.1849054
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1849054
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:5:p:1049-1064




Template-Type: ReDIF-Article 1.0
Author-Name: Florian Stark
Author-X-Name-First: Florian
Author-X-Name-Last: Stark
Author-Name: Sven Otto
Author-X-Name-First: Sven
Author-X-Name-Last: Otto
Title: Testing and dating structural changes in copula-based dependence measures
Abstract: 
 This paper is concerned with testing and dating structural breaks in the dependence structure of multivariate time series. We consider a cumulative sum (CUSUM) type test for constant copula-based dependence measures, such as Spearman's rank correlation and quantile dependencies. The asymptotic null distribution is not known in closed form and critical values are estimated by an i.i.d. bootstrap procedure. We analyze size and power properties in a simulation study under different dependence measure settings, such as skewed and fat-tailed distributions. To date breakpoints and to decide whether two estimated break locations belong to the same break event, we propose a pivot confidence interval procedure. Finally, we apply the test to the historical data of 10 large financial firms during the last financial crisis from 2002 to mid-2013.
Journal: Journal of Applied Statistics
Pages: 1121-1139
Issue: 5
Volume: 49
Year: 2022
Month: 04
X-DOI: 10.1080/02664763.2020.1850655
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1850655
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:5:p:1121-1139




Template-Type: ReDIF-Article 1.0
Author-Name: Maria Ioneris Oliveira
Author-X-Name-First: Maria
Author-X-Name-Last: Ioneris Oliveira
Author-Name: Michelli Barros
Author-X-Name-First: Michelli
Author-X-Name-Last: Barros
Author-Name: Joelson Campos
Author-X-Name-First: Joelson
Author-X-Name-Last: Campos
Author-Name: Francisco José A. Cysneiros
Author-X-Name-First: Francisco José A.
Author-X-Name-Last: Cysneiros
Title: Bivariate Birnbaum-Saunders accelerated lifetime model: estimation and diagnostic analysis
Abstract: 
 In this paper, we discuss the bivariate Birnbaum-Saunders accelerated lifetime model, in which we have modeled the dependence structure of bivariate survival data through the use of frailty models. Specifically, we propose the bivariate model Birnbaum-Saunders with the following frailty distributions: gamma, positive stable and logarithmic series. We present a study of inference and diagnostic analysis for the proposed model, more concisely, are proposed a diagnostic analysis based in local influence and residual analysis to assess the fit model, as well as, to detect influential observations. In this regard, we derived the normal curvatures of local influence under different perturbation schemes and we performed some simulation studies for assessing the potential of residuals to detect misspecification in the systematic component, the presence in the stochastic component of the model and to detect outliers. Finally, we apply the methodology studied to real data set from recurrence in times of infections of 38 kidney patients using a portable dialysis machine, we analyzed these data considering independence within the pairs and using the bivariate Birnbaum-Saunders accelerated lifetime model, so that we could make a comparison and verify the importance of modeling dependence within the times of infection associated with the same patient.
Journal: Journal of Applied Statistics
Pages: 1252-1276
Issue: 5
Volume: 49
Year: 2022
Month: 04
X-DOI: 10.1080/02664763.2020.1859466
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1859466
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:5:p:1252-1276




Template-Type: ReDIF-Article 1.0
Author-Name: Everestus O. Ossai
Author-X-Name-First: Everestus O.
Author-X-Name-Last: Ossai
Author-Name: Mbanefo S. Madukaife
Author-X-Name-First: Mbanefo S.
Author-X-Name-Last: Madukaife
Author-Name: Abimibola V. Oladugba
Author-X-Name-First: Abimibola V.
Author-X-Name-Last: Oladugba
Title: A review of tests for exponentiality with Monte Carlo comparisons
Abstract: 
 In this paper, 91 different tests for exponentiality are reviewed. Some of the tests are universally consistent while others are against some special classes of life distributions. Power performances of 40 of these different tests for exponentiality of datasets are compared through extensive Monte Carlo simulations. The comparisons are conducted for different sample sizes of 10, 25, 50 and 100 for different groups of distributions according to the shape of their hazard functions at 5 percent level of significance. Also, the techniques are applied to two real-world datasets and a measure of power is employed for the comparison of the tests. The results show that some tests which are very good under one group of alternative distributions are not so under another group. Also, some tests maintained relatively high power over all the groups of alternative distributions studied while some others maintained poor power performances over all the groups of alternative distributions. Again, the result obtained from real-world datasets agree completely with those of the simulation studies.
Journal: Journal of Applied Statistics
Pages: 1277-1304
Issue: 5
Volume: 49
Year: 2022
Month: 04
X-DOI: 10.1080/02664763.2020.1854202
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1854202
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:5:p:1277-1304




Template-Type: ReDIF-Article 1.0
Author-Name: Li-Pang Chen
Author-X-Name-First: Li-Pang
Author-X-Name-Last: Chen
Title: Ultrahigh-dimensional sufficient dimension reduction for censored data with measurement error in covariates
Abstract: 
 In this paper, we consider the ultrahigh-dimensional sufficient dimension reduction (SDR) for censored data and measurement error in covariates. We first propose the feature screening procedure based on censored data and the covariates subject to measurement error. With the suitable correction of mismeasurement, the error-contaminated variables detected by the proposed feature screening procedure are the same as the truly important variables. Based on the selected active variables, we develop the SDR method to estimate the central subspace and the structural dimension with both censored data and measurement error incorporated. The theoretical results of the proposed method are established. Simulation studies are reported to assess the performance of the proposed method. The proposed method is implemented to NKI breast cancer data.
Journal: Journal of Applied Statistics
Pages: 1154-1178
Issue: 5
Volume: 49
Year: 2022
Month: 04
X-DOI: 10.1080/02664763.2020.1856352
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1856352
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:5:p:1154-1178





Template-Type: ReDIF-Article 1.0
Author-Name: Benoît Lalloué
Author-X-Name-First: Benoît
Author-X-Name-Last: Lalloué
Author-Name: Jean-Marie Monnez
Author-X-Name-First: Jean-Marie
Author-X-Name-Last: Monnez
Author-Name: Eliane Albuisson
Author-X-Name-First: Eliane
Author-X-Name-Last: Albuisson
Title: Streaming constrained binary logistic regression with online standardized data
Abstract: 
 Online learning is a method for analyzing very large datasets (‘big data’) as well as data streams. In this article, we consider the case of constrained binary logistic regression and show the interest of using processes with an online standardization of the data, in particular to avoid numerical explosions or to allow the use of shrinkage methods. We prove the almost sure convergence of such a process and propose using a piecewise constant step-size such that the latter does not decrease too quickly and does not reduce the speed of convergence. We compare twenty-four stochastic approximation processes with raw or online standardized data on five real or simulated data sets. Results show that, unlike processes with raw data, processes with online standardized data can prevent numerical explosions and yield the best results.
Journal: Journal of Applied Statistics
Pages: 1519-1539
Issue: 6
Volume: 49
Year: 2022
Month: 04
X-DOI: 10.1080/02664763.2020.1870672
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1870672
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:6:p:1519-1539



Template-Type: ReDIF-Article 1.0
Author-Name: Xin Liu
Author-X-Name-First: Xin
Author-X-Name-Last: Liu
Author-Name: Wenqing He
Author-X-Name-First: Wenqing
Author-X-Name-Last: He
Title: Adaptive kernel scaling support vector machine with application to a prostate cancer image study
Abstract: 
 The support vector machine (SVM) is a popularly used classifier in applications such as pattern recognition, texture mining and image retrieval owing to its flexibility and interpretability. However, its performance deteriorates when the response classes are imbalanced. To enhance the performance of the support vector machine classifier in the imbalanced cases we investigate a new two stage method by adaptively scaling the kernel function. Based on the information obtained from the standard SVM in the first stage, we conformally rescale the kernel function in a data adaptive fashion in the second stage so that the separation between two classes can be effectively enlarged with incorporation of observation imbalance. The proposed method takes into account the location of the support vectors in the feature space, therefore is especially appealing when the response classes are imbalanced. The resulting algorithm can efficiently improve the classification accuracy, which is confirmed by intensive numerical studies as well as a real prostate cancer imaging data application.
Journal: Journal of Applied Statistics
Pages: 1465-1484
Issue: 6
Volume: 49
Year: 2022
Month: 04
X-DOI: 10.1080/02664763.2020.1870669
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1870669
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:6:p:1465-1484



Template-Type: ReDIF-Article 1.0
Author-Name: B. Curley
Author-X-Name-First: B.
Author-X-Name-Last: Curley
Title: A nonlinear measurement error model and its application to describing the dependency of health outcomes on dietary intake
Abstract: 
 Many nutritional studies focus on the relationship between individuals' diets and resulting health outcomes. When examining these relationships, researchers are generally interested in individuals' long-term, average intake of nutrients; however, typically only 1–2 days of data are collected. If analyses are performed without accounting for the error in estimating usual intake, estimates will be biased. In this work, we focus on situations where the association between intake and health outcomes is nonlinear. Since we can only obtain noisy measurements of intake, we propose implementing a nonlinear measurement error model which accounts for the nuisance day-to-day variance when estimating long-term average intake. Estimation of the model is performed using maximum likelihood. Properties of the estimators are explored for a model where we assume that the unobservable usual intake is normally distributed. We then propose an extended model where we no longer assume that the distribution for the unobservable predictor is normal, but is instead a finite mixture of discrete distributions. We finish with an application using data from the 2015–2016 National Health and Nutrition Examination Survey (NHANES) where we examine the association between potassium intake and systolic blood pressure.
Journal: Journal of Applied Statistics
Pages: 1485-1518
Issue: 6
Volume: 49
Year: 2022
Month: 04
X-DOI: 10.1080/02664763.2020.1870671
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1870671
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:6:p:1485-1518



Template-Type: ReDIF-Article 1.0
Author-Name: Jiefei Wang
Author-X-Name-First: Jiefei
Author-X-Name-Last: Wang
Author-Name: Jeffrey C. Miecznikowski
Author-X-Name-First: Jeffrey C.
Author-X-Name-Last: Miecznikowski
Title: High precision implementation of Steck's recursion method for use in goodness-of-fit tests
Abstract: 
 Classical continuous goodness-of-fit (GOF) testing is employed for examining whether the data come from an assumed parametric model. In many cases, GOF tests assume a uniform null distribution and examine extreme values of the order statistics of the samples. Many of these statistics can be expressed by a function of the order statistics and the p-values amount to a joint probability statement based on the uniform order statistics. In this paper, we utilize Steck's recursion method and propose two high precision computing algorithms to compute the p-values for these GOF statistics. The numerical difficulties in implementing Steck's method are discussed and compared with solutions provided in high precision libraries.
Journal: Journal of Applied Statistics
Pages: 1348-1363
Issue: 6
Volume: 49
Year: 2022
Month: 04
X-DOI: 10.1080/02664763.2020.1861224
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1861224
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:6:p:1348-1363



Template-Type: ReDIF-Article 1.0
Author-Name: Hongying Jing
Author-X-Name-First: Hongying
Author-X-Name-Last: Jing
Author-Name: Jian Li
Author-X-Name-First: Jian
Author-X-Name-Last: Li
Author-Name: Kaizong Bai
Author-X-Name-First: Kaizong
Author-X-Name-Last: Bai
Title: Directional monitoring and diagnosis for covariance matrices
Abstract: 
 Statistical surveillance for covariance matrices has attracted increasing attention recently. Many approaches have been developed for monitoring general shifts that are arbitrary deviations, as well as sparse shifts occurring in only a few elements. This paper considers directional shifts that occur in only one independent parameter, which is common if the process is relatively stable. A directional covariance matrix control chart is proposed, which fully exploits directional shift information and borrows the strong power of likelihood ratio test. Therefore, this chart provides a powerful tool for monitoring covariance matrices. In addition, the proposed chart does not require specifying the regularisation parameter, and it enjoys a concise quadratic form, thereby easy to implement. Furthermore, this chart naturally leads to a diagnostic prescription for identifying the shifting element in the covariance matrix. Simulation results have demonstrated the efficiency of the suggested control chart and its accompanying diagnostic scheme.
Journal: Journal of Applied Statistics
Pages: 1449-1464
Issue: 6
Volume: 49
Year: 2022
Month: 04
X-DOI: 10.1080/02664763.2020.1867830
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1867830
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:6:p:1449-1464



Template-Type: ReDIF-Article 1.0
Author-Name: Marcela de Marillac Carvalho
Author-X-Name-First: Marcela de Marillac
Author-X-Name-Last: Carvalho
Author-Name: Thelma Sáfadi
Author-X-Name-First: Thelma
Author-X-Name-Last: Sáfadi
Title: Risk analysis in the brazilian stock market: copula-APARCH modeling for value-at-risk
Abstract: 
 Risk management of stock portfolios is a fundamental problem for the financial analysis since it indicates the potential losses of an investment at any given time. The objective of this study is to use bivariate static conditional copulas to quantify the dependence structure and to estimate the risk measure Value-at-Risk (VaR). There were selected stocks that have been performing outstandingly on the Brazilian Stock Exchange to compose pairs trading portfolios (B3, Gerdau, Magazine Luiza, and Petrobras). Due to the flexibility that this methodology offers in the construction of multivariate distributions and risk aggregation in finance, we used the copula-APARCH approach with the Normal, T-student, and Joe-Clayton copula functions. In most scenarios, the results showed a pattern of dependence at the extremes. Moreover, the copula form seems not to be relevant for VaR estimation, since in most portfolios the appropriate copulas lead to significant VaR estimates. It has found that the best models fitted provided conservative risk measures, estimates at 5% and 1%, in a scenario more aggressive.
Journal: Journal of Applied Statistics
Pages: 1598-1610
Issue: 6
Volume: 49
Year: 2022
Month: 04
X-DOI: 10.1080/02664763.2020.1865883
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1865883
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:6:p:1598-1610



Template-Type: ReDIF-Article 1.0
Author-Name: M. Hemavathi
Author-X-Name-First: M.
Author-X-Name-Last: Hemavathi
Author-Name: Eldho Varghese
Author-X-Name-First: Eldho
Author-X-Name-Last: Varghese
Author-Name: Shashi Shekhar
Author-X-Name-First: Shashi
Author-X-Name-Last: Shekhar
Author-Name: Seema Jaggi
Author-X-Name-First: Seema
Author-X-Name-Last: Jaggi
Title: Sequential asymmetric third order rotatable designs (SATORDs)
Abstract: 
 Rotatable designs that are available for process/ product optimization trials are mostly symmetric in nature. In many practical situations, response surface designs (RSDs) with mixed factor (unequal) levels are more suitable as these designs explore more regions in the design space but it is hard to get rotatable designs with a given level of asymmetry. When experimenting with unequal factor levels via asymmetric second order rotatable design (ASORDs), the lack of fit of the model may become significant which ultimately leads to the estimation of parameters based on a higher (or third) order model. Experimenting with a new third order rotatable design (TORD) in such a situation would be expensive as the responses observed from the first stage runs would be kept underutilized. In this paper, we propose a method of constructing asymmetric TORD by sequentially augmenting some additional points to the ASORDs without discarding the runs in the first stage. The proposed designs will be more economical to obtain the optimum response as the design in the first stage can be used to fit the second order model and with some additional runs, third order model can be fitted without discarding the initial design.
Journal: Journal of Applied Statistics
Pages: 1364-1381
Issue: 6
Volume: 49
Year: 2022
Month: 04
X-DOI: 10.1080/02664763.2020.1864817
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1864817
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:6:p:1364-1381



Template-Type: ReDIF-Article 1.0
Author-Name: Milda Maria Burzala
Author-X-Name-First: Milda Maria
Author-X-Name-Last: Burzala
Title: The process of transferring negative impulses in capital markets – a wavelet analysis
Abstract: 
 The empirical research that is presented herein deals with the process of transferring negative impulses in capital markets during the subprime crisis (contagion, comovements, crisis transmission and shocks). A significant and positive contribution of the research conducted is the demonstration of how the wavelet analysis can be used in examining the various responses of the financial markets. The first stage of the research involved an analysis of the response of seven European markets (CAC40, DAX, FTSE100, IBEX, ATHEX, BUX and WIG20 indexes) to the proceedings in the US market, exemplified by the Dow Jones Industrial Average Index. The second stage involved examining the relationships of strong European markets (CAC40, DAX, FTSE100), and then the impact that the strongest German market DAX had on four other and weaker European markets – two from Western Europe (IBEX, ATHEX) and two from Central-Eastern Europe (BUX and WIG20). This article presents a methodological approach to transfer impulses on capital markets.
Journal: Journal of Applied Statistics
Pages: 1574-1597
Issue: 6
Volume: 49
Year: 2022
Month: 04
X-DOI: 10.1080/02664763.2020.1864811
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1864811
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:6:p:1574-1597



Template-Type: ReDIF-Article 1.0
Author-Name: Zainab Al-kaabawi
Author-X-Name-First: Zainab
Author-X-Name-Last: Al-kaabawi
Author-Name: Yinghui Wei
Author-X-Name-First: Yinghui
Author-X-Name-Last: Wei
Author-Name: Rana Moyeed
Author-X-Name-First: Rana
Author-X-Name-Last: Moyeed
Title: Bayesian hierarchical models for linear networks
Abstract: 
 The purpose of this study is to highlight dangerous motorways via estimating the intensity of accidents and study its pattern across the UK motorway network. Two methods have been developed to achieve this aim. First, the motorway-specific intensity is estimated by using a homogeneous Poisson process. The heterogeneity across motorways is incorporated using two-level hierarchical models. The data structure is multilevel since each motorway consists of junctions that are joined by grouped segments. In the second method, the segment-specific intensity is estimated. The homogeneous Poisson process is used to model accident data within grouped segments but heterogeneity across grouped segments is incorporated using three-level hierarchical models. A Bayesian method via Markov Chain Monte Carlo is used to estimate the unknown parameters in the models and the sensitivity to the choice of priors is assessed. The performance of the proposed models is evaluated by a simulation study and an application to traffic accidents in 2016 on the UK motorway network. The deviance information criterion (DIC) and the widely applicable information criterion (WAIC) are employed to choose between models.
Journal: Journal of Applied Statistics
Pages: 1421-1448
Issue: 6
Volume: 49
Year: 2022
Month: 04
X-DOI: 10.1080/02664763.2020.1864814
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1864814
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:6:p:1421-1448



Template-Type: ReDIF-Article 1.0
Author-Name: Gideon Mensah Engmann
Author-X-Name-First: Gideon Mensah
Author-X-Name-Last: Engmann
Author-Name: Dong Han
Author-X-Name-First: Dong
Author-X-Name-Last: Han
Title: The optimized CUSUM and EWMA multi-charts for jointly detecting a range of mean and variance change
Abstract: 
 This article considers the problem of jointly monitoring the mean and variance of a process by multi-chart schemes. Multi-chart is a combination of several single charts which detects changes in a process quickly. Asymptotic analyses and simulation studies show that the optimized CUSUM multi-chart has optimal performance than optimized EWMA multi-chart in jointly detecting mean and variance shifts in an 
 $ i.i.d. $ i.i.d. normal observation. A real example that monitors the changes in IBM's stock returns (mean) and risks (variance) is used to demonstrate the performance of the above two multi-charts. The proposed method has been compared to a benchmark and it performed better.
Journal: Journal of Applied Statistics
Pages: 1540-1558
Issue: 6
Volume: 49
Year: 2022
Month: 04
X-DOI: 10.1080/02664763.2020.1870670
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1870670
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:6:p:1540-1558



Template-Type: ReDIF-Article 1.0
Author-Name: Qianya Qi
Author-X-Name-First: Qianya
Author-X-Name-Last: Qi
Author-Name: Li Yan
Author-X-Name-First: Li
Author-X-Name-Last: Yan
Author-Name: Lili Tian
Author-X-Name-First: Lili
Author-X-Name-Last: Tian
Title: Analyzing partially paired data: when can the unpaired portion(s) be safely ignored?
Abstract: 
 Partially paired data, either with incompleteness in one or both arms, are common in practice. For testing equality of means of two arms, practitioners often use only the portion of data with complete pairs and perform paired tests. Although such tests (referred as ‘naive paired tests’) are legitimate, their powers might be low as only partial data are utilized. The recently proposed ‘P-value pooling methods’, based on combining P-values from two tests, use all data, have reasonable type-I error control and good power property. While it is generally believed that ‘P-value pooling methods’ are superior to ‘naive paired tests’ in terms of power as the former use more data than the latter, no detailed power comparison has been done. This paper aims to compare powers of ‘naive paired tests’ and ‘P-value pooling methods’ analytically and our findings are counterintuitive, i.e. the ‘P-value pooling methods’ do not always outperform the naive paired tests in terms of power. Based on these results, we present guidance on how to select the best test for testing equality of means with partially paired data.
Journal: Journal of Applied Statistics
Pages: 1402-1420
Issue: 6
Volume: 49
Year: 2022
Month: 04
X-DOI: 10.1080/02664763.2020.1864813
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1864813
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:6:p:1402-1420



Template-Type: ReDIF-Article 1.0
Author-Name: Parfait Munezero
Author-X-Name-First: Parfait
Author-X-Name-Last: Munezero
Author-Name: Gebrenegus Ghilagaber
Author-X-Name-First: Gebrenegus
Author-X-Name-Last: Ghilagaber
Title: Dynamic Bayesian adjustment of anticipatory covariates in retrospective data: application to the effect of education on divorce risk
Abstract: 
 We address a problem in inference from retrospective studies where the value of a variable is measured at the date of the survey but is used as covariate to events that have occurred long before the survey. This causes problem because the value of the current-date (anticipatory) covariate does not follow the temporal order of events. We propose a dynamic Bayesian approach for modelling jointly the anticipatory covariate and the event of interest, and allowing the effects of the anticipatory covariate to vary over time. The issues are illustrated with data on the effects of education attained by the survey-time on divorce risks among Swedish men. The overall results show that failure to adjust for the anticipatory nature of education leads to elevated relative risks of divorce across educational levels. The results are partially in accordance with previous findings based on analyses of the same data set. More importantly, our findings provide new insights in that the bias due to anticipatory covariates varies over marriage duration.
Journal: Journal of Applied Statistics
Pages: 1382-1401
Issue: 6
Volume: 49
Year: 2022
Month: 04
X-DOI: 10.1080/02664763.2020.1864812
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1864812
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:6:p:1382-1401



Template-Type: ReDIF-Article 1.0
Author-Name: The Editors
Title: Correction Notice
Journal: Journal of Applied Statistics
Pages: 1611-1611
Issue: 6
Volume: 49
Year: 2022
Month: 04
X-DOI: 10.1080/02664763.2022.2049996
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2049996
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:6:p:1611-1611



Template-Type: ReDIF-Article 1.0
Author-Name: Nipada Papukdee
Author-X-Name-First: Nipada
Author-X-Name-Last: Papukdee
Author-Name: Jeong-Soo Park
Author-X-Name-First: Jeong-Soo
Author-X-Name-Last: Park
Author-Name: Piyapatr Busababodhin
Author-X-Name-First: Piyapatr
Author-X-Name-Last: Busababodhin
Title: Penalized likelihood approach for the four-parameter kappa distribution
Abstract: 
 The four-parameter kappa distribution (K4D) is a generalized form of some commonly used distributions such as generalized logistic, generalized Pareto, generalized Gumbel, and generalized extreme value (GEV) distributions. Owing to its flexibility, the K4D is widely applied in modeling in several fields such as hydrology and climatic change. For the estimation of the four parameters, the maximum likelihood approach and the method of L-moments are usually employed. The L-moment estimator (LME) method works well for some parameter spaces, with up to a moderate sample size, but it is sometimes not feasible in terms of computing the appropriate estimates. Meanwhile, using the maximum likelihood estimator (MLE) with small sample sizes shows substantially poor performance in terms of a large variance of the estimator. We therefore propose a maximum penalized likelihood estimation (MPLE) of K4D by adjusting the existing penalty functions that restrict the parameter space. Eighteen combinations of penalties for two shape parameters are considered and compared. The MPLE retains modeling flexibility and large sample optimality while also improving on small sample properties. The properties of the proposed estimator are verified through a Monte Carlo simulation, and an application case is demonstrated taking Thailand’s annual maximum temperature data.
Journal: Journal of Applied Statistics
Pages: 1559-1573
Issue: 6
Volume: 49
Year: 2022
Month: 04
X-DOI: 10.1080/02664763.2021.1871592
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1871592
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:6:p:1559-1573



Template-Type: ReDIF-Article 1.0
Author-Name: The Editors
Title: Correction Notice
Journal: Journal of Applied Statistics
Pages: 1612-1614
Issue: 6
Volume: 49
Year: 2022
Month: 04
X-DOI: 10.1080/02664763.2022.2051984
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2051984
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:6:p:1612-1614

Template-Type: ReDIF-Article 1.0
Author-Name: Seyede Sedighe Azimi
Author-X-Name-First: Seyede Sedighe
Author-X-Name-Last: Azimi
Author-Name: Ehsan Bahrami Samani
Author-X-Name-First: Ehsan
Author-X-Name-Last: Bahrami Samani
Author-Name: Mojtaba Ganjali
Author-X-Name-First: Mojtaba
Author-X-Name-Last: Ganjali
Title: Analysis of mixed correlated overdispersed binomial and ordinal longitudinal responses: LogLindley-Binomial and ordinal random effects model
Abstract: 
 We propose a new model called LogLindley-Binomial and ordinal joint model with random effects for analyzing mixed overdispersed binomial and ordinal longitudinal responses. A new distribution called the LogLindley-Binomial is presented, which is appropriate for the analysis of overdispersed binomial variables. A full likelihood-based approach is used to obtain maximum likelihood estimates. A comparison between LogLindley-Binomial and Beta-Binomial distributions are given by a simulation study. Also, to illustrate the utility of the proposed model, some simulation studies are conducted. In simulation studies, the performances of the LogLindley-Binomial distribution and the proposed model are well in some situations. Also, the new model's performance for analyzing a real dataset, extracted from the British Household Panel Survey, is studied. The proposed model performs well in comparison with another model for analyzing real data. Finally, the proposed distribution and the new model are found to be applicable for analyzing overdispersed binomial and mixed data.
Journal: Journal of Applied Statistics
Pages: 1742-1768
Issue: 7
Volume: 49
Year: 2022
Month: 05
X-DOI: 10.1080/02664763.2021.1881455
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1881455
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:7:p:1742-1768



Template-Type: ReDIF-Article 1.0
Author-Name: Xiaolin Chen
Author-X-Name-First: Xiaolin
Author-X-Name-Last: Chen
Author-Name: Chenguang Li
Author-X-Name-First: Chenguang
Author-X-Name-Last: Li
Author-Name: Tao Zhang
Author-X-Name-First: Tao
Author-X-Name-Last: Zhang
Author-Name: Zhenlong Gao
Author-X-Name-First: Zhenlong
Author-X-Name-Last: Gao
Title: On correlation rank screening for ultra-high dimensional competing risks data
Abstract: 
 In recent years, numerous feature screening schemes have been developed for ultra-high dimensional standard survival data with only one failure event. Nevertheless, existing literature pays little attention to related investigations for competing risks data, in which subjects suffer from multiple mutually exclusive failures. In this article, we develop a new marginal feature screening for ultra-high dimensional time-to-event data to allow for competing risks. The proposed procedure is model-free, and robust against heavy-tailed distributions and potential outliers for time to the type of failure of interest. Apart from this, it is invariant to any monotone transformation of event time of interest. Under rather mild assumptions, it is shown that the newly suggested approach possesses the ranking consistency and sure independence screening properties. Some numerical studies are conducted to evaluate the finite-sample performance of our method and make a comparison with its competitor, while an application to a real data set is provided to serve as an illustration.
Journal: Journal of Applied Statistics
Pages: 1848-1864
Issue: 7
Volume: 49
Year: 2022
Month: 05
X-DOI: 10.1080/02664763.2021.1884209
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1884209
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:7:p:1848-1864



Template-Type: ReDIF-Article 1.0
Author-Name: Thomas Deregnaucourt
Author-X-Name-First: Thomas
Author-X-Name-Last: Deregnaucourt
Author-Name: Chafik Samir
Author-X-Name-First: Chafik
Author-X-Name-Last: Samir
Author-Name: Sebastian Kurtek
Author-X-Name-First: Sebastian
Author-X-Name-Last: Kurtek
Author-Name: Anne-Francoise Yao
Author-X-Name-First: Anne-Francoise
Author-X-Name-Last: Yao
Title: Shape-constrained Gaussian process regression for surface reconstruction and multimodal, non-rigid image registration
Abstract: 
 We present a new statistical framework for landmark ?>curve-based image registration and surface reconstruction. The proposed method first elastically aligns geometric features (continuous, parameterized curves) to compute local deformations, and then uses a Gaussian random field model to estimate the full deformation vector field as a spatial stochastic process on the entire surface or image domain. The statistical estimation is performed using two different methods: maximum likelihood and Bayesian inference via Markov Chain Monte Carlo sampling. The resulting deformations accurately match corresponding curve regions while also being sufficiently smooth over the entire domain. We present several qualitative and quantitative evaluations of the proposed method on both synthetic and real data. We apply our approach to two different tasks on real data: (1) multimodal medical image registration, and (2) anatomical and pottery surface reconstruction.
Journal: Journal of Applied Statistics
Pages: 1865-1889
Issue: 7
Volume: 49
Year: 2022
Month: 05
X-DOI: 10.1080/02664763.2021.1897970
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1897970
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:7:p:1865-1889



Template-Type: ReDIF-Article 1.0
Author-Name: Mohamed Milad
Author-X-Name-First: Mohamed
Author-X-Name-Last: Milad
Author-Name: Gayla R. Olbricht
Author-X-Name-First: Gayla R.
Author-X-Name-Last: Olbricht
Title: Testing differentially methylated regions through functional principal component analysis
Abstract: 
 DNA methylation is an epigenetic modification that plays an important role in many biological processes and diseases. Several statistical methods have been proposed to test for DNA methylation differences between conditions at individual cytosine sites, followed by a post hoc aggregation procedure to explore regional differences. While there are benefits to analyzing CpGs individually, there are both biological and statistical reasons to test entire genomic regions for differential methylation. Variability in methylation levels measured by Next-Generation Sequencing (NGS) is often observed across CpG sites in a genomic region. Evaluating meaningful changes in regional level methylation profiles between conditions over noisy site-level measurements is often difficult to implement with parametric models. To overcome these limitations, this study develops a nonparametric approach to detect predefined differentially methylated regions (DMR) based on functional principal component analysis (FPCA). The performance of this approach is compared with two alternative methods (GIFT and M3D), using real and simulated data.
Journal: Journal of Applied Statistics
Pages: 1677-1691
Issue: 7
Volume: 49
Year: 2022
Month: 05
X-DOI: 10.1080/02664763.2021.1877636
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1877636
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:7:p:1677-1691



Template-Type: ReDIF-Article 1.0
Author-Name: Fabrizio Ronchi
Author-X-Name-First: Fabrizio
Author-X-Name-Last: Ronchi
Author-Name: Solomon W. Harrar
Author-X-Name-First: Solomon W.
Author-X-Name-Last: Harrar
Author-Name: Luigi Salmaso
Author-X-Name-First: Luigi
Author-X-Name-Last: Salmaso
Title: Multivariate nonparametric methods in two-way balanced designs: performances and limitations in small samples
Abstract: 
 Investigations of multivariate population are pretty common in applied researches, and the two-way crossed factorial design is a common design used at the exploratory phase in industrial applications. When assumptions such as multivariate normality and covariance homogeneity are violated, the conventional wisdom is to resort to nonparametric tests for hypotheses testing. In this paper we compare the performances, and in particular the power, of some nonparametric and semi-parametric methods that have been developed in recent years. Specifically, we examined resampling methods and robust versions of classical multivariate analysis of variance (MANOVA) tests. In a simulation study, we generate data sets with different configurations of factor's effect, number of replicates, number of response variables under null hypothesis, and number of response variables under alternative hypothesis. The objective is to elicit practical advice and guides to practitioners regarding the sensitivity of the tests in the various configurations, the tradeoff between power and type I error, the strategic impact of increasing number of response variables, and the favourable performance of one test when the alternative is sparse. A real case study from an industrial engineering experiment in thermoformed packaging production is used to compare and illustrate the application of the various methods.
Journal: Journal of Applied Statistics
Pages: 1714-1741
Issue: 7
Volume: 49
Year: 2022
Month: 05
X-DOI: 10.1080/02664763.2021.1915256
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1915256
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:7:p:1714-1741



Template-Type: ReDIF-Article 1.0
Author-Name: Tamalika Koley
Author-X-Name-First: Tamalika
Author-X-Name-Last: Koley
Author-Name: Anup Dewanji
Author-X-Name-First: Anup
Author-X-Name-Last: Dewanji
Title: Current status data with two competing risks and missing failure types: a parametric approach
Abstract: 
 Missing cause of failure is a common problem in competing risks data. Here we consider a general missing pattern in which one observes a set of possible causes containing the true cause. In this work, we focus on the parametric analysis of current status data with two competing risks and the above-mentioned missing pattern. We make some simpler assumptions on the conditional probability of observing a set of possible causes of failure given the true cause and carry out maximum-likelihood estimation of the model parameters. Asymptotic properties of the maximum-likelihood estimators are also discussed. Simulation studies are performed to study the finite sample properties of the estimators and also to investigate the role of the monitoring time distribution. Finally, the method is illustrated through the analysis of a real data set.
Journal: Journal of Applied Statistics
Pages: 1769-1783
Issue: 7
Volume: 49
Year: 2022
Month: 05
X-DOI: 10.1080/02664763.2021.1881453
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1881453
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:7:p:1769-1783



Template-Type: ReDIF-Article 1.0
Author-Name: M. H. Tahir
Author-X-Name-First: M. H.
Author-X-Name-Last: Tahir
Author-Name: M. Adnan Hussain
Author-X-Name-First: M. Adnan
Author-X-Name-Last: Hussain
Author-Name: Gauss M. Cordeiro
Author-X-Name-First: Gauss M.
Author-X-Name-Last: Cordeiro
Title: A new flexible generalized family for constructing many families of distributions
Abstract: 
 We propose a new flexible generalized family (NFGF) for constructing many families of distributions. The importance of the NFGF is that any baseline distribution can be chosen and it does not involve any additional parameters. Some useful statistical properties of the NFGF are determined such as a linear representation for the family density, analytical shapes of the density and hazard rate, random variable generation, moments and generating function. Further, the structural properties of a special model named the new flexible Kumaraswamy (NFKw) distribution, are investigated, and the model parameters are estimated by maximum-likelihood method. A simulation study is carried out to assess the performance of the estimates. The usefulness of the NFKw model is proved empirically by means of three real-life data sets. In fact, the two-parameter NFKw model performs better than three-parameter transmuted-Kumaraswamy, three-parameter exponentiated-Kumaraswamy and the well-known two-parameter Kumaraswamy models.
Journal: Journal of Applied Statistics
Pages: 1615-1635
Issue: 7
Volume: 49
Year: 2022
Month: 05
X-DOI: 10.1080/02664763.2021.1874891
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1874891
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:7:p:1615-1635



Template-Type: ReDIF-Article 1.0
Author-Name: Sajjad Piradl
Author-X-Name-First: Sajjad
Author-X-Name-Last: Piradl
Author-Name: Ali Shadrokh
Author-X-Name-First: Ali
Author-X-Name-Last: Shadrokh
Author-Name: Masoud Yarmohammadi
Author-X-Name-First: Masoud
Author-X-Name-Last: Yarmohammadi
Title: A robust estimation method for the linear regression model parameters with correlated error terms and outliers
Abstract: 
 Independence of error terms in a linear regression model, often not established. So a linear regression model with correlated error terms appears in many applications. According to the earlier studies, this kind of error terms, basically can affect the robustness of the linear regression model analysis. It is also shown that the robustness of the parameters estimators of a linear regression model can stay using the M-estimator. But considering that, it acquires this feature as the result of establishment of its efficiency. Whereas, it has been shown that the minimum Matusita distance estimators, has both features robustness and efficiency at the same time. On the other hand, because the Cochrane and Orcutt adjusted least squares estimators are not affected by the dependence of the error terms, so they are efficient estimators. Here we are using of a non-parametric kernel density estimation method, to give a new method of obtaining the minimum Matusita distance estimators for the linear regression model with correlated error terms in the presence of outliers. Also, simulation and real data study both are done for the introduced estimation method. In each case, the proposed method represents lower biases and mean squared errors than the other two methods.
Journal: Journal of Applied Statistics
Pages: 1663-1676
Issue: 7
Volume: 49
Year: 2022
Month: 05
X-DOI: 10.1080/02664763.2021.1881454
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1881454
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:7:p:1663-1676



Template-Type: ReDIF-Article 1.0
Author-Name: H. Rehman
Author-X-Name-First: H.
Author-X-Name-Last: Rehman
Author-Name: N. Chandra
Author-X-Name-First: N.
Author-X-Name-Last: Chandra
Author-Name: Fatemeh Sadat Hosseini-Baharanchi
Author-X-Name-First: Fatemeh Sadat
Author-X-Name-Last: Hosseini-Baharanchi
Author-Name: Ahmad Reza Baghestani
Author-X-Name-First: Ahmad Reza
Author-X-Name-Last: Baghestani
Author-Name: Mohamad Amin Pourhoseingholi
Author-X-Name-First: Mohamad Amin
Author-X-Name-Last: Pourhoseingholi
Title: Cause-specific hazard regression estimation for modified Weibull distribution under a class of non-informative priors
Abstract: 
 In time to event analysis, the situation of competing risks arises when the individual (or subject) may experience p mutually exclusive causes of death (failure), where cause-specific hazard function is of great importance in this framework. For instance, in malignancy-related death, colorectal cancer is one of the leading causes of the death in the world and death due to other causes considered as competing causes. We include prognostic variables in the model through parametric Cox proportional hazards model. Mostly, in literature exponential, Weibull, etc. distributions have been used for parametric modelling of cause-specific hazard function but they are incapable to accommodate non-monotone failure rate. Therefore, in this article, we consider a modified Weibull distribution which is capable to model survival data with non-monotonic behaviour of hazard rate. For estimating the cumulative cause-specific hazard function, we utilized maximum likelihood and Bayesian methods. A class of non-informative types of prior (uniform, Jeffrey’s and half-t) is introduced for Bayes estimation under squared error (symmetric) as well as LINEX (asymmetric) loss functions. A simulation study is performed for a comprehensive comparison of Bayes and maximum likelihood estimators of cumulative cause-specific hazard function. Real data on colorectal cancer is used to demonstrate the proposed model.
Journal: Journal of Applied Statistics
Pages: 1784-1801
Issue: 7
Volume: 49
Year: 2022
Month: 05
X-DOI: 10.1080/02664763.2021.1882407
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1882407
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:7:p:1784-1801



Template-Type: ReDIF-Article 1.0
Author-Name: Francisco Corona
Author-X-Name-First: Francisco
Author-X-Name-Last: Corona
Author-Name: Nelson Muriel
Author-X-Name-First: Nelson
Author-X-Name-Last: Muriel
Author-Name: Graciela González-Farías
Author-X-Name-First: Graciela
Author-X-Name-Last: González-Farías
Title: Dynamic factor structure of team performances in Liga MX
Abstract: 
 Team performance of the Mexican Football League (Liga MX), measured as the percentage of the total points obtained during each short tournament, is analyzed using Dynamic Factor Models (DFMs). The estimation of the common components is carried out with Principal Components and the stochastic nature of the DFM is studied through Panel Analysis of Non-stationarity in Idiosyncratic and Common Components. The results reveal that there are two common factors, one being possibly non-stationary. These factors show an interesting dynamic behavior in the league and allow to split the teams into two groups, namely, top competitors and emerging or relegated teams. Some discussion is given in this direction.
Journal: Journal of Applied Statistics
Pages: 1900-1912
Issue: 7
Volume: 49
Year: 2022
Month: 05
X-DOI: 10.1080/02664763.2021.1881946
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1881946
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:7:p:1900-1912



Template-Type: ReDIF-Article 1.0
Author-Name: Yujie Zhao
Author-X-Name-First: Yujie
Author-X-Name-Last: Zhao
Author-Name: Hao Yan
Author-X-Name-First: Hao
Author-X-Name-Last: Yan
Author-Name: Sarah Holte
Author-X-Name-First: Sarah
Author-X-Name-Last: Holte
Author-Name: Yajun Mei
Author-X-Name-First: Yajun
Author-X-Name-Last: Mei
Title: Rapid detection of hot-spots via tensor decomposition with applications to crime rate data
Abstract: 
 In many real-world applications of monitoring multivariate spatio-temporal data that are non-stationary over time, one is often interested in detecting hot-spots with spatial sparsity and temporal consistency, instead of detecting system-wise changes as in traditional statistical process control (SPC) literature. In this paper, we propose an efficient method to detect hot-spots through tensor decomposition, and our method has three steps. First, we fit the observed data into a Smooth Sparse Decomposition Tensor (SSD-Tensor) model that serves as a dimension reduction and de-noising technique: it is an additive model decomposing the original data into: smooth but non-stationary global mean, sparse local anomalies, and random noises. Next, we estimate model parameters by the penalized framework that includes Least Absolute Shrinkage and Selection Operator (LASSO) and fused LASSO penalty. An efficient recursive optimization algorithm is developed based on Fast Iterative Shrinkage Thresholding Algorithm (FISTA). Finally, we apply a Cumulative Sum (CUSUM) Control Chart to monitor model residuals after removing global means, which helps to detect when and where hot-spots occur. To demonstrate the usefulness of our proposed SSD-Tensor method, we compare it with several other methods including scan statistics, LASSO-based, PCA-based, T2-based control chart in extensive numerical simulation studies and a real crime rate dataset.
Journal: Journal of Applied Statistics
Pages: 1636-1662
Issue: 7
Volume: 49
Year: 2022
Month: 05
X-DOI: 10.1080/02664763.2021.1874892
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1874892
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:7:p:1636-1662



Template-Type: ReDIF-Article 1.0
Author-Name: Sobom M. Somé
Author-X-Name-First: Sobom M.
Author-X-Name-Last: Somé
Author-Name: Célestin C. Kokonendji
Author-X-Name-First: Célestin C.
Author-X-Name-Last: Kokonendji
Title: Bayesian selector of adaptive bandwidth for multivariate gamma kernel estimator on [0,∞ )d
Abstract: 
 Bayesian bandwidth selections in multivariate associated kernel estimation of probability density functions are known to improve classical methods such as cross-validation techniques in terms of execution time and smoothing quality. The paper focuses on a basic multivariate gamma kernel which is appropriated to estimate densities with support 
 $ [0,\infty )^d $ [0,∞)d. For this purpose, we consider a Bayesian adaptive estimation of the bandwidths vector under the usual quadratic loss function. The exact expression of the posterior distribution and the vector of bandwidths are obtained. Simulations studies highlight the excellent performance of the proposed approach, comparing to the global cross-validation bandwidth selection, and under integrated squared errors. Two bivariate and trivariate applications to the Old Faithful geyser data and new ones on drinking water pumps in the Sahel, respectively, are made.
Journal: Journal of Applied Statistics
Pages: 1692-1713
Issue: 7
Volume: 49
Year: 2022
Month: 05
X-DOI: 10.1080/02664763.2021.1881456
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1881456
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:7:p:1692-1713



Template-Type: ReDIF-Article 1.0
Author-Name: Seonghun Cho
Author-X-Name-First: Seonghun
Author-X-Name-Last: Cho
Author-Name: Johan Lim
Author-X-Name-First: Johan
Author-X-Name-Last: Lim
Author-Name: Woncheol Jang
Author-X-Name-First: Woncheol
Author-X-Name-Last: Jang
Title: How many people participated in candlelight protests? Counting the size of a dynamic crowd
Abstract: 
 The recent controversy about the size of crowds at candlelight protests in Korea raises an interesting question regarding the methods used to estimate crowd size. Protest organizers tend to count all participants in the event from its start to finish, while the police usually report the crowd size at its peak. While several counting methods are available to estimate the size of a crowd at a given time, counting the total number of the participants at a protest is not straightforward. In this paper, we propose a new estimator to count the total number of participants that we call the size of a dynamic crowd. We assume that the arrival and departure times of the crowd are randomly observed and that the number of the attendees in the crowd at a specific time is estimable. We estimate the number of total attendees during the entire gathering based on the capture-recapture model. We also propose a bootstrap procedure to construct a confidence interval for the crowd size. We demonstrate the performance of the proposed method with simulation studies and the data from Korea's March for Science, a global event across the world on Earth Day, April 22, 2017.
Journal: Journal of Applied Statistics
Pages: 1890-1899
Issue: 7
Volume: 49
Year: 2022
Month: 05
X-DOI: 10.1080/02664763.2021.1871591
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1871591
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:7:p:1890-1899



Template-Type: ReDIF-Article 1.0
Author-Name: Somayeh Momenyan
Author-X-Name-First: Somayeh
Author-X-Name-Last: Momenyan
Author-Name: Farzane Ahmadi
Author-X-Name-First: Farzane
Author-X-Name-Last: Ahmadi
Author-Name: Jalal Poorolajal
Author-X-Name-First: Jalal
Author-X-Name-Last: Poorolajal
Title: Competing risks model for clustered data based on the subdistribution hazards with spatial random effects
Abstract: 
 In some applications, the clustered survival data are arranged spatially such as clinical centers or geographical regions. Incorporating spatial variation in these data not only can improve the accuracy and efficiency of the parameter estimation, but it also investigates the spatial patterns of survivorship for identifying high-risk areas. Competing risks in survival data concern a situation where there is more than one cause of failure, but only the occurrence of the first one is observable. In this paper, we considered Bayesian subdistribution hazard regression models with spatial random effects for the clustered HIV/AIDS data. An intrinsic conditional autoregressive (ICAR) distribution was employed to model the areal spatial random effects. Comparison among competing models was performed by the deviance information criterion. We illustrated the gains of our model through application to the HIV/AIDS data and the simulation studies.
Journal: Journal of Applied Statistics
Pages: 1802-1820
Issue: 7
Volume: 49
Year: 2022
Month: 05
X-DOI: 10.1080/02664763.2021.1884208
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1884208
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:7:p:1802-1820



Template-Type: ReDIF-Article 1.0
Author-Name: Cong Li
Author-X-Name-First: Cong
Author-X-Name-Last: Li
Author-Name: Haixiang Zhang
Author-X-Name-First: Haixiang
Author-X-Name-Last: Zhang
Author-Name: Dehui Wang
Author-X-Name-First: Dehui
Author-X-Name-Last: Wang
Title: Modelling and monitoring of INAR(1) process with geometrically inflated Poisson innovations
Abstract: 
 To analyse count time series data inflated at the r + 1 values 
 $ \{0,1,\ldots ,r\} $ {0,1,…,r}, we propose a new first-order integer-valued autoregressive process with r-geometrically inflated Poisson innovations. Some statistical properties together with conditional maximum likelihood estimate are provided. For the purpose of statistical monitoring, we focus on the cumulative sum chart, exponentially weighted moving average chart and combined jumps chart towards the proposed process. Numerical simulations indicate that the conditional maximum likelihood estimator is unbiased. Moreover, the cumulative sum chart is the best choice to monitor our model in practice. Some applications about telephone complaints data are provided to illustrate the proposed methods.
Journal: Journal of Applied Statistics
Pages: 1821-1847
Issue: 7
Volume: 49
Year: 2022
Month: 05
X-DOI: 10.1080/02664763.2021.1884206
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1884206
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:7:p:1821-1847

Template-Type: ReDIF-Article 1.0
Author-Name: Christian H. Weiß
Author-X-Name-First: Christian H.
Author-X-Name-Last: Weiß
Author-Name: Annika Homburg
Author-X-Name-First: Annika
Author-X-Name-Last: Homburg
Author-Name: Layth C. Alwan
Author-X-Name-First: Layth C.
Author-X-Name-Last: Alwan
Author-Name: Gabriel Frahm
Author-X-Name-First: Gabriel
Author-X-Name-Last: Frahm
Author-Name: Rainer Göb
Author-X-Name-First: Rainer
Author-X-Name-Last: Göb
Title: Efficient accounting for estimation uncertainty in coherent forecasting of count processes
Abstract: 
 Coherent forecasting techniques for count processes generate forecasts that consist of count values themselves. In practice, forecasting always relies on a fitted model and so the obtained forecast values are affected by estimation uncertainty. Thus, they may differ from the true forecast values as they would have been obtained from the true data generating process. We propose a computationally efficient resampling scheme that allows to express the uncertainty in common types of coherent forecasts for count processes. The performance of the resampling scheme, which results in ensembles of forecast values, is investigated in a simulation study. A real-data example is used to demonstrate the application of the proposed approach in practice. It is shown that the obtained ensembles of forecast values can be presented in a visual way that allows for an intuitive interpretation.
Journal: Journal of Applied Statistics
Pages: 1957-1978
Issue: 8
Volume: 49
Year: 2022
Month: 06
X-DOI: 10.1080/02664763.2021.1887104
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1887104
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:8:p:1957-1978



Template-Type: ReDIF-Article 1.0
Author-Name: Han Yu
Author-X-Name-First: Han
Author-X-Name-Last: Yu
Author-Name: Shanhe Jiang
Author-X-Name-First: Shanhe
Author-X-Name-Last: Jiang
Author-Name: Hong Huang
Author-X-Name-First: Hong
Author-X-Name-Last: Huang
Title: Spatio-temporal parse network-based trajectory modeling on the dynamics of criminal justice system
Abstract: 
 We extend the existing group-based trajectory modeling by proposing the network-based trajectory modeling based on judicious design and analysis of a spatio-temporal parse network (STPN) as a representation of neighborhood structure that evolves in time. The STPN offers a principled qualitative specification for an explicit paradigm framework to deal with complex real-world problems. The framework is completed by developing a quantitative specification of latent field representation to merge seamlessly on or alongside the established STPN via hierarchical modeling. The models adopt spatial random effects to characterize the heterogeneity and autocorrelation over the locations where nonlinear trajectories were observed. The trajectories are then investigated in the presence of the operational constraints of the dependence structure induced by the spatial and temporal dimensions. With the framework, complex developmental trajectory problems can be discerned, communicated, diagnosed and modeled in a relatively simple way that interpretation is accessible to nontechnical audiences and quickly comprehensible to technically sophisticated audiences. The proposed modeling is applied to address the challenges of the trajectory modeling of nonlinear dynamics arising from a motivating criminal justice empirical process.
Journal: Journal of Applied Statistics
Pages: 1979-2000
Issue: 8
Volume: 49
Year: 2022
Month: 06
X-DOI: 10.1080/02664763.2021.1887101
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1887101
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:8:p:1979-2000



Template-Type: ReDIF-Article 1.0
Author-Name: H. E. Correia
Author-X-Name-First: H. E.
Author-X-Name-Last: Correia
Author-Name: A. Abebe
Author-X-Name-First: A.
Author-X-Name-Last: Abebe
Title: Capturing spatiotemporal dynamics of Alaskan groundfish catch using signed-rank estimation for varying coefficient models
Abstract: 
 Varying coefficient models (VCMs) are commonly used for their high degree of flexibility in modeling complex systems. Many applications in fisheries utilize VCMs to capture spatial variation in populations of marine fishes. All of these applications use the penalized least squares method for estimation. However, this approach is known to be sensitive to non-normal distributions and outliers, a common feature of ecological data. Robust estimation methods are more appropriate for handling noisy and non-normal data. We present the application of a signed-rank-based procedure for obtaining robust estimates in VCMs on a fisheries dataset from the North Pacific Ocean. We demonstrates that the signed-rank-based estimation method provides better fit and improved prediction in comparison to the classical likelihood VCM fits in both simulations and the real data application, particularly when the distributions are non-normal and may be misspecified. Rank-based estimation of VCMs is therefore valuable for modeling ecological data and obtaining useful inferences where non-normality and outliers are common.
Journal: Journal of Applied Statistics
Pages: 2137-2156
Issue: 8
Volume: 49
Year: 2022
Month: 06
X-DOI: 10.1080/02664763.2021.1889996
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1889996
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:8:p:2137-2156



Template-Type: ReDIF-Article 1.0
Author-Name: Adewale F. Lukman
Author-X-Name-First: Adewale F.
Author-X-Name-Last: Lukman
Author-Name: Benedicta Aladeitan
Author-X-Name-First: Benedicta
Author-X-Name-Last: Aladeitan
Author-Name: Kayode Ayinde
Author-X-Name-First: Kayode
Author-X-Name-Last: Ayinde
Author-Name: Mohamed R. Abonazel
Author-X-Name-First: Mohamed R.
Author-X-Name-Last: Abonazel
Title: Modified ridge-type for the Poisson regression model: simulation and application
Abstract: 
 The Poisson regression model (PRM) is employed in modelling the relationship between a count variable (y) and one or more explanatory variables. The parameters of PRM are popularly estimated using the Poisson maximum likelihood estimator (PMLE). There is a tendency that the explanatory variables grow together, which results in the problem of multicollinearity. The variance of the PMLE becomes inflated in the presence of multicollinearity. The Poisson ridge regression (PRRE) and Liu estimator (PLE) have been suggested as an alternative to the PMLE. However, in this study, we propose a new estimator to estimate the regression coefficients for the PRM when multicollinearity is a challenge. We perform a simulation study under different specifications to assess the performance of the new estimator and the existing ones. The performance was evaluated using the scalar mean square error criterion and the mean squared error prediction error. The aircraft damage data was adopted for the application study and the estimators’ performance judged by the SMSE and the mean squared prediction error. The theoretical comparison shows that the proposed estimator outperforms other estimators. This is further supported by the simulation study and the application result.
Journal: Journal of Applied Statistics
Pages: 2124-2136
Issue: 8
Volume: 49
Year: 2022
Month: 06
X-DOI: 10.1080/02664763.2021.1889998
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1889998
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:8:p:2124-2136



Template-Type: ReDIF-Article 1.0
Author-Name: Sara Ghanbari
Author-X-Name-First: Sara
Author-X-Name-Last: Ghanbari
Author-Name: Abdolhamid Rezaei Roknabadi
Author-X-Name-First: Abdolhamid
Author-X-Name-Last: Rezaei Roknabadi
Author-Name: Mahdi Salehi
Author-X-Name-First: Mahdi
Author-X-Name-Last: Salehi
Title: Estimation of stress–strength reliability for Marshall–Olkin distributions based on progressively Type-II censored samples
Abstract: 
 We are mainly interested in estimating the stress–strength parameter, say 
 $ \mathcal {R} $ R, when the parent distribution follows the well-known Marshall–Olkin model and the accessible data has the form of the progressively Type-II censored samples. In this case, the stress–strength parameter is free of the base distribution employed in the Marshall–Olkin model. Thus, we use the exponential distribution for simplicity. The maximum likelihood methods as well as some Bayesian approaches are used for the estimation purpose. The corresponding estimators of the latter approach are obtained by using Lindley's approximation and Gibbs sampling methods since the Bayesian estimator of 
 $ \mathcal {R} $ R cannot be obtained as an explicit form. Moreover, some confidence intervals of various types are derived for 
 $ \mathcal {R} $ R and then compared via a Monte Carlo simulation. Finally, the survival times of head and neck cancer patients are analyzed by two therapies for illustrating purposes.
Journal: Journal of Applied Statistics
Pages: 1913-1934
Issue: 8
Volume: 49
Year: 2022
Month: 06
X-DOI: 10.1080/02664763.2021.1884207
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1884207
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:8:p:1913-1934



Template-Type: ReDIF-Article 1.0
Author-Name: Yasaman Jabbari
Author-X-Name-First: Yasaman
Author-X-Name-Last: Jabbari
Author-Name: Robert Cribbie
Author-X-Name-First: Robert
Author-X-Name-Last: Cribbie
Title: Negligible interaction test for continuous predictors
Abstract: 
 Behavioral science researchers are often interested in whether there is negligible interaction among continuous predictors of an outcome variable. For example, a researcher might be interested in demonstrating that the effect of perfectionism on depression is very consistent across age. In this case, the researcher is interested in assessing whether the interaction between the predictors is too small to be meaningful. Unfortunately, most researchers address the above research question using a traditional association-based null hypothesis test (e.g. regression) where their goal is to fail to reject the null hypothesis of no interaction. Common problems with traditional tests are their sensitivity to sample size and their opposite (and hence inappropriate) hypothesis setup for finding a negligible interaction effect. In this study, we investigated a method for testing for negligible interaction between continuous predictors using unstandardized and standardized regression-based models and equivalence testing. A Monte Carlo study provides evidence for the effectiveness of the equivalence-based test relative to traditional approaches.
Journal: Journal of Applied Statistics
Pages: 2001-2015
Issue: 8
Volume: 49
Year: 2022
Month: 06
X-DOI: 10.1080/02664763.2021.1887102
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1887102
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:8:p:2001-2015



Template-Type: ReDIF-Article 1.0
Author-Name: Hyunsung Kim
Author-X-Name-First: Hyunsung
Author-X-Name-Last: Kim
Author-Name: Yaeji Lim
Author-X-Name-First: Yaeji
Author-X-Name-Last: Lim
Title: Bootstrap aggregated classification for sparse functional data
Abstract: 
 Sparse functional data are commonly observed in real-data analyzes. For such data, we propose a new classification method based on functional principal component analysis (FPCA) and bootstrap aggregating. Bootstrap aggregating is believed to improve the single classifier. In this paper, we apply this belief to an FPCA based classification, and compare the classification performance with that of the single classifiers. The simulation results show that the proposed method performs better than the conventional single classifiers. We then conduct two real-data analyzes.
Journal: Journal of Applied Statistics
Pages: 2052-2063
Issue: 8
Volume: 49
Year: 2022
Month: 06
X-DOI: 10.1080/02664763.2021.1889997
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1889997
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:8:p:2052-2063



Template-Type: ReDIF-Article 1.0
Author-Name: Nileshkumar H. Jadhav
Author-X-Name-First: Nileshkumar H.
Author-X-Name-Last: Jadhav
Title: A new linearized ridge Poisson estimator in the presence of multicollinearity
Abstract: 
 Poisson regression is a very commonly used technique for modeling the count data in applied sciences, in which the model parameters are usually estimated by the maximum likelihood method. However, the presence of multicollinearity inflates the variance of maximum likelihood (ML) estimator and the estimated parameters give unstable results. In this article, a new linearized ridge Poisson estimator is introduced to deal with the problem of multicollinearity. Based on the asymptotic properties of ML estimator, the bias, covariance and mean squared error of the proposed estimator are obtained and the optimal choice of shrinkage parameter is derived. The performance of the existing estimators and proposed estimator is evaluated through Monte Carlo simulations and two real data applications. The results clearly reveal that the proposed estimator outperforms the existing estimators in the mean squared error sense.
Journal: Journal of Applied Statistics
Pages: 2016-2034
Issue: 8
Volume: 49
Year: 2022
Month: 06
X-DOI: 10.1080/02664763.2021.1887103
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1887103
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:8:p:2016-2034



Template-Type: ReDIF-Article 1.0
Author-Name: J. C. S. Vasconcelos
Author-X-Name-First: J. C. S.
Author-X-Name-Last: Vasconcelos
Author-Name: E. M. M. Ortega
Author-X-Name-First: E. M. M.
Author-X-Name-Last: Ortega
Author-Name: J. S. Vasconcelos
Author-X-Name-First: J. S.
Author-X-Name-Last: Vasconcelos
Author-Name: G. M. Cordeiro
Author-X-Name-First: G. M.
Author-X-Name-Last: Cordeiro
Author-Name: A. L. Vivan
Author-X-Name-First: A. L.
Author-X-Name-Last: Vivan
Author-Name: M. A. M. Biaggioni
Author-X-Name-First: M. A. M.
Author-X-Name-Last: Biaggioni
Title: A new heteroscedastic regression to analyze mass loss of wood in civil construction in Brazil
Abstract: 
 A heteroscedastic regression based on the odd log-logistic Marshall–Olkin normal (OLLMON) distribution is defined by extending previous models. Some structural properties of this distribution are presented. The estimation of the parameters is addressed by maximum likelihood. For different parameter settings, sample sizes and some scenarios, various simulations investigate the performance of the heteroscedastic OLLMON regression. We use residual analysis to detect influential observations and to check the model assumptions. The new regression explains the mass loss of different wood species in civil construction in Brazil.
Journal: Journal of Applied Statistics
Pages: 2035-2051
Issue: 8
Volume: 49
Year: 2022
Month: 06
X-DOI: 10.1080/02664763.2021.1890001
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1890001
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:8:p:2035-2051



Template-Type: ReDIF-Article 1.0
Author-Name: Amarnath Nandy
Author-X-Name-First: Amarnath
Author-X-Name-Last: Nandy
Author-Name: Ayanendranath Basu
Author-X-Name-First: Ayanendranath
Author-X-Name-Last: Basu
Author-Name: Abhik Ghosh
Author-X-Name-First: Abhik
Author-X-Name-Last: Ghosh
Title: Robust inference for skewed data in health sciences
Abstract: 
 Health data are often not symmetric to be adequately modeled through the usual normal distributions; most of them exhibit skewed patterns. They can indeed be modeled better through the larger family of skew-normal distributions covering both skewed and symmetric cases. Since outliers are not uncommon in complex real-life experimental datasets, a robust methodology automatically taking care of the noises in the data would be of great practical value to produce stable and more precise research insights leading to better policy formulation. In this paper, we develop a class of robust estimators and testing procedures for the family of skew-normal distributions using the minimum density power divergence approach with application to health data. In particular, a robust procedure for testing of symmetry is discussed in the presence of outliers. Two efficient computational algorithms are discussed. Besides deriving the asymptotic and robustness theory for the proposed methods, their advantages and utilities are illustrated through simulations and a couple of real-life applications for health data of athletes from Australian Institute of Sports and AIDS clinical trial data.
Journal: Journal of Applied Statistics
Pages: 2093-2123
Issue: 8
Volume: 49
Year: 2022
Month: 06
X-DOI: 10.1080/02664763.2021.1891527
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1891527
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:8:p:2093-2123



Template-Type: ReDIF-Article 1.0
Author-Name: Emrah Altun
Author-X-Name-First: Emrah
Author-X-Name-Last: Altun
Author-Name: Gauss M. Cordeiro
Author-X-Name-First: Gauss M.
Author-X-Name-Last: Cordeiro
Author-Name: Miroslav M. Ristić
Author-X-Name-First: Miroslav M.
Author-X-Name-Last: Ristić
Title: An one-parameter compounding discrete distribution
Abstract: 
 In this study, a new one-parameter discrete distribution obtained by compounding the Poisson and xgamma distributions is proposed. Some statistical properties of the new distribution are obtained including moments and probability and moment generating functions. Two methods are used for the estimation of the unknown parameter: the maximum likelihood method and the method of moments. Additionally, the count regression model and integer-valued autoregressive process of the proposed distribution are introduced. Some possible applications of the introduced models are considered and discussed.
Journal: Journal of Applied Statistics
Pages: 1935-1956
Issue: 8
Volume: 49
Year: 2022
Month: 06
X-DOI: 10.1080/02664763.2021.1884846
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1884846
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:8:p:1935-1956



Template-Type: ReDIF-Article 1.0
Author-Name: Amulya Kumar Mahto
Author-X-Name-First: Amulya Kumar
Author-X-Name-Last: Mahto
Author-Name: Chandrakant Lodhi
Author-X-Name-First: Chandrakant
Author-X-Name-Last: Lodhi
Author-Name: Yogesh Mani Tripathi
Author-X-Name-First: Yogesh Mani
Author-X-Name-Last: Tripathi
Author-Name: Liang Wang
Author-X-Name-First: Liang
Author-X-Name-Last: Wang
Title: Inference for partially observed competing risks model for Kumaraswamy distribution under generalized progressive hybrid censoring
Abstract: 
 In this paper, inference for a competing risks model is studied when latent failure times follow Kumaraswamy distribution and causes of failure are partially observed. Under generalized progressive hybrid censoring, existence and uniqueness of maximum likelihood estimators of model parameters are established. The confidence intervals are obtained by using asymptotic distribution theory. We further compute Bayes estimators along with credible intervals. In addition, inference is also discussed when there is order restricted shape parameters. The performance of all estimates is investigated using Monte-Carlo simulations. Finally, analysis of a real data set is presented for illustration purposes.
Journal: Journal of Applied Statistics
Pages: 2064-2092
Issue: 8
Volume: 49
Year: 2022
Month: 06
X-DOI: 10.1080/02664763.2021.1889999
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1889999
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:8:p:2064-2092



Template-Type: ReDIF-Article 1.0
Author-Name: Marcus Gerardus L. Nascimento
Author-X-Name-First: Marcus Gerardus
Author-X-Name-Last: L. Nascimento
Author-Name: Ralph S. Silva
Author-X-Name-First: Ralph S.
Author-X-Name-Last: Silva
Author-Name: Mario Jorge Mendonça
Author-X-Name-First: Mario Jorge
Author-X-Name-Last: Mendonça
Author-Name: Amaro Olimpio Pereira Jr.
Author-X-Name-First: Amaro Olimpio
Author-X-Name-Last: Pereira Jr.
Title: Estimating the efficiency of Brazilian electricity distribution utilities
Abstract: 
 This paper proposes a differing methodology from the Brazilian Electricity Regulatory Agency on the efficiency estimation for the Brazilian electricity distribution sector. Our proposal combines robust state-space models and stochastic frontier analysis to measure the operational cost efficiency in a panel data set from 60 Brazilian electricity distribution utilities. The modeling joins the main literature in energy economics with advanced econometric and statistic techniques in order to estimate the efficiencies. Moreover, the suggested model is able to deal with changes in the inefficiencies across time whilst the Bayesian paradigm – through Markov chain Monte Carlo techniques – facilitates the inference on all unknowns. The method enables a significant degree of flexibility in the resultant efficiencies and a complete photography about the distribution sector.
Journal: Journal of Applied Statistics
Pages: 2157-2166
Issue: 8
Volume: 49
Year: 2022
Month: 06
X-DOI: 10.1080/02664763.2021.1890000
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1890000
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:8:p:2157-2166

Template-Type: ReDIF-Article 1.0
Author-Name: Juan Sosa
Author-X-Name-First: Juan
Author-X-Name-Last: Sosa
Author-Name: Lina Buitrago
Author-X-Name-First: Lina
Author-X-Name-Last: Buitrago
Title: Time-varying coefficient model estimation through radial basis functions
Abstract: 
 In this paper, we estimate the dynamic parameters of a time-varying coefficient model through radial kernel functions in the context of a longitudinal study. Our proposal is based on a linear combination of weighted kernel functions involving a bandwidth, centered around a given set of time points. In addition, we study different alternatives of estimation and inference including a Frequentist approach using weighted least squares along with bootstrap methods, and a Bayesian approach through both Markov chain Monte Carlo and variational methods. We compare the estimation strategies mention above with each other, and our radial kernel functions proposal with an expansion based on regression spline, by means of an extensive simulation study considering multiples scenarios in terms of sample size, number of repeated measurements, and subject-specific correlation. Our experiments show that the capabilities of our proposal based on radial kernel functions are indeed comparable with or even better than those obtained from regression splines. We illustrate our methodology by analyzing data from two AIDS clinical studies.
Journal: Journal of Applied Statistics
Pages: 2510-2534
Issue: 10
Volume: 49
Year: 2022
Month: 07
X-DOI: 10.1080/02664763.2021.1910938
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1910938
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:10:p:2510-2534



Template-Type: ReDIF-Article 1.0
Author-Name: Rezoanoor Rahman
Author-X-Name-First: Rezoanoor
Author-X-Name-Last: Rahman
Author-Name: M. Iftakhar Alam
Author-X-Name-First: M.
Author-X-Name-Last: Iftakhar Alam
Title: Stopping for efficacy in single-arm phase II clinical trials
Abstract: 
 Phase II clinical trials investigate whether a new drug or treatment has sufficient evidence of effectiveness against the disease under study. Two-stage designs are popular for phase II since they can stop in the first stage if the drug is ineffective. Investigators often face difficulties in determining the target response rates, and adaptive designs can help to set the target response rate tested in the second stage based on the number of responses observed in the first stage. Popular adaptive designs consider two alternate response rates, and they generally minimise the expected sample size at the maximum uninterested response rate. Moreover, these designs consider only futility as the reason for early stopping and have high expected sample sizes if the provided drug is effective. Motivated by this problem, we propose an adaptive design that enables us to terminate the single-arm trial at the first stage for efficacy and conclude which alternate response rate to choose. Comparing the proposed design with a popular adaptive design from literature reveals that the expected sample size decreases notably if any of the two target response rates are correct. In contrast, the expected sample size remains almost the same under the null hypothesis.
Journal: Journal of Applied Statistics
Pages: 2447-2466
Issue: 10
Volume: 49
Year: 2022
Month: 07
X-DOI: 10.1080/02664763.2021.1904846
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1904846
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:10:p:2447-2466



Template-Type: ReDIF-Article 1.0
Author-Name: Yingzi Li
Author-X-Name-First: Yingzi
Author-X-Name-Last: Li
Author-Name: Huinan Liu
Author-X-Name-First: Huinan
Author-X-Name-Last: Liu
Author-Name: Nairanjana Dasgupta
Author-X-Name-First: Nairanjana
Author-X-Name-Last: Dasgupta
Title: Binary Dynamic Logit for Correlated Ordinal: estimation, application and simulation
Abstract: 
 We evaluate the estimation performance of the Binary Dynamic Logit model for correlated ordinal variables (BDLCO model), and compare it to GEE and Ordinal Logistic Regression performance in terms of bias and Mean Absolute Percentage Error (MAPE) via Monte Carlo simulation. Our results indicate that when the proportional-odds assumption does not hold, the proposed BDLCO method is superior to existing models in estimating correlated ordinal data. Moreover, this method is flexible in terms of modeling dependence and allows unequal slopes for each category, and can be used to estimate an apple bloom data set where the proportional-odds assumption is violated. We also provide a function in R to implement BDLCO.
Journal: Journal of Applied Statistics
Pages: 2657-2673
Issue: 10
Volume: 49
Year: 2022
Month: 07
X-DOI: 10.1080/02664763.2021.1906849
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1906849
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:10:p:2657-2673



Template-Type: ReDIF-Article 1.0
Author-Name: Diego Salmerón
Author-X-Name-First: Diego
Author-X-Name-Last: Salmerón
Title: Bayesian beta nonlinear models with constrained parameters to describe ruminal degradation kinetics
Abstract: 
 The models used to describe the kinetics of ruminal degradation are usually nonlinear models where the dependent variable is the proportion of degraded food. The method of least squares is the standard approach used to estimate the unknown parameters but this method can lead to unacceptable predictions. To solve this issue, a beta nonlinear model and the Bayesian perspective is proposed in this article. The application of standard methodologies to obtain prior distributions, such as the Jeffreys prior or the reference priors, involves serious difficulties here because this model is a nonlinear non-normal regression model, and the constrained parameters appear in the log-likelihood function through the Gamma function. This paper proposes an objective method to obtain the prior distribution, which can be applied to other models with similar complexity, can be easily implemented in OpenBUGS, and solves the problem of unacceptable predictions. The model is generalized to a larger class of models. The methodology was applied to real data with three models that were compared using the Deviance Information Criterion and the root mean square prediction error. A simulation study was performed to evaluate the coverage of the credible intervals.
Journal: Journal of Applied Statistics
Pages: 2612-2628
Issue: 10
Volume: 49
Year: 2022
Month: 07
X-DOI: 10.1080/02664763.2021.1913105
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1913105
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:10:p:2612-2628



Template-Type: ReDIF-Article 1.0
Author-Name: S. Arona Diop
Author-X-Name-First: S. Arona
Author-X-Name-Last: Diop
Author-Name: Thierry Duchesne
Author-X-Name-First: Thierry
Author-X-Name-Last: Duchesne
Author-Name: Steven G. Cumming
Author-X-Name-First: Steven
Author-X-Name-Last: G. Cumming
Author-Name: Awa Diop
Author-X-Name-First: Awa
Author-X-Name-Last: Diop
Author-Name: Denis Talbot
Author-X-Name-First: Denis
Author-X-Name-Last: Talbot
Title: Confounding adjustment methods for multi-level treatment comparisons under lack of positivity and unknown model specification
Abstract: 
 Imbalances in covariates between treatment groups are frequent in observational studies and can lead to biased comparisons. Various adjustment methods can be employed to correct these biases in the context of multi-level treatments (> 2). Analytical challenges, such as positivity violations and incorrect model specification due to unknown functional relationships between covariates and treatment or outcome, may affect their ability to yield unbiased results. Such challenges were expected in a comparison of fire-suppression interventions for preventing fire growth. We identified the overlap weights, augmented overlap weights, bias-corrected matching and targeted maximum likelihood as methods with the best potential to address those challenges. A simple variance estimator for the overlap weight estimators that can naturally be combined with machine learning is proposed. In a simulation study, we investigated the performance of these methods as well as those of simpler alternatives. Adjustment methods that included an outcome modeling component performed better than those that focused on the treatment mechanism in our simulations. Additionally, machine learning implementation was observed to efficiently compensate for the unknown model specification for the former methods, but not the latter. Based on these results, we compared the effectiveness of fire-suppression interventions using the augmented overlap weight estimator.
Journal: Journal of Applied Statistics
Pages: 2570-2592
Issue: 10
Volume: 49
Year: 2022
Month: 07
X-DOI: 10.1080/02664763.2021.1911966
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1911966
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:10:p:2570-2592



Template-Type: ReDIF-Article 1.0
Author-Name: Zhongnan Jin
Author-X-Name-First: Zhongnan
Author-X-Name-Last: Jin
Author-Name: Lu Lu
Author-X-Name-First: Lu
Author-X-Name-Last: Lu
Author-Name: Khaled Bedair
Author-X-Name-First: Khaled
Author-X-Name-Last: Bedair
Author-Name: Yili Hong
Author-X-Name-First: Yili
Author-X-Name-Last: Hong
Title: Modeling bivariate geyser eruption system with covariate-adjusted recurrent event process
Abstract: 
 Geyser eruption is one of the most popular signature attractions at the Yellowstone National Park. The interdependence of geyser eruptions and impacts of covariates are of interest to researchers in geyser studies. In this paper, we propose a parametric covariate-adjusted recurrent event model for estimating the eruption gap time. We describe a general bivariate recurrent event process, where a bivariate lognormal distribution and a Gumbel copula with different marginal distributions are used to model an interdependent dual-type event system. The maximum likelihood approach is used to estimate model parameters. The proposed method is applied to analyzing the Yellowstone geyser eruption data for a bivariate geyser system and offers a deeper understanding of the event occurrence mechanism of individual events as well as the system as a whole. A comprehensive simulation study is conducted to evaluate the performance of the proposed method.
Journal: Journal of Applied Statistics
Pages: 2488-2509
Issue: 10
Volume: 49
Year: 2022
Month: 07
X-DOI: 10.1080/02664763.2021.1910937
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1910937
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:10:p:2488-2509



Template-Type: ReDIF-Article 1.0
Author-Name: M. S. Eliwa
Author-X-Name-First: M. S.
Author-X-Name-Last: Eliwa
Author-Name: M. El-Morshedy
Author-X-Name-First: M.
Author-X-Name-Last: El-Morshedy
Title: A one-parameter discrete distribution for over-dispersed data: statistical and reliability properties with applications
Abstract: 
 In the literature of distribution theory, a vast proportion is acquired by discrete distributions and their applications in real-world phenomena. However, in a rapidly changing technological era, the data generated is becoming increasingly complex day by day, making it difficult for us to capture various aspects of this real data through existing discrete models. In view of this, we propose a new flexible discrete distribution with one parameter. Some statistical and reliability are derived. These properties can be expressed as closed-forms. One of the important virtues of this newly evolved model is that it can model not only over-dispersed, positively skewed and leptokurtic data sets, but it can also be utilized for modeling increasing, decreasing and unimodal failure rate. Various estimation approaches are utilized to estimate the model parameter. A simulation study is carried out to examine the performance of the estimators for different sample size. The flexibility of the new model for analyzing different types of data is explained by utilizing four real data sets in different fields. Finally, the proposed model can serve as an alternative model to other distributions in the existing literature for modeling positive real data in several areas.
Journal: Journal of Applied Statistics
Pages: 2467-2487
Issue: 10
Volume: 49
Year: 2022
Month: 07
X-DOI: 10.1080/02664763.2021.1905787
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1905787
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:10:p:2467-2487



Template-Type: ReDIF-Article 1.0
Author-Name: Guogen Shan
Author-X-Name-First: Guogen
Author-X-Name-Last: Shan
Title: Conservative confidence intervals for the intraclass correlation coefficient for clustered binary data
Abstract: 
 Asymptotic approaches are traditionally used to calculate confidence intervals for intraclass correlation coefficient in a clustered binary study. When sample size is small to medium, or correlation or response rate is near the boundary, asymptotic intervals often do not have satisfactory performance with regard to coverage. We propose using the importance sampling method to construct the profile confidence limits for the intraclass correlation coefficient. Importance sampling is a simulation based approach to reduce the variance of the estimated parameter. Four existing asymptotic limits are used as statistical quantities for sample space ordering in the importance sampling method. Simulation studies are performed to evaluate the performance of the proposed accurate intervals with regard to coverage and interval width. Simulation results indicate that the accurate intervals based on the asymptotic limits by Fleiss and Cuzick generally have shorter width than others in many cases, while the accurate intervals based on Zou and Donner asymptotic limits outperform others when correlation and response rate are close to their boundaries.
Journal: Journal of Applied Statistics
Pages: 2535-2549
Issue: 10
Volume: 49
Year: 2022
Month: 07
X-DOI: 10.1080/02664763.2021.1910939
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1910939
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:10:p:2535-2549



Template-Type: ReDIF-Article 1.0
Author-Name: Rosa Arboretti
Author-X-Name-First: Rosa
Author-X-Name-Last: Arboretti
Author-Name: Riccardo Ceccato
Author-X-Name-First: Riccardo
Author-X-Name-Last: Ceccato
Author-Name: Luca Pegoraro
Author-X-Name-First: Luca
Author-X-Name-Last: Pegoraro
Author-Name: Luigi Salmaso
Author-X-Name-First: Luigi
Author-X-Name-Last: Salmaso
Author-Name: Chris Housmekerides
Author-X-Name-First: Chris
Author-X-Name-Last: Housmekerides
Author-Name: Luca Spadoni
Author-X-Name-First: Luca
Author-X-Name-Last: Spadoni
Author-Name: Elisabetta Pierangelo
Author-X-Name-First: Elisabetta
Author-X-Name-Last: Pierangelo
Author-Name: Sara Quaggia
Author-X-Name-First: Sara
Author-X-Name-Last: Quaggia
Author-Name: Catherine Tveit
Author-X-Name-First: Catherine
Author-X-Name-Last: Tveit
Author-Name: Sebastiano Vianello
Author-X-Name-First: Sebastiano
Author-X-Name-Last: Vianello
Title: Machine learning and design of experiments with an application to product innovation in the chemical industry
Abstract: 
 Industrial statistics plays a major role in the areas of both quality management and innovation. However, existing methodologies must be integrated with the latest tools from the field of Artificial Intelligence. To this end, a background on the joint application of Design of Experiments (DOE) and Machine Learning (ML) methodologies in industrial settings is presented here, along with a case study from the chemical industry. A DOE study is used to collect data, and two ML models are applied to predict responses which performance show an advantage over the traditional modeling approach. Emphasis is placed on causal investigation and quantification of prediction uncertainty, as these are crucial for an assessment of the goodness and robustness of the models developed. Within the scope of the case study, the models learned can be implemented in a semi-automatic system that can assist practitioners who are inexperienced in data analysis in the process of new product development.
Journal: Journal of Applied Statistics
Pages: 2674-2699
Issue: 10
Volume: 49
Year: 2022
Month: 07
X-DOI: 10.1080/02664763.2021.1907840
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1907840
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:10:p:2674-2699



Template-Type: ReDIF-Article 1.0
Author-Name: Hannes Kazianka
Author-X-Name-First: Hannes
Author-X-Name-Last: Kazianka
Author-Name: Anna Morgenbesser
Author-X-Name-First: Anna
Author-X-Name-Last: Morgenbesser
Author-Name: Thomas Nowak
Author-X-Name-First: Thomas
Author-X-Name-Last: Nowak
Title: Assessing the discriminatory power of loss given default models
Abstract: 
 For banks using the Advanced Internal Ratings-Based Approach in accordance with Basel III requirements, the amount of required regulatory capital relies on the banks' estimates of the probability of default, the loss given default and the conversion factor for their credit risk portfolio. Therefore, for both model development and validation, assessing the models' predictive and discriminatory abilities is of key importance in order to ensure an adequate quantification of risk. This paper compares different measures of discriminatory power suitable for multi-class target variables such as in loss given default (LGD) models, which are currently used among banks and supervisory authorities. This analysis highlights the disadvantages of using measures that solely rely on pairwise comparisons when applied in a multi-class setting. Thus, for multi-class classification problems, we suggest using a generalisation of the well-known area under the receiver operating characteristic (ROC) curve known as the volume under the ROC surface (VUS). Furthermore, we present the R-package VUROCS, which allows for a time-efficient computation of the VUS as well as associated (co)variance estimates and illustrate its usage based on real-world loss data and validation principles.
Journal: Journal of Applied Statistics
Pages: 2700-2716
Issue: 10
Volume: 49
Year: 2022
Month: 07
X-DOI: 10.1080/02664763.2021.1910936
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1910936
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:10:p:2700-2716



Template-Type: ReDIF-Article 1.0
Author-Name: Abdullah Mohammed Rashid
Author-X-Name-First: Abdullah
Author-X-Name-Last: Mohammed Rashid
Author-Name: Habshah Midi
Author-X-Name-First: Habshah
Author-X-Name-Last: Midi
Author-Name: Waleed Dhhan
Author-X-Name-First: Waleed
Author-X-Name-Last: Dhhan
Author-Name: Jayanthi Arasan
Author-X-Name-First: Jayanthi
Author-X-Name-Last: Arasan
Title: Detection of outliers in high-dimensional data using nu-support vector regression
Abstract: 
 Support Vector Regression (SVR) is gaining in popularity in the detection of outliers and classification problems in high-dimensional data (HDD) as this technique does not require the data to be of full rank. In real application, most of the data are of high dimensional. Classification of high-dimensional data is needed in applied sciences, in particular, as it is important to discriminate cancerous cells from non-cancerous cells. It is also imperative that outliers are identified before constructing a model on the relationship between the dependent and independent variables to avoid misleading interpretations about the fitting of a model. The standard SVR and the μ-ε-SVR are able to detect outliers; however, they are computationally expensive. The fixed parameters support vector regression (FP-ε-SVR) was put forward to remedy this issue. However, the FP-ε-SVR using ε-SVR is not very successful in identifying outliers. In this article, we propose an alternative method to detect outliers i.e. by employing nu-SVR. The merit of our proposed method is confirmed by three real examples and the Monte Carlo simulation. The results show that our proposed nu-SVR method is very successful in identifying outliers under a variety of situations, and with less computational running time.
Journal: Journal of Applied Statistics
Pages: 2550-2569
Issue: 10
Volume: 49
Year: 2022
Month: 07
X-DOI: 10.1080/02664763.2021.1911965
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1911965
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:10:p:2550-2569



Template-Type: ReDIF-Article 1.0
Author-Name: Saisai Ding
Author-X-Name-First: Saisai
Author-X-Name-Last: Ding
Author-Name: Hongyan Fang
Author-X-Name-First: Hongyan
Author-X-Name-Last: Fang
Author-Name: Xiang Dong
Author-X-Name-First: Xiang
Author-X-Name-Last: Dong
Author-Name: Wenzhi Yang
Author-X-Name-First: Wenzhi
Author-X-Name-Last: Yang
Title: The CUSUM statistics of change-point models based on dependent sequences
Abstract: 
 In this paper, we investigate the mean change-point models based on associated sequences. Under some weak conditions, we obtain a limit distribution of CUSUM statistic which can be used to judge the mean change-mount 
 $ \delta_n $ δn is satisfied or dissatisfied 
 $ n^{1/2}\delta_n=o(1) $ n1/2δn=o(1). We also study the consistency of sample covariances and change-point location statistics. Based on Normality and Lognormality data, some simulations such as empirical sizes, empirical powers and convergence are presented to test our results. As an important application, we use CUSUM statistics to do the mean change-point analysis for a financial series.
Journal: Journal of Applied Statistics
Pages: 2593-2611
Issue: 10
Volume: 49
Year: 2022
Month: 07
X-DOI: 10.1080/02664763.2021.1913104
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1913104
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:10:p:2593-2611



Template-Type: ReDIF-Article 1.0
Author-Name: Chi Zhang
Author-X-Name-First: Chi
Author-X-Name-Last: Zhang
Author-Name: Guo-Liang Tian
Author-X-Name-First: Guo-Liang
Author-X-Name-Last: Tian
Author-Name: Kam Chuen Yuen
Author-X-Name-First: Kam Chuen
Author-X-Name-Last: Yuen
Author-Name: Pengyi Liu
Author-X-Name-First: Pengyi
Author-X-Name-Last: Liu
Author-Name: Man-Lai Tang
Author-X-Name-First: Man-Lai
Author-X-Name-Last: Tang
Title: A new multivariate t distribution with variant tail weights and its application in robust regression analysis
Abstract: 
 In this paper, we propose a new kind of multivariate t distribution by allowing different degrees of freedom for each univariate component. Compared with the classical multivariate t distribution, it is more flexible in the model specification that can be used to deal with the variant amounts of tail weights on marginals in multivariate data modeling. In particular, it could include components following the multivariate normal distribution, and it contains the product of independent t-distributions as a special case. Subsequently, it is extended to the regression model as the joint distribution of the error terms. Important distributional properties are explored and useful statistical methods are developed. The flexibility of the specified structure in better capturing the characteristic of data is exemplified by both simulation studies and real data analyses.
Journal: Journal of Applied Statistics
Pages: 2629-2656
Issue: 10
Volume: 49
Year: 2022
Month: 07
X-DOI: 10.1080/02664763.2021.1913106
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1913106
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:10:p:2629-2656

Template-Type: ReDIF-Article 1.0
Author-Name: Jingqin Luo
Author-X-Name-First: Jingqin
Author-X-Name-Last: Luo
Author-Name: Feng Gao
Author-X-Name-First: Feng
Author-X-Name-Last: Gao
Author-Name: Jingxia Liu
Author-X-Name-First: Jingxia
Author-X-Name-Last: Liu
Author-Name: Guoqiao Wang
Author-X-Name-First: Guoqiao
Author-X-Name-Last: Wang
Author-Name: Ling Chen
Author-X-Name-First: Ling
Author-X-Name-Last: Chen
Author-Name: Anne M. Fagan
Author-X-Name-First: Anne M.
Author-X-Name-Last: Fagan
Author-Name: Gregory S. Day
Author-X-Name-First: Gregory S.
Author-X-Name-Last: Day
Author-Name: Jonathan Vöglein
Author-X-Name-First: Jonathan
Author-X-Name-Last: Vöglein
Author-Name: Jasmeer P. Chhatwal
Author-X-Name-First: Jasmeer P.
Author-X-Name-Last: Chhatwal
Author-Name: Chengjie Xiong
Author-X-Name-First: Chengjie
Author-X-Name-Last: Xiong
Author-Name:  
Author-X-Name-First: 
Author-X-Name-Last: 
Title: Statistical estimation and comparison of group-specific bivariate correlation coefficients in family-type clustered studies
Abstract: 
 Bivariate correlation coefficients (BCCs) are often calculated to gauge the relationship between two variables in medical research. In a family-type clustered design where multiple participants from same units/families are enrolled, BCCs can be defined and estimated at various hierarchical levels (subject level, family level and marginal BCC). Heterogeneity usually exists between subject groups and, as a result, subject level BCCs may differ between subject groups. In the framework of bivariate linear mixed effects modeling, we define and estimate BCCs at various hierarchical levels in a family-type clustered design, accommodating subject group heterogeneity. Simplified and modified asymptotic confidence intervals are constructed to the BCC differences and Wald type tests are conducted. A real-world family-type clustered study of Alzheimer disease (AD) is analyzed to estimate and compare BCCs among well-established AD biomarkers between mutation carriers and non-carriers in autosomal dominant AD asymptomatic individuals. Extensive simulation studies are conducted across a wide range of scenarios to evaluate the performance of the proposed estimators and the type-I error rate and power of the proposed statistical tests.Abbreviations: BCC: bivariate correlation coefficient; BLM: bivariate linear mixed effects model; CI: confidence interval; AD: Alzheimer’s disease; DIAN: The Dominantly Inherited Alzheimer Network; SA: simple asymptotic; MA: modified asymptotic
Journal: Journal of Applied Statistics
Pages: 2246-2270
Issue: 9
Volume: 49
Year: 2022
Month: 07
X-DOI: 10.1080/02664763.2021.1899141
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1899141
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:9:p:2246-2270



Template-Type: ReDIF-Article 1.0
Author-Name: Srimanti Dutta
Author-X-Name-First: Srimanti
Author-X-Name-Last: Dutta
Author-Name: Geert Molenberghs
Author-X-Name-First: Geert
Author-X-Name-Last: Molenberghs
Author-Name: Arindom Chakraborty
Author-X-Name-First: Arindom
Author-X-Name-Last: Chakraborty
Title: Joint modelling of longitudinal response and time-to-event data using conditional distributions: a Bayesian perspective
Abstract: 
 Over the last 20 or more years a lot of clinical applications and methodological development in the area of joint models of longitudinal and time-to-event outcomes have come up. In these studies, patients are followed until an event, such as death, occurs. In most of the work, using subject-specific random-effects as frailty, the dependency of these two processes has been established. In this article, we propose a new joint model that consists of a linear mixed-effects model for longitudinal data and an accelerated failure time model for the time-to-event data. These two sub-models are linked via a latent random process. This model will capture the dependency of the time-to-event on the longitudinal measurements more directly. Using standard priors, a Bayesian method has been developed for estimation. All computations are implemented using OpenBUGS. Our proposed method is evaluated by a simulation study, which compares the conditional model with a joint model with local independence by way of calibration. Data on Duchenne muscular dystrophy (DMD) syndrome and a set of data in AIDS patients have been analysed.
Journal: Journal of Applied Statistics
Pages: 2228-2245
Issue: 9
Volume: 49
Year: 2022
Month: 07
X-DOI: 10.1080/02664763.2021.1897971
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1897971
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:9:p:2228-2245



Template-Type: ReDIF-Article 1.0
Author-Name: Çağlar Sözen
Author-X-Name-First: Çağlar
Author-X-Name-Last: Sözen
Author-Name: Yüksel Öner
Author-X-Name-First: Yüksel
Author-X-Name-Last: Öner
Title: The investigation of temperature data in Turkey’s Black Sea Region using functional data analysis
Abstract: 
 As the field of study expands, or as the number of observations in a sample increases, data observed at discrete points is generally assumed to be sampled from an underlying real function. As the number of observation points increases, those observations are likely to be sampled from a real-valued function. In this case, the derived data will be examples of a functional structure. We analyzed the daily average temperature data at 65 discrete points in 18 cities in Turkey's Black Sea Region. Fourier basis functions were used as a basis function approach because the temperature data had a periodic structure. The data were then transformed into a continuous function using the basis function and roughness penalty approach. Functional data were obtained using the roughness penalty approach. The generalized cross-validation method was used to determine the smoothing parameter of the variable (temperature variable). Finally, smoothed functional principal components analysis was applied to the functional data. In this way, changes in temperature functions, which seem hard to tackle, were evaluated on the same graph using the mean function generated for the principal component function and using the functions and the mean function obtained using the principal component function.
Journal: Journal of Applied Statistics
Pages: 2403-2415
Issue: 9
Volume: 49
Year: 2022
Month: 07
X-DOI: 10.1080/02664763.2021.1896683
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1896683
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:9:p:2403-2415



Template-Type: ReDIF-Article 1.0
Author-Name: H. S. Al-Kzzaz
Author-X-Name-First: H. S.
Author-X-Name-Last: Al-Kzzaz
Author-Name: M. M. E. Abd El-Monsef
Author-X-Name-First: M. M. E.
Author-X-Name-Last: Abd El-Monsef
Title: Inverse power Maxwell distribution: statistical properties, estimation and application
Abstract: 
 In this paper, we introduced a new probability distribution named as inverse power Maxwell distribution. The proposal distribution can be seen as an extension of the Maxwell distribution with more flexibility in modeling upside-down lifetime data. Some statistical properties of this distribution are derived. In estimation viewpoint, five methods are used for estimating the unknown parameters of the distribution and these methods are performed through the simulation study. Finally, two real data sets were analyzed to illustrate the applicability of the proposed distribution, proving that it fits each real data set much better than some other existing distributions.
Journal: Journal of Applied Statistics
Pages: 2287-2306
Issue: 9
Volume: 49
Year: 2022
Month: 07
X-DOI: 10.1080/02664763.2021.1899143
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1899143
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:9:p:2287-2306



Template-Type: ReDIF-Article 1.0
Author-Name: Ryan Wu
Author-X-Name-First: Ryan
Author-X-Name-Last: Wu
Author-Name: Mihye Ahn
Author-X-Name-First: Mihye
Author-X-Name-Last: Ahn
Author-Name: Hojin Yang
Author-X-Name-First: Hojin
Author-X-Name-Last: Yang
Title: Spike-and-slab type variable selection in the Cox proportional hazards model for high-dimensional features
Abstract: 
 In this paper, we develop a variable selection framework with the spike-and-slab prior distribution via the hazard function of the Cox model. Specifically, we consider the transformation of the score and information functions for the partial likelihood function evaluated at the given data from the parameter space into the space generated by the logarithm of the hazard ratio. Thereby, we reduce the nonlinear complexity of the estimation equation for the Cox model and allow the utilization of a wider variety of stable variable selection methods. Then, we use a stochastic variable search Gibbs sampling approach via the spike-and-slab prior distribution to obtain the sparsity structure of the covariates associated with the survival outcome. Additionally, we conduct numerical simulations to evaluate the finite-sample performance of our proposed method. Finally, we apply this novel framework on lung adenocarcinoma data to find important genes associated with decreased survival in subjects with the disease.
Journal: Journal of Applied Statistics
Pages: 2189-2207
Issue: 9
Volume: 49
Year: 2022
Month: 07
X-DOI: 10.1080/02664763.2021.1893285
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1893285
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:9:p:2189-2207



Template-Type: ReDIF-Article 1.0
Author-Name: Şenay Özdemir
Author-X-Name-First: Şenay
Author-X-Name-Last: Özdemir
Author-Name: Yeşim Güney
Author-X-Name-First: Yeşim
Author-X-Name-Last: Güney
Author-Name: Yetkin Tuaç
Author-X-Name-First: Yetkin
Author-X-Name-Last: Tuaç
Author-Name: Olcay Arslan
Author-X-Name-First: Olcay
Author-X-Name-Last: Arslan
Title: Empirical likelihood estimation for linear regression models with AR(p) error terms with numerical examples
Abstract: 
 Linear regression models are useful statistical tools to analyze data sets in different fields. There are several methods to estimate the parameters of a linear regression model. These methods usually perform under normally distributed and uncorrelated errors. If error terms are correlated the Conditional Maximum Likelihood (CML) estimation method under normality assumption is often used to estimate the parameters of interest. The CML estimation method is required a distributional assumption on error terms. However, in practice, such distributional assumptions on error terms may not be plausible. In this paper, we propose to estimate the parameters of a linear regression model with autoregressive error term using Empirical Likelihood (EL) method, which is a distribution free estimation method. A small simulation study is provided to evaluate the performance of the proposed estimation method over the CML method. The results of the simulation study show that the proposed estimators based on EL method are remarkably better than the estimators obtained from CML method in terms of mean squared errors (MSE) and bias in almost all the simulation configurations. These findings are also confirmed by the results of the numerical and real data examples.
Journal: Journal of Applied Statistics
Pages: 2271-2286
Issue: 9
Volume: 49
Year: 2022
Month: 07
X-DOI: 10.1080/02664763.2021.1899142
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1899142
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:9:p:2271-2286



Template-Type: ReDIF-Article 1.0
Author-Name: Michael Pokojovy
Author-X-Name-First: Michael
Author-X-Name-Last: Pokojovy
Author-Name: J. Marcus Jobe
Author-X-Name-First: J. Marcus
Author-X-Name-Last: Jobe
Title: Univariate fast initial response statistical process control with taut strings
Abstract: 
 We present a novel real-time univariate monitoring scheme for detecting a sustained departure of a process mean from some given standard assuming a constant variance. Our proposed stopping rule is based on the total variation of a nonparametric taut string estimator of the process mean and is designed to provide a desired average run length for an in-control situation. Compared to the more prominent CUSUM fast initial response (FIR) methodology and allowing for a restart following a false alarm, the proposed two-sided taut string (TS) scheme produces a significant reduction in average run length for a wide range of changes in the mean that occur at or immediately after process monitoring begins. A decision rule for when to choose our proposed TS chart compared to the CUSUM FIR chart that takes into account both false alarm rate and average run length to detect a shift in the mean is proposed and implemented. Supplementary materials are available online.
Journal: Journal of Applied Statistics
Pages: 2326-2348
Issue: 9
Volume: 49
Year: 2022
Month: 07
X-DOI: 10.1080/02664763.2021.1900798
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1900798
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:9:p:2326-2348



Template-Type: ReDIF-Article 1.0
Author-Name: Susan Gachau
Author-X-Name-First: Susan
Author-X-Name-Last: Gachau
Author-Name: Edmund Njeru Njagi
Author-X-Name-First: Edmund Njeru
Author-X-Name-Last: Njagi
Author-Name: Nelson Owuor
Author-X-Name-First: Nelson
Author-X-Name-Last: Owuor
Author-Name: Paul Mwaniki
Author-X-Name-First: Paul
Author-X-Name-Last: Mwaniki
Author-Name: Matteo Quartagno
Author-X-Name-First: Matteo
Author-X-Name-Last: Quartagno
Author-Name: Rachel Sarguta
Author-X-Name-First: Rachel
Author-X-Name-Last: Sarguta
Author-Name: Mike English
Author-X-Name-First: Mike
Author-X-Name-Last: English
Author-Name: Philip Ayieko
Author-X-Name-First: Philip
Author-X-Name-Last: Ayieko
Title: Handling missing data in a composite outcome with partially observed components: simulation study based on clustered paediatric routine data
Abstract: 
 Composite scores are useful in providing insights and trends about complex and multidimensional quality of care processes. However, missing data in subcomponents may hinder the overall reliability of a composite measure. In this study, strategies for handling missing data in Paediatric Admission Quality of Care (PAQC) score, an ordinal composite outcome, were explored through a simulation study. Specifically, the implications of the conventional method employed in addressing missing PAQC score subcomponents, consisting of scoring missing PAQC score components with a zero, and a multiple imputation (MI)-based strategy, were assessed. The latent normal joint modelling MI approach was used for the latter. Across simulation scenarios, MI of missing PAQC score elements at item level produced minimally biased estimates compared to the conventional method. Moreover, regression coefficients were more prone to bias compared to standards errors. Magnitude of bias was dependent on the proportion of missingness and the missing data generating mechanism. Therefore, incomplete composite outcome subcomponents should be handled carefully to alleviate potential for biased estimates and misleading inferences. Further research on other strategies of imputing at the component and composite outcome level and imputing compatibly with the substantive model in this setting, is needed.
Journal: Journal of Applied Statistics
Pages: 2389-2402
Issue: 9
Volume: 49
Year: 2022
Month: 07
X-DOI: 10.1080/02664763.2021.1895087
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1895087
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:9:p:2389-2402



Template-Type: ReDIF-Article 1.0
Author-Name: Jingyu Liu
Author-X-Name-First: Jingyu
Author-X-Name-Last: Liu
Author-Name: Walter W. Piegorsch
Author-X-Name-First: Walter W.
Author-X-Name-Last: Piegorsch
Author-Name: A. Grant Schissler
Author-X-Name-First: A. Grant
Author-X-Name-Last: Schissler
Author-Name: Rachel R. McCaster
Author-X-Name-First: Rachel R.
Author-X-Name-Last: McCaster
Author-Name: Susan L. Cutter
Author-X-Name-First: Susan L.
Author-X-Name-Last: Cutter
Title: Adjusting statistical benchmark risk analysis to account for non-spatial autocorrelation, with application to natural hazard risk assessment
Abstract: 
 We develop and study a quantitative, interdisciplinary strategy for conducting statistical risk analyses within the ‘benchmark risk’ paradigm of contemporary risk assessment when potential autocorrelation exists among sample units. We use the methodology to explore information on vulnerability to natural hazards across 3108 counties in the conterminous 48 US states, applying a place-based resilience index to an existing knowledgebase of hazardous incidents and related human casualties. An extension of a centered autologistic regression model is applied to relate local, county-level vulnerability to hazardous outcomes. Adjustments for autocorrelation embedded in the resiliency information are applied via a novel, non-spatial neighborhood structure. Statistical risk-benchmarking techniques are then incorporated into the modeling framework, wherein levels of high and low vulnerability to hazards are identified.
Journal: Journal of Applied Statistics
Pages: 2349-2369
Issue: 9
Volume: 49
Year: 2022
Month: 07
X-DOI: 10.1080/02664763.2021.1904385
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1904385
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:9:p:2349-2369



Template-Type: ReDIF-Article 1.0
Author-Name: Fiaz Ahmad Bhatti
Author-X-Name-First: Fiaz Ahmad
Author-X-Name-Last: Bhatti
Author-Name: Munir Ahmad
Author-X-Name-First: Munir
Author-X-Name-Last: Ahmad
Title: On the modified Burr IV model for hydrological events: development, properties, characterizations and applications
Abstract: 
 We introduce a new distribution for modeling extreme events about frequency analysis called modified Burr IV (MBIV) distribution. We derive the MBIV distribution on the basis of the generalized Pearson differential equation. The proposed model turns out to be flexible: its density function can be symmetrical, right-skewed, left-skewed, J and bimodal shaped. Its hazard rate has shapes such as bathtub and modified bathtub, increasing, decreasing, and increasing-decreasing-increasing. To show the importance of the MBIV distribution, we establish various mathematical properties such as random number generator, sub-models, moments related properties, inequality measures, reliability measures, uncertainty measures and characterizations. We utilize the maximum likelihood estimation technique to estimate the model parameters. We assess the behavior of the maximum likelihood estimators (MLEs) of the MBIV parameters via a simulation study. Five data sets related to frequency analysis are considered to elucidate the significance of the MBIV distribution. We show that the MBIV model is the best model to analyze data for hydrological events, motivating its high level of adaptability in the applied setting.
Journal: Journal of Applied Statistics
Pages: 2167-2188
Issue: 9
Volume: 49
Year: 2022
Month: 07
X-DOI: 10.1080/02664763.2021.1893284
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1893284
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:9:p:2167-2188



Template-Type: ReDIF-Article 1.0
Author-Name: Rana Jreich
Author-X-Name-First: Rana
Author-X-Name-Last: Jreich
Author-Name: Christine Hatte
Author-X-Name-First: Christine
Author-X-Name-Last: Hatte
Author-Name: Eric Parent
Author-X-Name-First: Eric
Author-X-Name-Last: Parent
Title: Review of Bayesian selection methods for categorical predictors using JAGS
Abstract: 
 The formulation of variable selection has been widely developed in the Bayesian literature by linking a random binary indicator to each variable. This Bayesian inference has the advantage of stochastically exploring the set of possible sub-models, whatever their dimension. Bayesian selection approaches, appropriate for categorical predictors, are generally beyond the scope of the standard Bayesian selection of regressors in the linear model since all levels of a categorical variable should be jointly handled in the selection procedure. For categorical covariates, new strategies have been developed to detect the effect of grouped covariates rather than the single effect of a quantitative regressor. In this paper, we review three Bayesian selection methods for categorical predictors: Bayesian Group Lasso with Spike and Slab priors, Bayesian Sparse Group Selection and Bayesian Effect Fusion using model-based clustering. The motivation behind this paper is to provide detailed information about the implementation of the three Bayesian selection methods mentioned above, appropriate for categorical predictors, using the JAGS software. Selection performance and sensitivity analysis of the hyperparameters tuning for prior specifications are assessed under various simulated scenarios. JAGS helps user implement these three Bayesian selection methods for more complex model structures such as hierarchical ones with latent layers.
Journal: Journal of Applied Statistics
Pages: 2370-2388
Issue: 9
Volume: 49
Year: 2022
Month: 07
X-DOI: 10.1080/02664763.2021.1902955
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1902955
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:9:p:2370-2388



Template-Type: ReDIF-Article 1.0
Author-Name: Hayala Cristina Cavenague de Souza
Author-X-Name-First: Hayala Cristina
Author-X-Name-Last: Cavenague de Souza
Author-Name: Francisco Louzada
Author-X-Name-First: Francisco
Author-X-Name-Last: Louzada
Author-Name: Mauro Ribeiro de Oliveira
Author-X-Name-First: Mauro Ribeiro
Author-X-Name-Last: de Oliveira
Author-Name: Bukola Fawole
Author-X-Name-First: Bukola
Author-X-Name-Last: Fawole
Author-Name: Adesina Akintan
Author-X-Name-First: Adesina
Author-X-Name-Last: Akintan
Author-Name: Lawal Oyeneyin
Author-X-Name-First: Lawal
Author-X-Name-Last: Oyeneyin
Author-Name: Wilfred Sanni
Author-X-Name-First: Wilfred
Author-X-Name-Last: Sanni
Author-Name: Gleici da Silva Castro Perdoná
Author-X-Name-First: Gleici da
Author-X-Name-Last: Silva Castro Perdoná
Title: The Log-Normal zero-inflated cure regression model for labor time in an African obstetric population
Abstract: 
 In obstetrics and gynecology, knowledge about how women's features are associated with childbirth is important. This leads to establishing guidelines and can help managers to describe the dynamics of pregnant women's hospital stays. Then, time is a variable of great importance and can be described by survival models. An issue that should be considered in the modeling is the inclusion of women for whom the duration of labor cannot be observed due to fetal death, generating a proportion of times equal to zero. Additionally, another proportion of women's time may be censored due to some intervention. The aim of this paper was to present the Log-Normal zero-inflated cure regression model and to evaluate likelihood-based parameter estimation by a simulation study. In general, the inference procedures showed a better performance for larger samples and low proportions of zero inflation and cure. To exemplify how this model can be an important tool for investigating the course of the childbirth process, we considered the Better Outcomes in Labor Difficulty project dataset and showed that parity and educational level are associated with the main outcomes. We acknowledge the World Health Organization for granting us permission to use the dataset.
Journal: Journal of Applied Statistics
Pages: 2416-2429
Issue: 9
Volume: 49
Year: 2022
Month: 07
X-DOI: 10.1080/02664763.2021.1896684
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1896684
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:9:p:2416-2429



Template-Type: ReDIF-Article 1.0
Author-Name: Tahir Ekin
Author-X-Name-First: Tahir
Author-X-Name-Last: Ekin
Author-Name: R. M. Musal
Author-X-Name-First: R. M.
Author-X-Name-Last: Musal
Title: Integrated statistical and decision models for multi-stage health care audit sampling
Abstract: 
 Health care audits are crucial in managing the government insurance programs that are estimated to have losses amounting to billions of dollars every year. Statistical methods such as sampling have long been used to handle their size and complexity. Sampling from health care claims data can benefit from multi-stage approaches, especially when the evaluation of the tradeoffs between precision and cost is important. The use of decision models could facilitate health care auditors and policy makers make the best use of these sampling outputs. This paper proposes an integrated multi-stage sampling and decision-making framework that enables auditors address the tradeoffs between audit costs and expected overpayment recovery. We illustrate the framework and discuss insights utilizing a variety of overpayment scenarios for payment populations including U.S. Medicare Part B claims payment data.
Journal: Journal of Applied Statistics
Pages: 2307-2325
Issue: 9
Volume: 49
Year: 2022
Month: 07
X-DOI: 10.1080/02664763.2021.1900797
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1900797
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:9:p:2307-2325



Template-Type: ReDIF-Article 1.0
Author-Name: Martina Vittorietti
Author-X-Name-First: Martina
Author-X-Name-Last: Vittorietti
Author-Name: Javier Hidalgo
Author-X-Name-First: Javier
Author-X-Name-Last: Hidalgo
Author-Name: Jilt Sietsma
Author-X-Name-First: Jilt
Author-X-Name-Last: Sietsma
Author-Name: Wei Li
Author-X-Name-First: Wei
Author-X-Name-Last: Li
Author-Name: Geurt Jongbloed
Author-X-Name-First: Geurt
Author-X-Name-Last: Jongbloed
Title: Isotonic regression for metallic microstructure data: estimation and testing under order restrictions
Abstract: 
 Investigating the main determinants of the mechanical performance of metals is not a simple task. Already known physically inspired qualitative relations between 2D microstructure characteristics and 3D mechanical properties can act as the starting point of the investigation. Isotonic regression allows to take into account ordering relations and leads to more efficient and accurate results when the underlying assumptions actually hold. The main goal in this paper is to test order relations in a model inspired by a materials science application. The statistical estimation procedure is described considering three different scenarios according to the knowledge of the variances: known variance ratio, completely unknown variances, and variances under order restrictions. New likelihood ratio tests are developed in the last two cases. Both parametric and non-parametric bootstrap approaches are developed for finding the distribution of the test statistics under the null hypothesis. Finally an application on the relation between geometrically necessary dislocations and number of observed microstructure precipitations is shown.
Journal: Journal of Applied Statistics
Pages: 2208-2227
Issue: 9
Volume: 49
Year: 2022
Month: 07
X-DOI: 10.1080/02664763.2021.1896685
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1896685
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:9:p:2208-2227



Template-Type: ReDIF-Article 1.0
Author-Name: Biviana M. Súarez-Sierra
Author-X-Name-First: Biviana M.
Author-X-Name-Last: Súarez-Sierra
Author-Name: Eliane R. Rodrigues
Author-X-Name-First: Eliane R.
Author-X-Name-Last: Rodrigues
Author-Name: Guadalupe Tzintzun
Author-X-Name-First: Guadalupe
Author-X-Name-Last: Tzintzun
Title: An application of a non-homogeneous Poisson model to study PM2.5 exceedances in Mexico City and Bogota
Abstract: 
 It is very important to study the occurrence of high levels of particulate matter due to the potential harm to people's health and to the environment. In the present work we use a non-homogeneous Poisson model to analyse the rate of exceedances of particulate matter with diameter smaller that 2.5 microns (PM
 $ _{2.5} $ 2.5). Models with and without change-points are considered and they are applied to data from Bogota, Colombia, and Mexico City, Mexico. Results show that whereas in Bogota larger particles pose a more serious problem, in Mexico City, even though nowadays levels are more controlled, in the recent past PM
 $ _{2.5} $ 2.5 were the ones causing serious problems.
Journal: Journal of Applied Statistics
Pages: 2430-2445
Issue: 9
Volume: 49
Year: 2022
Month: 07
X-DOI: 10.1080/02664763.2021.1897972
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1897972
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:9:p:2430-2445

Template-Type: ReDIF-Article 1.0
# input file: catalog-resolver-2362879763316292882.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220713T202513 git hash: 99d3863004
Author-Name: M. Maswadah
Author-X-Name-First: M.
Author-X-Name-Last: Maswadah
Title: Improved maximum likelihood estimation of the shape-scale family based on the generalized progressive hybrid censoring scheme
Abstract: 
 In parametric estimates, the maximum likelihood estimation method is the most popular method widely used in the social sciences and psychology, although it is biased in situations where sample sizes are small or the data are heavily censored. Therefore, the main objective of this research is to improve this estimation method using the Runge–Kutta technique. The improved method was applied to derive the estimators of the shape scale family parameters and compare them with Bayesian estimators based on the informative and kernel priors, via Monte Carlo simulation. The simulation results showed that the improved maximum likelihood estimation method is highly efficient and outperforms the Bayesian method for different sample sizes. Finally, from a future perspective, the proposed model could be important for analyzing real data sets including data on COVID-19 deaths in Egypt, for potential comparative studies with other countries.
Journal: Journal of Applied Statistics
Pages: 2825-2844
Issue: 11
Volume: 49
Year: 2022
Month: 08
X-DOI: 10.1080/02664763.2021.1924638
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1924638
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:11:p:2825-2844



Template-Type: ReDIF-Article 1.0
# input file: catalog-resolver-5461835493060998471.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220713T202513 git hash: 99d3863004
Author-Name: Ting Hsiang Lin
Author-X-Name-First: Ting Hsiang
Author-X-Name-Last: Lin
Author-Name: Min-Hsiao Tsai
Author-X-Name-First: Min-Hsiao
Author-X-Name-Last: Tsai
Title: Solving unobserved heterogeneity with latent class inflated Poisson regression model
Abstract: 
 Inflated data and over-dispersion are two common problems when modeling count data with traditional Poisson regression models. In this study, we propose a latent class inflated Poisson (LCIP) regression model to solve the unobserved heterogeneity that leads to inflations and over-dispersion. The performance of the model estimation is evaluated through simulation studies. We illustrate the usefulness of introducing a latent class variable by analyzing the Behavioral Risk Factor Surveillance System (BRFSS) data, which contain several excessive values and characterized by over-dispersion. As a result, the new model we proposed displays a better fit than the standard Poisson regression and zero-inflated Poisson regression models for the inflated counts.
Journal: Journal of Applied Statistics
Pages: 2953-2963
Issue: 11
Volume: 49
Year: 2022
Month: 08
X-DOI: 10.1080/02664763.2021.1929875
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1929875
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:11:p:2953-2963



Template-Type: ReDIF-Article 1.0
# input file: catalog-resolver233819172648051113.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220713T202513 git hash: 99d3863004
Author-Name: J. H. McVittie
Author-X-Name-First: J. H.
Author-X-Name-Last: McVittie
Author-Name: V. Addona
Author-X-Name-First: V.
Author-X-Name-Last: Addona
Title: A risk set adjustment for proportional hazards modeling of combined cohort data
Abstract: 
 Sporting careers observed over a preset time interval can be partitioned into two distinct subsamples. These samples consist of individuals whose careers had already commenced at the start of the time interval (prevalent subsample) and individuals whose careers began during the time interval (incident subsample) as well the respective individual-level covariate data such as salary, height, weight, performance statistics, draft position, etc. Under the assumption of a proportional hazards model, we propose a partial likelihood estimator to model the effect of covariates on survival via an adjusted risk set sampling procedure for when the incident cohort data is used in conjunction with the prevalent cohort data. We use simulated failure time data to validate the combined cohort proportional hazards methodology and illustrate our model using an NBA data set for career durations measured between 1990 and 2008.
Journal: Journal of Applied Statistics
Pages: 2913-2927
Issue: 11
Volume: 49
Year: 2022
Month: 08
X-DOI: 10.1080/02664763.2021.1928015
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1928015
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:11:p:2913-2927



Template-Type: ReDIF-Article 1.0
# input file: catalog-resolver7568146652705714345.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220713T202513 git hash: 99d3863004
Author-Name: Sanku Dey
Author-X-Name-First: Sanku
Author-X-Name-Last: Dey
Author-Name: Liang Wang
Author-X-Name-First: Liang
Author-X-Name-Last: Wang
Author-Name: Mazen Nassar
Author-X-Name-First: Mazen
Author-X-Name-Last: Nassar
Title: Inference on Nadarajah–Haghighi distribution with constant stress partially accelerated life tests under progressive type-II censoring
Abstract: 
 This paper presents methods of estimation of the parameters and acceleration factor for Nadarajah–Haghighi distribution based on constant-stress partially accelerated life tests. Based on progressive Type-II censoring, Maximum likelihood and Bayes estimates of the model parameters and acceleration factor are established, respectively. In addition, approximate confidence interval are constructed via asymptotic variance and covariance matrix, and Bayesian credible intervals are obtained based on importance sampling procedure. For comparison purpose, alternative bootstrap confidence intervals for unknown parameters and acceleration factor are also presented. Finally, extensive simulation studies are conducted for investigating the performance of the our results, and two data sets are analyzed to show the applicabilities of the proposed methods.
Journal: Journal of Applied Statistics
Pages: 2891-2912
Issue: 11
Volume: 49
Year: 2022
Month: 08
X-DOI: 10.1080/02664763.2021.1928014
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1928014
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:11:p:2891-2912



Template-Type: ReDIF-Article 1.0
# input file: catalog-resolver5766990061207766079.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220713T202513 git hash: 99d3863004
Author-Name: Paria Soleimani
Author-X-Name-First: Paria
Author-X-Name-Last: Soleimani
Author-Name: Shervin Asadzadeh
Author-X-Name-First: Shervin
Author-X-Name-Last: Asadzadeh
Title: Effect of non-normality on the monitoring of simple linear profiles in two-stage processes: a remedial measure for gamma-distributed responses
Abstract: 
 The relationship between the response variable and one or more independent variables refers to the quality characteristic in some statistical quality control applications, which is called profile. Most research dealt with the monitoring of profiles in single-stage processes considering a basic assumption of normality. However, some processes are made up of several sub-processes; thus, the effect of cascade property in multistage processes should be considered. Moreover, sometimes in practice, the assumption of normally distributed data does not hold. This paper first examines the effect of non-normal data to monitor simple linear profiles in two-stage processes in Phase II. We study non-normal distributions such as the skewed gamma distribution and the heavy-tailed symmetric t-distribution to measure the non-normality effect using the average run length criterion. Next, generalized linear models have been used and a monitoring approach based on generalized likelihood ratio (GLR) has been developed for gamma-distributed responses as a remedial measure to reduce the detrimental effects of non-normality. The results of simulation studies reveal that the performance of the GLR procedure is satisfactory for the multistage non-normal linear profiles. Finally, the simulated and real case studies with gamma-distributed data have been provided to show the application of the competing monitoring approaches.
Journal: Journal of Applied Statistics
Pages: 2870-2890
Issue: 11
Volume: 49
Year: 2022
Month: 08
X-DOI: 10.1080/02664763.2021.1928013
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1928013
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:11:p:2870-2890



Template-Type: ReDIF-Article 1.0
# input file: catalog-resolver1429486588680137146.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220713T202513 git hash: 99d3863004
Author-Name: Valdemiro P. Vigas
Author-X-Name-First: Valdemiro P.
Author-X-Name-Last: Vigas
Author-Name: Edwin M. M. Ortega
Author-X-Name-First: Edwin M. M.
Author-X-Name-Last: Ortega
Author-Name: Gauss M. Cordeiro
Author-X-Name-First: Gauss M.
Author-X-Name-Last: Cordeiro
Author-Name: Adriano K. Suzuki
Author-X-Name-First: Adriano K.
Author-X-Name-Last: Suzuki
Author-Name: Giovana O. Silva
Author-X-Name-First: Giovana O.
Author-X-Name-Last: Silva
Title: The new Neyman type A generalized odd log-logistic-G-family with cure fraction
Abstract: 
 The work proposes a new family of survival models called the Odd log-logistic generalized Neyman type A long-term. We consider different activation schemes in which the number of factors M has the Neyman type A distribution and the time of occurrence of an event follows the odd log-logistic generalized family. The parameters are estimated by the classical and Bayesian methods. We investigate the mean estimates, biases, and root mean square errors in different activation schemes using Monte Carlo simulations. The residual analysis via the frequentist approach is used to verify the model assumptions. We illustrate the applicability of the proposed model for patients with gastric adenocarcinoma. The choice of the adenocarcinoma data is because the disease is responsible for most cases of stomach tumors. The estimated cured proportion of patients under chemoradiotherapy is higher compared to patients undergoing only surgery. The estimated hazard function for the chemoradiotherapy level tends to decrease when the time increases. More information about the data is addressed in the application section.
Journal: Journal of Applied Statistics
Pages: 2805-2824
Issue: 11
Volume: 49
Year: 2022
Month: 08
X-DOI: 10.1080/02664763.2021.1922994
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1922994
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:11:p:2805-2824



Template-Type: ReDIF-Article 1.0
# input file: catalog-resolver2666411365973782405.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220713T202513 git hash: 99d3863004
Author-Name: Dongyu Li
Author-X-Name-First: Dongyu
Author-X-Name-Last: Li
Author-Name: Lei Wang
Author-X-Name-First: Lei
Author-X-Name-Last: Wang
Title: Improved kth power expectile regression with nonignorable dropouts
Abstract: 
 The kth (
 $ 1 <k \leq ~2 $ 1<k≤ 2) power expectile regression (ER) can balance robustness and effectiveness between the ordinary quantile regression and ER simultaneously. Motivated by a longitudinal ACTG 193A data with nonignorable dropouts, we propose a two-stage estimation procedure and statistical inference methods based on the kth power ER and empirical likelihood to accommodate both the within-subject correlations and nonignorable dropouts. Firstly, we construct the bias-corrected generalized estimating equations by combining the kth power ER and inverse probability weighting approaches. Subsequently, the generalized method of moments is utilized to estimate the parameters in the nonignorable dropout propensity based on sufficient instrumental estimating equations. Secondly, in order to incorporate the within-subject correlations under an informative working correlation structure, we borrow the idea of quadratic inference function to obtain the improved empirical likelihood procedures. The asymptotic properties of the corresponding estimators and their confidence regions are derived. The finite-sample performance of the proposed estimators is studied through simulation and an application to the ACTG 193A data is also presented.
Journal: Journal of Applied Statistics
Pages: 2767-2788
Issue: 11
Volume: 49
Year: 2022
Month: 08
X-DOI: 10.1080/02664763.2021.1919606
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1919606
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:11:p:2767-2788



Template-Type: ReDIF-Article 1.0
# input file: catalog-resolver6486564171287037823.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220713T202513 git hash: 99d3863004
Author-Name: M. El-Morshedy
Author-X-Name-First: M.
Author-X-Name-Last: El-Morshedy
Author-Name: M. S. Eliwa
Author-X-Name-First: M. S.
Author-X-Name-Last: Eliwa
Author-Name: Abhishek Tyagi
Author-X-Name-First: Abhishek
Author-X-Name-Last: Tyagi
Title: A discrete analogue of odd Weibull-G family of distributions: properties, classical and Bayesian estimation with applications to count data
Abstract: 
 In the statistical literature, several discrete distributions have been developed so far. However, in this progressive technological era, the data generated from different fields is getting complicated day by day, making it difficult to analyze this real data through the various discrete distributions available in the existing literature. In this context, we have proposed a new flexible family of discrete models named discrete odd Weibull-G (DOW-G) family. Its several impressive distributional characteristics are derived. A key feature of the proposed family is its failure rate function that can take a variety of shapes for distinct values of the unknown parameters, like decreasing, increasing, constant, J-, and bathtub-shaped. Furthermore, the presented family not only adequately captures the skewed and symmetric data sets, but it can also provide a better fit to equi-, over-, under-dispersed data. After producing the general class, two particular distributions of the DOW-G family are extensively studied. The parameters estimation of the proposed family, are explored by the method of maximum likelihood and Bayesian approach. A compact Monte Carlo simulation study is performed to assess the behavior of the estimation methods. Finally, we have explained the usefulness of the proposed family by using two different real data sets.
Journal: Journal of Applied Statistics
Pages: 2928-2952
Issue: 11
Volume: 49
Year: 2022
Month: 08
X-DOI: 10.1080/02664763.2021.1928018
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1928018
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:11:p:2928-2952



Template-Type: ReDIF-Article 1.0
# input file: catalog-resolver5590319579332340445.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220713T202513 git hash: 99d3863004
Author-Name: Huiping Xu
Author-X-Name-First: Huiping
Author-X-Name-Last: Xu
Author-Name: Xiaochun Li
Author-X-Name-First: Xiaochun
Author-X-Name-Last: Li
Author-Name: Shaun Grannis
Author-X-Name-First: Shaun
Author-X-Name-Last: Grannis
Title: A simple two-step procedure using the Fellegi–Sunter model for frequency-based record linkage
Abstract: 
 The widely used Fellegi–Sunter model for probabilistic record linkage does not leverage information contained in field values and consequently leads to identical classification of match status regardless of whether records agree on rare or common values. Since agreement on rare values is less likely to occur by chance than agreement on common values, records agreeing on rare values are more likely to be matches. Existing frequency-based methods typically rely on knowledge of error probabilities associated with field values and frequencies of agreed field values among matches, often derived using prior studies or training data. When such information is unavailable, applications of these methods are challenging. In this paper, we propose a simple two-step procedure for frequency-based matching using the Fellegi–Sunter framework to overcome these challenges. Matching weights are adjusted based on frequency distributions of the agreed field values among matches and non-matches, estimated by the Fellegi–Sunter model without relying on prior studies or training data. Through a real-world application and simulation, our method is found to produce comparable or better performance than the unadjusted method. Furthermore, frequency-based matching provides greater improvement in matching accuracy when using poorly discriminating fields with diminished benefit as the discriminating power of matching fields increases.
Journal: Journal of Applied Statistics
Pages: 2789-2804
Issue: 11
Volume: 49
Year: 2022
Month: 08
X-DOI: 10.1080/02664763.2021.1922615
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1922615
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:11:p:2789-2804



Template-Type: ReDIF-Article 1.0
# input file: catalog-resolver5450334875348267119.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220713T202513 git hash: 99d3863004
Author-Name: Chin-Shang Li
Author-X-Name-First: Chin-Shang
Author-X-Name-Last: Li
Author-Name: Minggen Lu
Author-X-Name-First: Minggen
Author-X-Name-Last: Lu
Title: Semiparametric zero-inflated Bernoulli regression with applications
Abstract: 
 When the observed proportion of zeros in a data set consisting of binary outcome data is larger than expected under a regular logistic regression model, it is frequently suggested to use a zero-inflated Bernoulli (ZIB) regression model. A spline-based ZIB regression model is proposed to describe the potentially nonlinear effect of a continuous covariate. A spline is used to approximate the unknown smooth function. Under the smoothness condition, the spline estimator of the unknown smooth function is uniformly consistent, and the regression parameter estimators are asymptotically normally distributed. We propose an easily implemented and consistent estimation method for the variances of the regression parameter estimators. Extensive simulations are conducted to investigate the finite-sample performance of the proposed method. A real-life data set is used to illustrate the practical use of the proposed methodology. The real-life data analysis indicates that the prediction performance of the proposed semiparametric ZIB regression model is better compared to the parametric ZIB regression model.
Journal: Journal of Applied Statistics
Pages: 2845-2869
Issue: 11
Volume: 49
Year: 2022
Month: 08
X-DOI: 10.1080/02664763.2021.1925228
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1925228
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:11:p:2845-2869



Template-Type: ReDIF-Article 1.0
# input file: catalog-resolver7531437084293406209.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220713T202513 git hash: 99d3863004
Author-Name: Aastha M. Sathe
Author-X-Name-First: Aastha M.
Author-X-Name-Last: Sathe
Author-Name: N. S. Upadhye
Author-X-Name-First: N. S.
Author-X-Name-Last: Upadhye
Title: Estimation of the parameters of symmetric stable ARMA and ARMA–GARCH models
Abstract: 
 In this article, we first propose the modified Hannan–Rissanen Method for estimating the parameters of autoregressive moving average (ARMA) process with symmetric stable noise and symmetric stable generalized autoregressive conditional heteroskedastic (GARCH) noise. Next, we propose the modified empirical characteristic function method for the estimation of GARCH parameters with symmetric stable noise. Further, we show the efficiency, accuracy and simplicity of our methods with Monte-Carlo simulation. Finally, we apply our proposed methods to model the financial data.
Journal: Journal of Applied Statistics
Pages: 2964-2980
Issue: 11
Volume: 49
Year: 2022
Month: 08
X-DOI: 10.1080/02664763.2021.1928019
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1928019
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:11:p:2964-2980



Template-Type: ReDIF-Article 1.0
# input file: catalog-resolver-4197662376885187138.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220713T202513 git hash: 99d3863004
Author-Name: Chenhui Zhang
Author-X-Name-First: Chenhui
Author-X-Name-Last: Zhang
Author-Name: Dehui Wang
Author-X-Name-First: Dehui
Author-X-Name-Last: Wang
Author-Name: Kai Yang
Author-X-Name-First: Kai
Author-X-Name-Last: Yang
Author-Name: Han Li
Author-X-Name-First: Han
Author-X-Name-Last: Li
Author-Name: Xiaohong Wang
Author-X-Name-First: Xiaohong
Author-X-Name-Last: Wang
Title: Generalized Poisson integer-valued autoregressive processes with structural changes
Abstract: 
 In this paper, we introduce a new first-order generalized Poisson integer-valued autoregressive process, for modeling integer-valued time series exhibiting a piecewise structure and overdispersion. Basic probabilistic and statistical properties of this model are discussed. Conditional least squares and conditional maximum likelihood estimators are derived. The asymptotic properties of the estimators are established. Moreover, two special cases of the process are discussed. Finally, some numerical results of the estimates and a real data example are presented.
Journal: Journal of Applied Statistics
Pages: 2717-2739
Issue: 11
Volume: 49
Year: 2022
Month: 08
X-DOI: 10.1080/02664763.2021.1915255
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1915255
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:11:p:2717-2739



Template-Type: ReDIF-Article 1.0
# input file: catalog-resolver4287097692070063735.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220713T202513 git hash: 99d3863004
Author-Name: Dianliang Deng
Author-X-Name-First: Dianliang
Author-X-Name-Last: Deng
Author-Name: Yiguang Sun
Author-X-Name-First: Yiguang
Author-X-Name-Last: Sun
Author-Name: Guo-Liang Tian
Author-X-Name-First: Guo-Liang
Author-X-Name-Last: Tian
Title: A multivariate zero-inflated binomial model for the analysis of correlated proportional data
Abstract: 
 In this paper, a new multivariate zero-inflated binomial (MZIB) distribution is proposed to analyse the correlated proportional data with excessive zeros. The distributional properties of purposed model are studied. The Fisher scoring algorithm and EM algorithm are given for the computation of estimates of parameters in the proposed MZIB model with/without covariates. The score tests and the likelihood ratio tests are derived for assessing both the zero-inflation and the equality of multiple binomial probabilities in correlated proportional data. A limited simulation study is performed to evaluate the performance of derived EM algorithms for the estimation of parameters in the model with/without covariates and to compare the nominal levels and powers of both score tests and likelihood ratio tests. The whitefly data is used to illustrate the proposed methodologies.
Journal: Journal of Applied Statistics
Pages: 2740-2766
Issue: 11
Volume: 49
Year: 2022
Month: 08
X-DOI: 10.1080/02664763.2021.1918649
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1918649
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:11:p:2740-2766

Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1937961_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220804T044749 git hash: 24b08f8188
Author-Name: Yuge Du
Author-X-Name-First: Yuge
Author-X-Name-Last: Du
Author-Name: Wenhao Gui
Author-X-Name-First: Wenhao
Author-X-Name-Last: Gui
Title: Statistical inference of adaptive type II progressive hybrid censored data with dependent competing risks under bivariate exponential distribution
Abstract: 
 Marshall–Olkin bivariate exponential distribution is used to statistically infer the adaptive type II progressive hybrid censored data under dependent competition risk model. For complex censored data with only partial failure reasons observed, maximum likelihood estimation and approximate confidence interval based on Fisher information are established. At the same time, Bayesian estimation is performed under the highly flexible Gamma–Dirichlet prior distribution and the highest posterior density interval using Gibbs sampling and Metropolis–Hastings algorithm is obtained. Then the performance of two methods is compared through several indexes. In addition, the Monte Carlo method is used for data simulation of multiple sets of variables to give experimental suggestions. Finally, a practical example is given to illustrate the operability and applicability of the proposed algorithm to efficiently carry out reliability test.
Journal: Journal of Applied Statistics
Pages: 3120-3140
Issue: 12
Volume: 49
Year: 2022
Month: 09
X-DOI: 10.1080/02664763.2021.1937961
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1937961
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:12:p:3120-3140



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1935803_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220804T044749 git hash: 24b08f8188
Author-Name: Lijuan Feng
Author-X-Name-First: Lijuan
Author-X-Name-Last: Feng
Author-Name: Murat Munkin
Author-X-Name-First: Murat
Author-X-Name-Last: Munkin
Title: Bayesian semiparametric analysis on the relationship between BMI and income for rural and urban workers in China
Abstract: 
 This study examines the nonlinear relationship between BMI and earnings for workers in China using Bayesian semiparametric methods. Markov chain Monte Carlo (MCMC) methods are used to obtain the posterior distribution. We stratify the whole sample into four subsamples based on gender and type of residence area. Using longitudinal data from the China Health and Nutrition Survey (CHNS) from 1989 to 2011, we find nonlinear relationship for each group of workers, especially for rural females. For females in both rural and urban areas, being overweight and obese is associated with lower earnings. However, for males in both areas, earnings are not penalized for extra weight.
Journal: Journal of Applied Statistics
Pages: 3215-3235
Issue: 12
Volume: 49
Year: 2022
Month: 09
X-DOI: 10.1080/02664763.2021.1935803
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1935803
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:12:p:3215-3235



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1931820_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220804T044749 git hash: 24b08f8188
Author-Name: Qiushi Yu
Author-X-Name-First: Qiushi
Author-X-Name-Last: Yu
Title: Dynamic Dirichlet process mixture model for identifying voting coalitions in the United Nations General Assembly human rights roll call votes
Abstract: 
 Scholars have been interested in the politicization of humans rights within the United Nations for some time. However, previous research typically looks at simple associations between voting coalitions and observable variables, such as geographic location or membership in international organizations. Our study is the first attempt at estimating the latent coalition structure based on the voting data. We propose a Bayesian Dynamic Dirichlet Process Mixture (DDPM) model to identify voting coalitions based on roll call vote data across multiple time periods. We also propose post-processing methods for analyzing the outputs of the DDPM model. We apply these methods to the United Nations General Assembly (UNGA) human rights roll call vote data from 1992 to 2017. We identify human rights voting coalitions in the UNGA after the Cold War, and polarizing resolutions that divide countries into different coalitions.
Journal: Journal of Applied Statistics
Pages: 3002-3021
Issue: 12
Volume: 49
Year: 2022
Month: 09
X-DOI: 10.1080/02664763.2021.1931820
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1931820
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:12:p:3002-3021



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1931819_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220804T044749 git hash: 24b08f8188
Author-Name: Hanan Haj Ahmad
Author-X-Name-First: Hanan
Author-X-Name-Last: Haj Ahmad
Author-Name: Mukhtar M. Salah
Author-X-Name-First: Mukhtar M.
Author-X-Name-Last: Salah
Author-Name: M. S. Eliwa
Author-X-Name-First: M. S.
Author-X-Name-Last: Eliwa
Author-Name: Ziyad Ali Alhussain
Author-X-Name-First: Ziyad
Author-X-Name-Last: Ali Alhussain
Author-Name: Ehab M. Almetwally
Author-X-Name-First: Ehab M.
Author-X-Name-Last: Almetwally
Author-Name: Essam A. Ahmed
Author-X-Name-First: Essam A.
Author-X-Name-Last: Ahmed
Title: Bayesian and non-Bayesian inference under adaptive type-II progressive censored sample with exponentiated power Lindley distribution
Abstract: 
 This paper deals with the statistical inference of the unknown parameters of three-parameter exponentiated power Lindley distribution under adaptive progressive type-II censored samples. The maximum likelihood estimator (MLE) cannot be expressed explicitly, hence approximate MLEs are conducted using the Newton–Raphson method. Bayesian estimation is studied and the Markov Chain Monte Carlo method is used for computing the Bayes estimation. For Bayesian estimation, we consider two loss functions, namely: squared error and linear exponential (LINEX) loss functions, furthermore, we perform asymptotic confidence intervals and the credible intervals for the unknown parameters. A comparison between Bayes estimation and the MLE is observed using simulation analysis and we perform an optimally criterion for some suggested censoring schemes by minimizing bias and mean square error for the point estimation of the parameters. Finally, a real data example is used for the illustration of the goodness of fit for this model.
Journal: Journal of Applied Statistics
Pages: 2981-3001
Issue: 12
Volume: 49
Year: 2022
Month: 09
X-DOI: 10.1080/02664763.2021.1931819
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1931819
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:12:p:2981-3001



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1931822_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220804T044749 git hash: 24b08f8188
Author-Name: Roberto Vila
Author-X-Name-First: Roberto
Author-X-Name-Last: Vila
Author-Name: Mehmet Niyazi Çankaya
Author-X-Name-First: Mehmet
Author-X-Name-Last: Niyazi Çankaya
Title: A bimodal Weibull distribution: properties and inference
Abstract: 
 Modelling is challenging topic and using parametric models is important stage to reach flexible function for modelling. Weibull distribution has shape and scale parameters which play the main role for modelling. Bimodality parameter is added and so bimodal Weibull distribution can capture real data set with bimodality which can be actually combination of two populations. The properties of the proposed distribution and estimation method are examined extensively to show its usability in modelling accurately and safely for practitioners. After examination as first stage in modelling issue, it is appropriate to use bimodal Weibull for modelling bimodality in real data sets if it exists. Two estimation methods including objective functions are used to estimate the parameters of shape, scale and bimodality parameters of function. The second stage in modelling is overcome by using heuristic algorithms for optimization of function according to parameters due to the fact that converging to global point of objective function is performed by heuristic algorithms from stochastic optimization. Real data sets are provided to show the modelling competence of objective functions from bimodal forms of Weibull and Gamma distributions having well defined shape, scale and bimodality parameters and potentially less parameters when compared with the existing distributions.
Journal: Journal of Applied Statistics
Pages: 3044-3062
Issue: 12
Volume: 49
Year: 2022
Month: 09
X-DOI: 10.1080/02664763.2021.1931822
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1931822
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:12:p:3044-3062



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1936466_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220804T044749 git hash: 24b08f8188
Author-Name: A. I. Katona
Author-X-Name-First: A. I.
Author-X-Name-Last: Katona
Title: Validation of risk-based quality control techniques: a case study from the automotive industry
Abstract: 
 Quality control is an outstanding area of production management. The effectiveness of applied quality control methods strongly depends on the performance of the measurement system. Many researchers aimed to analyze the effect of measurement errors on conformity or process control and proposed solutions to treat measurement uncertainty. Although both risk-based conformity control and process control solutions have been designed, verification and validation of these methods have not been provided through laboratory experiments. This paper proposes a case study from the automotive industry regarding the application of risk-based conformity control and risk-based control charts. Acceptance intervals and control limits are optimized to minimize the loss associated with incorrect decisions. The optimization is conducted assuming two scenarios: first, the process and measurement errors are simulated, and second, all data points are measured in the laboratory. This study verifies the applicability of risk-based approaches to real industrial problems and compares the results obtained by simulations and experiments, providing information about the achievable cost reduction opportunities granted by simulations.
Journal: Journal of Applied Statistics
Pages: 3236-3255
Issue: 12
Volume: 49
Year: 2022
Month: 09
X-DOI: 10.1080/02664763.2021.1936466
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1936466
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:12:p:3236-3255



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1933923_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220804T044749 git hash: 24b08f8188
Author-Name: Hayala Cristina Cavenague de Souza
Author-X-Name-First: Hayala Cristina Cavenague
Author-X-Name-Last: de Souza
Author-Name: Francisco Louzada
Author-X-Name-First: Francisco
Author-X-Name-Last: Louzada
Author-Name: Pedro Luiz Ramos
Author-X-Name-First: Pedro Luiz
Author-X-Name-Last: Ramos
Author-Name: Mauro Ribeiro de Oliveira Júnior
Author-X-Name-First: Mauro Ribeiro
Author-X-Name-Last: de Oliveira Júnior
Author-Name: Gleici da Silva Castro Perdoná
Author-X-Name-First: Gleici da Silva Castro
Author-X-Name-Last: Perdoná
Title: A Bayesian approach for the zero-inflated cure model: an application in a Brazilian invasive cervical cancer database
Abstract: 
 This paper aims to discuss the Bayesian estimation approach for the zero-inflated cure class of models, which extends the standard cure model by accommodating zero-inflated data in the survival analysis context. A comprehensive simulation study is carried out to assess the performance of the estimation procedure. A new estimation methodology is illustrated using a real dataset related to women diagnosed with invasive cervical cancer in Brazil.
Journal: Journal of Applied Statistics
Pages: 3178-3194
Issue: 12
Volume: 49
Year: 2022
Month: 09
X-DOI: 10.1080/02664763.2021.1933923
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1933923
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:12:p:3178-3194



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2104229_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220804T044749 git hash: 24b08f8188
Author-Name: Ayfer Ezgi Yilmaz
Author-X-Name-First: Ayfer Ezgi
Author-X-Name-Last: Yilmaz
Title: How reliable are the multiple comparison methods for odds ratio?
Abstract: 
 The homogeneity tests of odds ratios are used in clinical trials and epidemiological investigations as a preliminary step of meta-analysis. In recent studies, the severity or mortality of COVID-19 in relation to demographic characteristics, comorbidities, and other conditions has been popularly discussed by interpreting odds ratios and using meta-analysis. According to the homogeneity test results, a common odds ratio summarizes all of the odds ratios in a series of studies. If the aim is not to find a common odds ratio, but to find which of the sub-characteristics/groups is different from the others or is under risk, then the implementation of a multiple comparison procedure is required. In this article, the focus is placed on the accuracy and reliability of the homogeneity of odds ratio tests for multiple comparisons when the odds ratios are heterogeneous at the omnibus level. Three recently proposed multiple comparison tests and four homogeneity of odds ratios tests with six adjustment methods to control the type-I error rate are considered. The reliability and accuracy of the methods are discussed in relation to COVID-19 severity data associated with diabetes on a country-by-country basis, and a simulation study to assess the powers and type-I error rates of the tests is conducted.
Journal: Journal of Applied Statistics
Pages: 3141-3163
Issue: 12
Volume: 49
Year: 2022
Month: 09
X-DOI: 10.1080/02664763.2022.2104229
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2104229
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:12:p:3141-3163



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1937582_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220804T044749 git hash: 24b08f8188
Author-Name: Olatunde Adebayo Adeoti
Author-X-Name-First: Olatunde Adebayo
Author-X-Name-Last: Adeoti
Author-Name: Jean-Claude Malela-Majika
Author-X-Name-First: Jean-Claude
Author-X-Name-Last: Malela-Majika
Author-Name: Sandile Charles Shongwe
Author-X-Name-First: Sandile Charles
Author-X-Name-Last: Shongwe
Author-Name: Muhammad Aslam
Author-X-Name-First: Muhammad
Author-X-Name-Last: Aslam
Title: A homogeneously weighted moving average control chart for Conway–Maxwell Poisson distribution
Abstract: 
 The homogeneously weighted moving average (HWMA) control chart is a new memory-type chart that allocates a specific weight to the current sample and the remaining weight is distributed equally to the previous samples. In this paper, the HWMA control chart is proposed for monitoring count data. This chart is based on the Conway–Maxwell (COM) distribution, which can be used to model under-spread and over-spread count data. The performance of the proposed chart is evaluated in terms of the average run-length (ARL), standard deviation of the run-length (SDRL), median run-length (MRL) as well as the expected ARL, SDRL and MRL values for both location and dispersion shifts in the process. The sensitivity of the new control chart is compared with those of some existing well-known COM-Poisson memory-type control charts in terms of the out-of-control ARL. The results of this study reveal that the proposed control chart performs competitively well with the existing charts in detecting shifts in the location and dispersion parameters in many situations. Numerical examples are given to demonstrate the application of the proposed chart.
Journal: Journal of Applied Statistics
Pages: 3090-3119
Issue: 12
Volume: 49
Year: 2022
Month: 09
X-DOI: 10.1080/02664763.2021.1937582
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1937582
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:12:p:3090-3119



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1931821_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220804T044749 git hash: 24b08f8188
Author-Name: Mehrdad Naderi
Author-X-Name-First: Mehrdad
Author-X-Name-Last: Naderi
Author-Name: Elham Mirfarah
Author-X-Name-First: Elham
Author-X-Name-Last: Mirfarah
Author-Name: Matthew Bernhardt
Author-X-Name-First: Matthew
Author-X-Name-Last: Bernhardt
Author-Name: Ding-Geng Chen
Author-X-Name-First: Ding-Geng
Author-X-Name-Last: Chen
Title: Semiparametric inference for the scale-mixture of normal partial linear regression model with censored data
Abstract: 
 In the censored data exploration, the classical linear regression model which assumes normally distributed random errors is perhaps one of the commonly used frameworks. However, practical studies have often criticized the classical linear regression model because of its sensitivity to departure from the normality and partial nonlinearity. This paper proposes to solve these potential issues simultaneously in the context of the partial linear regression model by assuming that the random errors follow a scale-mixture of normal (SMN) family of distributions. The postulated method allows us to model data with great flexibility, accommodating heavy tails and outliers. By implementing the B-spline approximation and using the convenient hierarchical representation of the SMN distributions, a computationally analytical EM-type algorithm is developed for obtaining maximum likelihood (ML) parameter estimates. Various simulation studies are conducted to investigate the finite sample properties, as well as the robustness of the model in dealing with the heavy tails distributed datasets. Real-world data examples are finally analyzed for illustrating the usefulness of the proposed methodology.
Journal: Journal of Applied Statistics
Pages: 3022-3043
Issue: 12
Volume: 49
Year: 2022
Month: 09
X-DOI: 10.1080/02664763.2021.1931821
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1931821
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:12:p:3022-3043



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1935800_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220804T044749 git hash: 24b08f8188
Author-Name: Gill Giese
Author-X-Name-First: Gill
Author-X-Name-Last: Giese
Author-Name: Dayna P. Saldaña Zepeda
Author-X-Name-First: Dayna P.
Author-X-Name-Last: Saldaña Zepeda
Author-Name: Jacquelin Beacham
Author-X-Name-First: Jacquelin
Author-X-Name-Last: Beacham
Author-Name: Ciro Velasco Cruz
Author-X-Name-First: Ciro
Author-X-Name-Last: Velasco Cruz
Title: Modeling nematode population dynamics using a multivariate poisson model with spike and slab variable selection
Abstract: 
 Model-based learning of organism dynamics is challenging, particularly when modeling count correlated data. In this paper, we adapt the multivariate Poisson distribution to model nematode dynamics. This distribution relaxes the mean-equal-variance property of the univariate Poisson distribution and allows recovery of the correlation among nematode genera. An observational dataset with 68 soil samples, 11 nematode genera, and 12 soil parameters is analyzed. The Spike and Slab Variable Selection procedure is adapted to obtain parsimonious models for the nematode occurrence. Nematode genus to genus interaction is assessed through the correlation matrix of the model. A simulation study validated the model's implementation. As a result, the model determined the most important covariates for each nematode and classified pairs of nematodes as: sympathetic, antagonistic or neutral, based on their estimated correlations. The model is useful for researchers and practitioners interested in studying population dynamics. In particular, the current results are important inputs when planning strategies for improving or managing soil health regarding nematodes.
Journal: Journal of Applied Statistics
Pages: 3195-3214
Issue: 12
Volume: 49
Year: 2022
Month: 09
X-DOI: 10.1080/02664763.2021.1935800
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1935800
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:12:p:3195-3214



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1931055_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220804T044749 git hash: 24b08f8188
Author-Name: A. Ahmadi
Author-X-Name-First: A.
Author-X-Name-Last: Ahmadi
Author-Name: T. Baghfalaki
Author-X-Name-First: T.
Author-X-Name-Last: Baghfalaki
Author-Name: M. Ganjali
Author-X-Name-First: M.
Author-X-Name-Last: Ganjali
Author-Name: A. Kabir
Author-X-Name-First: A.
Author-X-Name-Last: Kabir
Author-Name: A. Pazouki
Author-X-Name-First: A.
Author-X-Name-Last: Pazouki
Title: A transition copula model for analyzing multivariate longitudinal data with missing responses
Abstract: 
 In multivariate longitudinal studies, several outcomes are repeatedly measured for each subject over time. The data structure of these studies creates two types of associations which should take into account by the model: association of outcomes at a given time point and association among repeated measurements over time for a specific outcome. In our approach, because of some advantageous arisen from features like flexibility of marginal distributions, a copula-based approach is used for joint modeling of multivariate outcomes at each time points, also a transition model is used for considering the association of longitudinal measurements over time. For the problem of incomplete data, missingness mechanism is assumed to be ignorable. Some simulation results are reported in different scenarios using the Gaussian, t and several commonly used copulas of the family of Archimedean copulas. Akaike information criterion (AIC) is used to select the best copula function. The proposed approach is also used for analyzing a real obesity data set.
Journal: Journal of Applied Statistics
Pages: 3164-3177
Issue: 12
Volume: 49
Year: 2022
Month: 09
X-DOI: 10.1080/02664763.2021.1931055
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1931055
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:12:p:3164-3177



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1935797_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220804T044749 git hash: 24b08f8188
Author-Name: Yangxin Huang
Author-X-Name-First: Yangxin
Author-X-Name-Last: Huang
Author-Name: Nian-Sheng Tang
Author-X-Name-First: Nian-Sheng
Author-X-Name-Last: Tang
Author-Name: Jiaqing Chen
Author-X-Name-First: Jiaqing
Author-X-Name-Last: Chen
Title: Multivariate piecewise joint models with random change-points for skewed-longitudinal and survival data
Abstract: 
 Methodological development and application of joint models for longitudinal and time-to-event data have mostly coupled a single longitudinal outcome-based linear mixed-effects model with normal distribution and Cox proportional hazards model. In practice, however, (i) profile of subject's longitudinal response may follow a `broken-stick nonlinear' (piecewise) trajectory. Such multiple phases are an important indicator to help quantify treatment effect, disease diagnosis and clinical decision-making. (ii) Normality in longitudinal models is a routine assumption, but it may be unrealistically obscuring important features of subject variations. (iii) Data collected are often featured by multivariate longitudinal outcomes which are significantly correlated, ignoring their correlation may lead to biased estimation. (iv) It is of importance to investigate how multivariate longitudinal outcomes are associated with event time of interest. In the article, driven by a motivating example, we propose Bayesian multivariate piecewise joint models with a skewed distribution and random change-points for longitudinal measures with an attempt to cope with correlated multivariate longitudinal data, adjust departures from normality, mediate accuracy from longitudinal trajectories with random change-point and tailor linkage in specifying a time-to-event process. A real example is analyzed to demonstrate methodology and simulation studies are conducted to evaluate performance of the proposed models and method.
Journal: Journal of Applied Statistics
Pages: 3063-3089
Issue: 12
Volume: 49
Year: 2022
Month: 09
X-DOI: 10.1080/02664763.2021.1935797
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1935797
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:12:p:3063-3089

Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1949440_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Shi-Fang Qiu
Author-X-Name-First: Shi-Fang
Author-X-Name-Last: Qiu
Author-Name: Ji-Ran Tao
Author-X-Name-First: Ji-Ran
Author-X-Name-Last: Tao
Title: Confidence intervals for assessing equivalence of two treatments with combined unilateral and bilateral data
Abstract: 
 Responses from the paired organs are generally highly correlated in bilateral studies, statistical procedures ignoring the correlation could lead to incorrect results. Note the intraclass correlation in the study of combined unilateral and bilateral outcomes; 11 confidence intervals (CIs) including 7 asymptotic CIs and 4 Bootstrap-resampling CIs for assessing the equivalence of 2 treatments are derived under Rosner's correlated binary data model. Performance is evaluated with respect to the empirical coverage probability (ECP), the empirical coverage width (ECW) and the ratio of the mesial non-coverage probability to the non-coverage probability (RMNCP) via simulation studies. Simulation results show that (i) all CIs except for the Wald CI and the bias-corrected Bootstrap percentile CI generally produce satisfactory ECPs and hence are recommended; (ii) all CIs except for the bias-corrected Bootstrap percentile CI provide preferred RMNCPs and are more symmetrical; (iii) as the measurement of the dependence increases, the ECWs of all CIs except for the score CI and the profile likelihood CI show increasing patterns that look like linear, while there is no obvious pattern on the ECPs of all CIs except for the profile likelihood CI. A data set from an otolaryngologic study is used to illustrate the proposed methods.
Journal: Journal of Applied Statistics
Pages: 3414-3435
Issue: 13
Volume: 49
Year: 2022
Month: 10
X-DOI: 10.1080/02664763.2021.1949440
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1949440
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:13:p:3414-3435



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1941805_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Ann-Kristin Kreutzmann
Author-X-Name-First: Ann-Kristin
Author-X-Name-Last: Kreutzmann
Author-Name: Philipp Marek
Author-X-Name-First: Philipp
Author-X-Name-Last: Marek
Author-Name: Marina Runge
Author-X-Name-First: Marina
Author-X-Name-Last: Runge
Author-Name: Nicola Salvati
Author-X-Name-First: Nicola
Author-X-Name-Last: Salvati
Author-Name: Timo Schmid
Author-X-Name-First: Timo
Author-X-Name-Last: Schmid
Title: The Fay–Herriot model for multiply imputed data with an application to regional wealth estimation in Germany
Abstract: 
 The increasing inequality of private income and wealth requires the redistribution of financial resources. Thus, several financial support schemes allocate budget across countries or regions. This work shows how to estimate private wealth at low regional levels by means of a modified Fay–Herriot approach that deals with (a) unit and item non-response, especially with used multiple imputation, (b) the skewness of the wealth distribution, and (c) inconsistencies of the regional estimates with the national direct estimate. One compelling example for financial redistribution is the promoted catching-up process of East Germany after the German reunification. This work shows that 25 years after the reunification differences are more diverse than just between the East and the West by estimating private wealth at two regional levels in Germany. The analysis is based on the Household Finance and Consumption Survey (HFCS) that the European Central Bank launched for all euro area countries in 2010. Although the application in this paper focuses particularly on Germany, the approach proposed is applicable to the other countries participating in the HFCS as well as to other surveys that make use of multiple imputation.
Journal: Journal of Applied Statistics
Pages: 3278-3299
Issue: 13
Volume: 49
Year: 2022
Month: 10
X-DOI: 10.1080/02664763.2021.1941805
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1941805
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:13:p:3278-3299



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2044018_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Seongoh Park
Author-X-Name-First: Seongoh
Author-X-Name-Last: Park
Author-Name: Johan Lim
Author-X-Name-First: Johan
Author-X-Name-Last: Lim
Title: An overview of heavy-tail extensions of multivariate Gaussian distribution and their relations
Abstract: 
 Many extensions of the multivariate normal distribution to heavy-tailed distributions are proposed in the literature, which includes scale Gaussian mixture distribution, elliptical distribution, generalized elliptical distribution and transelliptical distribution. The inferences for each family of distributions are well studied. However, extensions are overlapped or similar to each other, and it is hard to differentiate one extension from the other. For this reason, in practice, researchers simply pick one of many extensions and apply it to the analysis. In this paper, to enlighten practitioners who should conduct statistical procedures not based on their preferences but based on how data look like, we comparatively review various extensions and their estimators. Also, we fully investigate the inclusion and exclusion relations of different extensions by Venn diagrams and examples. Moreover, in the numerical study, we illustrate visual differences of the extensions by bivariate plots and analyze different scatter matrix estimators based on the microarray data.
Journal: Journal of Applied Statistics
Pages: 3477-3494
Issue: 13
Volume: 49
Year: 2022
Month: 10
X-DOI: 10.1080/02664763.2022.2044018
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2044018
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:13:p:3477-3494



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1951683_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Mohammed K. Shakhatreh
Author-X-Name-First: Mohammed K.
Author-X-Name-Last: Shakhatreh
Author-Name: Sanku Dey
Author-X-Name-First: Sanku
Author-X-Name-Last: Dey
Author-Name: Devendra Kumar
Author-X-Name-First: Devendra
Author-X-Name-Last: Kumar
Title: Inverse Lindley power series distributions: a new compounding family and regression model with censored data
Abstract: 
 This paper introduces a new class of distributions by compounding the inverse Lindley distribution and power series distributions which is called compound inverse Lindley power series (CILPS) distributions. An important feature of this distribution is that the lifetime of the component associated with a particular risk is not observable, rather only the minimum lifetime value among all risks is observable. Further, these distributions exhibit an unimodal failure rate. Various properties of the distribution are derived. Besides, two special models of the new family are investigated. The model parameters of the two sub-models of the new family are obtained by the methods of maximum likelihood, least square, weighted least square and maximum product of spacing and compared them using the Monte Carlo simulation study. Besides, the log compound inverse Lindley regression model for censored data is proposed. Three real data sets are analyzed to illustrate the flexibility and importance of the proposed models.
Journal: Journal of Applied Statistics
Pages: 3451-3476
Issue: 13
Volume: 49
Year: 2022
Month: 10
X-DOI: 10.1080/02664763.2021.1951683
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1951683
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:13:p:3451-3476



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1941807_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Samuel I. Watson
Author-X-Name-First: Samuel
Author-X-Name-Last: I. Watson
Title: Efficient design of geographically-defined clusters with spatial autocorrelation
Abstract: 
 Clusters form the basis of a number of research study designs including survey and experimental studies. Cluster-based designs can be less costly but also less efficient than individual-based designs due to correlation between individuals within the same cluster. Their design typically relies on ad hoc choices of correlation parameters, and is insensitive to variations in cluster design. This article examines how to efficiently design clusters where they are geographically defined by demarcating areas incorporating individuals and households or other units. Using geostatistical models for spatial autocorrelation, we generate approximations to within cluster average covariance in order to estimate the effective sample size given particular cluster design parameters. We show how the number of enumerated locations, cluster area, proportion sampled, and sampling method affect the efficiency of the design and consider the optimization problem of choosing the most efficient design subject to budgetary constraints. We also consider how the parameters from these approximations can be interpreted simply in terms of ‘real-world’ quantities and used in design analysis.
Journal: Journal of Applied Statistics
Pages: 3300-3318
Issue: 13
Volume: 49
Year: 2022
Month: 10
X-DOI: 10.1080/02664763.2021.1941807
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1941807
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:13:p:3300-3318



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1947997_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Maryam Rahmati
Author-X-Name-First: Maryam
Author-X-Name-Last: Rahmati
Author-Name: Parisa Rezanejad Asl
Author-X-Name-First: Parisa
Author-X-Name-Last: Rezanejad Asl
Author-Name: Javad Mikaeli
Author-X-Name-First: Javad
Author-X-Name-Last: Mikaeli
Author-Name: Hojjat Zeraati
Author-X-Name-First: Hojjat
Author-X-Name-Last: Zeraati
Author-Name: Aliakbar Rasekhi
Author-X-Name-First: Aliakbar
Author-X-Name-Last: Rasekhi
Title: Compound Poisson frailty model with a gamma process prior for the baseline hazard: accounting for a cured fraction
Abstract: 
 Cox model and traditional frailty models assume that all individuals will eventually experience the event of interest. This assumption is often overlooked, and situations will arise where it is not realistic. We introduce Compound Poisson frailty model for survival analysis to deal with populations in which some of the individuals will not experience the event of interest. This model assumes that the target population is a mixture of individuals with zero frailty and those with positive frailty. In this paper, we consider a compound Poisson frailty model for right-censored event times from a Bayesian perspective and compute the Bayesian estimator using the Markov Chain Monte Carlo method, where a Gamma process prior is adopted for the baseline hazard function. Furthermore, we evaluate the approach using simulation studies and demonstrate the methodology by analyzing the data from achalasia patient cohort.
Journal: Journal of Applied Statistics
Pages: 3377-3391
Issue: 13
Volume: 49
Year: 2022
Month: 10
X-DOI: 10.1080/02664763.2021.1947997
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1947997
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:13:p:3377-3391



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1945000_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Jung Yeon Park
Author-X-Name-First: Jung Yeon
Author-X-Name-Last: Park
Author-Name: Melanie M. Wall
Author-X-Name-First: Melanie M.
Author-X-Name-Last: Wall
Author-Name: Irini Moustaki
Author-X-Name-First: Irini
Author-X-Name-Last: Moustaki
Author-Name: Arnold H. Grossman
Author-X-Name-First: Arnold H.
Author-X-Name-Last: Grossman
Title: A Joint Modeling Approach for Longitudinal Outcomes and Non-ignorable Dropout under Population Heterogeneity in Mental Health Studies
Abstract: 
 The paper proposes a joint mixture model to model non-ignorable drop-out in longitudinal cohort studies of mental health outcomes. The model combines a (non)-linear growth curve model for the time-dependent outcomes and a discrete-time survival model for the drop-out with random effects shared by the two sub-models. The mixture part of the model takes into account population heterogeneity by accounting for latent subgroups of the shared effects that may lead to different patterns for the growth and the drop-out tendency. A simulation study shows that the joint mixture model provides greater precision in estimating the average slope and covariance matrix of random effects. We illustrate its benefits with data from a longitudinal cohort study that characterizes depression symptoms over time yet is hindered by non-trivial participant drop-out.
Journal: Journal of Applied Statistics
Pages: 3361-3376
Issue: 13
Volume: 49
Year: 2022
Month: 10
X-DOI: 10.1080/02664763.2021.1945000
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1945000
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:13:p:3361-3376



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1950654_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name:  Marcus G. L. Nascimento
Author-X-Name-First:  Marcus G. L.
Author-X-Name-Last: Nascimento
Author-Name: Kelly C. M. Gonçalves
Author-X-Name-First: Kelly C. M.
Author-X-Name-Last: Gonçalves
Title: Bayesian variable selection in quantile regression with random effects: an application to Municipal Human Development Index
Abstract: 
 According to the Atlas of Human Development in Brazil, the income dimension of Municipal Human Development Index (MHDI-I) is an indicator that shows the population's ability in a municipality to ensure a minimum standard of living to provide their basic needs, such as water, food and shelter. In public policy, one of the research objectives is to identify social and economic variables that are associated with this index. Due to the income inequality, evaluate these associations in quantiles, instead of the mean, could be more interest. Thus, in this paper, we develop a Bayesian variable selection in quantile regression models with hierarchical random effects. In particular, we assume a likelihood function based on the Generalized Asymmetric Laplace distribution, and a spike-and-slab prior is used to perform variable selection. The Generalized Asymmetric Laplace distribution is a more general alternative than the Asymmetric Laplace one, which is a common approach used in quantile regression under the Bayesian paradigm. The performance of the proposed method is evaluated via a comprehensive simulation study, and it is applied to the MHDI-I from municipalities located in the state of Rio de Janeiro.
Journal: Journal of Applied Statistics
Pages: 3436-3450
Issue: 13
Volume: 49
Year: 2022
Month: 10
X-DOI: 10.1080/02664763.2021.1950654
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1950654
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:13:p:3436-3450



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1949439_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Alexey Rey
Author-X-Name-First: Alexey
Author-X-Name-Last: Rey
Author-Name: Olga Rozanova
Author-X-Name-First: Olga
Author-X-Name-Last: Rozanova
Author-Name: Sergey Zhuk
Author-X-Name-First: Sergey
Author-X-Name-Last: Zhuk
Title: Estimating correlations between vaccine clinical trial outcomes
Abstract: 
 We demonstrate how a linear factor model with latent variables can be used to estimate correlations between the outcomes of clinical trials. These correlations are needed for many policy questions of drug/vaccine development (such as calculating the optimal size of financial incentives) and the literature so far has relied on expert opinions. We apply our methodology to the case of vaccines and show that the estimated correlations are highly significant. We also illustrate how the estimated correlations can be used to find the probability of obtaining a successful vaccine out of a certain number of candidates and to determine optimal investment in vaccine development.
Journal: Journal of Applied Statistics
Pages: 3392-3413
Issue: 13
Volume: 49
Year: 2022
Month: 10
X-DOI: 10.1080/02664763.2021.1949439
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1949439
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:13:p:3392-3413



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1944996_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Olivier Bouaziz
Author-X-Name-First: Olivier
Author-X-Name-Last: Bouaziz
Author-Name: Eva Lauridsen
Author-X-Name-First: Eva
Author-X-Name-Last: Lauridsen
Author-Name: Grégory Nuel
Author-X-Name-First: Grégory
Author-X-Name-Last: Nuel
Title: Regression modelling of interval censored data based on the adaptive ridge procedure
Abstract: 
 A new method for the analysis of time to ankylosis complication on a dataset of replanted teeth is proposed. In this context of left-censored, interval-censored and right-censored data, a Cox model with piecewise constant baseline hazard is introduced. Estimation is carried out with the expectation maximisation (EM) algorithm by treating the true event times as unobserved variables. This estimation procedure is shown to produce a block diagonal Hessian matrix of the baseline parameters. Taking advantage of this interesting feature in the EM algorithm, a 
 $ L_0 $ L0 penalised likelihood method is implemented in order to automatically determine the number and locations of the cuts of the baseline hazard. This procedure allows to detect specific areas of time where patients are at greater risks for ankylosis. The method can be directly extended to the inclusion of exact observations and to a cure fraction. Theoretical results are obtained which allow to derive statistical inference of the model parameters from asymptotic likelihood theory. Through simulation studies, the penalisation technique is shown to provide a good fit of the baseline hazard and precise estimations of the resulting regression parameters.
Journal: Journal of Applied Statistics
Pages: 3319-3343
Issue: 13
Volume: 49
Year: 2022
Month: 10
X-DOI: 10.1080/02664763.2021.1944996
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1944996
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:13:p:3319-3343



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1939662_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Lili Zhang
Author-X-Name-First: Lili
Author-X-Name-Last: Zhang
Author-Name: Trent Geisler
Author-X-Name-First: Trent
Author-X-Name-Last: Geisler
Author-Name: Herman Ray
Author-X-Name-First: Herman
Author-X-Name-Last: Ray
Author-Name: Ying Xie
Author-X-Name-First: Ying
Author-X-Name-Last: Xie
Title: Improving logistic regression on the imbalanced data by a novel penalized log-likelihood function
Abstract: 
 Logistic regression is estimated by maximizing the log-likelihood objective function formulated under the assumption of maximizing the overall accuracy. That does not apply to the imbalanced data. The resulting models tend to be biased towards the majority class (i.e. non-event), which can bring great loss in practice. One strategy for mitigating such bias is to penalize the misclassification costs of observations differently in the log-likelihood function. Existing solutions require either hard hyperparameter estimating or high computational complexity. We propose a novel penalized log-likelihood function by including penalty weights as decision variables for observations in the minority class (i.e. event) and learning them from data along with model coefficients. In the experiments, the proposed logistic regression model is compared with the existing ones on the statistics of area under receiver operating characteristics (ROC) curve from 10 public datasets and 16 simulated datasets, as well as the training time. A detailed analysis is conducted on an imbalanced credit dataset to examine the estimated probability distributions, additional performance measurements (i.e. type I error and type II error) and model coefficients. The results demonstrate that both the discrimination ability and computation efficiency of logistic regression models are improved using the proposed log-likelihood function as the learning objective.
Journal: Journal of Applied Statistics
Pages: 3257-3277
Issue: 13
Volume: 49
Year: 2022
Month: 10
X-DOI: 10.1080/02664763.2021.1939662
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1939662
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:13:p:3257-3277



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1944997_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Sudeep R. Bapat
Author-X-Name-First: Sudeep R.
Author-X-Name-Last: Bapat
Author-Name: Yan Zhuang
Author-X-Name-First: Yan
Author-X-Name-Last: Zhuang
Title: Maximum precision estimation for a step-stress model using two-stage methodologies
Abstract: 
 In this paper, we consider a two-stage sequential estimation procedure to estimate the parameters of a cumulative exposure model under an accelerated testing scenario. In particular, we focus on a step-stress model where the stress level changes after a pre-specified number of failures occur, which is also random. This is termed as a ‘random stress change time’ in the literature. We further aim to estimate these parameters using maximum precision and hence use a certain variance optimality criteria. Our proposed two-stage estimation procedures follow interesting efficiency properties and their applicability is seen through extensive simulation analyses and a pseudo-real data example from reliability studies.
Journal: Journal of Applied Statistics
Pages: 3344-3360
Issue: 13
Volume: 49
Year: 2022
Month: 10
X-DOI: 10.1080/02664763.2021.1944997
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1944997
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:13:p:3344-3360



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1939661_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: D. Chorozoglou
Author-X-Name-First: D.
Author-X-Name-Last: Chorozoglou
Author-Name: D. Kugiumtzis
Author-X-Name-First: D.
Author-X-Name-Last: Kugiumtzis
Author-Name: E. Papadimitriou
Author-X-Name-First: E.
Author-X-Name-Last: Papadimitriou
Title: Investigation of the correlation of successive earthquakes preceding main shocks in the Greek territory
Abstract: 
 The Canonical Correlation Analysis (CCA) estimates the correlation between two vector variables by maximizing the correlation of linear combinations of their respective components. Here, the CCA is used to find correlation patterns in the last five successive, per pairs, earthquakes (
 $ M \ge 4.0 $ M≥4.0) preceding 271 main shocks (
 $ M \ge 5.5 $ M≥5.5) that occurred in the Greek territory during 1964–2018. The vector variables have two components, the earthquake magnitude and interevent time. The statistical significance of CCA is determined by the standard parametric test along with two proposed randomization tests, one using random shuffling of each paired dataset and one using randomly selected pairs of successive earthquakes. Simulations were designed on synthetic data from vector variables having the statistical characteristics of the real observations. The results on uncorrelated variables showed the correct size for the two randomization tests but larger type I error for the parametric significance test for small sample size. For correlated variables, the test power was equally high for both test types. The application of CCA and the significance tests to the Greek seismicity evidence the significant correlation among the last five successive preshocks, proving to be a promising tool in an a posteriori short-term earthquake forecasting.
Journal: Journal of Applied Statistics
Pages: 3495-3512
Issue: 13
Volume: 49
Year: 2022
Month: 10
X-DOI: 10.1080/02664763.2021.1939661
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1939661
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:13:p:3495-3512

Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1957790_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Aniket Biswas
Author-X-Name-First: Aniket
Author-X-Name-Last: Biswas
Author-Name: Gaurangadeb Chattopadhyay
Author-X-Name-First: Gaurangadeb
Author-X-Name-Last: Chattopadhyay
Author-Name: Aditya Chatterjee
Author-X-Name-First: Aditya
Author-X-Name-Last: Chatterjee
Title: Bias-corrected estimators for proportion of true null hypotheses: application of adaptive FDR-controlling in segmented failure data
Abstract: 
 Two recently introduced model-based bias-corrected estimators for proportion of true null hypotheses (
 $ \pi _0 $ π0) under multiple hypotheses testing scenario have been restructured for random observations under a suitable failure model, available for each of the common hypotheses. Based on stochastic ordering, a new motivation behind formulation of some related estimators for 
 $ \pi _0 $ π0 is given. The reduction of bias for the model-based estimators are theoretically justified and algorithms for computing the estimators are also presented. The estimators are also used to formulate a popular adaptive multiple testing procedure. Extensive numerical study supports superiority of the bias-corrected estimators. The necessity of the proper distributional assumption for the failure data in the context of the model-based bias-corrected method has been highlighted. A case-study is done with a real-life dataset in connection with reliability and warranty studies to demonstrate the applicability of the procedure, under a non-Gaussian setup. The results obtained are in line with the intuition and experience of the subject expert. An intriguing discussion has been attempted to conclude the article that also indicates the future scope of study.
Journal: Journal of Applied Statistics
Pages: 3591-3613
Issue: 14
Volume: 49
Year: 2022
Month: 10
X-DOI: 10.1080/02664763.2021.1957790
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1957790
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:14:p:3591-3613



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1962259_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Zhuoran Yang
Author-X-Name-First: Zhuoran
Author-X-Name-Last: Yang
Author-Name: Liya Fu
Author-X-Name-First: Liya
Author-X-Name-Last: Fu
Author-Name: You-Gan Wang
Author-X-Name-First: You-Gan
Author-X-Name-Last: Wang
Author-Name: Zhixiong Dong
Author-X-Name-First: Zhixiong
Author-X-Name-Last: Dong
Author-Name: Yunlu Jiang
Author-X-Name-First: Yunlu
Author-X-Name-Last: Jiang
Title: A robust and efficient variable selection method for linear regression
Abstract: 
 Variable selection is fundamental to high dimensional statistical modeling, and many approaches have been proposed. However, existing variable selection methods do not perform well in presence of outliers in response variable or/and covariates. In order to ensure a high probability of correct selection and efficient parameter estimation, we investigate a robust variable selection method based on a modified Huber's function with an exponential squared loss tail. We also prove that the proposed method has oracle properties. Furthermore, we carry out simulation studies to evaluate the performance of the proposed method for both p<n and p>n. Our simulation results indicate that the proposed method is efficient and robust against outliers and heavy-tailed distributions. Finally, a real dataset from an air pollution mortality study is used to illustrate the proposed method.
Journal: Journal of Applied Statistics
Pages: 3677-3692
Issue: 14
Volume: 49
Year: 2022
Month: 10
X-DOI: 10.1080/02664763.2021.1962259
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1962259
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:14:p:3677-3692



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1959529_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Timothy Opheim
Author-X-Name-First: Timothy
Author-X-Name-Last: Opheim
Author-Name: Anuradha Roy
Author-X-Name-First: Anuradha
Author-X-Name-Last: Roy
Title: Doubly multivariate linear models with block exchangeable distributed errors and site-dependent covariates
Abstract: 
 The problem of testing the intercept and slope parameters of doubly multivariate linear models with site-dependent covariates using Rao's score test (RST) is studied. The RST statistic is developed for a block exchangeable covariance structure on the error vector under the assumption of multivariate normality. We compare our developed RST statistic with the likelihood ratio test (LRT) statistic. Monte Carlo simulations indicate that the RST statistic is much more accurate than its counterpart LRT statistic and it takes significantly less computation time than the LRT statistic. The proposed method is illustrated with an example of multiple response variables measured on multiple trees in a single plot in an agricultural study.
Journal: Journal of Applied Statistics
Pages: 3659-3676
Issue: 14
Volume: 49
Year: 2022
Month: 10
X-DOI: 10.1080/02664763.2021.1959529
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1959529
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:14:p:3659-3676



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1962261_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Zhen Sheng
Author-X-Name-First: Zhen
Author-X-Name-Last: Sheng
Author-Name: Yukun Liu
Author-X-Name-First: Yukun
Author-X-Name-Last: Liu
Author-Name: Pengfei Li
Author-X-Name-First: Pengfei
Author-X-Name-Last: Li
Author-Name: Jing Qin
Author-X-Name-First: Jing
Author-X-Name-Last: Qin
Title: Likelihood ratio test for genetic association study with case–control data under Probit model
Abstract: 
 Probit and Logit models are the most popular for binary disease statusing in genetic association studies. They are equally used and nearly exchangeable in the analysis of prospectively collected data. However, no strong inferences were made based on Probit models for the retrospectively collected case–control data, especially in the presence of random effects. This paper systematically investigates the performance of Probit mixed-effects models for case–control data. We find that the retrospective likelihood has a closed-form, which motivates the development of likelihood ratio tests for genetic association. Specifically, we developed four likelihood ratio tests based on whether the disease prevalence is completely unavailable, partly available, or completely available. We show that their limiting distribution without a genetic effect is an equal mixture of two chi-square distributions with degrees of freedom 1 and 2, respectively. Our simulations indicate that they can have a remarkable power gain against the popular Logit-model-based score tests, and the disease prevalence information can enhance the power of the likelihood ratio tests. After analyzing a Kenya malaria data, we found out that the proposed test produces a significant result on the association of the gene ABO with malaria, whereas the commonly used competitors fail.
Journal: Journal of Applied Statistics
Pages: 3717-3731
Issue: 14
Volume: 49
Year: 2022
Month: 10
X-DOI: 10.1080/02664763.2021.1962261
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1962261
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:14:p:3717-3731



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1951685_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Jeffrey Williams
Author-X-Name-First: Jeffrey
Author-X-Name-Last: Williams
Author-Name: Raymond R. Hill
Author-X-Name-First: Raymond R.
Author-X-Name-Last: Hill
Author-Name: Joseph J. Pignatiello Jr.
Author-X-Name-First: Joseph J.
Author-X-Name-Last: Pignatiello Jr.
Author-Name: Eric Chicken
Author-X-Name-First: Eric
Author-X-Name-Last: Chicken
Title: Wavelet analysis of variance box plot
Abstract: 
 Functional box plots satisfy two needs; visualization of functional data, and the calculation of important box plot statistics. Data visualization illuminates key characteristics of functional sets missed by statistical tests and summary statistics. The calculation of box plot statistics for functional sets permits a novel comparison more suited to functional data. The functional box plot uses a depth method to visualize and rank smooth functional curves in terms of a mean, box, whiskers, and outliers. The functional box plot improves upon other classic functional data analysis tools such as functional principal components and discriminant analysis for outlier detection. This research adds wavelet analysis as a generating mechanism along with depth for functional box plots to visualize functional data and calculate relevant statistics. The wavelet analysis of variance box plot tool gives competitive error rates in Gaussian test cases with magnitude outliers, and outperforms the functional box plot, for Gaussian test cases with shape outliers. Further, we show wavelet analysis is well suited at approximating irregular and noisy functional data and show the enhanced capability of WANOVA box plots to classify shape outliers which follow a different pattern than other functional data for both simulated and real data instances.
Journal: Journal of Applied Statistics
Pages: 3536-3563
Issue: 14
Volume: 49
Year: 2022
Month: 10
X-DOI: 10.1080/02664763.2021.1951685
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1951685
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:14:p:3536-3563



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1959527_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Guillermo Martínez-Flórez
Author-X-Name-First: Guillermo
Author-X-Name-Last: Martínez-Flórez
Author-Name: Eliseo Martínez
Author-X-Name-First: Eliseo
Author-X-Name-Last: Martínez
Author-Name: Roger Tovar-Falón
Author-X-Name-First: Roger
Author-X-Name-Last: Tovar-Falón
Author-Name: Héctor W. Gómez
Author-X-Name-First: Héctor W.
Author-X-Name-Last: Gómez
Title: A family of bimodal distributions generated by distributions with positive support
Abstract: 
 Bimodal data sets are very common in different areas of knowledge. The crude birth rates data, fish length data, egg diameter data, the eruption and interruption times of the Old Faithful geyser, are examples of this type of data. In this paper, a new class of symmetric density functions for modeling bimodal data as described above are presented. From density functions with support on 
 $ [ 0,+\infty ) $ [0,+∞), the symmetry is getting by reflecting the density function in the negative semi-axis with their respective normalization. In this way, if the primitive density function is unimodal, then the resulting density will be bimodal. We introduce asymmetry parameters and study their behavior, in particular the values of their modes and some other statistical values of interest. The cases for densities generated by Gamma, Weibull, Log-normal, and Birnbaum-Saunders densities, among others are studied. Statistical inference is performed from a classical perspective. A small simulation study to evaluate the benefits and limitations of the new proposal. In addition, an application to a data set related to the fetal weight in grams obtained through ultrasound in a sample of 500 units is also presented; the results show the great usefulness of the model in practical situations.
Journal: Journal of Applied Statistics
Pages: 3614-3637
Issue: 14
Volume: 49
Year: 2022
Month: 10
X-DOI: 10.1080/02664763.2021.1959527
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1959527
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:14:p:3614-3637



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1962262_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Yonggang Ji
Author-X-Name-First: Yonggang
Author-X-Name-Last: Ji
Author-Name: Haifang Shi
Author-X-Name-First: Haifang
Author-X-Name-Last: Shi
Title: Shrinkage estimation of fixed and random effects in linear quantile mixed models
Abstract: 
 This paper presents a Bayesian analysis of linear mixed models for quantile regression using a modified Cholesky decomposition for the covariance matrix of random effects and an asymmetric Laplace distribution for the error distribution. We consider several novel Bayesian shrinkage approaches for both fixed and random effects in a linear mixed quantile model using extended 
 $ L_1 $ L1 penalties. To improve mixing of the Markov chains, a simple and efficient partially collapsed Gibbs sampling algorithm is developed for posterior inference. We also extend the framework to a Bayesian mixed expectile model and develop a Metropolis–Hastings acceptance–rejection (MHAR) algorithm using proposal densities based on iteratively weighted least squares estimation. The proposed approach is then illustrated via both simulated and real data examples. Results indicate that the proposed approach performs very well in comparison to the other approaches.
Journal: Journal of Applied Statistics
Pages: 3693-3716
Issue: 14
Volume: 49
Year: 2022
Month: 10
X-DOI: 10.1080/02664763.2021.1962262
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1962262
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:14:p:3693-3716



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1962260_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Jiasheng Huang
Author-X-Name-First: Jiasheng
Author-X-Name-Last: Huang
Author-Name: Yehua Li
Author-X-Name-First: Yehua
Author-X-Name-Last: Li
Author-Name: Angelique G. Brellenthin
Author-X-Name-First: Angelique G.
Author-X-Name-Last: Brellenthin
Author-Name: Duck-chul Lee
Author-X-Name-First: Duck-chul
Author-X-Name-Last: Lee
Author-Name: Xuemei Sui
Author-X-Name-First: Xuemei
Author-X-Name-Last: Sui
Author-Name: Steven N. Blair
Author-X-Name-First: Steven N.
Author-X-Name-Last: Blair
Title: Causal mediation analysis between resistance exercise and reduced risk of cardiovascular disease based on the Aerobics Center Longitudinal Study
Abstract: 
 Health benefits of resistance exercise (RE), particularly in lowering cardiovascular disease (CVD) risks, are less understood in comparison to aerobic exercise (AE). Motivated by big data from the Aerobics Center Longitudinal Study (ACLS), we study the direct and indirect effects of RE on CVD risks. The primary outcome in our study, total CVD events (CVD morbidity and mortality combined), is modeled as a survival outcome. To investigate the pathway from RE to CVD outcome through potential mediators, we first conduct causal mediation analysis based on marginal structural models (MSMs). To fully account the information from repeated measurements of the mediators, we also adopt a joint model of the CVD survival outcome and multiple longitudinal trajectories of the mediators. Results show statistically significant direct effects of RE and AE on lowering the risk of total CVD events under each pathway. The causal effect of RE and AE on CVD risk is also studied across different age and gender groups. Furthermore, we produce a ranking for the relative importance of the potential risk factors for CVD, with total cholesterol ranking the highest.
Journal: Journal of Applied Statistics
Pages: 3750-3767
Issue: 14
Volume: 49
Year: 2022
Month: 10
X-DOI: 10.1080/02664763.2021.1962260
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1962260
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:14:p:3750-3767



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1953448_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Garo Panikian
Author-X-Name-First: Garo
Author-X-Name-Last: Panikian
Author-Name: Gabby Colmenares Reverol
Author-X-Name-First: Gabby
Author-X-Name-Last: Colmenares Reverol
Author-Name: Jayne Rhodes
Author-X-Name-First: Jayne
Author-X-Name-Last: Rhodes
Author-Name: Emma McLarnon
Author-X-Name-First: Emma
Author-X-Name-Last: McLarnon
Author-Name: Sarah Keast
Author-X-Name-First: Sarah
Author-X-Name-Last: Keast
Author-Name: Kokouvi Gamado
Author-X-Name-First: Kokouvi
Author-X-Name-Last: Gamado
Title: Time series modelling methods to forecast the volume of self-assessment tax returns in the UK
Abstract: 
 Her Majesty's Revenue and Customs (HMRC) has the ambitious target of making tax digital for all its customers and collecting tax in a more efficient, effective and accurate manner for both the government and UK taxpayers. Self-assessment tax returns, the biggest key business event for HMRC, is also one of the most popular digital services with over 90% of the approximately 12 million taxpayers in self assessment filing their return online each year. The majority of returns are filed in January immediately prior to the self-assessment deadline (31st January), putting significant pressure not only on the self-assessment digital service but also on all other HMRC digital services. Hence, understanding and predicting demand for the system is vital to provide a robust and responsive service. We therefore developed mathematical models with Bayesian inference techniques to forecast volumes of Self-assessment (SA) returns submitted online during January, providing accurate hourly predictions of traffic on the digital system in the run up to the SA deadline. Because none of the models being considered is believed to be the true model, we use an ensemble modelling technique that combines forecasts from different models to develop a less risky demand forecast.
Journal: Journal of Applied Statistics
Pages: 3732-3749
Issue: 14
Volume: 49
Year: 2022
Month: 10
X-DOI: 10.1080/02664763.2021.1953448
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1953448
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:14:p:3732-3749



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1957789_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Amadou Barry
Author-X-Name-First: Amadou
Author-X-Name-Last: Barry
Author-Name: Karim Oualkacha
Author-X-Name-First: Karim
Author-X-Name-Last: Oualkacha
Author-Name: Arthur Charpentier
Author-X-Name-First: Arthur
Author-X-Name-Last: Charpentier
Title: A new GEE method to account for heteroscedasticity using asymmetric least-square regressions
Abstract: 
 Generalized estimating equations 
 $ ({\rm GEE}) $ (GEE) are widely used to analyze longitudinal data; however, they are not appropriate for heteroscedastic data, because they only estimate regressor effects on the mean response – and therefore do not account for data heterogeneity. Here, we combine the 
 $ {\rm GEE} $ GEE with the asymmetric least squares (expectile) regression to derive a new class of estimators, which we call generalized expectile estimating equations 
 $ ({\rm GEEE}) $ (GEEE). The 
 $ {\rm GEEE} $ GEEE model estimates regressor effects on the expectiles of the response distribution, which provides a detailed view of regressor effects on the entire response distribution. In addition to capturing data heteroscedasticity, the GEEE extends the various working correlation structures to account for within-subject dependence. We derive the asymptotic properties of the 
 $ {\rm GEEE} $ GEEE estimators and propose a robust estimator of its covariance matrix for inference (see our R package, github.com/AmBarry/expectgee). Our simulations show that the GEEE estimator is non-biased and efficient, and our real data analysis shows it captures heteroscedasticity.
Journal: Journal of Applied Statistics
Pages: 3564-3590
Issue: 14
Volume: 49
Year: 2022
Month: 10
X-DOI: 10.1080/02664763.2021.1957789
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1957789
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:14:p:3564-3590



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1951684_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Seema Zubair
Author-X-Name-First: Seema
Author-X-Name-Last: Zubair
Author-Name: Sanjoy K. Sinha
Author-X-Name-First: Sanjoy K.
Author-X-Name-Last: Sinha
Title: Semiparametric methods for incomplete longitudinal count data with an application to health and retirement study
Abstract: 
 In this paper, we propose and explore a novel semiparametric approach to analyzing longitudinal count data. We address the issue of missingness in longitudinal data and propose a weighted generalized estimations equations approach to fitting marginal mean response models for count responses with dropouts. Also, we investigate a spline regression approach to approximating the curvilinear relationship between the mean response and covariates. The asymptotic properties of the proposed estimators are studied in some detail. The empirical properties of the estimators are investigated using Monte Carlo simulations. An application is also provided using actual survey data obtained from the Health and Retirement Study (HRS).
Journal: Journal of Applied Statistics
Pages: 3513-3535
Issue: 14
Volume: 49
Year: 2022
Month: 10
X-DOI: 10.1080/02664763.2021.1951684
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1951684
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:14:p:3513-3535



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1959528_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Babak Zafari
Author-X-Name-First: Babak
Author-X-Name-Last: Zafari
Author-Name: Tahir Ekin
Author-X-Name-First: Tahir
Author-X-Name-Last: Ekin
Author-Name: Fabrizio Ruggeri
Author-X-Name-First: Fabrizio
Author-X-Name-Last: Ruggeri
Title: Multicriteria decision frontiers for prescription anomaly detection over time
Abstract: 
 Health care prescription fraud and abuse result in major financial losses and adverse health effects. The growing budget deficits of health insurance programs and recent opioid drug abuse crisis in the United States have accelerated the use of analytical methods. Unsupervised methods such as clustering and anomaly detection could help the health care auditors to evaluate the billing patterns when embedded into rule-based frameworks. These decision models can aid policymakers in detecting potential suspicious activities. This manuscript proposes an unsupervised temporal learning-based decision frontier model using the real world Medicare Part D prescription data collected over 5 years. First, temporal probabilistic hidden groups of drugs are retrieved using a structural topic model with covariates. Next, we construct combined concentration curves and Gini measures considering the weighted impact of temporal observations for prescription patterns, in addition to the Gini values for the cost. The novel decision frontier utilizes this output and enables health care practitioners to assess the trade-offs among different criteria and to identify audit leads.
Journal: Journal of Applied Statistics
Pages: 3638-3658
Issue: 14
Volume: 49
Year: 2022
Month: 10
X-DOI: 10.1080/02664763.2021.1959528
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1959528
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:14:p:3638-3658

Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1968358_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Qingzhao Yu
Author-X-Name-First: Qingzhao
Author-X-Name-Last: Yu
Author-Name: Lu Zhang
Author-X-Name-First: Lu
Author-X-Name-Last: Zhang
Author-Name: Xiaocheng Wu
Author-X-Name-First: Xiaocheng
Author-X-Name-Last: Wu
Author-Name: Bin Li
Author-X-Name-First: Bin
Author-X-Name-Last: Li
Title: Inference on moderation effect with third-variable effect analysis – application to explore the trend of racial disparity in oncotype dx test for breast cancer treatment
Abstract: 
 Third variable effect refers to the effect from a third variable that explains an observed relationship between an exposure and an outcome. Depending on whether there is causal relationship, typically, a third variable takes the format of a mediator or a confounder. A moderation effect is a special case of the third-variable effect, where the moderator and other variables have an interactive effect on the outcome. In this paper, we extend the R package ‘mma’ for moderation analysis so that third-variable effects can be reported at different levels of the moderator. The proposed moderation analysis use tree-structured models to automatically detect moderation effects and can handle both categorical and numerical moderators. We propose algorithms and graphical methods for making inference on moderation effects and illustrate the method under different scenarios of moderation effects. Finally, we apply the proposed method to explore the trend of racial disparities in the use of Oncotype DX recurrence tests among breast cancer patients. We found that the unexplained racial differences in using the tests have decreased from 2010 to 2015.
Journal: Journal of Applied Statistics
Pages: 3958-3975
Issue: 15
Volume: 49
Year: 2022
Month: 11
X-DOI: 10.1080/02664763.2021.1968358
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1968358
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:15:p:3958-3975



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1967890_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Xin Yang
Author-X-Name-First: Xin
Author-X-Name-Last: Yang
Author-Name: Alan D. Hutson
Author-X-Name-First: Alan D.
Author-X-Name-Last: Hutson
Author-Name: Dongliang Wang
Author-X-Name-First: Dongliang
Author-X-Name-Last: Wang
Title: A generalized BLUE approach for combining location and scale information in a meta-analysis
Abstract: 
 In systematic reviews and meta-analyses, one is interested in combining information from a variety of sources in order to obtain unbiased and efficient pooled estimates of the mean treatment effect compared to a control group along with the corresponding standard errors and confidence intervals, particularly when the source data is unavailable. However, in many studies the mean and standard deviation are not reported in lieu of other descriptive measures such as the median and quartiles. In this note we provide a theoretically optimal best linear unbiased estimator (BLUE) strategy for combining different types of summary information in order to pool results and estimate the overall treatment effect and the corresponding confidence intervals. Our approach is less biased and much more flexible than past attempts at solving this problem and can accommodate combining a variety of summary information across studies. We show that confidence intervals based on our methods have the appropriate coverage probabilities. Our proposed methods are theoretically justified and verified by simulation studies. The BLUE method is illustrated via a real data application.
Journal: Journal of Applied Statistics
Pages: 3846-3867
Issue: 15
Volume: 49
Year: 2022
Month: 11
X-DOI: 10.1080/02664763.2021.1967890
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1967890
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:15:p:3846-3867



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1967894_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: FuPeng Xie
Author-X-Name-First: FuPeng
Author-X-Name-Last: Xie
Author-Name: Philippe Castagliola
Author-X-Name-First: Philippe
Author-X-Name-Last: Castagliola
Author-Name: YuLong Qiao
Author-X-Name-First: YuLong
Author-X-Name-Last: Qiao
Author-Name: XueLong Hu
Author-X-Name-First: XueLong
Author-X-Name-Last: Hu
Author-Name: JinSheng Sun
Author-X-Name-First: JinSheng
Author-X-Name-Last: Sun
Title: A one-sided exponentially weighted moving average control chart for time between events
Abstract: 
 Exponentially weighted moving average (EWMA) control charts for time-between-events (TBE) are commonly suggested to monitor high-quality processes for the early detection of process deteriorations. In this study, an enhanced one-sided EWMA TBE scheme is developed for rapid detection of increases or decreases in the process mean. The use of the truncation method helps to improve the sensitivity of the proposed scheme in the process mean detection. Moreover, by taking the effects of parameter estimation into account, the proposed scheme with estimated parameters is also investigated. Both the average run length (ARL) and standard deviation of run length (SDRL) performances of the proposed scheme with known and estimated parameters are studied using the Markov chain method, respectively. Furthermore, an optimal design procedure is developed for the recommended one-sided EWMA TBE chart based on ARL. Numerical results show that the proposed optimal one-sided EWMA TBE chart is more sensitive than the existing optimal one-sided exponential EWMA chart in monitoring both upward and downward mean shifts. Meanwhile, it also performs better than the existing comparative scheme in resisting the effect of parameter estimation. Finally, two illustrative examples are considered to show the implementation of the proposed scheme for simulated and real datasets.
Journal: Journal of Applied Statistics
Pages: 3928-3957
Issue: 15
Volume: 49
Year: 2022
Month: 11
X-DOI: 10.1080/02664763.2021.1967894
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1967894
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:15:p:3928-3957



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1967891_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Piotr Sliwka
Author-X-Name-First: Piotr
Author-X-Name-Last: Sliwka
Title: Markov (Set) chains application to predict mortality rates using extended Milevsky–Promislov generalized mortality models
Abstract: 
 The mortality rates (
 $ \mu _{x,t} $ μx,t) measure the frequency of deaths in a fixed: population and time interval. The ability to model and forecast 
 $ \mu _{x,t} $ μx,t allows determining, among others, fundamental characteristics of life expectancy tables, e.g. used to determine the amount of premium in life insurance, adequate to the risk of death. The article proposes a new method of modelling and forecasting 
 $ \mu _{x,t} $ μx,t, using the class of stochastic Milevsky–Promislov switch models with excitations. The excitations are modelled by second, fourth and sixth order polynomials of outputs from the non-Gaussian Linear Scalar Filter (nGLSF) model and taking into account the Markov (Set) chain. The Markov (Set) chain state space is defined based on even orders of the nGLSF polynomial. The model order determines the theoretical values of the death rates. The obtained results usually provide a more precise forecast of the mortality rates than the commonly used Lee–Carter model.
Journal: Journal of Applied Statistics
Pages: 3868-3888
Issue: 15
Volume: 49
Year: 2022
Month: 11
X-DOI: 10.1080/02664763.2021.1967891
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1967891
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:15:p:3868-3888



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1965966_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Weiwei Zhuang
Author-X-Name-First: Weiwei
Author-X-Name-Last: Zhuang
Author-Name: Yadong Li
Author-X-Name-First: Yadong
Author-X-Name-Last: Li
Author-Name: Guoxin Qiu
Author-X-Name-First: Guoxin
Author-X-Name-Last: Qiu
Title: Statistical inference for a relaxation index of stochastic dominance under density ratio model
Abstract: 
 Stochastic dominance is usually used to rank random variables by comparing their distributions, so it is widely applied in economics and finance. In actual applications, complete stochastic dominance is too demanding to meet, so relaxation indexes of stochastic dominance have attracted more attention. The π index, the biggest gap between two distributions, can be a measure of the degree of deviation from complete dominance. The traditional estimation method is to use the empirical distribution functions to estimate it. Considering the populations under comparison are generally of the same nature, we can link the populations through density ratio model under certain condition. Based on this model, we propose a new estimator and establish its statistical inference theory. Simulation results show that the proposed estimator substantially improves estimation efficiency and power of the tests and coverage probabilities satisfactorily match the confidence levels of the tests, which show the superiority of the proposed estimator. Finally we apply our method to a real example of the Chinese household incomes.
Journal: Journal of Applied Statistics
Pages: 3804-3822
Issue: 15
Volume: 49
Year: 2022
Month: 11
X-DOI: 10.1080/02664763.2021.1965966
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1965966
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:15:p:3804-3822



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1969342_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Yi-Ching Lee
Author-X-Name-First: Yi-Ching
Author-X-Name-Last: Lee
Author-Name: Junfeng Shang
Author-X-Name-First: Junfeng
Author-X-Name-Last: Shang
Title: Estimation and selection in linear mixed models with missing data under compound symmetric structure
Abstract: 
 It is quite appealing to extend existing theories in classical linear models to correlated responses where linear mixed-effects models are utilized and the dependency in the data is modeled by random effects. In the mixed modeling framework, missing values occur naturally due to dropouts or non-responses, which is frequently encountered when dealing with real data. Motivated by such problems, we aim to investigate the estimation and model selection performance in linear mixed models when missing data are present. Inspired by the property of the indicator function for missingness and its relation to missing rates, we propose an approach that records missingness in an indicator-based matrix and derive the likelihood-based estimators for all parameters involved in the linear mixed-effects models. Based on the proposed method for estimation, we explore the relationship between estimation and selection behavior over missing rates. Simulations and a real data application are conducted for illustrating the effectiveness of the proposed method in selecting the most appropriate model and in estimating parameters.
Journal: Journal of Applied Statistics
Pages: 4003-4027
Issue: 15
Volume: 49
Year: 2022
Month: 11
X-DOI: 10.1080/02664763.2021.1969342
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1969342
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:15:p:4003-4027



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1962264_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Junyi Lu
Author-X-Name-First: Junyi
Author-X-Name-Last: Lu
Author-Name: Sebastian Meyer
Author-X-Name-First: Sebastian
Author-X-Name-Last: Meyer
Title: An endemic–epidemic beta model for time series of infectious disease proportions
Abstract: 
 Time series of proportions of infected patients or positive specimens are frequently encountered in disease control and prevention. Since proportions are bounded and often asymmetrically distributed, conventional Gaussian time series models only apply to suitably transformed proportions. Here we borrow both from beta regression and from the well-established HHH model for infectious disease counts to propose an endemic–epidemic beta model for proportion time series. It accommodates the asymmetric shape and heteroskedasticity of proportion distributions and is consistent for complementary proportions. Coefficients can be interpreted in terms of odds ratios. A multivariate formulation with spatial power-law weights enables the joint estimation of model parameters from multiple regions. In our application to a flu activity index in the USA, we find that the endemic–epidemic beta model provides a better fit than a seasonal ARIMA model for the logit-transformed proportions. Furthermore, a multivariate approach can improve regional forecasts and reduce model complexity in comparison to univariate beta models stratified by region.
Journal: Journal of Applied Statistics
Pages: 3769-3783
Issue: 15
Volume: 49
Year: 2022
Month: 11
X-DOI: 10.1080/02664763.2021.1962264
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1962264
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:15:p:3769-3783



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1966397_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Xiaohong He
Author-X-Name-First: Xiaohong
Author-X-Name-Last: He
Author-Name: Lei Wang
Author-X-Name-First: Lei
Author-X-Name-Last: Wang
Title: Ensemble and calibration multiply robust estimation for quantile treatment effect
Abstract: 
 Quantile treatment effects can be important causal estimands in the evaluation of biomedical treatments or interventions for health outcomes such as birthweight and medical cost. However, the existing estimators require either a propensity score model or a conditional density vector model is correctly specified, which is difficult to verify in practice. In this paper, we allow multiple models for propensity score and conditional density vector, then construct a class of calibration estimators based on multiple imputation and inverse probability weighting approaches via empirical likelihood. The resulting estimators multiply robust in the sense that they are consistent if any one of these models is correctly specified. Moreover, we propose another class of ensemble estimators to reduce computational burden while ensuring multiple robustness. Simulations are performed to evaluate the finite sample performance of the proposed estimators. Two applications to the birthweight of infants born in the United States and AIDS Clinical Trials Group Protocol 175 data are also presented.
Journal: Journal of Applied Statistics
Pages: 3823-3845
Issue: 15
Volume: 49
Year: 2022
Month: 11
X-DOI: 10.1080/02664763.2021.1966397
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1966397
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:15:p:3823-3845



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1962263_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Xun Shen
Author-X-Name-First: Xun
Author-X-Name-Last: Shen
Author-Name: Pongsathorn Raksincharoensak
Author-X-Name-First: Pongsathorn
Author-X-Name-Last: Raksincharoensak
Title: Statistical models of near-accident event and pedestrian behavior at non-signalized intersections
Abstract: 
 This paper proposes an innovative framework of modeling the statistical properties of the near-accident event and pedestrian behavior at non-signalized intersections based on Poisson process and logistic regression. The first contribution of this study is that the predictive intensity model of the near-accident event is established by regarding the near-accident event as a Poisson process on space of the vehicle velocity, distance to the intersection and lateral distance to the pedestrian at the time when pedestrian appears. Besides, logistic regression is used to build the model which can predict the probability of pedestrian behavior. The two proposed models are validated in a generative simulation. The simulated pedestrian behavior data is generated by the proposed models and compared with the real data. The real data set is from the drive recorder data base of Smart Mobility Research Center (SMRC) at Tokyo University of Agriculture and Technology. Accident and near-accident data has been collected in the city streets with an image-captured drive recorder mounted on a taxi since 2006. The findings in this study are expected to be useful for constructions of traffic simulators or safety control design which considers the pedestrian-vehicle interaction.
Journal: Journal of Applied Statistics
Pages: 4028-4048
Issue: 15
Volume: 49
Year: 2022
Month: 11
X-DOI: 10.1080/02664763.2021.1962263
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1962263
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:15:p:4028-4048



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1967893_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Guglielmo D'Amico
Author-X-Name-First: Guglielmo
Author-X-Name-Last: D'Amico
Author-Name: Riccardo De Blasis
Author-X-Name-First: Riccardo
Author-X-Name-Last: De Blasis
Title: Confidence sets for dynamic poverty indexes
Abstract: 
 In this study, we consider different poverty indexes in a dynamic framework where individuals change their rate of income randomly in time. The primary objective of this paper is to assess the accuracy of the approximation of the indexes that can be obtained by applying the strong law of large numbers to an economic system composed of an infinite number of agents. The main result is a multivariate central limit theorem for dynamic poverty measures, which is obtained applying the theory of U-statistics. We also show how to get the confidence sets for the considered dynamic indexes, which show the appropriateness of the model. An application to the Italian income data from 1998 to 2012 confirms the effectiveness of the considered approach and the possibility to determine the evolution of poverty and inequality in real economies.
Journal: Journal of Applied Statistics
Pages: 3908-3927
Issue: 15
Volume: 49
Year: 2022
Month: 11
X-DOI: 10.1080/02664763.2021.1967893
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1967893
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:15:p:3908-3927



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1967892_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: J. C. Poythress
Author-X-Name-First: J. C.
Author-X-Name-Last: Poythress
Author-Name: Cheolwoo Park
Author-X-Name-First: Cheolwoo
Author-X-Name-Last: Park
Author-Name: Jeongyoun Ahn
Author-X-Name-First: Jeongyoun
Author-X-Name-Last: Ahn
Title: Dimension-wise sparse low-rank approximation of a matrix with application to variable selection in high-dimensional integrative analyzes of association
Abstract: 
 Many research proposals involve collecting multiple sources of information from a set of common samples, with the goal of performing an integrative analysis describing the associations between sources. We propose a method that characterizes the dominant modes of co-variation between the variables in two datasets while simultaneously performing variable selection. Our method relies on a sparse, low rank approximation of a matrix containing pairwise measures of association between the two sets of variables. We show that the proposed method shares a close connection with another group of methods for integrative data analysis – sparse canonical correlation analysis (CCA). Under some assumptions, the proposed method and sparse CCA aim to select the same subsets of variables. We show through simulation that the proposed method can achieve better variable selection accuracies than two state-of-the-art sparse CCA algorithms. Empirically, we demonstrate through the analysis of DNA methylation and gene expression data that the proposed method selects variables that have as high or higher canonical correlation than the variables selected by sparse CCA methods, which is a rather surprising finding given that objective function of the proposed method does not actually maximize the canonical correlation.
Journal: Journal of Applied Statistics
Pages: 3889-3907
Issue: 15
Volume: 49
Year: 2022
Month: 11
X-DOI: 10.1080/02664763.2021.1967892
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1967892
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:15:p:3889-3907



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1970120_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Qiang Zhao
Author-X-Name-First: Qiang
Author-X-Name-Last: Zhao
Author-Name: Liang Chen
Author-X-Name-First: Liang
Author-X-Name-Last: Chen
Author-Name: Jingjing Wu
Author-X-Name-First: Jingjing
Author-X-Name-Last: Wu
Title: Robust and efficient estimation of GARCH models based on Hellinger distance
Abstract: 
 It is well known that financial data frequently contain outlying observations. Almost all methods and techniques used to estimate GARCH models are likelihood-based and thus generally non-robust against outliers. Minimum distance method, as an important tool for statistical inferences and a competitive alternative for achieving robustness, has surprisingly not been well explored for GARCH models. In this paper, we proposed a minimum Hellinger distance estimator (MHDE) and a minimum profile Hellinger distance estimator (MPHDE), depending on whether the innovation distribution is specified or not, for estimating the parameters in GARCH models. The construction and investigation of the two estimators are quite involved due to the non-i.i.d. nature of data. We proved that the MHDE is a consistent estimator and derived its bias in explicit expression. For both of the proposed estimators, we demonstrated their finite-sample performance through simulation studies and compared with the well-established methods including MLE, Gaussian Quasi-MLE, Non-Gaussian Quasi-MLE and Least Absolute Deviation estimator. Our numerical results showed that MHDE and MPHDE have much better performance than MLE-based methods when data are contaminated while simultaneously they are very competitive when data is clean, which testified to the robustness and efficiency of the two proposed MHD-type estimations.
Journal: Journal of Applied Statistics
Pages: 3976-4002
Issue: 15
Volume: 49
Year: 2022
Month: 11
X-DOI: 10.1080/02664763.2021.1970120
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1970120
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:15:p:3976-4002



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1963422_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: João Victor B. de Freitas
Author-X-Name-First: João Victor B.
Author-X-Name-Last: de Freitas
Author-Name: Juvêncio S. Nobre
Author-X-Name-First: Juvêncio S.
Author-X-Name-Last: Nobre
Author-Name: Marcelo Bourguignon
Author-X-Name-First: Marcelo
Author-X-Name-Last: Bourguignon
Author-Name: Manoel Santos-Neto
Author-X-Name-First: Manoel
Author-X-Name-Last: Santos-Neto
Title: A new approach to modeling positive random variables with repeated measures
Abstract: 
 In many situations, it is common to have more than one observation per experimental unit, thus generating the experiments with repeated measures. In the modeling of such experiments, it is necessary to consider and model the intra-unit dependency structure. In the literature, there are several proposals to model positive continuous data with repeated measures. In this paper, we propose one more with the generalization of the beta prime regression model. We consider the possibility of dependence between observations of the same unit. Residuals and diagnostic tools also are discussed. To evaluate the finite-sample performance of the estimators, using different correlation matrices and distributions, we conducted a Monte Carlo simulation study. The methodology proposed is illustrated with an analysis of a real data set. Finally, we create an 
 $ \texttt {R} $ R package for easy access to publicly available the methodology described in this paper.
Journal: Journal of Applied Statistics
Pages: 3784-3803
Issue: 15
Volume: 49
Year: 2022
Month: 11
X-DOI: 10.1080/02664763.2021.1963422
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1963422
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:15:p:3784-3803

Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1973389_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Muhammad Qasim
Author-X-Name-First: Muhammad
Author-X-Name-Last: Qasim
Author-Name: Kristofer Månsson
Author-X-Name-First: Kristofer
Author-X-Name-Last: Månsson
Author-Name: Pär Sjölander
Author-X-Name-First: Pär
Author-X-Name-Last: Sjölander
Author-Name: B. M. Golam Kibria
Author-X-Name-First: B. M. Golam
Author-X-Name-Last: Kibria
Title: A new class of efficient and debiased two-step shrinkage estimators: method and application
Abstract: 
 This paper introduces a new class of efficient and debiased two-step shrinkage estimators for a linear regression model in the presence of multicollinearity. We derive the proposed estimators’ mean square error and define the necessary and sufficient conditions for superiority over the existing estimators. In addition, we develop an algorithm for selecting the shrinkage parameters for the proposed estimators. The comparison of the new estimators versus the traditional ordinary least squares, ridge regression, Liu, and the two-parameter estimators is done by a matrix mean square error criterion. The Monte Carlo simulation results show the superiority of the proposed estimators under certain conditions. In the presence of high but imperfect multicollinearity, the two-step shrinkage estimators’ performance is relatively better. Finally, two real-world chemical data are analyzed to demonstrate the advantages and the empirical relevance of our newly proposed estimators. It is shown that the standard errors and the estimated mean square error decrease substantially for the proposed estimator. Hence, the precision of the estimated parameters is increased, which of course is one of the main objectives of the practitioners.
Journal: Journal of Applied Statistics
Pages: 4181-4205
Issue: 16
Volume: 49
Year: 2022
Month: 12
X-DOI: 10.1080/02664763.2021.1973389
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1973389
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:16:p:4181-4205



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1976120_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Danúbia R. Cunha
Author-X-Name-First: Danúbia R.
Author-X-Name-Last: Cunha
Author-Name: Jose Angelo Divino
Author-X-Name-First: Jose Angelo
Author-X-Name-Last: Divino
Author-Name: Helton Saulo
Author-X-Name-First: Helton
Author-X-Name-Last: Saulo
Title: On a log-symmetric quantile tobit model applied to female labor supply data
Abstract: 
 The study of female labor supply has been a topic of relevance in the economic literature. Generally, the data are left-censored and the classic tobit model has been extensively used in the modeling strategy. This model, however, assumes normality for the error distribution and is not recommended for data with positive skewness, heavy-tails and heteroscedasticity, as is the case of female labor supply data. Moreover, it is well-known that the quantile regression approach accounts for the influences of different quantiles in the estimated coefficients. We take all these features into account and propose a parametric quantile tobit regression model based on quantile log-symmetric distributions. The proposed method allows one to model data with positive skewness (which is not suitable for the classic tobit model), to study the influence of the quantiles of interest, and to account for heteroscedasticity. The model parameters are estimated by maximum likelihood and a Monte Carlo experiment is performed to evaluate alternative estimators. The new method is applied to two distinct female labor supply data sets. The results indicate that the log-symmetric quantile tobit model fits better the data than the classic tobit model.
Journal: Journal of Applied Statistics
Pages: 4225-4253
Issue: 16
Volume: 49
Year: 2022
Month: 12
X-DOI: 10.1080/02664763.2021.1976120
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1976120
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:16:p:4225-4253



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1971631_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Derya Ersel
Author-X-Name-First: Derya
Author-X-Name-Last: Ersel
Author-Name: Yasemin Kayhan Atılgan
Author-X-Name-First: Yasemin Kayhan
Author-X-Name-Last: Atılgan
Title: An approach for knowledge acquisition from a survey data by conducting Bayesian network modeling, adopting the robust coplot method
Abstract: 
 This study proposes a methodological approach for extracting useful knowledge from survey data by performing Bayesian network (BN) modeling and adopting the robust coplot analysis results as prior knowledge about association patterns hidden in the data. By addressing the issue of BN construction when the expert knowledge is limited/not available, this proposed approach facilitates the modeling of large data sets describing numerously observed and latent variables. By answering the question of which node(s)/link(s) should be retained or discarded from a BN, we aim to determine a compact model of variables while considering the desired properties of data. The proposed method steps are explained on real data extracted from Turkey Demographic and Health Survey. First, a BN structure is created, which is based solely on the judgment of the analyst. Then the coplot results are employed to update the BN structure and the model parameters are updated using the updated structure and data. Loss scores of the BNs are used to ensure the success of the updated BN that inherits knowledge from coplot.
Journal: Journal of Applied Statistics
Pages: 4069-4096
Issue: 16
Volume: 49
Year: 2022
Month: 12
X-DOI: 10.1080/02664763.2021.1971631
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1971631
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:16:p:4069-4096



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1973386_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: A. Beretta
Author-X-Name-First: A.
Author-X-Name-Last: Beretta
Author-Name: C. Heuchenne
Author-X-Name-First: C.
Author-X-Name-Last: Heuchenne
Author-Name: M. Restaino
Author-X-Name-First: M.
Author-X-Name-Last: Restaino
Title: Competing risks proportional-hazards cure model and generalized extreme value regression: an application to bank failures and acquisitions in the United States
Abstract: 
 Several commercial banks in the United States disappeared during the last decades due to failure or acquisition by another entity. From a survival analysis perspective, however, the high censoring rate suggests that some institutions are likely to be immune to failure and/or acquisition. In this study, we use a competing risks proportional-hazards cure model in order to measure the impact of bank-specific and macroeconomic variables on the probabilities of being susceptible to these events (i.e. incidence) and on the survival time of susceptible banks (i.e. latency). Moreover, we propose to model the incidence distribution using Generalized Extreme Value regression and compare the results with the ones obtained by the usual logistic regression model. The proposed methodology is evaluated by means of a simulation study and then applied to a dataset of more than 4000 United States commercial banks spanning the period 1993–2018.
Journal: Journal of Applied Statistics
Pages: 4162-4180
Issue: 16
Volume: 49
Year: 2022
Month: 12
X-DOI: 10.1080/02664763.2021.1973386
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1973386
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:16:p:4162-4180



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1970121_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: David G. Sinclair
Author-X-Name-First: David G.
Author-X-Name-Last: Sinclair
Author-Name: Giles Hooker
Author-X-Name-First: Giles
Author-X-Name-Last: Hooker
Title: An expectation maximization algorithm for high-dimensional model selection for the Ising model with misclassified states*
Abstract: 
 We propose the misclassified Ising Model: a framework for analyzing dependent binary data where the binary state is susceptible to error. We extend previous theoretical results of a model selection method based on applying the LASSO to logistic regression at each node and show that the method will still correctly identify edges in the underlying graphical model under suitable misclassification settings. With knowledge of the misclassification process, an expectation maximization algorithm is developed that accounts for misclassification during model selection. We illustrate the increase of performance of the proposed expectation maximization algorithm with simulated data, and using data from a functional magnetic resonance imaging analysis.
Journal: Journal of Applied Statistics
Pages: 4049-4068
Issue: 16
Volume: 49
Year: 2022
Month: 12
X-DOI: 10.1080/02664763.2021.1970121
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1970121
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:16:p:4049-4068



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1971632_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Mahendra Saha
Author-X-Name-First: Mahendra
Author-X-Name-Last: Saha
Author-Name: Sanku Dey
Author-X-Name-First: Sanku
Author-X-Name-Last: Dey
Author-Name: Saralees Nadarajah
Author-X-Name-First: Saralees
Author-X-Name-Last: Nadarajah
Title: Parametric inference of the process capability index for exponentiated exponential distribution
Abstract: 
 Process capability indices (PCIs) are most effective devices/techniques used in industries for determining the quality of products and performance of manufacturing processes. In this article, we consider the PCI Cpc which is based on the proportion of conformance and is applicable to normally as well as non-normally and continuous as well as discrete distributed processes. In order to estimate the PCI Cpc when the process follows exponentiated exponential distribution, we have used five classical methods of estimation. The performances of these classical estimators are compared with respect to their biases and mean squared errors (MSEs) of the index Cpc through simulation study. Also, the confidence intervals for the index Cpc are constructed using five bootstrap confidence interval (BCIs) methods. Monte Carlo simulation study has been carried out to compare the performances of these five BCIs in terms of their average width and coverage probabilities. Besides, net sensitivity (NS) analysis for the given PCI Cpc is considered. We use two data sets related to electronic and food industries and two failure time data sets to illustrate the performance of the proposed methods of estimation and BCIs. Additionally, we have developed PCI Cpc using aforementioned methods for generalized Rayleigh distribution.
Journal: Journal of Applied Statistics
Pages: 4097-4121
Issue: 16
Volume: 49
Year: 2022
Month: 12
X-DOI: 10.1080/02664763.2021.1971632
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1971632
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:16:p:4097-4121



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1975661_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Jimmy Reyes
Author-X-Name-First: Jimmy
Author-X-Name-Last: Reyes
Author-Name: Jaime Arrué
Author-X-Name-First: Jaime
Author-X-Name-Last: Arrué
Author-Name: Osvaldo Venegas
Author-X-Name-First: Osvaldo
Author-X-Name-Last: Venegas
Author-Name: Héctor W. Gómez
Author-X-Name-First: Héctor W.
Author-X-Name-Last: Gómez
Title: The modified slash Lindley–Weibull distribution with applications to nutrition data
Abstract: 
 This work presents an extension of the slash Lindley–Weibull distribution, of which it can be considered a modification. The new family is obtained by using the quotient of two independent random variables: a two-parameter Lindley–Weibull distribution divided by a power of the exponential distribution with parameter equal to 2. We present the pdf and cdf of the new distribution, analyzing their risk functions. Some statistical properties are studied and the moments and coefficients of asymmetry and kurtosis are shown. The parameter estimation problem is carried out by the maximum likelihood method. The method is assessed by a Monte Carlo simulation study. We use nutrition data, which are characterized by high kurtosis, to illustrate the usefulness of the proposed model.
Journal: Journal of Applied Statistics
Pages: 4206-4224
Issue: 16
Volume: 49
Year: 2022
Month: 12
X-DOI: 10.1080/02664763.2021.1975661
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1975661
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:16:p:4206-4224



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1971633_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Zhiwen Fang
Author-X-Name-First: Zhiwen
Author-X-Name-Last: Fang
Author-Name: Wendong Li
Author-X-Name-First: Wendong
Author-X-Name-Last: Li
Author-Name: Xin Liu
Author-X-Name-First: Xin
Author-X-Name-Last: Liu
Author-Name: Xiaolong Pu
Author-X-Name-First: Xiaolong
Author-X-Name-Last: Pu
Author-Name: Dongdong Xiang
Author-X-Name-First: Dongdong
Author-X-Name-Last: Xiang
Title: Online monitoring of high-dimensional binary data streams with application to extreme weather surveillance
Abstract: 
 With the rapid development of modern sensor technology, high-dimensional data streams appear frequently nowadays, bringing urgent needs for effective statistical process control (SPC) tools. In such a context, the online monitoring problem of high-dimensional and correlated binary data streams is becoming very important. Conventional SPC methods for monitoring multivariate binary processes may fail when facing high-dimensional applications due to high computational complexity and the lack of efficiency. In this paper, motivated by an application in extreme weather surveillance, we propose a novel pairwise approach that considers the most informative pairwise correlation between any two data streams. The information is then integrated into an exponential weighted moving average (EWMA) charting scheme to monitor abnormal mean changes in high-dimensional binary data streams. Extensive simulation study together with a real-data analysis demonstrates the efficiency and applicability of the proposed control chart.
Journal: Journal of Applied Statistics
Pages: 4122-4136
Issue: 16
Volume: 49
Year: 2022
Month: 12
X-DOI: 10.1080/02664763.2021.1971633
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1971633
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:16:p:4122-4136



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1977260_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Nowrin Nusrat
Author-X-Name-First: Nowrin
Author-X-Name-Last: Nusrat
Author-Name: M. S. Rahman
Author-X-Name-First: M. S.
Author-X-Name-Last: Rahman
Title: Dealing with separation or near-to-separation in the model for multinomial response with application to childhood health seeking behavior data from a complex survey
Abstract: 
 Separation or monotone-likelihood can be observed in fitting process of a multinomial logistic model using maximum likelihood estimation (MLE) when sample size is small and/or one of the outcome categories is rare and/or there is one or more influential covariates, resulting in infinite or biased estimate of at least one regression coefficient of the model. This study investigated empirically to identify the optimal data condition to define both ‘separation’ and ‘near-to-separation’ (partial separation) and explored their consequences in MLE and provided a solution by applying a penalized likelihood approach, which has been proposed in the literature, by adding a Jeffreys prior-based penalty term to the original likelihood function to remove the first-order bias in the MLEs of the multinomial logit model via equivalent Poisson regression. Furthermore, the penalized estimating equation (PMLE) is extended to a weighted estimating equation allowing for survey-weight for analyzing data from a complex survey. The simulation study suggests that the PMLE outperforms the MLE by providing smaller amount of bias and mean squared of error and better coverage. The methods are applied to analyze data on choice of health facility for treatment of childhood diseases.
Journal: Journal of Applied Statistics
Pages: 4254-4277
Issue: 16
Volume: 49
Year: 2022
Month: 12
X-DOI: 10.1080/02664763.2021.1977260
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1977260
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:16:p:4254-4277



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1977785_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Jinjuan Wang
Author-X-Name-First: Jinjuan
Author-X-Name-Last: Wang
Author-Name: Yunpeng Zhao
Author-X-Name-First: Yunpeng
Author-X-Name-Last: Zhao
Author-Name: Larry L. Tang
Author-X-Name-First: Larry L.
Author-X-Name-Last: Tang
Author-Name: Claudius Mueller
Author-X-Name-First: Claudius
Author-X-Name-Last: Mueller
Author-Name: Qizhai Li
Author-X-Name-First: Qizhai
Author-X-Name-Last: Li
Title: A resample-replace lasso procedure for combining high-dimensional markers with limit of detection
Abstract: 
 In disease screening, a biomarker combination developed by combining multiple markers tends to have a higher sensitivity than an individual marker. Parametric methods for marker combination rely on the inverse of covariance matrices, which is often a non-trivial problem for high-dimensional data generated by modern high-throughput technologies. Additionally, another common problem in disease diagnosis is the existence of limit of detection (LOD) for an instrument – that is, when a biomarker's value falls below the limit, it cannot be observed and is assigned an NA value. To handle these two challenges in combining high-dimensional biomarkers with the presence of LOD, we propose a resample-replace lasso procedure. We first impute the values below LOD and then use the graphical lasso method to estimate the means and precision matrices for the high-dimensional biomarkers. The simulation results show that our method outperforms alternative methods such as either substitute NA values with LOD values or remove observations that have NA values. A real case analysis on a protein profiling study of glioblastoma patients on their survival status indicates that the biomarker combination obtained through the proposed method is more accurate in distinguishing between two groups.
Journal: Journal of Applied Statistics
Pages: 4278-4293
Issue: 16
Volume: 49
Year: 2022
Month: 12
X-DOI: 10.1080/02664763.2021.1977785
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1977785
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:16:p:4278-4293



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1986685_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: J. M. Thilini Jayasinghe
Author-X-Name-First: J. M. Thilini
Author-X-Name-Last: Jayasinghe
Author-Name: Leif Ellingson
Author-X-Name-First: Leif
Author-X-Name-Last: Ellingson
Author-Name: Chalani Prematilake
Author-X-Name-First: Chalani
Author-X-Name-Last: Prematilake
Title: Regression models using the LINEX loss to predict lower bounds for the number of points for approximating planar contour shapes
Abstract: 
 Researchers in statistical shape analysis often analyze outlines of objects. Even though these contours are infinite-dimensional in theory, they must be discretized in practice. When discretizing, it is important to reduce the number of sampling points considerably to reduce computational costs, but to not use too few points so as to result in too much approximation error. Unfortunately, determining the minimum number of points needed to achieve sufficiently approximate the contours is computationally expensive. In this paper, we fit regression models to predict these lower bounds using characteristics of the contours that are computationally cheap as predictor variables. However, least squares regression is inadequate for this task because it treats overestimation and underestimation equally, but underestimation of lower bounds is far more serious. Instead, to fit the models, we use the LINEX loss function, which allows us to penalize underestimation at an exponential rate while penalizing overestimation only linearly. We present a novel approach to select the shape parameter of the loss function and tools for analyzing how well the model fits the data. Through validation methods, we show that the LINEX models work well for reducing the underestimation for the lower bounds.
Journal: Journal of Applied Statistics
Pages: 4294-4313
Issue: 16
Volume: 49
Year: 2022
Month: 12
X-DOI: 10.1080/02664763.2021.1986685
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1986685
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:16:p:4294-4313



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1973385_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: F. Prataviera
Author-X-Name-First: F.
Author-X-Name-Last: Prataviera
Author-Name: G. M. Cordeiro
Author-X-Name-First: G. M.
Author-X-Name-Last: Cordeiro
Author-Name: E. M. M. Ortega
Author-X-Name-First: E. M. M.
Author-X-Name-Last: Ortega
Author-Name: E. M. Hashimoto
Author-X-Name-First: E. M.
Author-X-Name-Last: Hashimoto
Author-Name: V. G. Cancho
Author-X-Name-First: V. G.
Author-X-Name-Last: Cancho
Title: A new regression model for rates and proportions data with applications
Abstract: 
 We propose a new continuous distribution in the interval 
 $ (0,1) $ (0,1) based on the generalized odd log-logistic-G family, whose density function can be symmetrical, asymmetric, unimodal and bimodal. The new model is implemented using the gamlss packages in R. We propose an extended regression based on this distribution which includes as sub-models some important regressions. We employ a frequentist and Bayesian analysis to estimate the parameters and adopt the non-parametric and parametric bootstrap methods to obtain better efficiency of the estimators. Some simulations are conducted to verify the empirical distribution of the maximum likelihood estimators. We compare the empirical distribution of the quantile residuals with the standard normal distribution. The extended regression can give more realistic fits than other regressions in the analysis of proportional data.
Journal: Journal of Applied Statistics
Pages: 4137-4161
Issue: 16
Volume: 49
Year: 2022
Month: 12
X-DOI: 10.1080/02664763.2021.1973385
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1973385
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:49:y:2022:i:16:p:4137-4161

Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2004581_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Ran Bi
Author-X-Name-First: Ran
Author-X-Name-Last: Bi
Author-Name: Peng Liu
Author-X-Name-First: Peng
Author-X-Name-Last: Liu
Title: A semi-parametric Bayesian approach for detection of gene expression heterosis with RNA-seq data
Abstract: 
 Heterosis refers to the superior performance of a hybrid offspring over its two inbred parents. Although heterosis has been widely observed in agriculture, its molecular mechanism is not well studied. Recent advances in high-throughput genomic technologies such as RNA sequencing (RNA-seq) facilitate the investigation of heterosis at the gene expression level. However, it is challenging to identify genes exhibiting heterosis using RNA-seq data because high-dimension of hypotheses tests are conducted with limited sample size. Furthermore, detecting heterosis genes requires testing composite null hypotheses involving multiple mean expression levels instead of testing simple null hypotheses as in differential expression analysis. In this manuscript, we formulate a statistical model with parameters directly reflecting heterosis status, and develop a powerful test to detect heterosis genes. We employ a Bayesian framework where the RNA-seq count data are modeled through a Poisson-Gamma mixture with Dirichlet processes as priors for the distributions of the parameters of interest, the fold changes between each parent and the hybrid. Markov Chain Monte Carlo sampling with Gibbs algorithm is utilized to provide posterior inference to detect heterosis genes while controlling false discovery rate. Simulation results demonstrate that our proposed method outperformed other methods utilized to detect gene expression heterosis.
Journal: Journal of Applied Statistics
Pages: 214-230
Issue: 1
Volume: 50
Year: 2023
Month: 01
X-DOI: 10.1080/02664763.2021.2004581
File-URL: http://hdl.handle.net/10.1080/02664763.2021.2004581
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:1:p:214-230



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2025585_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Caner Taniş
Author-X-Name-First: Caner
Author-X-Name-Last: Taniş
Author-Name: Buğra Saraçoğlu
Author-X-Name-First: Buğra
Author-X-Name-Last: Saraçoğlu
Title: Cubic rank transmuted generalized Gompertz distribution: properties and applications
Abstract: 
 In this paper, we introduce a new lifetime distribution as an alternative to generalized Gompertz, Gompertz distribution and its modified ones. This new distribution is a special case of the family of distributions introduced by Granzotto et al. [D.C.T. Granzotto, F. Louzada and N. Balakrishnan, Cubic rank transmuted distributions: inferential issues and applications., J. Stat. Comput. Simul. 87 (2017), pp. 2760–2778]. We obtain some characteristic properties of suggested distribution such as hazard function, ordinary moments, coefficient of skewness, coefficient of kurtosis, moment generating function, quantile function and median. We discuss three different methods of estimation to estimate the parameters of proposed distribution. A comprehensive Monte Carlo simulation study is performed in order to compare the performances of estimators according to mean square errors and biases. Finally, three real data applications are performed to illustrate usefulness of suggested distribution.
Journal: Journal of Applied Statistics
Pages: 195-213
Issue: 1
Volume: 50
Year: 2023
Month: 01
X-DOI: 10.1080/02664763.2022.2025585
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2025585
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:1:p:195-213



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1981834_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Mustafa Ç. Korkmaz
Author-X-Name-First: Mustafa Ç.
Author-X-Name-Last: Korkmaz
Author-Name: Christophe Chesneau
Author-X-Name-First: Christophe
Author-X-Name-Last: Chesneau
Author-Name: Zehra Sedef Korkmaz
Author-X-Name-First: Zehra Sedef
Author-X-Name-Last: Korkmaz
Title: A new alternative quantile regression model for the bounded response with educational measurements applications of OECD countries
Abstract: 
 This article introduces a new distribution with two tuning parameters specified on the unit interval. It follows from a ‘hyperbolic secant transformation’ of a random variable following the Weibull distribution. The lack of research on the prospect of hyperbolic transformations providing flexible distributions over the unit interval is a motivation for the study. The main distributional structural properties of the new distribution are established. The different estimation methods and two simulation works have been derived for model parameters. Subsequently, we develop a related quantile regression model for further statistical perspectives. We consider two real data applications based on the educational measurements of both OECD and some non-members of OECD countries. Our regression model aims to relate the desire to get top grades on certain young students in the OECD countries with some of their Education and School Life Index such as reading performance, work environment at home, and paid work experience. It is shown that the elaborated quantile regression model has a better fitting power than famous regression models when the unit response variable possesses skewed distribution as well as two independent variables are significant in the statistical sense at any standard significance level for the median response.
Journal: Journal of Applied Statistics
Pages: 131-154
Issue: 1
Volume: 50
Year: 2023
Month: 01
X-DOI: 10.1080/02664763.2021.1981834
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1981834
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:1:p:131-154



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1981256_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Jianghu (James) Dong
Author-X-Name-First: Jianghu (James)
Author-X-Name-Last: Dong
Author-Name: Haolun Shi
Author-X-Name-First: Haolun
Author-X-Name-Last: Shi
Author-Name: Liangliang Wang
Author-X-Name-First: Liangliang
Author-X-Name-Last: Wang
Author-Name: Ying Zhang
Author-X-Name-First: Ying
Author-X-Name-Last: Zhang
Author-Name: Jiguo Cao
Author-X-Name-First: Jiguo
Author-X-Name-Last: Cao
Title: Jointly modelling multiple transplant outcomes by a competing risk model via functional principal component analysis
Abstract: 
 In many clinical studies, longitudinal biomarkers are often used to monitor the progression of a disease. For example, in a kidney transplant study, the glomerular filtration rate (GFR) is used as a longitudinal biomarker to monitor the progression of the kidney function and the patient's state of survival that is characterized by multiple time-to-event outcomes, such as kidney transplant failure and death. It is known that the joint modelling of longitudinal and survival data leads to a more accurate and comprehensive estimation of the covariates' effect. While most joint models use the longitudinal outcome as a covariate for predicting survival, very few models consider the further decomposition of the variation within the longitudinal trajectories and its effect on survival. We develop a joint model that uses functional principal component analysis (FPCA) to extract useful features from the longitudinal trajectories and adopt the competing risk model to handle multiple time-to-event outcomes. The longitudinal trajectories and the multiple time-to-event outcomes are linked via the shared functional features. The application of our model on a real kidney transplant data set reveals the significance of these functional features, and a simulation study is carried out to validate the accurateness of the estimation method.
Journal: Journal of Applied Statistics
Pages: 43-59
Issue: 1
Volume: 50
Year: 2023
Month: 01
X-DOI: 10.1080/02664763.2021.1981256
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1981256
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:1:p:43-59



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1981257_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Honggang Zhang
Author-X-Name-First: Honggang
Author-X-Name-Last: Zhang
Author-Name: Jingyong Su
Author-X-Name-First: Jingyong
Author-X-Name-Last: Su
Author-Name: Linlin Tang
Author-X-Name-First: Linlin
Author-X-Name-Last: Tang
Author-Name: Anuj Srivastava
Author-X-Name-First: Anuj
Author-X-Name-Last: Srivastava
Title: Elastic statistical analysis of interval-valued time series
Abstract: 
 We investigate the problem of statistical analysis of interval-valued time series data – two nonintersecting real-valued functions, representing lower and upper limits, over a period of time. Specifically, we pay attention to the two concepts of phase (or horizontal) variability and amplitude (or vertical) variability, and propose a phase-amplitude separation method. We view interval-valued time series as elements of a function (Hilbert) space and impose a Riemannian structure on it. We separate phase and amplitude variability in observed interval functions using a metric-based alignment solution. The key idea is to map an interval to a point in 
 $ {\mathbb {R}}^2 $ R2, view interval-valued time series as parameterized curves in 
 $ {\mathbb {R}}^2 $ R2, and borrow ideas from elastic shape analysis of planar curves, including PCA, to perform registration, summarization, analysis, and modeling of multiple series. The proposed phase-amplitude separation provides a new way of PCA and modeling for interval-valued time series, and enables shape clustering of interval-valued time series. We apply this framework to three different applications, including finance, meteorology and physiology, proves the effectiveness of proposed methods, and discovers some underlying patterns in the data. Experimental results on simulated data show that our method applies to the point-valued time series.
Journal: Journal of Applied Statistics
Pages: 60-85
Issue: 1
Volume: 50
Year: 2023
Month: 01
X-DOI: 10.1080/02664763.2021.1981257
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1981257
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:1:p:60-85



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1981832_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Shubham Agnihotri
Author-X-Name-First: Shubham
Author-X-Name-Last: Agnihotri
Author-Name: Sanjay Kumar Singh
Author-X-Name-First: Sanjay
Author-X-Name-Last: Kumar Singh
Author-Name: Umesh Singh
Author-X-Name-First: Umesh
Author-X-Name-Last: Singh
Title: Inferences for multiple interval type-I censoring scheme
Abstract: 
 In this paper, we have introduced a new type of censoring scheme named the multiple interval type-I censoring scheme. Further, We have assumed that the test units are drawn from the Weibull population. We have also proposed the maximum product of spacing estimators for unknown parameters under the multiple interval type-I censoring scheme and compare them with the existing maximum likelihood estimators. In addition to this, the Bayes estimators for shape and scale parameters are also obtained under the squared error loss function. Their corresponding asymptotic confidence/credible intervals are also discussed. A real data set containing the breakdown time of insulating fluids are used to demonstrate the appropriateness of the proposed methodology.
Journal: Journal of Applied Statistics
Pages: 86-105
Issue: 1
Volume: 50
Year: 2023
Month: 01
X-DOI: 10.1080/02664763.2021.1981832
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1981832
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:1:p:86-105



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1978955_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Vera Liu
Author-X-Name-First: Vera
Author-X-Name-Last: Liu
Author-Name: Stephen Walker
Author-X-Name-First: Stephen
Author-X-Name-Last: Walker
Title: Testing for genetic mutation of seasonal influenza virus
Abstract: 
 Influenza virus strains undergo genetic mutations every year and these changes in genetic makeup pose difficulties for effective vaccine selection. To better understand the problem it is important to statistically quantify the amount of genetic change between circulating strains from different years. In this paper, we propose the nonparametric crossmatch test applied to phylogenetic trees to assess the level of discrepancy between circulating flu virus strains between two years; the viruses being represented by a phylogenetic tree. The crossmatch test has advantages compared to parametric tests in that it preserves more information in the data. The outcome of the test would indicate whether the circulating influenza virus has mutated sufficiently in the past year to be considered as a new population of virus, suggesting the need to consider a new vaccine. We validate the test on simulated phylogenetic tree samples with varying branch lengths, as well as with publicly available virus sequence data from the ‘Global Initiative on Sharing All Influenza Data’ (GISAID: https://www.gisaid.org/)
Journal: Journal of Applied Statistics
Pages: 1-18
Issue: 1
Volume: 50
Year: 2023
Month: 01
X-DOI: 10.1080/02664763.2021.1978955
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1978955
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:1:p:1-18



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1981833_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: J. Bröcker
Author-X-Name-First: J.
Author-X-Name-Last: Bröcker
Title: Testing the reliability of forecasting systems
Abstract: 
 The problem of statistically evaluating forecasting systems is revisited. The forecaster claims the forecasts to exhibit a certain nominal statistical behaviour; for instance, the forecasts provide the expected value (or certain quantiles) of the verification, conditional on the information available at forecast time. Forecasting systems that indeed exhibit the nominal behaviour are referred to as reliable. Statistical tests for reliability are presented (based on an archive of verification–forecast pairs). As noted previously, devising such tests is encumbered by the fact that the dependence structure of the verification–forecast pairs is not known in general. Ignoring this dependence though might lead to incorrect tests and too-frequent rejection of forecasting systems that are actually reliable. On the other hand, reliability typically implies that the forecast provides information about the dependence structure, and using this in conjunction with judicious choices of the test statistic, rigorous results on the asymptotic distribution of the test statistic are obtained. These results are used to test for reliability under minimal additional assumptions on the statistical properties of the verification–forecast pairs. Applications to environmental forecasts are discussed. A python implementation of the discussed methods is available online.
Journal: Journal of Applied Statistics
Pages: 106-130
Issue: 1
Volume: 50
Year: 2023
Month: 01
X-DOI: 10.1080/02664763.2021.1981833
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1981833
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:1:p:106-130



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1982879_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Theodoros Perdikis
Author-X-Name-First: Theodoros
Author-X-Name-Last: Perdikis
Author-Name: Stelios Psarakis
Author-X-Name-First: Stelios
Author-X-Name-Last: Psarakis
Author-Name: Philippe Castagliola
Author-X-Name-First: Philippe
Author-X-Name-Last: Castagliola
Author-Name: Athanasios C. Rakitzis
Author-X-Name-First: Athanasios C.
Author-X-Name-Last: Rakitzis
Author-Name: Petros E. Maravelakis
Author-X-Name-First: Petros E.
Author-X-Name-Last: Maravelakis
Title: The EWMA sign chart revisited: performance and alternatives without and with ties
Abstract: 
 The EWMA Sign control chart is an efficient tool for monitoring shifts in a process regardless the observations' underlying distribution. Recent studies have shown that, for nonparametric control charts, due to the discrete nature of the statistics being used (such as the Sign statistic), it is impossible to accurately compute their Run Length properties using Markov chain or integral equation methods. In this work, a modified nonparametric Phase II EWMA chart based on the Sign statistic is proposed and its exact Run Length properties are discussed. A continuous transformation of the Sign statistic, combined with the classical Markov Chain method, is used for the determination of the chart's in- and out-of-control Run Length properties. Additionally, we show that when ties occur due to measurement rounding-off errors, the EWMA Sign control chart is no longer distribution-free and a Bernoulli trial approach is discussed to handle the occurrence of ties and makes the proposed chart almost distribution-free. Finally, an illustrative example is provided to show the practical implementation of our proposed chart.
Journal: Journal of Applied Statistics
Pages: 170-194
Issue: 1
Volume: 50
Year: 2023
Month: 01
X-DOI: 10.1080/02664763.2021.1982879
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1982879
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:1:p:170-194



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1982877_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Chun-Chao Wang
Author-X-Name-First: Chun-Chao
Author-X-Name-Last: Wang
Author-Name: Yi-Ting Hwang
Author-X-Name-First: Yi-Ting
Author-X-Name-Last: Hwang
Author-Name: Chung-Chuan Chou
Author-X-Name-First: Chung-Chuan
Author-X-Name-Last: Chou
Author-Name: Hui-Ling Lee
Author-X-Name-First: Hui-Ling
Author-X-Name-Last: Lee
Title: Misspecification of a binary dependent variable in the logistic model controlling for the repeated longitudinal measures
Abstract: 
 Many medical applications are interested to know the disease status. The disease status can be related to multiple serial measurements. Nevertheless, owing to various reasons, the binary outcome can be measured incorrectly. The estimators derived from the misspecified outcome can be biased. This paper derives the complete data likelihood function to incorporate both the multiple serial measurements and the misspecified outcome. Owing to the latent variables, EM algorithm is used to derive the maximum-likelihood estimators. Monte Carlo simulations are conducted to compare the impact of misspecification on the estimates. A retrospective data for the recurrence of atrial fibrillation is used to illustrate the usage of the proposed model.
Journal: Journal of Applied Statistics
Pages: 155-169
Issue: 1
Volume: 50
Year: 2023
Month: 01
X-DOI: 10.1080/02664763.2021.1982877
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1982877
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:1:p:155-169



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1980506_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Kashinath Chatterjee
Author-X-Name-First: Kashinath
Author-X-Name-Last: Chatterjee
Author-Name: Christos Koukouvinos
Author-X-Name-First: Christos
Author-X-Name-Last: Koukouvinos
Author-Name: Angeliki Lappa
Author-X-Name-First: Angeliki
Author-X-Name-Last: Lappa
Title: Monitoring process mean and dispersion with one double generally weighted moving average control chart
Abstract: 
 Control charts are widely known quality tools used to detect and control industrial process deviations in Statistical Process Control. In the current paper, we propose a new single memory-type control chart, called the maximum double generally weighted moving average chart (referred as Max-DGWMA), that simultaneously detects shifts in the process mean and/or process dispersion. The run length performance of the proposed Max-DGWMA chart is compared with that of the Max-EWMA, Max-DEWMA, Max-GWMA and SS-DGWMA charts, using time-varying control limits, through Monte–Carlo simulations. The comparisons reveal that the proposed chart is more efficient than the Max-EWMA, Max-DEWMA and Max-GWMA charts, while it is comparable with the SS-DGWMA chart. An automotive industry application is presented in order to implement the Max-DGWMA chart. The goal is to establish statistical control of the manufacturing process of the automobile engine piston rings. The source of the out-of-control signals is interpreted and the efficiency of the proposed chart in detecting shifts faster is evident.
Journal: Journal of Applied Statistics
Pages: 19-42
Issue: 1
Volume: 50
Year: 2023
Month: 01
X-DOI: 10.1080/02664763.2021.1980506
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1980506
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:1:p:19-42

Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1990225_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Xinyi Wang
Author-X-Name-First: Xinyi
Author-X-Name-Last: Wang
Author-Name: Zhenghui Feng
Author-X-Name-First: Zhenghui
Author-X-Name-Last: Feng
Title: Component selection for exponential power mixture models
Abstract: 
 Exponential Power (EP) family is a much flexible distribution family including Gaussian family as a sub-family. In this article, we study component selection and estimation for EP mixture models and regressions. The assumption on zero component mean in [X. Cao, Q. Zhao, D. Meng, Y. Chen, and Z. Xu, Robust low-rank matrix factorization under general mixture noise distributions, IEEE. Trans. Image. Process. 25 (2016), pp. 4677–4690.] is relaxed. To select components and estimate parameters simultaneously, we propose a penalized likelihood method, which can shrink mixing proportions to zero to achieve components selection. Modified EM algorithms are proposed, and the consistency of estimated component number is obtained. Simulation studies show the advantages of the proposed methods on accuracies of component number selection, parameter estimation, and density estimation. Analysis of value at risk of SHIBOR and a climate change data are given as illustration.
Journal: Journal of Applied Statistics
Pages: 291-314
Issue: 2
Volume: 50
Year: 2023
Month: 01
X-DOI: 10.1080/02664763.2021.1990225
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1990225
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:2:p:291-314



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2096875_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Yasin Altinisik
Author-X-Name-First: Yasin
Author-X-Name-Last: Altinisik
Title: Addressing overdispersion and zero-inflation for clustered count data via new multilevel heterogenous hurdle models
Abstract: 
 Unobserved heterogeneity causing overdispersion and the excessive number of zeros take a prominent place in the methodological development on count modeling. An insight into the mechanisms that induce heterogeneity is required for better understanding of the phenomenon of overdispersion. When the heterogeneity is sourced by the stochastic component of the model, the use of a heterogenous Poisson distribution for this part encounters as an elegant solution. Hierarchical design of the study is also responsible for the heterogeneity as the unobservable effects at various levels also contribute to the overdispersion. Zero-inflation, heterogeneity and multilevel nature in the count data present special challenges in their own respect, however the presence of all in one study adds more challenges to the modeling strategies. This study therefore is designed to merge the attractive features of the separate strand of the solutions in order to face such a comprehensive challenge. This study differs from the previous attempts by the choice of two recently developed heterogeneous distributions, namely Poisson–Lindley (PL) and Poisson–Ailamujia (PA) for the truncated part. Using generalized linear mixed modeling settings, predictive performances of the multilevel PL and PA models and their hurdle counterparts were assessed within a comprehensive simulation study in terms of bias, precision and accuracy measures. Multilevel models were applied to two separate real world examples for the assessment of practical implications of the new models proposed in this study.
Journal: Journal of Applied Statistics
Pages: 408-433
Issue: 2
Volume: 50
Year: 2023
Month: 01
X-DOI: 10.1080/02664763.2022.2096875
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2096875
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:2:p:408-433



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1985091_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Edward L. Boone
Author-X-Name-First: Edward L.
Author-X-Name-Last: Boone
Author-Name: Abdel-Salam G. Abdel-Salam
Author-X-Name-First: Abdel-Salam G.
Author-X-Name-Last: Abdel-Salam
Author-Name: Indranil Sahoo
Author-X-Name-First: Indranil
Author-X-Name-Last: Sahoo
Author-Name: Ryad Ghanam
Author-X-Name-First: Ryad
Author-X-Name-Last: Ghanam
Author-Name: Xi Chen
Author-X-Name-First: Xi
Author-X-Name-Last: Chen
Author-Name: Aiman Hanif
Author-X-Name-First: Aiman
Author-X-Name-Last: Hanif
Title: Monitoring SEIRD model parameters using MEWMA for the COVID-19 pandemic with application to the state of Qatar
Abstract: 
 During the current COVID-19 pandemic, decision-makers are tasked with implementing and evaluating strategies for both treatment and disease prevention. In order to make effective decisions, they need to simultaneously monitor various attributes of the pandemic such as transmission rate and infection rate for disease prevention, recovery rate which indicates treatment effectiveness as well as the mortality rate and others. This work presents a technique for monitoring the pandemic by employing an Susceptible, Exposed, Infected, Recovered, Death model regularly estimated by an augmented particle Markov chain Monte Carlo scheme in which the posterior distribution samples are monitored via Multivariate Exponentially Weighted Average process monitoring. This is illustrated on the COVID-19 data for the State of Qatar.
Journal: Journal of Applied Statistics
Pages: 231-246
Issue: 2
Volume: 50
Year: 2023
Month: 01
X-DOI: 10.1080/02664763.2021.1985091
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1985091
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:2:p:231-246



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1992360_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Joris Mulder
Author-X-Name-First: Joris
Author-X-Name-Last: Mulder
Author-Name: John P. T. M. Gelissen
Author-X-Name-First: John P. T. M.
Author-X-Name-Last: Gelissen
Title: Bayes factor testing of equality and order constraints on measures of association in social research
Abstract: 
 Measures of association play a central role in the social sciences to quantify the strength of a linear relationship between the variables of interest. In many applications researchers can translate scientific expectations to hypotheses with equality and/or order constraints on these measures of association. In this paper a Bayes factor test is proposed for testing multiple hypotheses with constraints on the measures of association between ordinal and/or continuous variables, possibly after correcting for certain covariates. This test can be used to obtain a direct answer to the research question how much evidence there is in the data for a social science theory relative to competing theories. The stand-alone software package ‘BCT’ allows users to apply the methodology in an easy manner. The methodology will also be available in the R package ‘BFpack’. An empirical application from leisure studies about the associations between life, leisure and relationship satisfaction and an application about the differences about egalitarian justice beliefs across countries are used to illustrate the methodology.
Journal: Journal of Applied Statistics
Pages: 315-351
Issue: 2
Volume: 50
Year: 2023
Month: 01
X-DOI: 10.1080/02664763.2021.1992360
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1992360
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:2:p:315-351



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1990224_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Takeshi Emura
Author-X-Name-First: Takeshi
Author-X-Name-Last: Emura
Author-Name: Wei-Chern Hsu
Author-X-Name-First: Wei-Chern
Author-X-Name-Last: Hsu
Author-Name: Wen-Chi Chou
Author-X-Name-First: Wen-Chi
Author-X-Name-Last: Chou
Title: A survival tree based on stabilized score tests for high-dimensional covariates
Abstract: 
 A survival tree can classify subjects into different survival prognostic groups. However, when data contains high-dimensional covariates, the two popular classification trees exhibit fatal drawbacks. The logrank tree is unstable and tends to have false nodes; the conditional inference tree is difficult to interpret the adjusted P-value for high-dimensional tests. Motivated by these problems, we propose a new survival tree based on the stabilized score tests. We propose a novel matrix-based algorithm in order to tests a number of nodes simultaneously via stabilized score tests. We propose a recursive partitioning algorithm to construct a survival tree and develop our original R package uni.survival.tree (https://cran.r-project.org/package=uni.survival.tree) for implementation. Simulations are performed to demonstrate the superiority of the proposed method over the existing methods. The lung cancer data analysis demonstrates the usefulness of the proposed method.
Journal: Journal of Applied Statistics
Pages: 264-290
Issue: 2
Volume: 50
Year: 2023
Month: 01
X-DOI: 10.1080/02664763.2021.1990224
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1990224
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:2:p:264-290



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1994529_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Zihang Lu
Author-X-Name-First: Zihang
Author-X-Name-Last: Lu
Author-Name: Wendy Lou
Author-X-Name-First: Wendy
Author-X-Name-Last: Lou
Title: Bayesian approaches to variable selection in mixture models with application to disease clustering
Abstract: 
 In biomedical research, cluster analysis is often performed to identify patient subgroups based on patients' characteristics or traits. In the model-based clustering for identifying patient subgroups, mixture models have played a fundamental role in modeling. While there is an increasing interest in using mixture modeling for identifying patient subgroups, little work has been done in selecting the predictors that are associated with the class assignment. In this study, we develop and compare two approaches to perform variable selection in the context of a mixture model to identify important predictors that are associated with the class assignment. These two approaches are the one-step approach and the stepwise approach. The former refers to an approach in which clustering and variable selection are performed simultaneously in one overall model, whereas the latter refers to an approach in which clustering and variable selection are performed in two sequential steps. We considered both shrinkage prior and spike-and-slab prior to select the importance of variables. Markov chain Monte Carlo algorithms are developed to estimate the posterior distribution of the model parameters. Practical applications and simulation studies are carried out to evaluate the clustering and variable selection performance of the proposed models.
Journal: Journal of Applied Statistics
Pages: 387-407
Issue: 2
Volume: 50
Year: 2023
Month: 01
X-DOI: 10.1080/02664763.2021.1994529
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1994529
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:2:p:387-407



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1993798_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Zezhun Chen
Author-X-Name-First: Zezhun
Author-X-Name-Last: Chen
Author-Name: Angelos Dassios
Author-X-Name-First: Angelos
Author-X-Name-Last: Dassios
Author-Name: George Tzougas
Author-X-Name-First: George
Author-X-Name-Last: Tzougas
Title: A first-order binomial-mixed Poisson integer-valued autoregressive model with serially dependent innovations
Abstract: 
 Motivated by the extended Poisson INAR(1), which allows innovations to be serially dependent, we develop a new family of binomial-mixed Poisson INAR(1) (BMP INAR(1)) processes by adding a mixed Poisson component to the innovations of the classical Poisson INAR(1) process. Due to the flexibility of the mixed Poisson component, the model includes a large class of INAR(1) processes with different transition probabilities. Moreover, it can capture some overdispersion features coming from the data while keeping the innovations serially dependent. We discuss its statistical properties, stationarity conditions and transition probabilities for different mixing densities (Exponential, Lindley). Then, we derive the maximum likelihood estimation method and its asymptotic properties for this model. Finally, we demonstrate our approach using a real data example of iceberg count data from a financial system.
Journal: Journal of Applied Statistics
Pages: 352-369
Issue: 2
Volume: 50
Year: 2023
Month: 01
X-DOI: 10.1080/02664763.2021.1993798
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1993798
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:2:p:352-369



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2008328_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Robert A. Bettinger
Author-X-Name-First: Robert A.
Author-X-Name-Last: Bettinger
Title: Bimodality in reentry latitude predictions for spacecraft in prograde orbits
Abstract: 
 Probability distribution functions (PDFs) of atmospheric reentry latitude predictions are shown to be bimodal for spacecraft in low-eccentricity, prograde low Earth orbits at altitudes of 300 km and lower. Using two-line element (TLE) data for initial orbit conditions, coupled with coarse estimates for spacecraft aerodynamic characteristics, parametric simulations produce bimodal distributions that suggest a greater likelihood of reentry near the latitudinal maxima of a given spacecraft's ground track. Various computational measures are used to test for and quantify bimodality in the reentry latitude data sets. Also, a method for approximating bandwidth is introduced for the kernel estimation of reentry latitude probability density. Overall, statistical analysis indicates that actual reentry latitudes are generally within 1-σ of observed hemisphere means as demonstrated by six historical reentry cases.
Journal: Journal of Applied Statistics
Pages: 434-450
Issue: 2
Volume: 50
Year: 2023
Month: 01
X-DOI: 10.1080/02664763.2021.2008328
File-URL: http://hdl.handle.net/10.1080/02664763.2021.2008328
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:2:p:434-450



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1987400_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Hao Ding
Author-X-Name-First: Hao
Author-X-Name-Last: Ding
Author-Name: Yan Zhang
Author-X-Name-First: Yan
Author-X-Name-Last: Zhang
Author-Name: Yuehua Wu
Author-X-Name-First: Yuehua
Author-X-Name-Last: Wu
Title: A novel group VIF regression for group variable selection with application to multiple change-point detection
Abstract: 
 In this paper, we propose a novel group variance inflation factor (VIF) regression model for tackling large data sets where data follows a grouped structure. Unlike classical penalized methods, this approach can perform group variable selection in a sparse model, which is quite different from the classical penalized methods. We further adapt the proposed method associated with a two-stage procedure for detecting multiple change-point in linear models. We carry out extensive simulation studies to show that the proposed group variable selection and change-point detection methods are stable and efficient. Finally, we provide two real data examples, including a body fat data set and an air pollution data set, to illustrate the performance of our algorithms in group selection and change-point detection.
Journal: Journal of Applied Statistics
Pages: 247-263
Issue: 2
Volume: 50
Year: 2023
Month: 01
X-DOI: 10.1080/02664763.2021.1987400
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1987400
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:2:p:247-263



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1993799_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Tao Wang
Author-X-Name-First: Tao
Author-X-Name-Last: Wang
Author-Name: Xiaona Yang
Author-X-Name-First: Xiaona
Author-X-Name-Last: Yang
Author-Name: Yunfei Guo
Author-X-Name-First: Yunfei
Author-X-Name-Last: Guo
Author-Name: Zhonghua Li
Author-X-Name-First: Zhonghua
Author-X-Name-Last: Li
Title: Identification of outlying observations for large-dimensional data
Abstract: 
 This work proposes a two-stage procedure for identifying outlying observations in a large-dimensional data set. In the first stage, an outlier identification measure is defined by using a max-normal statistic and a clean subset that contains non-outliers is obtained. The identification of outliers can be deemed as a multiple hypothesis testing problem, then, in the second stage, we explore the asymptotic distribution of the proposed measure, and obtain the threshold of the outlying observations. Furthermore, in order to improve the identification power and better control the misjudgment rate, a one-step refined algorithm is proposed. Simulation results and two real data analysis examples show that, compared with other methods, the proposed procedure has great advantages in identifying outliers in various data situations.
Journal: Journal of Applied Statistics
Pages: 370-386
Issue: 2
Volume: 50
Year: 2023
Month: 01
X-DOI: 10.1080/02664763.2021.1993799
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1993799
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:2:p:370-386

Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2068514_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Sixia Chen
Author-X-Name-First: Sixia
Author-X-Name-Last: Chen
Author-Name: Chao Xu
Author-X-Name-First: Chao
Author-X-Name-Last: Xu
Title: Handling high-dimensional data with missing values by modern machine learning techniques
Abstract: 
 High-dimensional data have been regarded as one of the most important types of big data in practice. It happens frequently in practice including genetic study, financial study, and geographical study. Missing data in high dimensional data analysis should be handled properly to reduce nonresponse bias. We discuss some modern machine learning techniques including penalized regression approaches, tree-based approaches, and deep learning (DL) for handling missing data with high dimensionality. Specifically, our proposed methods can be used for estimating general parameters of interest including population means and percentiles with imputation-based estimators, propensity score estimators, and doubly robust estimators. We compare those methods through some limited simulation studies and a real application. Both simulation studies and real application show the benefits of DL and XGboost approaches compared with other methods in terms of balancing bias and variance.
Journal: Journal of Applied Statistics
Pages: 786-804
Issue: 3
Volume: 50
Year: 2023
Month: 02
X-DOI: 10.1080/02664763.2022.2068514
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2068514
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:3:p:786-804



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1937581_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Wei Zhang
Author-X-Name-First: Wei
Author-X-Name-Last: Zhang
Author-Name: Colin O. Wu
Author-X-Name-First: Colin O.
Author-X-Name-Last: Wu
Author-Name: Xiaoyang Ma
Author-X-Name-First: Xiaoyang
Author-X-Name-Last: Ma
Author-Name: Xin Tian
Author-X-Name-First: Xin
Author-X-Name-Last: Tian
Author-Name: Qizhai Li
Author-X-Name-First: Qizhai
Author-X-Name-Last: Li
Title: Analysis of multivariate longitudinal data using dynamic lasso-regularized copula models with application to large pediatric cardiovascular studies
Abstract: 
 The National Heart, Lung and Blood Institute Growth and Health Study (NGHS) is a large longitudinal study of childhood health. A main objective of the study is to estimate the joint distributions of cardiovascular risk outcomes at any two time points conditioning on a large number of covariates. Existing multivariate longitudinal methods are not suitable for outcomes at multiple time points. We present a dynamic copula approach for estimating an outcome's joint distributions at two time points given a large number of time-varying covariates. Our models depend on the outcome's time-varying distributions at one time point, the bivariate copula densities and the functional copula parameters. We develop a three-step procedure for variable selection and estimation, which selects the influential covariates using a machine learning procedure based on spline Lasso-regularized least squares, computes the outcome's single-time distribution using splines, and estimates the functional copula parameter of the dynamic copula models. Pointwise confidence intervals are constructed through the resampling-subject bootstrap. We apply our procedure to the NGHS cardiovascular risk data and illustrate the clinical interpretations of the conditional distributions of a set of risk outcomes. We demonstrate the statistical properties of the dynamic models and estimation procedure through a simulation study.
Journal: Journal of Applied Statistics
Pages: 631-658
Issue: 3
Volume: 50
Year: 2023
Month: 02
X-DOI: 10.1080/02664763.2021.1937581
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1937581
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:3:p:631-658



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1834516_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Liang Zhang
Author-X-Name-First: Liang
Author-X-Name-Last: Zhang
Author-Name: Tianming Zhu
Author-X-Name-First: Tianming
Author-X-Name-Last: Zhu
Author-Name: Jin-Ting Zhang
Author-X-Name-First: Jin-Ting
Author-X-Name-Last: Zhang
Title: Two-sample Behrens–Fisher problems for high-dimensional data: a normal reference scale-invariant test
Abstract: 
 For high-dimensional two-sample Behrens–Fisher problems, several non-scale-invariant and scale-invariant tests have been proposed. Most of them impose strong assumptions on the underlying group covariance matrices so that their test statistics are asymptotically normal. However, in practice, these assumptions may not be satisfied or hardly be checked so that these tests may not be able to maintain the nominal size well in practice. To overcome this difficulty, in this paper, a normal reference scale-invariant test is proposed and studied. It works well by neither imposing strong assumptions on the underlying group covariance matrices nor assuming their equality. It is shown that under some regularity conditions and the null hypothesis, the proposed test and a chi-square-type mixture have the same normal and non-normal limiting distributions. It is then justifiable to approximate the null distribution of the proposed test using that of the chi-square-type mixture. The distribution of the chi-square type mixture can be well approximated by the Welch–Satterthwaite chi-square-approximation with the approximation parameter consistently estimated from the data. The asymptotic power of the proposed test is established. Numerical results demonstrate that the proposed test has much better size control and power than several well-known non-scale-invariant and scale-invariant tests.
Journal: Journal of Applied Statistics
Pages: 456-476
Issue: 3
Volume: 50
Year: 2023
Month: 02
X-DOI: 10.1080/02664763.2020.1834516
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1834516
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:3:p:456-476



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1975662_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: X. Zhi
Author-X-Name-First: X.
Author-X-Name-Last: Zhi
Author-Name: J. Liu
Author-X-Name-First: J.
Author-X-Name-Last: Liu
Author-Name: S. Wu
Author-X-Name-First: S.
Author-X-Name-Last: Wu
Author-Name: C. Niu
Author-X-Name-First: C.
Author-X-Name-Last: Niu
Title: A generalized l2,p-norm regression based feature selection algorithm
Abstract: 
 Feature selection is an important data dimension reduction method, and it has been used widely in applications involving high-dimensional data such as genetic data analysis and image processing. In order to achieve robust feature selection, the latest works apply the 
 $ l_{2,1} $ l2,1 or 
 $ l_{2,p} $ l2,p-norm of matrix to the loss function and regularization terms in regression, and have achieved encouraging results. However, these existing works rigidly set the matrix norms used in the loss function and the regularization terms to the same 
 $ l_{2,1} $ l2,1 or 
 $ l_{2,p} $ l2,p-norm, which limit their applications. In addition, the algorithms for solutions they present either have high computational complexity and are not suitable for large data sets, or cannot provide satisfying performance due to the approximate calculation. To address these problems, we present a generalized 
 $ \textit{l}_{2,p} $ l2,p-norm regression based feature selection (
 $ l_{2,p} $ l2,p-RFS) method based on a new optimization criterion. The criterion extends the optimization criterion of (
 $ l_{2,p} $ l2,p-RFS) when the loss function and the regularization terms in regression use different matrix norms. We cast the new optimization criterion in a regression framework without regularization. In this framework, the new optimization criterion can be solved using an iterative re-weighted least squares (IRLS) procedure in which the least squares problem can be solved efficiently by using the least square QR decomposition (LSQR) algorithm. We have conducted extensive experiments to evaluate the proposed algorithm on various well-known data sets of both gene expression and image data sets, and compare it with other related feature selection methods.
Journal: Journal of Applied Statistics
Pages: 703-723
Issue: 3
Volume: 50
Year: 2023
Month: 02
X-DOI: 10.1080/02664763.2021.1975662
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1975662
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:3:p:703-723



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1937583_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Xiaobin Zhi
Author-X-Name-First: Xiaobin
Author-X-Name-Last: Zhi
Author-Name: Tongjun Yu
Author-X-Name-First: Tongjun
Author-X-Name-Last: Yu
Author-Name: Longtao Bi
Author-X-Name-First: Longtao
Author-X-Name-Last: Bi
Author-Name: Yalan Li
Author-X-Name-First: Yalan
Author-X-Name-Last: Li
Title: Noise-insensitive discriminative subspace fuzzy clustering
Abstract: 
 Discriminative subspace clustering (DSC) can make full use of linear discriminant analysis (LDA) to reduce the dimension of data and achieve effective clustering high-dimension data by clustering low-dimension data in discriminant subspace. However, most existing DSC algorithms do not consider the noise and outliers that may be contained in data sets, and when they are applied to the data sets with noise or outliers, and they often obtain poor performance due to the influence of noise and outliers. In this paper, we address the problem of the sensitivity of DSC to noise and outlier. Replacing the Euclidean distance in the objective function of LDA by an exponential non-Euclidean distance, we first develop a noise-insensitive LDA (NILDA) algorithm. Then, combining the proposed NILDA and a noise-insensitive fuzzy clustering algorithm: AFKM, we propose a noise-insensitive discriminative subspace fuzzy clustering (NIDSFC) algorithm. Experiments on some benchmark data sets show the effectiveness of the proposed NIDSFC algorithm.
Journal: Journal of Applied Statistics
Pages: 659-674
Issue: 3
Volume: 50
Year: 2023
Month: 02
X-DOI: 10.1080/02664763.2021.1937583
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1937583
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:3:p:659-674



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1936468_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Mingyue Zhang Wu
Author-X-Name-First: Mingyue
Author-X-Name-Last: Zhang Wu
Author-Name: Jinzhu Luo
Author-X-Name-First: Jinzhu
Author-X-Name-Last: Luo
Author-Name: Xing Fang
Author-X-Name-First: Xing
Author-X-Name-Last: Fang
Author-Name: Maochao Xu
Author-X-Name-First: Maochao
Author-X-Name-Last: Xu
Author-Name: Peng Zhao
Author-X-Name-First: Peng
Author-X-Name-Last: Zhao
Title: Modeling multivariate cyber risks: deep learning dating extreme value theory
Abstract: 
 Modeling cyber risks has been an important but challenging task in the domain of cyber security, which is mainly caused by the high dimensionality and heavy tails of risk patterns. Those obstacles have hindered the development of statistical modeling of the multivariate cyber risks. In this work, we propose a novel approach for modeling the multivariate cyber risks which relies on the deep learning and extreme value theory. The proposed model not only enjoys the high accurate point predictions via deep learning but also can provide the satisfactory high quantile predictions via extreme value theory. Both the simulation and empirical studies show that the proposed approach can model the multivariate cyber risks very well and provide satisfactory prediction performances.
Journal: Journal of Applied Statistics
Pages: 610-630
Issue: 3
Volume: 50
Year: 2023
Month: 02
X-DOI: 10.1080/02664763.2021.1936468
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1936468
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:3:p:610-630



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1999398_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Mi Zhou
Author-X-Name-First: Mi
Author-X-Name-Last: Zhou
Author-Name: Weixin Yao
Author-X-Name-First: Weixin
Author-X-Name-Last: Yao
Title: Sensitivity analysis of unmeasured confounding in causal inference based on exponential tilting and super learner
Abstract: 
 Causal inference under the potential outcome framework relies on the strongly ignorable treatment assumption. This assumption is usually questionable in observational studies, and the unmeasured confounding is one of the fundamental challenges in causal inference. To this end, we propose a new sensitivity analysis method to evaluate the impact of the unmeasured confounder by leveraging ideas of doubly robust estimators, the exponential tilt method, and the super learner algorithm. Compared to other existing methods of sensitivity analysis that parameterize the unmeasured confounder as a latent variable in the working models, the exponential tilting method does not impose any restrictions on the structure or models of the unmeasured confounders. In addition, in order to reduce the modeling bias of traditional parametric methods, we propose incorporating the super learner machine learning algorithm to perform nonparametric model estimation and the corresponding sensitivity analysis. Furthermore, most existing sensitivity analysis methods require multivariate sensitivity parameters, which make its choice difficult and subjective in practice. In comparison, the new method has a univariate sensitivity parameter with a nice and simple interpretation of log-odds ratios for binary outcomes, which makes its choice and the application of the new sensitivity analysis method very easy for practitioners.
Journal: Journal of Applied Statistics
Pages: 744-760
Issue: 3
Volume: 50
Year: 2023
Month: 02
X-DOI: 10.1080/02664763.2021.1999398
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1999398
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:3:p:744-760



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1904847_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Mingtao Zhao
Author-X-Name-First: Mingtao
Author-X-Name-Last: Zhao
Author-Name: Xiaoli Xu
Author-X-Name-First: Xiaoli
Author-X-Name-Last: Xu
Author-Name: Yanling Zhu
Author-X-Name-First: Yanling
Author-X-Name-Last: Zhu
Author-Name: Kongsheng Zhang
Author-X-Name-First: Kongsheng
Author-X-Name-Last: Zhang
Author-Name: Yan Zhou
Author-X-Name-First: Yan
Author-X-Name-Last: Zhou
Title: Model estimation and selection for partial linear varying coefficient EV models with longitudinal data
Abstract: 
 In this paper, we consider the estimation and model selection for longitudinal partial linear varying coefficient errors-in-variables (EV) models when the covariates are measured with some additive errors. Bias-corrected penalized quadratic inference functions method is proposed based on quadratic inference functions with two penalty function terms. The proposed method can not only handle the measurement errors of covariates and within-subject correlations but also estimate and select significant non-zero parametric and nonparametric components simultaneously. With some regularization conditions, the resulting estimators of parameters are asymptotically normal and the estimators of nonparametric varying coefficient achieves the optimal convergence rate. Furthermore, we present simulation studies and a real example analysis to evaluate the finite sample performance of the proposed method.
Journal: Journal of Applied Statistics
Pages: 512-534
Issue: 3
Volume: 50
Year: 2023
Month: 02
X-DOI: 10.1080/02664763.2021.1904847
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1904847
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:3:p:512-534



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1884847_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Mingao Yuan
Author-X-Name-First: Mingao
Author-X-Name-Last: Yuan
Author-Name: Qian Wen
Author-X-Name-First: Qian
Author-X-Name-Last: Wen
Title: A practical two-sample test for weighted random graphs
Abstract: 
 Network (graph) data analysis is a popular research topic in statistics and machine learning. In application, one is frequently confronted with graph two-sample hypothesis testing where the goal is to test the difference between two graph populations. Several statistical tests have been devised for this purpose in the context of binary graphs. However, many of the practical networks are weighted and existing procedures cannot be directly applied to weighted graphs. In this paper, we study the weighted graph two-sample hypothesis testing problem and propose a practical test statistic. We prove that the proposed test statistic converges in distribution to the standard normal distribution under the null hypothesis and analyze its power theoretically. The simulation study shows that the proposed test has satisfactory performance and it substantially outperforms the existing counterpart in the binary graph case. A real data application is provided to illustrate the method.
Journal: Journal of Applied Statistics
Pages: 495-511
Issue: 3
Volume: 50
Year: 2023
Month: 02
X-DOI: 10.1080/02664763.2021.1884847
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1884847
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:3:p:495-511



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2162471_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Yichuan Zhao
Author-X-Name-First: Yichuan
Author-X-Name-Last: Zhao
Author-Name: Chi-Hua Chen
Author-X-Name-First: Chi-Hua
Author-X-Name-Last: Chen
Author-Name: Feng Feng
Author-X-Name-First: Feng
Author-X-Name-Last: Feng
Author-Name: Dragan Pamucar
Author-X-Name-First: Dragan
Author-X-Name-Last: Pamucar
Title: Editorial to the special issue: Statistical Approaches for Big Data and Machine Learning
Journal: Journal of Applied Statistics
Pages: 451-455
Issue: 3
Volume: 50
Year: 2023
Month: 02
X-DOI: 10.1080/02664763.2023.2162471
File-URL: http://hdl.handle.net/10.1080/02664763.2023.2162471
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:3:p:451-455



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1947996_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Ilsuk Kang
Author-X-Name-First: Ilsuk
Author-X-Name-Last: Kang
Author-Name: Cheolwoo Park
Author-X-Name-First: Cheolwoo
Author-X-Name-Last: Park
Author-Name: Young Joo Yoon
Author-X-Name-First: Young Joo
Author-X-Name-Last: Yoon
Author-Name: Changyi Park
Author-X-Name-First: Changyi
Author-X-Name-Last: Park
Author-Name: Soon-Sun Kwon
Author-X-Name-First: Soon-Sun
Author-X-Name-Last: Kwon
Author-Name: Hosik Choi
Author-X-Name-First: Hosik
Author-X-Name-Last: Choi
Title: Classification of histogram-valued data with support histogram machines
Abstract: 
 The current large amounts of data and advanced technologies have produced new types of complex data, such as histogram-valued data. The paper focuses on classification problems when predictors are observed as or aggregated into histograms. Because conventional classification methods take vectors as input, a natural approach converts histograms into vector-valued data using summary values, such as the mean or median. However, this approach forgoes the distributional information available in histograms. To address this issue, we propose a margin-based classifier called support histogram machine (SHM) for histogram-valued data. We adopt the support vector machine framework and the Wasserstein-Kantorovich metric to measure distances between histograms. The proposed optimization problem is solved by a dual approach. We then test the proposed SHM via simulated and real examples and demonstrate its superior performance to summary-value-based methods.
Journal: Journal of Applied Statistics
Pages: 675-690
Issue: 3
Volume: 50
Year: 2023
Month: 02
X-DOI: 10.1080/02664763.2021.1947996
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1947996
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:3:p:675-690



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1985090_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Yujia Cheng
Author-X-Name-First: Yujia
Author-X-Name-Last: Cheng
Author-Name: Yang Li
Author-X-Name-First: Yang
Author-X-Name-Last: Li
Author-Name: Matthew Lee Smith
Author-X-Name-First: Matthew
Author-X-Name-Last: Lee Smith
Author-Name: Changwei Li
Author-X-Name-First: Changwei
Author-X-Name-Last: Li
Author-Name: Ye Shen
Author-X-Name-First: Ye
Author-X-Name-Last: Shen
Title: Analyzing evidence-based falls prevention data with significant missing information using variable selection after multiple imputation
Abstract: 
 Falls are the leading cause of fatal and non-fatal injuries among older adults. Evidence-based fall prevention programs are delivered nationwide, largely supported by funding from the Administration for Community Living (ACL), to mitigate fall-related risk. This study utilizes data from 39 ACL grantees in 22 states from 2014 to 2017. The large amount of missing values for falls efficacy in this national database may lead to potentially biased statistical results and make it challenging to implement reliable variable selection. Multiple imputation is used to deal with missing values. To obtain a consistent result of variable selection in multiply-imputed datasets, multiple imputation-stepwise regression (MI-stepwise) and multiple imputation-least absolute shrinkage and selection operator (MI-LASSO) methods are used. To compare the performances of MI-stepwise and MI-LASSO, simulation studies were conducted. In particular, we extended prior work by considering several circumstances not covered in previous studies, including an extensive investigation of data with different signal-to-noise ratios and various missing data patterns across predictors, as well as a data structure that allowed the missingness mechanism to be missing not at random (MNAR). In addition, we evaluated the performance of MI-LASSO method with varying tuning parameters to address the overselection issue in cross-validation (CV)-based LASSO.
Journal: Journal of Applied Statistics
Pages: 724-743
Issue: 3
Volume: 50
Year: 2023
Month: 02
X-DOI: 10.1080/02664763.2021.1985090
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1985090
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:3:p:724-743



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1911967_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Xie Xiaoyue
Author-X-Name-First: Xie
Author-X-Name-Last: Xiaoyue
Author-Name: Jian Shi
Author-X-Name-First: Jian
Author-X-Name-Last: Shi
Author-Name: Kai Song
Author-X-Name-First: Kai
Author-X-Name-Last: Song
Title: A distributed multiple sample testing for massive data
Abstract: 
 When the data are stored in a distributed manner, direct application of traditional hypothesis testing procedures is often prohibitive due to communication costs and privacy concerns. This paper mainly develops and investigates a distributed two-node Kolmogorov–Smirnov hypothesis testing scheme, implemented by the divide-and-conquer strategy. In addition, this paper also provides a distributed fraud detection and a distribution-based classification for multi-node machines based on the proposed hypothesis testing scheme. The distributed fraud detection is to detect which node stores fraud data in multi-node machines and the distribution-based classification is to determine whether the multi-node distributions differ and classify different distributions. These methods can improve the accuracy of statistical inference in a distributed storage architecture. Furthermore, this paper verifies the feasibility of the proposed methods by simulation and real example studies.
Journal: Journal of Applied Statistics
Pages: 555-573
Issue: 3
Volume: 50
Year: 2023
Month: 02
X-DOI: 10.1080/02664763.2021.1911967
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1911967
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:3:p:555-573



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1929089_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Fang Fan
Author-X-Name-First: Fang
Author-X-Name-Last: Fan
Author-Name: Shu-Chuan Chu
Author-X-Name-First: Shu-Chuan
Author-X-Name-Last: Chu
Author-Name: Jeng-Shyang Pan
Author-X-Name-First: Jeng-Shyang
Author-X-Name-Last: Pan
Author-Name: Chuang Lin
Author-X-Name-First: Chuang
Author-X-Name-Last: Lin
Author-Name: Huiqi Zhao
Author-X-Name-First: Huiqi
Author-X-Name-Last: Zhao
Title: An optimized machine learning technology scheme and its application in fault detection in wireless sensor networks
Abstract: 
 Aiming at the problem of fault detection in data collection in wireless sensor networks, this paper combines evolutionary computing and machine learning to propose a productive technical solution. We choose the classical particle swarm optimization (PSO) and improve it, including the introduction of a biological population model to control the population size, and the addition of a parallel mechanism for further tuning. The proposed RS-PPSO algorithm was successfully used to optimize the initial weights and biases of back propagation neural network (BPNN), shortening the training time and raising the prediction accuracy. Wireless sensor networks (WSN) has become the key supporting platform of Internet of Things (IoT). The correctness of the data collected by the sensor nodes has a great influence on the reliability, real-time performance and energy saving of the entire network. The optimized machine learning technology scheme given in this paper can effectively identify the fault data, so as to ensure the effective operation of WSN.
Journal: Journal of Applied Statistics
Pages: 592-609
Issue: 3
Volume: 50
Year: 2023
Month: 02
X-DOI: 10.1080/02664763.2021.1929089
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1929089
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:3:p:592-609



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1849057_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Denis A. Pustokhin
Author-X-Name-First: Denis A.
Author-X-Name-Last: Pustokhin
Author-Name: Irina V. Pustokhina
Author-X-Name-First: Irina V.
Author-X-Name-Last: Pustokhina
Author-Name: Phuoc Nguyen Dinh
Author-X-Name-First: Phuoc Nguyen
Author-X-Name-Last: Dinh
Author-Name: Son Van Phan
Author-X-Name-First: Son Van
Author-X-Name-Last: Phan
Author-Name: Gia Nhu Nguyen
Author-X-Name-First: Gia Nhu
Author-X-Name-Last: Nguyen
Author-Name: Gyanendra Prasad Joshi
Author-X-Name-First: Gyanendra Prasad
Author-X-Name-Last: Joshi
Author-Name: Shankar K.
Author-X-Name-First: Shankar
Author-X-Name-Last: K.
Title: An effective deep residual network based class attention layer with bidirectional LSTM for diagnosis and classification of COVID-19
Abstract: 
 In recent days, COVID-19 pandemic has affected several people's lives globally and necessitates a massive number of screening tests to detect the existence of the coronavirus. At the same time, the rise of deep learning (DL) concepts helps to effectively develop a COVID-19 diagnosis model to attain maximum detection rate with minimum computation time. This paper presents a new Residual Network (ResNet) based Class Attention Layer with Bidirectional LSTM called RCAL-BiLSTM for COVID-19 Diagnosis. The proposed RCAL-BiLSTM model involves a series of processes namely bilateral filtering (BF) based preprocessing, RCAL-BiLSTM based feature extraction, and softmax (SM) based classification. Once the BF technique produces the preprocessed image, RCAL-BiLSTM based feature extraction process takes place using three modules, namely ResNet based feature extraction, CAL, and Bi-LSTM modules. Finally, the SM layer is applied to categorize the feature vectors into corresponding feature maps. The experimental validation of the presented RCAL-BiLSTM model is tested against Chest-X-Ray dataset and the results are determined under several aspects. The experimental outcome pointed out the superior nature of the RCAL-BiLSTM model by attaining maximum sensitivity of 93.28%, specificity of 94.61%, precision of 94.90%, accuracy of 94.88%, F-score of 93.10% and kappa value of 91.40%.
Journal: Journal of Applied Statistics
Pages: 477-494
Issue: 3
Volume: 50
Year: 2023
Month: 02
X-DOI: 10.1080/02664763.2020.1849057
File-URL: http://hdl.handle.net/10.1080/02664763.2020.1849057
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:3:p:477-494



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2017411_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Jin Jin
Author-X-Name-First: Jin
Author-X-Name-Last: Jin
Author-Name: Lin Zhang
Author-X-Name-First: Lin
Author-X-Name-Last: Zhang
Author-Name: Ethan Leng
Author-X-Name-First: Ethan
Author-X-Name-Last: Leng
Author-Name: Gregory J. Metzger
Author-X-Name-First: Gregory J.
Author-X-Name-Last: Metzger
Author-Name: Joseph S. Koopmeiners
Author-X-Name-First: Joseph S.
Author-X-Name-Last: Koopmeiners
Title: Multi-resolution super learner for voxel-wise classification of prostate cancer using multi-parametric MRI
Abstract: 
 Multi-parametric MRI (mpMRI) is a critical tool in prostate cancer (PCa) diagnosis and management. To further advance the use of mpMRI in patient care, computer aided diagnostic methods are under continuous development for supporting/supplanting standard radiological interpretation. While voxel-wise PCa classification models are the gold standard, few if any approaches have incorporated the inherent structure of the mpMRI data, such as spatial heterogeneity and between-voxel correlation, into PCa classification. We propose a machine learning-based method to fill in this gap. Our method uses an ensemble learning approach to capture regional heterogeneity in the data, where classifiers are developed at multiple resolutions and combined using the super learner algorithm, and further account for between-voxel correlation through a Gaussian kernel smoother. It allows any type of classifier to be the base learner and can be extended to further classify PCa sub-categories. We introduce the algorithms for binary PCa classification, as well as for classifying the ordinal clinical significance of PCa for which a weighted likelihood approach is implemented to improve the detection of less prevalent cancer categories. The proposed method has shown important advantages over conventional modeling and machine learning approaches in simulations and application to our motivating patient data.
Journal: Journal of Applied Statistics
Pages: 805-826
Issue: 3
Volume: 50
Year: 2023
Month: 02
X-DOI: 10.1080/02664763.2021.2017411
File-URL: http://hdl.handle.net/10.1080/02664763.2021.2017411
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:3:p:805-826



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1919063_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Anton Thielmann
Author-X-Name-First: Anton
Author-X-Name-Last: Thielmann
Author-Name: Christoph Weisser
Author-X-Name-First: Christoph
Author-X-Name-Last: Weisser
Author-Name: Astrid Krenz
Author-X-Name-First: Astrid
Author-X-Name-Last: Krenz
Author-Name: Benjamin Säfken
Author-X-Name-First: Benjamin
Author-X-Name-Last: Säfken
Title: Unsupervised document classification integrating web scraping, one-class SVM and LDA topic modelling
Abstract: 
 Unsupervised document classification for imbalanced data sets poses a major challenge. To obtain accurate classification results, training data sets are often created manually by humans which requires expert knowledge, time and money. Depending on the imbalance of the data set, this approach also either requires human labelling of all of the data or it fails to adequately recognize underrepresented categories. We propose an integration of web scraping, one-class Support Vector Machines (SVM) and Latent Dirichlet Allocation (LDA) topic modelling as a multi-step classification rule that circumvents manual labelling. Unsupervised one-class document classification with the integration of out-of-domain training data is achieved and >80% of the target data is correctly classified. The proposed method thus even outperforms common machine learning classifiers and is validated on multiple data sets.
Journal: Journal of Applied Statistics
Pages: 574-591
Issue: 3
Volume: 50
Year: 2023
Month: 02
X-DOI: 10.1080/02664763.2021.1919063
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1919063
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:3:p:574-591



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1982878_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Matthias Weber
Author-X-Name-First: Matthias
Author-X-Name-Last: Weber
Author-Name: Jonas Striaukas
Author-X-Name-First: Jonas
Author-X-Name-Last: Striaukas
Author-Name: Martin Schumacher
Author-X-Name-First: Martin
Author-X-Name-Last: Schumacher
Author-Name: Harald Binder
Author-X-Name-First: Harald
Author-X-Name-Last: Binder
Title: Regularized regression when covariates are linked on a network: the 3CoSE algorithm
Abstract: 
 Covariates in regressions may be linked to each other on a network. Knowledge of the network structure can be incorporated into regularized regression settings via a network penalty term. However, when it is unknown whether the connection signs in the network are positive (connected covariates reinforce each other) or negative (connected covariates repress each other), the connection signs have to be estimated jointly with the covariate coefficients. This can be done with an algorithm iterating a connection sign estimation step and a covariate coefficient estimation step. We develop such an algorithm, called 3CoSE, and show detailed simulation results and an application forecasting event times. The algorithm performs well in a variety of settings. We also briefly describe the publicly available R-package developed for this purpose.
Journal: Journal of Applied Statistics
Pages: 535-554
Issue: 3
Volume: 50
Year: 2023
Month: 02
X-DOI: 10.1080/02664763.2021.1982878
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1982878
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:3:p:535-554



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2047905_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Mehdi Dagdoug
Author-X-Name-First: Mehdi
Author-X-Name-Last: Dagdoug
Author-Name: Camelia Goga
Author-X-Name-First: Camelia
Author-X-Name-Last: Goga
Author-Name: David Haziza
Author-X-Name-First: David
Author-X-Name-Last: Haziza
Title: Model-assisted estimation in high-dimensional settings for survey data
Abstract: 
 Model-assisted estimators have attracted a lot of attention in the last three decades. These estimators attempt to make an efficient use of auxiliary information available at the estimation stage. A working model linking the survey variable to the auxiliary variables is specified and fitted on the sample data to obtain a set of predictions, which are then incorporated in the estimation procedures. A nice feature of model-assisted procedures is that they maintain important design properties such as consistency and asymptotic unbiasedness irrespective of whether or not the working model is correctly specified. In this article, we examine several model-assisted estimators from a design-based point of view and in a high-dimensional setting, including linear regression and penalized estimators. We conduct an extensive simulation study using data from the Irish Commission for Energy Regulation Smart Metering Project, to assess the performance of several model-assisted estimators in terms of bias and efficiency in this high-dimensional data set.
Journal: Journal of Applied Statistics
Pages: 761-785
Issue: 3
Volume: 50
Year: 2023
Month: 02
X-DOI: 10.1080/02664763.2022.2047905
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2047905
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:3:p:761-785



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1973387_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20220907T060133 git hash: 85d61bd949
Author-Name: Xiaotong Liu
Author-X-Name-First: Xiaotong
Author-X-Name-Last: Liu
Author-Name: Guoliang Tian
Author-X-Name-First: Guoliang
Author-X-Name-Last: Tian
Author-Name: Zhenqiu Liu
Author-X-Name-First: Zhenqiu
Author-X-Name-Last: Liu
Title: Identification of novel genes for triple-negative breast cancer with semiparametric gene-based analysis
Abstract: 
 Triple-negative breast cancer (TNBC) is generally considered an aggressive breast cancer subtype associated with poor prognostic outcomes. Up to now, the molecular and cellular mechanisms underlying TNBC pathology have not been fully understood. In this manuscript, we propose a novel semiparametric model with kernel for gene-based analysis with a breast cancer GWAS data. The software of SPMGBA (semiparametric method for gene-based analysis) in MATLAB is available at GitHub (https://github.com/zliu3/SPMGBA). Genetic signatures associated with breast cancer are discovered. We further validate the prognostic power of the identified genes with a large cohort of expression data from the European Genome-Phenome Archive, and discover that SEL1L is associated with the overall survival of TNBC with the p-value of .0002. We conclude that gene SEL1L is down-regulated in TNBC and the expression of SEL1L is positively associated with patient survival.
Journal: Journal of Applied Statistics
Pages: 691-702
Issue: 3
Volume: 50
Year: 2023
Month: 02
X-DOI: 10.1080/02664763.2021.1973387
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1973387
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:3:p:691-702

Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1994530_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Akira Suzuki
Author-X-Name-First: Akira
Author-X-Name-Last: Suzuki
Author-Name: Hidetoshi Murakami
Author-X-Name-First: Hidetoshi
Author-X-Name-Last: Murakami
Author-Name: Amitava Mukherjee
Author-X-Name-First: Amitava
Author-X-Name-Last: Mukherjee
Title: Distribution-free Phase-I scheme for location, scale and skewness shifts with an application in monitoring customers' waiting time
Abstract: 
 Phase-I analysis of historical data from a statistical process is a strategic problem in Statistical Process Monitoring and control. Before the establishment of process stability, it is challenging to model historical data. Consequently, a distribution-free approach is a natural choice in Phase-I monitoring. Existing distribution-free Phase-I control charts are suitable for detecting instability in location and scale parameters only and are often insensitive in complex processes involving skewness or shape parameters. A new Phase-I control chart is proposed to identify more general shifts, including location, scale and skewness. The proposed Phase-I scheme is efficient in such a situation. The proposed Phase-I scheme uses subsamples, and the plotting statistic is based on the omnibus multi-sample linear rank statistic corresponding to the location, scale and skewness shifts. The new scheme can identify subsamples that are not in control, and it can also indicate one or more process parameters where a deviation has occurred. The encouraging performance of the proposed scheme is established with a large-scale numerical study based on Monte-Carlo in detecting shifts of various nature in a comprehensive class of situations. An illustration based on monitoring the waiting time data from a customer service centre is given. Some concluding remarks and some future research problems are also offered.
Journal: Journal of Applied Statistics
Pages: 827-847
Issue: 4
Volume: 50
Year: 2023
Month: 03
X-DOI: 10.1080/02664763.2021.1994530
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1994530
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:4:p:827-847



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1998391_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Erina Paul
Author-X-Name-First: Erina
Author-X-Name-Last: Paul
Author-Name: Ram C. Tiwari
Author-X-Name-First: Ram C.
Author-X-Name-Last: Tiwari
Author-Name: Shrabanti Chowdhury
Author-X-Name-First: Shrabanti
Author-X-Name-Last: Chowdhury
Author-Name: Samiran Ghosh
Author-X-Name-First: Samiran
Author-X-Name-Last: Ghosh
Title: A more powerful test for three-arm non-inferiority via risk difference: Frequentist and Bayesian approaches
Abstract: 
 Necessity for finding improved intervention in many legacy therapeutic areas are of high priority. This has the potential to decrease the expense of medical care and poor outcomes for many patients. Typically, clinical efficacy is the primary evaluating criteria to measure any beneficial effect of a treatment. Albeit, there could be situations when several other factors (e.g. side-effects, cost-burden, less debilitating, less intensive, etc.) which can permit some slightly less efficacious treatment options favorable to a subgroup of patients. This often leads to non-inferiority (NI) testing. NI trials may or may not include a placebo arm due to ethical reasons. However, when included, the resulting three-arm trial is more prudent since it requires less stringent assumptions compared to a two-arm placebo-free trial. In this article, we consider both Frequentist and Bayesian procedures for testing NI in the three-arm trial with binary outcomes when the functional of interest is risk difference. An improved Frequentist approach is proposed first, which is then followed by a Bayesian counterpart. Bayesian methods have a natural advantage in many active-control trials, including NI trial, as it can seamlessly integrate substantial prior information. In addition, we discuss sample size calculation and draw an interesting connection between the two paradigms.
Journal: Journal of Applied Statistics
Pages: 848-870
Issue: 4
Volume: 50
Year: 2023
Month: 03
X-DOI: 10.1080/02664763.2021.1998391
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1998391
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:4:p:848-870



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2001442_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Mustafa Ç. Korkmaz
Author-X-Name-First: Mustafa Ç.
Author-X-Name-Last: Korkmaz
Author-Name: Zehra Sedef Korkmaz
Author-X-Name-First: Zehra Sedef
Author-X-Name-Last: Korkmaz
Title: The unit log–log distribution: a new unit distribution with alternative quantile regression modeling and educational measurements applications
Abstract: 
 In this paper, we propose a new distribution, named unit log–log distribution, defined on the bounded (0,1) interval. Basic distributional properties such as model shapes, stochastic ordering, quantile function, moments, and order statistics of the newly defined unit distribution are studied. The maximum likelihood estimation method has been pointed out to estimate its model parameters. The new quantile regression model based on the proposed distribution is introduced and it has been derived estimations of its model parameters also. The Monte Carlo simulation studies have been given to see the performance of the estimation method based on the new unit distribution and its regression modeling. Applications of the newly defined distribution and its quantile regression model to real data sets show that the proposed models have better modeling abilities than competitive models. The proposed unit quantile regression model has targeted to explain linear relation between educational measurements of both OECD (Organization for Economic Co-operation and Development) countries and some non-members of OECD countries, and their Better Life Index. The existence of the significant covariates has been seen on the real data applications for the unit median response.
Journal: Journal of Applied Statistics
Pages: 889-908
Issue: 4
Volume: 50
Year: 2023
Month: 03
X-DOI: 10.1080/02664763.2021.2001442
File-URL: http://hdl.handle.net/10.1080/02664763.2021.2001442
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:4:p:889-908



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2003760_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Rusul Mohsin Moharib Alsarray
Author-X-Name-First: Rusul Mohsin
Author-X-Name-Last: Moharib Alsarray
Author-Name: Jaber Kazempoor
Author-X-Name-First: Jaber
Author-X-Name-Last: Kazempoor
Author-Name: Adel Ahmadi Nadi
Author-X-Name-First: Adel
Author-X-Name-Last: Ahmadi Nadi
Title: Monitoring the Weibull shape parameter under progressive censoring in presence of independent competing risks
Abstract: 
 In this paper, monitoring the Weibull shape parameter arising from progressively censored competing risks data is investigated. The competing risks are assumed to be independent and not identically distributed from the Weibull distributions with different shape and scale parameters. Both the shape parameters can be monitored separately by the proposed control charts using censored and predicted observations. We also introduced a control chart for monitoring both shape parameters simultaneously to detect possible shifts in both opposite and the same directions. In addition, the problem of mask data is discussed and an efficient prediction method is proposed. The behavior of the average run length with and without mask data is investigated through extensive simulations. Furthermore, the effects of sample size, number of failures due to each risk, and censoring scheme on the charts' performance are also studied. Finally, an illustrative example is presented to demonstrate the application of the proposed control charts by investigating a real data set of the failure times of two-component ARC-1 VHF communication transmitter receivers of a single commercial airline. Although this data set has been widely investigated in reliability analysis studies, this is the first time it has been analyzed in a statistical process monitoring setting.
Journal: Journal of Applied Statistics
Pages: 945-962
Issue: 4
Volume: 50
Year: 2023
Month: 03
X-DOI: 10.1080/02664763.2021.2003760
File-URL: http://hdl.handle.net/10.1080/02664763.2021.2003760
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:4:p:945-962



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1998392_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Rosineide da Paz
Author-X-Name-First: Rosineide
Author-X-Name-Last: da Paz
Author-Name: Jorge Luis Bazán
Author-X-Name-First: Jorge Luis
Author-X-Name-Last: Bazán
Author-Name: Victor Hugo Lachos
Author-X-Name-First: Victor Hugo
Author-X-Name-Last: Lachos
Author-Name: Dipak Dey
Author-X-Name-First: Dipak
Author-X-Name-Last: Dey
Title: A finite mixture mixed proportion regression model for classification problems in longitudinal voting data
Abstract: 
 Continuous clustered proportion data often arise in various areas of the social and political sciences where the response variable of interest is a proportion (or percentage). An example is the behavior of the proportion of voters favorable to a political party in municipalities (or cities) of a country over time. This behavior can be different depending on the region of the country, giving rise to groups (or clusters) with similar profiles. For this kind of data, we propose a finite mixture of a random effects regression model based on the L-Logistic distribution. A Markov chain Monte Carlo algorithm is tailored to obtain posterior distributions of the unknown quantities of interest through a Bayesian approach. To illustrate the proposed method, with emphasis on analysis of clusters, we analyze the proportion of votes for a political party in presidential elections in different municipalities observed over time, and then identify groups according to electoral behavior at different levels of favorable votes.
Journal: Journal of Applied Statistics
Pages: 871-888
Issue: 4
Volume: 50
Year: 2023
Month: 03
X-DOI: 10.1080/02664763.2021.1998392
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1998392
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:4:p:871-888



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2004580_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Qianyi Li
Author-X-Name-First: Qianyi
Author-X-Name-Last: Li
Author-Name: Jianbo Li
Author-X-Name-First: Jianbo
Author-X-Name-Last: Li
Author-Name: Yongran Cheng
Author-X-Name-First: Yongran
Author-X-Name-Last: Cheng
Author-Name: Riquan Zhang
Author-X-Name-First: Riquan
Author-X-Name-Last: Zhang
Title: Curve fitting and jump detection on nonparametric regression with missing data
Abstract: 
 In this paper, by virtual of the inverse probability weighted technique, we considered the jump-preserving estimation on the nonparametric regression models with missing data on response variable. First, we used local piecewise-linear expansion respectively with left and right kernel to approximate the unknown regression function. Second, we obtained the left- and right-limit estimation of regression function at each observed points and then determinated the final estimators by residual sums of squares. Third, we presented the convergence rate of estimators and the residual sums of squares. Finally, we illustrated the performance of our proposed method through some simulation studies and a conjunctivitis example from The Affiliated Hospital of Hangzhou Normal University.
Journal: Journal of Applied Statistics
Pages: 963-983
Issue: 4
Volume: 50
Year: 2023
Month: 03
X-DOI: 10.1080/02664763.2021.2004580
File-URL: http://hdl.handle.net/10.1080/02664763.2021.2004580
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:4:p:963-983



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2001444_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Guanfu Liu
Author-X-Name-First: Guanfu
Author-X-Name-Last: Liu
Author-Name: Zongliang Hu
Author-X-Name-First: Zongliang
Author-X-Name-Last: Hu
Title: Testing quantitative trait locus effects in genetic backcross studies with double recombination occurring
Abstract: 
 Testing the existence of quantitative trait locus (QTL) effects is an important task in QTL mapping studies. In this paper, we assume the phenotype distributions from a location-scale distribution family, and consider to test the QTL effects in both location and scale in the backcross studies with double recombination occurring. Without equal scale assumption, the log-likelihood function is unbounded, which leads to the traditional likelihood ratio test being invalid. To deal with this problem, we propose a penalized likelihood ratio test (PLRT) for testing the QTL effects. The null limiting distribution of the PLRT is shown to be a supremum of a chi-square process. As a complement, we also investigate the null limiting distribution of the likelihood ratio test for the case with equal scale assumption. The limiting distributions of the two tests under local alternatives are also studied. Simulation studies are performed to evaluate the asymptotic results and a real-data example is given for illustration.
Journal: Journal of Applied Statistics
Pages: 927-944
Issue: 4
Volume: 50
Year: 2023
Month: 03
X-DOI: 10.1080/02664763.2021.2001444
File-URL: http://hdl.handle.net/10.1080/02664763.2021.2001444
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:4:p:927-944



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2006613_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: V. Mohtashami-Borzadaran
Author-X-Name-First: V.
Author-X-Name-Last: Mohtashami-Borzadaran
Author-Name: M. Amini
Author-X-Name-First: M.
Author-X-Name-Last: Amini
Author-Name: J. Ahmadi
Author-X-Name-First: J.
Author-X-Name-Last: Ahmadi
Title: Estimating the parameters of a dependent model and applying it to environmental data set
Abstract: 
 In this paper, a new dependent model is introduced. The model is motivated using the structure of series-parallel systems consisting of two series-parallel systems with a random number of parallel sub-systems that have fixed components connected in series. The dependence properties of the proposed model are studied. Two estimation methods, namely the moment method, and the maximum likelihood method are applied to estimate the parameters of the distributions of the components based on observing the system's lifetime data. A Monte Carlo simulation study is used to evaluate the performance of the estimators. Two real data sets are used to illustrate the proposed method. The results are useful for researchers and practitioners interested in analyzing bivariate data related to extreme events.
Journal: Journal of Applied Statistics
Pages: 984-1016
Issue: 4
Volume: 50
Year: 2023
Month: 03
X-DOI: 10.1080/02664763.2021.2006613
File-URL: http://hdl.handle.net/10.1080/02664763.2021.2006613
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:4:p:984-1016



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2008882_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Muhammad Atif
Author-X-Name-First: Muhammad
Author-X-Name-Last: Atif
Author-Name: Muhammad Shafiq
Author-X-Name-First: Muhammad
Author-X-Name-Last: Shafiq
Author-Name: Friedrich Leisch
Author-X-Name-First: Friedrich
Author-X-Name-Last: Leisch
Title: Applications of monitoring and tracing the evolution of clustering solutions in dynamic datasets
Abstract: 
 The clustering approach is widely accepted as the most prominent unsupervised learning problem in data mining techniques. This procedure deals with the identification of notable structures in unlabeled datasets. In modern days clustering of dynamic data, streams play a vital role in policy-making, and researchers are paying particular attention to monitoring the evolution of clustering solutions over time. The data streams evolve continually, and different sources generate data items over time. The clustering solution over this stream is not stationary and changes with the influx of new data items. This paper presents a comprehensive study of algorithms related to tracing the evolution of clusters over time in cumulative datasets. To demonstrate the applications and significance of the tracing cluster evolution, we implement the MONIC algorithm in R-software. This article illustrates how the data segmentation of dynamic streams is done and shows the applications of monitoring changes in clustering solutions with the help of real-life published datasets.
Journal: Journal of Applied Statistics
Pages: 1017-1035
Issue: 4
Volume: 50
Year: 2023
Month: 03
X-DOI: 10.1080/02664763.2021.2008882
File-URL: http://hdl.handle.net/10.1080/02664763.2021.2008882
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:4:p:1017-1035



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2001443_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: A. Venkatasubramaniam
Author-X-Name-First: A.
Author-X-Name-Last: Venkatasubramaniam
Author-Name: L. Evers
Author-X-Name-First: L.
Author-X-Name-Last: Evers
Author-Name: P. Thakuriah
Author-X-Name-First: P.
Author-X-Name-Last: Thakuriah
Author-Name: K. Ampountolas
Author-X-Name-First: K.
Author-X-Name-Last: Ampountolas
Title: Functional distributional clustering using spatio-temporal data
Abstract: 
 This paper presents a new method called the functional distributional clustering algorithm (FDCA) that seeks to identify spatially contiguous clusters and incorporate changes in temporal patterns across overcrowded networks. This method is motivated by a graph-based network composed of sensors arranged over space where recorded observations for each sensor represent a multi-modal distribution. The proposed method is fully non-parametric and generates clusters within an agglomerative hierarchical clustering approach based on a measure of distance that defines a cumulative distribution function over temporal changes for different locations in space. Traditional hierarchical clustering algorithms that are spatially adapted do not typically accommodate the temporal characteristics of the underlying data. The effectiveness of the FDCA is illustrated using an application to both empirical and simulated data from about 400 sensors in a 2.5 square miles network area in downtown San Francisco, California. The results demonstrate the superior ability of the the FDCA in identifying true clusters compared to functional only and distributional only algorithms and similar performance to a model-based clustering algorithm.
Journal: Journal of Applied Statistics
Pages: 909-926
Issue: 4
Volume: 50
Year: 2023
Month: 03
X-DOI: 10.1080/02664763.2021.2001443
File-URL: http://hdl.handle.net/10.1080/02664763.2021.2001443
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:4:p:909-926

Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2017412_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Ke-Yi Mou
Author-X-Name-First: Ke-Yi
Author-X-Name-Last: Mou
Author-Name: Chang-Xing Ma
Author-X-Name-First: Chang-Xing
Author-X-Name-Last: Ma
Author-Name: Zhi-Ming Li
Author-X-Name-First: Zhi-Ming
Author-X-Name-Last: Li
Title: Homogeneity test of relative risk ratios for stratified bilateral data under different algorithms
Abstract: 
 Medical clinical studies about paired body parts often involve stratified bilateral data. The correlation between responses from paired parts should be taken into account to avoid biased or misleading results. This paper aims to test if the relative risk ratios across strata are equal under the optimal algorithms. Based on different algorithms, we obtain the desired global and constrained maximum likelihood estimations (MLEs). Three asymptotic test statistics (i.e. 
 $ T_{L} $ TL, 
 $ T_{SC} $ TSC and 
 $ T_{W} $ TW) are proposed. Monte Carlo simulations are conducted to evaluate the performance of these algorithms with respect to mean square errors of MLEs and convergence rate. The empirical results show Fisher scoring algorithm is usually better than other methods since it has effective convergence rate for global MLEs, and makes mean-square error lower for constrained MLEs. Three test statistics are compared in terms of type I error rate (TIE) and power. Among these statistics, 
 $ T_{SC} $ TSC is recommended according to its robust TIEs and satisfactory power.
Journal: Journal of Applied Statistics
Pages: 1060-1077
Issue: 5
Volume: 50
Year: 2023
Month: 04
X-DOI: 10.1080/02664763.2021.2017412
File-URL: http://hdl.handle.net/10.1080/02664763.2021.2017412
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:5:p:1060-1077



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2023116_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Kenneth Flagg
Author-X-Name-First: Kenneth
Author-X-Name-Last: Flagg
Author-Name: Andrew Hoegh
Author-X-Name-First: Andrew
Author-X-Name-Last: Hoegh
Title: The integrated nested Laplace approximation applied to spatial log-Gaussian Cox process models
Abstract: 
 Spatial point process models are theoretically useful for mapping discrete events, such as plant or animal presence, across space; however, the computational complexity of fitting these models is often a barrier to their practical use. The log-Gaussian Cox process (LGCP) is a point process driven by a latent Gaussian field, and recent advances have made it possible to fit Bayesian LGCP models using approximate methods that facilitate rapid computation. These advances include the integrated nested Laplace approximation (INLA) with a stochastic partial differential equations (SPDE) approach to sparsely approximate the Gaussian field and an extension using pseudodata with a Poisson response. To help link the theoretical results to statistical practice, we provide an overview of INLA for point process data and then illustrate their implementation using freely available data. The analyzed datasets include both a completely observed spatial field and an incomplete data situation. Our well-commented R code is shared in the online supplement. Our intent is to make these methods accessible to the practitioner of spatial statistics without requiring deep knowledge of point process theory.
Journal: Journal of Applied Statistics
Pages: 1128-1151
Issue: 5
Volume: 50
Year: 2023
Month: 04
X-DOI: 10.1080/02664763.2021.2023116
File-URL: http://hdl.handle.net/10.1080/02664763.2021.2023116
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:5:p:1128-1151



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2023118_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Lin Tang
Author-X-Name-First: Lin
Author-X-Name-Last: Tang
Author-Name: Pengcheng Zeng
Author-X-Name-First: Pengcheng
Author-X-Name-Last: Zeng
Author-Name: Jian Qing Shi
Author-X-Name-First: Jian
Author-X-Name-Last: Qing Shi
Author-Name: Won-Seok Kim
Author-X-Name-First: Won-Seok
Author-X-Name-Last: Kim
Title: Model-based joint curve registration and classification
Abstract: 
 In this paper, we consider the problem of classification of misaligned multivariate functional data. We propose to use a model-based approach for the joint registration and classification of such data. The observed functional inputs are modeled as a functional nonlinear mixed effects model containing a nonlinear functional fixed effect constructed upon warping functions to account for curve alignment, and a nonlinear functional random effects component to address the variability among subjects. The warping functions are also modeled to accommodate common effect within groups and the variability between subjects. Then, a functional logistic regression model defined upon the representation of the aligned curves and scalar inputs is used to account for curve classification. EM-based algorithms are developed to perform maximum likelihood inference of the proposed models. The identifiability of the registration model and the asymptotical properties of the proposed method are established. The performance of the proposed procedure is illustrated via simulation studies and an analysis of a hyoid bone movement data application. The statistical developments proposed in this paper were motivated by the hyoid bone movement study, the methodology is designed and presented generality and can be applied to numerous areas of scientific research.
Journal: Journal of Applied Statistics
Pages: 1178-1198
Issue: 5
Volume: 50
Year: 2023
Month: 04
X-DOI: 10.1080/02664763.2021.2023118
File-URL: http://hdl.handle.net/10.1080/02664763.2021.2023118
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:5:p:1178-1198



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2017414_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Pengcheng Ren
Author-X-Name-First: Pengcheng
Author-X-Name-Last: Ren
Author-Name: Guanfu Liu
Author-X-Name-First: Guanfu
Author-X-Name-Last: Liu
Author-Name: Xiaolong Pu
Author-X-Name-First: Xiaolong
Author-X-Name-Last: Pu
Title: Generalized fiducial methods for testing the homogeneity of a three-sample problem with a mixture structure
Abstract: 
 Recently, the likelihood ratio (LR) test was proposed to test the homogeneity of a three-sample model with a mixture structure. Because of the presence of the mixture structure, the null limiting distribution of the LR test has a complicated supremum form, which leads to challenges in determining p-values. In addition, the LR test cannot control type-I errors well under small to moderate sample size. In this paper, we propose seven generalized fiducial methods to test the homogeneity of the three-sample model. Via simulation studies, we find that our methods perform significantly better than the LR test method in controlling the type-I errors under small to moderate sample size, while they have comparable powers in most cases. A halibut data example is used to illustrate the proposed methods.
Journal: Journal of Applied Statistics
Pages: 1094-1114
Issue: 5
Volume: 50
Year: 2023
Month: 04
X-DOI: 10.1080/02664763.2021.2017414
File-URL: http://hdl.handle.net/10.1080/02664763.2021.2017414
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:5:p:1094-1114



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2012563_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Reiko Aoki
Author-X-Name-First: Reiko
Author-X-Name-Last: Aoki
Author-Name: Dorival Leão
Author-X-Name-First: Dorival
Author-X-Name-Last: Leão
Author-Name: Juan P. Mamani Bustamante
Author-X-Name-First: Juan P. Mamani
Author-X-Name-Last: Bustamante
Author-Name: Filidor Vilca
Author-X-Name-First: Filidor
Author-X-Name-Last: Vilca
Title: Ultrastructural calibration model for proficiency testing
Abstract: 
 Proficiency testing (PT) determines the performance of individual laboratories for specific tests or measurements and it is used to monitor the reliability of laboratories measurements. PT plays a highly valuable role as it provides objective evidence of the competence of the participant laboratories. In this paper, we propose a multivariate calibration model to assess equivalence among laboratories measurements in PT. Our method allows to deal with multivariate data, where the item under test is measured at different levels. Although intuitive, the proposed model is nonergodic, which means that the asymptotic Fisher information matrix is random. As a consequence, a detailed asymptotic analysis was carried out to establish the strategy for comparing the results of the participating laboratories. To illustrate, we apply our method to analyze the data from the Brazilian engine test group, PT program, where the power of an engine was measured by eight laboratories at several levels of rotation.
Journal: Journal of Applied Statistics
Pages: 1037-1059
Issue: 5
Volume: 50
Year: 2023
Month: 04
X-DOI: 10.1080/02664763.2021.2012563
File-URL: http://hdl.handle.net/10.1080/02664763.2021.2012563
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:5:p:1037-1059



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2024515_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: J. C. S. Vasconcelos
Author-X-Name-First: J. C. S.
Author-X-Name-Last: Vasconcelos
Author-Name: G. M. Cordeiro
Author-X-Name-First: G. M.
Author-X-Name-Last: Cordeiro
Author-Name: E. M. M. Ortega
Author-X-Name-First: E. M. M.
Author-X-Name-Last: Ortega
Author-Name: G. O. Silva
Author-X-Name-First: G. O.
Author-X-Name-Last: Silva
Title: A random effect regression based on the odd log-logistic generalized inverse Gaussian distribution
Abstract: 
 In recent decades, the use of regression models with random effects has made great progress. Among these models' attractions is the flexibility to analyze correlated data. In various situations, the distribution of the response variable presents asymmetry or bimodality. In these cases, it is possible to use the normal regression with random effect at the intercept. In light of these contexts, i.e. the desire to analyze correlated data in the presence of bimodality or asymmetry, in this paper we propose a regression model with random effect at the intercept based onthe generalized inverse Gaussian distribution model with correlated data. The maximum likelihood is adopted to estimate the parameters and various simulations are performed for correlated data. A type of residuals for the new regression is proposed whose empirical distribution is close to normal. The versatility of the new regression is demonstrated by estimating the average price per hectare of bare land in 10 municipalities in the state of São Paulo (Brazil). In this context, various databases are constantly emerging, requiring flexible modeling. Thus, it is likely to be of interest to data analysts, and can make a good contribution to the statistical literature.
Journal: Journal of Applied Statistics
Pages: 1199-1214
Issue: 5
Volume: 50
Year: 2023
Month: 04
X-DOI: 10.1080/02664763.2021.2024515
File-URL: http://hdl.handle.net/10.1080/02664763.2021.2024515
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:5:p:1199-1214



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2019689_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: K. B. Kulasekera
Author-X-Name-First: K. B.
Author-X-Name-Last: Kulasekera
Author-Name: Sudaraka Tholkage
Author-X-Name-First: Sudaraka
Author-X-Name-Last: Tholkage
Author-Name: Maiying Kong
Author-X-Name-First: Maiying
Author-X-Name-Last: Kong
Title: Personalized treatment selection using observational data
Abstract: 
 Estimating the optimal treatment regime based on individual patient characteristics has been a topic of discussion in many forums. Advanced computational power has added momentum to this discussion over the last two decades and practitioners have been advocating the use of new methods in determining the best treatment. Treatments that are geared toward the ‘best’ outcome for a patient based on his/her genetic markers and characteristics are of high importance. In this article, we develop an approach to predict the optimal personalized treatment based on observational data. We have used inverse probability of treatment weighted machine learning methods to obtain score functions to predict the optimal treatment. Extensive simulation studies showed that our proposed method has desirable performance in selecting the optimal treatment. We provided a case study to examine the Statin use on cognitive function to illustrate the use of our proposed method.
Journal: Journal of Applied Statistics
Pages: 1115-1127
Issue: 5
Volume: 50
Year: 2023
Month: 04
X-DOI: 10.1080/02664763.2021.2019689
File-URL: http://hdl.handle.net/10.1080/02664763.2021.2019689
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:5:p:1115-1127



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2023117_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: O. Kharazmi
Author-X-Name-First: O.
Author-X-Name-Last: Kharazmi
Author-Name: G. G. Hamedani
Author-X-Name-First: G. G.
Author-X-Name-Last: Hamedani
Author-Name: G. M. Cordeiro
Author-X-Name-First: G. M.
Author-X-Name-Last: Cordeiro
Title: Log-mean distribution: applications to medical data, survival regression, Bayesian and non-Bayesian discussion with MCMC algorithm
Abstract: 
 We introduce a new family via the log mean of an underlying distribution and as baseline the proportional hazards model and derive some important properties. A special model is proposed by taking the Weibull for the baseline. We derive several properties of the sub-model such as moments, order statistics, hazard function, survival regression and certain characterization results. We estimate the parameters using frequentist and Bayesian approaches. Further, Bayes estimators, posterior risks, credible intervals and highest posterior density intervals are obtained under different symmetric and asymmetric loss functions. A Monte Carlo simulation study examines the biases and mean square errors of the maximum likelihood estimators. For the illustrative purposes, we consider heart transplant and bladder cancer data sets and investigate the efficiency of proposed model.
Journal: Journal of Applied Statistics
Pages: 1152-1177
Issue: 5
Volume: 50
Year: 2023
Month: 04
X-DOI: 10.1080/02664763.2021.2023117
File-URL: http://hdl.handle.net/10.1080/02664763.2021.2023117
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:5:p:1152-1177



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2017413_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Shuhua Chang
Author-X-Name-First: Shuhua
Author-X-Name-Last: Chang
Author-Name: Deli Li
Author-X-Name-First: Deli
Author-X-Name-Last: Li
Author-Name: Yongcheng Qi
Author-X-Name-First: Yongcheng
Author-X-Name-Last: Qi
Title: Pearson's goodness-of-fit tests for sparse distributions
Abstract: 
 Pearson's chi-squared test is widely used to test the goodness of fit between categorical data and a given discrete distribution function. When the number of sets of the categorical data, say k, is a fixed integer, Pearson's chi-squared test statistic converges in distribution to a chi-squared distribution with k−1 degrees of freedom when the sample size n goes to infinity. In real applications, the number k often changes with n and may be even much larger than n. By using the martingale techniques, we prove that Pearson's chi-squared test statistic converges to the normal under quite general conditions. We also propose a new test statistic which is more powerful than chi-squared test statistic based on our simulation study. A real application to lottery data is provided to illustrate our methodology.
Journal: Journal of Applied Statistics
Pages: 1078-1093
Issue: 5
Volume: 50
Year: 2023
Month: 04
X-DOI: 10.1080/02664763.2021.2017413
File-URL: http://hdl.handle.net/10.1080/02664763.2021.2017413
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:5:p:1078-1093



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2031128_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: David M. Ruth
Author-X-Name-First: David M.
Author-X-Name-Last: Ruth
Author-Name: Nicholas L. Wood
Author-X-Name-First: Nicholas L.
Author-X-Name-Last: Wood
Author-Name: Douglas N. VanDerwerken
Author-X-Name-First: Douglas N.
Author-X-Name-Last: VanDerwerken
Title: Fully nonparametric survival analysis in the presence of time-dependent covariates and dependent censoring
Abstract: 
 In the presence of informative right censoring and time-dependent covariates, we estimate the survival function in a fully nonparametric fashion. We introduce a novel method for incorporating multiple observations per subject when estimating the survival function at different covariate values and compare several competing methods via simulation. The proposed method is applied to survival data from people awaiting liver transplant.
Journal: Journal of Applied Statistics
Pages: 1215-1229
Issue: 5
Volume: 50
Year: 2023
Month: 04
X-DOI: 10.1080/02664763.2022.2031128
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2031128
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:5:p:1215-1229

Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2041568_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Richard F. Potthoff
Author-X-Name-First: Richard F.
Author-X-Name-Last: Potthoff
Title: Spot It! and balanced block designs: keys to better debate architecture for a plethora of candidates in presidential primaries?
Abstract: 
 U.S. presidential primary debates are influential but under-researched. Before 2015, all of these debates, both Democratic and Republican, had 10 candidates or fewer. The first Republican debate in 2015, however, abided 17 candidates. They were split into two segments, with the 10 best-polling candidates in the main (prime-time) segment and the others in an ‘undercard’ session. A comparable pattern applied for the next six Republican debates. Concern arose not only because many candidates were crowded into a session but also because the undercard candidates were seen as receiving inferior exposure. The Democratic presidential primary debates that started four years later encountered similar difficulty. An authorized policy caused their candidates in each of the first two debates to be limited to 20, randomly divided into two groups of 10 appearing on successive nights. For remedy, this paper examines innovative debate plans, for different numbers of candidates, that feature symmetry among all candidates and entail many short segments with relatively few candidates in each. We apply combinatorial designs—balanced incomplete block designs and regular pairwise balanced designs, which are analogous to the games Spot It Jr.! Animals and (full-fledged) Spot It!, respectively.
Journal: Journal of Applied Statistics
Pages: 1435-1454
Issue: 6
Volume: 50
Year: 2023
Month: 04
X-DOI: 10.1080/02664763.2022.2041568
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2041568
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:6:p:1435-1454



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2028745_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Jie Liu
Author-X-Name-First: Jie
Author-X-Name-Last: Liu
Author-Name: Haojie Chen
Author-X-Name-First: Haojie
Author-X-Name-Last: Chen
Author-Name: Yang Yang
Author-X-Name-First: Yang
Author-X-Name-Last: Yang
Title: Prediction models with graph kernel regularization for network data
Abstract: 
 Traditional regression methods typically consider only covariate information and assume that the observations are mutually independent samples. However, samples usually come from individuals connected by a network in many modern applications. We present a risk minimization formulation for learning from both covariates and network structure in the context of graph kernel regularization. The formulation involves a loss function with a penalty term. This penalty can be used not only to encourage similarity between linked nodes but also lead to improvement over traditional regression models. Furthermore, the penalty can be used with many loss-based predictive methods, such as linear regression with squared loss and logistic regression with log-likelihood loss. Simulations to evaluate the performance of this model in the cases of low dimensions and high dimensions show that our proposed approach outperforms all other benchmarks. We verify this for uniform graph, nonuniform graph, balanced-sample, and unbalanced-sample datasets. The approach was applied to predicting the response values on a ‘follow’ social network of Tencent Weibo users and on two citation networks (Cora and CiteSeer). Each instance verifies that the proposed method combining covariate information and link structure with the graph kernel regularization can improve predictive performance.
Journal: Journal of Applied Statistics
Pages: 1400-1417
Issue: 6
Volume: 50
Year: 2023
Month: 04
X-DOI: 10.1080/02664763.2022.2028745
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2028745
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:6:p:1400-1417



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2028130_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: A. D. C. Nascimento
Author-X-Name-First: A. D. C.
Author-X-Name-Last: Nascimento
Author-Name: Leandro C. Rêgo
Author-X-Name-First: Leandro C.
Author-X-Name-Last: Rêgo
Author-Name: Jonas W. A. Silva
Author-X-Name-First: Jonas W. A.
Author-X-Name-Last: Silva
Title: Compound truncated Poisson gamma distribution for understanding multimodal SAR intensities
Abstract: 
 In recent years, many works have addressed the proposal of new probability models by theoretic and applied reasons. Specifically, mixture models have been indicated to describe phenomena whose resulting data impose high flexibility. One drawback of these tools is the high number of parameters involved, which implies hard inference procedures. To outperform this gap, we propose a new model that is able to describe multimodal behaviors with only three parameters, called compound truncated Poisson gamma (CTrPGa) distribution. Some properties of the CTrPGa law are derived and discussed: hazard, characteristic and cumulative functions and ordinary moments. Beyond, moment estimation, maximum likelihood estimation (via the expectation maximization algorithm) and empirical characteristic function methods for CTrPGa parameters are furnished. The first of them may be reduced to solve one nonlinear equation, which facilitates its use. We perform a simulation analysis to compare the performance of the three estimation methods studied. Moreover, since the gamma distribution and its mixture versions are commonly used to characterize synthetic aperture radar (SAR) intensities, we perform some real experiments with SAR imagery. The results present evidence that our model is a reasonable assumption that can be taken into account in the pre-processing step of such images.
Journal: Journal of Applied Statistics
Pages: 1358-1377
Issue: 6
Volume: 50
Year: 2023
Month: 04
X-DOI: 10.1080/02664763.2022.2028130
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2028130
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:6:p:1358-1377



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2041565_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: ShengLi Tzeng
Author-X-Name-First: ShengLi
Author-X-Name-Last: Tzeng
Author-Name: Chun-Shu Chen
Author-X-Name-First: Chun-Shu
Author-X-Name-Last: Chen
Author-Name: Yu-Fen Li
Author-X-Name-First: Yu-Fen
Author-X-Name-Last: Li
Author-Name: Jin-Hua Chen
Author-X-Name-First: Jin-Hua
Author-X-Name-Last: Chen
Title: On summary ROC curve for dichotomous diagnostic studies: an application to meta-analysis of COVID-19
Abstract: 
 In a systematic review of a diagnostic performance, summarizing performance metrics is crucial. There are various summary models in the literature, and hence model selection becomes inevitable. However, most existing large-sample-based model selection approaches may not fit in a meta-analysis of diagnostic studies, typically having a rather small sample size. Researchers need to effectively determine the final model for further inference, which motivates this article to investigate existing methods and to suggest a more robust method for this need. We considered models covering several widely-used methods for bivariate summary of sensitivity and specificity. Simulation studies were conducted based on different number of studies and different population sensitivity and specificity. Then final models were selected using several existing criteria, and we compared the summary receiver operating characteristic (sROC) curves to the theoretical ROC curve given the generating model. Even though parametric likelihood-based criteria are often applied in practice for their asymptotic property, they fail to consistently choose appropriate models under the limited number of studies. When the number of studies is as small as 10 or 5, our suggestion is best in different scenarios. An example for summary ROC curves for chemiluminescence immunoassay (CLIA) used in COVID-19 diagnosis is also illustrated.
Journal: Journal of Applied Statistics
Pages: 1418-1434
Issue: 6
Volume: 50
Year: 2023
Month: 04
X-DOI: 10.1080/02664763.2022.2041565
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2041565
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:6:p:1418-1434



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2026896_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Panayotis Papoutsis
Author-X-Name-First: Panayotis
Author-X-Name-Last: Papoutsis
Author-Name: Tarn Duong
Author-X-Name-First: Tarn
Author-X-Name-Last: Duong
Author-Name: Bertrand Michel
Author-X-Name-First: Bertrand
Author-X-Name-Last: Michel
Author-Name: Anne Philippe
Author-X-Name-First: Anne
Author-X-Name-Last: Philippe
Title: Bayesian hierarchical models for the prediction of the driver flow and passenger waiting times in a stochastic carpooling service
Abstract: 
 Carpooling is an integral component in smart carbon-neutral cities, in particular to facilitate home-work commuting. We study an innovative carpooling service which offers stochastic passenger-driver matching. Stochastic matching is when a passenger makes a carpooling request, and then waits for the first driver from a population of drivers who are already en route. Crucially a designated driver is not assigned as in a traditional carpooling service. For this new form of stochastic carpooling, we propose a two-stage Bayesian hierarchical model to predict the driver flow and the passenger waiting times. The first stage focuses on prediction of the aggregated daily driver flows, and the second stage processes these daily driver flow into hourly predictions of the passenger waiting times. We demonstrate, for an operational carpooling service, that the predictions from our Bayesian hierarchical model outperform the predictions from a frequentist model and a Bayesian non-hierarchical model. The inferences from our proposed model provide insights for the service operator in their evidence-based decision making.
Journal: Journal of Applied Statistics
Pages: 1310-1333
Issue: 6
Volume: 50
Year: 2023
Month: 04
X-DOI: 10.1080/02664763.2022.2026896
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2026896
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:6:p:1310-1333



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2028131_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Jinwen Liang
Author-X-Name-First: Jinwen
Author-X-Name-Last: Liang
Author-Name: Maozai Tian
Author-X-Name-First: Maozai
Author-X-Name-Last: Tian
Title: Sparse regression for low-dimensional time-dynamic varying coefficient models with application to air quality data
Abstract: 
 Time dynamic varying coefficient models play an important role in applications of biology, medicine, environment, finance, etc. Traditional methods, such as kernel smoothing and spline smoothing, are popular. But explicit expressions are unavailable using these methods, and the convergence rate of coefficient function estimators is slow. To address these problems, we expand the varying component with appropriate basis functions. And then we solve a sparse regression problem via a sequential thresholded least-squares estimator. The “parameterization” leads to explicit expressions and fast computation speed. Convergence of the sequential thresholded least squares algorithm is guaranteed. The asymptotic distribution of the coefficient function estimator is derived under certain assumptions. Our simulation shows the proposed method has higher precision and computing speed. Finally, our proposed method is applied to the study of PM
 $ _{2.5} $ 2.5 concentration in Beijing. We analyze the relationship between PM
 $ _{2.5} $ 2.5 and other impact factors.
Journal: Journal of Applied Statistics
Pages: 1378-1399
Issue: 6
Volume: 50
Year: 2023
Month: 04
X-DOI: 10.1080/02664763.2022.2028131
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2028131
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:6:p:1378-1399



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2024154_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: F. Delgado-Vences
Author-X-Name-First: F.
Author-X-Name-Last: Delgado-Vences
Author-Name: F. Baltazar-Larios
Author-X-Name-First: F.
Author-X-Name-Last: Baltazar-Larios
Author-Name: A. Ornelas Vargas
Author-X-Name-First: A. Ornelas
Author-X-Name-Last: Vargas
Author-Name: E. Morales-Bojórquez
Author-X-Name-First: E.
Author-X-Name-Last: Morales-Bojórquez
Author-Name: V. H. Cruz-Escalona
Author-X-Name-First: V. H.
Author-X-Name-Last: Cruz-Escalona
Author-Name: C. Salomón Aguilar
Author-X-Name-First: C.
Author-X-Name-Last: Salomón Aguilar
Title: Inference for a discretized stochastic logistic differential equation and its application to biological growth
Abstract: 
 In this paper, we present a method to adjust a stochastic logistic differential equation (SLDE) to a set of highly sparse real data. We assume that the SLDE have two unknown parameters to be estimated. We calculate the Maximum Likelihood Estimator (MLE) to estimate the intrinsic growth rate. We prove that the MLE is strongly consistent and asymptotically normal. For estimating the diffusion parameter, the quadratic variation of the data is used. We validate our method with several types of simulated data. For more realistic cases in which we observe discretizations of the solution, we use diffusion bridges and the stochastic expectation-maximization algorithm to estimate the parameters. Furthermore, when we observe only one point for each path for a given number of trajectories we were still able to estimate the parameters of the SLDE. As far as we know, this is the first attempt to fit stochastic differential equations (SDEs) to these types of data. Finally, we apply our method to real data coming from fishery. The proposed adjustment method can be applied to other examples of SDEs and is highly applicable in several areas of science, especially in situations of sparse data.
Journal: Journal of Applied Statistics
Pages: 1231-1254
Issue: 6
Volume: 50
Year: 2023
Month: 04
X-DOI: 10.1080/02664763.2021.2024154
File-URL: http://hdl.handle.net/10.1080/02664763.2021.2024154
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:6:p:1231-1254



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2026898_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Diana Q. Chen
Author-X-Name-First: Diana Q.
Author-X-Name-Last: Chen
Author-Name: Si-Qi Mao
Author-X-Name-First: Si-Qi
Author-X-Name-Last: Mao
Author-Name: Xu-Feng Niu
Author-X-Name-First: Xu-Feng
Author-X-Name-Last: Niu
Title: Tests and classification methods in adaptive designs with applications
Abstract: 
 Statistical tests for biomarker identification and classification methods for patient grouping are two important topics in adaptive designs of clinical trials related to genomic studies. In this article, we evaluate four test methods for biomarker identification in the first stage of an adaptive design: a model-based identification method, the popular two-sided t-test, the nonparametric Wilcoxon Rank-Sum test (two-sided), and the Regularized Generalized Linear Models. For patients grouping in the second stage, we examine classification methods such as Random Forest, Elastic-net Regularized Generalized Linear Models, Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost). Simulation studies are carried out to assess the performance of the different methods. The best identification methods are chosen based on the well-known 
 $ F_1 $ F1 score, while the best classification techniques are selected based on the area under a receiver operating characteristic curve (AUC). The chosen methods are then applied to the Adaptive Signature Design (ASD) with a real data set from breast cancer patients for the purpose of evaluating the performance of ASD in different situations.
Journal: Journal of Applied Statistics
Pages: 1334-1357
Issue: 6
Volume: 50
Year: 2023
Month: 04
X-DOI: 10.1080/02664763.2022.2026898
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2026898
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:6:p:1334-1357



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2026895_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: O. Eguasa
Author-X-Name-First: O.
Author-X-Name-Last: Eguasa
Author-Name: E Edionwe
Author-X-Name-First: E
Author-X-Name-Last: Edionwe
Author-Name: J. I. Mbegbu
Author-X-Name-First: J. I.
Author-X-Name-Last: Mbegbu
Title: Local Linear Regression and the problem of dimensionality: a remedial strategy via a new locally adaptive bandwidths selector
Abstract: 
 Local Linear Regression (LLR) is a nonparametric regression model applied in the modeling phase of Response Surface Methodology (RSM). LLR does not make reference to any fixed parametric model. Hence, LLR is flexible and can capture local trends in the data that might be too complicated for the OLS. However, besides the small sample size and sparse data which characterizes RSM, the performance of the LLR model nosedives as the number of explanatory variables considered in the study increases. This phenomenon, popularly referred to as curse of dimensionality, results in the scanty application of LLR in RSM. In this paper, we propose a novel locally adaptive bandwidths selector, unlike the fixed bandwidths and existing locally adaptive bandwidths selectors, takes into account both the number of the explanatory variables in the study and their individual values at each data point. Single and multiple response problems from the literature and simulated data were used to compare the performance of the 
 $ LL{R_{PAB}} $ LLRPAB with those of the OLS, 
 $ LL{R_{FB}} $ LLRFB and 
 $ LL{R_{AB}} $ LLRAB. Neural network activation functions such ReLU, Leaky-ReLU, SELU and SPOCU was considered and give a remarkable improvement on the loss function (Mean Squared Error) over the regression models utilized in the three data.
Journal: Journal of Applied Statistics
Pages: 1283-1309
Issue: 6
Volume: 50
Year: 2023
Month: 04
X-DOI: 10.1080/02664763.2022.2026895
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2026895
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:6:p:1283-1309



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2024798_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Clécio S. Ferreira
Author-X-Name-First: Clécio S.
Author-X-Name-Last: Ferreira
Author-Name: Camila Borelli Zeller
Author-X-Name-First: Camila
Author-X-Name-Last: Borelli Zeller
Author-Name: Rafael R. de Oliveira Garcia
Author-X-Name-First: Rafael R.
Author-X-Name-Last: de Oliveira Garcia
Title: Heteroscedastic partially linear model under skew-normal distribution with application in ragweed pollen concentration
Abstract: 
 We introduce a new class of heteroscedastic partially linear model (PLM) with skew-normal distribution. Maximum likelihood estimation of the model parameters by the ECM algorithm (Expectation/Conditional Maximization) as well as influence diagnostics for the new model are investigated. In addition, a Likelihood Ratio test for assessing the homogeneity of the scale parameter is presented. Simulation studies for assessing the performance of the ECM algorithm and the Likelihood Ratio test statistics for homogeneity of variance are developed. Also, a study for misspecification of the structure function is considered. Finally, an application of the new heteroscedastic PLM to a real data set on ragweed pollen concentration is presented to show that it provides a better fit than the classic homocedastic PLM. We hope that the proposed model may attract applications in different areas of knowledge.
Journal: Journal of Applied Statistics
Pages: 1255-1282
Issue: 6
Volume: 50
Year: 2023
Month: 04
X-DOI: 10.1080/02664763.2021.2024798
File-URL: http://hdl.handle.net/10.1080/02664763.2021.2024798
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:6:p:1255-1282

Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2034760_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Qihuang Zhang
Author-X-Name-First: Qihuang
Author-X-Name-Last: Zhang
Author-Name: Grace Y. Yi
Author-X-Name-First: Grace Y.
Author-X-Name-Last: Yi
Title: Sensitivity analysis of error-contaminated time series data under autoregressive models with the application of COVID-19 data
Abstract: 
 Autoregressive (AR) models are useful in time series analysis. Inferences under such models are distorted in the presence of measurement error, a common feature in applications. In this article, we establish analytical results for quantifying the biases of the parameter estimation in AR models if the measurement error effects are neglected. We consider two measurement error models to describe different data contamination scenarios. We propose an estimating equation approach to estimate the AR model parameters with measurement error effects accounted for. We further discuss forecasting using the proposed method. Our work is inspired by COVID-19 data, which are error-contaminated due to multiple reasons including those related to asymptomatic cases and varying incubation periods. We implement the proposed method by conducting sensitivity analyses and forecasting the fatality rate of COVID-19 over time for the four most populated provinces in Canada. The results suggest that incorporating or not incorporating measurement error effects may yield rather different results for parameter estimation and forecasting.
Journal: Journal of Applied Statistics
Pages: 1611-1634
Issue: 7
Volume: 50
Year: 2023
Month: 05
X-DOI: 10.1080/02664763.2022.2034760
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2034760
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:7:p:1611-1634



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2032621_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Shubham Saini
Author-X-Name-First: Shubham
Author-X-Name-Last: Saini
Author-Name: Sachin Tomer
Author-X-Name-First: Sachin
Author-X-Name-Last: Tomer
Author-Name: Renu Garg
Author-X-Name-First: Renu
Author-X-Name-Last: Garg
Title: Inference of multicomponent stress-strength reliability following Topp-Leone distribution using progressively censored data
Abstract: 
 In this paper, the inference of multicomponent stress-strength reliability has been derived using progressively censored samples from Topp-Leone distribution. Both stress and strength variables are assumed to follow Topp-Leone distributions with different shape parameters. The maximum likelihood estimate along with the asymptotic confidence interval are developed. Boot-p and Boot-t confidence intervals are also constructed. The Bayes estimates under generalized entropy loss function based on gamma priors using Lindley's, Tierney-Kadane's approximation and Markov chain Monte Carlo methods are derived. A simulation study is considered to check the performance of various estimation methods and different censoring schemes. A real data study shows the applicability of the proposed estimation methods.
Journal: Journal of Applied Statistics
Pages: 1538-1567
Issue: 7
Volume: 50
Year: 2023
Month: 05
X-DOI: 10.1080/02664763.2022.2032621
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2032621
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:7:p:1538-1567



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2032619_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: T. S. McElroy
Author-X-Name-First: T. S.
Author-X-Name-Last: McElroy
Author-Name: Thomas Trimbur
Author-X-Name-First: Thomas
Author-X-Name-Last: Trimbur
Title: Variable targeting and reduction in large vector autoregressions with applications to workforce indicators
Abstract: 
 We develop statistical tools for time series analysis of large multivariate datasets, when a few core series are of principal interest and there are many potential auxiliary predictive variables. The methodology, based on Vector Autoregressions (VAR), handles the case where unrestricted fitting is precluded by a large number of series and a huge parameter space. In particular, we adopt a forecast error criterion and use Granger-causality tests in a sequential manner to build a VAR model that targets the main variables. This approach affects variable reduction (or equivalently, sparsity restrictions) in a computationally fast way that remains feasible for large dimensions. The search for the best model results in a VAR, fitted with a selection of supporting series, that has the best possible forecast performance with respect to the core variables. We apply the statistical methodology to model real Gross Domestic Product and the national Unemployment Rate, two time series widely monitored by economists and policy-makers, based on a large set of Quarterly Workforce Indicators comprising various major sectors of the economy and different measures of labor market conditions.
Journal: Journal of Applied Statistics
Pages: 1515-1537
Issue: 7
Volume: 50
Year: 2023
Month: 05
X-DOI: 10.1080/02664763.2022.2032619
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2032619
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:7:p:1515-1537



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2034759_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Brent Burch
Author-X-Name-First: Brent
Author-X-Name-Last: Burch
Author-Name: Jesse Egbert
Author-X-Name-First: Jesse
Author-X-Name-Last: Egbert
Title: Confidence intervals for ratios of means applied to corpus-based word frequency classes
Abstract: 
 The words we choose when we communicate with one another convey meaning and information. In written or spoken language, we tend to employ a relatively small number of words repeatedly whereas a large number of words in the lexicon are seldom used. By considering a ratio of means of the most prevalent word in a body of texts (or corpus) compared to that of the word in question, one can quantify the prevalence of the word in question. Furthermore, the concept of word classes or grouping words having similar measures of prevalence enables the investigator to compare the words. Using a sample of texts having varying lengths from a corpus, the sample mean relative frequency of a word and the maximum likelihood estimator using the zero-inflated beta distribution serve as two measures of the prevalence of a word. We construct and then compare asymptotic confidence intervals involving ratios of means for a number of words in the British National Corpus, a 100 million-word collection of written and spoken language of a wide range of British English. We also examine the sample sizes required to meet specific objectives regarding word classes and ratios of means.
Journal: Journal of Applied Statistics
Pages: 1592-1610
Issue: 7
Volume: 50
Year: 2023
Month: 05
X-DOI: 10.1080/02664763.2022.2034759
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2034759
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:7:p:1592-1610



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2031125_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Youssef Anzarmou
Author-X-Name-First: Youssef
Author-X-Name-Last: Anzarmou
Author-Name: Abdallah Mkhadri
Author-X-Name-First: Abdallah
Author-X-Name-Last: Mkhadri
Author-Name: Karim Oualkacha
Author-X-Name-First: Karim
Author-X-Name-Last: Oualkacha
Title: The Kendall interaction filter for variable interaction screening in high dimensional classification problems
Abstract: 
 Accounting for important interaction effects can improve the prediction of many statistical learning models. Identification of relevant interactions, however, is a challenging issue owing to their ultrahigh-dimensional nature. Interaction screening strategies can alleviate such issues. However, due to heavier tail distribution and complex dependence structure of interaction effects, innovative robust and/or model-free methods for screening interactions are required to better scale analysis of complex and high-throughput data. In this work, we develop a new model-free interaction screening method, termed Kendall Interaction Filter (KIF), for the classification in high-dimensional settings. KIF method suggests a weighted-sum measure, which compares the overall to the within-cluster Kendall's τ of pairs of predictors, to select interactive couples of features. The proposed KIF measure captures relevant interactions for the clusters response-variable, handles continuous, categorical or a mixture of continuous-categorical features, and is invariant under monotonic transformations. The tKIF measure enjoys the sure screening property in the high-dimensional setting under mild conditions, without imposing sub-exponential moment assumptions on the features' distribution. We illustrate the favorable behavior of the proposed methodology compared to the methods in the same category using simulation studies, and we conduct real data analyses to demonstrate its utility.
Journal: Journal of Applied Statistics
Pages: 1496-1514
Issue: 7
Volume: 50
Year: 2023
Month: 05
X-DOI: 10.1080/02664763.2022.2031125
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2031125
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:7:p:1496-1514



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2034141_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: José Clelto Barros Gomes
Author-X-Name-First: José Clelto Barros
Author-X-Name-Last: Gomes
Author-Name: Reiko Aoki
Author-X-Name-First: Reiko
Author-X-Name-Last: Aoki
Author-Name: Victor Hugo Lachos
Author-X-Name-First: Victor Hugo
Author-X-Name-Last: Lachos
Author-Name: Gilberto Alvarenga Paula
Author-X-Name-First: Gilberto Alvarenga
Author-X-Name-Last: Paula
Author-Name: Cibele Maria Russo
Author-X-Name-First: Cibele Maria
Author-X-Name-Last: Russo
Title: Fast inference for robust nonlinear mixed-effects models
Abstract: 
 The interest for nonlinear mixed-effects models comes from application areas as pharmacokinetics, growth curves and HIV viral dynamics. However, the modeling procedure usually leads to many difficulties, as the inclusion of random effects, the estimation process and the model sensitivity to atypical or nonnormal data. The scale mixture of normal distributions include heavy-tailed models, as the Student-t, slash and contaminated normal distributions, and provide competitive alternatives to the usual models, enabling the obtention of robust estimates against outlying observations. Our proposal is to compare two estimation methods in nonlinear mixed-effects models where the random components follow a multivariate scale mixture of normal distributions. For this purpose, a Monte Carlo expectation-maximization algorithm (MCEM) and an efficient likelihood-based approximate method are developed. Results show that the approximate method is much faster and enables a fairly efficient likelihood maximization, although a slightly larger bias may be produced, especially for the fixed-effects parameters. A discussion on the robustness aspects of the proposed models are also provided. Two real nonlinear applications are discussed and a brief simulation study is presented.
Journal: Journal of Applied Statistics
Pages: 1568-1591
Issue: 7
Volume: 50
Year: 2023
Month: 05
X-DOI: 10.1080/02664763.2022.2034141
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2034141
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:7:p:1568-1591



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2064439_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Masao Ueki
Author-X-Name-First: Masao
Author-X-Name-Last: Ueki
Title: Beta-negative binomial nonlinear spatio-temporal random effects modeling of COVID-19 case counts in Japan
Abstract: 
 Coronavirus disease 2019 (COVID-19) caused by the SARS-CoV-2 virus has spread seriously throughout the world. Predicting the spread, or the number of cases, in the future can facilitate preparation for, and prevention of, a worst-case scenario. To achieve these purposes, statistical modeling using past data is one feasible approach. This paper describes spatio-temporal modeling of COVID-19 case counts in 47 prefectures of Japan using a nonlinear random effects model, where random effects are introduced to capture the heterogeneity of a number of model parameters associated with the prefectures. The negative binomial distribution is frequently used with the Paul-Held random effects model to account for overdispersion in count data; however, the negative binomial distribution is known to be incapable of accommodating extreme observations such as those found in the COVID-19 case count data. We therefore propose use of the beta-negative binomial distribution with the Paul-Held model. This distribution is a generalization of the negative binomial distribution that has attracted much attention in recent years because it can model extreme observations with analytical tractability. The proposed beta-negative binomial model was applied to multivariate count time series data of COVID-19 cases in the 47 prefectures of Japan. Evaluation by one-step-ahead prediction showed that the proposed model can accommodate extreme observations without sacrificing predictive performance.
Journal: Journal of Applied Statistics
Pages: 1650-1663
Issue: 7
Volume: 50
Year: 2023
Month: 05
X-DOI: 10.1080/02664763.2022.2064439
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2064439
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:7:p:1650-1663



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2026897_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Woojoo Lee
Author-X-Name-First: Woojoo
Author-X-Name-Last: Lee
Author-Name: Jeonghwan Kim
Author-X-Name-First: Jeonghwan
Author-X-Name-Last: Kim
Author-Name: Donghwan Lee
Author-X-Name-First: Donghwan
Author-X-Name-Last: Lee
Title: Revisiting the analysis pipeline for overdispersed Poisson and binomial data
Abstract: 
 Overdispersion is a common feature in categorical data analysis and several methods have been developed for detecting and handling it in generalized linear models. The first aim of this study is to clarify the relationships among various score statistics for testing overdispersion and to compare their performances. In addition, we investigate a principled way to correct finite sample bias in the score statistic caused by estimating regression parameters with restricted likelihood. The second aim is to reconsider the current practice for handling overdispersed categorical data. Although the conventional models are based on substantially different mechanisms for generating overdispersion, model selection in practice has not been well studied. We perform an intensive numerical study for determining which method is more robust to various overdispersion mechanisms. In addition, we provide some graphical tools for identifying the better model. The last aim is to reconsider the key assumption for deriving the score statistics. We study the meaning of testing overdispersion when this assumption is violated, and we analytically show the conditions for which it is not appropriate to employ the current statistical practices for analyzing overdispersed data.
Journal: Journal of Applied Statistics
Pages: 1455-1476
Issue: 7
Volume: 50
Year: 2023
Month: 05
X-DOI: 10.1080/02664763.2022.2026897
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2026897
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:7:p:1455-1476



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2055749_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Suzanna-Maria Paleologou
Author-X-Name-First: Suzanna-Maria
Author-X-Name-Last: Paleologou
Title: Income and democracy: a bivariate copula approach
Abstract: 
 We propose a new approach for exploring the relationship between income and democracy by modeling the two most popular discrete democracy indexes, Polity IV and Freedom House, as a joint random variable by means of a copula function. Joint modeling is crucial for eliciting complementarity and/or substitutability amongst these indexes claiming to measure similar things, i.e. a country’s degree of democratization. We find strong evidence supporting both the existence of the relationship and the positive dependence between the two democracy indexes, suggesting that they are complements to each other. Our findings are robust to different samples and model specifications.
Journal: Journal of Applied Statistics
Pages: 1635-1649
Issue: 7
Volume: 50
Year: 2023
Month: 05
X-DOI: 10.1080/02664763.2022.2055749
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2055749
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:7:p:1635-1649



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2031123_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Chunjie Wu
Author-X-Name-First: Chunjie
Author-X-Name-Last: Wu
Author-Name: Zhijun Wang
Author-X-Name-First: Zhijun
Author-X-Name-Last: Wang
Author-Name: Steven MacEachern
Author-X-Name-First: Steven
Author-X-Name-Last: MacEachern
Author-Name: Jingjing Schneider
Author-X-Name-First: Jingjing
Author-X-Name-Last: Schneider
Title: A robust latent CUSUM chart for monitoring customer attrition
Abstract: 
 In competitive business, such as insurance and telecommunications, customers can easily replace one provider for another, which leads to customer attrition. Keeping customer attrition rate low is crucial for companies, since retaining a customer is more profitable than recruiting a new one. As a main statistical process control (SPC) method, the CUSUM scheme is able to detect small and persistent shifts in customer attrition. However, customer attrition summaries are typically available on an uneven time scale (e.g. 4-week and 5-week ‘business month’), which may not satisfy the assumptions of traditional CUSUM designs. This paper mainly develops a latent CUSUM chart based on an exponential model for monitoring ‘monthly’ customer attrition, under varying time scales. Both maximum likelihood and least squares methods are studied, where the former mostly performs better and the latter is advantageous for quite small shifts. We apply a Markov chain algorithm to obtain the average run length (ARL), make calibrations for different combinations of parameters, and present reference tables of cutoffs. Three more complicated models are considered to test the robustness of deviations from the initial model. Furthermore, a real example of monitoring monthly customer attrition from a Chinese insurance company is used to illustrate the scheme.
Journal: Journal of Applied Statistics
Pages: 1477-1495
Issue: 7
Volume: 50
Year: 2023
Month: 05
X-DOI: 10.1080/02664763.2022.2031123
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2031123
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:7:p:1477-1495

Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2036707_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: E. M. Hashimoto
Author-X-Name-First: E. M.
Author-X-Name-Last: Hashimoto
Author-Name: E. M. M. Ortega
Author-X-Name-First: E. M. M.
Author-X-Name-Last: Ortega
Author-Name: G. M. Cordeiro
Author-X-Name-First: G. M.
Author-X-Name-Last: Cordeiro
Author-Name: V. G. Cancho
Author-X-Name-First: V. G.
Author-X-Name-Last: Cancho
Author-Name: I. Silva
Author-X-Name-First: I.
Author-X-Name-Last: Silva
Title: The re-parameterized inverse Gaussian regression to model length of stay of COVID-19 patients in the public health care system of Piracicaba, Brazil
Abstract: 
 Among the models applied to analyze survival data, a standout is the inverse Gaussian distribution, which belongs to the class of models to analyze positive asymmetric data. However, the variance of this distribution depends on two parameters, which prevents establishing a functional relation with a linear predictor when the assumption of constant variance does not hold. In this context, the aim of this paper is to re-parameterize the inverse Gaussian distribution to enable establishing an association between a linear predictor and the variance. We propose deviance residuals to verify the model assumptions. Some simulations indicate that the distribution of these residuals approaches the standard normal distribution and the mean squared errors of the estimators are small for large samples. Further, we fit the new model to hospitalization times of COVID-19 patients in Piracicaba (Brazil) which indicates that men spend more time hospitalized than women, and this pattern is more pronounced for individuals older than 60 years. The re-parameterized inverse Gaussian model proved to be a good alternative to analyze censored data with non-constant variance.
Journal: Journal of Applied Statistics
Pages: 1665-1685
Issue: 8
Volume: 50
Year: 2023
Month: 06
X-DOI: 10.1080/02664763.2022.2036707
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2036707
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:8:p:1665-1685



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2046713_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Brian Neelon
Author-X-Name-First: Brian
Author-X-Name-Last: Neelon
Author-Name: Chun-Che Wen
Author-X-Name-First: Chun-Che
Author-X-Name-Last: Wen
Author-Name: Sara E. Benjamin-Neelon
Author-X-Name-First: Sara E.
Author-X-Name-Last: Benjamin-Neelon
Title: A multivariate spatiotemporal model for tracking COVID-19 incidence and death rates in socially vulnerable populations
Abstract: 
 Recent studies have produced inconsistent findings regarding the association between community social vulnerability and COVID-19 incidence and death rates. This inconsistency may be due, in part, to the fact that these studies modeled cases and deaths separately, ignoring their inherent association and thus yielding imprecise estimates. To improve inferences, we develop a Bayesian multivariate negative binomial model for exploring joint spatial and temporal trends in COVID-19 infections and deaths. The model introduces smooth functions that capture long-term temporal trends, while maintaining enough flexibility to detect local outbreaks in areas with vulnerable populations. Using multivariate autoregressive priors, we jointly model COVID-19 cases and deaths over time, taking advantage of convenient conditional representations to improve posterior computation. As such, the proposed model provides a general framework for multivariate spatiotemporal modeling of counts and rates. We adopt a fully Bayesian approach and develop an efficient posterior Markov chain Monte Carlo algorithm that relies on easily sampled Gibbs steps. We use the model to examine incidence and death rates among counties with high and low social vulnerability in the state of Georgia, USA, from 15 March to 15 December 2020.
Journal: Journal of Applied Statistics
Pages: 1812-1835
Issue: 8
Volume: 50
Year: 2023
Month: 06
X-DOI: 10.1080/02664763.2022.2046713
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2046713
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:8:p:1812-1835



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2043254_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Ruofei Du
Author-X-Name-First: Ruofei
Author-X-Name-Last: Du
Author-Name: Li Luo
Author-X-Name-First: Li
Author-X-Name-Last: Luo
Author-Name: Laurie G. Hudson
Author-X-Name-First: Laurie G.
Author-X-Name-Last: Hudson
Author-Name: Sara Nozadi
Author-X-Name-First: Sara
Author-X-Name-Last: Nozadi
Author-Name: Johnnye Lewis
Author-X-Name-First: Johnnye
Author-X-Name-Last: Lewis
Title: An adjusted partial least squares regression framework to utilize additional exposure information in environmental mixture data analysis
Abstract: 
 In a large-scale environmental health population study that is composed of subprojects, often different fractions of participants out of the total enrolled have measures of specific outcomes. It’s conceptually reasonable to assume the association study would benefit from utilizing additional exposure information from those with a specific outcome not measured. Partial least squares regression is a practical approach to determine the exposure-outcome associations for mixture data. Like a typical regression approach, however, the partial least squares regression requires that each data observation must have both complete covariate and outcome for model fitting. In this paper, we propose novel adjustments to the general partial least squares regression to estimate and examine the association effects of individual environmental exposure to an outcome within a more complete context of the study population’s environmental mixture exposures. The proposed framework takes advantage of the bilinear model structure. It allows information from all participants, with or without the outcome values, to contribute to the model fitting and the assessment of association effects. Using this proposed framework, incorporation of additional information will lead to smaller root mean square errors in the estimation of association effects, and improve the ability to assess the significance of the effects.
Journal: Journal of Applied Statistics
Pages: 1790-1811
Issue: 8
Volume: 50
Year: 2023
Month: 06
X-DOI: 10.1080/02664763.2022.2043254
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2043254
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:8:p:1790-1811



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2043255_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Tsirizani M. Kaombe
Author-X-Name-First: Tsirizani M.
Author-X-Name-Last: Kaombe
Author-Name: Samuel O. M. Manda
Author-X-Name-First: Samuel O. M.
Author-X-Name-Last: Manda
Title: A novel outlier statistic in multivariate survival models and its application to identify unusual under-five mortality sub-districts in Malawi
Abstract: 
 Although under-five mortality (U5M) rates have declined worldwide, many countries in sub-Saharan Africa still have much higher rates. Detection of subnational areas with unusually higher U5M rates could support targeted high impact child health interventions. We propose a novel group outlier detection statistic for identifying areas with extreme U5M rates under a multivariate survival data model. The performance of the proposed statistic was evaluated through a simulation study. We applied the proposed method to an analysis of child survival data in Malawi to identify sub-districts with unusually higher or lower U5M rates. The simulation study showed that the proposed outlier statistic can detect unusual high or low mortality groups with a high accuracy of at least 90%, for datasets with at least 50 clusters of size 80 or more. In the application, at most 7 U5M outlier sub-districts were identified, based on the best fitting model as measured by the Akaike information criterion (AIC).
Journal: Journal of Applied Statistics
Pages: 1836-1852
Issue: 8
Volume: 50
Year: 2023
Month: 06
X-DOI: 10.1080/02664763.2022.2043255
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2043255
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:8:p:1836-1852



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2036953_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Mingming Liu
Author-X-Name-First: Mingming
Author-X-Name-Last: Liu
Author-Name: Jing Yang
Author-X-Name-First: Jing
Author-X-Name-Last: Yang
Author-Name: Yushi Liu
Author-X-Name-First: Yushi
Author-X-Name-Last: Liu
Author-Name: Bochao Jia
Author-X-Name-First: Bochao
Author-X-Name-Last: Jia
Author-Name: Yun-Fei Chen
Author-X-Name-First: Yun-Fei
Author-X-Name-Last: Chen
Author-Name: Luna Sun
Author-X-Name-First: Luna
Author-X-Name-Last: Sun
Author-Name: Shujie Ma
Author-X-Name-First: Shujie
Author-X-Name-Last: Ma
Title: A fusion learning method to subgroup analysis of Alzheimer's disease
Abstract: 
 Uncovering the heterogeneity in the disease progression of Alzheimer's is a key factor to disease understanding and treatment development, so that interventions can be tailored to target the subgroups that will benefit most from the treatment, which is an important goal of precision medicine. However, in practice, one top methodological challenge hindering the heterogeneity investigation is that the true subgroup membership of each individual is often unknown. In this article, we aim to identify latent subgroups of individuals who share a common disorder progress over time, to predict latent subgroup memberships, and to estimate and infer the heterogeneous trajectories among the subgroups. To achieve these goals, we apply a concave fusion learning method to conduct subgroup analysis for longitudinal trajectories of the Alzheimer's disease data. The heterogeneous trajectories are represented by subject-specific unknown functions which are approximated by B-splines. The concave fusion method can simultaneously estimate the spline coefficients and merge them together for the subjects belonging to the same subgroup to automatically identify subgroups and recover the heterogeneous trajectories. The resulting estimator of the disease trajectory of each subgroup is supported by an asymptotic distribution. It provides a sound theoretical basis for further conducting statistical inference in subgroup analysis.
Journal: Journal of Applied Statistics
Pages: 1686-1708
Issue: 8
Volume: 50
Year: 2023
Month: 06
X-DOI: 10.1080/02664763.2022.2036953
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2036953
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:8:p:1686-1708



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2037528_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Kristopher Attwood
Author-X-Name-First: Kristopher
Author-X-Name-Last: Attwood
Author-Name: Surui Hou
Author-X-Name-First: Surui
Author-X-Name-Last: Hou
Author-Name: Alan Hutson
Author-X-Name-First: Alan
Author-X-Name-Last: Hutson
Title: Application of the skew exponential power distribution to ROC curves
Abstract: 
 The bi-Normal ROC model and corresponding metrics are commonly used in medical studies to evaluate the discriminatory ability of a biomarker. However, in practice, many clinical biomarkers tend to have skewed or other non-Normal distributions. And while the bi-Normal ROC model’s AUC tends to be unbiased in this setting, providing a reasonable measure of global performance, the corresponding decision thresholds tend to be biased. To correct this bias, we propose using an ROC model based on the skew exponential power (SEP) distribution, whose additional parameters can accommodate skewed, heavy tailed, or other non-Normal distributions. Additionally, the SEP distribution can be used to evaluate whether the bi-Normal model would be appropriate. The performance of these ROC models and the non-parametric approach are evaluated via a simulation study and applied to a real data set involving infections from Klebsiella pneumoniae. The SEP based ROC-model provides some efficiency gains with respect to estimation of the AUC and provides cut-points with improved classification rates. As such, in the presence non-Normal data, we suggest using the proposed SEP ROC model.
Journal: Journal of Applied Statistics
Pages: 1709-1724
Issue: 8
Volume: 50
Year: 2023
Month: 06
X-DOI: 10.1080/02664763.2022.2037528
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2037528
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:8:p:1709-1724



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2064440_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Wantanee Poonvoralak
Author-X-Name-First: Wantanee
Author-X-Name-Last: Poonvoralak
Title: Bayesian Markov Chain Monte Carlo for reparameterized Stochastic volatility models using Asian FX rates during Covid-19
Abstract: 
 In this paper, reparameterization and student-t are applied to Stochastic Volatility (SV) model. We aim to reduce the amount of autocorrelation of the SV parameters and to introduce heavy-tailed model via the Bayesian computation of the Markov Chain Monte Carlo (MCMC) samplers. This research paper helps support better MCMC estimation of the SV model for volatile Asian FX series during Covid-19.
Journal: Journal of Applied Statistics
Pages: 1853-1875
Issue: 8
Volume: 50
Year: 2023
Month: 06
X-DOI: 10.1080/02664763.2022.2064440
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2064440
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:8:p:1853-1875



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2041567_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Jing Kersey
Author-X-Name-First: Jing
Author-X-Name-Last: Kersey
Author-Name: Hani Samawi
Author-X-Name-First: Hani
Author-X-Name-Last: Samawi
Author-Name: Jingjing Yin
Author-X-Name-First: Jingjing
Author-X-Name-Last: Yin
Author-Name: Haresh Rochani
Author-X-Name-First: Haresh
Author-X-Name-Last: Rochani
Author-Name: Xinyan Zhang
Author-X-Name-First: Xinyan
Author-X-Name-Last: Zhang
Title: On diagnostic accuracy measure with cut-points criterion for ordinal disease classification based on concordance and discordance
Abstract: 
 The accuracy of a diagnostic test has always been essential in detecting disease staging. Many diagnostic tests of accuracy measures are used in binary diagnosis tests. Some measures apply to multi-stage diagnosis. Yet, there are limitations to the implementation, and the performance highly depends on the distribution of diagnostic outcomes. Another essential aspect of medical diagnostic testing using biomarkers is to find an optimal cut-point that categorizes a patient as diseased or healthy. This aspect was extended to the diseases with more than two stages. We propose a diagnostic accuracy measure and optimal cut-points selection (CD), using concordance and discordance for k-stages diseases. The CD measure uses the classification agreement and disagreement between tests outcomes and diseases stages. Simulations for power studies suggest that CD can detect the differences between the null and alternative hypotheses that other methods cannot for some scenarios. Simulation results indicate that using CD measures to select optimal cut-points can provide relatively high correct classification rates than the existing measures and more balanced accurate classification rates than the generalized Youden Index (GYI). An illustration is provided using the ANDI data to choose biomarkers for diagnosing Alzheimer's Disease (AD) and select optimal cut-points for the chosen biomarkers.
Journal: Journal of Applied Statistics
Pages: 1772-1789
Issue: 8
Volume: 50
Year: 2023
Month: 06
X-DOI: 10.1080/02664763.2022.2041567
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2041567
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:8:p:1772-1789



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2041566_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Angkana Kokaew
Author-X-Name-First: Angkana
Author-X-Name-Last: Kokaew
Author-Name: Winai Bodhisuwan
Author-X-Name-First: Winai
Author-X-Name-Last: Bodhisuwan
Author-Name: Su-Fen Yang
Author-X-Name-First: Su-Fen
Author-X-Name-Last: Yang
Author-Name: Andrei Volodin
Author-X-Name-First: Andrei
Author-X-Name-Last: Volodin
Title: Logarithmic confidence estimation of a ratio of binomial proportions for dependent populations
Abstract: 
 This article investigates the logarithmic interval estimation of a ratio of two binomial proportions in dependent samples. Previous studies suggest that the confidence intervals of the difference between two correlated proportions and their ratio typically do not possess closed-form solutions. Moreover, the computation process is complex and often based on a maximum likelihood estimator, which is a biased estimator of the ratio. We look at the data from two dependent samples and explore the general problem of estimating the ratio of two proportions. Each sample is obtained in the framework of direct binomial sampling. Our goal is to demonstrate that the normal approximation for the estimation of the ratio is reliable for the construction of a confidence interval. The main characteristics of confidence estimators will be investigated by a Monte Carlo simulation. We also provide recommendations for applying the asymptotic logarithmic interval. The estimations of the coverage probability, average width, standard deviation of interval width, and index H are presented as the criteria of our judgment. The simulation studies indicate that the proposed interval performs well based on the aforementioned criteria. Finally, the confidence intervals are illustrated with three real data examples.
Journal: Journal of Applied Statistics
Pages: 1750-1771
Issue: 8
Volume: 50
Year: 2023
Month: 06
X-DOI: 10.1080/02664763.2022.2041566
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2041566
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:8:p:1750-1771



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2038546_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Melissa C. Key
Author-X-Name-First: Melissa C.
Author-X-Name-Last: Key
Author-Name: Susanne Ragg
Author-X-Name-First: Susanne
Author-X-Name-Last: Ragg
Author-Name: Benzion Boukai
Author-X-Name-First: Benzion
Author-X-Name-Last: Boukai
Title: A statistical testing procedure for validating class labels
Abstract: 
 Motivated by an open problem of validating protein identities in label-free shotgun proteomics work-flows, we present a testing procedure to validate class (protein) labels using available measurements across N instances (peptides). More generally, we present a non-parametric solution to the problem of identifying instances that are deemed as outliers relative to the subset of instances assigned to the same class. The primary assumption is that measured distances between instances within the same class are stochastically smaller than measured distances between instances from different classes. We show that the overall type I error probability across all instances within a class can be controlled by some fixed value (say α). We also demonstrate conditions where similar results on type II error probability hold. The theoretical results are supplemented by an extensive numerical study illustrating the applicability and viability of our method. Even with up to 25% of instances initially mislabeled, our testing procedure maintains a high specificity and greatly reduces the proportion of mislabeled instances. The applicability and effectiveness of our testing procedure is further illustrated by a detailed example on a proteomics data set from children with sickle cell disease where five spike-in proteins acted as contrasting controls.
Journal: Journal of Applied Statistics
Pages: 1725-1749
Issue: 8
Volume: 50
Year: 2023
Month: 06
X-DOI: 10.1080/02664763.2022.2038546
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2038546
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:8:p:1725-1749

Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2060952_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Jung Wun Lee
Author-X-Name-First: Jung Wun
Author-X-Name-Last: Lee
Author-Name: Ofer Harel
Author-X-Name-First: Ofer
Author-X-Name-Last: Harel
Title: Incomplete clustering analysis via multiple imputation
Abstract: 
 Clustering analysis is a prevalent statistical method which divides populations into several subgroups of similar units. However, most existing clustering methods require complete data. One general method that addresses incomplete data is multiple imputation (MI) which avoids many limitations found in other single imputation-based methods and complete case analyses. Nevertheless, adopting MI framework to clustering analysis can be challenging since each imputed data might consist of a different number of clusters and there is not a unique parameter for clustering analysis. In response to this problem, we have developed MICA: Multiply Imputed Cluster Analysis. MICA is a framework for clustering incomplete data consisting of two clustering stages. We assess the properties of MICA and its superiority over other existing incomplete clustering strategies based on a simulation study under various data structures. In addition, we demonstrate the usage of MICA by applying it to the Youth Risk Behavior Surveillance System (YRBSS) 2019 data.
Journal: Journal of Applied Statistics
Pages: 1962-1979
Issue: 9
Volume: 50
Year: 2023
Month: 07
X-DOI: 10.1080/02664763.2022.2060952
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2060952
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:9:p:1962-1979



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2105827_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Shu Wang
Author-X-Name-First: Shu
Author-X-Name-Last: Wang
Author-Name: Elias Sayour
Author-X-Name-First: Elias
Author-X-Name-Last: Sayour
Author-Name: Ji-Hyun Lee
Author-X-Name-First: Ji-Hyun
Author-X-Name-Last: Lee
Title: Evaluation of phase I clinical trial designs for combinational agents along with guidance based on simulation studies
Abstract: 
 Combinational therapy that combines two or more therapeutic agents is very common in cancer treatment. Currently, many clinical trials aim to assess feasibility, safety and activity of combinational therapeutics to achieve synergistic response. Dose-finding for combinational agents is considerably more complex than single agent, because only partial order of dose combinations' toxicity is known. Prototypical phase I designs may not adequately capture this complexity thus limiting identification of the maximum tolerated dose (MTD) of combinational agents. In response, novel phase I clinical trial designs for combinational agents have been extensively proposed. However, with so many available designs, studies that compare their performances and explore the impact of design parameters, along with providing recommendations are limited. We are evaluating available phase I designs that identify a single MTD for combinational agents using simulation studies under various conditions. We are also exploring the influences of different design parameters and summarizing the risks/benefits of each design to provide general guidance in design selection.
Journal: Journal of Applied Statistics
Pages: 2055-2078
Issue: 9
Volume: 50
Year: 2023
Month: 07
X-DOI: 10.1080/02664763.2022.2105827
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2105827
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:9:p:2055-2078



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2053949_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Guangbao Guo
Author-X-Name-First: Guangbao
Author-X-Name-Last: Guo
Author-Name: Yue Sun
Author-X-Name-First: Yue
Author-X-Name-Last: Sun
Author-Name: Guoqi Qian
Author-X-Name-First: Guoqi
Author-X-Name-Last: Qian
Author-Name: Qian Wang
Author-X-Name-First: Qian
Author-X-Name-Last: Wang
Title: LIC criterion for optimal subset selection in distributed interval estimation
Abstract: 
 Distributed interval estimation in linear regression may be computationally infeasible in the presence of big data that are normally stored in different computer servers or in cloud. The existing challenge represents the results from the distributed estimation may still contain redundant information about the population characteristics of the data. To tackle this computing challenge, we develop an optimization procedure to select the best subset from the collection of data subsets, based on which we perform interval estimation in the context of linear regression. The procedure is derived based on minimizing the length of the final interval estimator and maximizing the information remained in the selected data subset, thus is named as the LIC criterion. Theoretical performance of the LIC criterion is studied in this paper together with a simulation study and real data analysis.
Journal: Journal of Applied Statistics
Pages: 1900-1920
Issue: 9
Volume: 50
Year: 2023
Month: 07
X-DOI: 10.1080/02664763.2022.2053949
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2053949
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:9:p:1900-1920



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2054962_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Kwan-Young Bak
Author-X-Name-First: Kwan-Young
Author-X-Name-Last: Bak
Author-Name: Jae-Kyung Shin
Author-X-Name-First: Jae-Kyung
Author-X-Name-Last: Shin
Author-Name: Ja-Yong Koo
Author-X-Name-First: Ja-Yong
Author-X-Name-Last: Koo
Title: Intrinsic spherical smoothing method based on generalized Bézier curves and sparsity inducing penalization
Abstract: 
 This study examines an intrinsic penalized smoothing method on the 2-sphere. We propose a method based on the spherical Bézier curves obtained using a generalized de Casteljau algorithm to provide a degree-based regularity constraint to the spherical smoothing problem. A smooth Bézier curve is found by minimizing the least squares criterion under the regularization constraint. The de Casteljau algorithm constructs higher-order Bézier curves in a recursive manner using linear Bézier curves. We introduce a local penalization scheme based on a penalty function that regularizes the velocity differences in consecutive linear Bézier curves. The imposed penalty induces sparsity on the control points so that the proposed method determines the number of control points, or equivalently the order of the Bézier curve, in a data-adaptive way. An efficient Riemannian block coordinate descent algorithm is devised to implement the proposed method. Numerical studies based on real and simulated data are provided to illustrate the performance and properties of the proposed method. The results show that the penalized Bézier curve adapts well to local data trends without compromising overall smoothness.
Journal: Journal of Applied Statistics
Pages: 1942-1961
Issue: 9
Volume: 50
Year: 2023
Month: 07
X-DOI: 10.1080/02664763.2022.2054962
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2054962
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:9:p:1942-1961



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2063266_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Xun Li
Author-X-Name-First: Xun
Author-X-Name-Last: Li
Author-Name: Joyee Ghosh
Author-X-Name-First: Joyee
Author-X-Name-Last: Ghosh
Author-Name: Gabriele Villarini
Author-X-Name-First: Gabriele
Author-X-Name-Last: Villarini
Title: Bayesian negative binomial regression model with unobserved covariates for predicting the frequency of north atlantic tropical storms
Abstract: 
 Predicting the annual frequency of tropical storms is of interest because it can provide basic information towards improved preparation against these storms. Sea surface temperatures (SSTs) averaged over the hurricane season can predict annual tropical cyclone activity well. But predictions need to be made before the hurricane season when the predictors are not yet observed. Several climate models issue forecasts of the SSTs, which can be used instead. Such models use the forecasts of SSTs as surrogates for the true SSTs. We develop a Bayesian negative binomial regression model, which makes a distinction between the true SSTs and their forecasts, both of which are included in the model. For prediction, the true SSTs may be regarded as unobserved predictors and sampled from their posterior predictive distribution. We also have a small fraction of missing data for the SST forecasts from the climate models. Thus, we propose a model that can simultaneously handle missing predictors and variable selection uncertainty. If the main goal is prediction, an interesting question is should we include predictors in the model that are missing at the time of prediction? We attempt to answer this question and demonstrate that our model can provide gains in prediction.
Journal: Journal of Applied Statistics
Pages: 2014-2035
Issue: 9
Volume: 50
Year: 2023
Month: 07
X-DOI: 10.1080/02664763.2022.2063266
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2063266
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:9:p:2014-2035



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2061430_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Xinhua Liu
Author-X-Name-First: Xinhua
Author-X-Name-Last: Liu
Author-Name: Zhezhen Jin
Author-X-Name-First: Zhezhen
Author-X-Name-Last: Jin
Title: On detecting the effect of exposure mixture
Abstract: 
 To study the effect of exposure mixture on the continuous health outcomes, one can use the linear model with a weighted sum of multiple standardized exposure variables as an index predictor and its coefficient for the overall effect. The unknown weights typically range between zero and one, indicating contributions of individual exposures to the overall effect. Because the weight parameters present only when the parameter for overall effect is non-zero, testing hypotheses on the overall effect can be challenging, especially when the number of exposure variables is above two. This paper presents a working model based approach to estimate the parameter for overall effect and to test specific hypotheses, including two tests for detecting the overall effect and one test for detecting unequal weights when the overall effect is evident. The statistics are computationally easy and one can apply existing statistical software to perform the analysis. A simulation study shows that the proposed estimators for the parameters of interest may have better finite sample performance than some other estimators.
Journal: Journal of Applied Statistics
Pages: 1980-1991
Issue: 9
Volume: 50
Year: 2023
Month: 07
X-DOI: 10.1080/02664763.2022.2061430
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2061430
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:9:p:1980-1991



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2063265_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Hyune-Ju Kim
Author-X-Name-First: Hyune-Ju
Author-X-Name-Last: Kim
Author-Name: Huann-Sheng Chen
Author-X-Name-First: Huann-Sheng
Author-X-Name-Last: Chen
Author-Name: Douglas Midthune
Author-X-Name-First: Douglas
Author-X-Name-Last: Midthune
Author-Name: Bill Wheeler
Author-X-Name-First: Bill
Author-X-Name-Last: Wheeler
Author-Name: Dennis W. Buckman
Author-X-Name-First: Dennis W.
Author-X-Name-Last: Buckman
Author-Name: Donald Green
Author-X-Name-First: Donald
Author-X-Name-Last: Green
Author-Name: Jeffrey Byrne
Author-X-Name-First: Jeffrey
Author-X-Name-Last: Byrne
Author-Name: Jun Luo
Author-X-Name-First: Jun
Author-X-Name-Last: Luo
Author-Name: Eric J. Feuer
Author-X-Name-First: Eric J.
Author-X-Name-Last: Feuer
Title: Data-driven choice of a model selection method in joinpoint regression
Abstract: 
 Selecting the number of change points in segmented line regression is an important problem in trend analysis, and there have been various approaches proposed in the literature. We first study the empirical properties of several model selection procedures and propose a new method based on two Schwarz type criteria, a classical Bayes Information Criterion (BIC) and the one with a harsher penalty than BIC (
 $ \hbox {BIC}_3 $ BIC3). The proposed rule is designed to use the former when effect sizes are small and the latter when the effect sizes are large and employs the partial 
 $ R^2 $ R2 to determine the weight between BIC and 
 $ \hbox {BIC}_3 $ BIC3. The proposed method is computationally much more efficient than the permutation test procedure that has been the default method of Joinpoint software developed for cancer trend analysis, and its satisfactory performance is observed in our simulation study. Simulations indicate that the proposed method performs well in keeping the probability of correct selection at least as large as that of 
 $ \hbox {BIC}_3 $ BIC3, whose performance is comparable to that of the permutation test procedure, and improves 
 $ \hbox {BIC}_3 $ BIC3 when it performs worse than 
 $ \hbox {BIC}. $ BIC. The proposed method is applied to the U.S. prostate cancer incidence and mortality rates.
Journal: Journal of Applied Statistics
Pages: 1992-2013
Issue: 9
Volume: 50
Year: 2023
Month: 07
X-DOI: 10.1080/02664763.2022.2063265
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2063265
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:9:p:1992-2013



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2064976_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Xin Zhao
Author-X-Name-First: Xin
Author-X-Name-Last: Zhao
Author-Name: Stuart Barber
Author-X-Name-First: Stuart
Author-X-Name-Last: Barber
Author-Name: Charles C Taylor
Author-X-Name-First: Charles C
Author-X-Name-Last: Taylor
Author-Name: Xiaokai Nie
Author-X-Name-First: Xiaokai
Author-X-Name-Last: Nie
Author-Name: Wenqian Shen
Author-X-Name-First: Wenqian
Author-X-Name-Last: Shen
Title: Spatio-temporal forecasting using wavelet transform-based decision trees with application to air quality and covid-19 forecasting
Abstract: 
 We develop a new method that combines a decision tree with a wavelet transform to forecast time series data with spatial spillover effects. The method can not only improve prediction but also give good interpretability of the time series mechanism. As a feature exploration method, the wavelet transform represents information at different resolution levels, which may improve the performance of decision trees. The method is applied to simulated data, air pollution and COVID time series data sets. In the simulation, Haar, LA8, D4 and D6 wavelets are compared, with the Haar wavelet having the best performance. In the air pollution application, by using wavelet transform-based decision trees, the temporal effect of air quality index including autoregressive and seasonal effects can be described as well as the spatial correlation effect. To describe the spillover spatial effect in contiguous regions, a spatial weight is constructed to improve the modeling performance. The results show that air quality index has autoregressive, seasonal and spatial spillover effects. The wavelet transformed variables have a better forecasting performance and enhanced interpretability than the original variables. For the COVID time series of cumulative cases, spatial weighted variables are not selected which shows the lock-down policies are truly effective.
Journal: Journal of Applied Statistics
Pages: 2036-2054
Issue: 9
Volume: 50
Year: 2023
Month: 07
X-DOI: 10.1080/02664763.2022.2064976
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2064976
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:9:p:2036-2054



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2052821_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: C. P. Yadav
Author-X-Name-First: C. P.
Author-X-Name-Last: Yadav
Author-Name: Sanjeev K. Tomer
Author-X-Name-First: Sanjeev K.
Author-X-Name-Last: Tomer
Author-Name: M. S. Panwar
Author-X-Name-First: M. S.
Author-X-Name-Last: Panwar
Title: A competing risk study of menarcheal age distribution based on non-recall current status data
Abstract: 
 In many cross-sectional studies, the chances that an individual will be able to exactly recall the event are very low. The possibility of recalling the exact time as well as the cause of occurrence of an event usually decreases as the gap between event and monitoring time increases. This gives rise to non-recall current status data. In this article, an efficient approach to deal with such non-recall current status data is established in a competing risk set up. In the classical method, a nested Expectation–Maximization technique is worked out for the estimation purpose and the information matrix is evaluated using the missing information principle. In the Bayesian paradigm, point and interval estimates are obtained using the Gibbs sampling algorithm. A recent anthropometric study data containing the menarcheal status of girls and age at menarche is analyzed using the considered methodology.
Journal: Journal of Applied Statistics
Pages: 1877-1899
Issue: 9
Volume: 50
Year: 2023
Month: 07
X-DOI: 10.1080/02664763.2022.2052821
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2052821
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:9:p:1877-1899



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2053950_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Yanqin Feng
Author-X-Name-First: Yanqin
Author-X-Name-Last: Feng
Author-Name: Jie Wang
Author-X-Name-First: Jie
Author-X-Name-Last: Wang
Author-Name: Yang Li
Author-X-Name-First: Yang
Author-X-Name-Last: Li
Title: Goodness-of-fit inference for the additive hazards regression model with clustered current status data
Abstract: 
 Clustered current status data are frequently encountered in biomedical research and other areas that require survival analysis. This paper proposes graphical and formal model assessment procedures to evaluate the goodness of fit of the additive hazards model to clustered current status data. The test statistics proposed are based on sums of martingale-based residuals. Relevant asymptotic properties are established, and empirical distributions of the test statistics can be simulated utilizing Gaussian multipliers. Extensive simulation studies confirmed that the proposed test procedures work well for practical scenarios. This proposed method applies when failure times within the same cluster are correlated, and in particular, when cluster sizes can be informative about intra-cluster correlations. The method is applied to analyze clustered current status data from a lung tumorigenicity study.
Journal: Journal of Applied Statistics
Pages: 1921-1941
Issue: 9
Volume: 50
Year: 2023
Month: 07
X-DOI: 10.1080/02664763.2022.2053950
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2053950
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:9:p:1921-1941

Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2064977_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Vasileios Alevizakos
Author-X-Name-First: Vasileios
Author-X-Name-Last: Alevizakos
Author-Name: Kashinath Chatterjee
Author-X-Name-First: Kashinath
Author-X-Name-Last: Chatterjee
Author-Name: Christos Koukouvinos
Author-X-Name-First: Christos
Author-X-Name-Last: Koukouvinos
Author-Name: Angeliki Lappa
Author-X-Name-First: Angeliki
Author-X-Name-Last: Lappa
Title: A double generally weighted moving average control chart for monitoring the process variability
Abstract: 
 In the present article, a double generally weighted moving average (DGWMA) control chart based on a three-parameter logarithmic transformation is proposed for monitoring the process variability, namely the 
 $ S^2 $ S2-DGWMA chart. Monte-Carlo simulations are utilized in order to evaluate the run-length performance of the 
 $ S^2 $ S2-DGWMA chart. In addition, a detailed comparative study is conducted to compare the performance of the 
 $ S^2 $ S2-DGWMA chart with several well-known memory-type control charts in the literature. The comparisons indicate that the proposed one is more efficient in detecting small shifts, while it is more sensitive in identifying upward shifts in the process variability. A real data example is given to present the implementation of the new 
 $ S^2 $ S2-DGWMA chart.
Journal: Journal of Applied Statistics
Pages: 2079-2107
Issue: 10
Volume: 50
Year: 2023
Month: 07
X-DOI: 10.1080/02664763.2022.2064977
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2064977
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:10:p:2079-2107



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2064978_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Fidel Ernesto Castro Morales
Author-X-Name-First: Fidel Ernesto Castro
Author-X-Name-Last: Morales
Author-Name: Daniele Torres Rodrigues
Author-X-Name-First: Daniele Torres
Author-X-Name-Last: Rodrigues
Title: Spatiotemporal nonhomogeneous poisson model with a seasonal component applied to the analysis of extreme rainfall
Abstract: 
 This paper develops an extension of spatiotemporal models that handle count data using nonhomogeneous Poisson processes. In this new proposal, we incorporate a seasonal cycle component in the definition of the intensity function to control possible effects produced by the occurrence of the event of interest in regular periods. The seasonal cycle can cause problems in estimating the shape parameter of the Weibull and generalized Goel intensity functions. This shape parameter serves to confront the research hypothesis that seeks to identify a trend in the occurrence rate of an event of interest. In the case of the Weibull intensity function, a value significantly equal to one of the shape parameters indicates a constant rate of occurrence, less than one indicates a decreasing rate, and greater than one indicates an increasing rate. In the case of the Goel intensity function, parameter values less than or equal to one indicate a decreasing occurrence rate, and values greater than one indicate the presence of a change point. We also built a spatial model using the Musa-Okumoto intensity function as an alternative to approximate counting processes for which there is a decreasing trend in the occurrence rate of the event of interest. We estimated the parameters of the proposed method from a Bayesian perspective. Finally, we fitted the proposed model and compared it with other approximations to analyze the frequency of extreme rainfall in the northern region of the states of Maranhão and Piauí in northeastern Brazil over ten years. Among the main results, we found that (1) the proposed method has proven superior in terms of fit and prediction performance than the other models, and (2) unlike other approximations, the proposed model does not detect changes in the rate of extreme rainfall occurrences.
Journal: Journal of Applied Statistics
Pages: 2108-2126
Issue: 10
Volume: 50
Year: 2023
Month: 07
X-DOI: 10.1080/02664763.2022.2064978
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2064978
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:10:p:2108-2126



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2065468_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: N Vipin
Author-X-Name-First: N
Author-X-Name-Last: Vipin
Author-Name: Indranil Ghosh
Author-X-Name-First: Indranil
Author-X-Name-Last: Ghosh
Author-Name: S. M. Sunoj
Author-X-Name-First: S. M.
Author-X-Name-Last: Sunoj
Title: Some properties of stop-loss moments under biased sampling
Abstract: 
 The stop-loss moments have generally been used as useful summary measures for analyzing the data which exceeds specific threshold levels. In many scientific studies, the investigator cannot record the sampling units with equal probability, and in such a scenario, the selected sample units appear with unequal probability, in other words with different weights, which leads to a biased or weighted sampling. In the present study, we examine the usefulness of stop-loss moments in biased sampling. The application of the weighted stop-loss moments in analyzing biased data has been investigated and compared using different empirical estimators through simulated and real data sets.
Journal: Journal of Applied Statistics
Pages: 2127-2150
Issue: 10
Volume: 50
Year: 2023
Month: 07
X-DOI: 10.1080/02664763.2022.2065468
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2065468
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:10:p:2127-2150



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2068512_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Pei-Fang Su
Author-X-Name-First: Pei-Fang
Author-X-Name-Last: Su
Author-Name: Junjiang Zhong
Author-X-Name-First: Junjiang
Author-X-Name-Last: Zhong
Author-Name: Yi-Chia Liu
Author-X-Name-First: Yi-Chia
Author-X-Name-Last: Liu
Author-Name: Tzu-Hsuan Lin
Author-X-Name-First: Tzu-Hsuan
Author-X-Name-Last: Lin
Author-Name: Huang-Tz Ou
Author-X-Name-First: Huang-Tz
Author-X-Name-Last: Ou
Title: Efficient estimation of a Cox model when integrating the subgroup incidence rate information
Abstract: 
 Incidence rates for diseases are widely used in the field of medical research because they lead to clear and simple physical and clinical interpretations. In this study, we propose an efficient estimation method that incorporates auxiliary subgroup information related to the incidence rate into the estimation of the Cox proportional hazard model. The results show that utilizing the incidence rate information improves the efficiency of the estimation of regression parameters based on the double empirical likelihood method compared to that for conventional models that do not incorporation such information. We show that estimators of regression parameters asymptotically follow a multivariate normal distribution with a variance-covariance matrix that can be consistently estimated. Simulation results indicate that the proposed estimators significantly increase efficiency. Finally, an example of the effects of type 2 diabetes on stroke is applied to demonstrate the proposed method.
Journal: Journal of Applied Statistics
Pages: 2151-2170
Issue: 10
Volume: 50
Year: 2023
Month: 07
X-DOI: 10.1080/02664763.2022.2068512
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2068512
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:10:p:2151-2170



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2068513_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Yulan B. van Oppen
Author-X-Name-First: Yulan B.
Author-X-Name-Last: van Oppen
Author-Name: Gabi Milder-Mulderij
Author-X-Name-First: Gabi
Author-X-Name-Last: Milder-Mulderij
Author-Name: Christophe Brochard
Author-X-Name-First: Christophe
Author-X-Name-Last: Brochard
Author-Name: Rink Wiggers
Author-X-Name-First: Rink
Author-X-Name-Last: Wiggers
Author-Name: Saskia de Vries
Author-X-Name-First: Saskia
Author-X-Name-Last: de Vries
Author-Name: Wim P. Krijnen
Author-X-Name-First: Wim P.
Author-X-Name-Last: Krijnen
Author-Name: Marco A. Grzegorczyk
Author-X-Name-First: Marco A.
Author-X-Name-Last: Grzegorczyk
Title: Modeling dragonfly population data with a Bayesian bivariate geometric mixed-effects model
Abstract: 
 We develop a generalized linear mixed model (GLMM) for bivariate count responses for statistically analyzing dragonfly population data from the Northern Netherlands. The populations of the threatened dragonfly species Aeshna viridis were counted in the years 2015–2018 at 17 different locations (ponds and ditches). Two different widely applied population size measures were used to quantify the population sizes, namely the number of found exoskeletons (‘exuviae’) and the number of spotted egg-laying females were counted. Since both measures (responses) led to many zero counts but also feature very large counts, our GLMM model builds on a zero-inflated bivariate geometric (ZIBGe) distribution, for which we show that it can be easily parameterized in terms of a correlation parameter and its two marginal medians. We model the medians with linear combinations of fixed (environmental covariates) and random (location-specific intercepts) effects. Modeling the medians yields a decreased sensitivity to overly large counts; in particular, in light of growing marginal zero inflation rates. Because of the relatively small sample size (n = 114) we follow a Bayesian modeling approach and use Metropolis-Hastings Markov Chain Monte Carlo (MCMC) simulations for generating posterior samples.
Journal: Journal of Applied Statistics
Pages: 2171-2193
Issue: 10
Volume: 50
Year: 2023
Month: 07
X-DOI: 10.1080/02664763.2022.2068513
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2068513
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:10:p:2171-2193



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2070136_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: E. F. Saraiva
Author-X-Name-First: E. F.
Author-X-Name-Last: Saraiva
Author-Name: L. Sauer
Author-X-Name-First: L.
Author-X-Name-Last: Sauer
Author-Name: C. A. B. Pereira
Author-X-Name-First: C. A. B.
Author-X-Name-Last: Pereira
Title: A hierarchical Bayesian approach for modeling the evolution of the 7-day moving average of the number of deaths by COVID-19
Abstract: 
 In this paper, we propose a hierarchical Bayesian approach for modeling the evolution of the 7-day moving average for the number of deaths due to COVID-19 in a country, state or city. The proposed approach is based on a Gaussian process regression model. The main advantage of this model is that it assumes that a nonlinear function f used for modeling the observed data is an unknown random parameter in opposite to usual approaches that set up f as being a known mathematical function. This assumption allows the development of a Bayesian approach with a Gaussian process prior over f. In order to estimate the parameters of interest, we develop an MCMC algorithm based on the Metropolis-within-Gibbs sampling algorithm. We also present a procedure for making predictions. The proposed method is illustrated in a case study, in which, we model the 7-day moving average for the number of deaths recorded in the state of São Paulo, Brazil. Results obtained show that the proposed method is very effective in modeling and predicting the values of the 7-day moving average.
Journal: Journal of Applied Statistics
Pages: 2194-2208
Issue: 10
Volume: 50
Year: 2023
Month: 07
X-DOI: 10.1080/02664763.2022.2070136
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2070136
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:10:p:2194-2208



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2070137_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Simon Boge Brant
Author-X-Name-First: Simon Boge
Author-X-Name-Last: Brant
Author-Name: Ingrid Hobæk Haff
Author-X-Name-First: Ingrid
Author-X-Name-Last: Hobæk Haff
Title: The fraud loss for selecting the model complexity in fraud detection
Abstract: 
 Statistical fraud detection consists in making a system that automatically selects a subset of all cases (insurance claims, financial transactions, etc.) that are the most interesting for further investigation. The reason why such a system is needed is that the total number of cases typically is much higher than one realistically could investigate manually and that fraud tends to be quite rare. Further, the investigator is typically limited to controlling a restricted number k of cases, due to limited resources. The most efficient manner of allocating these resources is then to try selecting the k cases with the highest probability of being fraudulent. The prediction model used for this purpose must normally be regularised to avoid overfitting and consequently bad prediction performance. A loss function, denoted the fraud loss, is proposed for selecting the model complexity via a tuning parameter. A simulation study is performed to find the optimal settings for validation. Further, the performance of the proposed procedure is compared to the most relevant competing procedure, based on the area under the receiver operating characteristic curve (AUC), in a set of simulations, as well as on a credit card default dataset. Choosing the complexity of the model by the fraud loss resulted in either comparable or better results in terms of the fraud loss than choosing it according to the AUC.
Journal: Journal of Applied Statistics
Pages: 2209-2227
Issue: 10
Volume: 50
Year: 2023
Month: 07
X-DOI: 10.1080/02664763.2022.2070137
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2070137
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:10:p:2209-2227



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2071419_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Ana F. Best
Author-X-Name-First: Ana F.
Author-X-Name-Last: Best
Author-Name: Yaakov Malinovsky
Author-X-Name-First: Yaakov
Author-X-Name-Last: Malinovsky
Author-Name: Paul S. Albert
Author-X-Name-First: Paul S.
Author-X-Name-Last: Albert
Title: The efficient design of Nested Group Testing algorithms for disease identification in clustered data
Abstract: 
 Group testing study designs have been used since the 1940s to reduce screening costs for uncommon diseases; for rare diseases, all cases are identifiable with substantially fewer tests than the population size. Substantial research has identified efficient designs under this paradigm. However, little work has focused on the important problem of disease screening among clustered data, such as geographic heterogeneity in HIV prevalence. We evaluated designs where we first estimate disease prevalence and then apply efficient group testing algorithms using these estimates. Specifically, we evaluate prevalence using individual testing on a fixed-size subset of each cluster and use these prevalence estimates to choose group sizes that minimize the corresponding estimated average number of tests per subject. We compare designs where we estimate cluster-specific prevalences as well as a common prevalence across clusters, use different group testing algorithms, construct groups from individuals within and in different clusters, and consider misclassification. For diseases with low prevalence, our results suggest that accounting for clustering is unnecessary. However, for diseases with higher prevalence and sizeable between-cluster heterogeneity, accounting for clustering in study design and implementation improves efficiency. We consider the practical aspects of our design recommendations with two examples with strong clustering effects: (1) Identification of HIV carriers in the US population and (2) Laboratory screening of anti-cancer compounds using cell lines.
Journal: Journal of Applied Statistics
Pages: 2228-2245
Issue: 10
Volume: 50
Year: 2023
Month: 07
X-DOI: 10.1080/02664763.2022.2071419
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2071419
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:10:p:2228-2245



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2073336_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Daniel Ries
Author-X-Name-First: Daniel
Author-X-Name-Last: Ries
Author-Name: Alicia Carriquiry
Author-X-Name-First: Alicia
Author-X-Name-Last: Carriquiry
Title: The relationship between moderate to vigorous physical activity and metabolic syndrome: a Bayesian measurement error approach
Abstract: 
 Metabolic Syndrome (MetS) is a serious condition that can be an early warning sign of heart disease and Type 2 diabetes. MetS is characterized by having elevated levels of blood pressure, cholesterol, waist circumference, and fasting glucose. There are many articles in the literature exploring the relationship between physical activity and MetS, but most do not consider the measurement error in the physical activity measurements nor the correlations among the MetS risk factors. Furthermore, previous work has generally treated MetS as binary, rather than directly modeling the risk factors on their measured, continuous space. Using data from the National Health and Nutrition Examination Survey (NHANES), we explore the relationship between minutes of moderate to vigorous physical activity (MVPA) and MetS risk factors. We construct a measurement error model for the accelerometry data, and then model its relationship between MetS risk factors with nonlinear seemingly unrelated regressions, incorporating dependence among MetS risk factors. The novel features of this model give the medical research community a new way to understand relationships between MVPA and MetS. The results of this approach present the field with a different modeling perspective than previously taken and suggest future avenues of scientific discovery.
Journal: Journal of Applied Statistics
Pages: 2246-2266
Issue: 10
Volume: 50
Year: 2023
Month: 07
X-DOI: 10.1080/02664763.2022.2073336
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2073336
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:10:p:2246-2266



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2108773_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: A. Elayouty
Author-X-Name-First: A.
Author-X-Name-Last: Elayouty
Author-Name: H. Abou-Ali
Author-X-Name-First: H.
Author-X-Name-Last: Abou-Ali
Title: Functional data analysis of the relationship between electricity consumption and climate change drivers
Abstract: 
 Climate change has become increasingly important in recent years. It is the outcome of the burning of fossil fuels that increased the concentration of atmospheric carbon dioxide (CO
 $ _2 $ 2), over the last century. Mitigating the impacts of climate change requires a better understanding and assessment of the countries' economic decisions on the amount of CO
 $ _2 $ 2 emissions. This paper assesses the variability between the different countries in the trends of CO
 $ _2 $ 2 emissions and electricity consumption from 1975 to 2014, while identifying clusters of countries of similar trends over time. The novel methodology applied in this paper enables us to assess long-debated issues in climate literature. The temporal dynamic effects of electricity consumption and economic growth on CO
 $ _2 $ 2 emissions across countries are studied using functional data analysis (FDA) methods. The latter have proven to be useful tools for visualising similarities and differences in the non-linear trends of CO
 $ _2 $ 2 emissions without forcing linear trends and stationary relationships which can be unrealistic and misleading. The results indicate the possibility of identifying changes in the trends of CO
 $ _2 $ 2 emissions and electricity consumption for a wide range of heterogeneous countries over the study period. The findings also reveal that economic growth puts a strain on the environment, where many high-income countries are still away from attaining economic-energy sustainability.
Journal: Journal of Applied Statistics
Pages: 2267-2285
Issue: 10
Volume: 50
Year: 2023
Month: 07
X-DOI: 10.1080/02664763.2022.2108773
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2108773
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:10:p:2267-2285

Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1937584_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: S. Bacci
Author-X-Name-First: S.
Author-X-Name-Last: Bacci
Author-Name: R. Fabbricatore
Author-X-Name-First: R.
Author-X-Name-Last: Fabbricatore
Author-Name: Maria Iannario
Author-X-Name-First: Maria
Author-X-Name-Last: Iannario
Title: Latent trait models for perceived risk assessment using a Covid-19 data survey
Abstract: 
 Aim of the contribution is analyzing potential events that may negatively impact individuals, assets, and/or the environment, and making judgments about the perceived personal and social riskiness of Covid-19 compared to other hazards belonging to health (AIDS, cancer, infarction), environmental (climate change), behavioral (serious car accidents), and technological (nuclear weapons) domains. The comparative risk analysis has been performed on a survey data collected during the first Italian Covid-19 lockdown. An item response theory model for polytomously scored items has been implemented for the analysis of the positioning of Covid-19 with respect to the other hazards in terms of perceived risk. Among the attributes determining the hazard's perceived risk, Covid-19 distinguishes for the knowledge of risks from the hazard, media attention, and fear caused by the hazard in the peers. Besides, through a latent regression analysis, the role of some individual characteristics on the perceived risk for Covid-19 has been examined. Our contribution allows us to disentangle among several aspects of hazards and describe the main factors affecting the perceived risk. It also contributes to determine if existing control measures are perceived as adequate and the interest for new media with related impact on a person's reaction.
Journal: Journal of Applied Statistics
Pages: 2575-2598
Issue: 11-12
Volume: 50
Year: 2023
Month: 09
X-DOI: 10.1080/02664763.2021.1937584
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1937584
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:11-12:p:2575-2598



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1953449_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Peiyi Zhang
Author-X-Name-First: Peiyi
Author-X-Name-Last: Zhang
Author-Name: Tianning Dong
Author-X-Name-First: Tianning
Author-X-Name-Last: Dong
Author-Name: Ninghui Li
Author-X-Name-First: Ninghui
Author-X-Name-Last: Li
Author-Name: Faming Liang
Author-X-Name-First: Faming
Author-X-Name-Last: Liang
Title: Identification of factors impacting on the transmission and mortality of COVID-19
Abstract: 
 This paper proposes a dynamic infectious disease model for COVID-19 daily counts data and estimate the model using the Langevinized EnKF algorithm, which is scalable for large-scale spatio-temporal data, converges to the right filtering distribution, and is thus suitable for performing statistical inference and quantifying uncertainty for the underlying dynamic system. Under the framework of the proposed dynamic infectious disease model, we tested the impact of temperature, precipitation, state emergency order and stay home order on the spread of COVID-19 based on the United States county-wise daily counts data. Our numerical results show that warm and humid weather can significantly slow the spread of COVID-19, and the state emergency and stay home orders also help to slow it. This finding provides guidance and support to future policies or acts for mitigating the community transmission and lowering the mortality rate of COVID-19.
Journal: Journal of Applied Statistics
Pages: 2624-2647
Issue: 11-12
Volume: 50
Year: 2023
Month: 09
X-DOI: 10.1080/02664763.2021.1953449
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1953449
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:11-12:p:2624-2647



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1941806_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Claire Donnat
Author-X-Name-First: Claire
Author-X-Name-Last: Donnat
Author-Name: Susan Holmes
Author-X-Name-First: Susan
Author-X-Name-Last: Holmes
Title: Modeling the heterogeneity in COVID-19's reproductive number and its impact on predictive scenarios
Abstract: 
 The correct evaluation of the reproductive number R for COVID-19 is central in the quantification of the potential scope of the pandemic and the selection of an appropriate course of action. In most models, R is modeled as a constant - effectively averaging out the inherent variability of the transmission process due to varying individual contact rates, population densities, or temporal factors amongst many. Yet, due to the exponential nature of epidemic growth, the error due to this simplification can be rapidly amplified, and its extent remains unknown. How can this intrinsic variability be percolated into epidemic models, and its impact, better quantified? We study this question here through a Bayesian perspective that captures at scale the heterogeneity of a population and environmental conditions, creating a bridge between the traditional agent-based and compartmental approaches. We use our model to simulate the spread as well as the impact of different social distancing strategies on real COVID-19 data, and highlight the significant impact of the heterogeneity. We emphasize that the contribution of this paper focuses on discussing the importance of the impact of R's heterogeneity on uncertainty quantification from a statistical viewpoint, rather than developing new predictive models.
Journal: Journal of Applied Statistics
Pages: 2518-2546
Issue: 11-12
Volume: 50
Year: 2023
Month: 09
X-DOI: 10.1080/02664763.2021.1941806
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1941806
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:11-12:p:2518-2546



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1895089_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Ting Tian
Author-X-Name-First: Ting
Author-X-Name-Last: Tian
Author-Name: Jingwen Zhang
Author-X-Name-First: Jingwen
Author-X-Name-Last: Zhang
Author-Name: Shiyun Lin
Author-X-Name-First: Shiyun
Author-X-Name-Last: Lin
Author-Name: Yukang Jiang
Author-X-Name-First: Yukang
Author-X-Name-Last: Jiang
Author-Name: Jianbin Tan
Author-X-Name-First: Jianbin
Author-X-Name-Last: Tan
Author-Name: Zhongfei Li
Author-X-Name-First: Zhongfei
Author-X-Name-Last: Li
Author-Name: Xueqin Wang
Author-X-Name-First: Xueqin
Author-X-Name-Last: Wang
Title: Data-driven analysis of the simulations of the spread of COVID-19 under different interventions of China
Abstract: 
 Since February 2020, COVID-19 has spread rapidly to more than 200 countries in the world. During the pandemic, local governments in China have implemented different interventions to efficiently control the spread of the epidemic. Characterizing transmission of COVID-19 under some typical interventions is essential to help countries develop appropriate interventions. Based on the pre-symptomatic transmission patterns of COVID-19, we established a novel compartmental model: Susceptible-Infectious-Confirmed-Removed (SICR) model, which allowed the effective reproduction number to change over time, thus the effects of policies could be reasonably estimated. Using the epidemic data of Wuhan, Wenzhou, and Shenzhen, we migrated the corresponding estimated policy modes to South Korea, Italy, and the United States and simulated the potential outcomes for these countries when they adopted similar policy strategies to China. We found that the mild interventions implemented in Shenzhen were effective in controlling the epidemic in the early stage, while more stringent policies which were implemented in Wuhan and Wenzhou were necessary if the epidemic became severe and needed to be controlled in a short time.
Journal: Journal of Applied Statistics
Pages: 2547-2560
Issue: 11-12
Volume: 50
Year: 2023
Month: 09
X-DOI: 10.1080/02664763.2021.1895089
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1895089
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:11-12:p:2547-2560



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1907839_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Álvaro Gajardo
Author-X-Name-First: Álvaro
Author-X-Name-Last: Gajardo
Author-Name: Hans-Georg Müller
Author-X-Name-First: Hans-Georg
Author-X-Name-Last: Müller
Title: Point process models for COVID-19 cases and deaths
Abstract: 
 The study of events distributed over time which can be quantified as point processes has attracted much interest over the years due to its wide range of applications. It has recently gained new relevance due to the COVID-19 case and death processes associated with SARS-CoV-2 that characterize the COVID-19 pandemic and are observed across different countries. It is of interest to study the behavior of these point processes and how they may be related to covariates such as mobility restrictions, gross domestic product per capita, and fraction of population of older age. As infections and deaths in a region are intrinsically events that arrive at random times, a point process approach is natural for this setting. We adopt techniques for conditional functional point processes that target point processes as responses with vector covariates as predictors, to study the interaction and optimal transport between case and death processes and doubling times conditional on covariates.
Journal: Journal of Applied Statistics
Pages: 2294-2309
Issue: 11-12
Volume: 50
Year: 2023
Month: 09
X-DOI: 10.1080/02664763.2021.1907839
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1907839
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:11-12:p:2294-2309



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2064975_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Teng Chen
Author-X-Name-First: Teng
Author-X-Name-Last: Chen
Author-Name: Paweł Polak
Author-X-Name-First: Paweł
Author-X-Name-Last: Polak
Author-Name: Stanislav Uryasev
Author-X-Name-First: Stanislav
Author-X-Name-Last: Uryasev
Title: Classification and severity progression measure of COVID-19 patients using pairs of multi-omic factors
Abstract: 
 Early detection and effective treatment of severe COVID-19 patients remain two major challenges during the current pandemic. Analysis of molecular changes in blood samples of severe patients is one of the promising approaches to this problem. From thousands of proteomic, metabolomic, lipidomic, and transcriptomic biomarkers selected in other research, we identify several pairs of biomarkers that after additional nonlinear spline transformation are highly effective in classifying and predicting severe COVID-19 cases. The performance of these pairs is evaluated in-sample, in a cross-validation exercise, and in an out-of-sample analysis on two independent datasets. We further improve our classifier by identifying complementary pairs using hierarchical clustering. In a result, we achieve 96–98% AUC on the validation data. Our findings can help medical experts to identify small groups of biomarkers that after nonlinear transformation can be used to construct a cost-effective test for patient screening and prediction of severity progression.
Journal: Journal of Applied Statistics
Pages: 2473-2503
Issue: 11-12
Volume: 50
Year: 2023
Month: 09
X-DOI: 10.1080/02664763.2022.2064975
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2064975
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:11-12:p:2473-2503



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2019687_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Bryan Cai
Author-X-Name-First: Bryan
Author-X-Name-Last: Cai
Author-Name: John P. A. Ioannidis
Author-X-Name-First: John P. A.
Author-X-Name-Last: Ioannidis
Author-Name: Eran Bendavid
Author-X-Name-First: Eran
Author-X-Name-Last: Bendavid
Author-Name: Lu Tian
Author-X-Name-First: Lu
Author-X-Name-Last: Tian
Title: Exact inference for disease prevalence based on a test with unknown specificity and sensitivity
Abstract: 
 To make informative public policy decisions in battling the ongoing COVID-19 pandemic, it is important to know the disease prevalence in a population. There are two intertwined difficulties in estimating this prevalence based on testing results from a group of subjects. First, the test is prone to measurement error with unknown sensitivity and specificity. Second, the prevalence tends to be low at the initial stage of the pandemic and we may not be able to determine if a positive test result is a false positive due to the imperfect test specificity. The statistical inference based on a large sample approximation or conventional bootstrap may not be valid in such cases. In this paper, we have proposed a set of confidence intervals, whose validity doesn't depend on the sample size in the unweighted setting. For the weighted setting, the proposed inference is equivalent to hybrid bootstrap methods, whose performance is also more robust than those based on asymptotic approximations. The methods are used to reanalyze data from a study investigating the antibody prevalence in Santa Clara County, California in addition to several other seroprevalence studies. Simulation studies have been conducted to examine the finite-sample performance of the proposed method.
Journal: Journal of Applied Statistics
Pages: 2599-2623
Issue: 11-12
Volume: 50
Year: 2023
Month: 09
X-DOI: 10.1080/02664763.2021.2019687
File-URL: http://hdl.handle.net/10.1080/02664763.2021.2019687
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:11-12:p:2599-2623



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2228597_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Arnold Stromberg
Author-X-Name-First: Arnold
Author-X-Name-Last: Stromberg
Author-Name: Jie Chen
Author-X-Name-First: Jie
Author-X-Name-Last: Chen
Author-Name: Teresa Paula Costa Azinheira Oliveira
Author-X-Name-First: Teresa Paula Costa Azinheira
Author-X-Name-Last: Oliveira
Author-Name: Yichuan Zhao
Author-X-Name-First: Yichuan
Author-X-Name-Last: Zhao
Author-Name: Ramin Moghaddass
Author-X-Name-First: Ramin
Author-X-Name-Last: Moghaddass
Author-Name: Milan Stehlik
Author-X-Name-First: Milan
Author-X-Name-Last: Stehlik
Title: Editorial to the special issue: statistical perspectives on analytics for COVID-19 data
Journal: Journal of Applied Statistics
Pages: 2287-2293
Issue: 11-12
Volume: 50
Year: 2023
Month: 09
X-DOI: 10.1080/02664763.2023.2228597
File-URL: http://hdl.handle.net/10.1080/02664763.2023.2228597
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:11-12:p:2287-2293



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1976119_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Xiaolei Zhang
Author-X-Name-First: Xiaolei
Author-X-Name-Last: Zhang
Author-Name: Renjun Ma
Author-X-Name-First: Renjun
Author-X-Name-Last: Ma
Title: Forecasting waved daily COVID-19 death count series with a novel combination of segmented Poisson model and ARIMA models
Abstract: 
 Autoregressive Integrated Moving Average (ARIMA) models have been widely used to forecast and model the development of various infectious diseases including COVID-19 outbreaks; however, such use of ARIMA models does not respect the count nature of the pandemic development data. For example, the daily COVID-19 death count series data for Canada and the United States (USA) are generally skewed with lots of low counts. In addition, there are generally waved patterns with turning points influenced by government major interventions against the spread of COVID-19 during different periods and seasons. In this study, we propose a novel combination of the segmented Poisson model and ARIMA models to handle these features and correlation structures in a two-stage process. The first stage of this process is a generalization of trend analysis of time series data. Our approach is illustrated with forecasting and modeling of daily COVID-19 death count series data for Canada and the USA.
Journal: Journal of Applied Statistics
Pages: 2561-2574
Issue: 11-12
Volume: 50
Year: 2023
Month: 09
X-DOI: 10.1080/02664763.2021.1976119
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1976119
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:11-12:p:2561-2574



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2019688_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Mohsen Maleki
Author-X-Name-First: Mohsen
Author-X-Name-Last: Maleki
Author-Name: Hamid Bidram
Author-X-Name-First: Hamid
Author-X-Name-Last: Bidram
Author-Name: Darren Wraith
Author-X-Name-First: Darren
Author-X-Name-Last: Wraith
Title: Robust clustering of COVID-19 cases across U.S. counties using mixtures of asymmetric time series models with time varying and freely indexed covariates
Abstract: 
 In this paper, we develop a mixture of autoregressive (MoAR) process model with time varying and freely indexed covariates under the flexible class of two–piece distributions using the scale mixtures of normal (TP-SMN) family. This novel family of time series (TP-SMN-MoAR) models was used to examine flexible and robust clustering of reported cases of Covid-19 across 313 counties in the U.S. The TP-SMN distributions allow for symmetrical/ asymmetrical distributions as well as heavy-tailed distributions providing for flexibility to handle outliers and complex data. Developing a suitable hierarchical representation of the TP-SMN family enabled the construction of a pseudo-likelihood function to derive the maximum pseudo-likelihood estimates via an EM-type algorithm.
Journal: Journal of Applied Statistics
Pages: 2648-2662
Issue: 11-12
Volume: 50
Year: 2023
Month: 09
X-DOI: 10.1080/02664763.2021.2019688
File-URL: http://hdl.handle.net/10.1080/02664763.2021.2019688
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:11-12:p:2648-2662



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2177625_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: A. Tchorbadjieff
Author-X-Name-First: A.
Author-X-Name-Last: Tchorbadjieff
Author-Name: L. P. Tomov
Author-X-Name-First: L. P.
Author-X-Name-Last: Tomov
Author-Name: V. Velev
Author-X-Name-First: V.
Author-X-Name-Last: Velev
Author-Name: G. Dezhov
Author-X-Name-First: G.
Author-X-Name-Last: Dezhov
Author-Name: V. Manev
Author-X-Name-First: V.
Author-X-Name-Last: Manev
Author-Name: P. Mayster
Author-X-Name-First: P.
Author-X-Name-Last: Mayster
Title: On regime changes of COVID-19 outbreak
Abstract: 
 The COVID-19 pandemic has had a very serious impact on societies and caused large-scale economic changes and death toll worldwide. The first cases were detected in China, but soon the virus spread quickly worldwide and the intensity of newly reported infections grew high during this initial period almost everywhere. Later, despite all imposed measures, the intensity shifted abruptly multiple times during the two-year period between 2020 and 2022 causing waves of too high infection rates in almost every part of the world. To target this problem, we assume the data heterogeneity as multiple consecutive regime changes. The research study includes the development of a model based on automatic regime change detection and their combination with the linear birth-death process for long-run data fits. The results are empirically verified on data for 38 countries and US states for the period from February 2020 to April 2022. Finally, the initial phase (conditions) properties of infection development are studied.
Journal: Journal of Applied Statistics
Pages: 2343-2359
Issue: 11-12
Volume: 50
Year: 2023
Month: 09
X-DOI: 10.1080/02664763.2023.2177625
File-URL: http://hdl.handle.net/10.1080/02664763.2023.2177625
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:11-12:p:2343-2359



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2006153_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Estate Khmaladze
Author-X-Name-First: Estate
Author-X-Name-Last: Khmaladze
Author-Name: Giorgi Kvizhinadze
Author-X-Name-First: Giorgi
Author-X-Name-Last: Kvizhinadze
Title: On evolution model for SARS-Cov-2-infected population: the case of New Zealand
Abstract: 
 The work proposes a mathematical model of the process of COVID-19 epidemic as it evolved in New Zealand. The model uses a system of differential equations which emanate from natural assumptions on some probability measure and evolution of this measure on evolving family of simplexes. The authors tried to create the model which, at one hand, is simple and easy to follow. and, at the other hand, reflects the observed epidemic process correctly. The practical aim was to come to justifiable estimations of important parameters like the rate of infection as function of time, thus quantifying effectiveness of the Government measures. Another parameters estimated were the probability distribution of detection times and recovery times.
Journal: Journal of Applied Statistics
Pages: 2435-2449
Issue: 11-12
Volume: 50
Year: 2023
Month: 09
X-DOI: 10.1080/02664763.2021.2006153
File-URL: http://hdl.handle.net/10.1080/02664763.2021.2006153
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:11-12:p:2435-2449



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2145459_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Fatemeh Elhambakhsh
Author-X-Name-First: Fatemeh
Author-X-Name-Last: Elhambakhsh
Author-Name: Kamyar Sabri-Laghaie
Author-X-Name-First: Kamyar
Author-X-Name-Last: Sabri-Laghaie
Author-Name: Rassoul Noorossana
Author-X-Name-First: Rassoul
Author-X-Name-Last: Noorossana
Title: A latent space model and Hotelling's T2 control chart to monitor the networks of Covid-19 symptoms
Abstract: 
 In the COVID-19 coronavirus pandemic, potential patients that suffer from different symptoms can be diagnosed with COVID-19. At the early stages of the pandemic, patients were mainly diagnosed with fever and respiratory symptoms. Recently, patients with new symptoms, such as gastrointestinal or loss of senses, are also diagnosed with COVID-19. Monitoring these symptoms can help the healthcare system to be aware of new symptoms that can be related to the COVID-19 coronavirus. This article focuses on monitoring the behavior of COVID-19 symptoms over time. In this regard, a Latent space model (LSM) and a Generalized linear model (GLM) are introduced to model the networks of symptoms. We apply Hotelling's T2 control chart to the estimated parameters of the LSM and GLM, to identify significant changes and detect anomalies in the networks. The performance of the proposed methods is evaluated using simulation and calculating average run length (ARL). Then, dynamic networks are generated from a COVID-19 epidemic survey dataset.
Journal: Journal of Applied Statistics
Pages: 2450-2472
Issue: 11-12
Volume: 50
Year: 2023
Month: 09
X-DOI: 10.1080/02664763.2022.2145459
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2145459
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:11-12:p:2450-2472



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1947995_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Peipei Du
Author-X-Name-First: Peipei
Author-X-Name-Last: Du
Author-Name: Peihua Cao
Author-X-Name-First: Peihua
Author-X-Name-Last: Cao
Author-Name: Xiaodong Yan
Author-X-Name-First: Xiaodong
Author-X-Name-Last: Yan
Author-Name: Daihai He
Author-X-Name-First: Daihai
Author-X-Name-Last: He
Author-Name: Xiaotong Zhang
Author-X-Name-First: Xiaotong
Author-X-Name-Last: Zhang
Author-Name: Weixiang Chen
Author-X-Name-First: Weixiang
Author-X-Name-Last: Chen
Author-Name: Jiawei Luo
Author-X-Name-First: Jiawei
Author-X-Name-Last: Luo
Author-Name: Ziqian Zeng
Author-X-Name-First: Ziqian
Author-X-Name-Last: Zeng
Author-Name: Yaolong Chen
Author-X-Name-First: Yaolong
Author-X-Name-Last: Chen
Author-Name: Lin Yang
Author-X-Name-First: Lin
Author-X-Name-Last: Yang
Author-Name: Shu Yang
Author-X-Name-First: Shu
Author-X-Name-Last: Yang
Author-Name: Xixi Feng
Author-X-Name-First: Xixi
Author-X-Name-Last: Feng
Title: A continuous age-specific standardized mortality ratio for estimating the unascertained rates in the early epidemic of COVID-19 in different regions
Abstract: 
 The difference in age structure and aging population level was an important factor that caused the difference in COVID-19’s case fatality rate (CFR) in various regions. To eliminate the age effect on estimating the CFR of COVID-19, our study applied nonlinear logistic model and maximum likelihood method to fit the age-fatality curves of COVID-19 in different countries and regions. We further computed the standardized mortality ratio from the age-fatality curves of COVID-19 in the above regions and found that the risk of COVID-19 death in Wuhan was of a moderate level, while the non-Hubei region was even lower, compared with other regions. Regarding the disparity of CFRs among different regions in the country, we believed that there might be an unascertained phenomenon in high-endemic regions. Based on age-fatality rate curves, we estimated unascertained rates in cities with severe epidemics such as Wuhan and New York, and it was found that the total unascertained rates in Wuhan and New York were 81.6% and 81.2%, respectively. Meanwhile, we also found that the unascertained rates varied greatly with age.
Journal: Journal of Applied Statistics
Pages: 2504-2517
Issue: 11-12
Volume: 50
Year: 2023
Month: 09
X-DOI: 10.1080/02664763.2021.1947995
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1947995
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:11-12:p:2504-2517



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1970122_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Siddharth Rawat
Author-X-Name-First: Siddharth
Author-X-Name-Last: Rawat
Author-Name: Soudeep Deb
Author-X-Name-First: Soudeep
Author-X-Name-Last: Deb
Title: A spatio-temporal statistical model to analyze COVID-19 spread in the USA
Abstract: 
 Coronavirus pandemic has affected the whole world extensively and it is of immense importance to understand how the disease is spreading. In this work, we provide evidence of spatial dependence in the pandemic data and accordingly develop a new statistical technique that captures the spatio-temporal dependence pattern of the COVID-19 spread appropriately. The proposed model uses a separable Gaussian spatio-temporal process, in conjunction with an additive mean structure and a random error process. The model is implemented through a Bayesian framework, thereby providing a computational advantage over the classical way. We use state-level data from the United States of America in this study. We show that a quadratic trend pattern is most appropriate in this context. Interestingly, the population is found not to affect the numbers significantly, whereas the number of deaths in the previous week positively affects the spread of the disease. Residual diagnostics establish that the model is adequate enough to understand the spatio-temporal dependence pattern in the data. It is also shown to have superior predictive power than other spatial and temporal models. In fact, we show that the proposed approach can predict well for both short term (1 week) and long term (up to three months).
Journal: Journal of Applied Statistics
Pages: 2310-2329
Issue: 11-12
Volume: 50
Year: 2023
Month: 09
X-DOI: 10.1080/02664763.2021.1970122
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1970122
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:11-12:p:2310-2329



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2022607_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: E. Skamnia
Author-X-Name-First: E.
Author-X-Name-Last: Skamnia
Author-Name: P. Economou
Author-X-Name-First: P.
Author-X-Name-Last: Economou
Author-Name: S. Bersimis
Author-X-Name-First: S.
Author-X-Name-Last: Bersimis
Author-Name: M. Frouda
Author-X-Name-First: M.
Author-X-Name-Last: Frouda
Author-Name: A. Politis
Author-X-Name-First: A.
Author-X-Name-Last: Politis
Author-Name: P. Alexopoulos
Author-X-Name-First: P.
Author-X-Name-Last: Alexopoulos
Title: Hot spot identification method based on Andrews curves: an application on the COVID-19 crisis effects on caregiver distress in neurocognitive disorder
Abstract: 
 Identifying and locating areas – hot spots – that present high concentration of observations in a high-dimensional data set is crucial in many data processing and analysis methods and techniques, since observations that belong to the same hot spot share information and behave in a similar way. A useful tool towards that aim is the reduction of the data dimensionality and the graphical representation of them. In the present paper, a new method to identify and locate hot spots is proposed, based on the Andrews curves. Simulations results demonstrate the performance of the proposed method, which is also applied to a high-dimensional data set, regarding caregiver distress related to symptoms of people with neurocognitive disorder and to the mental effects of the recent outbreak of the COVID-19 pandemic.
Journal: Journal of Applied Statistics
Pages: 2388-2407
Issue: 11-12
Volume: 50
Year: 2023
Month: 09
X-DOI: 10.1080/02664763.2021.2022607
File-URL: http://hdl.handle.net/10.1080/02664763.2021.2022607
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:11-12:p:2388-2407



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2069232_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Kevin D. Dayaratna
Author-X-Name-First: Kevin D.
Author-X-Name-Last: Dayaratna
Author-Name: Drew Gonshorowski
Author-X-Name-First: Drew
Author-X-Name-Last: Gonshorowski
Author-Name: Mary Kolesar
Author-X-Name-First: Mary
Author-X-Name-Last: Kolesar
Title: Hierarchical Bayesian spatio-temporal modeling of COVID-19 in the United States
Abstract: 
 We examine the impact of economic, demographic, and mobility-related factors have had on the transmission of COVID-19 in 2020. While many models in the academic literature employ linear/generalized linear models, few contributions exist that incorporate spatial analysis, which is useful for understanding factors influencing the proliferation of the disease before the introduction of vaccines. We utilize a Poisson generalized linear model coupled with a spatial autoregressive structure to do so. Our analysis yields a number of insights including that, in some areas of the country, the counterintuitive result that staying at home can lead to increased disease proliferation. Additionally, we find some positive effects from increased gathering at grocery stores, negative effects of visiting retail stores and workplaces, and even small effects on visiting parks highlighting the complexities travel and migration have on the transmission of diseases.
Journal: Journal of Applied Statistics
Pages: 2663-2680
Issue: 11-12
Volume: 50
Year: 2023
Month: 09
X-DOI: 10.1080/02664763.2022.2069232
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2069232
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:11-12:p:2663-2680



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2028744_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Yılmaz Akdi
Author-X-Name-First: Yılmaz
Author-X-Name-Last: Akdi
Author-Name: Yunus Emre Karamanoğlu
Author-X-Name-First: Yunus
Author-X-Name-Last: Emre Karamanoğlu
Author-Name: Kamil Demirberk Ünlü
Author-X-Name-First: Kamil Demirberk
Author-X-Name-Last: Ünlü
Author-Name: Cem Baş
Author-X-Name-First: Cem
Author-X-Name-Last: Baş
Title: Identifying the cycles in COVID-19 infection: the case of Turkey
Abstract: 
 The new coronavirus disease, called COVID-19, has spread extremely quickly to more than 200 countries since its detection in December 2019 in China. COVID-19 marks the return of a very old and familiar enemy. Throughout human history, disasters such as earthquakes, volcanic eruptions and even wars have not caused more human losses than lethal diseases, which are caused by viruses, bacteria and parasites. The first COVID-19 case was detected in Turkey on 12 March 2020 and researchers have since then attempted to examine periodicity in the number of daily new cases. One of the most curious questions in the pandemic process that affects the whole world is whether there will be a second wave. Such questions can be answered by examining any periodicities in the series of daily cases. Periodic series are frequently seen in many disciplines. An important method based on harmonic regression is the focus of the study. The main aim of this study is to identify the hidden periodic structure of the daily infected cases. Infected case of Turkey is analyzed by using periodogram-based methodology. Our results revealed that there are 4, 5 and 62 days cycles in the daily new cases of Turkey.
Journal: Journal of Applied Statistics
Pages: 2360-2372
Issue: 11-12
Volume: 50
Year: 2023
Month: 09
X-DOI: 10.1080/02664763.2022.2028744
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2028744
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:11-12:p:2360-2372



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1936467_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Hou-Cheng Yang
Author-X-Name-First: Hou-Cheng
Author-X-Name-Last: Yang
Author-Name: Yishu Xue
Author-X-Name-First: Yishu
Author-X-Name-Last: Xue
Author-Name: Yuqing Pan
Author-X-Name-First: Yuqing
Author-X-Name-Last: Pan
Author-Name: Qingyang Liu
Author-X-Name-First: Qingyang
Author-X-Name-Last: Liu
Author-Name: Guanyu Hu
Author-X-Name-First: Guanyu
Author-X-Name-Last: Hu
Title: Time fused coefficient SIR model with application to COVID-19 epidemic in the United States
Abstract: 
 In this paper, we propose a Susceptible–Infected–Removal (SIR) model with time fused coefficients. In particular, our proposed model discovers the underlying time homogeneity pattern for the SIR model's transmission rate and removal rate via Bayesian shrinkage priors. MCMC sampling for the proposed method is facilitated by the nimble package in R. Extensive simulation studies are carried out to examine the empirical performance of the proposed methods. We further apply the proposed methodology to analyze different levels of COVID-19 data in the United States.
Journal: Journal of Applied Statistics
Pages: 2373-2387
Issue: 11-12
Volume: 50
Year: 2023
Month: 09
X-DOI: 10.1080/02664763.2021.1936467
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1936467
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:11-12:p:2373-2387



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2006154_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: D. Atanasov
Author-X-Name-First: D.
Author-X-Name-Last: Atanasov
Author-Name: Vessela Stoimenova
Author-X-Name-First: Vessela
Author-X-Name-Last: Stoimenova
Author-Name: Nikolay M. Yanev
Author-X-Name-First: Nikolay M.
Author-X-Name-Last: Yanev
Title: Statistical modelling of COVID-19 pandemic development applying branching processes
Abstract: 
 In this paper, a statistical model for COVID-19 infection dynamics is described, using only the observed daily statistics of infected individuals. For this purpose, two special classes of branching processes without or with an immigration component are considered. These models are intended to estimate the main parameter of the infection and to give a prediction of the mean value of the non-observed population of the infected individuals. This is a serious advantage in comparison with other more complicated models where the officially reported data are not sufficient for estimation of the model parameters. The model is applied for different regions in the world and the corresponding parameters of the infection dynamics are estimated.
Journal: Journal of Applied Statistics
Pages: 2330-2342
Issue: 11-12
Volume: 50
Year: 2023
Month: 09
X-DOI: 10.1080/02664763.2021.2006154
File-URL: http://hdl.handle.net/10.1080/02664763.2021.2006154
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:11-12:p:2330-2342



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_1928016_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Guannan Wang
Author-X-Name-First: Guannan
Author-X-Name-Last: Wang
Author-Name: Zhiling Gu
Author-X-Name-First: Zhiling
Author-X-Name-Last: Gu
Author-Name: Xinyi Li
Author-X-Name-First: Xinyi
Author-X-Name-Last: Li
Author-Name: Shan Yu
Author-X-Name-First: Shan
Author-X-Name-Last: Yu
Author-Name: Myungjin Kim
Author-X-Name-First: Myungjin
Author-X-Name-Last: Kim
Author-Name: Yueying Wang
Author-X-Name-First: Yueying
Author-X-Name-Last: Wang
Author-Name: Lei Gao
Author-X-Name-First: Lei
Author-X-Name-Last: Gao
Author-Name: Li Wang
Author-X-Name-First: Li
Author-X-Name-Last: Wang
Title: Comparing and integrating US COVID-19 data from multiple sources with anomaly detection and repairing
Abstract: 
 Over the past few months, the outbreak of Coronavirus disease (COVID-19) has been expanding over the world. A reliable and accurate dataset of the cases is vital for scientists to conduct related research and policy-makers to make better decisions. We collect the United States COVID-19 daily reported data from four open sources: the New York Times, the COVID-19 Data Repository by Johns Hopkins University, the COVID Tracking Project at the Atlantic, and the USAFacts, then compare the similarities and differences among them. To obtain reliable data for further analysis, we first examine the cyclical pattern and the following anomalies, which frequently occur in the reported cases: (1) the order dependencies violation, (2) the point or period anomalies, and (3) the issue of reporting delay. To address these detected issues, we propose the corresponding repairing methods and procedures if corrections are necessary. In addition, we integrate the COVID-19 reported cases with the county-level auxiliary information of the local features from official sources, such as health infrastructure, demographic, socioeconomic, and environmental information, which are also essential for understanding the spread of the virus.
Journal: Journal of Applied Statistics
Pages: 2408-2434
Issue: 11-12
Volume: 50
Year: 2023
Month: 09
X-DOI: 10.1080/02664763.2021.1928016
File-URL: http://hdl.handle.net/10.1080/02664763.2021.1928016
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:11-12:p:2408-2434

Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2095362_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Jasper Velthoen
Author-X-Name-First: Jasper
Author-X-Name-Last: Velthoen
Author-Name: Juan-Juan Cai
Author-X-Name-First: Juan-Juan
Author-X-Name-Last: Cai
Author-Name: Geurt Jongbloed
Author-X-Name-First: Geurt
Author-X-Name-Last: Jongbloed
Title: Forward variable selection for random forest models
Abstract: 
 Random forest is a popular prediction approach for handling high dimensional covariates. However, it often becomes infeasible to interpret the obtained high dimensional and non-parametric model. Aiming for an interpretable predictive model, we develop a forward variable selection method using the continuous ranked probability score (CRPS) as the loss function. eOur stepwise procedure selects at each step a variable that minimizes the CRPS risk and a stopping criterion for selection is designed based on an estimation of the CRPS risk difference of two consecutive steps. We provide mathematical motivation for our method by proving that in a population sense, the method attains the optimal set. In a simulation study, we compare the performance of our method with an existing variable selection method, for different sample sizes and correlation strength of covariates. Our method is observed to have a much lower false positive rate. We also demonstrate an application of our method to statistical post-processing of daily maximum temperature forecasts in the Netherlands. Our method selects about 10% covariates while retaining the same predictive power.
Journal: Journal of Applied Statistics
Pages: 2836-2856
Issue: 13
Volume: 50
Year: 2023
Month: 10
X-DOI: 10.1080/02664763.2022.2095362
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2095362
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:13:p:2836-2856



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2073585_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Debasis Kundu
Author-X-Name-First: Debasis
Author-X-Name-Last: Kundu
Title: A stationary Weibull process and its applications
Abstract: 
 In this paper we introduce a discrete-time and continuous state-space Markov stationary process 
 $ \{X_n; n = 1, 2, \ldots \} $ {Xn;n=1,2,…}, where 
 $ X_n $ Xn has a two-parameter Weibull distribution, 
 $ X_n $ Xn's are dependent and there is a positive probability that 
 $ X_n = X_{n+1} $ Xn=Xn+1. The motivation came from the gold price data where there are several instances for which 
 $ X_n = X_{n+1} $ Xn=Xn+1. Hence, the existing methods cannot be used to analyze this data. We derive different properties of the proposed Weibull process. It is observed that the joint cumulative distribution function of 
 $ X_n $ Xn and 
 $ X_{n+1} $ Xn+1 has a very convenient copula structure. Hence, different dependence properties and dependence measures can be obtained. The maximum likelihood estimators cannot be obtained in explicit forms, we have proposed a simple profile likelihood method to compute these estimators. We have used this model to analyze two synthetic data sets and one gold price data set of the Indian market, and it is observed that the proposed model fits quite well with the data set.
Journal: Journal of Applied Statistics
Pages: 2681-2700
Issue: 13
Volume: 50
Year: 2023
Month: 10
X-DOI: 10.1080/02664763.2022.2073585
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2073585
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:13:p:2681-2700



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2081965_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Zili Zhang
Author-X-Name-First: Zili
Author-X-Name-Last: Zhang
Author-Name: Christiana Charalambous
Author-X-Name-First: Christiana
Author-X-Name-Last: Charalambous
Author-Name: Peter Foster
Author-X-Name-First: Peter
Author-X-Name-Last: Foster
Title: Joint modelling of longitudinal measurements and survival times via a multivariate copula approach
Abstract: 
 Joint modelling of longitudinal and time-to-event data is usually described by a joint model which uses shared or correlated latent effects to capture associations between the two processes. Under this framework, the joint distribution of the two processes can be derived straightforwardly by assuming conditional independence given the random effects. Alternative approaches to induce interdependency into sub-models have also been considered in the literature and one such approach is using copulas to introduce non-linear correlation between the marginal distributions of the longitudinal and time-to-event processes. The multivariate Gaussian copula joint model has been proposed in the literature to fit joint data by applying a Monte Carlo expectation-maximisation algorithm. In this paper, we propose an exact likelihood estimation approach to replace the more computationally expensive Monte Carlo expectation-maximisation algorithm and we consider results based on using both the multivariate Gaussian and t copula functions. We also provide a straightforward way to compute dynamic predictions of survival probabilities, showing that our proposed model is comparable in prediction performance to the shared random effects joint model.
Journal: Journal of Applied Statistics
Pages: 2739-2759
Issue: 13
Volume: 50
Year: 2023
Month: 10
X-DOI: 10.1080/02664763.2022.2081965
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2081965
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:13:p:2739-2759



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2078289_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Kai Qu
Author-X-Name-First: Kai
Author-X-Name-Last: Qu
Author-Name: Jonathan R. Bradley
Author-X-Name-First: Jonathan R.
Author-X-Name-Last: Bradley
Title: Bayesian models for spatial count data with informative finite populations with application to the American community survey
Abstract: 
 The American Community Survey (ACS) is an ongoing program conducted by the US Census Bureau that publishes estimates of important demographic statistics over pre-specified administrative areas. ACS provides spatially referenced count-valued outcomes that are paired with finite populations. For example, the number of people below the poverty line and the total population for each county are estimated by ACS. One common assumption is that the spatially referenced count-valued outcome given the finite population is binomial distributed. This conditionally specified (CS) model does not define the joint relationship between the count-valued outcome and the finite population. Thus, we consider a joint model for the count-valued outcome and the finite population. When cross-dependence in our joint model can be leveraged to ‘improve spatial prediction’ we say that the finite population is ‘informative.’ We model the count given the finite population as binomial and the finite population as negative binomial and use multivariate logit-beta prior distributions. This leads to closed-form expressions of the full-conditional distributions for an efficient Gibbs sampler. We illustrate our model through simulations and our motivating application of ACS poverty estimates. These empirical analyses show the benefits of using our proposed model over the more traditional CS binomial model.
Journal: Journal of Applied Statistics
Pages: 2701-2716
Issue: 13
Volume: 50
Year: 2023
Month: 10
X-DOI: 10.1080/02664763.2022.2078289
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2078289
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:13:p:2701-2716



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2084719_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: M. Martel
Author-X-Name-First: M.
Author-X-Name-Last: Martel
Author-Name: M. A. Negrín
Author-X-Name-First: M. A.
Author-X-Name-Last: Negrín
Author-Name: F. J. Vázquez–Polo
Author-X-Name-First: F. J.
Author-X-Name-Last: Vázquez–Polo
Title: Bayesian heterogeneity in a meta–analysis with two studies and binary data
Abstract: 
 The meta–analysis of two trials is valuable in many practical situations, such as studies of rare and/or orphan diseases focussed on a single intervention. In this context, additional concerns, like small sample size and/or heterogeneity in the results obtained, might make standard frequentist and Bayesian techniques inappropriate. In a meta–analysis, moreover, the presence of between–sample heterogeneity adds model uncertainty, which must be taken into consideration when drawing inferences. We suggest that the most appropriate way to measure this heterogeneity is by clustering the samples and then determining the posterior probability of the cluster models. The meta–inference is obtained as a mixture of all the meta–inferences for the cluster models, where the mixing distribution is the posterior model probability. We present a simple two–component form of Bayesian model averaging that is unaffected by characteristics such as small study size or zero–cell counts, and which is capable of incorporating uncertainties into the estimation process. Illustrative examples are given and analysed, using real sparse binomial data.
Journal: Journal of Applied Statistics
Pages: 2760-2776
Issue: 13
Volume: 50
Year: 2023
Month: 10
X-DOI: 10.1080/02664763.2022.2084719
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2084719
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:13:p:2760-2776



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2093842_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Siti Zahariah
Author-X-Name-First: Siti
Author-X-Name-Last: Zahariah
Author-Name: Habshah Midi
Author-X-Name-First: Habshah
Author-X-Name-Last: Midi
Title: Minimum regularized covariance determinant and principal component analysis-based method for the identification of high leverage points in high dimensional sparse data
Abstract: 
 The main aim of this paper is to propose a novel method (RMD-MRCD-PCA) of identification of High Leverage Points (HLPs) in high-dimensional sparse data. It is to address the weakness of the Robust Mahalanobis Distance (RMD) method which is based on the Minimum Regularized Covariance Determinant (RMD-MRCD), which indicates a decrease in its performance as the number of independent variables (p) increases. The RMD-MRCD-PCA is developed by incorporating the Principal Component Analysis (PCA) in the MRCD algorithm whereby this robust approach shrinks the covariance matrix to make it invertible and thus, can be employed to compute the RMD for high dimensional data. A simulation study and two real data sets are used to illustrate the merit of our proposed method compared to the RMD-MRCD and Robust PCA (ROBPCA) methods. Findings show that the performance of the RMD-MRCD is similar to the performance of the RMD-MRCD-PCA for p close to 200. However, its performance tends to decrease when the number of p is more than 200 and worsens at p equals 700 and larger. On the other hand, the ROBPCA is not effective for less than 20% contamination as it suffers from serious swamping problems.
Journal: Journal of Applied Statistics
Pages: 2817-2835
Issue: 13
Volume: 50
Year: 2023
Month: 10
X-DOI: 10.1080/02664763.2022.2093842
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2093842
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:13:p:2817-2835



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2078798_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: S. M. Patil
Author-X-Name-First: S. M.
Author-X-Name-Last: Patil
Author-Name: H. V. Kulkarni
Author-X-Name-First: H. V.
Author-X-Name-Last: Kulkarni
Title: Analysis of medians under two-way model with and without interaction for Birnbaum–Saunders distributed response
Abstract: 
 The Birnbaum–Saunders (BS) distribution, well-known as the fatigue-life distribution, has been used in numerous disciplines ranging from engineering to medical sciences. In this article, we develop a test for analysis of medians for BS distributed response to assess the impact of two interacting factors on the median, where no test is presently available. The proposed integrated likelihood ratio test (ILRT) eliminates the nuisance shape parameters by integrating them out. The second-order accurate asymptotic chi-square distribution of ILRT is derived. An in-depth simulation study strongly supports its excellent performance even under small group sizes. Furthermore, ILRT developed under the one-way model is found uniformly superior over its peers, is straightway extendable under general multiway setup, and has potential to be extended to other non-normal response variables. Its genuine need in industry, where non-normal responses are commonly encountered, is highlighted through analysis of three real data sets: ILRT strongly picked out the deposition time as influential factor in epitaxial layer experiment, revealed significant impact of spools on fiber life for the failure times of Kevlar 49 fiber data, and gave more accurate parameter estimates in delivery time data experiment, as assessed by various model adequacy tools, where its competitors failed to deliver desired results.
Journal: Journal of Applied Statistics
Pages: 2717-2738
Issue: 13
Volume: 50
Year: 2023
Month: 10
X-DOI: 10.1080/02664763.2022.2078798
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2078798
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:13:p:2717-2738



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2088706_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Daniel Ries
Author-X-Name-First: Daniel
Author-X-Name-Last: Ries
Author-Name: Alicia Carriquiry
Author-X-Name-First: Alicia
Author-X-Name-Last: Carriquiry
Title: Assessing adult physical activity and compliance with 2008 CDC guidelines using a Bayesian two-part measurement error model
Abstract: 
 While there is wide agreement that physical activity is an important component of a healthy lifestyle, it is unclear how many people adhere to public health recommendations on physical activity. The Physical Activity Guidelines (PAG), published by the CDC, provides guidelines to American adults, but it is difficult to assess compliance with these guidelines. The PAG further complicates adherence assessment by recommending activity to occur in at least 10 min bouts. To better understand the measurement capabilities of various instruments to quantify activity, and to propose an approach to evaluate activity relative to the PAG, researchers at Iowa State University administered the Physical Activity Measurement Survey (PAMS) to over 1000 participants in four different Iowa counties. In this paper, we develop a two-part Bayesian measurement error model and apply it to the PAMS data in order to assess compliance with the PAG in the Iowa adult population. The model accurately accounts for the 10 min bout requirement put forth in the PAG. The measurement error model corrects biased estimates and accounts for day-to-day variation in activity. The model is also applied to the nationally representative National Health and Nutrition Examination Survey.
Journal: Journal of Applied Statistics
Pages: 2777-2795
Issue: 13
Volume: 50
Year: 2023
Month: 10
X-DOI: 10.1080/02664763.2022.2088706
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2088706
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:13:p:2777-2795



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2091526_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Jeyadurga Periyasamypandian
Author-X-Name-First: Jeyadurga
Author-X-Name-Last: Periyasamypandian
Author-Name: Saminathan Balamurali
Author-X-Name-First: Saminathan
Author-X-Name-Last: Balamurali
Title: Determination of new multiple deferred state sampling plan with economic perspective under Weibull distribution
Abstract: 
 This study focuses on designing a new multiple deferred state sampling plan to ensure products’ mean lifetime that complies with Weibull distribution. The parameters that characterize the proposed plan are determined by considering two specified points on the operating characteristic curve. Practical applications of the proposed plan for assuring mean lifetimes of electrical appliances as well as Lithium-ion batteries are explained by using real-time data and simulated data respectively. Sensitivity analysis on testing time of the life test is done and theoretical average sample number is compared with the same obtained by simulation. By comparing the proposed plan with other existing sampling plans based on discriminating power, the number of units required for lot sentencing, it is observed that the new multiple deferred state sampling plan provides quality assurance for the products with low inspection costs compared to the other existing sampling plans. Besides, this study investigates the economic design of a new multiple deferred state sampling plan and compares the total cost needed in the proposed plan with the same required for some other existing sampling plans.
Journal: Journal of Applied Statistics
Pages: 2796-2816
Issue: 13
Volume: 50
Year: 2023
Month: 10
X-DOI: 10.1080/02664763.2022.2091526
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2091526
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:13:p:2796-2816

Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2112557_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Yujie Zhao
Author-X-Name-First: Yujie
Author-X-Name-Last: Zhao
Author-Name: Xiaoming Huo
Author-X-Name-First: Xiaoming
Author-X-Name-Last: Huo
Author-Name: Yajun Mei
Author-X-Name-First: Yajun
Author-X-Name-Last: Mei
Title: Hot-spots detection in count data by Poisson assisted smooth sparse tensor decomposition
Abstract: 
 Count data occur widely in many bio-surveillance and healthcare applications, e.g. the numbers of new patients of different types of infectious diseases from different cities/counties/states repeatedly over time, say, daily/weekly/monthly. For this type of count data, one important task is the quick detection and localization of hot-spots in terms of unusual infectious rates so that we can respond appropriately. In this paper, we develop a method called Poisson assisted Smooth Sparse Tensor Decomposition (PoSSTenD), which not only detect when hot-spots occur but also localize where hot-spots occur. The main idea of our proposed PoSSTenD method is articulated as follows. First, we represent the observed count data as a three-dimensional tensor including (1) a spatial dimension for location patterns, e.g. different cities/countries/states; (2) a temporal domain for time patterns, e.g. daily/weekly/monthly; (3) a categorical dimension for different types of data sources, e.g. different types of diseases. Second, we fit this tensor into a Poisson regression model, and then we further decompose the infectious rate into two components: smooth global trend and local hot-spots. Third, we detect when hot-spots occur by building a cumulative sum (CUSUM) control chart and localize where hot-spots occur by their LASSO-type sparse estimation. The usefulness of our proposed methodology is validated through numerical simulation studies and a real-world dataset, which records the annual number of 10 different infectious diseases from 1993 to 2018 for 49 mainland states in the United States.
Journal: Journal of Applied Statistics
Pages: 2999-3029
Issue: 14
Volume: 50
Year: 2023
Month: 10
X-DOI: 10.1080/02664763.2022.2112557
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2112557
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:14:p:2999-3029



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2150753_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Yinglun Zhan
Author-X-Name-First: Yinglun
Author-X-Name-Last: Zhan
Author-Name: Ruizhi Zhang
Author-X-Name-First: Ruizhi
Author-X-Name-Last: Zhang
Author-Name: Yuzhen Zhou
Author-X-Name-First: Yuzhen
Author-X-Name-Last: Zhou
Author-Name: Vincent Stoerger
Author-X-Name-First: Vincent
Author-X-Name-Last: Stoerger
Author-Name: Jeremy Hiller
Author-X-Name-First: Jeremy
Author-X-Name-Last: Hiller
Author-Name: Tala Awada
Author-X-Name-First: Tala
Author-X-Name-Last: Awada
Author-Name: Yufeng Ge
Author-X-Name-First: Yufeng
Author-X-Name-Last: Ge
Title: Rapid online plant leaf area change detection with high-throughput plant image data
Abstract: 
 High-throughput plant phenotyping (HTPP) has become an emerging technique to study plant traits due to its fast, labor-saving, accurate and non-destructive nature. It has wide applications in plant breeding and crop management. However, the resulting massive image data has raised a challenge associated with efficient plant traits prediction and anomaly detection. In this paper, we propose a two-step image-based online detection framework for monitoring and quick change detection of the individual plant leaf area via real-time imaging data. Our proposed method is able to achieve a smaller detection delay compared with some baseline methods under some predefined false alarm rate constraint. Moreover, it does not need to store all past image information and can be implemented in real time. The efficiency of the proposed framework is validated by a real data analysis.
Journal: Journal of Applied Statistics
Pages: 2984-2998
Issue: 14
Volume: 50
Year: 2023
Month: 10
X-DOI: 10.1080/02664763.2022.2150753
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2150753
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:14:p:2984-2998



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2137115_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: David Lun
Author-X-Name-First: David
Author-X-Name-Last: Lun
Author-Name: Svenja Fischer
Author-X-Name-First: Svenja
Author-X-Name-Last: Fischer
Author-Name: Alberto Viglione
Author-X-Name-First: Alberto
Author-X-Name-Last: Viglione
Author-Name: Günter Blöschl
Author-X-Name-First: Günter
Author-X-Name-Last: Blöschl
Title: Significance testing of rank cross-correlations between autocorrelated time series with short-range dependence
Abstract: 
 Statistical dependency measures such as Kendall’s Tau or Spearman’s Rho are frequently used to analyse the coherence between time series in environmental data analyses. Autocorrelation of the data can, however, result in spurious cross correlations if not accounted for. Here, we present the asymptotic distribution of the estimators of Spearman’s Rho and Kendall’s Tau, which can be used for statistical hypothesis testing of cross-correlations between autocorrelated observations. The results are derived using U-statistics under the assumption of absolutely regular (or β-mixing) processes. These comprise many short-range dependent processes, such as ARMA-, GARCH- and some copula-based models relevant in the environmental sciences. We show that while the assumption of absolute regularity is required, the specific type of model does not have to be specified for the hypothesis test. Simulations show the improved performance of the modified hypothesis test for some common stochastic models and small to moderate sample sizes under autocorrelation. The methodology is applied to observed climatological time series of flood discharges and temperatures in Europe. While the standard test results in spurious correlations between floods and temperatures, this is not the case for the proposed test, which is more consistent with the literature on flood regime changes in Europe.
Journal: Journal of Applied Statistics
Pages: 2934-2950
Issue: 14
Volume: 50
Year: 2023
Month: 10
X-DOI: 10.1080/02664763.2022.2137115
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2137115
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:14:p:2934-2950



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2147150_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Heng-Hui Lue
Author-X-Name-First: Heng-Hui
Author-X-Name-Last: Lue
Author-Name: ShengLi Tzeng
Author-X-Name-First: ShengLi
Author-X-Name-Last: Tzeng
Title: Interpretable, predictive spatio-temporal models via enhanced pairwise directions estimation
Abstract: 
 This article concerns predictive modeling for spatio-temporal data as well as model interpretation using data information in space and time. We develop a novel approach based on supervised dimension reduction for such data in order to capture nonlinear mean structures without requiring a prespecified parametric model. In addition to prediction as a common interest, this approach emphasizes the exploration of geometric information from the data. The method of Pairwise Directions Estimation (PDE) is implemented in our approach as a data-driven function searching for spatial patterns and temporal trends. The benefit of using geometric information from the method of PDE is highlighted, which aids effectively in exploring data structures. We further enhance PDE, referring to it as PDE+, by incorporating kriging to estimate the random effects not explained in the mean functions. Our proposal can not only increase prediction accuracy but also improve the interpretation for modeling. Two simulation examples are conducted and comparisons are made with several existing methods. The results demonstrate that the proposed PDE+ method is very useful for exploring and interpreting the patterns and trends for spatio-temporal data. Illustrative applications to two real datasets are also presented.
Journal: Journal of Applied Statistics
Pages: 2914-2933
Issue: 14
Volume: 50
Year: 2023
Month: 10
X-DOI: 10.1080/02664763.2022.2147150
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2147150
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:14:p:2914-2933



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2174257_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Michael Baron
Author-X-Name-First: Michael
Author-X-Name-Last: Baron
Author-Name: Sergey V. Malov
Author-X-Name-First: Sergey V.
Author-X-Name-Last: Malov
Title: Detection and estimation of multiple transient changes
Abstract: 
 Change-point detection methods are proposed for the case of temporary failures, or transient changes, when an unexpected disorder is ultimately followed by a re-adjustment and return to the initial state. A base distribution of the ‘in-control’ state changes to an ‘out-of-control’ distribution for unknown periods of time. Likelihood based sequential and retrospective tools are proposed for the detection and estimation of each pair of change-points. The accuracy of the obtained change-point estimates is assessed. Proposed methods offer simultaneous control of the familywise false alarm and false re-adjustment rates at the pre-chosen levels.
Journal: Journal of Applied Statistics
Pages: 2862-2888
Issue: 14
Volume: 50
Year: 2023
Month: 10
X-DOI: 10.1080/02664763.2023.2174257
File-URL: http://hdl.handle.net/10.1080/02664763.2023.2174257
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:14:p:2862-2888



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2117288_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Jiuyun Hu
Author-X-Name-First: Jiuyun
Author-X-Name-Last: Hu
Author-Name: Yajun Mei
Author-X-Name-First: Yajun
Author-X-Name-Last: Mei
Author-Name: Sarah Holte
Author-X-Name-First: Sarah
Author-X-Name-Last: Holte
Author-Name: Hao Yan
Author-X-Name-First: Hao
Author-X-Name-Last: Yan
Title: Adaptive resources allocation CUSUM for binomial count data monitoring with application to COVID-19 hotspot detection
Abstract: 
 In this paper, we present an efficient statistical method (denoted as ‘Adaptive Resources Allocation CUSUM’) to robustly and efficiently detect the hotspot with limited sampling resources. Our main idea is to combine the multi-arm bandit (MAB) and change-point detection methods to balance the exploration and exploitation of resource allocation for hotspot detection. Further, a Bayesian weighted update is used to update the posterior distribution of the infection rate. Then, the upper confidence bound (UCB) is used for resource allocation and planning. Finally, CUSUM monitoring statistics to detect the change point as well as the change location. For performance evaluation, we compare the performance of the proposed method with several benchmark methods in the literature and showed the proposed algorithm is able to achieve a lower detection delay and higher detection precision. Finally, this method is applied to hotspot detection in a real case study of county-level daily positive COVID-19 cases in Washington State WA) and demonstrates the effectiveness with very limited distributed samples.
Journal: Journal of Applied Statistics
Pages: 2889-2913
Issue: 14
Volume: 50
Year: 2023
Month: 10
X-DOI: 10.1080/02664763.2022.2117288
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2117288
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:14:p:2889-2913



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2200496_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Ka Wai Tsang
Author-X-Name-First: Ka
Author-X-Name-Last: Wai Tsang
Author-Name: Fugee Tsung
Author-X-Name-First: Fugee
Author-X-Name-Last: Tsung
Author-Name: Zhihao Xu
Author-X-Name-First: Zhihao
Author-X-Name-Last: Xu
Title: Knockoff procedure for false discovery rate control in high-dimensional data streams
Abstract: 
 Motivated by applications to root-cause identification of faults in high-dimensional data streams that may have very limited samples after faults are detected, we consider multiple testing in models for multivariate statistical process control (SPC). With quick fault detection, only small portion of data streams being out-of-control (OC) can be assumed. It is a long standing problem to identify those OC data streams while controlling the number of false discoveries. It is challenging due to the limited number of OC samples after the termination of the process when faults are detected. Although several false discovery rate (FDR) controlling methods have been proposed, people may prefer other methods for quick detection. With a recently developed method called Knockoff filtering, we propose a knockoff procedure that can combine with other fault detection methods in the sense that the knockoff procedure does not change the stopping time, but may identify another set of faults to control FDR. A theorem for the FDR control of the proposed procedure is provided. Simulation studies show that the proposed procedure can control FDR while maintaining high power. We also illustrate the performance in an application to semiconductor manufacturing processes that motivated this development.
Journal: Journal of Applied Statistics
Pages: 2970-2983
Issue: 14
Volume: 50
Year: 2023
Month: 10
X-DOI: 10.1080/02664763.2023.2200496
File-URL: http://hdl.handle.net/10.1080/02664763.2023.2200496
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:14:p:2970-2983



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2164885_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Hongzhen Tian
Author-X-Name-First: Hongzhen
Author-X-Name-Last: Tian
Author-Name: Reuven Zev Cohen
Author-X-Name-First: Reuven Zev
Author-X-Name-Last: Cohen
Author-Name: Chuck Zhang
Author-X-Name-First: Chuck
Author-X-Name-Last: Zhang
Author-Name: Yajun Mei
Author-X-Name-First: Yajun
Author-X-Name-Last: Mei
Title: Active learning-based multistage sequential decision-making model with application on common bile duct stone evaluation
Abstract: 
 Multistage sequential decision-making occurs in many real-world applications such as healthcare diagnosis and treatment. One concrete example is when the doctors need to decide to collect which kind of information from subjects so as to make the good medical decision cost-effectively. In this paper, an active learning-based method is developed to model the doctors' decision-making process that actively collects necessary information from each subject in a sequential manner. The effectiveness of the proposed model, especially its two-stage version, is validated on both simulation studies and a case study of common bile duct stone evaluation for pediatric patients.
Journal: Journal of Applied Statistics
Pages: 2951-2969
Issue: 14
Volume: 50
Year: 2023
Month: 10
X-DOI: 10.1080/02664763.2023.2164885
File-URL: http://hdl.handle.net/10.1080/02664763.2023.2164885
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:14:p:2951-2969



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2247646_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Yajun Mei
Author-X-Name-First: Yajun
Author-X-Name-Last: Mei
Author-Name: Jay Bartroff
Author-X-Name-First: Jay
Author-X-Name-Last: Bartroff
Author-Name: Jie Chen
Author-X-Name-First: Jie
Author-X-Name-Last: Chen
Author-Name: Georgios Fellouris
Author-X-Name-First: Georgios
Author-X-Name-Last: Fellouris
Author-Name: Ruizhi Zhang
Author-X-Name-First: Ruizhi
Author-X-Name-Last: Zhang
Title: Editorial to the special issue: modern streaming data analytics
Journal: Journal of Applied Statistics
Pages: 2857-2861
Issue: 14
Volume: 50
Year: 2023
Month: 10
X-DOI: 10.1080/02664763.2023.2247646
File-URL: http://hdl.handle.net/10.1080/02664763.2023.2247646
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:14:p:2857-2861

Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2097204_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: R. N. Montgomery
Author-X-Name-First: R. N.
Author-X-Name-Last: Montgomery
Author-Name: L.T. Ptomey
Author-X-Name-First: L.T.
Author-X-Name-Last: Ptomey
Author-Name: J. D. Mahnken
Author-X-Name-First: J. D.
Author-X-Name-Last: Mahnken
Title: A flexible test for early-stage studies with multiple endpoints
Abstract: 
 This paper builds on the recently proposed prediction test for muliple endpoints. The prediction test combines information across multiple endpoints while accounting for the correlation between them. The test performs well with small samples relative to the number of endpoints of interest and is flexible in the hypotheses across the individual endpoints that can be combined. The prediction test addresses a global hypothesis that is of particular interest in early-stage studies and can be used as justification for continuing on to a larger trial. However, the prediction test has several limitations which we seek to address. First, the prediction test is overly conservative when both the effect sizes across all endpoints and the number of endpoints are small. By using a parametric bootstrap to estimate the null distribution, we show that the test achieves the nominal error rate in this situation and increases the power of the test. Second, we provide a framework to allow for predictions of a difference on one or more endpoints. Finally, we extend the test with a composite null hypothesis that allows for different null hypothesized predictive abilities across the endpoints which can be especially useful if the study contains both familiar and novel endpoints. We use an example from a physical activity trial to illustrate these extensions.
Journal: Journal of Applied Statistics
Pages: 3048-3061
Issue: 15
Volume: 50
Year: 2023
Month: 11
X-DOI: 10.1080/02664763.2022.2097204
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2097204
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:15:p:3048-3061



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2099816_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Termeh Shafie
Author-X-Name-First: Termeh
Author-X-Name-Last: Shafie
Title: Goodness of fit tests for random multigraph models
Abstract: 
 Goodness of fit tests for two probabilistic multigraph models are presented. The first model is random stub matching given fixed degrees (RSM) so that edge assignments to vertex pair sites are dependent, and the second is independent edge assignments (IEA) according to a common probability distribution. Tests are performed using goodness of fit measures between the edge multiplicity sequence of an observed multigraph, and the expected one according to a simple or composite hypothesis. Test statistics of Pearson type and of likelihood ratio type are used, and the expected values of the Pearson statistic under the different models are derived. Test performances based on simulations indicate that even for small number of edges, the null distributions of both statistics are well approximated by their asymptotic 
 $ \chi ^{2} $ χ2-distribution. The non-null distributions of the test statistics can be well approximated by proposed adjusted 
 $ \chi ^{2} $ χ2-distributions used for power approximations. The influence of RSM on both test statistics is substantial for small number of edges and implies a shift of their distributions towards smaller values compared to what holds true for the null distributions under IEA. Two applications on social networks are included to illustrate how the tests can guide in the analysis of social structure.
Journal: Journal of Applied Statistics
Pages: 3062-3087
Issue: 15
Volume: 50
Year: 2023
Month: 11
X-DOI: 10.1080/02664763.2022.2099816
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2099816
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:15:p:3062-3087



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2101045_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: İsmet Birbiçer
Author-X-Name-First: İsmet
Author-X-Name-Last: Birbiçer
Author-Name: Ali İ. Genç
Author-X-Name-First: Ali İ.
Author-X-Name-Last: Genç
Title: On parameter estimation of the standard omega distribution
Abstract: 
 The standard omega distribution is defined on the unit interval so that it is a probabilistic model for observations in rates and percentages. It is, in fact, the unit form of the exponentiated half logistic distribution. In this work, we first give a detailed shape analysis from which we observe that it is another flexible beta-like distribution. We observe that it can be J-shaped, reverse J-shaped, U-shaped, unimodal and show left and right skewness according to the values of its shape parameters. Contrary to the ordinary beta, it has the advantage of having a clear distribution function. We then discuss the existence and uniqueness of the maximum likelihood estimators and the Bayesian estimate of the parameters. The existence and uniqueness of the maximum likelihood estimators of the parameters will give a great advantage to the possible practitioners of this model since the possibility of finding a spurious solution to the likelihood equations disappears then. The comparison of these estimators with the existing ones for the general omega distribution is made with the help of a simulation study. Two real data fitting demonstrations prove its usefulness among other beta-like distributions such as Kumaraswamy, log-Lindley and Topp–Leone.
Journal: Journal of Applied Statistics
Pages: 3108-3124
Issue: 15
Volume: 50
Year: 2023
Month: 11
X-DOI: 10.1080/02664763.2022.2101045
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2101045
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:15:p:3108-3124



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2096209_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Jody Krahn
Author-X-Name-First: Jody
Author-X-Name-Last: Krahn
Author-Name: Shakhawat Hossain
Author-X-Name-First: Shakhawat
Author-X-Name-Last: Hossain
Author-Name: Shahedul Khan
Author-X-Name-First: Shahedul
Author-X-Name-Last: Khan
Title: An efficient estimation approach to joint modeling of longitudinal and survival data
Abstract: 
 The joint models for longitudinal and survival data have recently received significant attention in medical and epidemiological studies. Joint models typically combine linear mixed effects models for repeated measurement data and Cox models for survival time. When we are jointly modeling the longitudinal and survival data, variable selection and efficient estimation of parameters are especially important for performing reliable statistical analyzes, both of which are currently lacking in the literature. In this paper we discuss the pretest and shrinkage estimation methods for jointly modeling longitudinal data and survival time data when some of the covariates in both longitudinal and survival components may not be relevant for predicting survival times. In this situation, we fit two models: the full model that contains all the covariates and the subset model that contains a reduced number of covariates. We combine the full model estimators and the estimators that are restricted by a linear hypothesis to define pretest and shrinkage estimators. We provide their numerical mean squared errors (MSE) and relative MSE. We show that if the shrinkage dimension exceeds two, the risk of the shrinkage estimators is strictly less than that of the full model estimators. Our proposed methods are illustrated by extensive simulation studies and a real-data example.
Journal: Journal of Applied Statistics
Pages: 3031-3047
Issue: 15
Volume: 50
Year: 2023
Month: 11
X-DOI: 10.1080/02664763.2022.2096209
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2096209
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:15:p:3031-3047



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2102158_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Jan Kalina
Author-X-Name-First: Jan
Author-X-Name-Last: Kalina
Author-Name: Patrik Janáček
Author-X-Name-First: Patrik
Author-X-Name-Last: Janáček
Title: Testing exchangeability of multivariate distributions
Abstract: 
 Although there have been a number of available tests of bivariate exchangeability, i.e. bivariate symmetry for bivariate distributions, the literature is void of tests whether a multivariate distribution with more than two dimensions is exchangeable or not. In this paper, multivariate permutation tests of exchangeability of multivariate distributions are proposed, which are based on the non-parametric combination methodology, i.e. on combining non-parametric bivariate exchangeability tests. Numerical experiments on real as well as simulated multivariate data with more than two dimensions are presented here. The multivariate permutation test turns out to be typically more powerful than a bivariate exchangeability test performed only over a single pair of variables, and also more suitable compared to tests exploiting the approaches of Benjamini–Yekutieli or Bonferroni.
Journal: Journal of Applied Statistics
Pages: 3142-3156
Issue: 15
Volume: 50
Year: 2023
Month: 11
X-DOI: 10.1080/02664763.2022.2102158
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2102158
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:15:p:3142-3156



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2103101_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Minh Thu Bui
Author-X-Name-First: Minh Thu
Author-X-Name-Last: Bui
Author-Name: Cornelis J. Potgieter
Author-X-Name-First: Cornelis J.
Author-X-Name-Last: Potgieter
Author-Name: Akihito Kamata
Author-X-Name-First: Akihito
Author-X-Name-Last: Kamata
Title: Penalized likelihood methods for modeling count data
Abstract: 
 The paper considers parameter estimation in count data models using penalized likelihood methods. The motivating data consists of multiple independent count variables with a moderate sample size per variable. The data were collected during the assessment of oral reading fluency (ORF) in school-aged children. A sample of fourth-grade students were given one of ten available passages to read with these differing in length and difficulty. The observed number of words read incorrectly (WRI) is used to measure ORF. Three models are considered for WRI scores, namely the binomial, the zero-inflated binomial, and the beta-binomial. We aim to efficiently estimate passage difficulty, a quantity expressed as a function of the underlying model parameters. Two types of penalty functions are considered for penalized likelihood with respective goals of shrinking parameter estimates closer to zero or closer to one another. A simulation study evaluates the efficacy of the shrinkage estimates using Mean Square Error (MSE) as metric. Big reductions in MSE relative to unpenalized maximum likelihood are observed. The paper concludes with an analysis of the motivating ORF data.
Journal: Journal of Applied Statistics
Pages: 3157-3176
Issue: 15
Volume: 50
Year: 2023
Month: 11
X-DOI: 10.1080/02664763.2022.2103101
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2103101
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:15:p:3157-3176



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2104228_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Ka Kin Lam
Author-X-Name-First: Ka Kin
Author-X-Name-Last: Lam
Author-Name: Bo Wang
Author-X-Name-First: Bo
Author-X-Name-Last: Wang
Title: Multipopulation mortality modelling and forecasting: the weighted multivariate functional principal component approaches
Abstract: 
 Human mortality patterns and trajectories in closely related populations are likely linked together and share similarities. It is always desirable to model them simultaneously while taking their heterogeneity into account. This article introduces two new models for joint mortality modelling and forecasting multiple subpopulations using the multivariate functional principal component analysis techniques. The first model extends the independent functional data model to a multipopulation modelling setting. In the second one, we propose a novel multivariate functional principal component method for coherent modelling. Its design primarily fulfils the idea that when several subpopulation groups have similar socio-economic conditions or common biological characteristics such close connections are expected to evolve in a non-diverging fashion. We demonstrate the proposed methods by using sex-specific mortality data. Their forecast performances are further compared with several existing models, including the independent functional data model and the Product-Ratio model, through comparisons with mortality data of ten developed countries. The numerical examples show that the first proposed model maintains a comparable forecast ability with the existing methods. In contrast, the second proposed model outperforms the first model as well as the existing models in terms of forecast accuracy.
Journal: Journal of Applied Statistics
Pages: 3177-3198
Issue: 15
Volume: 50
Year: 2023
Month: 11
X-DOI: 10.1080/02664763.2022.2104228
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2104228
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:15:p:3177-3198



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2101631_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Yamin Sayyari
Author-X-Name-First: Yamin
Author-X-Name-Last: Sayyari
Author-Name: Mohammad Reza Molaei
Author-X-Name-First: Mohammad Reza
Author-X-Name-Last: Molaei
Author-Name: Adel Mehrpooya
Author-X-Name-First: Adel
Author-X-Name-Last: Mehrpooya
Title: Complexities of information sources
Abstract: 
 Calculating the entropy for complex systems is a significant problem in science and engineering problems. However, this calculation is usually computationally expensive when the entropy is computed directly. This paper introduces three classes of information sources that for all members of each class, the entropy value is the same. These classes are characterized according to special dynamics created by three kinds of self-mappings on Ω, and A, where Ω is a probability space and A is a finite set. An approximation of rank variables of the product of information sources is made, and it is proved that the topological entropy of the product of two information sources is equal to the summation of their topological entropies.
Journal: Journal of Applied Statistics
Pages: 3125-3141
Issue: 15
Volume: 50
Year: 2023
Month: 11
X-DOI: 10.1080/02664763.2022.2101631
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2101631
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:15:p:3125-3141



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2101044_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Mattia Cefis
Author-X-Name-First: Mattia
Author-X-Name-Last: Cefis
Title: Observed heterogeneity in players' football performance analysis using PLS-PM
Abstract: 
 Nowadays, data science is applied in several areas of daily life. There have been many applications to sports. In this context, the attention will be focused on football (i.e. ‘soccer’ for Americans): the making of strategic choices, whether by the scouting department of the football club, or the technical staff, up to the management, is crucial. It has been measured and monitored football players' performance in the season 2018/2019, for the top five European Leagues, using data provided by Electronic Arts (EA) experts and available on the Kaggle data science platform. For this purpose, with the help of football experts, a third-order partial least-squares path model (PLS-PM) approach was adopted to the sofifa key performance indices in order to compute a composite indicator differentiated by role and compare it with the well-known overall indicator from EA Sports. It has been taken into account players' observed heterogeneity (i.e. roles and leagues), since often experts refer to differences in these features, and so the objective is to verify their importance scientifically. The results are very consistent with this because they underline how some sub-areas of performance have different significance weights depending on the role.
Journal: Journal of Applied Statistics
Pages: 3088-3107
Issue: 15
Volume: 50
Year: 2023
Month: 11
X-DOI: 10.1080/02664763.2022.2101044
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2101044
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:15:p:3088-3107

Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2112556_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Nirosha Rathnayake
Author-X-Name-First: Nirosha
Author-X-Name-Last: Rathnayake
Author-Name: Hongying Daisy Dai
Author-X-Name-First: Hongying Daisy
Author-X-Name-Last: Dai
Author-Name: Richard Charnigo
Author-X-Name-First: Richard
Author-X-Name-Last: Charnigo
Author-Name: Kendra Schmid
Author-X-Name-First: Kendra
Author-X-Name-Last: Schmid
Author-Name: Jane Meza
Author-X-Name-First: Jane
Author-X-Name-Last: Meza
Title: A general class of small area estimation using calibrated hierarchical likelihood approach with applications to COVID-19 data
Abstract: 
 The direct estimation techniques in small area estimation (SAE) models require sufficiently large sample sizes to provide accurate estimates. Hence, indirect model-based methodologies are developed to incorporate auxiliary information. The most commonly used SAE models, including the Fay-Herriot (FH) model and its extended models, are estimated using marginal likelihood estimation and the Bayesian methods, which rely heavily on the computationally intensive integration of likelihood function. In this article, we propose a Calibrated Hierarchical (CH) likelihood approach to obtain SAE through hierarchical estimation of fixed effects and random effects with the regression calibration method for bias correction. The latent random variables at the domain level are treated as ‘parameters’ and estimated jointly with other parameters of interest. Then the dispersion parameters are estimated iteratively based on the Laplace approximation of the profile likelihood. The proposed method avoids the intractable integration to estimate the marginal distribution. Hence, it can be applied to a wide class of distributions, including generalized linear mixed models, survival analysis, and joint modeling with distinct distributions. We demonstrate our method using an area-level analysis of publicly available count data from the novel coronavirus (COVID-19) positive cases.
Journal: Journal of Applied Statistics
Pages: 3384-3404
Issue: 16
Volume: 50
Year: 2023
Month: 12
X-DOI: 10.1080/02664763.2022.2112556
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2112556
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:16:p:3384-3404



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2111678_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Carolina Luque
Author-X-Name-First: Carolina
Author-X-Name-Last: Luque
Author-Name: Juan Sosa
Author-X-Name-First: Juan
Author-X-Name-Last: Sosa
Title: A Bayesian spatial voting model to characterize the legislative behavior of the Colombian Senate 2010–2014
Abstract: 
 This paper characterizes the legislators voting behavior in the Colombian Senate 2010–2014, by implementing a one-dimensional standard Bayesian ideal point estimator via Markov chain Monte Carlo algorithms. Our main goal is to retrieve the political preferences of legislators from their roll-call voting records, which individualizes the electoral behavior of the legislative chamber. Furthermore, we conclude about the nature of the latent trait underlying the deputies voting decisions and the legislators locations in political space. Finally, we also offer several methodological and theoretical tools to guide the analysis of nominal voting data in the context of unbalanced parliaments (multi-party systems), taking as reference the particular case of the Colombian Senate.
Journal: Journal of Applied Statistics
Pages: 3362-3383
Issue: 16
Volume: 50
Year: 2023
Month: 12
X-DOI: 10.1080/02664763.2022.2111678
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2111678
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:16:p:3362-3383



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2110860_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Onur Camli
Author-X-Name-First: Onur
Author-X-Name-Last: Camli
Author-Name: Zeynep Kalaylioglu
Author-X-Name-First: Zeynep
Author-X-Name-Last: Kalaylioglu
Author-Name: Ashis SenGupta
Author-X-Name-First: Ashis
Author-X-Name-Last: SenGupta
Title: Variable selection in linear-circular regression models
Abstract: 
 Applications of circular regression models are ubiquitous in many disciplines, particularly in meteorology, biology and geology. In circular regression models, variable selection problem continues to be a remarkable open question. In this paper, we address variable selection in linear-circular regression models where uni-variate linear dependent and a mixed set of circular and linear independent variables constitute the data set. We consider Bayesian lasso which is a popular choice for variable selection in classical linear regression models. We show that Bayesian lasso in linear-circular regression models is not able to produce robust inference as the coefficient estimates are sensitive to the choice of hyper-prior setting for the tuning parameter. To eradicate the problem, we propose a robustified Bayesian lasso that is based on an empirical Bayes (EB) type methodology to construct a hyper-prior for the tuning parameter while using Gibbs Sampling. This hyper-prior construction is computationally more feasible than the hyper-priors that are based on correlation measures. We show in a comprehensive simulation study that Bayesian lasso with EB-GS hyper-prior leads to a more robust inference. Overall, the method offers an efficient Bayesian lasso for variable selection in linear-circular regression while reducing model complexity.
Journal: Journal of Applied Statistics
Pages: 3337-3361
Issue: 16
Volume: 50
Year: 2023
Month: 12
X-DOI: 10.1080/02664763.2022.2110860
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2110860
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:16:p:3337-3361



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2107187_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Zhan Liu
Author-X-Name-First: Zhan
Author-X-Name-Last: Liu
Author-Name: Junbo Zheng
Author-X-Name-First: Junbo
Author-X-Name-Last: Zheng
Author-Name: Chaofeng Tu
Author-X-Name-First: Chaofeng
Author-X-Name-Last: Tu
Author-Name: Yingli Pan
Author-X-Name-First: Yingli
Author-X-Name-Last: Pan
Title: Estimation for volunteer web survey samples using a model-averaging approach
Abstract: 
 Propensity score approach is a popular technique for estimating the population based on volunteer web survey samples. Various models have been used to estimate propensity scores and produce different population estimates. To obtain more accurate population estimators, we propose a model-averaging estimation approach based on propensity score estimates from a parametric logistic regression model and a nonparametric generalized boosted model. Consistency and asymptotic normality of the proposed estimators are established. A computation algorithm is also developed to implement the proposed method. Simulation studies are conducted to compare the performance of the proposed method with the other methods. A survey data from the Netizen Social Awareness Survey (NSAS) is used to illustrate the proposed methodology.
Journal: Journal of Applied Statistics
Pages: 3251-3271
Issue: 16
Volume: 50
Year: 2023
Month: 12
X-DOI: 10.1080/02664763.2022.2107187
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2107187
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:16:p:3251-3271



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2108386_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Soyun Park
Author-X-Name-First: Soyun
Author-X-Name-Last: Park
Author-Name: Jihnhee Yu
Author-X-Name-First: Jihnhee
Author-X-Name-Last: Yu
Author-Name: Hwa-Hyoung Woo
Author-X-Name-First: Hwa-Hyoung
Author-X-Name-Last: Woo
Author-Name: Chun Gun Park
Author-X-Name-First: Chun Gun
Author-X-Name-Last: Park
Title: A novel network architecture combining central-peripheral deviation with image-based convolutional neural networks for diffusion tensor imaging studies
Abstract: 
 Brain imaging research is a very challenging topic due to complex structure and lack of explicitly identifiable features in the image. With the advancement of magnetic resonance imaging (MRI) technologies, such as diffusion tensor imaging (DTI), developing classification methods to improve clinical diagnosis is crucial. This paper proposes a classification method for DTI data based on a novel neural network strategy that combines a convolutional neural network (CNN) with a multilayer neural network using central-peripheral deviation (CPD), which reflects diffusion dynamics in the white matter by spatially evaluating the deviation of diffusion coefficients between the inner and outer parts of the brain. In our method, a multilayer perceptron (MLP) using CPD is combined with the final layers for classification after reducing the dimensions of images in the convolutional layers of the neural network architecture. In terms of training loss and the classification error, the proposed classification method improves the existing image classification with CNN. For real data analysis, we demonstrate how to process raw DTI image data sets obtained from a traumatic brain injury study (MagNeTS) and a brain atlas construction study (ICBM), and apply the proposed approach to the data, successfully improving classification performance with two age groups.
Journal: Journal of Applied Statistics
Pages: 3294-3311
Issue: 16
Volume: 50
Year: 2023
Month: 12
X-DOI: 10.1080/02664763.2022.2108386
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2108386
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:16:p:3294-3311



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2109129_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Wei Xiong
Author-X-Name-First: Wei
Author-X-Name-Last: Xiong
Author-Name: Maozai Tian
Author-X-Name-First: Maozai
Author-X-Name-Last: Tian
Author-Name: Manlai Tang
Author-X-Name-First: Manlai
Author-X-Name-Last: Tang
Author-Name: Han Pan
Author-X-Name-First: Han
Author-X-Name-Last: Pan
Title: Robust and sparse learning of varying coefficient models with high-dimensional features
Abstract: 
 Varying coefficient model (VCM) is extensively used in various scientific fields due to its capability of capturing the changing structure of predictors. Classical mean regression analysis is often complicated in the existence of skewed, heterogeneous and heavy-tailed data. For this purpose, this work employs the idea of model averaging and introduces a novel comprehensive approach by incorporating quantile-adaptive weights across different quantile levels to further improve both least square (LS) and quantile regression (QR) methods. The proposed procedure that adaptively takes advantage of the heterogeneous and sparse nature of input data can gain more efficiency and be well adapted to extreme event case and high-dimensional setting. Motivated by its nice properties, we develop several robust methods to reveal the dynamic close-to-truth structure for VCM and consistently uncover the zero and nonzero patterns in high-dimensional scientific discoveries. We provide a new iterative algorithm that is proven to be asymptotic consistent and can attain the optimal nonparametric convergence rate given regular conditions. These introduced procedures are highlighted with extensive simulation examples and several real data analyses to further show their stronger predictive power compared with LS, composite quantile regression (CQR) and QR methods.
Journal: Journal of Applied Statistics
Pages: 3312-3336
Issue: 16
Volume: 50
Year: 2023
Month: 12
X-DOI: 10.1080/02664763.2022.2109129
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2109129
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:16:p:3312-3336



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2104822_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Somnath Chaudhuri
Author-X-Name-First: Somnath
Author-X-Name-Last: Chaudhuri
Author-Name: Pablo Juan
Author-X-Name-First: Pablo
Author-X-Name-Last: Juan
Author-Name: Jorge Mateu
Author-X-Name-First: Jorge
Author-X-Name-Last: Mateu
Title: Spatio-temporal modeling of traffic accidents incidence on urban road networks based on an explicit network triangulation
Abstract: 
 Traffic deaths and injuries are one of the major global public health concerns. The present study considers accident records in an urban environment to explore and analyze spatial and temporal in the incidence of road traffic accidents. We propose a spatio-temporal model to provide predictions of the number of traffic collisions on any given road segment, to further generate a risk map of the entire road network. A Bayesian methodology using Integrated nested Laplace approximations with stochastic partial differential equations (SPDE) has been applied in the modeling process. As a novelty, we have introduced SPDE network triangulation to estimate the spatial autocorrelation restricted to the linear network. The resulting risk maps provide information to identify safe routes between source and destination points, and can be useful for accident prevention and multi-disciplinary road safety measures.
Journal: Journal of Applied Statistics
Pages: 3229-3250
Issue: 16
Volume: 50
Year: 2023
Month: 12
X-DOI: 10.1080/02664763.2022.2104822
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2104822
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:16:p:3229-3250



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2108007_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Germà Coenders
Author-X-Name-First: Germà
Author-X-Name-Last: Coenders
Author-Name: Michael Greenacre
Author-X-Name-First: Michael
Author-X-Name-Last: Greenacre
Title: Three approaches to supervised learning for compositional data with pairwise logratios
Abstract: 
 Logratios between pairs of compositional parts (pairwise logratios) are the easiest to interpret in compositional data analysis, and include the well-known additive logratios as particular cases. When the number of parts is large (sometimes even larger than the number of cases), some form of logratio selection is needed. In this article, we present three alternative stepwise supervised learning methods to select the pairwise logratios that best explain a dependent variable in a generalized linear model, each geared for a specific problem. The first method features unrestricted search, where any pairwise logratio can be selected. This method has a complex interpretation if some pairs of parts in the logratios overlap, but it leads to the most accurate predictions. The second method restricts parts to occur only once, which makes the corresponding logratios intuitively interpretable. The third method uses additive logratios, so that K−1 selected logratios involve a K-part subcomposition. Our approach allows logratios or non-compositional covariates to be forced into the models based on theoretical knowledge, and various stopping criteria are available based on information measures or statistical significance with the Bonferroni correction. We present an application on a dataset from a study predicting Crohn's disease.
Journal: Journal of Applied Statistics
Pages: 3272-3293
Issue: 16
Volume: 50
Year: 2023
Month: 12
X-DOI: 10.1080/02664763.2022.2108007
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2108007
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:16:p:3272-3293



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2104230_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20230119T200553 git hash: 724830af20
Author-Name: Fatemeh Hassantabar Darzi
Author-X-Name-First: Fatemeh Hassantabar
Author-X-Name-Last: Darzi
Author-Name: Samaneh Eftekhari Mahabadi
Author-X-Name-First: Samaneh Eftekhari
Author-X-Name-Last: Mahabadi
Author-Name: Firoozeh Haghighi
Author-X-Name-First: Firoozeh
Author-X-Name-Last: Haghighi
Title: Type-II progressive censoring with GLM-based random removal mechanism dependent on the experimental conditions
Abstract: 
 This article presents a novel stochastic removal mechanism under Type-II progressive random censoring in which removal probabilities are allowed to be dependent on the lifetime conditions through Generalized Linear Models (GLM). These conditions potentially include failure distances (the time required to observe the next failure) or other covariate information available in the experiment. The proposed GLM-based random removal mechanism includes a set of tuning parameters that are determined by the researcher according to the possible failure distance category. These parameters allow flexible determination of the removal probabilities leading to necessary experimental cost and time reductions. To establish the proposed mechanism, the Proportional Hazard Rate (PHR) family of distributions is considered. Also, the maximum likelihood estimators of parameters and their asymptotic variances are derived for the Weibull distributed lifetime data. A simple simulation algorithm for generating Type-II progressive censoring samples with GLM-based dependent removal probabilities is also presented. The expected experiment time required to complete the life test under this censoring scheme is also investigated using the Monte Carlo integration method. Several simulation studies are conducted to evaluate and compare the performance of the proposed mechanism. A sensitivity analysis is also considered to study the effect of misspecification of removal mechanism coefficients. Finally, two real data sets are analyzed for illustrative purposes.
Journal: Journal of Applied Statistics
Pages: 3199-3228
Issue: 16
Volume: 50
Year: 2023
Month: 12
X-DOI: 10.1080/02664763.2022.2104230
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2104230
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:50:y:2023:i:16:p:3199-3228

Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2116409_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20231214T103247 git hash: d7a2cb0857
Author-Name: A. Fanjul-Hevia
Author-X-Name-First: A.
Author-X-Name-Last: Fanjul-Hevia
Author-Name: J. C. Pardo-Fernández
Author-X-Name-First: J. C.
Author-X-Name-Last: Pardo-Fernández
Author-Name: I. Van Keilegom
Author-X-Name-First: I.
Author-X-Name-Last: Van Keilegom
Author-Name: W. González-Manteiga
Author-X-Name-First: W.
Author-X-Name-Last: González-Manteiga
Title: A test for comparing conditional ROC curves with multidimensional covariates
Abstract: 
 The comparison of Receiver Operating Characteristic (ROC) curves is frequently used in the literature to compare the discriminatory capability of different classification procedures based on diagnostic variables. The performance of these variables can be sometimes influenced by the presence of other covariates, and thus they should be taken into account when making the comparison. A new non-parametric test is proposed here for testing the equality of two or more dependent ROC curves conditioned to the value of a multidimensional covariate. Projections are used for transforming the problem into a one-dimensional approach easier to handle. Simulations are carried out to study the practical performance of the new methodology. The procedure is then used to analyse a real data set of patients with Pleural Effusion to compare the diagnostic capability of different markers.
Journal: Journal of Applied Statistics
Pages: 87-113
Issue: 1
Volume: 51
Year: 2024
Month: 01
X-DOI: 10.1080/02664763.2022.2116409
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2116409
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:1:p:87-113



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2114431_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20231214T103247 git hash: d7a2cb0857
Author-Name: Wenchao Xu
Author-X-Name-First: Wenchao
Author-X-Name-Last: Xu
Author-Name: Hongmei Lin
Author-X-Name-First: Hongmei
Author-X-Name-Last: Lin
Author-Name: Tiejun Tong
Author-X-Name-First: Tiejun
Author-X-Name-Last: Tong
Author-Name: Riquan Zhang
Author-X-Name-First: Riquan
Author-X-Name-Last: Zhang
Title: A new method for estimating Sharpe ratio function via local maximum likelihood
Abstract: 
 The Sharpe ratio function is a commonly used risk/return measure in financial econometrics. To estimate this function, most existing methods take a two-step procedure that first estimates the mean and volatility functions separately and then applies the plug-in method. In this paper, we propose a direct method via local maximum likelihood to simultaneously estimate the Sharpe ratio function and the negative log-volatility function as well as their derivatives. We establish the joint limiting distribution of the proposed estimators, and moreover extend the proposed method to estimate the multivariate Sharpe ratio function. We also evaluate the numerical performance of the proposed estimators through simulation studies, and compare them with existing methods. Finally, we apply the proposed method to the three-month US Treasury bill data and that captures a well-known covariate-dependent effect on the Sharpe ratio.
Journal: Journal of Applied Statistics
Pages: 34-52
Issue: 1
Volume: 51
Year: 2024
Month: 01
X-DOI: 10.1080/02664763.2022.2114431
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2114431
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:1:p:34-52



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2125502_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20231214T103247 git hash: d7a2cb0857
Author-Name: Jianguo Sun
Author-X-Name-First: Jianguo
Author-X-Name-Last: Sun
Title: New advances in statistics and data science
Journal: Journal of Applied Statistics
Pages: 193-195
Issue: 1
Volume: 51
Year: 2024
Month: 01
X-DOI: 10.1080/02664763.2022.2125502
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2125502
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:1:p:193-195



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2118245_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20231214T103247 git hash: d7a2cb0857
Author-Name: Peter Chang
Author-X-Name-First: Peter
Author-X-Name-Last: Chang
Author-Name: Rongzi Liu
Author-X-Name-First: Rongzi
Author-X-Name-Last: Liu
Author-Name: Tingting Hou
Author-X-Name-First: Tingting
Author-X-Name-Last: Hou
Author-Name: Xinyu Yan
Author-X-Name-First: Xinyu
Author-X-Name-Last: Yan
Author-Name: Guogen Shan
Author-X-Name-First: Guogen
Author-X-Name-Last: Shan
Title: Continuity corrected score confidence interval for the difference in proportions in paired data
Abstract: 
 For paired binary data, the hybrid method and the score method are often recommended for use to calculate the confidence interval for risk difference. These asymptotic intervals do not control the coverage probability. We propose to develop a new score interval with continuity correction to further improve the performance of the existing intervals. The traditional correction value may be too large which leads to a wide interval. For that reason, we propose three different correction values to identify the optimal correction interval with balanced coverage probability and interval width. From simulation studies, we find that a small correction value for the score interval has good performance. In addition, we derive the non-iterative solutions for the developed continuity correction score intervals.
Journal: Journal of Applied Statistics
Pages: 139-152
Issue: 1
Volume: 51
Year: 2024
Month: 01
X-DOI: 10.1080/02664763.2022.2118245
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2118245
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:1:p:139-152



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2138838_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20231214T103247 git hash: d7a2cb0857
Author-Name: Cindy Feng
Author-X-Name-First: Cindy
Author-X-Name-Last: Feng
Author-Name: Xi Chen
Author-X-Name-First: Xi
Author-X-Name-Last: Chen
Title: A two-stage latent factor regression method to model the common and unique effects of multiple highly correlated exposure variables
Abstract: 
 In many epidemiological and environmental health studies, developing an accurate exposure assessment of multiple exposures on a health outcome is often of interest. However, the problem is challenging in the presence of multicollinearity, which can lead to biased estimates of regression coefficients and inflated variance estimators. Selecting one exposure variable as a surrogate of multiple highly correlated exposure variables is often suggested in the literature as a solution to handle the multicollinearity problem. However, this may lead to loss of information, since the exposure variables that are highly correlated tend to have not only common but also additional effects on the outcome variable. In this study, a two-stage latent factor regression method is proposed. The key idea is to regress the dependent variable not only on the common latent factor(s) of the explanatory variables, but also on the residuals terms from the factor analysis as the explanatory variables. The proposed method is compared to the traditional latent factor regression and principal component regression for their performance of handling multicollinearity. Two case studies are presented. Simulation studies are performed to assess their performances in terms of the epidemiological interpretation and stability of parameter estimates.
Journal: Journal of Applied Statistics
Pages: 168-192
Issue: 1
Volume: 51
Year: 2024
Month: 01
X-DOI: 10.1080/02664763.2022.2138838
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2138838
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:1:p:168-192



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2113865_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20231214T103247 git hash: d7a2cb0857
Author-Name: Subhankar Dutta
Author-X-Name-First: Subhankar
Author-X-Name-Last: Dutta
Author-Name: Suchandan Kayal
Author-X-Name-First: Suchandan
Author-X-Name-Last: Kayal
Title: Estimation and prediction for Burr type III distribution based on unified progressive hybrid censoring scheme
Abstract: 
 The present communication develops the tools for estimation and prediction of the Burr-III distribution under unified progressive hybrid censoring scheme. The maximum likelihood estimates of model parameters are obtained. It is shown that the maximum likelihood estimates exist uniquely. Expectation maximization and stochastic expectation maximization methods are employed to compute the point estimates of unknown parameters. Based on the asymptotic distribution of the maximum likelihood estimators, approximate confidence intervals are proposed. In addition, the bootstrap confidence intervals are constructed. Furthermore, the Bayes estimates are derived with respect to squared error and LINEX loss functions. To compute the approximate Bayes estimates, Metropolis–Hastings algorithm is adopted. The highest posterior density credible intervals are obtained. Further, maximum a posteriori estimates of the model parameters are computed. The Bayesian predictive point, as well as interval estimates, are proposed. A Monte Carlo simulation study is employed in order to evaluate the performance of the proposed statistical procedures. Finally, two real data sets are considered and analysed to illustrate the methodologies established in this paper.
Journal: Journal of Applied Statistics
Pages: 1-33
Issue: 1
Volume: 51
Year: 2024
Month: 01
X-DOI: 10.1080/02664763.2022.2113865
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2113865
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:1:p:1-33



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2112937_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20231214T103247 git hash: d7a2cb0857
Author-Name: Carlos E. Rodríguez
Author-X-Name-First: Carlos E.
Author-X-Name-Last: Rodríguez
Author-Name: Luis E. Nieto-Barajas
Author-X-Name-First: Luis E.
Author-X-Name-Last: Nieto-Barajas
Author-Name: Carlos S. Pérez-Pérez
Author-X-Name-First: Carlos S.
Author-X-Name-Last: Pérez-Pérez
Title: Dealing with missing data under stratified sampling designs where strata are study domains
Abstract: 
 A quick count seeks to estimate the voting trends of an election and communicate them to the population on the evening of the same day of the election. In quick counts, the sampling is based on a stratified design of polling stations. Voting information is gathered gradually, often with no guarantee of obtaining the complete sample or even information in all the strata. However, accurate interval estimates with partial information must be obtained. Furthermore, this becomes more challenging if the strata are additionally study domains. To produce partial estimates, two strategies are proposed: (1) a Bayesian model using a dynamic post-stratification strategy and a single imputation process defined after a thorough analysis of historic voting information; additionally, a credibility level correction is included to solve the underestimation of the variance and (2) a frequentist alternative that combines standard multiple imputation ideas with classic sampling techniques to obtain estimates under a missing information framework. Both solutions are illustrated and compared using information from the 2021 quick count. The aim was to estimate the composition of the Chamber of Deputies in Mexico.
Journal: Journal of Applied Statistics
Pages: 153-167
Issue: 1
Volume: 51
Year: 2024
Month: 01
X-DOI: 10.1080/02664763.2022.2112937
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2112937
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:1:p:153-167



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2116746_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20231214T103247 git hash: d7a2cb0857
Author-Name: Marco Molinari
Author-X-Name-First: Marco
Author-X-Name-Last: Molinari
Author-Name: Andrea Cremaschi
Author-X-Name-First: Andrea
Author-X-Name-Last: Cremaschi
Author-Name: Maria De Iorio
Author-X-Name-First: Maria
Author-X-Name-Last: De Iorio
Author-Name: Nishi Chaturvedi
Author-X-Name-First: Nishi
Author-X-Name-Last: Chaturvedi
Author-Name: Alun Hughes
Author-X-Name-First: Alun
Author-X-Name-Last: Hughes
Author-Name: Therese Tillin
Author-X-Name-First: Therese
Author-X-Name-Last: Tillin
Title: Bayesian dynamic network modelling: an application to metabolic associations in cardiovascular diseases
Abstract: 
 We propose a novel approach to the estimation of multiple Graphical Models to analyse temporal patterns of association among a set of metabolites over different groups of patients. Our motivating application is the Southall And Brent REvisited (SABRE) study, a tri-ethnic cohort study conducted in the UK. We are interested in identifying potential ethnic differences in metabolite levels and associations as well as their evolution over time, with the aim of gaining a better understanding of different risk of cardio-metabolic disorders across ethnicities. Within a Bayesian framework, we employ a nodewise regression approach to infer the structure of the graphs, borrowing information across time as well as across ethnicities. The response variables of interest are metabolite levels measured at two time points and for two ethnic groups, Europeans and South-Asians. We use nodewise regression to estimate the high-dimensional precision matrices of the metabolites, imposing sparsity on the regression coefficients through the dynamic horseshoe prior, thus favouring sparser graphs. We provide the code to fit the proposed model using the software Stan, which performs posterior inference using Hamiltonian Monte Carlo sampling, as well as a detailed description of a block Gibbs sampling scheme.
Journal: Journal of Applied Statistics
Pages: 114-138
Issue: 1
Volume: 51
Year: 2024
Month: 01
X-DOI: 10.1080/02664763.2022.2116746
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2116746
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:1:p:114-138



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2115985_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20231214T103247 git hash: d7a2cb0857
Author-Name: Ryan H.L. Ip
Author-X-Name-First: Ryan H.L.
Author-X-Name-Last: Ip
Author-Name: K.Y.K. Wu
Author-X-Name-First: K.Y.K.
Author-X-Name-Last: Wu
Title: A Markov random field model with cumulative logistic functions for spatially dependent ordinal data
Abstract: 
 This paper presents a class of regression models with cumulative logistic functions that are chiefly designed to analyse spatially dependent ordinal data. In contrast to previous works, the proposed model requires neither the sites to be regularly spaced nor the assumption of an underlying continuous variable. It belongs to a more general class of Markov random field models, and can be considered an extension of the ordinal regression model with the proportional odds link function. Our proposed model allows practitioners to interpret the model parameters using odds ratios. Apart from the theoretical developments, this work also highlights the practical aspects of model fitting, including parameterisation, selection of neighbourhood, and calculation of standard errors. Simulation studies with regularly and irregularly spaced sites were conducted. Modelling strategies including pseudo-likelihood methods were found to be useful in both settings. The proposed model and the non-spatial counterpart were applied to the daily air quality index measured in the United Kingdom. The results indicate the presence of spatial effects and the incorporation of spatial effects led to better model performance in terms of various goodness-of-fit measures.
Journal: Journal of Applied Statistics
Pages: 70-86
Issue: 1
Volume: 51
Year: 2024
Month: 01
X-DOI: 10.1080/02664763.2022.2115985
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2115985
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:1:p:70-86



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2114432_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20231214T103247 git hash: d7a2cb0857
Author-Name: Hao Hao
Author-X-Name-First: Hao
Author-X-Name-Last: Hao
Author-Name: Bai Huang
Author-X-Name-First: Bai
Author-X-Name-Last: Huang
Author-Name: Tae-hwy Lee
Author-X-Name-First: Tae-hwy
Author-X-Name-Last: Lee
Title: Model averaging estimation of panel data models with many instruments and boosting
Abstract: 
 Applied researchers often confront two issues when using the fixed effect-two-stage least squares (FE-2SLS) estimator for panel data models. One is that it may lose its consistency due to too many instruments. The other is that the gain of using FE-2SLS may not exceed its loss when the endogeneity is weak. In this paper, an 
 $ L_{2} $ L2Boosting regularization procedure for panel data models is proposed to tackle the many instruments issue. We then construct a Stein-like model-averaging estimator to take advantage of FE and FE-2SLS-Boosting estimators. Finite sample properties are examined in Monte Carlo and an empirical application is presented.
Journal: Journal of Applied Statistics
Pages: 53-69
Issue: 1
Volume: 51
Year: 2024
Month: 01
X-DOI: 10.1080/02664763.2022.2114432
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2114432
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:1:p:53-69

Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2154329_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20231214T103247 git hash: d7a2cb0857
Author-Name: Shengxiao Vincent Feng
Author-X-Name-First: Shengxiao Vincent
Author-X-Name-Last: Feng
Author-Name: Willem van den Boom
Author-X-Name-First: Willem
Author-X-Name-Last: van den Boom
Author-Name: Maria De Iorio
Author-X-Name-First: Maria
Author-X-Name-Last: De Iorio
Author-Name: Gladi J. Thng
Author-X-Name-First: Gladi J.
Author-X-Name-Last: Thng
Author-Name: Jerry K. Y. Chan
Author-X-Name-First: Jerry K. Y.
Author-X-Name-Last: Chan
Author-Name: Helen Y. Chen
Author-X-Name-First: Helen Y.
Author-X-Name-Last: Chen
Author-Name: Kok Hian Tan
Author-X-Name-First: Kok Hian
Author-X-Name-Last: Tan
Author-Name: Michelle Z. L. Kee
Author-X-Name-First: Michelle Z. L.
Author-X-Name-Last: Kee
Title: Joint modelling of mental health markers through pregnancy: a Bayesian semi-parametric approach
Abstract: 
 Maternal depression and anxiety through pregnancy have lasting societal impacts. It is thus crucial to understand the trajectories of its progression from preconception to postnatal period, and the risk factors associated with it. Within the Bayesian framework, we propose to jointly model seven outcomes, of which two are physiological and five non-physiological indicators of maternal depression and anxiety over time. We model the former two by a Gaussian process and the latter by an autoregressive model, while imposing a multidimensional Dirichlet process prior on the subject-specific random effects to account for subject heterogeneity and induce clustering. The model allows for the inclusion of covariates through a regression term. Our findings reveal four distinct clusters of trajectories of the seven health outcomes, characterising women's mental health progression from before to after pregnancy. Importantly, our results caution against the loose use of hair corticosteroids as a biomarker, or even a causal factor, for pregnancy mental health progression. Additionally, the regression analysis reveals a range of preconception determinants and risk factors for depressive and anxiety symptoms during pregnancy.
Journal: Journal of Applied Statistics
Pages: 388-405
Issue: 2
Volume: 51
Year: 2024
Month: 01
X-DOI: 10.1080/02664763.2022.2154329
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2154329
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:2:p:388-405



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2123460_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20231214T103247 git hash: d7a2cb0857
Author-Name: Qian Xu
Author-X-Name-First: Qian
Author-X-Name-Last: Xu
Author-Name: Demetra Antimisiaris
Author-X-Name-First: Demetra
Author-X-Name-Last: Antimisiaris
Author-Name: Maiying Kong
Author-X-Name-First: Maiying
Author-X-Name-Last: Kong
Title: Statistical methods for assessing drug interactions using observational data
Abstract: 
 With advances in medicine, many drugs and treatments become available. On the one hand, polydrug use (i.e. using more than one drug at a time) has been used to treat patients with multiple morbid conditions, and polydrug use may cause severe side effects. On the other hand, combination treatments have been successfully developed to treat severe diseases such as cancer and chronic diseases. Observational data, such as electronic health record data, may provide useful information for assessing drug interactions. In this article, we propose using marginal structural models to assess the average treatment effect and causal interaction of two drugs by controlling confounding variables. The causal effect and the interaction of two drugs are assessed using the weighted likelihood approach, with weights being the inverse probability of the treatment assigned. Simulation studies were conducted to examine the performance of the proposed method, which showed that the proposed method was able to estimate the causal parameters consistently. Case studies were conducted to examine the joint effect of metformin and glyburide use on reducing the hospital readmission for type 2 diabetic patients, and to examine the joint effect of antecedent statins and opioids use on the immune and inflammatory biomarkers for COVID-19 hospitalized patients.
Journal: Journal of Applied Statistics
Pages: 298-323
Issue: 2
Volume: 51
Year: 2024
Month: 01
X-DOI: 10.1080/02664763.2022.2123460
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2123460
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:2:p:298-323



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2120973_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20231214T103247 git hash: d7a2cb0857
Author-Name: Muhammad Mohsin
Author-X-Name-First: Muhammad
Author-X-Name-Last: Mohsin
Author-Name: Albrecht Gebhardt
Author-X-Name-First: Albrecht
Author-X-Name-Last: Gebhardt
Title: A stochastic model for NFL games and point spread assessment
Abstract: 
 Statistical modelling of sports data is indispensable to analyse the sports behaviour and apprehend significant inferences that are helpful to adopt decisive strategies before or during the sports events. This paper introduces a stochastic model as the distribution of difference derived from the Bivariate Affine-Linear Exponential distribution. The distribution of difference is first ever used to model the margin of victory that provides an adequate fitting on the observed data. A simulation study is carried out to observe the stability of the model parameters through their average estimated values, biases, standard errors, root mean square errors and confidence intervals. The performance of the proposed model is examined by applying it on the real data of the National Football League and comparing the results with those of the existing models. Finally, the quantile function of the proposed distribution is used to assess the possible range of point spreads for winning the bet in a particular game.
Journal: Journal of Applied Statistics
Pages: 216-229
Issue: 2
Volume: 51
Year: 2024
Month: 01
X-DOI: 10.1080/02664763.2022.2120973
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2120973
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:2:p:216-229



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2122947_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20231214T103247 git hash: d7a2cb0857
Author-Name: Deiby Tineke Salaki
Author-X-Name-First: Deiby Tineke
Author-X-Name-Last: Salaki
Author-Name: Anang Kurnia
Author-X-Name-First: Anang
Author-X-Name-Last: Kurnia
Author-Name: Bagus Sartono
Author-X-Name-First: Bagus
Author-X-Name-Last: Sartono
Author-Name: I Wayan Mangku
Author-X-Name-First: I Wayan
Author-X-Name-Last: Mangku
Author-Name: Arief Gusnanto
Author-X-Name-First: Arief
Author-X-Name-Last: Gusnanto
Title: Model averaging in calibration of near-infrared instruments with correlated high-dimensional data
Abstract: 
 Model averaging (MA) is a modelling strategy where the uncertainty in the configuration of selected variables is taken into account by weight-combining each estimate of the so-called ‘candidate model’. Some studies have shown that MA enables better prediction, even in high-dimensional cases. However, little is known about the model prediction performance at different types of multicollinearity in high-dimensional data. Motivated by calibration of near-infrared (NIR) instruments,we focus on MA prediction performance in such data. The weighting schemes that we consider are based on the Akaike’s information criterion (AIC), Mallows’ Cp, and cross-validation. For estimating the model parameters, we consider the standard least squares and the ridge regression methods. The results indicate that MA outperforms model selection methods such as LASSO and SCAD in high-correlation data. The use of Mallows’ Cp and cross-validation for the weights tends to yield similar results in all structures of correlation, although the former is generally preferred. We also find that the ridge model averaging outperforms the least-squares model averaging. This research suggests ridge model averaging to build a relatively better prediction of the NIR calibration model.
Journal: Journal of Applied Statistics
Pages: 279-297
Issue: 2
Volume: 51
Year: 2024
Month: 01
X-DOI: 10.1080/02664763.2022.2122947
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2122947
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:2:p:279-297



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2121384_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20231214T103247 git hash: d7a2cb0857
Author-Name: Esam Mahdi
Author-X-Name-First: Esam
Author-X-Name-Last: Mahdi
Author-Name: Thomas J. Fisher
Author-X-Name-First: Thomas J.
Author-X-Name-Last: Fisher
Title: Bootstrapping a powerful mixed portmanteau test for time series
Abstract: 
 A new portmanteau test statistic is proposed for detecting nonlinearity in time series data. The new portmanteau statistic is calculated from the log of the determinant of a matrix comprised of the autocorrelations and cross-correlations of the residuals and squared residuals of a fitted time series. The asymptotic distribution of the proposed test statistic is derived as a linear combination of chi-square distributed random variables and can be approximated by a gamma distribution. A bootstrapping approach is shown to be robust when distributional assumptions are relaxed. The efficacy of the statistic is studied against linear and nonlinear dependency structures of some stationary time series models. It is shown that the new test can provide higher power than other tests in many situations. We demonstrate the advantages of the proposed test by investigating linear and nonlinear effects in an economic series and two environmental time series.
Journal: Journal of Applied Statistics
Pages: 230-255
Issue: 2
Volume: 51
Year: 2024
Month: 01
X-DOI: 10.1080/02664763.2022.2121384
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2121384
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:2:p:230-255



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2151576_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20231214T103247 git hash: d7a2cb0857
Author-Name: Semhar B. Ogbagaber
Author-X-Name-First: Semhar B.
Author-X-Name-Last: Ogbagaber
Author-Name: Yifan Cui
Author-X-Name-First: Yifan
Author-X-Name-Last: Cui
Author-Name: Kaigang Li
Author-X-Name-First: Kaigang
Author-X-Name-Last: Li
Author-Name: Ronald J. Iannotti
Author-X-Name-First: Ronald J.
Author-X-Name-Last: Iannotti
Author-Name: Paul S. Albert
Author-X-Name-First: Paul S.
Author-X-Name-Last: Albert
Title: A hidden Markov modeling approach combining objective measure of activity and subjective measure of self-reported sleep to estimate the sleep-wake cycle
Abstract: 
 Characterizing the sleep-wake cycle in adolescents is an important prerequisite to better understand the association of abnormal sleep patterns with subsequent clinical and behavioral outcomes. The aim of this research was to develop hidden Markov models (HMM) that incorporate both objective (actigraphy) and subjective (sleep log) measures to estimate the sleep-wake cycle using data from the NEXT longitudinal study, a large population-based cohort study. The model was estimated with a negative binomial distribution for the activity counts (1-minute epochs) to account for overdispersion relative to a Poisson process. Furthermore, self-reported measures were dichotomized (for each one-minute interval) and subject to misclassification. We assumed that the unobserved sleep-wake cycle follows a two-state Markov chain with transitional probabilities varying according to a circadian rhythm. Maximum-likelihood estimation using a backward–forward algorithm was applied to fit the longitudinal data on a subject by subject basis. The algorithm was used to reconstruct the sleep-wake cycle from sequences of self-reported sleep and activity data. Furthermore, we conduct simulations to examine the properties of this approach under different observational patterns including both complete and partially observed measurements on each individual.
Journal: Journal of Applied Statistics
Pages: 370-387
Issue: 2
Volume: 51
Year: 2024
Month: 01
X-DOI: 10.1080/02664763.2022.2151576
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2151576
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:2:p:370-387



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2118678_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20231214T103247 git hash: d7a2cb0857
Author-Name: Juan Sosa
Author-X-Name-First: Juan
Author-X-Name-Last: Sosa
Author-Name: Abel Rodríguez
Author-X-Name-First: Abel
Author-X-Name-Last: Rodríguez
Title: A Bayesian approach for de-duplication in the presence of relational data
Abstract: 
 In this paper, we study the impact of combining profile and network data in solving record de-duplication problems. We also assess the influence of a range of prior distributions on the linkage structure, and explore the use of stochastic gradient Hamiltonian Monte Carlo methods as a faster alternative to obtain samples from the posterior distribution for network parameters. Our methodology is evaluated using the RLdata500 data, which is a popular dataset in the record linkage literature.
Journal: Journal of Applied Statistics
Pages: 197-215
Issue: 2
Volume: 51
Year: 2024
Month: 01
X-DOI: 10.1080/02664763.2022.2118678
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2118678
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:2:p:197-215



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2122027_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20231214T103247 git hash: d7a2cb0857
Author-Name: Faryal Ibrar
Author-X-Name-First: Faryal
Author-X-Name-Last: Ibrar
Author-Name: Sajid Ali
Author-X-Name-First: Sajid
Author-X-Name-Last: Ali
Author-Name: Ismail Shah
Author-X-Name-First: Ismail
Author-X-Name-Last: Shah
Title: A comparison of single- and double-threshold ROC plots for mixture distributions
Abstract: 
 The receiver operating characteristics (ROC) analysis is commonly used in clinical settings to check the performance of a single threshold for distinguishing population-wise bimodal-distributed test results. However, for population-wise three-modal distributed test results, a single threshold ROC (stROC) analysis showed poor discriminative performance. The purpose of this study is to use a double-threshold ROC analysis for the three-modal distributed test results to provide better discriminative performance than the stROC analysis. A double-threshold receiver operating characteristic plot (dtROC) is constructed by replacing the single threshold with a double threshold. The sensitivity and specificity coordinates are chosen to maximize sensitivity for a given specificity value. Besides a simulation study assuming a mixture of lognormal, Poisson, and Weibull distributions, a clinical application is examined by a secondary data analysis of palpation test results of the C7 spinous process using the modified thorax–rib static technique. For the assumed mixture models, the discrimination performance of dtROC analysis outperforms the stROC analysis (area under ROC (AUROC) increased from 0.436 to 0.983 for lognormal distributed test results, 0.676 to 0.752 for the Poisson distribution, and 0.674 to 0.804 for Weibull distribution).
Journal: Journal of Applied Statistics
Pages: 256-278
Issue: 2
Volume: 51
Year: 2024
Month: 01
X-DOI: 10.1080/02664763.2022.2122027
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2122027
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:2:p:256-278



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2125936_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20231214T103247 git hash: d7a2cb0857
Author-Name: Heba Soltan Mohamed
Author-X-Name-First: Heba Soltan
Author-X-Name-Last: Mohamed
Author-Name: Gauss M. Cordeiro
Author-X-Name-First: Gauss M.
Author-X-Name-Last: Cordeiro
Author-Name: R. Minkah
Author-X-Name-First: R.
Author-X-Name-Last: Minkah
Author-Name: Haitham M. Yousof
Author-X-Name-First: Haitham M.
Author-X-Name-Last: Yousof
Author-Name: Mohamed Ibrahim
Author-X-Name-First: Mohamed
Author-X-Name-Last: Ibrahim
Title: A size-of-loss model for the negatively skewed insurance claims data: applications, risk analysis using different methods and statistical forecasting
Abstract: 
 The future values of the expected claims are very important for the insurance companies for avoiding the big losses under uncertainty which may be produced from future claims. In this paper, we define a new size-of-loss distribution for the negatively skewed insurance claims data. Four key risk indicators are defined and analyzed under four estimation methods: maximum likelihood, ordinary least squares, weighted least squares, and Anderson Darling. The insurance claims data are modeled using many competitive models and comprehensive comparison is performed under nine statistical tests. The autoregressive model is proposed to analyze the insurance claims data and estimate the future values of the expected claims. The value-at-risk estimation and the peaks-over random threshold mean-of-order-p methodology are considered.
Journal: Journal of Applied Statistics
Pages: 348-369
Issue: 2
Volume: 51
Year: 2024
Month: 01
X-DOI: 10.1080/02664763.2022.2125936
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2125936
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:2:p:348-369



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2125935_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20231214T103247 git hash: d7a2cb0857
Author-Name: A. D. C. Nascimento
Author-X-Name-First: A. D. C.
Author-X-Name-Last: Nascimento
Author-Name: P. M. Almeida-Junior
Author-X-Name-First: P. M.
Author-X-Name-Last: Almeida-Junior
Author-Name: J. M. Vasconcelos
Author-X-Name-First: J. M.
Author-X-Name-Last: Vasconcelos
Author-Name: A. P. M. Borges-Junior
Author-X-Name-First: A. P. M.
Author-X-Name-Last: Borges-Junior
Title: K-Bessel regression model for speckled data
Abstract: 
 Synthetic aperture radar (SAR) provides an efficient way to monitor the Earth's surface. But the speckle noise that the SAR system generates when acquiring images makes it difficult to understand and interpret SAR intensity features. To automatically analyze SAR images, this paper presents a K-Bessel regression (KBR) model in which a function of the mean intensity response is explained by other features (or covariates) determined in parallel. Some mathematical properties of this regression are derived and discussed in the context of the physical origin of the SAR image. A maximum likelihood estimation procedure is planned and its performance is quantified by Monte Carlo experiments. An application to real data obtained from a polarimetric SAR image of San Francisco Bay is realized. Results show that both the KBR-based processing is more informative than the unconditional approach to describe SAR intensity and that our proposal can outperform the normal and gamma regression models. Finally, it is shown that the KBR model is useful to reproduce the relief signal of one channel from the intensity values of the other.
Journal: Journal of Applied Statistics
Pages: 324-347
Issue: 2
Volume: 51
Year: 2024
Month: 01
X-DOI: 10.1080/02664763.2022.2125935
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2125935
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:2:p:324-347

Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2166905_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20231214T103247 git hash: d7a2cb0857
Author-Name: A. Skripnikov
Author-X-Name-First: A.
Author-X-Name-Last: Skripnikov
Title: Partially constrained group variable selection to adjust for complementary unit performance in American college football
Abstract: 
 Given the importance of accurate team rankings in American college football (CFB) – due to heavy title and playoff implications – strides have been made to improve metrics for team performance evaluation, going from basic averages (e.g. points scored per game) to metrics that adjust for a team's strength of schedule, but one aspect that's yet to be accounted for is the ability of team's offense and defense to complement one another, termed ‘complementary football’. American football is unique because the same team's offensive and defensive units typically consist of separate player sets that don't share the field simultaneously, which tempts one to evaluate them independently. Yet, some aspects of your team's defensive (offensive) performance may directly impact the complementary unit, e.g. turnovers forced by your defense could lead to easier scoring chances for your offense. Our main goal is to identify the most consistently influential features of complementary football in a data-driven way, subsequently adjusting each team's offensive (defensive) performance for that of their complementary unit. To achieve that, for the 2009–2019 CFB seasons, we incorporate natural splines with group penalty approaches, conducting partially constrained optimization to guarantee the full adjustment for the strength of schedule and home-field factor.
Journal: Journal of Applied Statistics
Pages: 606-620
Issue: 3
Volume: 51
Year: 2024
Month: 02
X-DOI: 10.1080/02664763.2023.2166905
File-URL: http://hdl.handle.net/10.1080/02664763.2023.2166905
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:3:p:606-620



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2129044_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20231214T103247 git hash: d7a2cb0857
Author-Name: Pushkal Kumar
Author-X-Name-First: Pushkal
Author-X-Name-Last: Kumar
Author-Name: Manas Ranjan Tripathy
Author-X-Name-First: Manas Ranjan
Author-X-Name-Last: Tripathy
Author-Name: Somesh Kumar
Author-X-Name-First: Somesh
Author-X-Name-Last: Kumar
Title: Alternative classification rules for two inverse gaussian populations with a common mean and order restricted scale-like parameters
Abstract: 
 The problem of classification into two inverse Gaussian populations with a common mean and ordered scale-like parameters is considered. Surprisingly, the maximum likelihood estimators (MLEs) of the associated model parameters have not been utilized for classification purposes. Note that the MLEs of the model parameters, including the MLE of the common mean, do not have closed-form expressions. In this paper, several classification rules are proposed that use the MLEs and some plug-in type estimators under order restricted scale-like parameters. In the sequel, the risk values of all the proposed estimators are compared numerically, which shows that the proposed plug-in type restricted MLE performs better than others, including the Graybill-Deal type estimator of the common mean. Further, the proposed classification rules are compared in terms of the expected probability of correct classification (EPC) numerically. It is seen that some of our proposed rules have better performance than the existing ones in most of the parameter space. Two real-life examples are considered for application purposes.
Journal: Journal of Applied Statistics
Pages: 407-429
Issue: 3
Volume: 51
Year: 2024
Month: 02
X-DOI: 10.1080/02664763.2022.2129044
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2129044
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:3:p:407-429



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2138837_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20231214T103247 git hash: d7a2cb0857
Author-Name: Cees Diks
Author-X-Name-First: Cees
Author-X-Name-Last: Diks
Author-Name: Marcin Wolski
Author-X-Name-First: Marcin
Author-X-Name-Last: Wolski
Title: New nonparametric measures for instantaneous and granger-causality tail co-dependence
Abstract: 
 We propose a new methodology to asses risk spillovers in a time-series framework. Firstly, we introduce an explicit nonparametric measure of cross-sectional conditional tail co-movement, which is intuitively comparable to the Conditional Value-at-Risk (CoVaR). We show that nonlinear CoVaR (NCoVaR) is able to capture even highly nonlinear dependence structures. Secondly, for the purpose of potential contagion analysis, we adapt the measure to be informative about the causality direction between the variables in the Granger causality sense. By showing that the natural estimators of the two metrics are U-statistics, we construct formal nonparametric tests for independence and Granger non-causality. Numerical simulations confirm that in common situations the nonparametric tests have better size and power properties than their parametric counterparts. The methodology is illustrated empirically by assessing risk transmissions between sovereigns and banking sectors in the euro area, which observed highly irregular co-movements between asset prices after the global financial crisis. The new measures seem to be less susceptible to these irregularities than their parametric analogues, providing a clearer overview of the underlying sovereign-bank risk feedback loops.
Journal: Journal of Applied Statistics
Pages: 515-533
Issue: 3
Volume: 51
Year: 2024
Month: 02
X-DOI: 10.1080/02664763.2022.2138837
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2138837
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:3:p:515-533



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2137478_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20231214T103247 git hash: d7a2cb0857
Author-Name: Hani Samawi
Author-X-Name-First: Hani
Author-X-Name-Last: Samawi
Author-Name: Ding-Geng Chen
Author-X-Name-First: Ding-Geng
Author-X-Name-Last: Chen
Author-Name: Jingjing Yin
Author-X-Name-First: Jingjing
Author-X-Name-Last: Yin
Author-Name: Marwan Alsharman
Author-X-Name-First: Marwan
Author-X-Name-Last: Alsharman
Title: Performance of diagnostic tests based on continuous bivariate markers
Abstract: 
 In medical diagnostic research, it is customary to collect multiple continuous biomarker measures to improve the accuracy of diagnostic tests. A prevalent practice is to combine the measurements of these biomarkers into one single composite score. However, incorporating those biomarker measurements into a single score depends on the combination of methods and may lose vital information needed to make an effective and accurate decision. Furthermore, a diagnostic cut-off is required for such a combined score, and it is difficult to interpret in actual clinical practice. The paper extends the classical biomarkers’ accuracy and predictive values from univariate to bivariate markers. Also, we will develop a novel pseudo-measures system to maximize the vital information from multiple biomarkers. We specified these pseudo-and-or classifiers for the true positive rate, true negative rate, false-positive rate, and false-negative rate. We used them to redefine classical measures such as the Youden index, diagnostics odds ratio, likelihood ratios, and predictive values. We provide optimal cut-off point selection based on the modified Youden index with numerical illustrations and real data analysis for this paper's newly developed pseudo measures.
Journal: Journal of Applied Statistics
Pages: 497-514
Issue: 3
Volume: 51
Year: 2024
Month: 02
X-DOI: 10.1080/02664763.2022.2137478
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2137478
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:3:p:497-514



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2164562_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20231214T103247 git hash: d7a2cb0857
Author-Name: Archi Roy
Author-X-Name-First: Archi
Author-X-Name-Last: Roy
Author-Name: Soudeep Deb
Author-X-Name-First: Soudeep
Author-X-Name-Last: Deb
Author-Name: Divya Chakarwarti
Author-X-Name-First: Divya
Author-X-Name-Last: Chakarwarti
Title: Impact of COVID-19 on public social life and mental health: a statistical study of google trends data from the USA
Abstract: 
 The COVID-19 pandemic has caused a significant disruption in the social lives and mental health of people across the world. This study aims to asses the effect of using internet search volume data. We categorize the widely searched keywords on the internet in several categories, which are relevant in analyzing the public mental health status. Corresponding to each category of keywords, we conduct an appropriate statistical analysis to identify significant changes in the search pattern during the course of the pandemic. Binary segmentation method of changepoint detection, along with the combination of ARMA-GARCH models are utilized in this analysis. It helps us detect how people's behavior changed in phases and whether the severity of the pandemic brought forth those shifts in behaviors. Interestingly, we find that rather than the severity of the outbreak, the long duration of the pandemic has affected the public health status more. The phases, however, align well with the so-called COVID-19 waves and are consistent for different aspects of social and mental health. We further observe that the results are typically similar for different states as well.
Journal: Journal of Applied Statistics
Pages: 581-605
Issue: 3
Volume: 51
Year: 2024
Month: 02
X-DOI: 10.1080/02664763.2022.2164562
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2164562
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:3:p:581-605



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2134316_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20231214T103247 git hash: d7a2cb0857
Author-Name: Le Chen
Author-X-Name-First: Le
Author-X-Name-Last: Chen
Author-Name: Ruochen Tian
Author-X-Name-First: Ruochen
Author-X-Name-Last: Tian
Author-Name: Guanjie Chen
Author-X-Name-First: Guanjie
Author-X-Name-Last: Chen
Author-Name: Ao Yuan
Author-X-Name-First: Ao
Author-X-Name-Last: Yuan
Author-Name: Chuan-Ming Li
Author-X-Name-First: Chuan-Ming
Author-X-Name-Last: Li
Author-Name: Amy R. Bentley
Author-X-Name-First: Amy R.
Author-X-Name-Last: Bentley
Author-Name: Howard J. Hoffman
Author-X-Name-First: Howard J.
Author-X-Name-Last: Hoffman
Author-Name: Charles Rotimi
Author-X-Name-First: Charles
Author-X-Name-Last: Rotimi
Title: Semiparametric partial linear modeling of risk factors for ear infections: the Early Childhood Longitudinal Study
Abstract: 
 The Early Childhood Longitudinal Study–Kindergarten Class of 2010–2011 (ECLS-K:2011) ascertained timing of ear infections within age specified intervals and parent's/caregiver's report of medically diagnosed hearing loss. In this nationally representative, school-based sample of children followed from kindergarten entry through fifth grade, academic performance in reading, mathematics, and science was assessed longitudinally. Prior investigations of this ECLS-K:2011 cohort showed that age has a non-linear, monotonically increasing functional relationship with academic performance. Because of this knowledge, a semiparametric partial linear model is proposed, in which the effect of age is modeled by an unknown monotonically increasing function along with other regression parameters. The parameters are estimated by a semiparametric maximum likelihood estimator. A test of a constant effect of age is also proposed. Simulation studies are conducted to evaluate the performance of the proposed method, as compared with the commonly used linear model; the former outperforms the latter based on several criteria. We then analyzed ECLS-K:2011 data to compare results of the partial linear parametric model estimation with that of classical linear regression models.
Journal: Journal of Applied Statistics
Pages: 430-450
Issue: 3
Volume: 51
Year: 2024
Month: 02
X-DOI: 10.1080/02664763.2022.2134316
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2134316
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:3:p:430-450



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2136147_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20231214T103247 git hash: d7a2cb0857
Author-Name: Qi Lv
Author-X-Name-First: Qi
Author-X-Name-Last: Lv
Author-Name: Yajie Tian
Author-X-Name-First: Yajie
Author-X-Name-Last: Tian
Author-Name: Wenhao Gui
Author-X-Name-First: Wenhao
Author-X-Name-Last: Gui
Title: Statistical inference for Gompertz distribution under adaptive type-II progressive hybrid censoring
Abstract: 
 Gompertz distribution is a significant and commonly used lifetime distribution, which plays an important role in reliability engineering. In this paper, we study the statistical inference of Gompertz distribution based on adaptive Type-II hybrid progressive censored schemes. From the perspective of frequentist, we derive the point estimations through the method of maximum likelihood estimation (MLE) and the existence of MLE is proved. Besides MLE, we propose the stochastic EM algorithm to reduce complexity and simplify computing. We also apply the method of Bootstraps (Bootstrap-p and Bootstrap-t) to construct confidence intervals. From Bayesian aspect, the Bayes estimates of the unknown parameters are evaluated by applying the MCMC method, the average length and coverage rate of credible intervals are also carried out. The Bayes inference is based on the squared error loss function and LINEX loss function. Furthermore, a numerical simulation is conducted to assess the performance of the proposed methods. Finally, a real-life example is considered to illustrate the application and development of the inference methods. In summary, the Bayesian method seems to perform the best among all approaches, while other approaches also present different advantages.
Journal: Journal of Applied Statistics
Pages: 451-480
Issue: 3
Volume: 51
Year: 2024
Month: 02
X-DOI: 10.1080/02664763.2022.2136147
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2136147
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:3:p:451-480



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2137477_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20231214T103247 git hash: d7a2cb0857
Author-Name: Han Yu
Author-X-Name-First: Han
Author-X-Name-Last: Yu
Author-Name: Alan D. Hutson
Author-X-Name-First: Alan D.
Author-X-Name-Last: Hutson
Title: Inferential procedures based on the weighted Pearson correlation coefficient test statistic
Abstract: 
 In this note, we evaluated the type I error control of the commonly used t-test found in most statistical software packages for testing the hypothesis on 
 $ H_0: \rho = 0 $ H0:ρ=0 vs. 
 $ H_1: \rho > 0 $ H1:ρ>0 based on the sample weighted Pearson correlation coefficient. We found the type I error rate is severely inflated in general cases, even under bivariate normality. To address this issue, we derived the large sample variance of the weighted Pearson correlation. Based on this result, we proposed an asymptotic test and a set of studentized permutation tests. A comprehensive set of simulation studies with a range of sample sizes and a variety of underlying distributions were conducted. The studentized permutation test based on Fisher's Z statistic was shown to robustly control the type I error even in the small sample and non-normality settings. The method was demonstrated with an example data of country-level preterm birth rates.
Journal: Journal of Applied Statistics
Pages: 481-496
Issue: 3
Volume: 51
Year: 2024
Month: 02
X-DOI: 10.1080/02664763.2022.2137477
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2137477
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:3:p:481-496



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2142537_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20231214T103247 git hash: d7a2cb0857
Author-Name: Ting Zeng
Author-X-Name-First: Ting
Author-X-Name-Last: Zeng
Author-Name: Solomon W. Harrar
Author-X-Name-First: Solomon W.
Author-X-Name-Last: Harrar
Title: Robust tests for multivariate repeated measures with small samples
Abstract: 
 Multivariate repeated measures data naturally arise in clinical trials and other fields such as biomedical science, public health, agriculture, social science and so on. For data of this type, the classical approach is to conduct multivariate analysis of variance (MANOVA) based on Wilks' Lambda and other multivariate statistics, which require the assumptions of multivariate normality and homogeneity of within-cell covariance matrices. However, data being analyzed nowadays show marked departure from multivariate normality and homogeneity. This paper proposes a finite-sample test by modifying the sums of squares matrices to make them insensitive to the heterogeneity in MANOVA. The proposed test is invariant to affine transformation and robust against nonnormality. The proposed method can be used in various experimental designs, for example, factorial design and crossover design. Under various simulation settings, the proposed method outperforms the classical Doubly Multivariate Model and Multivariate Mixed Model proposed elsewhere, especially for unbalanced sample sizes with heteroscedasticity. The applications of the proposed method are illustrated with ophthalmology data in factorial and crossover designs. The proposed method successfully identified and validated a significant main effect and demonstrated that univariate analysis could be oversensitive to small but clinically unimportant interactions.
Journal: Journal of Applied Statistics
Pages: 555-580
Issue: 3
Volume: 51
Year: 2024
Month: 02
X-DOI: 10.1080/02664763.2022.2142537
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2142537
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:3:p:555-580



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2140332_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20231214T103247 git hash: d7a2cb0857
Author-Name: Helmi Shat
Author-X-Name-First: Helmi
Author-X-Name-Last: Shat
Title: Optimal design of stress levels in accelerated degradation testing for multivariate linear degradation models
Abstract: 
 In recent years, more attention has been paid prominently to accelerated degradation testing (ADT) in order to characterize accurate estimation of reliability properties for systems that are designed to work properly for years or even decades. In this paper, we propose optimal experimental designs for repeated measures ADTs with competing failure modes that correspond to multiple response components. The marginal degradation paths are expressed using linear mixed effects models. The optimal design is obtained by minimizing the asymptotic variance of the estimator of some quantile of the failure time distribution at the normal use conditions. Numerical examples are introduced to ensure the robustness of the proposed optimal designs and compare their efficiency with standard experimental designs.
Journal: Journal of Applied Statistics
Pages: 534-554
Issue: 3
Volume: 51
Year: 2024
Month: 02
X-DOI: 10.1080/02664763.2022.2140332
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2140332
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:3:p:534-554

Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2153812_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a
Author-Name: Fernando Ferraz do Nascimento
Author-X-Name-First: Fernando Ferraz do
Author-X-Name-Last: Nascimento
Author-Name: Aline Raquel Assunção Nunes
Author-X-Name-First: Aline Raquel
Author-X-Name-Last: Assunção Nunes
Title: Regression models for the full distribution to exceedance data
Abstract: 
 The list of occurrences linked to significant climate change has grown in recent decades. These changes can be influenced by a set of covariates, such as temperature, location and period of the year. Analyzing the relation among elements and factors that influence the behavior of such events is extremely important for decision-making in order to minimize damages and losses. Exceedance analysis uses the tail of the distribution based on Extreme Value Theory (EVT). Extensions for these models have been proposed in literature, such as regression models for the tail parameters and a parametric or semi-parametric distribution for the part that comes before the tail (well known as bulk distribution). This work presents a new extension to exceedance model, in which the parameters for the bulk distribution capture the effect of covariates such as location and seasonality. We considered a Bayesian approach in the inference procedure. The estimation was done using MCMC -- Markov Chain Monte Carlo methods. Application results for modeling maximum and minimum temperature data showed an efficient estimation of extreme quantiles and a predictive advantage compared to models previously used in literature.
Journal: Journal of Applied Statistics
Pages: 701-720
Issue: 4
Volume: 51
Year: 2024
Month: 03
X-DOI: 10.1080/02664763.2022.2153812
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2153812
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:4:p:701-720



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2145272_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a
Author-Name: Zihang Zhong
Author-X-Name-First: Zihang
Author-X-Name-Last: Zhong
Author-Name: Min Yang
Author-X-Name-First: Min
Author-X-Name-Last: Yang
Author-Name: Senmiao Ni
Author-X-Name-First: Senmiao
Author-X-Name-Last: Ni
Author-Name: Lixin Cai
Author-X-Name-First: Lixin
Author-X-Name-Last: Cai
Author-Name: Jingwei Wu
Author-X-Name-First: Jingwei
Author-X-Name-Last: Wu
Author-Name: Jianling Bai
Author-X-Name-First: Jianling
Author-X-Name-Last: Bai
Author-Name: Hao Yu
Author-X-Name-First: Hao
Author-X-Name-Last: Yu
Title: The heterogeneity effect of surveillance intervals on progression free survival
Abstract: 
 Progression-free survival (PFS) is an increasingly important surrogate endpoint in cancer clinical trials. However, the true time of progression is typically unknown if the evaluation of progression status is only scheduled at given surveillance intervals. In addition, comparison between treatment arms under different surveillance schema is not uncommon. Our aim is to explore whether the heterogeneity of the surveillance intervals may interfere with the validity of the conclusion of efficacy based on PFS, and the extent to which the variation would bias the results. We conduct comprehensive simulation studies to explore the aforementioned goals in a two-arm randomized control trial. We introduce three steps to simulate survival data with predefined surveillance intervals under different censoring rate considerations. We report the estimated hazard ratios and examine false positive rate, power and bias under different surveillance intervals, given different baseline median PFS, hazard ratio and censoring rate settings. Results show that larger heterogeneous lengths of surveillance intervals lead to higher false positive rate and overestimate the power, and the effect of the heterogeneous surveillance intervals may depend upon both the life expectancy of the tumor prognoses and the censoring proportion of the survival data. We also demonstrate such heterogeneity effect of surveillance intervals on PFS in a phase III metastatic colorectal cancer trial. In our opinions, adherence to consistent surveillance intervals should be favored in designing the comparative trials. Otherwise, it needs to be appropriately taken into account when analyzing data.
Journal: Journal of Applied Statistics
Pages: 646-663
Issue: 4
Volume: 51
Year: 2024
Month: 03
X-DOI: 10.1080/02664763.2022.2145272
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2145272
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:4:p:646-663



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2156485_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a
Author-Name: Emiliano Geneyro
Author-X-Name-First: Emiliano
Author-X-Name-Last: Geneyro
Author-Name: Gabriel Núñez-Antonio
Author-X-Name-First: Gabriel
Author-X-Name-Last: Núñez-Antonio
Title: A Bayesian nonparametric model for bounded directional data on the positive orthant of the unit sphere
Abstract: 
 Directional data appears in several branches of research. In some cases, those directional variables are only defined in subsets of the K-dimensional unit sphere. For example, in some applications, angles as measured responses are limited on the positive orthant. Analysis on subsets of the K-dimensional unit sphere is challenging and nowadays there are not many proposals that discuss this topic. Thus, from a methodological point of view, it is important to have probability distributions defined on bounded subsets of the K-dimensional unit sphere. Specifically, in this paper, we introduce a nonparametric Bayesian model to describe directional variables restricted to the first orthant. This model is based on a Dirichlet process mixture model with multivariate projected Gamma densities as kernel distributions. We show how to carry out inference for the proposed model based on a slice sampling scheme. The proposed methodology is illustrated using simulated data sets as well as a real data set.
Journal: Journal of Applied Statistics
Pages: 721-739
Issue: 4
Volume: 51
Year: 2024
Month: 03
X-DOI: 10.1080/02664763.2022.2156485
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2156485
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:4:p:721-739



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2151989_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a
Author-Name: Padma Sharma
Author-X-Name-First: Padma
Author-X-Name-Last: Sharma
Title: Selection of random coefficients in ordered response models: a framework to detect heterogeneity in household surveys
Abstract: 
 This paper develops a Bayesian method to detect heterogeneity in the relationship between covariates and the outcome in models with ordered responses. To this end, we construct an efficient Markov chain Monte Carlo algorithm for a hierarchical Bayesian model that selects random coefficients in ordered models. This method extends an approach for selecting random coefficients in linear mixed models into the ordered setting by adding two enhancements that are relevant to the latter category of models. First, we construct steps to efficiently estimate cut-points by addressing identification and ordering constraints. Second, we develop a framework to evaluate marginal effects that combine the fixed and random effects of each covariate. The marginal effects additionally allow for model uncertainty by averaging across models visited by the selection algorithm. Simulation studies demonstrate that this method detects random effects when they are present, estimates parameters accurately and efficiently samples from the posterior with low autocorrelations across successive draws. On applying this method on data from the survey of consumer expectations, we find clear support for the presence of household-level heterogeneity in relationships between demographic variables, and current as well as expected financial conditions.
Journal: Journal of Applied Statistics
Pages: 682-700
Issue: 4
Volume: 51
Year: 2024
Month: 03
X-DOI: 10.1080/02664763.2022.2151989
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2151989
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:4:p:682-700



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2178641_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a
Author-Name: J. de Uña-Álvarez
Author-X-Name-First: J.
Author-X-Name-Last: de Uña-Álvarez
Author-Name: A. I. Martínez-Senra
Author-X-Name-First: A. I.
Author-X-Name-Last: Martínez-Senra
Author-Name: M. S. Otero-Giráldez
Author-X-Name-First: M. S.
Author-X-Name-Last: Otero-Giráldez
Author-Name: M. A. Quintás
Author-X-Name-First: M. A.
Author-X-Name-Last: Quintás
Title: Cox regression with doubly truncated responses and time-dependent covariates: the impact of innovation on firm survival
Abstract: 
 The creation of new firms is an important incentive for the economic growth of a country, since it generates employment, it encourages the competition, and promotes innovation. In this work, we investigate the survival of Spanish firms which were created since 2001 and closed down between 2004 and 2012. The information was gathered from Technological Innovation Panel (PITEC), a survey with a focus the technological innovation in Spanish firms. In particular, a Cox regression model with time-dependent covariates was used in order to identify and quantify the determinants of the risk of exit for the firm. The selection bias due to the interval sampling for the firms was corrected by using methods for doubly truncated lifetimes. Interestingly, it is seen how the correction for the selection bias changes both the size and the statistical significance of the effects provided by standard Cox regression.
Journal: Journal of Applied Statistics
Pages: 780-792
Issue: 4
Volume: 51
Year: 2024
Month: 03
X-DOI: 10.1080/02664763.2023.2178641
File-URL: http://hdl.handle.net/10.1080/02664763.2023.2178641
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:4:p:780-792



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2143484_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a
Author-Name: Wentao Ge
Author-X-Name-First: Wentao
Author-X-Name-Last: Ge
Author-Name: Junfeng Shang
Author-X-Name-First: Junfeng
Author-X-Name-Last: Shang
Title: Bootstrap-adjusted quasi-likelihood information criteria for mixed model selection
Abstract: 
 We propose two model selection criteria relying on the bootstrap approach, denoted by QAICb1 and QAICb2, in the framework of linear mixed models. Similar to the justification of Akaike Information Criterion (AIC), the proposed QAICb1 and QAICb2 are proved as asymptotically unbiased estimators of the Kullback–Leibler discrepancy between a candidate model and the true model. However, they are defined on the quasi-likelihood function instead of the likelihood and are proven to be asymptotically equivalent. The proposed selection criteria are constructed by the quasi-likelihood of a candidate model and a bias estimation term in which the bootstrap method is adopted to improve the estimation for the bias caused by using the candidate model to estimate the true model. The simulations across a variety of mixed model settings are conducted to demonstrate that the proposed selection criteria outperform some other existing model selection criteria in selecting the true model. Generalized estimating equations (GEE) are utilized to calculate QAICb1 and QAICb2 in the simulations. The effectiveness of the proposed selection criteria is also demonstrated in an application of Parkinson's Progression Markers Initiative (PPMI) data.
Journal: Journal of Applied Statistics
Pages: 621-645
Issue: 4
Volume: 51
Year: 2024
Month: 03
X-DOI: 10.1080/02664763.2022.2143484
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2143484
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:4:p:621-645



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2161488_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a
Author-Name: Lin Luo
Author-X-Name-First: Lin
Author-X-Name-Last: Luo
Author-Name: Jinzhao Yu
Author-X-Name-First: Jinzhao
Author-X-Name-Last: Yu
Author-Name: Hui Zhao
Author-X-Name-First: Hui
Author-X-Name-Last: Zhao
Title: The sparse estimation of the semiparametric linear transformation model with dependent current status data
Abstract: 
 In this paper, we study the sparse estimation under the semiparametric linear transformation models for the current status data, also called type I interval-censored data. For the problem, the failure time of interest may be dependent on the censoring time and the association parameter between them is left unspecified. To address this, we employ the copula model to describe the dependence between them and a two-stage estimation procedure to estimate both the association parameter and the regression parameter. In addition, we propose a penalized maximum likelihood estimation procedure based on the broken adaptive ridge regression, and Bernstein polynomials are used to approximate the nonparametric functions involved. The oracle property of the proposed method is established and the numerical studies suggest that the method works well for practical situations. Finally, the method is applied to an Alzheimer's disease study that motivated this investigation.
Journal: Journal of Applied Statistics
Pages: 759-779
Issue: 4
Volume: 51
Year: 2024
Month: 03
X-DOI: 10.1080/02664763.2022.2161488
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2161488
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:4:p:759-779



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2146661_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a
Author-Name: Roberto Vila
Author-X-Name-First: Roberto
Author-X-Name-Last: Vila
Author-Name: Lucas Alfaia
Author-X-Name-First: Lucas
Author-X-Name-Last: Alfaia
Author-Name: André F.B. Menezes
Author-X-Name-First: André F.B.
Author-X-Name-Last: Menezes
Author-Name: Mehmet N. Çankaya
Author-X-Name-First: Mehmet N.
Author-X-Name-Last: Çankaya
Author-Name: Marcelo Bourguignon
Author-X-Name-First: Marcelo
Author-X-Name-Last: Bourguignon
Title: A model for bimodal rates and proportions
Abstract: 
 The beta model is the most important distribution for fitting data with the unit interval. However, the beta distribution is not suitable to model bimodal unit interval data. In this paper, we propose a bimodal beta distribution constructed by using an approach based on the alpha-skew-normal model. We discuss several properties of this distribution, such as bimodality, real moments, entropies and identifiability. Furthermore, we propose a new regression model based on the proposed model and discuss residuals. Estimation is performed by maximum likelihood. A Monte Carlo experiment is conducted to evaluate the performances of these estimators in finite samples with a discussion of the results. An application is provided to show the modelling competence of the proposed distribution when the data sets show bimodality.
Journal: Journal of Applied Statistics
Pages: 664-681
Issue: 4
Volume: 51
Year: 2024
Month: 03
X-DOI: 10.1080/02664763.2022.2146661
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2146661
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:4:p:664-681



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2192445_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a
Author-Name: Katherine Vorpe
Author-X-Name-First: Katherine
Author-X-Name-Last: Vorpe
Author-Name: Sierra Hessinger
Author-X-Name-First: Sierra
Author-X-Name-Last: Hessinger
Author-Name: Rebekah Poth
Author-X-Name-First: Rebekah
Author-X-Name-Last: Poth
Author-Name: Tatjana Miljkovic
Author-X-Name-First: Tatjana
Author-X-Name-Last: Miljkovic
Title: Clustering regions with dynamic time warping to model obesity prevalence disparities in the United States
Abstract: 
 Current methods for clustering adult obesity prevalence by state focus on creating a single map of obesity prevalence for a given year in the United States. Comparing these maps for different years may limit our understanding of the progression of state and regional obesity prevalence over time for the purpose of developing targeted regional health policies. In this application note, we adopt the non-parametric Dynamic Time Warping method for clustering longitudinal time series of obesity prevalence by state. This method captures the lead and lag relationship between the time series as part of the temporal alignment, allowing us to produce a single map that captures the regional and temporal clusters of obesity prevalence from 1990 to 2019 in the United States. We identify six regions of obesity prevalence in the United States and forecast future estimates of obesity prevalence based on ARIMA models.
Journal: Journal of Applied Statistics
Pages: 793-807
Issue: 4
Volume: 51
Year: 2024
Month: 03
X-DOI: 10.1080/02664763.2023.2192445
File-URL: http://hdl.handle.net/10.1080/02664763.2023.2192445
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:4:p:793-807



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2159339_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a
Author-Name: D. Scaldelai
Author-X-Name-First: D.
Author-X-Name-Last: Scaldelai
Author-Name: L. C. Matioli
Author-X-Name-First: L. C.
Author-X-Name-Last: Matioli
Author-Name: S. R. Santos
Author-X-Name-First: S. R.
Author-X-Name-Last: Santos
Title: TreeKDE: clustering multivariate data based on decision tree and using one-dimensional kernel density estimation
Abstract: 
 In this paper, we present an algorithm for clustering multidimensional data, which we named TreeKDE. It is based on a tree structure decision associated with the optimization of the one-dimensional kernel density estimator function constructed from the orthogonal projections of the data on the coordinate axes. Among the main features of the proposed algorithm, we highlight the automatic determination of the number of clusters and their insertion in a rectangular region. Comparative numerical experiments are presented to illustrate the performance of the proposed algorithm and the results indicate that the TreeKDE is efficient and competitive when compared to other algorithms from the literature. Features such as simplicity and efficiency make the proposed algorithm an attractive and promising research field, which can be used as a basis for its improvement, and also for the development of new clustering algorithms based on the association between decision tree and kernel density estimator.
Journal: Journal of Applied Statistics
Pages: 740-758
Issue: 4
Volume: 51
Year: 2024
Month: 03
X-DOI: 10.1080/02664763.2022.2159339
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2159339
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:4:p:740-758

Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2170991_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a
Author-Name: Yifan Li
Author-X-Name-First: Yifan
Author-X-Name-Last: Li
Author-Name: Chunjie Wu
Author-X-Name-First: Chunjie
Author-X-Name-Last: Wu
Author-Name: Zhijun Wang
Author-X-Name-First: Zhijun
Author-X-Name-Last: Wang
Author-Name: Zhiming Hu
Author-X-Name-First: Zhiming
Author-X-Name-Last: Hu
Title: Aggregated parameter update schemes for monitoring binary profiles
Abstract: 
 Profile monitoring is one of the most important topics for statistical process control. Traditional self-starting profile monitoring schemes generally use all historical observations to estimate parameters. Because of the rapid increase in the complexity of modern statistical processes, the practitioners often need to deal with massive datasets in process monitoring. However, when observations of each period are of large sample size and the computation is of high complexity, the traditional method is not economical and urgently needs a parameter update strategy. Under the framework of binary profile monitoring, this paper proposes a novel recursive update strategy based on the aggregated estimation equation (AEE) for massive datasets and designs a self-starting control chart accordingly. Numerical simulation verifies that the proposed method performs better in parameter estimation and process monitoring. In addition, we give the asymptotic property of the proposed monitoring statistic and illustrate our method's superiority by a real-data example.
Journal: Journal of Applied Statistics
Pages: 935-957
Issue: 5
Volume: 51
Year: 2024
Month: 04
X-DOI: 10.1080/02664763.2023.2170991
File-URL: http://hdl.handle.net/10.1080/02664763.2023.2170991
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:5:p:935-957



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2207786_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a
Author-Name: Achmad Choiruddin
Author-X-Name-First: Achmad
Author-X-Name-Last: Choiruddin
Author-Name: Tabita Yuni Susanto
Author-X-Name-First: Tabita
Author-X-Name-Last: Yuni Susanto
Author-Name: Ahmad Husain
Author-X-Name-First: Ahmad
Author-X-Name-Last: Husain
Author-Name: Yuniar Mega Kartikasari
Author-X-Name-First: Yuniar
Author-X-Name-Last: Mega Kartikasari
Title: kppmenet: combining the kppm and elastic net regularization for inhomogeneous Cox point process with correlated covariates
Abstract: 
 The 
 $ \mathtt {kppm} $ kppm is a standard procedure to estimate the parameters of the inhomogeneous Cox point process. However, the procedure cannot handle the problem when the models involve correlated covariates. In this study, we develop the 
 $ \mathtt {kppmenet} $ kppmenet, the modified version of the 
 $ \mathtt {kppm} $ kppm, for the inhomogeneous Cox point process involving correlated covariates by considering elastic net regularization. We compare the methodology in a simulation study and apply it to model major-shallow earthquake distribution in Sumatra, Indonesia. We conclude that the 
 $ \mathtt {kppmenet} $ kppmenet outperforms 
 $ \mathtt {kppm} $ kppm when correlated covariates are involved.
Journal: Journal of Applied Statistics
Pages: 993-1006
Issue: 5
Volume: 51
Year: 2024
Month: 04
X-DOI: 10.1080/02664763.2023.2207786
File-URL: http://hdl.handle.net/10.1080/02664763.2023.2207786
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:5:p:993-1006



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2164561_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a
Author-Name: Osafu Augustine Egbon
Author-X-Name-First: Osafu Augustine
Author-X-Name-Last: Egbon
Author-Name: Ezra Gayawan
Author-X-Name-First: Ezra
Author-X-Name-Last: Gayawan
Title: Modeling the spatial patterns of antenatal care utilization in Nigeria with inference based on Pólya-Gamma mixtures
Abstract: 
 Despite the vast advantages of making antenatal care visits, the service utilization among pregnant women in Nigeria is suboptimal. A five-year monitoring estimate indicated that about 24% of the women who had live births made no visit. The non-utilization induced excessive zeroes in the outcome of interest. Thus, this study adopted a zero-inflated negative binomial model within a Bayesian framework to identify the spatial pattern and the key factors hindering antenatal care utilization in Nigeria. We overcome the intractability associated with posterior inference by adopting a Pólya-Gamma data-augmentation technique to facilitate inference. The Gibbs sampling algorithm was used to draw samples from the joint posterior distribution. Results revealed that type of place of residence, maternal level of education, access to mass media, household work index, and woman's working status have significant effects on the use of antenatal care services. Findings identified substantial state-level spatial disparity in antenatal care utilization across the country. Cost-effective techniques to achieve an acceptable frequency of utilization include the creation of a community-specific awareness to emphasize the importance and benefits of the appropriate utilization. Special consideration should be given to older pregnant women, women in poor antenatal utilization states, and women residing in poor road network regions.
Journal: Journal of Applied Statistics
Pages: 866-890
Issue: 5
Volume: 51
Year: 2024
Month: 04
X-DOI: 10.1080/02664763.2022.2164561
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2164561
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:5:p:866-890



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2229973_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a
Author-Name: Julio C. S. Vasconcelos
Author-X-Name-First: Julio C. S.
Author-X-Name-Last: Vasconcelos
Author-Name: Thiago da Costa Travassos
Author-X-Name-First: Thiago da Costa
Author-X-Name-Last: Travassos
Author-Name: Edwin M. M. Ortega
Author-X-Name-First: Edwin M. M.
Author-X-Name-Last: Ortega
Author-Name: Gauss M. Cordeiro
Author-X-Name-First: Gauss M.
Author-X-Name-Last: Cordeiro
Author-Name: Leonardo Oliveira Reis
Author-X-Name-First: Leonardo
Author-X-Name-Last: Oliveira Reis
Title: Alternative statistical modeling for radical prostatectomy data
Abstract: 
 Several statistical models have been proposed in recent years, among them is the semiparametric regression. In medicine, there are several situations in which it is impracticable to consider a linear regression for statistical modeling, especially when the data contain explanatory variables that present a nonlinear relationship with the response variable. Another common situation is when the response variable does not have a unimodal shape, and it is not possible to adopt distributions belonging to the symmetric or asymmetric classes. In this context, a semiparametric heteroskedastic regression is proposed based on an extension of the normal distribution. Then, we show the usefulness of this model to analyze the cost of prostate cancer surgery. The predictor variables refer to two groups of patients such that one group receives a multimodal local anesthetic solution (Preemptive Target Anesthetic Solution) and the second group is treated with neuraxial blockade (spinal anesthesia/traditional standard). The other relevant predictor variables are also evaluated, thus allowing for the in-depth interpretation of the predictor variables with a nonlinear effect on the dependent variable cost. The penalized maximum likelihood method is adopted to estimate the model parameters. The new regression is a useful statistical tool for analyzing medical data.
Journal: Journal of Applied Statistics
Pages: 1007-1022
Issue: 5
Volume: 51
Year: 2024
Month: 04
X-DOI: 10.1080/02664763.2023.2229973
File-URL: http://hdl.handle.net/10.1080/02664763.2023.2229973
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:5:p:1007-1022



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2172143_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a
Author-Name: Pedro Henrique T. O. Sousa
Author-X-Name-First: Pedro Henrique T. O.
Author-X-Name-Last: Sousa
Author-Name: Camila P. E. de Souza
Author-X-Name-First: Camila P. E.
Author-X-Name-Last: de Souza
Author-Name: Ronaldo Dias
Author-X-Name-First: Ronaldo
Author-X-Name-Last: Dias
Title: Bayesian adaptive selection of basis functions for functional data representation
Abstract: 
 Considering the context of functional data analysis, we developed and applied a new Bayesian approach via the Gibbs sampler to select basis functions for a finite representation of functional data. The proposed methodology uses Bernoulli latent variables to assign zero to some of the basis function coefficients with a positive probability. This procedure allows for an adaptive basis selection since it can determine the number of bases and which ones should be selected to represent functional data. Moreover, the proposed procedure measures the uncertainty of the selection process and can be applied to multiple curves simultaneously. The methodology developed can deal with observed curves that may differ due to experimental error and random individual differences between subjects, which one can observe in a real dataset application involving daily numbers of COVID-19 cases in Brazil. Simulation studies show the main properties of the proposed method, such as its accuracy in estimating the coefficients and the strength of the procedure to find the true set of basis functions. Despite having been developed in the context of functional data analysis, we also compared the proposed model via simulation with the well-established LASSO and Bayesian LASSO, which are methods developed for non-functional data.
Journal: Journal of Applied Statistics
Pages: 958-992
Issue: 5
Volume: 51
Year: 2024
Month: 04
X-DOI: 10.1080/02664763.2023.2172143
File-URL: http://hdl.handle.net/10.1080/02664763.2023.2172143
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:5:p:958-992



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2170336_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a
Author-Name: Muhammad Imran
Author-X-Name-First: Muhammad
Author-X-Name-Last: Imran
Author-Name: Jinsheng Sun
Author-X-Name-First: Jinsheng
Author-X-Name-Last: Sun
Author-Name: Xuelong Hu
Author-X-Name-First: Xuelong
Author-X-Name-Last: Hu
Author-Name: Fatima Sehar Zaidi
Author-X-Name-First: Fatima Sehar
Author-X-Name-Last: Zaidi
Author-Name: Anan Tang
Author-X-Name-First: Anan
Author-X-Name-Last: Tang
Title: Investigating zero-state and steady-state performance of MEWMA-CoDa control chart using variable sampling interval
Abstract: 
 Traditional process monitoring control charts (CCs) focused on sampling methods using fixed sampling intervals (
 $ \mathrm {FSI} $ FSIs). The variable sampling intervals (
 $ \mathrm {VSI} $ VSIs) scheme is receiving increasing attention, in which the sampling interval (
 $ \mathrm {SI} $ SI) length varies according to the process monitoring statistics. A shorter 
 $ \mathrm {SI} $ SI is considered when the process quality indicates the possibility of an out-of-control (OOC) situation; otherwise, a longer 
 $ \mathrm {SI} $ SI is preferred. The 
 $ \mathrm {VSI} $ VSI multivariate exponentially moving average for compositional data (
 $ \mathrm {VSI} $ VSI-
 $ \mathrm {MEWMA}\,\mathrm {CoDa} $ MEWMACoDa) CC based on a coordinate representation using isometric log-ratio (
 $ \operatorname {ilr} $ ilr) transformation is proposed in this study. A methodology is proposed to obtain the optimal parameters by considering the zero-state (
 $ \mathrm {ZS} $ ZS) average time to signal (
 $ \mathrm {ZATS} $ ZATS) and the steady-state (SS) average time to signal (
 $ \mathrm {SATS} $ SATS). The statistical performance of the proposed CC is evaluated based on a continuous-time Markov chain (
 $ \mathrm {CTMC} $ CTMC) method for both cases, the 
 $ \mathrm {ZS} $ ZS and the SS using a fixed value of in-control (IC) 
 $ \mathrm {ATS}_0 $ ATS0. Simulation results demonstrate that the 
 $ \mathrm {VSI} $ VSI-
 $ \mathrm {MEWMA}\,\mathrm {CoDa} $ MEWMACoDa CC has significantly decreased the OOC average time to signal (
 $ \mathrm {ATS} $ ATS) than the 
 $ \mathrm {FSI}\,\mathrm {MEWMA}\,\mathrm {CoDa} $ FSIMEWMACoDa CC. Moreover, it is found that the number of variables (d) has a negative impact on the 
 $ \mathrm {ATS} $ ATS of the 
 $ \mathrm {VSI} $ VSI-
 $ \mathrm {MEWMA}\,\mathrm {CoDa} $ MEWMACoDa CC, and the subgroup size (n) has a mildly positive impact on the 
 $ \mathrm {ATS} $ ATS of the 
 $ \mathrm {VSI} $ VSI-
 $ \mathrm {MEWMA}\,\mathrm {CoDa} $ MEWMACoDa CC. At the same time, the 
 $ \mathrm {SATS} $ SATS of the 
 $ \mathrm {VSI} $ VSI-
 $ \mathrm {MEWMA}\,\mathrm {CoDa} $ MEWMACoDa CC is less than the 
 $ \mathrm {ZATS} $ ZATS of the 
 $ \mathrm {VSI} $ VSI-
 $ \mathrm {MEWMA}\,\mathrm {CoDa} $ MEWMACoDa CC for all the values of n and d. The proposed 
 $ \mathrm {VSI} $ VSI-
 $ \mathrm {MEWMA}\,\mathrm {CoDa} $ MEWMACoDa CC under steady-State performs effectively compared to its competitors, such as the 
 $ \mathrm {FSI} $ FSI-
 $ \mathrm {MEWMA}\,\mathrm {CoDa} $ MEWMACoDa CC, the 
 $ \mathrm {VSI} $ VSI-
 $ T^2\,\mathrm {CoDa} $ T2CoDa CC and the 
 $ \mathrm {FSI} $ FSI-
 $ T^2\,\mathrm {CoDa} $ T2CoDa CC. An example of an industrial problem from a plant in Europe is also given to study the statistical significance of the 
 $ \mathrm {VSI} $ VSI-
 $ \mathrm {MEWMA}\,\mathrm {CoDa} $ MEWMACoDa CC.
Journal: Journal of Applied Statistics
Pages: 913-934
Issue: 5
Volume: 51
Year: 2024
Month: 04
X-DOI: 10.1080/02664763.2023.2170336
File-URL: http://hdl.handle.net/10.1080/02664763.2023.2170336
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:5:p:913-934



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2163229_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a
Author-Name: F. Prataviera
Author-X-Name-First: F.
Author-X-Name-Last: Prataviera
Author-Name: E. M. Hashimoto
Author-X-Name-First: E. M.
Author-X-Name-Last: Hashimoto
Author-Name: E. M. M. Ortega
Author-X-Name-First: E. M. M.
Author-X-Name-Last: Ortega
Author-Name: G. M. Cordeiro
Author-X-Name-First: G. M.
Author-X-Name-Last: Cordeiro
Author-Name: V. G. Cancho
Author-X-Name-First: V. G.
Author-X-Name-Last: Cancho
Author-Name: R. Vila
Author-X-Name-First: R.
Author-X-Name-Last: Vila
Title: A new flexible regression model with application to recovery probability Covid-19 patients
Abstract: 
 The aim of this study is to propose a generalized odd log-logistic Maxwell mixture model to analyze the effect of gender and age groups on lifetimes and on the recovery probabilities of Chinese individuals with COVID-19. We add new properties of the generalized Maxwell model. The coefficients of the regression and the recovered fraction are estimated by maximum likelihood and Bayesian methods. Further, some simulation studies are done to compare the regressions for different scenarios. Model-checking techniques based on the quantile residuals are addressed. The estimated survival functions for the patients are reported by age range and sex. The simulation study showed that mean squared errors decay toward zero and the average estimates converge to the true parameters when sample size increases. According to the fitted model, there is a significant difference only in the age group on the lifetime of individuals with COVID-19. Women have higher probability of recovering than men and individuals aged 
 $ \geq $ ≥60 years have lower recovered probabilities than those who aged 
 $ {<}60 $ <60 years. The findings suggest that the proposed model could be a good alternative to analyze censored lifetime of individuals with COVID-19.
Journal: Journal of Applied Statistics
Pages: 826-844
Issue: 5
Volume: 51
Year: 2024
Month: 04
X-DOI: 10.1080/02664763.2022.2163229
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2163229
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:5:p:826-844



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2163379_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a
Author-Name: Abdul Salam
Author-X-Name-First: Abdul
Author-X-Name-Last: Salam
Author-Name: Marco Grzegorczyk
Author-X-Name-First: Marco
Author-X-Name-Last: Grzegorczyk
Title: Learning the structure of the mTOR protein signaling pathway from protein phosphorylation data
Abstract: 
 Statistical learning of the structures of cellular networks, such as protein signaling pathways, is a topical research field in computational systems biology. To get the most information out of experimental data, it is often required to develop a tailored statistical approach rather than applying one of the off-the-shelf network reconstruction methods. The focus of this paper is on learning the structure of the mTOR protein signaling pathway from immunoblotting protein phosphorylation data. Under two experimental conditions eleven phosphorylation sites of eight key proteins of the mTOR pathway were measured at ten non-equidistant time points. For the statistical analysis we propose a new advanced hierarchically coupled non-homogeneous dynamic Bayesian network (NH-DBN) model, and we consider various data imputation methods for dealing with non-equidistant temporal observations. Because of the absence of a true gold standard network, we propose to use predictive probabilities in combination with a leave-one-out cross validation strategy to objectively cross-compare the accuracies of different NH-DBN models and data imputation methods. Finally, we employ the best combination of model and data imputation method for predicting the structure of the mTOR protein signaling pathway.
Journal: Journal of Applied Statistics
Pages: 845-865
Issue: 5
Volume: 51
Year: 2024
Month: 04
X-DOI: 10.1080/02664763.2022.2163379
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2163379
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:5:p:845-865



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2162863_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a
Author-Name: Dong Han
Author-X-Name-First: Dong
Author-X-Name-Last: Han
Author-Name: Fugee Tsung
Author-X-Name-First: Fugee
Author-X-Name-Last: Tsung
Author-Name: Lei Qiao
Author-X-Name-First: Lei
Author-X-Name-Last: Qiao
Title: The optimal CUSUM control chart with a dynamic non-random control limit and a given sampling strategy for small samples sequence
Abstract: 
 This article proposes a performance measure to evaluate the detection performance of a control chart with a given sampling strategy for finite or small samples sequence and prove that the CUSUM control chart with dynamic non-random control limit and a given sampling strategy can be optimal under the measure. Numerical simulations and real data for an earthquake are provided to illustrate that for different sampling strategies, the CUSUM chart will have different monitoring performance in change-point detection. Among the six sampling strategies that take only a part of samples, the numerical comparing results illustrate that the uniform sampling strategy (uniformly dispersed sampling strategy) has the best monitoring effect.
Journal: Journal of Applied Statistics
Pages: 809-825
Issue: 5
Volume: 51
Year: 2024
Month: 04
X-DOI: 10.1080/02664763.2022.2162863
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2162863
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:5:p:809-825



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2164759_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a
Author-Name: Chathura Siriwardhana
Author-X-Name-First: Chathura
Author-X-Name-Last: Siriwardhana
Author-Name: K.B. Kulasekera
Author-X-Name-First: K.B.
Author-X-Name-Last: Kulasekera
Author-Name: Somnath Datta
Author-X-Name-First: Somnath
Author-X-Name-Last: Datta
Title: Selection of the optimal personalized treatment from multiple treatments with right-censored multivariate outcome measures
Abstract: 
 We propose a novel personalized concept for the optimal treatment selection for a situation where the response is a multivariate vector that could contain right-censored variables such as survival time. The proposed method can be applied with any number of treatments and outcome variables, under a broad set of models. Following a working semiparametric Single Index Model that relates covariates and responses, we first define a patient-specific composite score, constructed from individual covariates. We then estimate conditional means of each response, given the patient score, correspond to each treatment, using a nonparametric smooth estimator. Next, a rank aggregation technique is applied to estimate an ordering of treatments based on ranked lists of treatment performance measures given by conditional means. We handle the right-censored data by incorporating the inverse probability of censoring weighting to the corresponding estimators. An empirical study illustrates the performance of the proposed method in finite sample problems. To show the applicability of the proposed procedure for real data, we also present a data analysis using HIV clinical trial data, that contained a right-censored survival event as one of the endpoints.
Journal: Journal of Applied Statistics
Pages: 891-912
Issue: 5
Volume: 51
Year: 2024
Month: 04
X-DOI: 10.1080/02664763.2022.2164759
File-URL: http://hdl.handle.net/10.1080/02664763.2022.2164759
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:5:p:891-912

Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2176470_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a
Author-Name: Jinwen Liang
Author-X-Name-First: Jinwen
Author-X-Name-Last: Liang
Author-Name: Maozai Tian
Author-X-Name-First: Maozai
Author-X-Name-Last: Tian
Title: Imputed mean tensor regression for near-sited spatial temporal data
Abstract: 
 Modern spatial temporal data are often collected from sensor networks. Missing data problems are common to this kind of data. Making accurate imputation is important for many applications. In the unsupervised setting, one technique is to minimize the rank of a tensor or matrix. If we add related covariates, can we get more accurate imputation results? To address this, we transform the original sensor×time measurements to high order tensors by adding additional temporal dimensions and then integrate tensor regression with tensor completion using nuclear norm penalty. One advantage is we can simultaneously estimate parameters and impute missing values due to clear spatial consistency for near-sited spatial-temporal data. The proposed method doesn't assume missing mechanism of the response. Theoretical properties of the proposed estimator are investigated. Simulation studies and real data analysis are conducted to verify the efficiency of the estimation procedure.
Journal: Journal of Applied Statistics
Pages: 1057-1075
Issue: 6
Volume: 51
Year: 2024
Month: 04
X-DOI: 10.1080/02664763.2023.2176470
File-URL: http://hdl.handle.net/10.1080/02664763.2023.2176470
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:6:p:1057-1075



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2178642_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a
Author-Name: Bo Peng
Author-X-Name-First: Bo
Author-X-Name-Last: Peng
Author-Name: Kai Yang
Author-X-Name-First: Kai
Author-X-Name-Last: Yang
Author-Name: Xiaogang Dong
Author-X-Name-First: Xiaogang
Author-X-Name-Last: Dong
Title: Variable selection for quantile autoregressive model: Bayesian methods versus classical methods
Abstract: 
 In this article, we introduce three Bayesian variable selection methods for the quantile autoregressive model with explanatory variables. The Gibbs sampling algorithms are developed for each method by setting different priors. The numerical simulations suggest that the Gibbs sampling algorithms converge fast and Bayesian variable selection methods are reliable. A real example is given to analysis the relationship between the count of total rental bikes and five explanatory variables. Both simulations and data example indicate that the proposed methods are feasible, reliable, and appropriate for analyzing the Bike Sharing data set.
Journal: Journal of Applied Statistics
Pages: 1098-1130
Issue: 6
Volume: 51
Year: 2024
Month: 04
X-DOI: 10.1080/02664763.2023.2178642
File-URL: http://hdl.handle.net/10.1080/02664763.2023.2178642
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:6:p:1098-1130



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2175799_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a
Author-Name: Zhou Yu
Author-X-Name-First: Zhou
Author-X-Name-Last: Yu
Author-Name: Jie Yang
Author-X-Name-First: Jie
Author-X-Name-Last: Yang
Author-Name: Hsin-Hsiung Huang
Author-X-Name-First: Hsin-Hsiung
Author-X-Name-Last: Huang
Title: Smoothing regression and impact measures for accidents of traffic flows
Abstract: 
 Traffic pattern identification and accident evaluation are essential for improving traffic planning, road safety, and traffic management. In this paper, we establish classification and regression models to characterize the relationship between traffic flows and different time points and identify different patterns of traffic flows by a negative binomial model with smoothing splines. It provides mean response curves and Bayesian credible bands for traffic flows, a single index, and the log-likelihood difference, for traffic flow pattern recognition. We further propose an impact measure for evaluating the influence of accidents on traffic flows based on the fitted negative binomial model. The proposed method has been successfully applied to real-world traffic flows, and it can be used for improving traffic management.
Journal: Journal of Applied Statistics
Pages: 1041-1056
Issue: 6
Volume: 51
Year: 2024
Month: 04
X-DOI: 10.1080/02664763.2023.2175799
File-URL: http://hdl.handle.net/10.1080/02664763.2023.2175799
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:6:p:1041-1056



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2233143_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a
Author-Name: David Angeles
Author-X-Name-First: David
Author-X-Name-Last: Angeles
Author-Name: Sebastian Kurtek
Author-X-Name-First: Sebastian
Author-X-Name-Last: Kurtek
Author-Name: Elizabeth Klein
Author-X-Name-First: Elizabeth
Author-X-Name-Last: Klein
Author-Name: Marielle Brinkman
Author-X-Name-First: Marielle
Author-X-Name-Last: Brinkman
Author-Name: Amy Ferketich
Author-X-Name-First: Amy
Author-X-Name-Last: Ferketich
Title: Geometric framework for statistical analysis of eye tracking heat maps, with application to a tobacco waterpipe study
Abstract: 
 Health warning labels have been found to increase awareness of the harmful effects of tobacco products. An eye tracking study was conducted to determine the optimal placement and type of a health warning label on tobacco waterpipes. Participants viewed images that contained one of (1) four waterpipes, (2) three different types of warning labels, (3) placed in three locations. Typically, statistical analysis of eye tracking data is conducted based on summary statistics such as total dwell time, duration score, and number of visits to an area of interest. However, these summary statistics fail to capture the complete variability in a participant's eye movement. Instead, we propose to estimate heat maps defined on the entire image domain using the raw two-dimensional coordinates of eye movement via kernel density estimation. For statistical analysis of heat maps, we adopt the Fisher–Rao Riemannian geometric framework, which enables computationally efficient comparisons of heat maps, statistical summarization and exploration of variability in a sample of heat maps, and metric-based hierarchical clustering. We apply this framework to eye tracking data from the tobacco waterpipe study and comment on the results in the context of the optimal placement and type of health warning labels on tobacco waterpipes.
Journal: Journal of Applied Statistics
Pages: 1191-1209
Issue: 6
Volume: 51
Year: 2024
Month: 04
X-DOI: 10.1080/02664763.2023.2233143
File-URL: http://hdl.handle.net/10.1080/02664763.2023.2233143
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:6:p:1191-1209



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2176834_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a
Author-Name: Serenay Cakar
Author-X-Name-First: Serenay
Author-X-Name-Last: Cakar
Author-Name: Fulya Gokalp Yavuz
Author-X-Name-First: Fulya Gokalp
Author-X-Name-Last: Yavuz
Title: Hybrid statistical and machine learning modeling of cognitive neuroscience data
Abstract: 
 The nested data structure is prevalent for cognitive measure experiments due to repeatedly taken observations from different brain locations within subjects. The analysis methods used for this data type should consider the dependency structure among the repeated measurements. However, the dependency assumption is mainly ignored in the cognitive neuroscience data analysis literature. We consider both statistical, and machine learning methods extended to repeated data analysis and compare distinct algorithms in terms of their advantage and disadvantages. Unlike basic algorithm comparison studies, this article analyzes novel neuroscience data considering the dependency structure for the first time with several statistical and machine learning methods and their hybrid forms. In addition, the fitting performances of different algorithms are compared using contaminated data sets, and the cross-validation approach. One of our findings suggests that the GLMM tree, including random term indices indicating the location of functional near-infrared spectroscopy optodes nested within experimental units, shows the best predictive performance with the lowest MSE, RMSE, and MAE model performance metrics. However, there is a trade-off between accuracy and speed since this algorithm is required the highest computational time.
Journal: Journal of Applied Statistics
Pages: 1076-1097
Issue: 6
Volume: 51
Year: 2024
Month: 04
X-DOI: 10.1080/02664763.2023.2176834
File-URL: http://hdl.handle.net/10.1080/02664763.2023.2176834
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:6:p:1076-1097



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2248413_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a
Author-Name: Laura L. Tupper
Author-X-Name-First: Laura L.
Author-X-Name-Last: Tupper
Author-Name: Charles R. Keese
Author-X-Name-First: Charles R.
Author-X-Name-Last: Keese
Author-Name: David S. Matteson
Author-X-Name-First: David S.
Author-X-Name-Last: Matteson
Title: Classifying contaminated cell cultures using time series features
Abstract: 
 We examine the use of time series data, derived from Electric Cell-substrate Impedance Sensing (ECIS), to differentiate between standard mammalian cell cultures and those infected with a mycoplasma organism. With the goal of easy visualization and interpretation, we perform low-dimensional feature-based classification, extracting application-relevant features from the ECIS time courses. We can achieve very high classification accuracy using only two features, which depend on the cell line under examination. Initial results also show the existence of experimental variation between plates and suggest types of features that may prove more robust to such variation. Our paper is the first to perform a broad examination of ECIS time course features in the context of detecting contamination; to combine different types of features to achieve classification accuracy while preserving interpretability; and to describe and suggest possibilities for ameliorating plate-to-plate variation.
Journal: Journal of Applied Statistics
Pages: 1210-1226
Issue: 6
Volume: 51
Year: 2024
Month: 04
X-DOI: 10.1080/02664763.2023.2248413
File-URL: http://hdl.handle.net/10.1080/02664763.2023.2248413
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:6:p:1210-1226



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2189771_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a
Author-Name: Vasileios Alevizakos
Author-X-Name-First: Vasileios
Author-X-Name-Last: Alevizakos
Author-Name: Kashinath Chatterjee
Author-X-Name-First: Kashinath
Author-X-Name-Last: Chatterjee
Author-Name: Christos Koukouvinos
Author-X-Name-First: Christos
Author-X-Name-Last: Koukouvinos
Title: Distribution-free Phase II triple EWMA control chart for joint monitoring the process location and scale parameters
Abstract: 
 Distribution-free or nonparametric control charts are used for monitoring the process parameters when there is a lack of knowledge about the underlying distribution. In this paper, we investigate a single distribution-free triple exponentially weighted moving average control chart based on the Lepage statistic (referred as TL chart) for simultaneously monitoring shifts in the unknown location and scale parameters of a univariate continuous distribution. The design and implementation of the proposed chart are discussed using time-varying and steady-state control limits for the zero-state case. The run-length distribution of the TL chart is evaluated by performing Monte Carlo simulations. The performance of the proposed chart is compared to those of the existing EWMA-Lepage (EL) and DEWMA-Lepage (DL) charts. It is observed that the TL chart with a time-varying control limit is superior to its competitors, especially for small to moderate shifts in the process parameters. We also provide a real example from a manufacturing process to illustrate the application of the proposed chart.
Journal: Journal of Applied Statistics
Pages: 1171-1190
Issue: 6
Volume: 51
Year: 2024
Month: 04
X-DOI: 10.1080/02664763.2023.2189771
File-URL: http://hdl.handle.net/10.1080/02664763.2023.2189771
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:6:p:1171-1190



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2180167_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a
Author-Name: Canhui Li
Author-X-Name-First: Canhui
Author-X-Name-Last: Li
Author-Name: Weirong Li
Author-X-Name-First: Weirong
Author-X-Name-Last: Li
Author-Name: Wensheng Zhu
Author-X-Name-First: Wensheng
Author-X-Name-Last: Zhu
Title: Penalized robust learning for optimal treatment regimes with heterogeneous individualized treatment effects
Abstract: 
 The growing popularity of personalized medicine motivates people to explore individualized treatment regimes according to heterogeneous characteristics of the patients. For the large-scale data analysis, however, the data are collected at different times and different locations, i.e. subjects are usually from a heterogeneous population, which causes that the optimal treatment regimes also vary for patients across different subgroups. In this paper, we mainly focus on the estimation of optimal treatment regimes for subjects come from a heterogeneous population with high-dimensional data. We first remove the main effects of the covariates for each subgroup to eliminate non-ignorable residual confounding. Based on the centralized outcome, we propose a penalized robust learning that estimates the coefficient matrix of the interactions between covariates and treatment by penalizing pairwise differences of the coefficients of any two subgroups for the same covariate, which can automatically identify the latent complex structure of the coefficient matrix with heterogeneous and homogeneous columns. At the same time, the penalized robust learning can also select the important variables that truly contribute to the individualized treatment decisions with commonly used sparsity structure penalty. Extensive simulation studies show that our proposed method outperforms current popular methods, and it is further illustrated in the real analysis of the Tamoxifen breast cancer data.
Journal: Journal of Applied Statistics
Pages: 1151-1170
Issue: 6
Volume: 51
Year: 2024
Month: 04
X-DOI: 10.1080/02664763.2023.2180167
File-URL: http://hdl.handle.net/10.1080/02664763.2023.2180167
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:6:p:1151-1170



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2173156_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a
Author-Name: Satwik Acharyya
Author-X-Name-First: Satwik
Author-X-Name-Last: Acharyya
Author-Name: Debdeep Pati
Author-X-Name-First: Debdeep
Author-X-Name-Last: Pati
Author-Name: Shumei Sun
Author-X-Name-First: Shumei
Author-X-Name-Last: Sun
Author-Name: Dipankar Bandyopadhyay
Author-X-Name-First: Dipankar
Author-X-Name-Last: Bandyopadhyay
Title: A monotone single index model for missing-at-random longitudinal proportion data
Abstract: 
 Beta distributions are commonly used to model proportion valued response variables, often encountered in longitudinal studies. In this article, we develop semi-parametric Beta regression models for proportion valued responses, where the aggregate covariate effect is summarized and flexibly modeled, using a interpretable monotone time-varying single index transform of a linear combination of the potential covariates. We utilize the potential of single index models, which are effective dimension reduction tools and accommodate link function misspecification in generalized linear mixed models. Our Bayesian methodology incorporates the missing-at-random feature of the proportion response and utilize Hamiltonian Monte Carlo sampling to conduct inference. We explore finite-sample frequentist properties of our estimates and assess the robustness via detailed simulation studies. Finally, we illustrate our methodology via application to a motivating longitudinal dataset on obesity research recording proportion body fat.
Journal: Journal of Applied Statistics
Pages: 1023-1040
Issue: 6
Volume: 51
Year: 2024
Month: 04
X-DOI: 10.1080/02664763.2023.2173156
File-URL: http://hdl.handle.net/10.1080/02664763.2023.2173156
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:6:p:1023-1040



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2179567_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a
Author-Name: Mamadou Lamine Diop
Author-X-Name-First: Mamadou Lamine
Author-X-Name-Last: Diop
Author-Name: William Kengne
Author-X-Name-First: William
Author-X-Name-Last: Kengne
Title: Epidemic change-point detection in general integer-valued time series
Abstract: 
 In this paper, we consider the structural change in a class of discrete valued time series, where the true conditional distribution of the observations is assumed to be unknown. The conditional mean of the process depends on a parameter 
 $ \theta ^* $ θ∗ which may change over time. We provide sufficient conditions for the consistency and the asymptotic normality of the Poisson quasi-maximum likelihood estimator (QMLE) of the model. We consider an epidemic change-point detection and propose a test statistic based on the QMLE of the parameter. Under the null hypothesis of a constant parameter (no change), the test statistic converges to a distribution obtained from increments of a Browninan bridge. The test statistic diverges to infinity under the epidemic alternative, which establishes that the proposed procedure is consistent in power. The effectiveness of the proposed procedure is illustrated by simulated and real data examples.
Journal: Journal of Applied Statistics
Pages: 1131-1150
Issue: 6
Volume: 51
Year: 2024
Month: 04
X-DOI: 10.1080/02664763.2023.2179567
File-URL: http://hdl.handle.net/10.1080/02664763.2023.2179567
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:6:p:1131-1150

Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2196752_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a
Author-Name: Rui Ma
Author-X-Name-First: Rui
Author-X-Name-Last: Ma
Author-Name: Shishun Zhao
Author-X-Name-First: Shishun
Author-X-Name-Last: Zhao
Author-Name: Jianguo Sun
Author-X-Name-First: Jianguo
Author-X-Name-Last: Sun
Author-Name: Shuying Wang
Author-X-Name-First: Shuying
Author-X-Name-Last: Wang
Title: Estimation of accelerated hazards models based on case K informatively interval-censored failure time data
Abstract: 
 The accelerated hazards model is one of the most commonly used models for regression analysis of failure time data and this is especially the case when, for example, the hazard functions may have monotonicity property. Correspondingly a large literature has been established for its estimation or inference when right-censored data are observed. Although several methods have also been developed for its inference based on interval-censored data, they apply only to limited situations or rely on some assumptions such as independent censoring. In this paper, we consider the situation where one observes case K interval-censored data, the type of failure time data that occur most in, for example, medical research such as clinical trials or periodical follow-up studies. For inference, we propose a sieve borrow-strength method and in particular, it allows for informative censoring. The asymptotic properties of the proposed estimators are established. Simulation studies demonstrate that the proposed inference procedure performs well. The method is applied to a set of real data set arising from an AIDS clinical trial.
Journal: Journal of Applied Statistics
Pages: 1251-1270
Issue: 7
Volume: 51
Year: 2024
Month: 05
X-DOI: 10.1080/02664763.2023.2196752
File-URL: http://hdl.handle.net/10.1080/02664763.2023.2196752
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:7:p:1251-1270



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2203882_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a
Author-Name: Isaac E. Cortés
Author-X-Name-First: Isaac E.
Author-X-Name-Last: Cortés
Author-Name: Mário de Castro
Author-X-Name-First: Mário
Author-X-Name-Last: de Castro
Author-Name: Diego I. Gallardo
Author-X-Name-First: Diego I.
Author-X-Name-Last: Gallardo
Title: A new family of quantile regression models applied to nutritional data
Abstract: 
 This paper introduces a new family of quantile regression models whose response variable follows a reparameterized Marshall-Olkin distribution indexed by quantile, scale, and asymmetry parameters. The family has arisen by applying the Marshall-Olkin approach to distributions belonging to the location-scale family. Models of higher flexibility and whose structure is similar to generalized linear models were generated by quantile reparameterization. The maximum likelihood (ML) method is presented for the estimation of the model parameters, and simulation studies evaluated the performance of the ML estimators. The advantages of the family are illustrated through an application to a set of nutritional data, whose results indicate it is a good alternative for modeling slightly asymmetric response variables with support on the real line.
Journal: Journal of Applied Statistics
Pages: 1378-1398
Issue: 7
Volume: 51
Year: 2024
Month: 05
X-DOI: 10.1080/02664763.2023.2203882
File-URL: http://hdl.handle.net/10.1080/02664763.2023.2203882
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:7:p:1378-1398



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2194582_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a
Author-Name: Seyedeh Mahbubeh Hoseini Baladezaei
Author-X-Name-First: Seyedeh Mahbubeh Hoseini
Author-X-Name-Last: Baladezaei
Author-Name: Einolah Deiri
Author-X-Name-First: Einolah
Author-X-Name-Last: Deiri
Author-Name: Ezzatallah Baloui Jamkhaneh
Author-X-Name-First: Ezzatallah Baloui
Author-X-Name-Last: Jamkhaneh
Title: The balanced discrete Burr–Hatke model and mixing INAR(1) process: properties, estimation, forecasting and COVID-19 applications
Abstract: 
 The main concern of this paper is providing a flexible discrete model that captures every kind of dispersion (equi-, over- and under-dispersion). Based on the balanced discretization method, a new discrete version of Burr–Hatke distribution is introduced with the partial moment-preserving property. Some statistical properties of the new distribution are introduced, and the applicability of proposed model is evaluated by considering counting series. A new integer-valued autoregressive (INAR) process based on the mixing Pegram and binomial thinning operators with discrete Burr–Hatke innovations is introduced, which can model contagious data properly. The different estimation approaches of parameters of the new process are provided and compared through the Monte Carlo simulation scheme. The performance of the proposed process is evaluated by four data sets of the daily death counts of the COVID-19 in Austria, Switzerland, Nigeria and Slovenia in comparison with some competitor INAR(1) models, along with the Pearson residual analysis of the assessing model. The goodness of fit measures affirm the adequacy of the proposed process in modeling all COVID-19 data sets. The fundamental prediction procedures are considered for new process by classic, modified Sieve bootstrap and Bayesian forecasting methods for all COVID-19 data sets, which is concluded that the Bayesian forecasting approach provides more reliable results.
Journal: Journal of Applied Statistics
Pages: 1227-1250
Issue: 7
Volume: 51
Year: 2024
Month: 05
X-DOI: 10.1080/02664763.2023.2194582
File-URL: http://hdl.handle.net/10.1080/02664763.2023.2194582
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:7:p:1227-1250



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2197571_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a
Author-Name: Sajid Ali
Author-X-Name-First: Sajid
Author-X-Name-Last: Ali
Author-Name: Mariyam Waheed
Author-X-Name-First: Mariyam
Author-X-Name-Last: Waheed
Author-Name: Ismail Shah
Author-X-Name-First: Ismail
Author-X-Name-Last: Shah
Author-Name: Syed Muhammad Muslim Raza
Author-X-Name-First: Syed Muhammad Muslim
Author-X-Name-Last: Raza
Title: Bayesian sample size determination for coefficient of variation of normal distribution
Abstract: 
 Sample size determination is an active area of research in statistics. Generally, Bayesian methods provide relatively smaller sample sizes than the classical techniques, particularly average length criterion is more conventional and gives relatively small sample sizes under the given constraints. The objective of this study is to utilize major Bayesian sample size determination techniques for the coefficient of variation of normal distribution and assess their performance by comparing the results with the freqentist approach. To this end, we noticed that the average coverage criterion is the one that provides relatively smaller sample sizes than the worst outcome criterion. By comparing with the existing frequentist studies, we show that a smaller sample size is required in Bayesian methods to achieve the same efficiency.
Journal: Journal of Applied Statistics
Pages: 1271-1286
Issue: 7
Volume: 51
Year: 2024
Month: 05
X-DOI: 10.1080/02664763.2023.2197571
File-URL: http://hdl.handle.net/10.1080/02664763.2023.2197571
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:7:p:1271-1286



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2277115_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a
Author-Name: Sameer Poongadan
Author-X-Name-First: Sameer
Author-X-Name-Last: Poongadan
Author-Name: M. C. Lineesh
Author-X-Name-First: M. C.
Author-X-Name-Last: Lineesh
Title: Forecasting of the true satellite carbon monoxide data with ensemble empirical mode decomposition, singular value decomposition and moving average
Abstract: 
 The forecasting of carbon monoxide in the atmosphere is essential as it causes the pollution of the atmosphere and hence severe health problems for humans. This study proposes a time-series prognosis EEMD-SVD-MA technique which incorporates Ensemble Empirical Mode Decomposition, Singular Value Decomposition and Moving Average, to predict the prospects of carbon monoxide data taken from the Indian region. The collected data are non-linear. The technique can be applied for non-stationary and non-linear data. In this approach, there are three levels: EEMD level, SVD level and MA level. The first level deploys EEMD to fragment data series into a limited number of Intrinsic Mode Function (IMF) components along with a residue. To denoise each IMF component, SVD is deployed in the second level. In the third level, each denoised IMF component is predicted by MA. The future values of the original data are obtained by adding all the predicted series of the components. In this study, we proposed two variants of the model: EEMD-SVD-MA(3) and EEMD-SVD-MA(4) and compared the results with other forecasting techniques, namely LSTM (Long Short Term Memory network), EMD-LSTM, EMD-MA, EEMD-MA and CEEMDAN-MA. The results show that the proposed EEMD-SVD-MA model is more efficient than other models.
Journal: Journal of Applied Statistics
Pages: 1412-1426
Issue: 7
Volume: 51
Year: 2024
Month: 05
X-DOI: 10.1080/02664763.2023.2277115
File-URL: http://hdl.handle.net/10.1080/02664763.2023.2277115
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:7:p:1412-1426



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2272223_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a
Author-Name: Nikola Surjanovic
Author-X-Name-First: Nikola
Author-X-Name-Last: Surjanovic
Author-Name: Thomas M. Loughin
Author-X-Name-First: Thomas M.
Author-X-Name-Last: Loughin
Title: Improving the Hosmer-Lemeshow goodness-of-fit test in large models with replicated Bernoulli trials
Abstract: 
 The Hosmer-Lemeshow (HL) test is a commonly used global goodness-of-fit (GOF) test that assesses the quality of the overall fit of a logistic regression model. In this paper, we give results from simulations showing that the type I error rate (and hence power) of the HL test decreases as model complexity grows, provided that the sample size remains fixed and binary replicates (multiple Bernoulli trials) are present in the data. We demonstrate that a generalized version of the HL test (GHL) presented in previous work can offer some protection against this power loss. These results are also supported by application of both the HL and GHL test to a real-life data set. We conclude with a brief discussion explaining the behavior of the HL test, along with some guidance on how to choose between the two tests. In particular, we suggest the GHL test to be used when there are binary replicates or clusters in the covariate space, provided that the sample size is sufficiently large.
Journal: Journal of Applied Statistics
Pages: 1399-1411
Issue: 7
Volume: 51
Year: 2024
Month: 05
X-DOI: 10.1080/02664763.2023.2272223
File-URL: http://hdl.handle.net/10.1080/02664763.2023.2272223
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:7:p:1399-1411



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2202464_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a
Author-Name: Ufuk Beyaztas
Author-X-Name-First: Ufuk
Author-X-Name-Last: Beyaztas
Author-Name: Mujgan Tez
Author-X-Name-First: Mujgan
Author-X-Name-Last: Tez
Author-Name: Han Lin Shang
Author-X-Name-First: Han
Author-X-Name-Last: Lin Shang
Title: Robust scalar-on-function partial quantile regression
Abstract: 
 Compared with the conditional mean regression-based scalar-on-function regression model, the scalar-on-function quantile regression is robust to outliers in the response variable. However, it is susceptible to outliers in the functional predictor (called leverage points). This is because the influence function of the regression quantiles is bounded in the response variable but unbounded in the predictor space. The leverage points may alter the eigenstructure of the predictor matrix, leading to poor estimation and prediction results. This study proposes a robust procedure to estimate the model parameters in the scalar-on-function quantile regression method and produce reliable predictions in the presence of both outliers and leverage points. The proposed method is based on a functional partial quantile regression procedure. We propose a weighted partial quantile covariance to obtain functional partial quantile components of the scalar-on-function quantile regression model. After the decomposition, the model parameters are estimated via a weighted loss function, where the robustness is obtained by iteratively reweighting the partial quantile components. The estimation and prediction performance of the proposed method is evaluated by a series of Monte-Carlo experiments and an empirical data example. The results are compared favorably with several existing methods. The method is implemented in an R package robfpqr.
Journal: Journal of Applied Statistics
Pages: 1359-1377
Issue: 7
Volume: 51
Year: 2024
Month: 05
X-DOI: 10.1080/02664763.2023.2202464
File-URL: http://hdl.handle.net/10.1080/02664763.2023.2202464
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:7:p:1359-1377



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2197587_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a
Author-Name: Jun Ye
Author-X-Name-First: Jun
Author-X-Name-Last: Ye
Title: Functional principal component models for sparse and irregularly spaced data by Bayesian inference
Abstract: 
 The area of functional principal component analysis (FPCA) has seen relatively few contributions from the Bayesian inference. A Bayesian method in FPCA is developed under the cases of continuous and binary observations for sparse and irregularly spaced data. In the proposed Markov chain Monte Carlo (MCMC) method, Gibbs sampler approach is adopted to update the different variables based on their conditional posterior distributions. In FPCA, a set of eigenfunctions is suggested under Stiefel manifold, and samples are drawn from a Langevin–Bingham matrix variate distribution. Penalized splines are used to model mean trajectory and eigenfunction trajectories in generalized functional mixed models; and the proposed model is casted into a mixed-effects model framework for Bayesian inference. To determine the number of principal components, reversible jump Markov chain Monte Carlo (RJ-MCMC) algorithm is implemented. Four different simulation settings are conducted to demonstrate competitive performance against non-Bayesian approaches in FPCA. Finally, the proposed method is illustrated to the analysis of body mass index (BMI) data by gender and ethnicity.
Journal: Journal of Applied Statistics
Pages: 1287-1317
Issue: 7
Volume: 51
Year: 2024
Month: 05
X-DOI: 10.1080/02664763.2023.2197587
File-URL: http://hdl.handle.net/10.1080/02664763.2023.2197587
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:7:p:1287-1317



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2198178_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a
Author-Name: Yonghui Liu
Author-X-Name-First: Yonghui
Author-X-Name-Last: Liu
Author-Name: Jing Wang
Author-X-Name-First: Jing
Author-X-Name-Last: Wang
Author-Name: Víctor Leiva
Author-X-Name-First: Víctor
Author-X-Name-Last: Leiva
Author-Name: Alejandra Tapia
Author-X-Name-First: Alejandra
Author-X-Name-Last: Tapia
Author-Name: Wei Tan
Author-X-Name-First: Wei
Author-X-Name-Last: Tan
Author-Name: Shuangzhe Liu
Author-X-Name-First: Shuangzhe
Author-X-Name-Last: Liu
Title: Robust autoregressive modeling and its diagnostic analytics with a COVID-19 related application
Abstract: 
 Autoregressive models in time series are useful in various areas. In this article, we propose a skew-t autoregressive model. We estimate its parameters using the expectation-maximization (EM) method and develop the influence methodology based on local perturbations for its validation. We obtain the normal curvatures for four perturbation strategies to identify influential observations, and then to assess their performance through Monte Carlo simulations. An example of financial data analysis is presented to study daily log-returns for Brent crude futures and investigate possible impact by the COVID-19 pandemic.
Journal: Journal of Applied Statistics
Pages: 1318-1343
Issue: 7
Volume: 51
Year: 2024
Month: 05
X-DOI: 10.1080/02664763.2023.2198178
File-URL: http://hdl.handle.net/10.1080/02664763.2023.2198178
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:7:p:1318-1343



Template-Type: ReDIF-Article 1.0
# input file: CJAS_A_2199177_J.xml processed with: repec_from_jats12.xsl darts-xml-transformations-20240209T083504 git hash: db97ba8e3a
Author-Name: Rouba A. Chahine
Author-X-Name-First: Rouba A.
Author-X-Name-Last: Chahine
Author-Name: Inmaculada Aban
Author-X-Name-First: Inmaculada
Author-X-Name-Last: Aban
Title: Analysis of survival outcomes using likelihood ratio test in trials incorporating patient's treatment choice
Abstract: 
 Methods for designing and analyzing multiple arms survival trials that incorporate patient's treatment choice are needed. In these trials, patients are randomized into two groups, random and choice. Participants in the choice group choose their treatment, which is not a current standard practice in randomized clinical trials. In this paper, we propose a new method based on the likelihood function to design and analyze these trials with time to event outcomes in the presence of non-informative right censoring. We use simulations to evaluate the methods for Weibull outcomes, complete and censored. Finally, we provide an illustration for designing a study in which we discuss some design considerations and demonstrate the methods.
Journal: Journal of Applied Statistics
Pages: 1344-1358
Issue: 7
Volume: 51
Year: 2024
Month: 05
X-DOI: 10.1080/02664763.2023.2199177
File-URL: http://hdl.handle.net/10.1080/02664763.2023.2199177
File-Format: text/html
File-Restriction: Access to full text is restricted to subscribers.
Handle: RePEc:taf:japsta:v:51:y:2024:i:7:p:1344-1358